core[minor]: update @tool inferred schema and description

.github/ISSUE_TEMPLATE/documentation.yml

@@ -26,13 +26,6 @@ body:
       [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
       [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
       [LangChain ChatBot](https://chat.langchain.com/)
-- type: input
-  id: url
-  attributes:
-    label: URL
-    description: URL to documentation
-  validations:
-    required: false
 - type: checkboxes
   id: checks
   attributes:
@@ -55,4 +48,4 @@ body:
     label: "Idea or request for content:"
     description: >
       Please describe as clearly as possible what topics you think are missing
-      from the current documentation.
+      from the current documentation.

.github/PULL_REQUEST_TEMPLATE.md

@@ -26,4 +26,4 @@ Additional guidelines:
 - Changes should be backwards compatible.
 - If you are adding something to community, do not re-import it in langchain.
 
-If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
+If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17.

.github/actions/people/app/main.py

@@ -537,9 +537,7 @@ if __name__ == "__main__":
         "nfcampos",
         "efriis",
         "eyurtsev",
-        "rlancemartin",
-        "ccurme",
-        "vbarda",
+        "rlancemartin"
     }
     hidden_logins = {
         "dev2049",

.github/workflows/_release.yml

@@ -177,7 +177,7 @@ jobs:
         env:
           MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
         run: |
-          poetry run pip install --force-reinstall $MIN_VERSIONS --editable .
+          poetry run pip install --force-reinstall $MIN_VERSIONS
           make tests
         working-directory: ${{ inputs.working-directory }}
 
@@ -222,7 +222,6 @@ jobs:
           MONGODB_ATLAS_URI: ${{ secrets.MONGODB_ATLAS_URI }}
           VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
           UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
-          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
@@ -308,4 +307,3 @@ jobs:
           tag: ${{needs.build.outputs.pkg-name}}==${{ needs.build.outputs.version }}
           body: "# Release ${{needs.build.outputs.pkg-name}}==${{ needs.build.outputs.version }}\n\nPackage-specific release note generation coming soon."
           commit: ${{ github.sha }}
-          makeLatest: ${{ needs.build.outputs.pkg-name == 'langchain-core'}}

Makefile

@@ -3,7 +3,7 @@
 ## help: Show this help info.
 help: Makefile
 	@printf "\n\033[1mUsage: make <TARGETS> ...\033[0m\n\n\033[1mTargets:\033[0m\n\n"
-	@sed -n 's/^## //p' $< | awk -F':' '{printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' | sort | sed -e 's/^/  /'
+	@sed -n 's/^##//p' $< | awk -F':' '{printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' | sort | sed -e 's/^/ /'
 
 ## all: Default target, shows help.
 all: help

cookbook/oracleai_demo.ipynb

@@ -6,24 +6,23 @@
    "source": [
     "# Oracle AI Vector Search with Document Processing\n",
     "Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords.\n",
-    "One of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system.\n",
-    "This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems.\n",
+    "One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems.\n",
     "\n",
-    "In addition, your vectors can benefit from all of Oracle Database’s most powerful features, like the following:\n",
+    "In addition, because Oracle has been building database technologies for so long, your vectors can benefit from all of Oracle Database's most powerful features, like the following:\n",
     "\n",
-    " * [Partitioning Support](https://www.oracle.com/database/technologies/partitioning.html)\n",
-    " * [Real Application Clusters scalability](https://www.oracle.com/database/real-application-clusters/)\n",
-    " * [Exadata smart scans](https://www.oracle.com/database/technologies/exadata/software/smartscan/)\n",
-    " * [Shard processing across geographically distributed databases](https://www.oracle.com/database/distributed-database/)\n",
-    " * [Transactions](https://docs.oracle.com/en/database/oracle/oracle-database/23/cncpt/transactions.html)\n",
-    " * [Parallel SQL](https://docs.oracle.com/en/database/oracle/oracle-database/21/vldbg/parallel-exec-intro.html#GUID-D28717E4-0F77-44F5-BB4E-234C31D4E4BA)\n",
-    " * [Disaster recovery](https://www.oracle.com/database/data-guard/)\n",
-    " * [Security](https://www.oracle.com/security/database-security/)\n",
-    " * [Oracle Machine Learning](https://www.oracle.com/artificial-intelligence/database-machine-learning/)\n",
-    " * [Oracle Graph Database](https://www.oracle.com/database/integrated-graph-database/)\n",
-    " * [Oracle Spatial and Graph](https://www.oracle.com/database/spatial/)\n",
-    " * [Oracle Blockchain](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_blockchain_table.html#GUID-B469E277-978E-4378-A8C1-26D3FF96C9A6)\n",
-    " * [JSON](https://docs.oracle.com/en/database/oracle/oracle-database/23/adjsn/json-in-oracle-database.html)\n",
+    " * Partitioning Support\n",
+    " * Real Application Clusters scalability\n",
+    " * Exadata smart scans\n",
+    " * Shard processing across geographically distributed databases\n",
+    " * Transactions\n",
+    " * Parallel SQL\n",
+    " * Disaster recovery\n",
+    " * Security\n",
+    " * Oracle Machine Learning\n",
+    " * Oracle Graph Database\n",
+    " * Oracle Spatial and Graph\n",
+    " * Oracle Blockchain\n",
+    " * JSON\n",
     "\n",
     "This guide demonstrates how Oracle AI Vector Search can be used with Langchain to serve an end-to-end RAG pipeline. This guide goes through examples of:\n",
     "\n",
@@ -34,13 +33,6 @@
     " * Storing and Indexing them in a Vector Store and querying them for queries in OracleVS"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you are just starting with Oracle Database, consider exploring the [free Oracle 23 AI](https://www.oracle.com/database/free/#resources) which provides a great introduction to setting up your database environment. While working with the database, it is often advisable to avoid using the system user by default; instead, you can create your own user for enhanced security and customization. For detailed steps on user creation, refer to our [end-to-end guide](https://github.com/langchain-ai/langchain/blob/master/cookbook/oracleai_demo.ipynb) which also shows how to set up a user in Oracle. Additionally, understanding user privileges is crucial for managing database security effectively. You can learn more about this topic in the official [Oracle guide](https://docs.oracle.com/en/database/oracle/oracle-database/19/admqs/administering-user-accounts-and-security.html#GUID-36B21D72-1BBB-46C9-A0C9-F0D2A8591B8D) on administering user accounts and security."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -139,13 +131,13 @@
    "metadata": {},
    "source": [
     "## Process Documents using Oracle AI\n",
-    "Consider the following scenario: users possess documents stored either in an Oracle Database or a file system and intend to utilize this data with Oracle AI Vector Search powered by Langchain.\n",
+    "Let's think about a scenario that the users have some documents in Oracle Database or in a file system. They want to use the data for Oracle AI Vector Search using Langchain.\n",
     "\n",
-    "To prepare the documents for analysis, a comprehensive preprocessing workflow is necessary. Initially, the documents must be retrieved, summarized (if required), and chunked as needed. Subsequent steps involve generating embeddings for these chunks and integrating them into the Oracle AI Vector Store. Users can then conduct semantic searches on this data.\n",
+    "For that, the users need to do some document preprocessing. The first step would be to read the documents, generate their summary(if needed) and then chunk/split them if needed. After that, they need to generate the embeddings for those chunks and store into Oracle AI Vector Store. Finally, the users will perform some semantic queries on those data. \n",
     "\n",
-    "The Oracle AI Vector Search Langchain library encompasses a suite of document processing tools that facilitate document loading, chunking, summary generation, and embedding creation.\n",
+    "Oracle AI Vector Search Langchain library provides a range of document processing functionalities including document loading, splitting, generating summary and embeddings.\n",
     "\n",
-    "In the sections that follow, we will detail the utilization of Oracle AI Langchain APIs to effectively implement each of these processes."
+    "In the following sections, we will go through how to use Oracle AI Langchain APIs to achieve each of these functionalities individually. "
    ]
   },
   {
@@ -153,7 +145,7 @@
    "metadata": {},
    "source": [
     "### Connect to Demo User\n",
-    "The following sample code will show how to connect to Oracle Database. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You might want to switch to thick-mode if you are unable to use thin-mode."
+    "The following sample code will show how to connect to Oracle Database. "
    ]
   },
   {
@@ -250,7 +242,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With the inclusion of a demo user and a populated sample table, the remaining configuration involves setting up embedding and summary functionalities. Users are presented with multiple provider options, including local database solutions and third-party services such as Ocigenai, Hugging Face, and OpenAI. Should users opt for a third-party provider, they are required to establish credentials containing the necessary authentication details. Conversely, if selecting a database as the provider for embeddings, it is necessary to upload an ONNX model to the Oracle Database. No additional setup is required for summary functionalities when using the database option."
+    "\n",
+    "\n",
+    "Now that we have a demo user and a demo table with some data, we just need to do one more setup. For embedding and summary, we have a few provider options that the users can choose from such as database, 3rd party providers like ocigenai, huggingface, openai, etc. If the users choose to use 3rd party provider, they need to create a credential with corresponding authentication information. On the other hand, if the users choose to use 'database' as provider, they need to load an onnx model to Oracle Database for embeddings; however, for summary, they don't need to do anything."
    ]
   },
   {
@@ -259,13 +253,13 @@
    "source": [
     "### Load ONNX Model\n",
     "\n",
-    "Oracle accommodates a variety of embedding providers, enabling users to choose between proprietary database solutions and third-party services such as OCIGENAI and HuggingFace. This selection dictates the methodology for generating and managing embeddings.\n",
+    "To generate embeddings, Oracle provides a few provider options for users to choose from. The users can choose 'database' provider or some 3rd party providers like OCIGENAI, HuggingFace, etc.\n",
     "\n",
-    "***Important*** : Should users opt for the database option, they must upload an ONNX model into the Oracle Database. Conversely, if a third-party provider is selected for embedding generation, uploading an ONNX model to Oracle Database is not required.\n",
+    "***Note*** If the users choose database option, they need to load an ONNX model to Oracle Database. The users do not need to load an ONNX model to Oracle Database if they choose to use 3rd party provider to generate embeddings.\n",
     "\n",
-    "A significant advantage of utilizing an ONNX model directly within Oracle is the enhanced security and performance it offers by eliminating the need to transmit data to external parties. Additionally, this method avoids the latency typically associated with network or REST API calls.\n",
+    "One of the core benefits of using an ONNX model is that the users do not need to transfer their data to 3rd party to generate embeddings. And also, since it does not involve any network or REST API calls, it may provide better performance.\n",
     "\n",
-    "Below is the example code to upload an ONNX model into Oracle Database:"
+    "Here is the sample code to load an ONNX model to Oracle Database:"
    ]
   },
   {
@@ -304,11 +298,11 @@
    "source": [
     "### Create Credential\n",
     "\n",
-    "When selecting third-party providers for generating embeddings, users are required to establish credentials to securely access the provider's endpoints.\n",
+    "On the other hand, if the users choose to use 3rd party provider to generate embeddings and summary, they need to create credential to access 3rd party provider's end points.\n",
     "\n",
-    "***Important:*** No credentials are necessary when opting for the 'database' provider to generate embeddings. However, should users decide to utilize a third-party provider, they must create credentials specific to the chosen provider.\n",
+    "***Note:*** The users do not need to create any credential if they choose to use 'database' provider to generate embeddings and summary. Should the users choose to 3rd party provider, they need to create credential for the 3rd party provider they want to use. \n",
     "\n",
-    "Below is an illustrative example:"
+    "Here is a sample example:"
    ]
   },
   {
@@ -358,11 +352,11 @@
    "metadata": {},
    "source": [
     "### Load Documents\n",
-    "Users have the flexibility to load documents from either the Oracle Database, a file system, or both, by appropriately configuring the loader parameters. For comprehensive details on these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-73397E89-92FB-48ED-94BB-1AD960C4EA1F).\n",
+    "The users can load the documents from Oracle Database or a file system or both. They just need to set the loader parameters accordingly. Please refer to the Oracle AI Vector Search Guide book for complete information about these parameters.\n",
     "\n",
-    "A significant advantage of utilizing OracleDocLoader is its capability to process over 150 distinct file formats, eliminating the need for multiple loaders for different document types. For a complete list of the supported formats, please refer to the [Oracle Text Supported Document Formats](https://docs.oracle.com/en/database/oracle/oracle-database/23/ccref/oracle-text-supported-document-formats.html).\n",
+    "The main benefit of using OracleDocLoader is that it can handle 150+ different file formats. You don't need to use different types of loader for different file formats. Here is the list formats that we support: [Oracle Text Supported Document Formats](https://docs.oracle.com/en/database/oracle/oracle-database/23/ccref/oracle-text-supported-document-formats.html)\n",
     "\n",
-    "Below is a sample code snippet that demonstrates how to use OracleDocLoader"
+    "The following sample code will show how to do that:"
    ]
   },
   {
@@ -405,7 +399,7 @@
    "metadata": {},
    "source": [
     "### Generate Summary\n",
-    "Now that the user loaded the documents, they may want to generate a summary for each document. The Oracle AI Vector Search Langchain library offers a suite of APIs designed for document summarization. It supports multiple summarization providers such as Database, OCIGENAI, HuggingFace, among others, allowing users to select the provider that best meets their needs. To utilize these capabilities, users must configure the summary parameters as specified. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-EC9DDB58-6A15-4B36-BA66-ECBA20D2CE57)."
+    "Now that the user loaded the documents, they may want to generate a summary for each document. The Oracle AI Vector Search Langchain library provides an API to do that. There are a few summary generation provider options including Database, OCIGENAI, HuggingFace and so on. The users can choose their preferred provider to generate a summary. Like before, they just need to set the summary parameters accordingly. Please refer to the Oracle AI Vector Search Guide book for complete information about these parameters."
    ]
   },
   {
@@ -476,9 +470,9 @@
    "metadata": {},
    "source": [
     "### Split Documents\n",
-    "The documents may vary in size, ranging from small to very large. Users often prefer to chunk their documents into smaller sections to facilitate the generation of embeddings. A wide array of customization options is available for this splitting process. For comprehensive details regarding these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-4E145629-7098-4C7C-804F-FC85D1F24240).\n",
+    "The documents can be in different sizes: small, medium, large, or very large. The users like to split/chunk their documents into smaller pieces to generate embeddings. There are lots of different splitting customizations the users can do. Please refer to the Oracle AI Vector Search Guide book for complete information about these parameters.\n",
     "\n",
-    "Below is a sample code illustrating how to implement this:"
+    "The following sample code will show how to do that:"
    ]
   },
   {
@@ -519,16 +513,14 @@
    "metadata": {},
    "source": [
     "### Generate Embeddings\n",
-    "Now that the documents are chunked as per requirements, the users may want to generate embeddings for these chunks. Oracle AI Vector Search provides multiple methods for generating embeddings, utilizing either locally hosted ONNX models or third-party APIs. For comprehensive instructions on configuring these alternatives, please refer to the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-C6439E94-4E86-4ECD-954E-4B73D53579DE)."
+    "Now that the documents are chunked as per requirements, the users may want to generate embeddings for these chunks. Oracle AI Vector Search provides a number of ways to generate embeddings. The users can load an ONNX embedding model to Oracle Database and use it to generate embeddings or use some 3rd party API's end points to generate embeddings. Please refer to the Oracle AI Vector Search Guide book for complete information about these parameters."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***Note:*** Currently, OracleEmbeddings processes each embedding generation request individually, without batching, by calling REST endpoints separately for each request. This method could potentially lead to exceeding the maximum request per minute quota set by some providers. However, we are actively working to enhance this process by implementing request batching, which will allow multiple embedding requests to be combined into fewer API calls, thereby optimizing our use of provider resources and adhering to their request limits. This update is expected to be rolled out soon, eliminating the current limitation.\n",
-    "\n",
-    "***Note:*** Users may need to configure a proxy to utilize third-party embedding generation providers, excluding the 'database' provider that utilizes an ONNX model."
+    "***Note:*** The users may need to set proxy if they want to use some 3rd party embedding generation providers other than 'database' provider (aka using ONNX model)."
    ]
   },
   {
@@ -760,18 +752,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The example provided illustrates the creation of a vector store using the DOT_PRODUCT distance strategy. Users have the flexibility to employ various distance strategies with the Oracle AI Vector Store, as detailed in our [comprehensive guide](https://python.langchain.com/v0.1/docs/integrations/vectorstores/oracle/)."
+    "The above example creates a vector store with DOT_PRODUCT distance strategy. \n",
+    "\n",
+    "However, the users can create Oracle AI Vector Store provides different distance strategies. Please see the [comprehensive guide](/docs/integrations/vectorstores/oracle) for more information."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With embeddings now stored in vector stores, it is advisable to establish an index to enhance semantic search performance during query execution.\n",
+    "Now that we have embeddings stored in vector stores, let's create an index on them to get better semantic search performance during query time.\n",
     "\n",
-    "***Note*** Should you encounter an \"insufficient memory\" error, it is recommended to increase the  ***vector_memory_size*** in your database configuration\n",
+    "***Note*** If you are getting some insufficient memory error, please increase ***vector_memory_size*** in your database.\n",
     "\n",
-    "Below is a sample code snippet for creating an index:"
+    "Here is the sample code to create an index:"
    ]
   },
   {
@@ -791,9 +785,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This example demonstrates the creation of a default HNSW index on embeddings within the 'oravs' table. Users may adjust various parameters according to their specific needs. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/manage-different-categories-vector-indexes.html).\n",
+    "The above example creates a default HNSW index on the embeddings stored in 'oravs' table. The users can set different parameters as per their requirements. Please refer to the Oracle AI Vector Search Guide book for complete information about these parameters.\n",
     "\n",
-    "Additionally, various types of vector indices can be created to meet diverse requirements. More details can be found in our [comprehensive guide](https://python.langchain.com/v0.1/docs/integrations/vectorstores/oracle/).\n"
+    "Also, there are different types of vector indices that the users can create. Please see the [comprehensive guide](/docs/integrations/vectorstores/oracle) for more information.\n"
    ]
   },
   {
@@ -803,9 +797,9 @@
     "## Perform Semantic Search\n",
     "All set!\n",
     "\n",
-    "We have successfully processed the documents and stored them in the vector store, followed by the creation of an index to enhance query performance. We are now prepared to proceed with semantic searches.\n",
+    "We have processed the documents, stored them to vector store, and then created index to get better query performance. Now let's do some semantic searches.\n",
     "\n",
-    "Below is the sample code for this process:"
+    "Here is the sample code for this:"
    ]
   },
   {

docs/Makefile

@@ -13,7 +13,7 @@ OUTPUT_NEW_DOCS_DIR = $(OUTPUT_NEW_DIR)/docs
 
 PYTHON = .venv/bin/python
 
-PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec test -e "{}/pyproject.toml" \; -print | grep -vE "airbyte|ibm" | tr '\n' ' ')
+PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec test -e "{}/pyproject.toml" \; -print | grep -vE "airbyte|ibm|ai21" | tr '\n' ' ')
 
 PORT ?= 3001
 
@@ -45,6 +45,9 @@ generate-files:
 	wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O $(INTERMEDIATE_DIR)/langserve.md
 	$(PYTHON) scripts/resolve_local_links.py $(INTERMEDIATE_DIR)/langserve.md https://github.com/langchain-ai/langserve/tree/main/
 
+	wget -q https://raw.githubusercontent.com/langchain-ai/langgraph/main/README.md -O $(INTERMEDIATE_DIR)/langgraph.md
+	$(PYTHON) scripts/resolve_local_links.py $(INTERMEDIATE_DIR)/langgraph.md https://github.com/langchain-ai/langgraph/tree/main/
+
 copy-infra:
 	mkdir -p $(OUTPUT_NEW_DIR)
 	cp -r src $(OUTPUT_NEW_DIR)
@@ -66,9 +69,9 @@ md-sync:
 generate-references:
 	$(PYTHON) scripts/generate_api_reference_links.py --docs_dir $(OUTPUT_NEW_DOCS_DIR)
 
-build: install-py-deps generate-files copy-infra render md-sync
+build: install-py-deps generate-files copy-infra render md-sync generate-references
 
-vercel-build: install-vercel-deps build generate-references
+vercel-build: install-vercel-deps build
 	rm -rf docs
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
 	rm -rf build

docs/docs/additional_resources/arxiv_references.mdx

@@ -1,569 +0,0 @@
-# arXiv
-            
-LangChain implements the latest research in the field of Natural Language Processing.
-This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference,
-and Templates.
-
-## Summary
-
-| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation|
-|------------------|---------|-------------------|------------------------|
-| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen,  et al. | 2023-12-11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
-| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al. | 2023-11-15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
-| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023-10-09 | `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
-| `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He,  et al. | 2023-05-23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read)
-| `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023-05-15 | `API:` [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
-| `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan,  et al. | 2023-03-30 | `API:` [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
-| `2303.08774v6` [GPT-4 Technical Report](http://arxiv.org/abs/2303.08774v6) | OpenAI, Josh Achiam, Steven Adler,  et al. | 2023-03-15 | `Docs:` [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
-| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023-01-24 | `API:` [langchain_community.llms...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022-12-20 | `API:` [langchain.chains...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde)
-| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022-12-12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
-| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022-11-25 | `API:` [langchain_core.example_selectors...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
-| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022-11-18 | `API:` [langchain_experimental.pal_chain...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain)
-| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022-09-22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
-| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022-05-25 | `API:` [langchain_community.embeddings...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `API:` [langchain_community.utilities...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community.utilities...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
-| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022-02-01 | `API:` [langchain_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021-02-26 | `API:` [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
-| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019-09-11 | `API:` [langchain_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `1908.10084v1` [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](http://arxiv.org/abs/1908.10084v1) | Nils Reimers, Iryna Gurevych | 2019-08-27 | `Docs:` [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
-
-## Dense X Retrieval: What Retrieval Granularity Should We Use?
-
-- **arXiv id:** 2312.06648v2
-- **Title:** Dense X Retrieval: What Retrieval Granularity Should We Use?
-- **Authors:** Tong Chen, Hongwei Wang, Sihao Chen,  et al.
-- **Published Date:** 2023-12-11
-- **URL:** http://arxiv.org/abs/2312.06648v2
-- **LangChain:**
-
-   - **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
-
-**Abstract:** Dense retrieval has become a prominent method to obtain relevant context or
-world knowledge in open-domain NLP tasks. When we use a learned dense retriever
-on a retrieval corpus at inference time, an often-overlooked design choice is
-the retrieval unit in which the corpus is indexed, e.g. document, passage, or
-sentence. We discover that the retrieval unit choice significantly impacts the
-performance of both retrieval and downstream tasks. Distinct from the typical
-approach of using passages or sentences, we introduce a novel retrieval unit,
-proposition, for dense retrieval. Propositions are defined as atomic
-expressions within text, each encapsulating a distinct factoid and presented in
-a concise, self-contained natural language format. We conduct an empirical
-comparison of different retrieval granularity. Our results reveal that
-proposition-based retrieval significantly outperforms traditional passage or
-sentence-based methods in dense retrieval. Moreover, retrieval by proposition
-also enhances the performance of downstream QA tasks, since the retrieved texts
-are more condensed with question-relevant information, reducing the need for
-lengthy input tokens and minimizing the inclusion of extraneous, irrelevant
-information.
-                
-## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
-
-- **arXiv id:** 2311.09210v1
-- **Title:** Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
-- **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al.
-- **Published Date:** 2023-11-15
-- **URL:** http://arxiv.org/abs/2311.09210v1
-- **LangChain:**
-
-   - **Template:** [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
-
-**Abstract:** Retrieval-augmented language models (RALMs) represent a substantial
-advancement in the capabilities of large language models, notably in reducing
-factual hallucination by leveraging external knowledge sources. However, the
-reliability of the retrieved information is not always guaranteed. The
-retrieval of irrelevant data can lead to misguided responses, and potentially
-causing the model to overlook its inherent knowledge, even when it possesses
-adequate information to address the query. Moreover, standard RALMs often
-struggle to assess whether they possess adequate knowledge, both intrinsic and
-retrieved, to provide an accurate answer. In situations where knowledge is
-lacking, these systems should ideally respond with "unknown" when the answer is
-unattainable. In response to these challenges, we introduces Chain-of-Noting
-(CoN), a novel approach aimed at improving the robustness of RALMs in facing
-noisy, irrelevant documents and in handling unknown scenarios. The core idea of
-CoN is to generate sequential reading notes for retrieved documents, enabling a
-thorough evaluation of their relevance to the given question and integrating
-this information to formulate the final answer. We employed ChatGPT to create
-training data for CoN, which was subsequently trained on an LLaMa-2 7B model.
-Our experiments across four open-domain QA benchmarks show that RALMs equipped
-with CoN significantly outperform standard RALMs. Notably, CoN achieves an
-average improvement of +7.9 in EM score given entirely noisy retrieved
-documents and +10.5 in rejection rates for real-time questions that fall
-outside the pre-training knowledge scope.
-                
-## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
-
-- **arXiv id:** 2310.06117v2
-- **Title:** Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
-- **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al.
-- **Published Date:** 2023-10-09
-- **URL:** http://arxiv.org/abs/2310.06117v2
-- **LangChain:**
-
-   - **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
-
-**Abstract:** We present Step-Back Prompting, a simple prompting technique that enables
-LLMs to do abstractions to derive high-level concepts and first principles from
-instances containing specific details. Using the concepts and principles to
-guide reasoning, LLMs significantly improve their abilities in following a
-correct reasoning path towards the solution. We conduct experiments of
-Step-Back Prompting with PaLM-2L, GPT-4 and Llama2-70B models, and observe
-substantial performance gains on various challenging reasoning-intensive tasks
-including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back
-Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7%
-and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.
-                
-## Query Rewriting for Retrieval-Augmented Large Language Models
-
-- **arXiv id:** 2305.14283v3
-- **Title:** Query Rewriting for Retrieval-Augmented Large Language Models
-- **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He,  et al.
-- **Published Date:** 2023-05-23
-- **URL:** http://arxiv.org/abs/2305.14283v3
-- **LangChain:**
-
-   - **Template:** [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read)
-
-**Abstract:** Large Language Models (LLMs) play powerful, black-box readers in the
-retrieve-then-read pipeline, making remarkable progress in knowledge-intensive
-tasks. This work introduces a new framework, Rewrite-Retrieve-Read instead of
-the previous retrieve-then-read for the retrieval-augmented LLMs from the
-perspective of the query rewriting. Unlike prior studies focusing on adapting
-either the retriever or the reader, our approach pays attention to the
-adaptation of the search query itself, for there is inevitably a gap between
-the input text and the needed knowledge in retrieval. We first prompt an LLM to
-generate the query, then use a web search engine to retrieve contexts.
-Furthermore, to better align the query to the frozen modules, we propose a
-trainable scheme for our pipeline. A small language model is adopted as a
-trainable rewriter to cater to the black-box LLM reader. The rewriter is
-trained using the feedback of the LLM reader by reinforcement learning.
-Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice
-QA. Experiments results show consistent performance improvement, indicating
-that our framework is proven effective and scalable, and brings a new framework
-for retrieval-augmented LLM.
-                
-## Large Language Model Guided Tree-of-Thought
-
-- **arXiv id:** 2305.08291v1
-- **Title:** Large Language Model Guided Tree-of-Thought
-- **Authors:** Jieyi Long
-- **Published Date:** 2023-05-15
-- **URL:** http://arxiv.org/abs/2305.08291v1
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
-
-**Abstract:** In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
-approach aimed at improving the problem-solving capabilities of auto-regressive
-large language models (LLMs). The ToT technique is inspired by the human mind's
-approach for solving complex reasoning tasks through trial and error. In this
-process, the human mind explores the solution space through a tree-like thought
-process, allowing for backtracking when necessary. To implement ToT as a
-software system, we augment an LLM with additional modules including a prompter
-agent, a checker module, a memory module, and a ToT controller. In order to
-solve a given problem, these modules engage in a multi-round conversation with
-the LLM. The memory module records the conversation and state history of the
-problem solving process, which allows the system to backtrack to the previous
-steps of the thought-process and explore other directions from there. To verify
-the effectiveness of the proposed technique, we implemented a ToT-based solver
-for the Sudoku Puzzle. Experimental results show that the ToT framework can
-significantly increase the success rate of Sudoku puzzle solving. Our
-implementation of the ToT-based Sudoku solver is available on GitHub:
-\url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.
-                
-## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
-
-- **arXiv id:** 2303.17580v4
-- **Title:** HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
-- **Authors:** Yongliang Shen, Kaitao Song, Xu Tan,  et al.
-- **Published Date:** 2023-03-30
-- **URL:** http://arxiv.org/abs/2303.17580v4
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
-
-**Abstract:** Solving complicated AI tasks with different domains and modalities is a key
-step toward artificial general intelligence. While there are numerous AI models
-available for various domains and modalities, they cannot handle complicated AI
-tasks autonomously. Considering large language models (LLMs) have exhibited
-exceptional abilities in language understanding, generation, interaction, and
-reasoning, we advocate that LLMs could act as a controller to manage existing
-AI models to solve complicated AI tasks, with language serving as a generic
-interface to empower this. Based on this philosophy, we present HuggingGPT, an
-LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI
-models in machine learning communities (e.g., Hugging Face) to solve AI tasks.
-Specifically, we use ChatGPT to conduct task planning when receiving a user
-request, select models according to their function descriptions available in
-Hugging Face, execute each subtask with the selected AI model, and summarize
-the response according to the execution results. By leveraging the strong
-language capability of ChatGPT and abundant AI models in Hugging Face,
-HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different
-modalities and domains and achieve impressive results in language, vision,
-speech, and other challenging tasks, which paves a new way towards the
-realization of artificial general intelligence.
-                
-## GPT-4 Technical Report
-
-- **arXiv id:** 2303.08774v6
-- **Title:** GPT-4 Technical Report
-- **Authors:** OpenAI, Josh Achiam, Steven Adler,  et al.
-- **Published Date:** 2023-03-15
-- **URL:** http://arxiv.org/abs/2303.08774v6
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
-
-**Abstract:** We report the development of GPT-4, a large-scale, multimodal model which can
-accept image and text inputs and produce text outputs. While less capable than
-humans in many real-world scenarios, GPT-4 exhibits human-level performance on
-various professional and academic benchmarks, including passing a simulated bar
-exam with a score around the top 10% of test takers. GPT-4 is a
-Transformer-based model pre-trained to predict the next token in a document.
-The post-training alignment process results in improved performance on measures
-of factuality and adherence to desired behavior. A core component of this
-project was developing infrastructure and optimization methods that behave
-predictably across a wide range of scales. This allowed us to accurately
-predict some aspects of GPT-4's performance based on models trained with no
-more than 1/1,000th the compute of GPT-4.
-                
-## A Watermark for Large Language Models
-
-- **arXiv id:** 2301.10226v4
-- **Title:** A Watermark for Large Language Models
-- **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al.
-- **Published Date:** 2023-01-24
-- **URL:** http://arxiv.org/abs/2301.10226v4
-- **LangChain:**
-
-   - **API Reference:** [langchain_community.llms...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-
-**Abstract:** Potential harms of large language models can be mitigated by watermarking
-model output, i.e., embedding signals into generated text that are invisible to
-humans but algorithmically detectable from a short span of tokens. We propose a
-watermarking framework for proprietary language models. The watermark can be
-embedded with negligible impact on text quality, and can be detected using an
-efficient open-source algorithm without access to the language model API or
-parameters. The watermark works by selecting a randomized set of "green" tokens
-before a word is generated, and then softly promoting use of green tokens
-during sampling. We propose a statistical test for detecting the watermark with
-interpretable p-values, and derive an information-theoretic framework for
-analyzing the sensitivity of the watermark. We test the watermark using a
-multi-billion parameter model from the Open Pretrained Transformer (OPT)
-family, and discuss robustness and security.
-                
-## Precise Zero-Shot Dense Retrieval without Relevance Labels
-
-- **arXiv id:** 2212.10496v1
-- **Title:** Precise Zero-Shot Dense Retrieval without Relevance Labels
-- **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin,  et al.
-- **Published Date:** 2022-12-20
-- **URL:** http://arxiv.org/abs/2212.10496v1
-- **LangChain:**
-
-   - **API Reference:** [langchain.chains...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
-   - **Template:** [hyde](https://python.langchain.com/docs/templates/hyde)
-
-**Abstract:** While dense retrieval has been shown effective and efficient across tasks and
-languages, it remains difficult to create effective fully zero-shot dense
-retrieval systems when no relevance label is available. In this paper, we
-recognize the difficulty of zero-shot learning and encoding relevance. Instead,
-we propose to pivot through Hypothetical Document Embeddings~(HyDE). Given a
-query, HyDE first zero-shot instructs an instruction-following language model
-(e.g. InstructGPT) to generate a hypothetical document. The document captures
-relevance patterns but is unreal and may contain false details. Then, an
-unsupervised contrastively learned encoder~(e.g. Contriever) encodes the
-document into an embedding vector. This vector identifies a neighborhood in the
-corpus embedding space, where similar real documents are retrieved based on
-vector similarity. This second step ground the generated document to the actual
-corpus, with the encoder's dense bottleneck filtering out the incorrect
-details. Our experiments show that HyDE significantly outperforms the
-state-of-the-art unsupervised dense retriever Contriever and shows strong
-performance comparable to fine-tuned retrievers, across various tasks (e.g. web
-search, QA, fact verification) and languages~(e.g. sw, ko, ja).
-                
-## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
-
-- **arXiv id:** 2212.07425v3
-- **Title:** Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
-- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al.
-- **Published Date:** 2022-12-12
-- **URL:** http://arxiv.org/abs/2212.07425v3
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
-
-**Abstract:** The spread of misinformation, propaganda, and flawed argumentation has been
-amplified in the Internet era. Given the volume of data and the subtlety of
-identifying violations of argumentation norms, supporting information analytics
-tasks, like content moderation, with trustworthy methods that can identify
-logical fallacies is essential. In this paper, we formalize prior theoretical
-work on logical fallacies into a comprehensive three-stage evaluation framework
-of detection, coarse-grained, and fine-grained classification. We adapt
-existing evaluation datasets for each stage of the evaluation. We employ three
-families of robust and explainable methods based on prototype reasoning,
-instance-based reasoning, and knowledge injection. The methods combine language
-models with background knowledge and explainable mechanisms. Moreover, we
-address data sparsity with strategies for data augmentation and curriculum
-learning. Our three-stage framework natively consolidates prior datasets and
-methods from existing tasks, like propaganda detection, serving as an
-overarching evaluation testbed. We extensively evaluate these methods on our
-datasets, focusing on their robustness and explainability. Our results provide
-insight into the strengths and weaknesses of the methods on different
-components and fallacy classes, indicating that fallacy identification is a
-challenging task that may require specialized forms of reasoning to capture
-various classes. We share our open-source code and data on GitHub to support
-further work on logical fallacy identification.
-                
-## Complementary Explanations for Effective In-Context Learning
-
-- **arXiv id:** 2211.13892v2
-- **Title:** Complementary Explanations for Effective In-Context Learning
-- **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al.
-- **Published Date:** 2022-11-25
-- **URL:** http://arxiv.org/abs/2211.13892v2
-- **LangChain:**
-
-   - **API Reference:** [langchain_core.example_selectors...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
-
-**Abstract:** Large language models (LLMs) have exhibited remarkable capabilities in
-learning from explanations in prompts, but there has been limited understanding
-of exactly how these explanations function or why they are effective. This work
-aims to better understand the mechanisms by which explanations are used for
-in-context learning. We first study the impact of two different factors on the
-performance of prompts with explanations: the computation trace (the way the
-solution is decomposed) and the natural language used to express the prompt. By
-perturbing explanations on three controlled tasks, we show that both factors
-contribute to the effectiveness of explanations. We further study how to form
-maximally effective sets of explanations for solving a given test query. We
-find that LLMs can benefit from the complementarity of the explanation set:
-diverse reasoning skills shown by different exemplars can lead to better
-performance. Therefore, we propose a maximal marginal relevance-based exemplar
-selection approach for constructing exemplar sets that are both relevant as
-well as complementary, which successfully improves the in-context learning
-performance across three real-world tasks on multiple LLMs.
-                
-## PAL: Program-aided Language Models
-
-- **arXiv id:** 2211.10435v2
-- **Title:** PAL: Program-aided Language Models
-- **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou,  et al.
-- **Published Date:** 2022-11-18
-- **URL:** http://arxiv.org/abs/2211.10435v2
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.pal_chain...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain)
-
-**Abstract:** Large language models (LLMs) have recently demonstrated an impressive ability
-to perform arithmetic and symbolic reasoning tasks, when provided with a few
-examples at test time ("few-shot prompting"). Much of this success can be
-attributed to prompting methods such as "chain-of-thought'', which employ LLMs
-for both understanding the problem description by decomposing it into steps, as
-well as solving each step of the problem. While LLMs seem to be adept at this
-sort of step-by-step decomposition, LLMs often make logical and arithmetic
-mistakes in the solution part, even when the problem is decomposed correctly.
-In this paper, we present Program-Aided Language models (PAL): a novel approach
-that uses the LLM to read natural language problems and generate programs as
-the intermediate reasoning steps, but offloads the solution step to a runtime
-such as a Python interpreter. With PAL, decomposing the natural language
-problem into runnable steps remains the only learning task for the LLM, while
-solving is delegated to the interpreter. We demonstrate this synergy between a
-neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and
-algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all
-these natural language reasoning tasks, generating code using an LLM and
-reasoning using a Python interpreter leads to more accurate results than much
-larger models. For example, PAL using Codex achieves state-of-the-art few-shot
-accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B
-which uses chain-of-thought by absolute 15% top-1. Our code and data are
-publicly available at http://reasonwithpal.com/ .
-                
-## Deep Lake: a Lakehouse for Deep Learning
-
-- **arXiv id:** 2209.10785v2
-- **Title:** Deep Lake: a Lakehouse for Deep Learning
-- **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al.
-- **Published Date:** 2022-09-22
-- **URL:** http://arxiv.org/abs/2209.10785v2
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
-
-**Abstract:** Traditional data lakes provide critical data infrastructure for analytical
-workloads by enabling time travel, running SQL queries, ingesting data with
-ACID transactions, and visualizing petabyte-scale datasets on cloud storage.
-They allow organizations to break down data silos, unlock data-driven
-decision-making, improve operational efficiency, and reduce costs. However, as
-deep learning usage increases, traditional data lakes are not well-designed for
-applications such as natural language processing (NLP), audio processing,
-computer vision, and applications involving non-tabular datasets. This paper
-presents Deep Lake, an open-source lakehouse for deep learning applications
-developed at Activeloop. Deep Lake maintains the benefits of a vanilla data
-lake with one key difference: it stores complex data, such as images, videos,
-annotations, as well as tabular data, in the form of tensors and rapidly
-streams the data over the network to (a) Tensor Query Language, (b) in-browser
-visualization engine, or (c) deep learning frameworks without sacrificing GPU
-utilization. Datasets stored in Deep Lake can be accessed from PyTorch,
-TensorFlow, JAX, and integrate with numerous MLOps tools.
-                
-## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
-
-- **arXiv id:** 2205.12654v1
-- **Title:** Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
-- **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
-- **Published Date:** 2022-05-25
-- **URL:** http://arxiv.org/abs/2205.12654v1
-- **LangChain:**
-
-   - **API Reference:** [langchain_community.embeddings...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-
-**Abstract:** Scaling multilingual representation learning beyond the hundred most frequent
-languages is challenging, in particular to cover the long tail of low-resource
-languages. A promising approach has been to train one-for-all multilingual
-models capable of cross-lingual transfer, but these models often suffer from
-insufficient capacity and interference between unrelated languages. Instead, we
-move away from this approach and focus on training multiple language (family)
-specific representations, but most prominently enable all languages to still be
-encoded in the same representational space. To achieve this, we focus on
-teacher-student training, allowing all encoders to be mutually compatible for
-bitext mining, and enabling fast learning of new languages. We introduce a new
-teacher-student training scheme which combines supervised and self-supervised
-training, allowing encoders to take advantage of monolingual training data,
-which is valuable in the low-resource setting.
-  Our approach significantly outperforms the original LASER encoder. We study
-very low-resource languages and handle 50 African languages, many of which are
-not covered by any other model. For these languages, we train sentence
-encoders, mine bitexts, and validate the bitexts by training NMT systems.
-                
-## Evaluating the Text-to-SQL Capabilities of Large Language Models
-
-- **arXiv id:** 2204.00498v1
-- **Title:** Evaluating the Text-to-SQL Capabilities of Large Language Models
-- **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
-- **Published Date:** 2022-03-15
-- **URL:** http://arxiv.org/abs/2204.00498v1
-- **LangChain:**
-
-   - **API Reference:** [langchain_community.utilities...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community.utilities...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
-
-**Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
-language model. We find that, without any finetuning, Codex is a strong
-baseline on the Spider benchmark; we also analyze the failure modes of Codex in
-this setting. Furthermore, we demonstrate on the GeoQuery and Scholar
-benchmarks that a small number of in-domain examples provided in the prompt
-enables Codex to perform better than state-of-the-art models finetuned on such
-few-shot examples.
-                
-## Locally Typical Sampling
-
-- **arXiv id:** 2202.00666v5
-- **Title:** Locally Typical Sampling
-- **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher,  et al.
-- **Published Date:** 2022-02-01
-- **URL:** http://arxiv.org/abs/2202.00666v5
-- **LangChain:**
-
-   - **API Reference:** [langchain_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-
-**Abstract:** Today's probabilistic language generators fall short when it comes to
-producing coherent and fluent text despite the fact that the underlying models
-perform well under standard metrics, e.g., perplexity. This discrepancy has
-puzzled the language generation community for the last few years. In this work,
-we posit that the abstraction of natural language generation as a discrete
-stochastic process--which allows for an information-theoretic analysis--can
-provide new insights into the behavior of probabilistic language generators,
-e.g., why high-probability texts can be dull or repetitive. Humans use language
-as a means of communicating information, aiming to do so in a simultaneously
-efficient and error-minimizing manner; in fact, psycholinguistics research
-suggests humans choose each word in a string with this subconscious goal in
-mind. We formally define the set of strings that meet this criterion: those for
-which each word has an information content close to the expected information
-content, i.e., the conditional entropy of our model. We then propose a simple
-and efficient procedure for enforcing this criterion when generating from
-probabilistic models, which we call locally typical sampling. Automatic and
-human evaluations show that, in comparison to nucleus and top-k sampling,
-locally typical sampling offers competitive performance (in both abstractive
-summarization and story generation) in terms of quality while consistently
-reducing degenerate repetitions.
-                
-## Learning Transferable Visual Models From Natural Language Supervision
-
-- **arXiv id:** 2103.00020v1
-- **Title:** Learning Transferable Visual Models From Natural Language Supervision
-- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy,  et al.
-- **Published Date:** 2021-02-26
-- **URL:** http://arxiv.org/abs/2103.00020v1
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
-
-**Abstract:** State-of-the-art computer vision systems are trained to predict a fixed set
-of predetermined object categories. This restricted form of supervision limits
-their generality and usability since additional labeled data is needed to
-specify any other visual concept. Learning directly from raw text about images
-is a promising alternative which leverages a much broader source of
-supervision. We demonstrate that the simple pre-training task of predicting
-which caption goes with which image is an efficient and scalable way to learn
-SOTA image representations from scratch on a dataset of 400 million (image,
-text) pairs collected from the internet. After pre-training, natural language
-is used to reference learned visual concepts (or describe new ones) enabling
-zero-shot transfer of the model to downstream tasks. We study the performance
-of this approach by benchmarking on over 30 different existing computer vision
-datasets, spanning tasks such as OCR, action recognition in videos,
-geo-localization, and many types of fine-grained object classification. The
-model transfers non-trivially to most tasks and is often competitive with a
-fully supervised baseline without the need for any dataset specific training.
-For instance, we match the accuracy of the original ResNet-50 on ImageNet
-zero-shot without needing to use any of the 1.28 million training examples it
-was trained on. We release our code and pre-trained model weights at
-https://github.com/OpenAI/CLIP.
-                
-## CTRL: A Conditional Transformer Language Model for Controllable Generation
-
-- **arXiv id:** 1909.05858v2
-- **Title:** CTRL: A Conditional Transformer Language Model for Controllable Generation
-- **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al.
-- **Published Date:** 2019-09-11
-- **URL:** http://arxiv.org/abs/1909.05858v2
-- **LangChain:**
-
-   - **API Reference:** [langchain_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-
-**Abstract:** Large-scale language models show promising text generation capabilities, but
-users cannot easily control particular aspects of the generated text. We
-release CTRL, a 1.63 billion-parameter conditional transformer language model,
-trained to condition on control codes that govern style, content, and
-task-specific behavior. Control codes were derived from structure that
-naturally co-occurs with raw text, preserving the advantages of unsupervised
-learning while providing more explicit control over text generation. These
-codes also allow CTRL to predict which parts of the training data are most
-likely given a sequence. This provides a potential method for analyzing large
-amounts of data via model-based source attribution. We have released multiple
-full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.
-                
-## Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
-
-- **arXiv id:** 1908.10084v1
-- **Title:** Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
-- **Authors:** Nils Reimers, Iryna Gurevych
-- **Published Date:** 2019-08-27
-- **URL:** http://arxiv.org/abs/1908.10084v1
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
-
-**Abstract:** BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new
-state-of-the-art performance on sentence-pair regression tasks like semantic
-textual similarity (STS). However, it requires that both sentences are fed into
-the network, which causes a massive computational overhead: Finding the most
-similar pair in a collection of 10,000 sentences requires about 50 million
-inference computations (~65 hours) with BERT. The construction of BERT makes it
-unsuitable for semantic similarity search as well as for unsupervised tasks
-like clustering.
-  In this publication, we present Sentence-BERT (SBERT), a modification of the
-pretrained BERT network that use siamese and triplet network structures to
-derive semantically meaningful sentence embeddings that can be compared using
-cosine-similarity. This reduces the effort for finding the most similar pair
-from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while
-maintaining the accuracy from BERT.
-  We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning
-tasks, where it outperforms other state-of-the-art sentence embeddings methods.
-                

docs/docs/additional_resources/tutorials.mdx

@@ -1,10 +1,18 @@
-# 3rd Party Tutorials
+# Tutorials
+
+## Books and Handbooks
+
+- [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
+- [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
+- [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
+
 
 ##  Tutorials
 
 ### [LangChain v 0.1 by LangChain.ai](https://www.youtube.com/playlist?list=PLfaIDFEXuae0gBSJ9T0w7cu7iJZbH3T31)
 ### [Build with Langchain - Advanced by LangChain.ai](https://www.youtube.com/playlist?list=PLfaIDFEXuae06tclDATrMYY0idsTdLg9v)
 ### [LangGraph by LangChain.ai](https://www.youtube.com/playlist?list=PLfaIDFEXuae16n2TWUkKq5PgJ0w6Pkwtg)
+
 ### [by Greg Kamradt](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5)
 ### [by Sam Witteveen](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ)
 ### [by James Briggs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F)
@@ -12,6 +20,7 @@
 ### [by Mayo Oshin](https://www.youtube.com/@chatwithdata/search?query=langchain)
 ### [by 1 little Coder](https://www.youtube.com/playlist?list=PLpdmBGJ6ELUK-v0MK-t4wZmVEbxM5xk6L)
 
+
 ## Courses
 
 ### Featured courses on Deeplearning.AI
@@ -24,7 +33,6 @@
 ### Online courses
 
 - [Udemy](https://www.udemy.com/courses/search/?q=langchain)
-- [DataCamp](https://www.datacamp.com/courses/developing-llm-applications-with-langchain)
 - [Pluralsight](https://www.pluralsight.com/search?q=langchain)
 - [Coursera](https://www.coursera.org/search?query=langchain)
 - [Maven](https://maven.com/courses?query=langchain)
@@ -40,11 +48,7 @@
 - [by Rabbitmetrics](https://youtu.be/aywZrzNaKjs)
 - [by Ivan Reznikov](https://medium.com/@ivanreznikov/langchain-101-course-updated-668f7b41d6cb)
 
-## Books and Handbooks
-
-- [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
-- [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
-- [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
+## [Documentation: Use cases](/docs/how_to#use-cases)
 
 ---------------------
 

docs/docs/additional_resources/youtube.mdx

@@ -1,63 +1,137 @@
 # YouTube videos
 
-[Updated 2024-05-16]
+⛓ icon marks a new addition [last update 2023-09-21]
 
 ### [Official LangChain YouTube channel](https://www.youtube.com/@LangChain)
 
-### [Tutorials on YouTube](/docs/additional_resources/tutorials/#tutorials)
+### Introduction to LangChain with Harrison Chase, creator of LangChain
+- [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
+- [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
+- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@The_Full_Stack)
+- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
 
 ## Videos (sorted by views)
 
-Only videos with 40K+ views:
+- [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
+- [First look - `ChatGPT` + `WolframAlpha` (`GPT-3.5` and Wolfram|Alpha via LangChain by James Weaver)](https://youtu.be/wYGbY811oMo) by [Dr Alan D. Thompson](https://www.youtube.com/@DrAlanDThompson) 
+- [LangChain explained - The hottest new Python framework](https://youtu.be/RoR4XJw8wIc) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
+- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
+- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
+- [Build your own LLM Apps with LangChain & `GPT-Index`](https://youtu.be/-75p09zFUJY) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- [`BabyAGI` - New System of Autonomous AI Agents with LangChain](https://youtu.be/lg3kJvf1kXo) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- [Run `BabyAGI` with Langchain Agents (with Python Code)](https://youtu.be/WosPGHPObx8) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- [How to Use Langchain With `Zapier` | Write and Send Email with GPT-3 | OpenAI API Tutorial](https://youtu.be/p9v2-xEa9A0) by [StarMorph AI](https://www.youtube.com/@starmorph)
+- [Use Your Locally Stored Files To Get Response From GPT - `OpenAI` | Langchain | Python](https://youtu.be/NC1Ni9KS-rk) by [Shweta Lodha](https://www.youtube.com/@shweta-lodha)
+- [`Langchain JS` | How to Use GPT-3, GPT-4 to Reference your own Data | `OpenAI Embeddings` Intro](https://youtu.be/veV2I-NEjaM) by [StarMorph AI](https://www.youtube.com/@starmorph)
+- [The easiest way to work with large language models | Learn LangChain in 10min](https://youtu.be/kmbS6FDQh7c) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
+- [4 Autonomous AI Agents: “Westworld” simulation `BabyAGI`, `AutoGPT`, `Camel`, `LangChain`](https://youtu.be/yWbnH6inT_U) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
+- [AI CAN SEARCH THE INTERNET? Langchain Agents + OpenAI ChatGPT](https://youtu.be/J-GL0htqda8) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
+- [Query Your Data with GPT-4 | Embeddings, Vector Databases | Langchain JS Knowledgebase](https://youtu.be/jRnUPUTkZmU) by [StarMorph AI](https://www.youtube.com/@starmorph)
+- [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
+- [Langchain Overview — How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
+- [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
+- [LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
+  - [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
+  - [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI)
+- [Custom langchain Agent & Tools with memory. Turn any `Python function` into langchain tool with Gpt 3](https://youtu.be/NIG8lXk0ULg) by [echohive](https://www.youtube.com/@echohive)
+- [Building AI LLM Apps with LangChain (and more?) - LIVE STREAM](https://www.youtube.com/live/M-2Cj_2fzWI?feature=share) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
+- [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
+- [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
+- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@heymichaeldaigler) 
+- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nickdaigler)
+- [LangChain. Crear aplicaciones Python impulsadas por GPT](https://youtu.be/DkW_rDndts8) by [Jesús Conde](https://www.youtube.com/@0utKast)
+- [Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial](https://youtu.be/fLy0VenZyGc) by [Rachel Woods](https://www.youtube.com/@therachelwoods)
+- [`BabyAGI` + `GPT-4` Langchain Agent with Internet Access](https://youtu.be/wx1z_hs5P6E) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
+- [Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI](https://youtu.be/mb_YAABSplk) by [Arnoldas Kemeklis](https://www.youtube.com/@processusAI)
+- [Get Started with LangChain in `Node.js`](https://youtu.be/Wxx1KUWJFv4) by [Developers Digest](https://www.youtube.com/@DevelopersDigest)
+- [LangChain + `OpenAI` tutorial: Building a Q&A system w/ own text data](https://youtu.be/DYOU_Z0hAwo) by [Samuel Chan](https://www.youtube.com/@SamuelChan)
+- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@heymichaeldaigler)
+- [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
+- [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
+- [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
+- [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
+- [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
+- [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
+- [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
+- [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
+- [KI schreibt krasses Youtube Skript 😲😳 | LangChain Tutorial Deutsch](https://youtu.be/QpTiXyK1jus) by [SimpleKI](https://www.youtube.com/@simpleki)
+- [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
+- [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
+- [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
+- [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
+- [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
+- [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
+- [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
+- [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
+- [9 LangChain UseCases | Beginner's Guide | 2023](https://youtu.be/zS8_qosHNMw) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
+- [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
+- [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
+- [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
+- [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
+- [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
+- [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
+- [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
+- [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@heymichaeldaigler)
+- [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
+- [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
+- [Build AI chatbot with custom knowledge base using OpenAI API and GPT Index](https://youtu.be/vDZAZuaXf48) by [Irina Nik](https://www.youtube.com/@irina_nik)
+- [Build Your Own Auto-GPT Apps with LangChain (Python Tutorial)](https://youtu.be/NYSWn1ipbgg) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
+- [Chat with Multiple `PDFs` | LangChain App Tutorial in Python (Free LLMs and Embeddings)](https://youtu.be/dXxQ0LR-3Hg) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- [Chat with a `CSV` | `LangChain Agents` Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- [Create Your Own ChatGPT with `PDF` Data in 5 Minutes (LangChain Tutorial)](https://youtu.be/au2WVVGUvc8) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
+- [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod)
+- [`Flowise` is an open-source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
+- [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Girlfriend GPT](https://www.youtube.com/@girlfriendGPT)
+- [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ)
+- [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg) by [Krish Naik](https://www.youtube.com/@krishnaik06)
+- ⛓ [Vector Embeddings Tutorial – Code Your Own AI Assistant with `GPT-4 API` + LangChain + NLP](https://youtu.be/yfHHvmaMkcA?si=5uJhxoh2tvdnOXok) by [FreeCodeCamp.org](https://www.youtube.com/@freecodecamp)
+- ⛓ [Fully LOCAL `Llama 2` Q&A with LangChain](https://youtu.be/wgYctKFnQ74?si=UX1F3W-B3MqF4-K-) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- ⛓ [Fully LOCAL `Llama 2` Langchain on CPU](https://youtu.be/yhECvKMu8kM?si=IvjxwlA1c09VwHZ4) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- ⛓ [Build LangChain Audio Apps with Python in 5 Minutes](https://youtu.be/7w7ysaDz2W4?si=BvdMiyHhormr2-vr) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
+- ⛓ [`Voiceflow` & `Flowise`: Want to Beat Competition? New Tutorial with Real AI Chatbot](https://youtu.be/EZKkmeFwag0?si=-4dETYDHEstiK_bb) by [AI SIMP](https://www.youtube.com/@aisimp)
+- ⛓ [THIS Is How You Build Production-Ready AI Apps (`LangSmith` Tutorial)](https://youtu.be/tFXm5ijih98?si=lfiqpyaivxHFyI94) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
+- ⛓ [Build POWERFUL LLM Bots EASILY with Your Own Data - `Embedchain` - Langchain 2.0? (Tutorial)](https://youtu.be/jE24Y_GasE8?si=0yEDZt3BK5Q-LIuF) by [WorldofAI](https://www.youtube.com/@intheworldofai)
+- ⛓ [`Code Llama` powered Gradio App for Coding: Runs on CPU](https://youtu.be/AJOhV6Ryy5o?si=ouuQT6IghYlc1NEJ) by [AI Anytime](https://www.youtube.com/@AIAnytime)
+- ⛓ [LangChain Complete Course in One Video | Develop LangChain (AI) Based Solutions for Your Business](https://youtu.be/j9mQd-MyIg8?si=_wlNT3nP2LpDKztZ) by [UBprogrammer](https://www.youtube.com/@UBprogrammer)
+- ⛓ [How to Run `LLaMA` Locally on CPU or GPU | Python & Langchain & CTransformers Guide](https://youtu.be/SvjWDX2NqiM?si=DxFml8XeGhiLTzLV) by [Code With Prince](https://www.youtube.com/@CodeWithPrince)
+- ⛓ [PyData Heidelberg #11 - TimeSeries Forecasting & LLM Langchain](https://www.youtube.com/live/Glbwb5Hxu18?si=PIEY8Raq_C9PCHuW) by [PyData](https://www.youtube.com/@PyDataTV)
+- ⛓ [Prompt Engineering in Web Development | Using LangChain and Templates with OpenAI](https://youtu.be/pK6WzlTOlYw?si=fkcDQsBG2h-DM8uQ) by [Akamai Developer
+](https://www.youtube.com/@AkamaiDeveloper)
+- ⛓ [Retrieval-Augmented Generation (RAG) using LangChain and `Pinecone` - The RAG Special Episode](https://youtu.be/J_tCD_J6w3s?si=60Mnr5VD9UED9bGG) by [Generative AI and Data Science On AWS](https://www.youtube.com/@GenerativeAIOnAWS)
+- ⛓ [`LLAMA2 70b-chat` Multiple Documents Chatbot with Langchain & Streamlit |All OPEN SOURCE|Replicate API](https://youtu.be/vhghB81vViM?si=dszzJnArMeac7lyc) by [DataInsightEdge](https://www.youtube.com/@DataInsightEdge01)
+- ⛓ [Chatting with 44K Fashion Products: LangChain Opportunities and Pitfalls](https://youtu.be/Zudgske0F_s?si=8HSshHoEhh0PemJA) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- ⛓ [Structured Data Extraction from `ChatGPT` with LangChain](https://youtu.be/q1lYg8JISpQ?si=0HctzOHYZvq62sve) by [MG](https://www.youtube.com/@MG_cafe)
+- ⛓ [Chat with Multiple PDFs using `Llama 2`, `Pinecone` and LangChain (Free LLMs and Embeddings)](https://youtu.be/TcJ_tVSGS4g?si=FZYnMDJyoFfL3Z2i) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal)
+- ⛓ [Integrate Audio into `LangChain.js` apps in 5 Minutes](https://youtu.be/hNpUSaYZIzs?si=Gb9h7W9A8lzfvFKi) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
+- ⛓ [`ChatGPT` for your data with Local LLM](https://youtu.be/bWrjpwhHEMU?si=uM6ZZ18z9og4M90u) by [Jacob Jedryszek](https://www.youtube.com/@jj09)
+- ⛓ [Training `Chatgpt` with your personal data using langchain step by step in detail](https://youtu.be/j3xOMde2v9Y?si=179HsiMU-hEPuSs4) by [NextGen Machines](https://www.youtube.com/@MayankGupta-kb5yc)
+- ⛓ [Use ANY language in `LangSmith` with REST](https://youtu.be/7BL0GEdMmgY?si=iXfOEdBLqXF6hqRM) by [Nerding I/O](https://www.youtube.com/@nerding_io)
+- ⛓ [How to Leverage the Full Potential of LLMs for Your Business with Langchain - Leon Ruddat](https://youtu.be/vZmoEa7oWMg?si=ZhMmydq7RtkZd56Q) by [PyData](https://www.youtube.com/@PyDataTV)
+- ⛓ [`ChatCSV` App: Chat with CSV files using LangChain and `Llama 2`](https://youtu.be/PvsMg6jFs8E?si=Qzg5u5gijxj933Ya) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal)
+- ⛓ [Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
+- ⛓ [Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
+
+
+### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
+- [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
+- [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
+- [LangChain Models: `ChatGPT`, `Flan Alpaca`, `OpenAI Embeddings`, Prompt Templates & Streaming](https://www.youtube.com/watch?v=zy6LiK5F5-s)
+- [LangChain Chains: Use `ChatGPT` to Build Conversational Agents, Summaries and Q&A on Text With LLMs](https://www.youtube.com/watch?v=h1tJZQPcimM)
+- [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
+- [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
 
-- [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain `OpenAI API`)](https://youtu.be/9AXP7tCI9PI)
-- [Chat with Multiple `PDFs` | LangChain App Tutorial in Python (Free LLMs and Embeddings)](https://youtu.be/dXxQ0LR-3Hg?si=pjXKhsHRzn10vOqX)
-- [`Hugging Face` + Langchain in 5 mins | Access 200k+ FREE AI models for your AI apps](https://youtu.be/_j7JEDWuqLE?si=psimQscN3qo2dOa9)
-- [LangChain Crash Course For Beginners | LangChain Tutorial](https://youtu.be/nAmC7SoVLd8?si=qJdvyG5-rnjqfdj1)
-- [Vector Embeddings Tutorial – Code Your Own AI Assistant with GPT-4 API + LangChain + NLP](https://youtu.be/yfHHvmaMkcA?si=UBP3yw50cLm3a2nj)
-- [Development with Large Language Models Tutorial – `OpenAI`, Langchain, Agents, `Chroma`](https://youtu.be/xZDB1naRUlk?si=v8J1q6oFHRyTkf7Y)
-- [Langchain: `PDF` Chat App (GUI) | ChatGPT for Your PDF FILES | Step-by-Step Tutorial](https://youtu.be/RIWbalZ7sTo?si=LbKsCcuyv0BtnrTY)
-- [Vector Search `RAG` Tutorial – Combine Your Data with LLMs with Advanced Search](https://youtu.be/JEBDfGqrAUA?si=pD7oxpfwWeJCxfBt)
-- [LangChain Crash Course for Beginners](https://youtu.be/lG7Uxts9SXs?si=Yte4S5afN7KNCw0F)
-- [Learn `RAG` From Scratch – Python AI Tutorial from a LangChain Engineer](https://youtu.be/sVcwVQRHIc8?si=_LN4g0vOgSdtlB3S)
-- [`Llama 2` in LangChain — FIRST Open Source Conversational Agent!](https://youtu.be/6iHVJyX2e50?si=rtq1maPrzWKHbwVV)
-- [LangChain Tutorial for Beginners | Generative AI Series](https://youtu.be/cQUUkZnyoD0?si=KYz-bvcocdqGh9f_)
-- [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=yS7T98VLfcWdkDek)
-- [LangChain Explained In 15 Minutes - A MUST Learn For Python Programmers](https://youtu.be/mrjq3lFz23s?si=wkQGcSKUJjuiiEPf)
-- [LLM Project | End to End LLM Project Using Langchain, `OpenAI` in Finance Domain](https://youtu.be/MoqgmWV1fm8?si=oVl-5kJVgd3a07Y_)
-- [What is LangChain?](https://youtu.be/1bUy-1hGZpI?si=NZ0D51VM5y-DhjGe)
-- [`RAG` + Langchain Python Project: Easy AI/Chat For Your Doc](https://youtu.be/tcqEUSNCn8I?si=RLcWPBVLIErRqdmU)
-- [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg?si=X9qVazlXYucN_JBP)
-- [LangChain GEN AI Tutorial – 6 End-to-End Projects using OpenAI, Google `Gemini Pro`, `LLAMA2`](https://youtu.be/x0AnCE9SE4A?si=_92gJYm7kb-V2bi0)
-- [Complete Langchain GEN AI Crash Course With 6 End To End LLM Projects With OPENAI, `LLAMA2`, `Gemini Pro`](https://youtu.be/aWKrL4z5H6w?si=NVLi7Yiq0ccE7xXE)
-- [AI Leader Reveals The Future of AI AGENTS (LangChain CEO)](https://youtu.be/9ZhbA0FHZYc?si=1r4P6kRvKVvEhRgE)
-- [Learn How To Query Pdf using Langchain Open AI in 5 min](https://youtu.be/5Ghv-F1wF_0?si=ZZRjrWfeiFOVrcvu)
-- [Reliable, fully local RAG agents with `LLaMA3`](https://youtu.be/-ROS6gfYIts?si=75CXA8W_BbnkIxcV)
-- [Learn `LangChain.js` - Build LLM apps with JavaScript and `OpenAI`](https://youtu.be/HSZ_uaif57o?si=Icj-RAhwMT-vHaYA)
-- [LLM Project | End to End LLM Project Using LangChain, Google Palm In Ed-Tech Industry](https://youtu.be/AjQPRomyd-k?si=eC3NT6kn02Lhpz-_)
-- [Chatbot Answering from Your Own Knowledge Base: Langchain, `ChatGPT`, `Pinecone`, and `Streamlit`: | Code](https://youtu.be/nAKhxQ3hcMA?si=9Zd_Nd_jiYhtml5w)
-- [LangChain is AMAZING | Quick Python Tutorial](https://youtu.be/I4mFqyqFkxg?si=aJ66qh558OfNAczD)
-- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw?si=kZR-lnJwixeVrjmh)
-- [Using NEW `MPT-7B` in `Hugging Face` and LangChain](https://youtu.be/DXpk9K7DgMo?si=99JDpV_ueimwJhMi)
-- [LangChain - COMPLETE TUTORIAL - Basics to advanced concept!](https://youtu.be/a89vqgK-Qcs?si=0aVO2EOqsw7GE5e3)
-- [LangChain Agents: Simply Explained!](https://youtu.be/Xi9Ui-9qcPw?si=DCuG7nGx8dxcfhkx)
-- [Chat With Multiple `PDF` Documents With Langchain And Google `Gemini Pro`](https://youtu.be/uus5eLz6smA?si=YUwvHtaZsGeIl0WD)
-- [LLM Project | End to end LLM project Using Langchain, `Google Palm` in Retail Industry](https://youtu.be/4wtrl4hnPT8?si=_eOKPpdLfWu5UXMQ)
-- [Tutorial | Chat with any Website using Python and Langchain](https://youtu.be/bupx08ZgSFg?si=KRrjYZFnuLsstGwW)
-- [Prompt Engineering And LLM's With LangChain In One Shot-Generative AI](https://youtu.be/t2bSApmPzU4?si=87vPQQtYEWTyu2Kx)
-- [Build a Custom Chatbot with `OpenAI`: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU?si=gR1u3DUG9lvzBIKK)
-- [Search Your `PDF` App using Langchain, `ChromaDB`, and Open Source LLM: No OpenAI API (Runs on CPU)](https://youtu.be/rIV1EseKwU4?si=UxZEoXSiPai8fXgl)
-- [Building a `RAG` application from scratch using Python, LangChain, and the `OpenAI API`](https://youtu.be/BrsocJb-fAo?si=hvkh9iTGzJ-LnsX-)
-- [Function Calling via `ChatGPT API` - First Look With LangChain](https://youtu.be/0-zlUy7VUjg?si=Vc6LFseckEc6qvuk)
-- [Private GPT, free deployment! Langchain-Chachat helps you easily play with major mainstream AI models! | Zero Degree Commentary](https://youtu.be/3LLUyaHP-3I?si=AZumEeFXsvqaLl0f)
-- [Create a ChatGPT clone using `Streamlit` and LangChain](https://youtu.be/IaTiyQ2oYUQ?si=WbgsYmqPDnMidSUK)
-- [What's next for AI agents ft. LangChain's Harrison Chase](https://youtu.be/pBBe1pk8hf4?si=H4vdBF9nmkNZxiHt)
-- [`LangFlow`: Build Chatbots without Writing Code - LangChain](https://youtu.be/KJ-ux3hre4s?si=TJuDu4bAlva1myNL)
-- [Building a LangChain Custom Medical Agent with Memory](https://youtu.be/6UFtRwWnHws?si=wymYad26VgigRkHy)
-- [`Ollama` meets LangChain](https://youtu.be/k_1pOF1mj8k?si=RlBiCrmaR3s7SnMK)
-- [End To End LLM Langchain Project using `Pinecone` Vector Database](https://youtu.be/erUfLIi9OFM?si=aHpuHXdIEmAfS4eF)
-- [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=FUs0OLVJpzKhut0h)
-- [Understanding `ReACT` with LangChain](https://youtu.be/Eug2clsLtFs?si=imgj534ggxlypS0d)
 
 ---------------------
-[Updated 2024-05-16]
+⛓ icon marks a new addition [last update 2024-02-04]

docs/docs/concepts.mdx

@@ -33,7 +33,7 @@ Key partner packages are separated out (see below).
 This contains all integrations for various components (LLMs, vectorstores, retrievers).
 All dependencies in this package are optional to keep the package as lightweight as possible.
 
-### [`langgraph`](https://langchain-ai.github.io/langgraph)
+### [`langgraph`](/docs/langgraph)
 
 `langgraph` is an extension of `langchain` aimed at
 building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
@@ -44,7 +44,7 @@ LangGraph exposes high level interfaces for creating common types of agents, as
 
 A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.
 
-### [LangSmith](https://docs.smith.langchain.com)
+### [LangSmith](/docs/langsmith)
 
 A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
 
@@ -66,7 +66,7 @@ LCEL was designed from day 1 to **support putting prototypes in production, with
 When you build your chains with LCEL you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out). For some chains this means eg. we stream tokens straight from an LLM to a streaming output parser, and you get back parsed, incremental chunks of output at the same rate as the LLM provider outputs the raw tokens.
 
 **Async support**
-Any chain built with LCEL can be called both with the synchronous API (eg. in your Jupyter notebook while prototyping) as well as with the asynchronous API (eg. in a [LangServe](/docs/langserve/) server). This enables using the same code for prototypes and in production, with great performance, and the ability to handle many concurrent requests in the same server.
+Any chain built with LCEL can be called both with the synchronous API (eg. in your Jupyter notebook while prototyping) as well as with the asynchronous API (eg. in a [LangServe](/docs/langsmith) server). This enables using the same code for prototypes and in production, with great performance, and the ability to handle many concurrent requests in the same server.
 
 **Optimized parallel execution**
 Whenever your LCEL chains have steps that can be executed in parallel (eg if you fetch documents from multiple retrievers) we automatically do it, both in the sync and the async interfaces, for the smallest possible latency.
@@ -80,9 +80,9 @@ For more complex chains it’s often very useful to access the results of interm
 **Input and output schemas**
 Input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.
 
-[**Seamless LangSmith tracing**](https://docs.smith.langchain.com)
+[**Seamless LangSmith tracing**](/docs/langsmith)
 As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step.
-With LCEL, **all** steps are automatically logged to [LangSmith](https://docs.smith.langchain.com/) for maximum observability and debuggability.
+With LCEL, **all** steps are automatically logged to [LangSmith](/docs/langsmith/) for maximum observability and debuggability.
 
 [**Seamless LangServe deployment**](/docs/langserve)
 Any chain created with LCEL can be easily deployed using [LangServe](/docs/langserve).
@@ -128,14 +128,13 @@ LangChain provides standard, extendable interfaces and external integrations for
 Some components LangChain implements, some components we rely on third-party integrations for, and others are a mix.
 
 ### Chat models
-
 Language models that use a sequence of messages as inputs and return chat messages as outputs (as opposed to using plain text).
 These are traditionally newer models (older models are generally `LLMs`, see above).
 Chat models support the assignment of distinct roles to conversation messages, helping to distinguish messages from the AI, users, and instructions such as system messages.
 
-Although the underlying models are messages in, message out, the LangChain wrappers also allow these models to take a string as input. This means you can easily use chat models in place of LLMs.
-
-When a string is passed in as input, it is converted to a HumanMessage and then passed to the underlying model.
+Although the underlying models are messages in, message out, the LangChain wrappers also allow these models to take a string as input.
+This makes them interchangeable with LLMs (and simpler to use).
+When a string is passed in as input, it will be converted to a HumanMessage under the hood before being passed to the underlying model.
 
 LangChain does not provide any ChatModels, rather we rely on third party integrations.
 
@@ -144,14 +143,7 @@ We have some standardized parameters when constructing ChatModels:
 
 ChatModels also accept other parameters that are specific to that integration.
 
-:::important
-**Tool Calling** Some chat models have been fine-tuned for tool calling and provide a dedicated API for tool calling.
-Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling.
-Please see the [tool calling section](/docs/concepts/#functiontool-calling) for more information.
-:::
-
 ### LLMs
-
 Language models that takes a string as input and returns a string.
 These are traditionally older models (newer models generally are `ChatModels`, see below).
 
@@ -247,7 +239,7 @@ from langchain_core.prompts import ChatPromptTemplate
 
 prompt_template = ChatPromptTemplate.from_messages([
     ("system", "You are a helpful assistant"),
-    ("user", "Tell me a joke about {topic}")
+    ("user", "Tell me a joke about {topic}"
 ])
 
 prompt_template.invoke({"topic": "cats"})
@@ -417,30 +409,22 @@ Retrievers can be created from vectorstores, but are also broad enough to includ
 Retrievers accept a string query as input and return a list of Document's as output.
 
 ### Tools
-
-Tools are interfaces that an agent, a chain, or a chat model / LLM can use to interact with the world.
-
-A tool consists of the following components:
+Tools are interfaces that an agent, chain, or LLM can use to interact with the world.
+They combine a few things:
 
 1. The name of the tool
-2. A description of what the tool does
+2. A description of what the tool is
 3. JSON schema of what the inputs to the tool are
 4. The function to call
-5. Whether the result of a tool should be returned directly to the user (only relevant for agents)
+5. Whether the result of a tool should be returned directly to the user
 
-The name, description and JSON schema are provided as context
-to the LLM, allowing the LLM to determine how to use the tool
-appropriately.
+It is useful to have all this information because this information can be used to build action-taking systems! The name, description, and JSON schema can be used to prompt the LLM so it knows how to specify what action to take, and then the function to call is equivalent to taking that action.
 
-Given a list of available tools and a prompt, an LLM can request
-that one or more tools be invoked with appropriate arguments.
+The simpler the input to a tool is, the easier it is for an LLM to be able to use it.
+Many agents will only work with tools that have a single string input.
 
-Generally, when designing tools to be used by a chat model or LLM, it is important to keep in mind the following:
+Importantly, the name, description, and JSON schema (if used) are all used in the prompt. Therefore, it is really important that they are clear and describe exactly how the tool should be used. You may need to change the default name, description, or JSON schema if the LLM is not understanding how to use the tool.
 
-- Chat models that have been fine-tuned for tool calling will be better at tool calling than non-fine-tuned models.
-- Non fine-tuned models may not be able to use tools at all, especially if the tools are complex or require multiple tool calls.
-- Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas.
-- Simpler tools are generally easier for models to use than more complex tools.
 
 ### Toolkits
 
@@ -476,87 +460,6 @@ If you are still using AgentExecutor, do not fear: we still have a guide on [how
 It is recommended, however, that you start to transition to LangGraph.
 In order to assist in this we have put together a [transition guide on how to do so](/docs/how_to/migrate_agent)
 
-### Callbacks
-
-LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
-
-You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.
-
-#### Callback handlers
-
-`CallbackHandlers` are objects that implement the [`CallbackHandler`](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) interface, which has a method for each event that can be subscribed to.
-The `CallbackManager` will call the appropriate method on each handler when the event is triggered.
-
-```python
-class BaseCallbackHandler:
-    """Base callback handler that can be used to handle callbacks from langchain."""
-
-    def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> Any:
-        """Run when LLM starts running."""
-
-    def on_chat_model_start(
-        self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any
-    ) -> Any:
-        """Run when Chat Model starts running."""
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> Any:
-        """Run on new LLM token. Only available when streaming is enabled."""
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
-        """Run when LLM ends running."""
-
-    def on_llm_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> Any:
-        """Run when LLM errors."""
-
-    def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> Any:
-        """Run when chain starts running."""
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
-        """Run when chain ends running."""
-
-    def on_chain_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> Any:
-        """Run when chain errors."""
-
-    def on_tool_start(
-        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
-    ) -> Any:
-        """Run when tool starts running."""
-
-    def on_tool_end(self, output: Any, **kwargs: Any) -> Any:
-        """Run when tool ends running."""
-
-    def on_tool_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> Any:
-        """Run when tool errors."""
-
-    def on_text(self, text: str, **kwargs: Any) -> Any:
-        """Run on arbitrary text."""
-
-    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
-        """Run on agent action."""
-
-    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
-        """Run on agent end."""
-```
-
-#### Passing callbacks
-
-The `callbacks` property is available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:
-
-- **Constructor callbacks**: defined in the constructor, e.g. `ChatAnthropic(callbacks=[handler], tags=['a-tag'])`. In this case, the callbacks will be used for all calls made on that object, and will be scoped to that object only.
-  For example, if you initialize a chat model with constructor callbacks, then use it within a chain, the callbacks will only be invoked for calls to that model.
-- **Request callbacks**: passed into the `invoke` method used for issuing a request. In this case, the callbacks will be used for that specific request only, and all sub-requests that it contains (e.g. a call to a sequence that triggers a call to a model, which uses the same handler passed in the `invoke()` method).
-  In the `invoke()` method, callbacks are passed through the `config` parameter.
-
 ## Techniques
 
 ### Function/tool calling
@@ -591,18 +494,12 @@ receive the tool call, execute it, and return the output to the LLM to inform it
 response. LangChain includes a suite of [built-in tools](/docs/integrations/tools/)
 and supports several methods for defining your own [custom tools](/docs/how_to/custom_tools).
 
-LangChain provides a standardized interface for tool calling that is consistent across different models.
-
-The standard interface consists of:
-
-* `ChatModel.bind_tools()`: a method for specifying which tools are available for a model to call.
-* `AIMessage.tool_calls`: an attribute on the `AIMessage` returned from the model for accessing the tool calls requested by the model.
-
 There are two main use cases for function/tool calling:
 
 - [How to return structured data from an LLM](/docs/how_to/structured_output/)
 - [How to use a model to call tools](/docs/how_to/tool_calling/)
 
+
 ### Retrieval
 
 LangChain provides several advanced retrieval types. A full list is below, along with the following information:

docs/docs/contributing/documentation/style_guide.mdx

@@ -88,7 +88,7 @@ Concepts covered in `Integrations` should generally exist in `langchain_communit
 
 ### Guides and Ecosystem
 
-The [Guides](/docs/tutorials) and [Ecosystem](https://docs.smith.langchain.com/) sections should contain guides that address higher-level problems than the sections above.
+The [Guides](/docs/tutorials) and [Ecosystem](/docs/langsmith/) sections should contain guides that address higher-level problems than the sections above.
 This includes, but is not limited to, considerations around productionization and development workflows.
 
 These should contain mostly **How-to guides**, **Explanations**, and **Tutorials**.

docs/docs/contributing/repo_structure.mdx

@@ -6,7 +6,7 @@ sidebar_position: 0.5
 If you plan on contributing to LangChain code or documentation, it can be useful
 to understand the high level structure of the repository.
 
-LangChain is organized as a [monorepo](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages.
+LangChain is organized as a [monorep](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages.
 
 Here's the structure visualized as a tree:
 

docs/docs/how_to/HTML_section_aware_splitter.ipynb

@@ -132,7 +132,7 @@
     }
    ],
    "source": [
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "\n",
     "html_string = \"\"\"\n",
     "    <!DOCTYPE html>\n",

docs/docs/how_to/MultiQueryRetriever.ipynb

@@ -138,10 +138,20 @@
    "execution_count": 5,
    "id": "d9afb0ca",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/chestercurme/.pyenv/versions/3.10.4/envs/sandbox310/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 0.3.0. Use RunnableSequence, e.g., `prompt | llm` instead.\n",
+      "  warn_deprecated(\n"
+     ]
+    }
+   ],
    "source": [
     "from typing import List\n",
     "\n",
+    "from langchain.chains import LLMChain\n",
     "from langchain_core.output_parsers import BaseOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
@@ -170,7 +180,7 @@
     "llm = ChatOpenAI(temperature=0)\n",
     "\n",
     "# Chain\n",
-    "llm_chain = QUERY_PROMPT | llm | output_parser\n",
+    "llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)\n",
     "\n",
     "# Other inputs\n",
     "question = \"What are the approaches to Task Decomposition?\""
@@ -179,14 +189,14 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "59c75c56-dbd7-4887-b9ba-0b5b21069f51",
+   "id": "2eca2d96-8057-4ed9-873d-fa1064c09acf",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer about regression?', '4. In what way is regression covered in the course?', '5. What are the teachings of the course regarding regression?']\n"
+      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer about regression analysis?', '4. What are the teachings of the course regarding regression?', '5. In what manner is regression covered in the course curriculum?']\n"
      ]
     },
     {

docs/docs/how_to/agent_executor.ipynb

@@ -66,7 +66,7 @@
     "```\n",
     "\n",
     "\n",
-    "For more details, see our [Installation guide](/docs/how_to/installation).\n",
+    "For more details, see our [Installation guide](/docs/installation).\n",
     "\n",
     "### LangSmith\n",
     "\n",

docs/docs/how_to/binding.ipynb

@@ -16,7 +16,7 @@
    "id": "711752cb-4f15-42a3-9838-a0c67f397771",
    "metadata": {},
    "source": [
-    "# How to add default invocation args to a Runnable\n",
+    "# How to attach runtime arguments to a Runnable\n",
     "\n",
     ":::info Prerequisites\n",
     "\n",

docs/docs/how_to/callbacks_async.ipynb

@@ -1,171 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to use callbacks in async environments\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
-    "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "If you are planning to use the async APIs, it is recommended to use and extend [`AsyncCallbackHandler`](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) to avoid blocking the runloop.\n",
-    "\n",
-    "**Note**: if you use a sync `CallbackHandler` while using an async method to run your LLM / Chain / Tool / Agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "%pip install -qU langchain langchain_anthropic\n",
-    "\n",
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zzzz....\n",
-      "Hi! I just woke up. Your llm is starting\n",
-      "Sync handler being called in a `thread_pool_executor`: token: Here\n",
-      "Sync handler being called in a `thread_pool_executor`: token: 's\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  a\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  little\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  joke\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  for\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  you\n",
-      "Sync handler being called in a `thread_pool_executor`: token: :\n",
-      "Sync handler being called in a `thread_pool_executor`: token: \n",
-      "\n",
-      "Why\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  can\n",
-      "Sync handler being called in a `thread_pool_executor`: token: 't\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  a\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  bicycle\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  stan\n",
-      "Sync handler being called in a `thread_pool_executor`: token: d up\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  by\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  itself\n",
-      "Sync handler being called in a `thread_pool_executor`: token: ?\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  Because\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  it\n",
-      "Sync handler being called in a `thread_pool_executor`: token: 's\n",
-      "Sync handler being called in a `thread_pool_executor`: token:  two\n",
-      "Sync handler being called in a `thread_pool_executor`: token: -\n",
-      "Sync handler being called in a `thread_pool_executor`: token: tire\n",
-      "zzzz....\n",
-      "Hi! I just woke up. Your llm is ending\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", message=AIMessage(content=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", id='run-8afc89e8-02c0-4522-8480-d96977240bd4-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('8afc89e8-02c0-4522-8480-d96977240bd4'))])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import asyncio\n",
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_core.outputs import LLMResult\n",
-    "\n",
-    "\n",
-    "class MyCustomSyncHandler(BaseCallbackHandler):\n",
-    "    def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
-    "        print(f\"Sync handler being called in a `thread_pool_executor`: token: {token}\")\n",
-    "\n",
-    "\n",
-    "class MyCustomAsyncHandler(AsyncCallbackHandler):\n",
-    "    \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
-    "\n",
-    "    async def on_llm_start(\n",
-    "        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any\n",
-    "    ) -> None:\n",
-    "        \"\"\"Run when chain starts running.\"\"\"\n",
-    "        print(\"zzzz....\")\n",
-    "        await asyncio.sleep(0.3)\n",
-    "        class_name = serialized[\"name\"]\n",
-    "        print(\"Hi! I just woke up. Your llm is starting\")\n",
-    "\n",
-    "    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n",
-    "        \"\"\"Run when chain ends running.\"\"\"\n",
-    "        print(\"zzzz....\")\n",
-    "        await asyncio.sleep(0.3)\n",
-    "        print(\"Hi! I just woke up. Your llm is ending\")\n",
-    "\n",
-    "\n",
-    "# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
-    "# Additionally, we pass in a list with our custom handler\n",
-    "chat = ChatAnthropic(\n",
-    "    model=\"claude-3-sonnet-20240229\",\n",
-    "    max_tokens=25,\n",
-    "    streaming=True,\n",
-    "    callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],\n",
-    ")\n",
-    "\n",
-    "await chat.agenerate([[HumanMessage(content=\"Tell me a joke\")]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    "You've now learned how to create your own custom callback handlers.\n",
-    "\n",
-    "Next, check out the other how-to guides in this section, such as [how to attach callbacks to a runnable](/docs/how_to/callbacks_attach)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/callbacks_attach.ipynb

@@ -1,144 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to attach callbacks to a module\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
-    "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
-    "- [Chaining runnables](/docs/how_to/sequence)\n",
-    "- [Attach runtime arguments to a Runnable](/docs/how_to/binding)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "If you are composing a chain of runnables and want to reuse callbacks across multiple executions, you can attach callbacks with the [`.with_config()`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method. This saves you the need to pass callbacks in each time you invoke the chain.\n",
-    "\n",
-    "Here's an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "%pip install -qU langchain langchain_anthropic\n",
-    "\n",
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chain RunnableSequence started\n",
-      "Chain ChatPromptTemplate started\n",
-      "Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n",
-      "Chat model started\n",
-      "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0'))]] llm_output={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n",
-      "Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0'\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.callbacks import BaseCallbackHandler\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "from langchain_core.outputs import LLMResult\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "\n",
-    "class LoggingHandler(BaseCallbackHandler):\n",
-    "    def on_chat_model_start(\n",
-    "        self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
-    "    ) -> None:\n",
-    "        print(\"Chat model started\")\n",
-    "\n",
-    "    def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
-    "        print(f\"Chat model ended, response: {response}\")\n",
-    "\n",
-    "    def on_chain_start(\n",
-    "        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
-    "    ) -> None:\n",
-    "        print(f\"Chain {serialized.get('name')} started\")\n",
-    "\n",
-    "    def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
-    "        print(f\"Chain ended, outputs: {outputs}\")\n",
-    "\n",
-    "\n",
-    "callbacks = [LoggingHandler()]\n",
-    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
-    "prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "\n",
-    "chain_with_callbacks = chain.with_config(callbacks=callbacks)\n",
-    "\n",
-    "chain_with_callbacks.invoke({\"number\": \"2\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The bound callbacks will run for all nested module runs.\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You've now learned how to attach callbacks to a chain.\n",
-    "\n",
-    "Next, check out the other how-to guides in this section, such as how to [pass callbacks in at runtime](/docs/how_to/callbacks_runtime)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/callbacks_constructor.ipynb

@@ -1,136 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to pass callbacks into a module constructor\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
-    "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "Most LangChain modules allow you to pass `callbacks` directly into the constructor. In this case, the callbacks will only be called for that instance (and any nested runs).\n",
-    "\n",
-    "Here's an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "%pip install -qU langchain langchain_anthropic\n",
-    "\n",
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chat model started\n",
-      "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0'))]] llm_output={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.callbacks import BaseCallbackHandler\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "from langchain_core.outputs import LLMResult\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "\n",
-    "class LoggingHandler(BaseCallbackHandler):\n",
-    "    def on_chat_model_start(\n",
-    "        self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
-    "    ) -> None:\n",
-    "        print(\"Chat model started\")\n",
-    "\n",
-    "    def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
-    "        print(f\"Chat model ended, response: {response}\")\n",
-    "\n",
-    "    def on_chain_start(\n",
-    "        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
-    "    ) -> None:\n",
-    "        print(f\"Chain {serialized.get('name')} started\")\n",
-    "\n",
-    "    def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
-    "        print(f\"Chain ended, outputs: {outputs}\")\n",
-    "\n",
-    "\n",
-    "callbacks = [LoggingHandler()]\n",
-    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", callbacks=callbacks)\n",
-    "prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "\n",
-    "chain.invoke({\"number\": \"2\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see that we only see events from the chat model run - no chain events from the prompt or broader chain.\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You've now learned how to pass callbacks into a constructor.\n",
-    "\n",
-    "Next, check out the other how-to guides in this section, such as how to [pass callbacks at runtime](/docs/how_to/callbacks_runtime)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/callbacks_propagate.ipynb

@@ -1,340 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to propagate callbacks to child components\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
-    "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "If you're creating a custom chain that's composed entirely using LCEL, then callbacks will be propagated automatically for you.\n",
-    "\n",
-    "If you're using `RunnableLambda`, `RunnableGenerator` or `@tool` to create custom components, `langchain` will attempt to propagate\n",
-    "callbacks on your behalf from those components to any child `Runnables` if possible.\n",
-    "\n",
-    ":::{.callout-important}\n",
-    "If you're working in an `async` code base and are using `python<=3.10`, you will need to propagate `config` or `callbacks`  since LangChain is unable to do this automatically. `sync` code has no such limitation.\n",
-    "::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "%pip install -qU langchain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sync (All Python Versions)\n",
-    "\n",
-    "You should see that the custom call back is invoked twice!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---\n",
-      "Invoking custom callback. Recevied inputs: hello\n",
-      "---\n",
-      "Invoking custom callback. Recevied inputs: goodbye\n"
-     ]
-    }
-   ],
-   "source": [
-    "import asyncio\n",
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from langchain_core.callbacks import BaseCallbackHandler\n",
-    "from langchain_core.runnables import RunnableLambda\n",
-    "\n",
-    "class MyCustomHandler(BaseCallbackHandler):\n",
-    "    \"\"\"Sync callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
-    "    def on_chain_start(self, \n",
-    "        serialized: Dict[str, Any],\n",
-    "        inputs: Dict[str, Any],\n",
-    "        **kwargs: Any,\n",
-    "    ) -> None:\n",
-    "        \"\"\"Run when chain starts running.\"\"\"\n",
-    "        print('---')\n",
-    "        print(f'Invoking custom callback. Recevied inputs: {inputs}')\n",
-    "\n",
-    "@RunnableLambda\n",
-    "def foo(inputs):\n",
-    "    return 'world'\n",
-    "\n",
-    "@RunnableLambda\n",
-    "def bar(inputs):\n",
-    "    return foo.invoke('goodbye')\n",
-    "\n",
-    "bar.invoke('hello', {'callbacks': [MyCustomHandler()]});"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Async Python >=3.11\n",
-    "\n",
-    "The custom callback will be invoked twice!\n",
-    "\n",
-    "This code will work correctly even thought `bar` does not propagate config to `foo` does not propagate config!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'3.11.4 (main, Sep 25 2023, 10:06:23) [GCC 11.4.0]'"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---\n",
-      "Invoking custom callback. Recevied inputs: hello\n",
-      "---\n",
-      "Invoking custom callback. Recevied inputs: goodbye\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'world'"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import asyncio\n",
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from langchain_core.callbacks import AsyncCallbackHandler\n",
-    "from langchain_core.runnables import RunnableLambda\n",
-    "\n",
-    "class AsyncMyCustomHandler(AsyncCallbackHandler):\n",
-    "    \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
-    "    async def on_chain_start(self, \n",
-    "        serialized: Dict[str, Any],\n",
-    "        inputs: Dict[str, Any],\n",
-    "        **kwargs: Any,\n",
-    "    ) -> None:\n",
-    "        \"\"\"Run when chain starts running.\"\"\"\n",
-    "        print('---')\n",
-    "        print(f'Invoking custom callback. Recevied inputs: {inputs}')\n",
-    "\n",
-    "@RunnableLambda\n",
-    "async def foo(inputs):\n",
-    "    return 'world'\n",
-    "\n",
-    "@RunnableLambda\n",
-    "async def bar(inputs):\n",
-    "    return await foo.ainvoke('goodbye')\n",
-    "\n",
-    "await bar.ainvoke('hello', {'callbacks': [AsyncMyCustomHandler()]})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Async Python <=3.10\n",
-    "\n",
-    "### Incorrect\n",
-    "\n",
-    "This code will not work!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'3.9.6 (default, Sep 26 2023, 21:46:56) \\n[GCC 11.4.0]'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---\n",
-      "Invoking custom callback. Recevied inputs: hello\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'world'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import asyncio\n",
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from langchain_core.callbacks import AsyncCallbackHandler\n",
-    "from langchain_core.runnables import RunnableLambda\n",
-    "\n",
-    "class AsyncMyCustomHandler(AsyncCallbackHandler):\n",
-    "    \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
-    "    async def on_chain_start(self, \n",
-    "        serialized: Dict[str, Any],\n",
-    "        inputs: Dict[str, Any],\n",
-    "        **kwargs: Any,\n",
-    "    ) -> None:\n",
-    "        \"\"\"Run when chain starts running.\"\"\"\n",
-    "        print('---')\n",
-    "        print(f'Invoking custom callback. Recevied inputs: {inputs}')\n",
-    "\n",
-    "@RunnableLambda\n",
-    "async def foo(inputs):\n",
-    "    return 'world'\n",
-    "\n",
-    "@RunnableLambda\n",
-    "async def bar(inputs):\n",
-    "    return await foo.ainvoke('goodbye')\n",
-    "\n",
-    "await bar.ainvoke('hello', {'callbacks': [AsyncMyCustomHandler()]})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Correct\n",
-    "\n",
-    "This code will work correctly across all supported versions of python.\n",
-    "\n",
-    ":::{.callout-tip}\n",
-    "\n",
-    "This code exposes `config` and propagates it to child component as `foo.ainvoke('goodbye', config=config)`\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---\n",
-      "Invoking custom callback. Recevied inputs: hello\n",
-      "---\n",
-      "Invoking custom callback. Recevied inputs: goodbye\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'world'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "@RunnableLambda\n",
-    "async def foo(inputs, config):\n",
-    "    return 'world'\n",
-    "\n",
-    "@RunnableLambda\n",
-    "async def bar(inputs, config):\n",
-    "    return await foo.ainvoke('goodbye', config=config)\n",
-    "\n",
-    "await bar.ainvoke('hello', {'callbacks': [AsyncMyCustomHandler()]})"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/how_to/callbacks_runtime.ipynb

@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to pass callbacks in at runtime\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
-    "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "In many cases, it is advantageous to pass in handlers instead when running the object. When we pass through [`CallbackHandlers`](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) using the `callbacks` keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an Agent, it will be used for all callbacks related to the agent and all the objects involved in the agent's execution, in this case, the Tools and LLM.\n",
-    "\n",
-    "This prevents us from having to manually attach the handlers to each individual nested object. Here's an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "%pip install -qU langchain langchain_anthropic\n",
-    "\n",
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chain RunnableSequence started\n",
-      "Chain ChatPromptTemplate started\n",
-      "Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n",
-      "Chat model started\n",
-      "Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'))]] llm_output={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n",
-      "Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.callbacks import BaseCallbackHandler\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "from langchain_core.outputs import LLMResult\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "\n",
-    "class LoggingHandler(BaseCallbackHandler):\n",
-    "    def on_chat_model_start(\n",
-    "        self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
-    "    ) -> None:\n",
-    "        print(\"Chat model started\")\n",
-    "\n",
-    "    def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
-    "        print(f\"Chat model ended, response: {response}\")\n",
-    "\n",
-    "    def on_chain_start(\n",
-    "        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
-    "    ) -> None:\n",
-    "        print(f\"Chain {serialized.get('name')} started\")\n",
-    "\n",
-    "    def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
-    "        print(f\"Chain ended, outputs: {outputs}\")\n",
-    "\n",
-    "\n",
-    "callbacks = [LoggingHandler()]\n",
-    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
-    "prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "\n",
-    "chain.invoke({\"number\": \"2\"}, config={\"callbacks\": callbacks})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If there are already existing callbacks associated with a module, these will run in addition to any passed in at runtime.\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You've now learned how to pass callbacks at runtime.\n",
-    "\n",
-    "Next, check out the other how-to guides in this section, such as how to [pass callbacks into a module constructor](/docs/how_to/custom_callbacks)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/chat_model_caching.ipynb

@@ -170,7 +170,7 @@
    "outputs": [],
    "source": [
     "# We can do the same thing with a SQLite cache\n",
-    "from langchain_community.cache import SQLiteCache\n",
+    "from langchain.cache import SQLiteCache\n",
     "\n",
     "set_llm_cache(SQLiteCache(database_path=\".langchain.db\"))"
    ]

docs/docs/how_to/chatbots_memory.ipynb

@@ -165,7 +165,7 @@
     }
    ],
    "source": [
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
+    "from langchain.memory import ChatMessageHistory\n",
     "\n",
     "demo_ephemeral_chat_history = ChatMessageHistory()\n",
     "\n",

docs/docs/how_to/chatbots_tools.ipynb

@@ -336,7 +336,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
+    "from langchain.memory import ChatMessageHistory\n",
     "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
     "\n",
     "demo_ephemeral_chat_history_for_chain = ChatMessageHistory()\n",

docs/docs/how_to/configure.ipynb

@@ -89,7 +89,7 @@
     }
    ],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_core.runnables import ConfigurableField\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
@@ -312,8 +312,8 @@
     }
    ],
    "source": [
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_core.runnables import ConfigurableField\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",

docs/docs/how_to/custom_callbacks.ipynb

@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to create custom callback handlers\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "LangChain has some built-in callback handlers, but you will often want to create your own handlers with custom logic.\n",
-    "\n",
-    "To create a custom callback handler, we need to determine the [event(s)](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) we want our callback handler to handle as well as what we want our callback handler to do when the event is triggered. Then all we need to do is attach the callback handler to the object, for example via [the constructor](/docs/how_to/callbacks_constructor) or [at runtime](/docs/how_to/callbacks_runtime).\n",
-    "\n",
-    "In the example below, we'll implement streaming with a custom handler.\n",
-    "\n",
-    "In our custom callback handler `MyCustomHandler`, we implement the `on_llm_new_token` handler to print the token we have just received. We then attach our custom handler to the model object as a constructor callback."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "%pip install -qU langchain langchain_anthropic\n",
-    "\n",
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "My custom handler, token: Here\n",
-      "My custom handler, token: 's\n",
-      "My custom handler, token:  a\n",
-      "My custom handler, token:  bear\n",
-      "My custom handler, token:  joke\n",
-      "My custom handler, token:  for\n",
-      "My custom handler, token:  you\n",
-      "My custom handler, token: :\n",
-      "My custom handler, token: \n",
-      "\n",
-      "Why\n",
-      "My custom handler, token:  di\n",
-      "My custom handler, token: d the\n",
-      "My custom handler, token:  bear\n",
-      "My custom handler, token:  dissol\n",
-      "My custom handler, token: ve\n",
-      "My custom handler, token:  in\n",
-      "My custom handler, token:  water\n",
-      "My custom handler, token: ?\n",
-      "My custom handler, token: \n",
-      "Because\n",
-      "My custom handler, token:  it\n",
-      "My custom handler, token:  was\n",
-      "My custom handler, token:  a\n",
-      "My custom handler, token:  polar\n",
-      "My custom handler, token:  bear\n",
-      "My custom handler, token: !\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.callbacks import BaseCallbackHandler\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "\n",
-    "class MyCustomHandler(BaseCallbackHandler):\n",
-    "    def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
-    "        print(f\"My custom handler, token: {token}\")\n",
-    "\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages([\"Tell me a joke about {animal}\"])\n",
-    "\n",
-    "# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
-    "# Additionally, we pass in our custom handler as a list to the callbacks parameter\n",
-    "model = ChatAnthropic(\n",
-    "    model=\"claude-3-sonnet-20240229\", streaming=True, callbacks=[MyCustomHandler()]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | model\n",
-    "\n",
-    "response = chain.invoke({\"animal\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see [this reference page](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) for a list of events you can handle. Note that the `handle_chain_*` events run for most LCEL runnables.\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You've now learned how to create your own custom callback handlers.\n",
-    "\n",
-    "Next, check out the other how-to guides in this section, such as [how to attach callbacks to a runnable](/docs/how_to/callbacks_attach)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/custom_tools.ipynb

@@ -5,29 +5,35 @@
    "id": "5436020b",
    "metadata": {},
    "source": [
-    "# How to create custom tools\n",
+    "# How to create custom Tools\n",
     "\n",
-    "When constructing an agent, you will need to provide it with a list of `Tool`s that it can use. Besides the actual function that is called, the Tool consists of several components:\n",
+    "When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:\n",
     "\n",
-    "| Attribute       | Type                      | Description                                                                                                      |\n",
-    "|-----------------|---------------------------|------------------------------------------------------------------------------------------------------------------|\n",
-    "| name          | str                     | Must be unique within a set of tools provided to an LLM or agent.                                           |\n",
-    "| description   | str                     | Describes what the tool does. Used as context by the LLM or agent.                                       |\n",
-    "| args_schema   | Pydantic BaseModel      | Optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters |\n",
-    "| return_direct   | boolean      | Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user.  |\n",
+    "- `name` (str), is required and must be unique within a set of tools provided to an agent\n",
+    "- `description` (str), is optional but recommended, as it is used by an agent to determine tool use\n",
+    "- `args_schema` (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.\n",
     "\n",
-    "LangChain provides 3 ways to create tools:\n",
     "\n",
-    "1. Using [@tool decorator](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.tool.html#langchain_core.tools.tool) -- the simplest way to define a custom tool.\n",
-    "2. Using [StructuredTool.from_function](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method -- this is similar to the `@tool` decorator, but allows more configuration and specification of both sync and async implementations.\n",
-    "3. By sub-classing from [BaseTool](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
+    "There are multiple ways to define a tool. In this guide, we will walk through how to do for two functions:\n",
     "\n",
-    "The `@tool` or the `StructuredTool.from_function` class method should be sufficient for most use cases.\n",
+    "1. A made up search function that always returns the string \"LangChain\"\n",
+    "2. A multiplier function that will multiply two numbers by eachother\n",
     "\n",
-    ":::{.callout-tip}\n",
-    "\n",
-    "Models will perform better if the tools have well chosen names, descriptions and JSON schemas.\n",
-    ":::"
+    "The biggest difference here is that the first function only requires one input, while the second one requires multiple. Many agents only work with functions that require single inputs, so it's important to know how to work with those. For the most part, defining these custom tools is the same, but there are some differences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "1aaba18c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Import things that are needed generically\n",
+    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from langchain.tools import BaseTool, StructuredTool, tool"
    ]
   },
   {
@@ -42,8 +48,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "cc7005cd-072f-4d37-8453-6297468e5192",
+   "execution_count": 4,
+   "id": "b0ce7de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool\n",
+    "def search(query: str) -> str:\n",
+    "    \"\"\"Look up things online.\"\"\"\n",
+    "    return \"LangChain\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e889fa34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "search\n",
+      "search(query: str) -> str - Look up things online.\n",
+      "{'query': {'title': 'Query', 'type': 'string'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(search.name)\n",
+    "print(search.description)\n",
+    "print(search.args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0b9694d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool\n",
+    "def multiply(a: int, b: int) -> int:\n",
+    "    \"\"\"Multiply two numbers.\"\"\"\n",
+    "    return a * b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d7f9395b",
    "metadata": {},
    "outputs": [
     {
@@ -57,45 +111,11 @@
     }
    ],
    "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "# Let's inspect some of the attributes associated with the tool.\n",
     "print(multiply.name)\n",
     "print(multiply.description)\n",
     "print(multiply.args)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "96698b67-993a-4c97-b867-333132e1eb14",
-   "metadata": {},
-   "source": [
-    "Or create an **async** implementation, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0c0991db-b997-4611-be37-4346e660506b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "async def amultiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "98d6eee9",
@@ -106,164 +126,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "9216d03a-f6ea-4216-b7e1-0661823a4c0b",
+   "execution_count": 43,
+   "id": "dbbf4b6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SearchInput(BaseModel):\n",
+    "    query: str = Field(description=\"should be a search query\")\n",
+    "\n",
+    "\n",
+    "@tool(\"search-tool\", args_schema=SearchInput, return_direct=True)\n",
+    "def search(query: str) -> str:\n",
+    "    \"\"\"Look up things online.\"\"\"\n",
+    "    return \"LangChain\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "5950ce32",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "multiplication-tool\n",
-      "multiplication-tool(a: int, b: int) -> int - Multiply two numbers.\n",
-      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n",
+      "search-tool\n",
+      "search-tool(query: str) -> str - Look up things online.\n",
+      "{'query': {'title': 'Query', 'description': 'should be a search query', 'type': 'string'}}\n",
       "True\n"
      ]
     }
    ],
    "source": [
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class CalculatorInput(BaseModel):\n",
-    "    a: int = Field(description=\"first number\")\n",
-    "    b: int = Field(description=\"second number\")\n",
-    "\n",
-    "\n",
-    "@tool(\"multiplication-tool\", args_schema=CalculatorInput, return_direct=True)\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "# Let's inspect some of the attributes associated with the tool.\n",
-    "print(multiply.name)\n",
-    "print(multiply.description)\n",
-    "print(multiply.args)\n",
-    "print(multiply.return_direct)"
+    "print(search.name)\n",
+    "print(search.description)\n",
+    "print(search.args)\n",
+    "print(search.return_direct)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b63fcc3b",
-   "metadata": {},
-   "source": [
-    "## StructuredTool\n",
-    "\n",
-    "The `StrurcturedTool.from_function` class method provides a bit more configurability than the `@tool` decorator, without requiring much additional code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "564fbe6f-11df-402d-b135-ef6ff25e1e63",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n",
-      "10\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.tools import StructuredTool\n",
-    "\n",
-    "\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "async def amultiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "calculator = StructuredTool.from_function(func=multiply, coroutine=amultiply)\n",
-    "\n",
-    "print(calculator.invoke({\"a\": 2, \"b\": 3}))\n",
-    "print(await calculator.ainvoke({\"a\": 2, \"b\": 5}))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26b3712a-b38d-4582-b6e6-bc7cfb1d6680",
-   "metadata": {},
-   "source": [
-    "To configure it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6bc055d4-1fbe-4db5-8881-9c382eba6b1b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n",
-      "Calculator\n",
-      "Calculator(a: int, b: int) -> int - multiply numbers\n",
-      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "class CalculatorInput(BaseModel):\n",
-    "    a: int = Field(description=\"first number\")\n",
-    "    b: int = Field(description=\"second number\")\n",
-    "\n",
-    "\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "calculator = StructuredTool.from_function(\n",
-    "    func=multiply,\n",
-    "    name=\"Calculator\",\n",
-    "    description=\"multiply numbers\",\n",
-    "    args_schema=CalculatorInput,\n",
-    "    return_direct=True,\n",
-    "    # coroutine= ... <- you can specify an async method if desired as well\n",
-    ")\n",
-    "\n",
-    "print(calculator.invoke({\"a\": 2, \"b\": 3}))\n",
-    "print(calculator.name)\n",
-    "print(calculator.description)\n",
-    "print(calculator.args)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b840074b-9c10-4ca0-aed8-626c52b2398f",
+   "id": "9d11e80c",
    "metadata": {},
    "source": [
     "## Subclass BaseTool\n",
     "\n",
-    "You can define a custom tool by sub-classing from `BaseTool`. This provides maximal control over the tool definition, but requires writing more code."
+    "You can also explicitly define a custom tool by subclassing the BaseTool class. This provides maximal control over the tool definition, but is a bit more work."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 45,
    "id": "1dad8f8e",
    "metadata": {},
    "outputs": [],
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
-    "from langchain_core.callbacks import (\n",
+    "from langchain.callbacks.manager import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
-    "from langchain_core.tools import BaseTool\n",
+    "\n",
+    "\n",
+    "class SearchInput(BaseModel):\n",
+    "    query: str = Field(description=\"should be a search query\")\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",
@@ -271,6 +199,24 @@
     "    b: int = Field(description=\"second number\")\n",
     "\n",
     "\n",
+    "class CustomSearchTool(BaseTool):\n",
+    "    name = \"custom_search\"\n",
+    "    description = \"useful for when you need to answer questions about current events\"\n",
+    "    args_schema: Type[BaseModel] = SearchInput\n",
+    "\n",
+    "    def _run(\n",
+    "        self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None\n",
+    "    ) -> str:\n",
+    "        \"\"\"Use the tool.\"\"\"\n",
+    "        return \"LangChain\"\n",
+    "\n",
+    "    async def _arun(\n",
+    "        self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None\n",
+    "    ) -> str:\n",
+    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
+    "        raise NotImplementedError(\"custom_search does not support async\")\n",
+    "\n",
+    "\n",
     "class CustomCalculatorTool(BaseTool):\n",
     "    name = \"Calculator\"\n",
     "    description = \"useful for when you need to answer questions about math\"\n",
@@ -290,17 +236,35 @@
     "        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n",
     "    ) -> str:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        # If the calculation is cheap, you can just delegate to the sync implementation\n",
-    "        # as shown below.\n",
-    "        # If the sync calculation is expensive, you should delete the entire _arun method.\n",
-    "        # LangChain will automatically provide a better implementation that will\n",
-    "        # kick off the task in a thread to make sure it doesn't block other async code.\n",
-    "        return self._run(a, b, run_manager=run_manager.get_sync())"
+    "        raise NotImplementedError(\"Calculator does not support async\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 46,
+   "id": "89933e27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "custom_search\n",
+      "useful for when you need to answer questions about current events\n",
+      "{'query': {'title': 'Query', 'description': 'should be a search query', 'type': 'string'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "search = CustomSearchTool()\n",
+    "print(search.name)\n",
+    "print(search.description)\n",
+    "print(search.args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "id": "bb551c33",
    "metadata": {},
    "outputs": [
@@ -311,9 +275,7 @@
       "Calculator\n",
       "useful for when you need to answer questions about math\n",
       "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n",
-      "True\n",
-      "6\n",
-      "6\n"
+      "True\n"
      ]
     }
    ],
@@ -322,50 +284,80 @@
     "print(multiply.name)\n",
     "print(multiply.description)\n",
     "print(multiply.args)\n",
-    "print(multiply.return_direct)\n",
-    "\n",
-    "print(multiply.invoke({\"a\": 2, \"b\": 3}))\n",
-    "print(await multiply.ainvoke({\"a\": 2, \"b\": 3}))"
+    "print(multiply.return_direct)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "97aba6cc-4bdf-4fab-aff3-d89e7d9c3a09",
+   "id": "b63fcc3b",
    "metadata": {},
    "source": [
-    "## How to create async tools\n",
+    "## StructuredTool dataclass\n",
     "\n",
-    "LangChain Tools implement the [Runnable interface 🏃](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html).\n",
-    "\n",
-    "All Runnables expose the `invoke` and `ainvoke` methods (as well as other methods like `batch`, `abatch`, `astream` etc).\n",
-    "\n",
-    "So even if you only provide an `sync` implementation of a tool, you could still use the `ainvoke` interface, but there\n",
-    "are some important things to know:\n",
-    "\n",
-    "* LangChain's by default provides an async implementation that assumes that the function is expensive to compute, so it'll delegate execution to another thread.\n",
-    "* If you're working in an async codebase, you should create async tools rather than sync tools, to avoid incuring a small overhead due to that thread.\n",
-    "* If you need both sync and async implementations, use `StructuredTool.from_function` or sub-class from `BaseTool`.\n",
-    "* If implementing both sync and async, and the sync code is fast to run, override the default LangChain async implementation and simply call the sync code.\n",
-    "* You CANNOT and SHOULD NOT use the sync `invoke` with an `async` tool."
+    "You can also use a `StructuredTool` dataclass. This methods is a mix between the previous two. It's more convenient than inheriting from the BaseTool class, but provides more functionality than just using a decorator."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "6615cb77-fd4c-4676-8965-f92cc71d4944",
+   "execution_count": 35,
+   "id": "56ff7670",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def search_function(query: str):\n",
+    "    return \"LangChain\"\n",
+    "\n",
+    "\n",
+    "search = StructuredTool.from_function(\n",
+    "    func=search_function,\n",
+    "    name=\"Search\",\n",
+    "    description=\"useful for when you need to answer questions about current events\",\n",
+    "    # coroutine= ... <- you can specify an async method if desired as well\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "d3fd3896",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6\n",
-      "10\n"
+      "Search\n",
+      "Search(query: str) - useful for when you need to answer questions about current events\n",
+      "{'query': {'title': 'Query', 'type': 'string'}}\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.tools import StructuredTool\n",
+    "print(search.name)\n",
+    "print(search.description)\n",
+    "print(search.args)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9b560f7",
+   "metadata": {},
+   "source": [
+    "You can also define a custom `args_schema` to provide more information about inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "712c1967",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CalculatorInput(BaseModel):\n",
+    "    a: int = Field(description=\"first number\")\n",
+    "    b: int = Field(description=\"second number\")\n",
     "\n",
     "\n",
     "def multiply(a: int, b: int) -> int:\n",
@@ -373,223 +365,185 @@
     "    return a * b\n",
     "\n",
     "\n",
-    "calculator = StructuredTool.from_function(func=multiply)\n",
-    "\n",
-    "print(calculator.invoke({\"a\": 2, \"b\": 3}))\n",
-    "print(\n",
-    "    await calculator.ainvoke({\"a\": 2, \"b\": 5})\n",
-    ")  # Uses default LangChain async implementation incurs small overhead"
+    "calculator = StructuredTool.from_function(\n",
+    "    func=multiply,\n",
+    "    name=\"Calculator\",\n",
+    "    description=\"multiply numbers\",\n",
+    "    args_schema=CalculatorInput,\n",
+    "    return_direct=True,\n",
+    "    # coroutine= ... <- you can specify an async method if desired as well\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "bb2af583-eadd-41f4-a645-bf8748bd3dcd",
+   "execution_count": 42,
+   "id": "f634081e",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6\n",
-      "10\n"
+      "Calculator\n",
+      "Calculator(a: int, b: int) -> int - multiply numbers\n",
+      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.tools import StructuredTool\n",
-    "\n",
-    "\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "async def amultiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "calculator = StructuredTool.from_function(func=multiply, coroutine=amultiply)\n",
-    "\n",
-    "print(calculator.invoke({\"a\": 2, \"b\": 3}))\n",
-    "print(\n",
-    "    await calculator.ainvoke({\"a\": 2, \"b\": 5})\n",
-    ")  # Uses use provided amultiply without additional overhead"
+    "print(calculator.name)\n",
+    "print(calculator.description)\n",
+    "print(calculator.args)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c80ffdaa-e4ba-4a70-8500-32bf4f60cc1a",
-   "metadata": {},
-   "source": [
-    "You should not and cannot use `.invoke` when providing only an async definition."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "4ad0932c-8610-4278-8c57-f9218f654c8a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Raised not implemented error. You should not be doing this.\n"
-     ]
-    }
-   ],
-   "source": [
-    "@tool\n",
-    "async def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiply two numbers.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "try:\n",
-    "    multiply.invoke({\"a\": 2, \"b\": 3})\n",
-    "except NotImplementedError:\n",
-    "    print(\"Raised not implemented error. You should not be doing this.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9c746a7-88d7-4afb-bcb8-0e98b891e8b6",
+   "id": "f1da459d",
    "metadata": {},
    "source": [
     "## Handling Tool Errors \n",
+    "When a tool encounters an error and the exception is not caught, the agent will stop executing. If you want the agent to continue execution, you can raise a `ToolException` and set `handle_tool_error` accordingly. \n",
     "\n",
-    "If you're using tools with agents, you will likely need an error handling strategy, so the agent can recover from the error and continue execution.\n",
+    "When `ToolException` is thrown, the agent will not stop working, but will handle the exception according to the `handle_tool_error` variable of the tool, and the processing result will be returned to the agent as observation, and printed in red.\n",
     "\n",
-    "A simple strategy is to throw a `ToolException` from inside the tool and specify an error handler using `handle_tool_error`. \n",
-    "\n",
-    "When the error handler is specified, the exception will be caught and the error handler will decide which output to return from the tool.\n",
-    "\n",
-    "You can set `handle_tool_error` to `True`, a string value, or a function. If it's a function, the function should take a `ToolException` as a parameter and return a value.\n",
+    "You can set `handle_tool_error` to `True`, set it a unified string value, or set it as a function. If it's set as a function, the function should take a `ToolException` as a parameter and return a `str` value.\n",
     "\n",
     "Please note that only raising a `ToolException` won't be effective. You need to first set the `handle_tool_error` of the tool because its default value is `False`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "7094c0e8-6192-4870-a942-aad5b5ae48fd",
+   "execution_count": null,
+   "id": "f8bf4668",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.tools import ToolException\n",
     "\n",
     "\n",
-    "def get_weather(city: str) -> int:\n",
-    "    \"\"\"Get weather for the given city.\"\"\"\n",
-    "    raise ToolException(f\"Error: There is no city by the name of {city}.\")"
+    "def search_tool1(s: str):\n",
+    "    raise ToolException(\"The search tool1 is not available.\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9d93b217-1d44-4d31-8956-db9ea680ff4f",
+   "id": "7fb56757",
    "metadata": {},
    "source": [
-    "Here's an example with the default `handle_tool_error=True` behavior."
+    "First, let's see what happens if we don't set `handle_tool_error` - it will error."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "b4d22022-b105-4ccc-a15b-412cb9ea3097",
+   "execution_count": 58,
+   "id": "f3dfbcb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ToolException",
+     "evalue": "The search tool1 is not available.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mToolException\u001b[0m                             Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[58], line 7\u001b[0m\n\u001b[1;32m      1\u001b[0m search \u001b[38;5;241m=\u001b[39m StructuredTool\u001b[38;5;241m.\u001b[39mfrom_function(\n\u001b[1;32m      2\u001b[0m     func\u001b[38;5;241m=\u001b[39msearch_tool1,\n\u001b[1;32m      3\u001b[0m     name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSearch_tool1\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      4\u001b[0m     description\u001b[38;5;241m=\u001b[39mdescription,\n\u001b[1;32m      5\u001b[0m )\n\u001b[0;32m----> 7\u001b[0m \u001b[43msearch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/tools.py:344\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_tool_error:\n\u001b[1;32m    343\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_tool_error(e)\n\u001b[0;32m--> 344\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_tool_error, \u001b[38;5;28mbool\u001b[39m):\n\u001b[1;32m    346\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39margs:\n",
+      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/tools.py:337\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    335\u001b[0m     tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_args_and_kwargs(parsed_input)\n\u001b[1;32m    336\u001b[0m     observation \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 337\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtool_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtool_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    339\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run(\u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    340\u001b[0m     )\n\u001b[1;32m    341\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ToolException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    342\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_tool_error:\n",
+      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/tools.py:631\u001b[0m, in \u001b[0;36mStructuredTool._run\u001b[0;34m(self, run_manager, *args, **kwargs)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc:\n\u001b[1;32m    623\u001b[0m     new_argument_supported \u001b[38;5;241m=\u001b[39m signature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    624\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m    625\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(\n\u001b[1;32m    626\u001b[0m             \u001b[38;5;241m*\u001b[39margs,\n\u001b[1;32m    627\u001b[0m             callbacks\u001b[38;5;241m=\u001b[39mrun_manager\u001b[38;5;241m.\u001b[39mget_child() \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    628\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    629\u001b[0m         )\n\u001b[1;32m    630\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_argument_supported\n\u001b[0;32m--> 631\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    632\u001b[0m     )\n\u001b[1;32m    633\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool does not support sync\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[55], line 5\u001b[0m, in \u001b[0;36msearch_tool1\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msearch_tool1\u001b[39m(s: \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m----> 5\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ToolException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe search tool1 is not available.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mToolException\u001b[0m: The search tool1 is not available."
+     ]
+    }
+   ],
+   "source": [
+    "search = StructuredTool.from_function(\n",
+    "    func=search_tool1,\n",
+    "    name=\"Search_tool1\",\n",
+    "    description=\"A bad tool\",\n",
+    ")\n",
+    "\n",
+    "search.run(\"test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2475acd",
+   "metadata": {},
+   "source": [
+    "Now, let's set `handle_tool_error` to be True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "ab81e0f0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Error: There is no city by the name of foobar.'"
+       "'The search tool1 is not available.'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "get_weather_tool = StructuredTool.from_function(\n",
-    "    func=get_weather,\n",
+    "search = StructuredTool.from_function(\n",
+    "    func=search_tool1,\n",
+    "    name=\"Search_tool1\",\n",
+    "    description=\"A bad tool\",\n",
     "    handle_tool_error=True,\n",
     ")\n",
     "\n",
-    "get_weather_tool.invoke({\"city\": \"foobar\"})"
+    "search.run(\"test\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f91d6dc0-3271-4adc-a155-21f2e62ffa56",
+   "id": "dafbbcbe",
    "metadata": {},
    "source": [
-    "We can set `handle_tool_error` to a string that will always be returned."
+    "We can also define a custom way to handle the tool error"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "3fad1728-d367-4e1b-9b54-3172981271cf",
+   "execution_count": 60,
+   "id": "ad16fbcf",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"There is no such city, but it's probably above 0K there!\""
+       "'The following errors occurred during tool execution:The search tool1 is not available.Please try another tool.'"
       ]
      },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_weather_tool = StructuredTool.from_function(\n",
-    "    func=get_weather,\n",
-    "    handle_tool_error=\"There is no such city, but it's probably above 0K there!\",\n",
-    ")\n",
-    "\n",
-    "get_weather_tool.invoke({\"city\": \"foobar\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b0a640c1-e08f-4413-83b6-f599f304935f",
-   "metadata": {},
-   "source": [
-    "Handling the error using a function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ebfe7c1f-318d-4e58-99e1-f31e69473c46",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The following errors occurred during tool execution: `Error: There is no city by the name of foobar.`'"
-      ]
-     },
-     "execution_count": 14,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def _handle_error(error: ToolException) -> str:\n",
-    "    return f\"The following errors occurred during tool execution: `{error.args[0]}`\"\n",
+    "    return (\n",
+    "        \"The following errors occurred during tool execution:\"\n",
+    "        + error.args[0]\n",
+    "        + \"Please try another tool.\"\n",
+    "    )\n",
     "\n",
     "\n",
-    "get_weather_tool = StructuredTool.from_function(\n",
-    "    func=get_weather,\n",
+    "search = StructuredTool.from_function(\n",
+    "    func=search_tool1,\n",
+    "    name=\"Search_tool1\",\n",
+    "    description=\"A bad tool\",\n",
     "    handle_tool_error=_handle_error,\n",
     ")\n",
     "\n",
-    "get_weather_tool.invoke({\"city\": \"foobar\"})"
+    "search.run(\"test\")"
    ]
   }
  ],
@@ -609,7 +563,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.1"
   },
   "vscode": {
    "interpreter": {

docs/docs/how_to/debugging.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "- Verbose Mode: This adds print statements for \"important\" events in your chain.\n",
     "- Debug Mode: This add logging statements for ALL events in your chain.\n",
-    "- LangSmith Tracing: This logs events to [LangSmith](https://docs.smith.langchain.com/) to allow for visualization there.\n",
+    "- LangSmith Tracing: This logs events to [LangSmith](/docs/langsmith/) to allow for visualization there.\n",
     "\n",
     "|                        | Verbose Mode | Debug Mode | LangSmith Tracing |\n",
     "|------------------------|--------------|------------|-------------------|\n",

docs/docs/how_to/document_loader_pdf.ipynb

@@ -463,7 +463,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.documents import Document\n",
+    "from langchain.docstore.document import Document\n",
     "\n",
     "cur_idx = -1\n",
     "semantic_snippets = []\n",

docs/docs/how_to/dynamic_chain.ipynb

@@ -1,200 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "77bf57fb-e990-45f2-8b5f-c76388b05966",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "keywords: [LCEL]\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50d57bf2-7104-4570-b3e5-90fd71e1bea1",
-   "metadata": {},
-   "source": [
-    "# How to create a dynamic (self-constructing) chain\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
-    "- [How to turn any function into a runnable](/docs/how_to/functions)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "Sometimes we want to construct parts of a chain at runtime, depending on the chain inputs ([routing](/docs/how_to/routing/) is the most common example of this). We can create dynamic chains like this using a very useful property of RunnableLambda's, which is that if a RunnableLambda returns a Runnable, that Runnable is itself invoked. Let's see an example.\n",
-    "\n",
-    "```{=mdx}\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs\n",
-    "  customVarName=\"llm\"\n",
-    "/>\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "406bffc2-86d0-4cb9-9262-5c1e3442397a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | echo: false\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0ae6692b-983e-40b8-aa2a-6c078d945b9e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"According to the context provided, Egypt's population in 2024 is estimated to be about 111 million.\""
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import Runnable, RunnablePassthrough, chain\n",
-    "\n",
-    "contextualize_instructions = \"\"\"Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text).\"\"\"\n",
-    "contextualize_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", contextualize_instructions),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{question}\"),\n",
-    "    ]\n",
-    ")\n",
-    "contextualize_question = contextualize_prompt | llm | StrOutputParser()\n",
-    "\n",
-    "qa_instructions = (\n",
-    "    \"\"\"Answer the user question given the following context:\\n\\n{context}.\"\"\"\n",
-    ")\n",
-    "qa_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"system\", qa_instructions), (\"human\", \"{question}\")]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "@chain\n",
-    "def contextualize_if_needed(input_: dict) -> Runnable:\n",
-    "    if input_.get(\"chat_history\"):\n",
-    "        # NOTE: This is returning another Runnable, not an actual output.\n",
-    "        return contextualize_question\n",
-    "    else:\n",
-    "        return RunnablePassthrough()\n",
-    "\n",
-    "\n",
-    "@chain\n",
-    "def fake_retriever(input_: dict) -> str:\n",
-    "    return \"egypt's population in 2024 is about 111 million\"\n",
-    "\n",
-    "\n",
-    "full_chain = (\n",
-    "    RunnablePassthrough.assign(question=contextualize_if_needed).assign(\n",
-    "        context=fake_retriever\n",
-    "    )\n",
-    "    | qa_prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "full_chain.invoke(\n",
-    "    {\n",
-    "        \"question\": \"what about egypt\",\n",
-    "        \"chat_history\": [\n",
-    "            (\"human\", \"what's the population of indonesia\"),\n",
-    "            (\"ai\", \"about 276 million\"),\n",
-    "        ],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5076ddb4-4a99-47ad-b549-8ac27ca3e2c6",
-   "metadata": {},
-   "source": [
-    "The key here is that `contextualize_if_needed` returns another Runnable and not an actual output. This returned Runnable is itself run when the full chain is executed.\n",
-    "\n",
-    "Looking at the trace we can see that, since we passed in chat_history, we executed the contextualize_question chain as part of the full chain: https://smith.langchain.com/public/9e0ae34c-4082-4f3f-beed-34a2a2f4c991/r"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fe6ca44-a643-4859-a290-be68403f51f0",
-   "metadata": {},
-   "source": [
-    "Note that the streaming, batching, etc. capabilities of the returned Runnable are all preserved"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6def37fa-5105-4090-9b07-77cb488ecd9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "What\n",
-      " is\n",
-      " the\n",
-      " population\n",
-      " of\n",
-      " Egypt\n",
-      "?\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in contextualize_if_needed.stream(\n",
-    "    {\n",
-    "        \"question\": \"what about egypt\",\n",
-    "        \"chat_history\": [\n",
-    "            (\"human\", \"what's the population of indonesia\"),\n",
-    "            (\"ai\", \"about 276 million\"),\n",
-    "        ],\n",
-    "    }\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv-2",
-   "language": "python",
-   "name": "poetry-venv-2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/how_to/example_selectors_length_based.ipynb

@@ -17,8 +17,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.example_selectors import LengthBasedExampleSelector\n",
-    "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "from langchain.prompts.example_selector import LengthBasedExampleSelector\n",
     "\n",
     "# Examples of a pretend task of creating antonyms.\n",
     "examples = [\n",

docs/docs/how_to/example_selectors_mmr.ipynb

@@ -17,12 +17,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_core.example_selectors import (\n",
+    "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "from langchain.prompts.example_selector import (\n",
     "    MaxMarginalRelevanceExampleSelector,\n",
     "    SemanticSimilarityExampleSelector,\n",
     ")\n",
-    "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "from langchain_community.vectorstores import FAISS\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "example_prompt = PromptTemplate(\n",

docs/docs/how_to/example_selectors_ngram.ipynb

@@ -19,8 +19,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.example_selectors import NGramOverlapExampleSelector\n",
-    "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "from langchain.prompts.example_selector.ngram_overlap import NGramOverlapExampleSelector\n",
     "\n",
     "example_prompt = PromptTemplate(\n",
     "    input_variables=[\"input\", \"output\"],\n",

docs/docs/how_to/example_selectors_similarity.ipynb

@@ -17,9 +17,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "from langchain.prompts.example_selector import SemanticSimilarityExampleSelector\n",
     "from langchain_chroma import Chroma\n",
-    "from langchain_core.example_selectors import SemanticSimilarityExampleSelector\n",
-    "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "example_prompt = PromptTemplate(\n",

docs/docs/how_to/extraction_parse.ipynb

@@ -69,7 +69,7 @@
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.output_parsers import PydanticOutputParser\n",
+    "from langchain.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
     "\n",

docs/docs/how_to/fallbacks.ipynb

@@ -1,21 +1,11 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "018f3868-e60d-4db6-a1c6-c6633c66b1f4",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "keywords: [LCEL, fallbacks]\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "19c9cbd6",
    "metadata": {},
    "source": [
-    "# How to add fallbacks to a runnable\n",
+    "# Fallbacks\n",
     "\n",
     "When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safeguard against these. That's why we've introduced the concept of fallbacks. \n",
     "\n",
@@ -53,7 +43,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_anthropic import ChatAnthropic\n",
+    "from langchain_community.chat_models import ChatAnthropic\n",
     "from langchain_openai import ChatOpenAI"
    ]
   },
@@ -90,8 +80,8 @@
    "outputs": [],
    "source": [
     "# Note that we set max_retries = 0 to avoid retrying on RateLimits, etc\n",
-    "openai_llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", max_retries=0)\n",
-    "anthropic_llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n",
+    "openai_llm = ChatOpenAI(max_retries=0)\n",
+    "anthropic_llm = ChatAnthropic()\n",
     "llm = openai_llm.with_fallbacks([anthropic_llm])"
    ]
   },
@@ -457,7 +447,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/how_to/few_shot_examples.ipynb

@@ -45,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
     "\n",
     "example_prompt = PromptTemplate.from_template(\"Question: {question}\\n{answer}\")"
    ]
@@ -222,7 +222,7 @@
     }
    ],
    "source": [
-    "from langchain_core.prompts import FewShotPromptTemplate\n",
+    "from langchain.prompts.few_shot import FewShotPromptTemplate\n",
     "\n",
     "prompt = FewShotPromptTemplate(\n",
     "    examples=examples,\n",
@@ -282,8 +282,8 @@
     }
    ],
    "source": [
+    "from langchain.prompts.example_selector import SemanticSimilarityExampleSelector\n",
     "from langchain_chroma import Chroma\n",
-    "from langchain_core.example_selectors import SemanticSimilarityExampleSelector\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "example_selector = SemanticSimilarityExampleSelector.from_examples(\n",

docs/docs/how_to/few_shot_examples_chat.ipynb

@@ -88,7 +88,10 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
+    "from langchain.prompts import (\n",
+    "    ChatPromptTemplate,\n",
+    "    FewShotChatMessagePromptTemplate,\n",
+    ")\n",
     "\n",
     "examples = [\n",
     "    {\"input\": \"2+2\", \"output\": \"4\"},\n",
@@ -215,8 +218,8 @@
    },
    "outputs": [],
    "source": [
+    "from langchain.prompts import SemanticSimilarityExampleSelector\n",
     "from langchain_chroma import Chroma\n",
-    "from langchain_core.example_selectors import SemanticSimilarityExampleSelector\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "examples = [\n",
@@ -302,7 +305,10 @@
     }
    ],
    "source": [
-    "from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
+    "from langchain.prompts import (\n",
+    "    ChatPromptTemplate,\n",
+    "    FewShotChatMessagePromptTemplate,\n",
+    ")\n",
     "\n",
     "# Define the few-shot prompt.\n",
     "few_shot_prompt = FewShotChatMessagePromptTemplate(\n",

docs/docs/how_to/functions.ipynb

@@ -167,7 +167,7 @@
    "source": [
     "Above, the `@chain` decorator is used to convert `custom_chain` into a runnable, which we invoke with the `.invoke()` method.\n",
     "\n",
-    "If you are using a tracing with [LangSmith](https://docs.smith.langchain.com/), you should see a `custom_chain` trace in there, with the calls to OpenAI nested underneath.\n",
+    "If you are using a tracing with [LangSmith](/docs/langsmith/), you should see a `custom_chain` trace in there, with the calls to OpenAI nested underneath.\n",
     "\n",
     "## Automatic coercion in chains\n",
     "\n",

docs/docs/how_to/graph_semantic.ipynb

@@ -177,13 +177,14 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_core.callbacks import (\n",
+    "from langchain.callbacks.manager import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
-    "from langchain_core.tools import BaseTool\n",
+    "\n",
+    "# Import things that are needed generically\n",
+    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from langchain.tools import BaseTool\n",
     "\n",
     "description_query = \"\"\"\n",
     "MATCH (m:Movie|Person)\n",
@@ -226,13 +227,14 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_core.callbacks import (\n",
+    "from langchain.callbacks.manager import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
-    "from langchain_core.tools import BaseTool\n",
+    "\n",
+    "# Import things that are needed generically\n",
+    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from langchain.tools import BaseTool\n",
     "\n",
     "\n",
     "class InformationInput(BaseModel):\n",
@@ -285,8 +287,8 @@
     "from langchain.agents import AgentExecutor\n",
     "from langchain.agents.format_scratchpad import format_to_openai_function_messages\n",
     "from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\n",
+    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_core.messages import AIMessage, HumanMessage\n",
-    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_core.utils.function_calling import convert_to_openai_function\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",

docs/docs/how_to/index.mdx

@@ -7,41 +7,35 @@ sidebar_class_name: hidden
 
 Here you’ll find answers to “How do I….?” types of questions.
 These guides are *goal-oriented* and *concrete*; they're meant to help you complete a specific task.
-For conceptual explanations see the [Conceptual guide](/docs/concepts/).
+For conceptual explanations see [Conceptual Guides](/docs/concepts/).
 For end-to-end walkthroughs see [Tutorials](/docs/tutorials).
-For comprehensive descriptions of every class and function see the [API Reference](https://api.python.langchain.com/en/latest/).
-
-## Installation
-
-- [How to: install LangChain packages](/docs/how_to/installation/)
+For comprehensive descriptions of every class and function see [API Reference](https://api.python.langchain.com/en/latest/).
 
 ## Key features
 
 This highlights functionality that is core to using LangChain.
 
-- [How to: return structured data from a model](/docs/how_to/structured_output/)
-- [How to: use a model to call tools](/docs/how_to/tool_calling/)
+- [How to: return structured data from an LLM](/docs/how_to/structured_output/)
+- [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
 - [How to: stream runnables](/docs/how_to/streaming)
 - [How to: debug your LLM apps](/docs/how_to/debugging/)
 
 ## LangChain Expression Language (LCEL)
 
-[LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html) protocol.
-
-[**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
+LangChain Expression Language is a way to create arbitrary custom chains. It is built on the Runnable protocol.
 
 - [How to: chain runnables](/docs/how_to/sequence)
 - [How to: stream runnables](/docs/how_to/streaming)
 - [How to: invoke runnables in parallel](/docs/how_to/parallel/)
-- [How to: add default invocation args to runnables](/docs/how_to/binding/)
-- [How to: turn any function into a runnable](/docs/how_to/functions)
-- [How to: pass through inputs from one chain step to the next](/docs/how_to/passthrough)
-- [How to: configure runnable behavior at runtime](/docs/how_to/configure)
-- [How to: add message history (memory) to a chain](/docs/how_to/message_history)
-- [How to: route between sub-chains](/docs/how_to/routing)
-- [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/)
+- [How to: attach runtime arguments to a runnable](/docs/how_to/binding/)
+- [How to: run custom functions](/docs/how_to/functions)
+- [How to: pass through arguments from one step to the next](/docs/how_to/passthrough)
+- [How to: add values to a chain's state](/docs/how_to/assign)
+- [How to: configure a chain at runtime](/docs/how_to/configure)
+- [How to: add message history](/docs/how_to/message_history)
+- [How to: route execution within a chain](/docs/how_to/routing)
 - [How to: inspect runnables](/docs/how_to/inspect)
-- [How to: add fallbacks to a runnable](/docs/how_to/fallbacks)
+- [How to: add fallbacks](/docs/how_to/fallbacks)
 
 ## Components
 
@@ -168,11 +162,15 @@ Indexing is the process of keeping your vectorstore in-sync with the underlying
 
 LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call).
 
-- [How to: create custom tools](/docs/how_to/custom_tools)
-- [How to: use built-in tools and built-in toolkits](/docs/how_to/tools_builtin)
+- [How to: use LangChain tools](/docs/how_to/tools)
 - [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
-- [How to: add ad-hoc tool calling capability to LLMs and chat models](/docs/how_to/tools_prompting)
+- [How to: use LangChain toolkits](/docs/how_to/toolkits)
+- [How to: define a custom tool](/docs/how_to/custom_tools)
+- [How to: convert LangChain tools to OpenAI functions](/docs/how_to/tools_as_openai_functions)
+- [How to: use tools without function calling](/docs/how_to/tools_prompting)
+- [How to: let the LLM choose between multiple tools](/docs/how_to/tools_multiple)
 - [How to: add a human in the loop to tool usage](/docs/how_to/tools_human)
+- [How to: do parallel tool use](/docs/how_to/tools_parallel)
 - [How to: handle errors when calling tools](/docs/how_to/tools_error)
 - [How to: call tools using multi-modal data](/docs/how_to/tool_calls_multi_modal)
 
@@ -187,14 +185,6 @@ For in depth how-to guides for agents, please check out [LangGraph](https://gith
 - [How to: use legacy LangChain Agents (AgentExecutor)](/docs/how_to/agent_executor)
 - [How to: migrate from legacy LangChain agents to LangGraph](/docs/how_to/migrate_agent)
 
-### Callbacks
-
-- [How to: pass in callbacks at runtime](/docs/how_to/callbacks_runtime)
-- [How to: attach callbacks to a module](/docs/how_to/callbacks_attach)
-- [How to: pass callbacks into a module constructor](/docs/how_to/callbacks_constructor)
-- [How to: create custom callback handlers](/docs/how_to/custom_callbacks)
-- [How to: use callbacks in async environments](/docs/how_to/callbacks_async)
-
 ### Custom
 
 All of LangChain components can easily be extended to support your own versions.
@@ -204,7 +194,6 @@ All of LangChain components can easily be extended to support your own versions.
 - [How to: write a custom retriever class](/docs/how_to/custom_retriever)
 - [How to: write a custom document loader](/docs/how_to/document_loader_custom)
 - [How to: write a custom output parser class](/docs/how_to/output_parser_custom)
-- [How to: create custom callback handlers](/docs/how_to/custom_callbacks)
 - [How to: define a custom tool](/docs/how_to/custom_tools)
 
 

docs/docs/how_to/indexing.ipynb

@@ -60,7 +60,7 @@
     "   * document addition by id (`add_documents` method with `ids` argument)\n",
     "   * delete by id (`delete` method with `ids` argument)\n",
     "\n",
-    "Compatible Vectorstores: `Aerospike`, `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
+    "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
     "  \n",
     "## Caution\n",
     "\n",
@@ -786,7 +786,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.document_loaders import BaseLoader\n",
+    "from langchain_community.document_loaders.base import BaseLoader\n",
     "\n",
     "\n",
     "class MyCustomLoader(BaseLoader):\n",

docs/docs/how_to/inspect.ipynb

@@ -39,9 +39,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain_community.vectorstores import FAISS\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "\n",

docs/docs/how_to/llm_caching.ipynb

@@ -119,7 +119,7 @@
    "outputs": [],
    "source": [
     "# We can do the same thing with a SQLite cache\n",
-    "from langchain_community.cache import SQLiteCache\n",
+    "from langchain.cache import SQLiteCache\n",
     "\n",
     "set_llm_cache(SQLiteCache(database_path=\".langchain.db\"))"
    ]

docs/docs/how_to/local_llms.ipynb

@@ -134,7 +134,8 @@
     }
    ],
    "source": [
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "\n",
     "llm = Ollama(\n",
     "    model=\"llama2\", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])\n",
@@ -287,8 +288,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain_community.llms import LlamaCpp\n",
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler\n",
     "\n",
     "llm = LlamaCpp(\n",
     "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",

docs/docs/how_to/long_context_reorder.ipynb

@@ -52,11 +52,11 @@
    ],
    "source": [
     "from langchain.chains import LLMChain, StuffDocumentsChain\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_chroma import Chroma\n",
     "from langchain_community.document_transformers import (\n",
     "    LongContextReorder,\n",
     ")\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_huggingface import HuggingFaceEmbeddings\n",
     "from langchain_openai import OpenAI\n",
     "\n",

docs/docs/how_to/message_history.ipynb

@@ -512,7 +512,7 @@
    "id": "36f43b87-655c-4f64-aa7b-bd8c1955d8e5",
    "metadata": {},
    "source": [
-    "### [LangSmith](https://docs.smith.langchain.com)\n",
+    "### [LangSmith](/docs/langsmith)\n",
     "\n",
     "LangSmith is especially useful for something like message history injection, where it can be hard to otherwise understand what the inputs are to various parts of the chain.\n",
     "\n",

docs/docs/how_to/migrate_agent.ipynb

@@ -18,13 +18,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "662fac50",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain langchain-openai"
+    "%pip install -U langchain-openai langchain langgraph"
    ]
   },
   {
@@ -34,12 +34,12 @@
    "source": [
     "## Basic Usage\n",
     "\n",
-    "For basic creation and usage of a tool-calling ReAct-style agent, the functionality is the same. First, let's define a model and tool(s), then we'll use those to create an agent."
+    "First, let's define a model and tool."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "1e425fea-2796-4b99-bee6-9a6ffe73f756",
    "metadata": {},
    "outputs": [],
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "id": "03ea357c-9c36-4464-b2cc-27bd150e1554",
    "metadata": {},
    "outputs": [
@@ -83,7 +83,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "id": "53a3737a-d167-4255-89bf-20ac37f89a3e",
    "metadata": {},
    "outputs": [
@@ -130,7 +130,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "id": "74ecebe3-512e-409c-a661-bdd5b0a2b782",
    "metadata": {},
    "outputs": [
@@ -158,10 +158,10 @@
      "data": {
       "text/plain": [
        "{'input': 'Pardon?',\n",
-       " 'output': 'The result of applying `magic_function` to the input 3 is 5.'}"
+       " 'output': 'The result of applying the `magic_function` to the input `3` is `5`.'}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "id": "a9a11ccd-75e2-4c11-844d-a34870b0ff91",
    "metadata": {},
    "outputs": [
@@ -211,7 +211,7 @@
        " 'output': 'El valor de `magic_function(3)` es 5.'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "id": "a9486805-676a-4d19-a5c4-08b41b172989",
    "metadata": {},
    "outputs": [],
@@ -272,16 +272,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "id": "d369ab45-0c82-45f4-9d3e-8efb8dd47e2c",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input': 'what is the value of magic_function(3)?', 'output': 'El valor de magic_function(3) es 5. ¡Pandamonium!'}\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'input': 'what is the value of magic_function(3)?',\n",
+       " 'output': 'El valor de magic_function(3) es 5. ¡Pandamonium!'}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -306,278 +310,10 @@
     "\n",
     "\n",
     "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
-    "print(\n",
-    "    {\n",
-    "        \"input\": query,\n",
-    "        \"output\": messages[\"messages\"][-1].content,\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "68df3a09",
-   "metadata": {},
-   "source": [
-    "## Memory\n",
-    "\n",
-    "With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could add chat [Memory](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.memory) so it can engage in a multi-turn conversation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "1fb52a2c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hi Polly! The output of the magic function for the input 3 is 5.\n",
-      "---\n",
-      "Yes, I remember your name, Polly! How can I assist you further?\n",
-      "---\n",
-      "The output of the magic function for the input 3 is 5.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "from langchain_core.tools import tool\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")\n",
-    "memory = ChatMessageHistory(session_id=\"test-session\")\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        # First put the history\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        # Then the new input\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        # Finally the scratchpad\n",
-    "        (\"placeholder\", \"{agent_scratchpad}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def magic_function(input: int) -> int:\n",
-    "    \"\"\"Applies a magic function to an input.\"\"\"\n",
-    "    return input + 2\n",
-    "\n",
-    "\n",
-    "tools = [magic_function]\n",
-    "\n",
-    "\n",
-    "agent = create_tool_calling_agent(model, tools, prompt)\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
-    "\n",
-    "agent_with_chat_history = RunnableWithMessageHistory(\n",
-    "    agent_executor,\n",
-    "    # This is needed because in most real world scenarios, a session id is needed\n",
-    "    # It isn't really used here because we are using a simple in memory ChatMessageHistory\n",
-    "    lambda session_id: memory,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")\n",
-    "\n",
-    "config = {\"configurable\": {\"session_id\": \"test-session\"}}\n",
-    "print(\n",
-    "    agent_with_chat_history.invoke(\n",
-    "        {\"input\": \"Hi, I'm polly! What's the output of magic_function of 3?\"}, config\n",
-    "    )[\"output\"]\n",
-    ")\n",
-    "print(\"---\")\n",
-    "print(agent_with_chat_history.invoke({\"input\": \"Remember my name?\"}, config)[\"output\"])\n",
-    "print(\"---\")\n",
-    "print(\n",
-    "    agent_with_chat_history.invoke({\"input\": \"what was that output again?\"}, config)[\n",
-    "        \"output\"\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2a5a32f",
-   "metadata": {},
-   "source": [
-    "#### In LangGraph\n",
-    "\n",
-    "Memory is just [persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence/), aka [checkpointing](https://langchain-ai.github.io/langgraph/reference/checkpoints/).\n",
-    "\n",
-    "Add a `checkpointer` to the agent and you get chat memory for free."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "035e1253",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hi Polly! The output of the magic_function for the input 3 is 5.\n",
-      "---\n",
-      "Yes, your name is Polly!\n",
-      "---\n",
-      "The output of the magic_function for the input 3 was 5.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import SystemMessage\n",
-    "from langgraph.checkpoint import MemorySaver  # an in-memory checkpointer\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "system_message = \"You are a helpful assistant.\"\n",
-    "# This could also be a SystemMessage object\n",
-    "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
-    "\n",
-    "memory = MemorySaver()\n",
-    "app = create_react_agent(\n",
-    "    model, tools, messages_modifier=system_message, checkpointer=memory\n",
-    ")\n",
-    "\n",
-    "config = {\"configurable\": {\"thread_id\": \"test-thread\"}}\n",
-    "print(\n",
-    "    app.invoke(\n",
-    "        {\n",
-    "            \"messages\": [\n",
-    "                (\"user\", \"Hi, I'm polly! What's the output of magic_function of 3?\")\n",
-    "            ]\n",
-    "        },\n",
-    "        config,\n",
-    "    )[\"messages\"][-1].content\n",
-    ")\n",
-    "print(\"---\")\n",
-    "print(\n",
-    "    app.invoke({\"messages\": [(\"user\", \"Remember my name?\")]}, config)[\"messages\"][\n",
-    "        -1\n",
-    "    ].content\n",
-    ")\n",
-    "print(\"---\")\n",
-    "print(\n",
-    "    app.invoke({\"messages\": [(\"user\", \"what was that output again?\")]}, config)[\n",
-    "        \"messages\"\n",
-    "    ][-1].content\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7cf24a8",
-   "metadata": {},
-   "source": [
-    "## Iterating through steps\n",
-    "\n",
-    "With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could iterate over the steps using the [stream](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) (or async `astream`) methods or the [iter](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter) method. LangGraph supports stepwise iteration using [stream](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d640feb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])]}\n",
-      "{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
-      "{'output': 'The value of `magic_function(3)` is 5.', 'messages': [AIMessage(content='The value of `magic_function(3)` is 5.')]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.tools import tool\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")\n",
-    "\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        # Placeholders fill up a **list** of messages\n",
-    "        (\"placeholder\", \"{agent_scratchpad}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def magic_function(input: int) -> int:\n",
-    "    \"\"\"Applies a magic function to an input.\"\"\"\n",
-    "    return input + 2\n",
-    "\n",
-    "\n",
-    "tools = [magic_function]\n",
-    "\n",
-    "agent = create_tool_calling_agent(model, tools, prompt=prompt)\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
-    "\n",
-    "for step in agent_executor.stream({\"input\": query}):\n",
-    "    print(step)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46ccbcbf",
-   "metadata": {},
-   "source": [
-    "#### In LangGraph\n",
-    "\n",
-    "In LangGraph, things are handled natively using [stream](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.graph.CompiledGraph.stream) or the asynchronous `astream` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "86abbe07",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yTjXXibj76tyFyPRa1soLo0S', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 70, 'total_tokens': 84}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b275f314-c42e-4e77-9dec-5c23f7dbd53b-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_yTjXXibj76tyFyPRa1soLo0S'}])]}}\n",
-      "{'tools': {'messages': [ToolMessage(content='5', name='magic_function', id='41c5f227-528d-4483-a313-b03b23b1d327', tool_call_id='call_yTjXXibj76tyFyPRa1soLo0S')]}}\n",
-      "{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 93, 'total_tokens': 107}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-0ef12b6e-415d-4758-9b62-5e5e1b350072-0')]}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import AnyMessage\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"placeholder\", \"{messages}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def _modify_messages(messages: list[AnyMessage]):\n",
-    "    return prompt.invoke({\"messages\": messages}).to_messages()\n",
-    "\n",
-    "\n",
-    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
-    "\n",
-    "\n",
-    "for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
-    "    print(step)"
+    "{\n",
+    "    \"input\": query,\n",
+    "    \"output\": messages[\"messages\"][-1].content,\n",
+    "}"
    ]
   },
   {
@@ -592,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "id": "4eff44bc-a620-4c8a-97b1-268692a842bb",
    "metadata": {},
    "outputs": [
@@ -600,7 +336,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-837e794f-cfd8-40e0-8abc-4d98ced11b75', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'index': 0}])], tool_call_id='call_ABI4hftfEdnVgKyfF6OzZbca'), 5)]\n"
+      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_lIjE9voYOCFAVoUXSDPQ5bFI', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-7a23003a-ab50-4d7c-b14b-86129d1cacfe', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_lIjE9voYOCFAVoUXSDPQ5bFI'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_lIjE9voYOCFAVoUXSDPQ5bFI', 'index': 0}])], tool_call_id='call_lIjE9voYOCFAVoUXSDPQ5bFI'), 5)]\n"
      ]
     }
    ],
@@ -620,20 +356,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "id": "4f4364ea-dffe-4d25-bdce-ef7d0020b880",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='0f63e437-c4d8-4da9-b6f5-b293ebfe4a64'),\n",
-       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_S96v28LlI6hNkQrNnIio0JPh', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ffef7898-14b1-4537-ad90-7c000a8a5d25-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_S96v28LlI6hNkQrNnIio0JPh'}]),\n",
-       "  ToolMessage(content='5', name='magic_function', id='fbd9df4e-1dda-4d3e-9044-b001f7875476', tool_call_id='call_S96v28LlI6hNkQrNnIio0JPh'),\n",
-       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 87, 'total_tokens': 101}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-e5d94c54-d9f4-45cd-be8e-a9101a8d88d6-0')]}"
+       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='8c252eb2-9496-4ad0-b3ae-9ecb2f6c406e'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_xmBLOw2pRqB1aRTTiwqEEftW', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2393b69c-7c52-4771-8bec-aca0e097fcc1-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_xmBLOw2pRqB1aRTTiwqEEftW'}]),\n",
+       "  ToolMessage(content='5', name='magic_function', id='bec0d0f9-bbaf-49fb-b0cb-46a658658f87', tool_call_id='call_xmBLOw2pRqB1aRTTiwqEEftW'),\n",
+       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 87, 'total_tokens': 101}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-5904d36f-b2a4-4f55-b431-12c82992c92c-0')]}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -664,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "id": "16f189a7-fc78-4cb5-aa16-a94ca06401a6",
    "metadata": {},
    "outputs": [],
@@ -680,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "id": "c96aefd7-6f6e-4670-aca6-1ac3d4e7871f",
    "metadata": {},
    "outputs": [
@@ -695,11 +431,7 @@
       "Invoking: `magic_function` with `{'input': '3'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `magic_function` with `{'input': '3'}`\n",
-      "responded: Parece que hubo un error al intentar obtener el valor de `magic_function(3)`. Permíteme intentarlo de nuevo.\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mAún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mParece que hubo un error al intentar obtener el valor de `magic_function(3)`. ¿Te gustaría que lo intente de nuevo?\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -708,10 +440,10 @@
      "data": {
       "text/plain": [
        "{'input': 'what is the value of magic_function(3)?',\n",
-       " 'output': 'Aún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?'}"
+       " 'output': 'Parece que hubo un error al intentar obtener el valor de `magic_function(3)`. ¿Te gustaría que lo intente de nuevo?'}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -739,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 29,
    "id": "b974a91f-6ae8-4644-83d9-73666258a6db",
    "metadata": {},
    "outputs": [
@@ -748,11 +480,14 @@
      "output_type": "stream",
      "text": [
       "('human', 'what is the value of magic_function(3)?')\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_pFdKcCu5taDTtOOfX14vEDRp', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-25836468-ba7e-43be-a7cf-76bba06a2a08-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_pFdKcCu5taDTtOOfX14vEDRp'}]\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='1a08b883-9c7b-4969-9e9b-67ce64cdcb5f' tool_call_id='call_pFdKcCu5taDTtOOfX14vEDRp'\n",
-      "content='It seems there was an error when trying to apply the magic function. Let me try again.' additional_kwargs={'tool_calls': [{'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 34, 'prompt_tokens': 97, 'total_tokens': 131}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-d571b774-0ea3-4e35-8b7d-f32932c3f3cc-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K'}]\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='0b45787b-c82a-487f-9a5a-de129c30460f' tool_call_id='call_DA0lpDIkBFg2GHy4WsEcZG4K'\n",
-      "content='It appears that there is a consistent issue when trying to apply the magic function to the input \"3.\" This could be due to various reasons, such as the input not being in the correct format or an internal error.\\n\\nIf you have any other questions or if there\\'s something else you\\'d like to try, please let me know!' response_metadata={'token_usage': {'completion_tokens': 66, 'prompt_tokens': 153, 'total_tokens': 219}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None} id='run-50a962e6-21b7-4327-8dea-8e2304062627-0'\n"
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_9fMkSAUGRa2BsADwF32ct1m1', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-79084bff-6e10-49bb-b7f0-f613ebcc68ac-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_9fMkSAUGRa2BsADwF32ct1m1'}]\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='06f997fd-5309-4d56-afa3-2fe8cbf0d04f' tool_call_id='call_9fMkSAUGRa2BsADwF32ct1m1'\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_Fg92zoL8oS5q6im2jR1INRvH', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 97, 'total_tokens': 111}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-fc2e201f-6330-4330-8c4e-1a66e85c1ffa-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_Fg92zoL8oS5q6im2jR1INRvH'}]\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='a931dd6e-2ed7-42ea-a58c-5ffb4041d7c9' tool_call_id='call_Fg92zoL8oS5q6im2jR1INRvH'\n",
+      "content='It seems there is an issue with processing the request for the value of `magic_function(3)`. Let me try a different approach.' additional_kwargs={'tool_calls': [{'id': 'call_lbYBMptprZ6HMqNiTvoqhmwP', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 43, 'prompt_tokens': 130, 'total_tokens': 173}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-2e0baab0-c4c1-42e8-b49d-a2704ae977c0-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_lbYBMptprZ6HMqNiTvoqhmwP'}]\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='9957435a-5de3-4662-b23c-abfa31e71208' tool_call_id='call_lbYBMptprZ6HMqNiTvoqhmwP'\n",
+      "content='It appears that the `magic_function` is currently experiencing issues when attempting to process the input \"3\". Unfortunately, I can\\'t provide the value of `magic_function(3)` at this moment.\\n\\nIf you have any other questions or need assistance with something else, please let me know!' response_metadata={'token_usage': {'completion_tokens': 58, 'prompt_tokens': 195, 'total_tokens': 253}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None} id='run-bb68d7ca-da76-43ad-80ab-23737a70c391-0'\n",
+      "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
     }
    ],
@@ -787,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 30,
    "id": "4b8498fc-a7af-4164-a401-d8714f082306",
    "metadata": {},
    "outputs": [
@@ -814,7 +549,7 @@
        " 'output': 'Agent stopped due to max iterations.'}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -855,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 31,
    "id": "a2b29113-e6be-4f91-aa4c-5c63dea3e423",
    "metadata": {},
    "outputs": [
@@ -863,7 +598,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_HaQkeCwD5QskzJzFixCBacZ4', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-596c9200-771f-436d-8576-72fcb81620f1-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_HaQkeCwD5QskzJzFixCBacZ4'}])]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_GlXWTlJ0jQc2B8jQuDVFzmnc', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-38a0459b-a363-4181-b7a3-f25cb5c5d728-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_GlXWTlJ0jQc2B8jQuDVFzmnc'}])]}}\n",
       "------\n",
       "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
@@ -889,12 +624,12 @@
    "id": "32a9db70",
    "metadata": {},
    "source": [
-    "The other way to set a single max timeout for an entire run is to directly use the python stdlib [asyncio](https://docs.python.org/3/library/asyncio.html) library."
+    "The other way to set a max timeout is just via python's stdlib [asyncio](https://docs.python.org/3/library/asyncio.html)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 34,
    "id": "e9eb55f4-a321-4bac-b52d-9e43b411cf92",
    "metadata": {},
    "outputs": [
@@ -902,9 +637,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a1c77db7-405f-43d9-8d57-751f2ca1a58c-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv'}])]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_cR1oJuYcNrOmcaaIRRvh5dSr', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-1c03c5d6-4883-4ccd-aa78-53dbafa99622-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_cR1oJuYcNrOmcaaIRRvh5dSr'}])]}}\n",
       "------\n",
-      "Task Cancelled.\n"
+      "{'action': {'messages': [ToolMessage(content='Sorry, there was an error. Please try again.', name='magic_function', id='596baf13-de35-4a4f-8b78-475b387a1f40', tool_call_id='call_cR1oJuYcNrOmcaaIRRvh5dSr')]}}\n",
+      "------\n",
+      "{'input': 'what is the value of magic_function(3)?', 'output': 'Task Cancelled.'}\n"
      ]
     }
    ],
@@ -928,290 +665,6 @@
     "except TimeoutError:\n",
     "    print(\"Task Cancelled.\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4884ac87",
-   "metadata": {},
-   "source": [
-    "## `early_stopping_method`\n",
-    "\n",
-    "With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could configure an [early_stopping_method](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.early_stopping_method) to either return a string saying \"Agent stopped due to iteration limit or time limit.\" (`\"force\"`) or prompt the LLM a final time to respond (`\"generate\"`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "3f6e2cf2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output with early_stopping_method='force':\n",
-      "Agent stopped due to max iterations.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.tools import tool\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")\n",
-    "\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        # Placeholders fill up a **list** of messages\n",
-    "        (\"placeholder\", \"{agent_scratchpad}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def magic_function(input: int) -> int:\n",
-    "    \"\"\"Applies a magic function to an input.\"\"\"\n",
-    "    return \"Sorry there was an error, please try again.\"\n",
-    "\n",
-    "\n",
-    "tools = [magic_function]\n",
-    "\n",
-    "agent = create_tool_calling_agent(model, tools, prompt=prompt)\n",
-    "agent_executor = AgentExecutor(\n",
-    "    agent=agent, tools=tools, early_stopping_method=\"force\", max_iterations=1\n",
-    ")\n",
-    "\n",
-    "result = agent_executor.invoke({\"input\": query})\n",
-    "print(\"Output with early_stopping_method='force':\")\n",
-    "print(result[\"output\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "706e05c4",
-   "metadata": {},
-   "source": [
-    "#### In LangGraph\n",
-    "\n",
-    "In LangGraph, you can explicitly handle the response behavior outside the agent, since the full state can be accessed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "73cabbc4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('human', 'what is the value of magic_function(3)?')\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_bTURmOn9C8zslmn0kMFeykIn', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-0844a504-7e6b-4ea6-a069-7017e38121ee-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_bTURmOn9C8zslmn0kMFeykIn'}]\n",
-      "content='Sorry there was an error, please try again.' name='magic_function' id='00d5386f-eb23-4628-9a29-d9ce6a7098cc' tool_call_id='call_bTURmOn9C8zslmn0kMFeykIn'\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_JYqvvvWmXow2u012DuPoDHFV', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 96, 'total_tokens': 110}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-b73b1b1c-c829-4348-98cd-60b315c85448-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_JYqvvvWmXow2u012DuPoDHFV'}]\n",
-      "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langgraph.errors import GraphRecursionError\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "RECURSION_LIMIT = 2 * 1 + 1\n",
-    "\n",
-    "app = create_react_agent(model, tools=tools)\n",
-    "\n",
-    "try:\n",
-    "    for chunk in app.stream(\n",
-    "        {\"messages\": [(\"human\", query)]},\n",
-    "        {\"recursion_limit\": RECURSION_LIMIT},\n",
-    "        stream_mode=\"values\",\n",
-    "    ):\n",
-    "        print(chunk[\"messages\"][-1])\n",
-    "except GraphRecursionError:\n",
-    "    print({\"input\": query, \"output\": \"Agent stopped due to max iterations.\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "017fe20e",
-   "metadata": {},
-   "source": [
-    "## `trim_intermediate_steps`\n",
-    "\n",
-    "With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor), you could trim the intermediate steps of long-running agents using [trim_intermediate_steps](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.trim_intermediate_steps), which is either an integer (indicating the agent should keep the last N steps) or a custom function.\n",
-    "\n",
-    "For instance, we could trim the value so the agent only sees the most recent intermediate step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "b94bb169",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Call number: 1\n",
-      "Call number: 2\n",
-      "Call number: 3\n",
-      "Call number: 4\n",
-      "Call number: 5\n",
-      "Call number: 6\n",
-      "Call number: 7\n",
-      "Call number: 8\n",
-      "Call number: 9\n",
-      "Call number: 10\n",
-      "Call number: 11\n",
-      "Call number: 12\n",
-      "Call number: 13\n",
-      "Call number: 14\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Stopping agent prematurely due to triggering stop condition\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Call number: 15\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.tools import tool\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")\n",
-    "\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        # Placeholders fill up a **list** of messages\n",
-    "        (\"placeholder\", \"{agent_scratchpad}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "magic_step_num = 1\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def magic_function(input: int) -> int:\n",
-    "    \"\"\"Applies a magic function to an input.\"\"\"\n",
-    "    global magic_step_num\n",
-    "    print(f\"Call number: {magic_step_num}\")\n",
-    "    magic_step_num += 1\n",
-    "    return input + magic_step_num\n",
-    "\n",
-    "\n",
-    "tools = [magic_function]\n",
-    "\n",
-    "agent = create_tool_calling_agent(model, tools, prompt=prompt)\n",
-    "\n",
-    "\n",
-    "def trim_steps(steps: list):\n",
-    "    # Let's give the agent amnesia\n",
-    "    return []\n",
-    "\n",
-    "\n",
-    "agent_executor = AgentExecutor(\n",
-    "    agent=agent, tools=tools, trim_intermediate_steps=trim_steps\n",
-    ")\n",
-    "\n",
-    "\n",
-    "query = \"Call the magic function 4 times in sequence with the value 3. You cannot call it multiple times at once.\"\n",
-    "\n",
-    "for step in agent_executor.stream({\"input\": query}):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d450c5a",
-   "metadata": {},
-   "source": [
-    "#### In LangGraph\n",
-    "\n",
-    "We can use the [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "b309ba9a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Call number: 1\n",
-      "Call number: 2\n",
-      "Call number: 3\n",
-      "Call number: 4\n",
-      "Call number: 5\n",
-      "Call number: 6\n",
-      "Call number: 7\n",
-      "Call number: 8\n",
-      "Call number: 9\n",
-      "Call number: 10\n",
-      "Call number: 11\n",
-      "Call number: 12\n",
-      "Stopping agent prematurely due to triggering stop condition\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import AnyMessage\n",
-    "from langgraph.errors import GraphRecursionError\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "magic_step_num = 1\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def magic_function(input: int) -> int:\n",
-    "    \"\"\"Applies a magic function to an input.\"\"\"\n",
-    "    global magic_step_num\n",
-    "    print(f\"Call number: {magic_step_num}\")\n",
-    "    magic_step_num += 1\n",
-    "    return input + magic_step_num\n",
-    "\n",
-    "\n",
-    "tools = [magic_function]\n",
-    "\n",
-    "\n",
-    "def _modify_messages(messages: list[AnyMessage]):\n",
-    "    # Give the agent amnesia, only keeping the original user query\n",
-    "    return [(\"system\", \"You are a helpful assistant\"), messages[0]]\n",
-    "\n",
-    "\n",
-    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
-    "\n",
-    "try:\n",
-    "    for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
-    "        pass\n",
-    "except GraphRecursionError as e:\n",
-    "    print(\"Stopping agent prematurely due to triggering stop condition\")"
-   ]
   }
  ],
  "metadata": {

docs/docs/how_to/multi_vector.ipynb

@@ -423,7 +423,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.output_parsers.openai_functions import JsonKeyOutputFunctionsParser\n",
+    "from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser\n",
     "\n",
     "chain = (\n",
     "    {\"doc\": lambda x: x.page_content}\n",

docs/docs/how_to/output_parser_fixing.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "from typing import List\n",
     "\n",
-    "from langchain_core.output_parsers import PydanticOutputParser\n",
+    "from langchain.output_parsers import PydanticOutputParser\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI"
    ]

docs/docs/how_to/output_parser_retry.ipynb

@@ -17,9 +17,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.output_parsers import OutputFixingParser\n",
-    "from langchain_core.output_parsers import PydanticOutputParser\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.output_parsers import (\n",
+    "    OutputFixingParser,\n",
+    "    PydanticOutputParser,\n",
+    ")\n",
+    "from langchain.prompts import (\n",
+    "    PromptTemplate,\n",
+    ")\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI, OpenAI"
    ]

docs/docs/how_to/parent_document_retriever.ipynb

@@ -57,7 +57,7 @@
    "outputs": [],
    "source": [
     "loaders = [\n",
-    "    TextLoader(\"paul_graham_essay.txt\"),\n",
+    "    TextLoader(\"../../paul_graham_essay.txt\"),\n",
     "    TextLoader(\"state_of_the_union.txt\"),\n",
     "]\n",
     "docs = []\n",
@@ -124,8 +124,8 @@
     {
      "data": {
       "text/plain": [
-       "['9a63376c-58cc-42c9-b0f7-61f0e1a3a688',\n",
-       " '40091598-e918-4a18-9be0-f46413a95ae4']"
+       "['cfdf4af7-51f2-4ea3-8166-5be208efa040',\n",
+       " 'bf213c21-cc66-4208-8a72-733d030187e6']"
       ]
      },
      "execution_count": 6,
@@ -190,7 +190,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "retrieved_docs = retriever.invoke(\"justice breyer\")"
+    "retrieved_docs = retriever.get_relevant_documents(\"justice breyer\")"
    ]
   },
   {
@@ -338,17 +338,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "3a3202df",
    "metadata": {},
    "outputs": [],
    "source": [
-    "retrieved_docs = retriever.invoke(\"justice breyer\")"
+    "retrieved_docs = retriever.get_relevant_documents(\"justice breyer\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "684fdb2c",
    "metadata": {},
    "outputs": [
@@ -358,7 +358,7 @@
        "1849"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "9f17f662",
    "metadata": {},
    "outputs": [
@@ -424,7 +424,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/paul_graham_essay.txt

@@ -1,351 +0,0 @@
-What I Worked On
-
-February 2021
-
-Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.
-
-The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.
-
-The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.
-
-I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.
-
-With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]
-
-The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.
-
-Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.
-
-Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.
-
-I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.
-
-AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words.
-
-There weren't any classes in AI at Cornell then, not even graduate classes, so I started trying to teach myself. Which meant learning Lisp, since in those days Lisp was regarded as the language of AI. The commonly used programming languages then were pretty primitive, and programmers' ideas correspondingly so. The default language at Cornell was a Pascal-like language called PL/I, and the situation was similar elsewhere. Learning Lisp expanded my concept of a program so fast that it was years before I started to have a sense of where the new limits were. This was more like it; this was what I had expected college to do. It wasn't happening in a class, like it was supposed to, but that was ok. For the next couple years I was on a roll. I knew what I was going to do.
-
-For my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence.
-
-I had gotten into a program at Cornell that didn't make you choose a major. You could take whatever classes you liked, and choose whatever you liked to put on your degree. I of course chose "Artificial Intelligence." When I got the actual physical diploma, I was dismayed to find that the quotes had been included, which made them read as scare-quotes. At the time this bothered me, but now it seems amusingly accurate, for reasons I was about to discover.
-
-I applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I'd visited because Rich Draves went there, and was also home to Bill Woods, who'd invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went.
-
-I don't remember the moment it happened, or if there even was a specific moment, but during the first year of grad school I realized that AI, as practiced at the time, was a hoax. By which I mean the sort of AI in which a program that's told "the dog is sitting on the chair" translates this into some formal representation and adds it to the list of things it knows.
-
-What these programs really showed was that there's a subset of natural language that's a formal language. But a very proper subset. It was clear that there was an unbridgeable gap between what they could do and actually understanding natural language. It was not, in fact, simply a matter of teaching SHRDLU more words. That whole way of doing AI, with explicit data structures representing concepts, was not going to work. Its brokenness did, as so often happens, generate a lot of opportunities to write papers about various band-aids that could be applied to it, but it was never going to get us Mike.
-
-So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp. I knew from experience that Lisp was interesting for its own sake and not just for its association with AI, even though that was the main reason people cared about it at the time. So I decided to focus on Lisp. In fact, I decided to write a book about Lisp hacking. It's scary to think how little I knew about Lisp hacking when I started writing that book. But there's nothing like writing a book about something to help you learn it. The book, On Lisp, wasn't published till 1993, but I wrote much of it in grad school.
-
-Computer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting.
-
-The problem with systems work, though, was that it didn't last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good.
-
-There were some surplus Xerox Dandelions floating around the computer lab at one point. Anyone who wanted one to play around with could have one. I was briefly tempted, but they were so slow by present standards; what was the point? No one else wanted one either, so off they went. That was what happened to systems work.
-
-I wanted not just to build things, but to build things that would last.
-
-In this dissatisfied state I went in 1988 to visit Rich Draves at CMU, where he was in grad school. One day I went to visit the Carnegie Institute, where I'd spent a lot of time as a kid. While looking at a painting there I realized something that might seem obvious, but was a big surprise to me. There, right on the wall, was something you could make that would last. Paintings didn't become obsolete. Some of the best ones were hundreds of years old.
-
-And moreover this was something you could make a living doing. Not as easily as you could by writing software, of course, but I thought if you were really industrious and lived really cheaply, it had to be possible to make enough to survive. And as an artist you could be truly independent. You wouldn't have a boss, or even need to get research funding.
-
-I had always liked looking at paintings. Could I make them? I had no idea. I'd never imagined it was even possible. I knew intellectually that people made art — that it didn't just appear spontaneously — but it was as if the people who made it were a different species. They either lived long ago or were mysterious geniuses doing strange things in profiles in Life magazine. The idea of actually being able to make art, to put that verb before that noun, seemed almost miraculous.
-
-That fall I started taking art classes at Harvard. Grad students could take classes in any department, and my advisor, Tom Cheatham, was very easy going. If he even knew about the strange classes I was taking, he never said anything.
-
-So now I was in a PhD program in computer science, yet planning to be an artist, yet also genuinely in love with Lisp hacking and working away at On Lisp. In other words, like many a grad student, I was working energetically on multiple projects that were not my thesis.
-
-I didn't see a way out of this situation. I didn't want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he'd found such a spectacular way to get out of grad school.
-
-Then one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn't have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay "Yes, I think so. I'll give you something to read in a few days."
-
-I picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There's a whole world there that's barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely.
-
-Meanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went.
-
-I'd applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design.
-
-Toward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they'd sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I'd done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian.
-
-Only stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don't know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2]
-
-I'm only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn't require the faculty to teach anything, and in return the faculty wouldn't require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines.
-
-Our model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She'd copy an obscure old painting out of a book, and then he'd take the copy and maltreat it to make it look old. [3]
-
-While I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can't move. People can't sit for more than about 15 minutes at a time, and when they do they don't sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you're painting. Whereas a still life you can, if you want, copy pixel by pixel from what you're seeing. You don't want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it's been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it's the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4]
-
-I liked painting still lives because I was curious about what I was seeing. In everyday life, we aren't consciously aware of much we're seeing. Most visual perception is handled by low-level processes that merely tell your brain "that's a water droplet" without telling you details like where the lightest and darkest points are, or "that's a bush" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there's a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted.
-
-This is not the only way to paint. I'm not 100% sure it's even a good way to paint. But it seemed a good enough bet to be worth trying.
-
-Our teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn't teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US.
-
-I wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5]
-
-Interleaf had done something pretty bold. Inspired by Emacs, they'd added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I've had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn't know C and didn't want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours. That seemed unnatural to me, and on this point the rest of the world is coming around to my way of thinking, but at the time it caused a lot of friction. Toward the end of the year I spent much of my time surreptitiously working on On Lisp, which I had by this time gotten a contract to publish.
-
-The good part was that I got paid huge amounts of money, especially by art student standards. In Florence, after paying my part of the rent, my budget for everything else had been $7 a day. Now I was getting paid more than 4 times that every hour, even when I was just sitting in a meeting. By living cheaply I not only managed to save enough to go back to RISD, but also paid off my college loans.
-
-I learned some useful things at Interleaf, though they were mostly about what not to do. I learned that it's better for technology companies to be run by product people than sales people (though sales is a real skill and people who are good at it are really good at it), that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it's depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there's not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it.
-
-But the most important thing I learned, and which I used in both Viaweb and Y Combinator, is that the low end eats the high end: that it's good to be the "entry level" option, even though that will be less prestigious, because if you're not, someone else will be, and will squash you against the ceiling. Which in turn means that prestige is a danger sign.
-
-When I left to go back to RISD the next fall, I arranged to do freelance work for the group that did projects for customers, and this was how I survived for the next several years. When I came back to visit for a project later on, someone told me about a new thing called HTML, which was, as he described it, a derivative of SGML. Markup language enthusiasts were an occupational hazard at Interleaf and I ignored him, but this HTML thing later became a big part of my life.
-
-In the fall of 1992 I moved back to Providence to continue at RISD. The foundation had merely been intro stuff, and the Accademia had been a (very civilized) joke. Now I was going to see what real art school was like. But alas it was more like the Accademia than not. Better organized, certainly, and a lot more expensive, but it was now becoming clear that art school did not bear the same relationship to art that medical school bore to medicine. At least not the painting department. The textile department, which my next door neighbor belonged to, seemed to be pretty rigorous. No doubt illustration and architecture were too. But painting was post-rigorous. Painting students were supposed to express themselves, which to the more worldly ones meant to try to cook up some sort of distinctive signature style.
-
-A signature style is the visual equivalent of what in show business is known as a "schtick": something that immediately identifies the work as yours and no one else's. For example, when you see a painting that looks like a certain kind of cartoon, you know it's by Roy Lichtenstein. So if you see a big painting of this type hanging in the apartment of a hedge fund manager, you know he paid millions of dollars for it. That's not always why artists have a signature style, but it's usually why buyers pay a lot for such work. [6]
-
-There were plenty of earnest students too: kids who "could draw" in high school, and now had come to what was supposed to be the best art school in the country, to learn to draw even better. They tended to be confused and demoralized by what they found at RISD, but they kept going, because painting was what they did. I was not one of the kids who could draw in high school, but at RISD I was definitely closer to their tribe than the tribe of signature style seekers.
-
-I learned a lot in the color class I took at RISD, but otherwise I was basically teaching myself to paint, and I could do that for free. So in 1993 I dropped out. I hung around Providence for a bit, and then my college friend Nancy Parmet did me a big favor. A rent-controlled apartment in a building her mother owned in New York was becoming vacant. Did I want it? It wasn't much more than my current place, and New York was supposed to be where the artists were. So yes, I wanted it! [7]
-
-Asterix comics begin by zooming in on a tiny corner of Roman Gaul that turns out not to be controlled by the Romans. You can do something similar on a map of New York City: if you zoom in on the Upper East Side, there's a tiny corner that's not rich, or at least wasn't in 1993. It's called Yorkville, and that was my new home. Now I was a New York artist — in the strictly technical sense of making paintings and living in New York.
-
-I was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn't want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.)
-
-The best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I'd taken her painting class at Harvard. I've never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant.
-
-She liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn't that much older than me, and was super rich. The thought suddenly occurred to me: why don't I become rich? Then I'll be able to work on whatever I want.
-
-Meanwhile I'd been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I'd seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet.
-
-If I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can't honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn't want to be online, and still don't, not the fancy ones. That's not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us.
-
-Then some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we'd been generating for galleries. This impressive-sounding thing called an "internet storefront" was something we already knew how to build.
-
-So in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we'd at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course.
-
-We were working out of Robert's apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we'd never have to write anything to run on users' computers. We could generate the sites on the same server we'd serve them from. Users wouldn't need anything more than a browser.
-
-This kind of software, known as a web app, is common now, but at the time it wasn't clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server.
-
-Now we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn't need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server.
-
-We started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator's. We knew founders needed something like this, because we'd needed it ourselves.
-
-At this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I'd made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on.
-
-We originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.
-
-It helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.
-
-(If you're curious why my site looks so old-fashioned, it's because it's still made with this software. It may look clunky today, but in 1996 it was the last word in slick.)
-
-In September, Robert rebelled. "We've been working on this for a month," he said, "and it's still not done." This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker.
-
-It was a lot of fun working with Robert and Trevor. They're the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm's brain it would look like a colonial New England church, and if you could see inside Trevor's it would look like the worst excesses of Austrian Rococo.
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-We opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8]
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-There were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn't have to integrate with any other software except Robert's and Trevor's, so it was quite fun to work on. If all I'd had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.
-
-There were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn't because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us.
-
-We did a lot of things right by accident like that. For example, we did what's now called "doing things that don't scale," although at the time we would have described it as "being so lame that we're driven to the most desperate measures to get users." The most common of which was building stores for them. This seemed particularly humiliating, since the whole reason d'etre of our software was that people could use it to make their own stores. But anything to get users.
-
-We learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man's shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men's shirts. My first set of scans were so beautiful too.
-
-Though this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by "business" and thought we needed a "business person" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we'd have so many users that I couldn't scan their images for them, but in the meantime there was nothing more important to do.
-
-Another thing I didn't get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that's how much money you're making, and if you're not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we'd been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you're doing fine. You're growing 7x a year. Just don't hire too many more people and you'll soon be profitable, and then you'll control your own destiny.
-
-Alas I hired lots more people, partly because our investors wanted me to, and partly because that's what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn't reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards.
-
-It was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn't feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned.
-
-The next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn't realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo's prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf.
-
-Yahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they'd never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I'd painted anything that I'd half forgotten why I was doing this. My brain had been entirely full of software and men's shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint.
-
-When I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan.
-
-But I really was quitting to paint, and I started immediately. There was no time to lose. I'd already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That's what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me.
-
-So I tried to paint, but I just didn't seem to have any energy or ambition. Part of the problem was that I didn't know many people in California. I'd compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you'll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn't know any of them.
-
-When I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn't been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I'd paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn't rotted yet).
-
-Meanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn't one. Huh.
-
-Around this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc.
-
-I got so excited about this idea that I couldn't think about anything else. It seemed obvious that this was the future. I didn't particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he'd made a lot of money the last time I'd lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.
-
-Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open-source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did.
-
-By then there was a name for the kind of company Viaweb was, an "application service provider," or ASP. This name didn't last long before it was replaced by "software as a service," but it was current for long enough that I named this new company after it: it was going to be called Aspra.
-
-I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company — especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open-source project.
-
-Much to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.
-
-The subset I would build as an open-source project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.
-
-The following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we'd used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I'd created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]
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-Wow, I thought, there's an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.
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-This had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]
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-In the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]
-
-I've worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I'd always write essays too.
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-I knew that online essays would be a marginal medium at first. Socially they'd seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.
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-One of the most conspicuous patterns I've noticed in my life is how well it has worked, for me at least, to work on things that weren't prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I'm writing, and I explain that it's an essay I'm going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip.
-
-It's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one.
-
-Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.
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-One night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn't know but would probably like. One of the guests was someone I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.
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-Jessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.
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-When the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.
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-One of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won't waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they'd be able to avoid the worst of the mistakes we'd made.
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-So I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they'd be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I'd only known), I blurted out "But not me!" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I'd been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn't done one angel investment.
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-Meanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.
-
-As Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We'd start our own investment firm and actually implement the ideas we'd been talking about. I'd fund it, and Jessica could quit her job and work for it, and we'd get Robert and Trevor as partners too. [13]
-
-Once again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.
-
-There are multiple components to Y Combinator, and we didn't figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn't go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we'd been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us.
-
-YC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking "Wow, that means they got all the returns." But once again, this was not due to any particular insight on our part. We didn't know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn't have known where to start. [14]
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-The most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they'd start startups instead? We wouldn't feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn't make much money out of it, we'd at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft.
-
-We'd use the building I owned in Cambridge as our headquarters. We'd all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we'd bring in experts on startups to give talks.
-
-We knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get "deal flow," as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who'd already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we'd intended.
-
-We invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don't think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs.
-
-The deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16]
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-Fairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them.
-
-As YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another's customers. We used to refer jokingly to the "YC GDP," but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates.
-
-I had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things.
-
-In the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn't startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one's intellectual curiosity.
-
-HN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I'd had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one's work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]
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-As well as HN, I wrote all of YC's internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn't have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC.
-
-YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it.
-
-There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: "No one works harder than the boss." He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I'd better work very hard.
-
-One day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. "You know," he said, "you should make sure Y Combinator isn't the last cool thing you do."
-
-At the time I didn't understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life's work or I'd have to leave eventually. And it wasn't, so I would.
-
-In the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else.
-
-I asked Jessica if she wanted to be president, but she didn't, so we decided we'd try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn't be controlled by the founders. So if Sam said yes, we'd let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners.
-
-When we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.
-
-She died on January 15, 2014. We knew this was coming, but it was still hard when it did.
-
-I kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.)
-
-What should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]
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-I spent most of the rest of 2014 painting. I'd never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I'd always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven't painted since. So far anyway.
-
-I realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that's not the best one (or at least a good one) for you, then it's getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.
-
-I started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren't about startups. Then in March 2015 I started working on Lisp again.
-
-The distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn't originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what's the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19]
-
-McCarthy didn't realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy's interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn't match. It was this that attracted me in college, though I didn't understand why at the time.
-
-McCarthy's 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you'd want in a programming language. So these had to be added, and when they were, they weren't defined using McCarthy's original axiomatic approach. That wouldn't have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you'd have had to run it, and computers then weren't powerful enough.
-
-Now they are, though. Now you could continue using McCarthy's axiomatic approach till you'd defined a complete programming language. And as long as every change you made to McCarthy's Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long.
-
-I wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it's an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test.
-
-I had to ban myself from writing essays during most of this time, or I'd never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you're working on an interpreter written in itself, it's hard to keep track of what's happening at what level, and errors can be practically encrypted by the time you get them.
-
-So I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I'd ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I'd check Twitter or HN and see someone asking "Does Paul Graham still code?"
-
-Working on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.
-
-In the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.
-
-In the fall of 2019, Bel was finally finished. Like McCarthy's original Lisp, it's a spec rather than an implementation, although like McCarthy's Lisp it's a spec expressed as code.
-
-Now that I could write essays again, I wrote a bunch about topics I'd had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who'd lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.
-
-
-
-
-
-
-
-
-
-Notes
-
-[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.
-
-[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It's the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.
-
-[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists.
-
-[4] You can of course paint people like still lives if you want to, and they're willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters.
-
-[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore's Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer.
-
-[6] The signature style seekers at RISD weren't specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.
-
-[7] Technically the apartment wasn't rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.
-
-[8] Most software you can launch as soon as it's done. But when the software is an online store builder and you're hosting the stores, if you don't have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.
-
-[9] We'd had a code editor in Viaweb for users to define their own page styles. They didn't know it, but they were editing Lisp expressions underneath. But this wasn't an app editor, because the code ran when the merchants' sites were generated, not when shoppers visited them.
-
-[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren't they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I'm not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don't already know, and some people dislike being told such things.
-
-[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.
-
-[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era.
-
-Which in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete).
-
-Here's an interesting point, though: you can't always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be?
-
-[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn't want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator.
-
-I picked orange as our color partly because it's the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.
-
-[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.
-
-[15] I've never liked the term "deal flow," because it implies that the number of new startups at any given time is fixed. This is not only false, but it's the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.
-
-[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now.
-
-[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you're assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you're present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it's correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance.
-
-[18] The worst thing about leaving YC was not working with Jessica anymore. We'd been working on YC almost the whole time we'd known each other, and we'd neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree.
-
-[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy's 1960 paper.
-
-But if so there's no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy's Lisp along which discoveredness is preserved.
-
-
-
-Thanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this.

docs/docs/how_to/qa_chat_history_how_to.ipynb

@@ -941,7 +941,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_multiple_queries.ipynb

@@ -83,9 +83,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain_chroma import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
     "texts = [\"Harrison worked at Kensho\", \"Ankush worked at Facebook\"]\n",
     "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",

docs/docs/how_to/query_multiple_retrievers.ipynb

@@ -83,9 +83,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain_chroma import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
     "texts = [\"Harrison worked at Kensho\"]\n",
     "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",

docs/docs/how_to/query_no_queries.ipynb

@@ -85,9 +85,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain_chroma import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
     "texts = [\"Harrison worked at Kensho\"]\n",
     "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",

docs/docs/how_to/routing.ipynb

@@ -16,7 +16,7 @@
    "id": "4b47436a",
    "metadata": {},
    "source": [
-    "# How to route between sub-chains\n",
+    "# How to route execution within a chain\n",
     "\n",
     ":::info Prerequisites\n",
     "\n",
@@ -335,7 +335,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.utils.math import cosine_similarity\n",
+    "from langchain.utils.math import cosine_similarity\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",

docs/docs/how_to/sequence.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "The resulting [`RunnableSequence`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableSequence.html) is itself a runnable, which means it can be invoked, streamed, or further chained just like any other runnable. Advantages of chaining runnables in this way are efficient streaming (the sequence will stream output as soon as it is available), and debugging and tracing with tools like [LangSmith](/docs/how_to/debugging).\n",
     "\n",
-    "## The pipe operator: `|`\n",
+    "## The pipe operator\n",
     "\n",
     "To show off how this works, let's go through an example. We'll walk through a common pattern in LangChain: using a [prompt template](/docs/how_to#prompt-templates) to format input into a [chat model](/docs/how_to#chat-models), and finally converting the chat message output into a string with an [output parser](/docs/how_to#output-parsers).\n",
     "\n",
@@ -230,28 +230,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or the abbreviated:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "composed_chain_with_pipe = RunnableParallel({\"joke\": chain}).pipe(\n",
-    "    analysis_prompt, model, StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Related\n",
+    "## Next steps\n",
     "\n",
-    "- [Streaming](/docs/how_to/streaming/): Check out the streaming guide to understand the streaming behavior of a chain\n",
-    "- "
+    "You now know some ways to chain two runnables together.\n",
+    "\n",
+    "To learn more, see the other how-to guides on runnables in this section."
    ]
   }
  ],

docs/docs/how_to/sql_csv.ipynb

@@ -428,7 +428,7 @@
     }
    ],
    "source": [
-    "from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsParser\n",
+    "from langchain.output_parsers.openai_tools import JsonOutputKeyToolsParser\n",
     "\n",
     "parser = JsonOutputKeyToolsParser(key_name=tool.name, first_tool_only=True)\n",
     "(llm_with_tools | parser).invoke(\n",

docs/docs/how_to/sql_large_db.ipynb

@@ -473,7 +473,7 @@
    "id": "12b0ed60-2536-4f82-85df-e096a272072a",
    "metadata": {},
    "source": [
-    "To try out our chain, let's see what happens when we try filtering on \"elenis moriset\", a misspelling of Alanis Morissette, without and with retrieval:"
+    "To try out our chain, let's see what happens when we try filtering on \"elenis moriset\", a mispelling of Alanis Morissette, without and with retrieval:"
    ]
   },
   {

docs/docs/how_to/streaming.ipynb

@@ -71,17 +71,16 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "f123bdcb-8c8b-440c-9bbd-aa5ed4e9cd17",
+   "id": "cd351cf4",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
+      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.0 is available.\n",
+      "You should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
+      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
@@ -94,69 +93,25 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "keys = [\n",
-    "    \"ANTHROPIC_API_KEY\",\n",
-    "    \"OPENAI_API_KEY\",\n",
-    "]\n",
-    "\n",
-    "for key in keys:\n",
-    "    if key not in os.environ:\n",
-    "        os.environ[key] = getpass(f\"Enter API Key for {key}=?\")\n",
-    "\n",
-    "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
+    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "\n",
     "model = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "a2464c57-0e89-4159-b21f-5859a21be658",
-   "metadata": {},
-   "source": [
-    "Let's start with the sync `stream` API:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "8b44dfb2-0749-487a-8918-f8b6b8233093",
+   "id": "91787fc7-d941-48c0-a8b4-0ee61ab7dd5d",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The| sky| appears| blue| during| the| day|.|"
-     ]
-    }
-   ],
-   "source": [
-    "chunks = []\n",
-    "for chunk in model.stream(\"what color is the sky?\"):\n",
-    "    chunks.append(chunk)\n",
-    "    print(chunk.content, end=\"|\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d835b5c-cbb7-41ab-8905-bdc24d515d29",
-   "metadata": {},
-   "source": [
-    "Alternatively, if you're working in an async environment, you may consider using the async `astream` API:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f180b6a0-0027-4bd8-8bab-fde76e282609",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The| sky| appears| blue| during| the| day|.|"
+      "The| sky| appears| blue| during| the| da|ytime|.|"
      ]
     }
    ],
@@ -177,17 +132,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "dade3000-1ac4-4f5c-b5c6-a0217f9f8a6b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessageChunk(content='The', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')"
+       "AIMessageChunk(content='The', id='run-c3885fff-3783-4b6d-85c4-4aeb45a02b1a')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -208,17 +163,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "d3cf5f38-249c-4da0-94e6-5e5203fad52e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessageChunk(content='The sky appears blue during', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')"
+       "AIMessageChunk(content='The sky appears blue during', id='run-c3885fff-3783-4b6d-85c4-4aeb45a02b1a')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "a8562ae2-3fd1-4829-9801-a5a732b1798d",
    "metadata": {},
    "outputs": [
@@ -259,13 +214,17 @@
       "\n",
       "A man| goes| to| a| pet| shop| to| buy| a| par|rot|.| The| shop| owner| shows| him| two| stunning| pa|rr|ots| with| beautiful| pl|um|age|.|\n",
       "\n",
-      "\"|There|'s| a| talking| par|rot| an|d a| non|-|talking| par|rot|,\"| the| owner| says|.| \"|The| talking| par|rot| costs| $|100|,| an|d the| non|-|talking| par|rot| is| $|20|.\"|\n",
+      "\"|There|'s| a| talking| par|rot| and| a| non|-|talking| par|rot|,\"| the| shop| owner| says|.| \"|The| talking| par|rot| costs| $|100|,| and| the| non|-|talking| par|rot| is| $|20|.\"|\n",
       "\n",
-      "The| man| says|,| \"|I|'ll| take| the| non|-|talking| par|rot| at| $|20|.\"|\n",
+      "The| man| thinks| about| it| and| decides| to| buy| the| cheaper| non|-|talking| par|rot|.|\n",
       "\n",
-      "He| pays| an|d leaves| with| the| par|rot|.| As| he|'s| walking| down| the| street|,| the| par|rot| looks| up| at| him| an|d says|,| \"|You| know|,| you| really| are| a| stupi|d man|!\"|\n",
+      "When| he| gets| home|,| the| par|rot| immediately| speaks| up| and| says|,| \"|Hey|,| buddy|,| I|'m| actually| the| talking| par|rot|,| and| you| got| an| amazing| deal|!\"|\n",
       "\n",
-      "The| man| is| stun|ne|d an|d looks| at| the| par|rot| in| dis|bel|ief|.| The| par|rot| continues|,| \"|Yes|,| you| got| r|ippe|d off| big| time|!| I| can| talk| just| as| well| as| that| other| par|rot|,| an|d you| only| pai|d $|20| |for| me|!\"|"
+      "The| man| is| stun|ned| and| rush|es| back| to| the| pet| shop| the| next| day|.|\n",
+      "\n",
+      "\"|That| par|rot| you| sold| me| can| talk|!\"| he| tells| the| shop| owner|.| \"|You| said| it| was| the| non|-|talking| par|rot|,| but| it|'s| been| talking| up| a| storm|!\"|\n",
+      "\n",
+      "The| shop| owner| n|ods| and| says|,| \"|Yeah|,| I| know|.| But| did| you| really| think| I| was| going| to| sell| you| the| talking| par|rot| for| just| $|20|?\"|"
      ]
     }
    ],
@@ -286,11 +245,9 @@
    "id": "868bc412",
    "metadata": {},
    "source": [
-    "Note that we're getting streaming output even though we're using `parser` at the end of the chain above. The `parser` operates on each streaming chunk individidually. Many of the [LCEL primitives](/docs/how_to#langchain-expression-language-lcel) also support this kind of transform-style passthrough streaming, which can be very convenient when constructing apps. \n",
+    "You might notice above that `parser` actually doesn't block the streaming output from the model, and instead processes each chunk individually. Many of the [LCEL primitives](/docs/how_to#langchain-expression-language-lcel) also support this kind of transform-style passthrough streaming, which can be very convenient when constructing apps.\n",
     "\n",
-    "Custom functions can be [designed to return generators](/docs/how_to/functions#streaming), which are able to operate on streams.\n",
-    "\n",
-    "Certain runnables, like [prompt templates](/docs/how_to#prompt-templates) and [chat models](/docs/how_to#chat-models), cannot process individual chunks and instead aggregate all previous steps. Such runnables can interrupt the streaming process."
+    "Certain runnables, like [prompt templates](/docs/how_to#prompt-templates) and [chat models](/docs/how_to#chat-models), cannot process individual chunks and instead aggregate all previous steps. This will interrupt the streaming process. Custom functions can be [designed to return generators](/docs/how_to/functions#streaming), which"
    ]
   },
   {
@@ -299,9 +256,10 @@
    "metadata": {},
    "source": [
     ":::{.callout-note}\n",
-    "The LangChain Expression language allows you to separate the construction of a chain from the mode in which it is used (e.g., sync/async, batch/streaming etc.). If this is not relevant to what you're building, you can also rely on a standard **imperative** programming approach by\n",
+    "If the above functionality is not relevant to what you're building, you do not have to use the `LangChain Expression Language` to use LangChain and can instead rely on a standard **imperative** programming approach by\n",
     "caling `invoke`, `batch` or `stream` on each component individually, assigning the results to variables and then using them downstream as you see fit.\n",
     "\n",
+    "If that works for your needs, then that's fine by us 👌!\n",
     ":::"
    ]
   },
@@ -325,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "5ff63cce-715a-4561-951f-9321c82e8d81",
    "metadata": {},
    "outputs": [
@@ -363,9 +321,7 @@
     "    model | JsonOutputParser()\n",
     ")  # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n",
     "async for text in chain.astream(\n",
-    "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "    \"Each country should have the key `name` and `population`\"\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n",
     "):\n",
     "    print(text, flush=True)"
    ]
@@ -388,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "d9c90117-9faa-4a01-b484-0db071808d1f",
    "metadata": {},
    "outputs": [
@@ -396,7 +352,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['France', 'Spain', 'Japan']|"
+      "[None, '', 'France', 'France', 'France', 'France', 'France', None, 'France', '', 'France', 'Spain', 'France', 'Spain', 'France', 'Spain', 'France', 'Spain', 'France', 'Spain', None, 'France', 'Spain', '', 'France', 'Spain', 'Japan', 'France', 'Spain', 'Japan', 'France', 'Spain', 'Japan', 'France', 'Spain', 'Japan']|"
      ]
     }
    ],
@@ -430,9 +386,7 @@
     "chain = model | JsonOutputParser() | _extract_country_names\n",
     "\n",
     "async for text in chain.astream(\n",
-    "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "    \"Each country should have the key `name` and `population`\"\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n",
     "):\n",
     "    print(text, end=\"|\", flush=True)"
    ]
@@ -447,13 +401,13 @@
     "Le'ts fix the streaming using a generator function that can operate on the **input stream**.\n",
     "\n",
     ":::{.callout-tip}\n",
-    "A generator function (a function that uses `yield`) allows writing code that operates on **input streams**\n",
+    "A generator function (a function that uses `yield`) allows writing code that operators on **input streams**\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "15984b2b-315a-4119-945b-2a3dabea3082",
    "metadata": {},
    "outputs": [
@@ -497,9 +451,7 @@
     "chain = model | JsonOutputParser() | _extract_country_names_streaming\n",
     "\n",
     "async for text in chain.astream(\n",
-    "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "    \"Each country should have the key `name` and `population`\",\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n",
     "):\n",
     "    print(text, end=\"|\", flush=True)"
    ]
@@ -528,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "b9b1c00d-8b44-40d0-9e2b-8a70d238f82b",
    "metadata": {},
    "outputs": [
@@ -539,7 +491,7 @@
        "  Document(page_content='harrison likes spicy food')]]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -584,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "957447e6-1e60-41ef-8c10-2654bd9e738d",
    "metadata": {},
    "outputs": [],
@@ -602,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "94e50b5d-bf51-4eee-9da0-ee40dd9ce42b",
    "metadata": {},
    "outputs": [
@@ -610,15 +562,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Base|d on| the| given| context|,| Harrison| worke|d at| K|ens|ho|.|\n",
+      "Based| on| the| given| context|,| Harrison| worked| at| K|ens|ho|.|\n",
       "\n",
       "Here| are| |3| |made| up| sentences| about| this| place|:|\n",
       "\n",
-      "1|.| K|ens|ho| was| a| cutting|-|edge| technology| company| known| for| its| innovative| solutions| in| artificial| intelligence| an|d data| analytics|.|\n",
+      "1|.| K|ens|ho| was| a| cutting|-|edge| technology| company| known| for| its| innovative| solutions| in| artificial| intelligence| and| data| analytics|.|\n",
       "\n",
-      "2|.| The| modern| office| space| at| K|ens|ho| feature|d open| floor| plans|,| collaborative| work|sp|aces|,| an|d a| vib|rant| atmosphere| that| fos|tere|d creativity| an|d team|work|.|\n",
+      "2|.| The| modern| office| space| at| K|ens|ho| featured| open| floor| plans|,| collaborative| work|sp|aces|,| and| a| vib|rant| atmosphere| that| fos|tered| creativity| and| team|work|.|\n",
       "\n",
-      "3|.| With| its| prime| location| in| the| heart| of| the| city|,| K|ens|ho| attracte|d top| talent| from| aroun|d the| worl|d,| creating| a| diverse| an|d dynamic| work| environment|.|"
+      "3|.| With| its| prime| location| in| the| heart| of| the| city|,| K|ens|ho| attracted| top| talent| from| around| the| world|,| creating| a| diverse| and| dynamic| work| environment|.|"
      ]
     }
    ],
@@ -647,17 +599,27 @@
     "Event Streaming is a **beta** API. This API may change a bit based on feedback.\n",
     "\n",
     ":::{.callout-note}\n",
-    "\n",
-    "This guide demonstrates the `V2` API and requires langchain-core >= 0.2. For the `V1` API compatible with older versions of LangChain, see [here](https://python.langchain.com/v0.1/docs/expression_language/streaming/#using-stream-events).\n",
+    "Introduced in langchain-core **0.1.14**.\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "61348df9-ec58-401e-be89-68a70042f88e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.1.45'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import langchain_core\n",
     "\n",
@@ -685,21 +647,24 @@
     "When streaming is implemented properly, the inputs to a runnable will not be known until after the input stream has been entirely consumed. This means that `inputs` will often be included only for `end` events and rather than for `start` events.\n",
     ":::\n",
     "\n",
+    "\n",
     "| event                | name             | chunk                           | input                                         | output                                          |\n",
     "|----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------|\n",
     "| on_chat_model_start  | [model name]     |                                 | {\"messages\": [[SystemMessage, HumanMessage]]} |                                                 |\n",
     "| on_chat_model_stream | [model name]     | AIMessageChunk(content=\"hello\") |                                               |                                                 |\n",
-    "| on_chat_model_end    | [model name]     |                                 | {\"messages\": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content=\"hello world\")           |\n",
+    "| on_chat_model_end    | [model name]     |                                 | {\"messages\": [[SystemMessage, HumanMessage]]} | {\"generations\": [...], \"llm_output\": None, ...} |\n",
     "| on_llm_start         | [model name]     |                                 | {'input': 'hello'}                            |                                                 |\n",
     "| on_llm_stream        | [model name]     | 'Hello'                         |                                               |                                                 |\n",
-    "| on_llm_end           | [model name]     |                                 | 'Hello human!'                                |                                                 |\n",
+    "| on_llm_end           | [model name]     |                                 | 'Hello human!'                                |\n",
     "| on_chain_start       | format_docs      |                                 |                                               |                                                 |\n",
     "| on_chain_stream      | format_docs      | \"hello world!, goodbye world!\"  |                                               |                                                 |\n",
     "| on_chain_end         | format_docs      |                                 | [Document(...)]                               | \"hello world!, goodbye world!\"                  |\n",
     "| on_tool_start        | some_tool        |                                 | {\"x\": 1, \"y\": \"2\"}                            |                                                 |\n",
+    "| on_tool_stream       | some_tool        | {\"x\": 1, \"y\": \"2\"}              |                                               |                                                 |\n",
     "| on_tool_end          | some_tool        |                                 |                                               | {\"x\": 1, \"y\": \"2\"}                              |\n",
     "| on_retriever_start   | [retriever name] |                                 | {\"query\": \"hello\"}                            |                                                 |\n",
-    "| on_retriever_end     | [retriever name] |                                 | {\"query\": \"hello\"}                            | [Document(...), ..]                             |\n",
+    "| on_retriever_chunk   | [retriever name] | {documents: [...]}              |                                               |                                                 |\n",
+    "| on_retriever_end     | [retriever name] |                                 | {\"query\": \"hello\"}                            | {documents: [...]}                              |\n",
     "| on_prompt_start      | [template_name]  |                                 | {\"question\": \"hello\"}                         |                                                 |\n",
     "| on_prompt_end        | [template_name]  |                                 | {\"question\": \"hello\"}                         | ChatPromptValue(messages: [SystemMessage, ...]) |"
    ]
@@ -716,22 +681,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "c00df46e-7f6b-4e06-8abf-801898c8d57f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.\n",
-      "  warn_beta(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "events = []\n",
-    "async for event in model.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in model.astream_events(\"hello\", version=\"v1\"):\n",
     "    events.append(event)"
    ]
   },
@@ -742,16 +698,13 @@
    "source": [
     ":::{.callout-note}\n",
     "\n",
-    "Hey what's that funny version=\"v2\" parameter in the API?! 😾\n",
+    "Hey what's that funny version=\"v1\" parameter in the API?! 😾\n",
     "\n",
-    "This is a **beta API**, and we're almost certainly going to make some changes to it (in fact, we already have!)\n",
+    "This is a **beta API**, and we're almost certainly going to make some changes to it.\n",
     "\n",
     "This version parameter will allow us to minimize such breaking changes to your code. \n",
     "\n",
     "In short, we are annoying you now, so we don't have to annoy you later.\n",
-    "\n",
-    "`v2` is only available for langchain-core>=0.2.0.\n",
-    "\n",
     ":::"
    ]
   },
@@ -765,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "ce31b525-f47d-4828-85a7-912ce9f2e79b",
    "metadata": {},
    "outputs": [
@@ -773,26 +726,26 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chat_model_start',\n",
-       "  'data': {'input': 'hello'},\n",
+       "  'run_id': '26134ba4-e486-4552-94d9-a31a2dfe7f4a',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {},\n",
+       "  'data': {'input': 'hello'}},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='Hello', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
-       "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '26134ba4-e486-4552-94d9-a31a2dfe7f4a',\n",
        "  'tags': [],\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {},\n",
+       "  'name': 'ChatAnthropic',\n",
+       "  'data': {'chunk': AIMessageChunk(content='Hello', id='run-26134ba4-e486-4552-94d9-a31a2dfe7f4a')}},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='!', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
-       "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '26134ba4-e486-4552-94d9-a31a2dfe7f4a',\n",
        "  'tags': [],\n",
-       "  'metadata': {}}]"
+       "  'metadata': {},\n",
+       "  'name': 'ChatAnthropic',\n",
+       "  'data': {'chunk': AIMessageChunk(content='!', id='run-26134ba4-e486-4552-94d9-a31a2dfe7f4a')}}]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -803,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "76cfe826-ee63-4310-ad48-55a95eb3b9d6",
    "metadata": {},
    "outputs": [
@@ -811,20 +764,20 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
-       "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '26134ba4-e486-4552-94d9-a31a2dfe7f4a',\n",
        "  'tags': [],\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {},\n",
+       "  'name': 'ChatAnthropic',\n",
+       "  'data': {'chunk': AIMessageChunk(content='?', id='run-26134ba4-e486-4552-94d9-a31a2dfe7f4a')}},\n",
        " {'event': 'on_chat_model_end',\n",
-       "  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
        "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '26134ba4-e486-4552-94d9-a31a2dfe7f4a',\n",
        "  'tags': [],\n",
-       "  'metadata': {}}]"
+       "  'metadata': {},\n",
+       "  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', id='run-26134ba4-e486-4552-94d9-a31a2dfe7f4a')}}]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -845,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "4328c56c-a303-427b-b1f2-f354e9af555c",
    "metadata": {},
    "outputs": [],
@@ -857,10 +810,8 @@
     "events = [\n",
     "    event\n",
     "    async for event in chain.astream_events(\n",
-    "        \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "        'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "        \"Each country should have the key `name` and `population`\",\n",
-    "        version=\"v2\",\n",
+    "        'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
+    "        version=\"v1\",\n",
     "    )\n",
     "]"
    ]
@@ -881,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "8e66ea3d-a450-436a-aaac-d9478abc6c28",
    "metadata": {},
    "outputs": [
@@ -889,26 +840,26 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chain_start',\n",
-       "  'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'},\n",
+       "  'run_id': '93c65519-a480-43f2-b340-851706799c57',\n",
        "  'name': 'RunnableSequence',\n",
        "  'tags': [],\n",
-       "  'run_id': '4765006b-16e2-4b1d-a523-edd9fd64cb92',\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {},\n",
+       "  'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}},\n",
        " {'event': 'on_chat_model_start',\n",
-       "  'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}},\n",
        "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '6075a178-bc34-4ef2-bbb4-75c3ed96eb9c',\n",
        "  'tags': ['seq:step:1'],\n",
-       "  'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {},\n",
+       "  'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='{', id='run-0320c234-7b52-4a14-ae4e-5f100949e589')},\n",
-       "  'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',\n",
        "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '6075a178-bc34-4ef2-bbb4-75c3ed96eb9c',\n",
        "  'tags': ['seq:step:1'],\n",
-       "  'metadata': {}}]"
+       "  'metadata': {},\n",
+       "  'data': {'chunk': AIMessageChunk(content='{', id='run-6075a178-bc34-4ef2-bbb4-75c3ed96eb9c')}}]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -935,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "630c71d6-8d94-4ce0-a78a-f20e90f628df",
    "metadata": {},
    "outputs": [
@@ -974,10 +925,8 @@
     "num_events = 0\n",
     "\n",
     "async for event in chain.astream_events(\n",
-    "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
+    "    version=\"v1\",\n",
     "):\n",
     "    kind = event[\"event\"]\n",
     "    if kind == \"on_chat_model_stream\":\n",
@@ -1018,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "4f0b581b-be63-4663-baba-c6d2b625cdf9",
    "metadata": {},
    "outputs": [
@@ -1026,17 +975,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': []}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
+      "{'event': 'on_parser_start', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': ''}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'b817e94b-db03-4b6f-8432-019dd59a2d93', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}}}\n",
       "...\n"
      ]
     }
@@ -1048,10 +997,8 @@
     "\n",
     "max_events = 0\n",
     "async for event in chain.astream_events(\n",
-    "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
+    "    version=\"v1\",\n",
     "    include_names=[\"my_parser\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1072,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "096cd904-72f0-4ebe-a8b7-d0e730faea7f",
    "metadata": {},
    "outputs": [
@@ -1080,17 +1027,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n    ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n      ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
+      "{'event': 'on_chat_model_start', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\"', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' [', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\\n    ', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\\n      ', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '02b68bbd-e99b-4a66-bf5f-6e238bfd0182', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\"', id='run-02b68bbd-e99b-4a66-bf5f-6e238bfd0182')}}\n",
       "...\n"
      ]
     }
@@ -1103,7 +1050,7 @@
     "max_events = 0\n",
     "async for event in chain.astream_events(\n",
     "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
-    "    version=\"v2\",\n",
+    "    version=\"v1\",\n",
     "    include_types=[\"chat_model\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1131,7 +1078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "26bac0d2-76d9-446e-b346-82790236b88d",
    "metadata": {},
    "outputs": [
@@ -1139,17 +1086,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chain_stream', 'data': {'chunk': {}}, 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
+      "{'event': 'on_chain_start', 'run_id': '55ab7082-7200-4545-8f45-bb0997b0bce8', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}}\n",
+      "{'event': 'on_chat_model_start', 'name': 'ChatAnthropic', 'run_id': 'd2efdbe8-77e4-4b29-ae68-be163239385e', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'd2efdbe8-77e4-4b29-ae68-be163239385e', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{', id='run-d2efdbe8-77e4-4b29-ae68-be163239385e')}}\n",
+      "{'event': 'on_parser_start', 'name': 'JsonOutputParser', 'run_id': 'bc80bc6d-5ae5-4d3a-9bb6-006c0e9c67c5', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {}}\n",
+      "{'event': 'on_parser_stream', 'name': 'JsonOutputParser', 'run_id': 'bc80bc6d-5ae5-4d3a-9bb6-006c0e9c67c5', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {}}}\n",
+      "{'event': 'on_chain_stream', 'run_id': '55ab7082-7200-4545-8f45-bb0997b0bce8', 'tags': ['my_chain'], 'metadata': {}, 'name': 'RunnableSequence', 'data': {'chunk': {}}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'd2efdbe8-77e4-4b29-ae68-be163239385e', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-d2efdbe8-77e4-4b29-ae68-be163239385e')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'd2efdbe8-77e4-4b29-ae68-be163239385e', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\"', id='run-d2efdbe8-77e4-4b29-ae68-be163239385e')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'd2efdbe8-77e4-4b29-ae68-be163239385e', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries', id='run-d2efdbe8-77e4-4b29-ae68-be163239385e')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'd2efdbe8-77e4-4b29-ae68-be163239385e', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":', id='run-d2efdbe8-77e4-4b29-ae68-be163239385e')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'd2efdbe8-77e4-4b29-ae68-be163239385e', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' [', id='run-d2efdbe8-77e4-4b29-ae68-be163239385e')}}\n",
       "...\n"
      ]
     }
@@ -1160,7 +1107,7 @@
     "max_events = 0\n",
     "async for event in chain.astream_events(\n",
     "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
-    "    version=\"v2\",\n",
+    "    version=\"v1\",\n",
     "    include_tags=[\"my_chain\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1185,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "0e6451d3-3b11-4a71-ae19-998f4c10180f",
    "metadata": {},
    "outputs": [],
@@ -1227,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "f9a8fe35-faab-4970-b8c0-5c780845d98a",
    "metadata": {},
    "outputs": [
@@ -1235,15 +1182,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['France', 'Spain', 'Japan']\n"
+      "[None, '', 'France', 'France', 'France', 'France', 'France', None, 'France', '', 'France', 'Spain', 'France', 'Spain', 'France', 'Spain', 'France', 'Spain', 'France', 'Spain', None, 'France', 'Spain', '', 'France', 'Spain', 'Japan', 'France', 'Spain', 'Japan', 'France', 'Spain', 'Japan', 'France', 'Spain', 'Japan']\n"
      ]
     }
    ],
    "source": [
     "async for chunk in chain.astream(\n",
-    "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "    \"Each country should have the key `name` and `population`\",\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
     "):\n",
     "    print(chunk, flush=True)"
    ]
@@ -1258,7 +1203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "b08215cd-bffa-4e76-aaf3-c52ee34f152c",
    "metadata": {},
    "outputs": [
@@ -1301,10 +1246,8 @@
     "num_events = 0\n",
     "\n",
     "async for event in chain.astream_events(\n",
-    "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
-    "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
-    "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
+    "    version=\"v1\",\n",
     "):\n",
     "    kind = event[\"event\"]\n",
     "    if kind == \"on_chat_model_stream\":\n",
@@ -1339,7 +1282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "1854206d-b3a5-4f91-9e00-bccbaebac61f",
    "metadata": {},
    "outputs": [
@@ -1347,10 +1290,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'bad_tool', 'tags': [], 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'metadata': {}}\n",
-      "{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'metadata': {}}\n",
-      "{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
-      "{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'name': 'bad_tool', 'tags': [], 'metadata': {}}\n"
+      "{'event': 'on_tool_start', 'run_id': 'b5ffad93-6dcf-4c95-9dfa-a35675c6bbc3', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
+      "{'event': 'on_tool_stream', 'run_id': 'b5ffad93-6dcf-4c95-9dfa-a35675c6bbc3', 'tags': [], 'metadata': {}, 'name': 'bad_tool', 'data': {'chunk': 'olleh'}}\n",
+      "{'event': 'on_tool_end', 'name': 'bad_tool', 'run_id': 'b5ffad93-6dcf-4c95-9dfa-a35675c6bbc3', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n"
      ]
     }
    ],
@@ -1372,7 +1314,7 @@
     "    return reverse_word.invoke(word)\n",
     "\n",
     "\n",
-    "async for event in bad_tool.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in bad_tool.astream_events(\"hello\", version=\"v1\"):\n",
     "    print(event)"
    ]
   },
@@ -1386,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "a20a6cb3-bb43-465c-8cfc-0a7349d70968",
    "metadata": {},
    "outputs": [
@@ -1394,10 +1336,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'correct_tool', 'tags': [], 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'metadata': {}}\n",
-      "{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'metadata': {}}\n",
-      "{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
-      "{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'name': 'correct_tool', 'tags': [], 'metadata': {}}\n"
+      "{'event': 'on_tool_start', 'run_id': 'be7f9379-5340-433e-b1fc-84314353cd17', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
+      "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '50bfe8a9-64c5-4ed8-8dae-03415b5b7c6e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '50bfe8a9-64c5-4ed8-8dae-03415b5b7c6e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello', 'output': 'olleh'}}\n",
+      "{'event': 'on_tool_stream', 'run_id': 'be7f9379-5340-433e-b1fc-84314353cd17', 'tags': [], 'metadata': {}, 'name': 'correct_tool', 'data': {'chunk': 'olleh'}}\n",
+      "{'event': 'on_tool_end', 'name': 'correct_tool', 'run_id': 'be7f9379-5340-433e-b1fc-84314353cd17', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n"
      ]
     }
    ],
@@ -1408,7 +1351,7 @@
     "    return reverse_word.invoke(word, {\"callbacks\": callbacks})\n",
     "\n",
     "\n",
-    "async for event in correct_tool.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in correct_tool.astream_events(\"hello\", version=\"v1\"):\n",
     "    print(event)"
    ]
   },
@@ -1422,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "0ac0a3c1-f3a4-4157-b053-4fec8d2e698c",
    "metadata": {},
    "outputs": [
@@ -1430,11 +1373,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'metadata': {}}\n",
-      "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'metadata': {}}\n",
-      "{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
-      "{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n",
-      "{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n"
+      "{'event': 'on_chain_start', 'run_id': 'a5d11046-93fa-4cd9-9854-d3afa3d686ef', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
+      "{'event': 'on_chain_stream', 'run_id': 'a5d11046-93fa-4cd9-9854-d3afa3d686ef', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': 'a5d11046-93fa-4cd9-9854-d3afa3d686ef', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n"
      ]
     }
    ],
@@ -1450,7 +1391,7 @@
     "\n",
     "await reverse_and_double.ainvoke(\"1234\")\n",
     "\n",
-    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
+    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v1\"):\n",
     "    print(event)"
    ]
   },
@@ -1464,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "c896bb94-9d10-41ff-8fe2-d6b05b1ed74b",
    "metadata": {},
    "outputs": [
@@ -1472,11 +1413,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'metadata': {}}\n",
-      "{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'metadata': {}}\n",
-      "{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
-      "{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n",
-      "{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n"
+      "{'event': 'on_chain_start', 'run_id': 'b3eff5c2-8339-4e15-98b3-85148d9ae350', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
+      "{'event': 'on_chain_stream', 'run_id': 'b3eff5c2-8339-4e15-98b3-85148d9ae350', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': 'b3eff5c2-8339-4e15-98b3-85148d9ae350', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n"
      ]
     }
    ],
@@ -1491,7 +1430,7 @@
     "\n",
     "await reverse_and_double.ainvoke(\"1234\")\n",
     "\n",
-    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
+    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v1\"):\n",
     "    print(event)"
    ]
   },

docs/docs/how_to/structured_output.ipynb

@@ -518,7 +518,7 @@
     "\n",
     "### Using `PydanticOutputParser`\n",
     "\n",
-    "The following example uses the built-in [`PydanticOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html) to parse the output of a chat model prompted to match the given Pydantic schema. Note that we are adding `format_instructions` directly to the prompt from a method on the parser:"
+    "The following example uses the built-in [`PydanticOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html) to parse the output of a chat model prompted to match a the given Pydantic schema. Note that we are adding `format_instructions` directly to the prompt from a method on the parser:"
    ]
   },
   {

docs/docs/how_to/time_weighted_vectorstore.ipynb

@@ -205,7 +205,7 @@
    "source": [
     "import datetime\n",
     "\n",
-    "from langchain_core.utils import mock_now"
+    "from langchain.utils import mock_now"
    ]
   },
   {

docs/docs/how_to/tool_calling.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How to use a model to call tools\n",
+    "# How to use a chat model to call tools\n",
     "\n",
     ":::info Prerequisites\n",
     "\n",
@@ -33,7 +33,7 @@
     "result.\n",
     "\n",
     "However, tool calling goes beyond [structured output](/docs/how_to/structured_output/)\n",
-    "since you can pass responses from called tools back to the model to create longer interactions.\n",
+    "since you can pass responses to caled tools back to the model to create longer interactions.\n",
     "For instance, given a search engine tool, an LLM might handle a \n",
     "query by first issuing a call to the search engine with arguments. The system calling the LLM can \n",
     "receive the tool call, execute it, and return the output to the LLM to inform its \n",
@@ -705,7 +705,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools.ipynb

@@ -0,0 +1,450 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "7f219241",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 4\n",
+    "sidebar_class_name: hidden\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15780a65",
+   "metadata": {},
+   "source": [
+    "# How to use LangChain tools\n",
+    "\n",
+    "Tools are interfaces that an agent, chain, or LLM can use to interact with the world.\n",
+    "They combine a few things:\n",
+    "\n",
+    "1. The name of the tool\n",
+    "2. A description of what the tool is\n",
+    "3. JSON schema of what the inputs to the tool are\n",
+    "4. The function to call \n",
+    "5. Whether the result of a tool should be returned directly to the user\n",
+    "\n",
+    "It is useful to have all this information because this information can be used to build action-taking systems! The name, description, and JSON schema can be used to prompt the LLM so it knows how to specify what action to take, and then the function to call is equivalent to taking that action.\n",
+    "\n",
+    "The simpler the input to a tool is, the easier it is for an LLM to be able to use it.\n",
+    "Many agents will only work with tools that have a single string input.\n",
+    "For a list of agent types and which ones work with more complicated inputs, please see [this documentation](https://python.langchain.com/v0.1/docs/modules/agents/agent_types/)\n",
+    "\n",
+    "Importantly, the name, description, and JSON schema (if used) are all used in the prompt. Therefore, it is really important that they are clear and describe exactly how the tool should be used. You may need to change the default name, description, or JSON schema if the LLM is not understanding how to use the tool.\n",
+    "\n",
+    "## Default Tools\n",
+    "\n",
+    "Let's take a look at how to work with tools. To do this, we'll work with a built in tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "19297004",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.tools import WikipediaQueryRun\n",
+    "from langchain_community.utilities import WikipediaAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1098e51a",
+   "metadata": {},
+   "source": [
+    "Now we initialize the tool. This is where we can configure it as we please"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "27a48655",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)\n",
+    "tool = WikipediaQueryRun(api_wrapper=api_wrapper)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7db48439",
+   "metadata": {},
+   "source": [
+    "This is the default name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "50f1ece1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Wikipedia'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "075499b1",
+   "metadata": {},
+   "source": [
+    "This is the default description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e9be09e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.description"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89c86b00",
+   "metadata": {},
+   "source": [
+    "This is the default JSON schema of the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "963a2e8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'query': {'title': 'Query', 'type': 'string'}}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.args"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c467a35",
+   "metadata": {},
+   "source": [
+    "We can see if the tool should return directly to the user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "039334b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.return_direct"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc421b02",
+   "metadata": {},
+   "source": [
+    "We can call this tool with a dictionary input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6669a13c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Page: LangChain\\nSummary: LangChain is a framework designed to simplify the creation of applications '"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.run({\"query\": \"langchain\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "587d6a58",
+   "metadata": {},
+   "source": [
+    "We can also call this tool with a single string input. \n",
+    "We can do this because this tool expects only a single input.\n",
+    "If it required multiple inputs, we would not be able to do that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8cb23935",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Page: LangChain\\nSummary: LangChain is a framework designed to simplify the creation of applications '"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.run(\"langchain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19eee1d5",
+   "metadata": {},
+   "source": [
+    "## Customizing Default Tools\n",
+    "We can also modify the built in name, description, and JSON schema of the arguments.\n",
+    "\n",
+    "When defining the JSON schema of the arguments, it is important that the inputs remain the same as the function, so you shouldn't change that. But you can define custom descriptions for each input easily."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "599c4da7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class WikiInputs(BaseModel):\n",
+    "    \"\"\"Inputs to the wikipedia tool.\"\"\"\n",
+    "\n",
+    "    query: str = Field(\n",
+    "        description=\"query to look up in Wikipedia, should be 3 or less words\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "6bde63e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool = WikipediaQueryRun(\n",
+    "    name=\"wiki-tool\",\n",
+    "    description=\"look up things in wikipedia\",\n",
+    "    args_schema=WikiInputs,\n",
+    "    api_wrapper=api_wrapper,\n",
+    "    return_direct=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "eeaa1d9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'wiki-tool'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "7599d88c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'look up things in wikipedia'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "80042cb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'query': {'title': 'Query',\n",
+       "  'description': 'query to look up in Wikipedia, should be 3 or less words',\n",
+       "  'type': 'string'}}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.args"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "8455fb9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.return_direct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "86f731a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Page: LangChain\\nSummary: LangChain is a framework designed to simplify the creation of applications '"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.run(\"langchain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5b8b6bc",
+   "metadata": {},
+   "source": [
+    "## More Topics\n",
+    "\n",
+    "This was a quick introduction to tools in LangChain, but there is a lot more to learn\n",
+    "\n",
+    "**[Built-In Tools](/docs/integrations/tools/)**: For a list of all built-in tools, see [this page](/docs/integrations/tools/)\n",
+    "    \n",
+    "**[Custom Tools](/docs/how_to/custom_tools)**: Although built-in tools are useful, it's highly likely that you'll have to define your own tools. See [this guide](/docs/how_to/custom_tools) for instructions on how to do so.\n",
+    "    \n",
+    "**[Toolkits](/docs/how_to/toolkits)**: Toolkits are collections of tools that work well together. For a more in depth description as well as a list of all built-in toolkits, see [this page](/docs/how_to/toolkits)\n",
+    "\n",
+    "**[Tools as OpenAI Functions](/docs/how_to/tools_as_openai_functions/)**: Tools are very similar to OpenAI Functions, and can easily be converted to that format. See [this notebook](/docs/how_to/tools_as_openai_functions) for instructions on how to do that.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78e2d0b3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how_to/tools_builtin.ipynb

@@ -1,236 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "7f219241",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_position: 4\n",
-    "sidebar_class_name: hidden\n",
-    "---"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e8f68de0-7df7-4bfd-9207-3258431426ef",
-   "metadata": {},
-   "source": [
-    "# How to use built-in tools and toolkits\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [LangChain Toolkits](/docs/concepts/#tools)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Tools\n",
-    "\n",
-    "LangChain has a large collection of 3rd party tools. Please visit [Tool Integrations](/docs/integrations/tools/) for a list of the available tools.\n",
-    "\n",
-    ":::{.callout-important}\n",
-    "\n",
-    "When using 3rd party tools, make sure that you understand how the tool works, what permissions\n",
-    "it has. Read over its documentation and check if anything is required from you\n",
-    "from a security point of view. Please see our [security](https://python.langchain.com/v0.1/docs/security/) \n",
-    "guidelines for more information.\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "Let's try out the [Wikipedia integration](/docs/integrations/tools/wikipedia/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "84f70856-b865-4658-9930-7577fb4712ce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -qU wikipedia"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "b4eaed85-c5a6-4ba9-b401-40258b0131c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Page: LangChain\n",
-      "Summary: LangChain is a framework designed to simplify the creation of applications \n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.tools import WikipediaQueryRun\n",
-    "from langchain_community.utilities import WikipediaAPIWrapper\n",
-    "\n",
-    "api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)\n",
-    "tool = WikipediaQueryRun(api_wrapper=api_wrapper)\n",
-    "\n",
-    "print(tool.invoke({\"query\": \"langchain\"}))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cb870984-52d5-4453-be35-7072a08c6c14",
-   "metadata": {},
-   "source": [
-    "The tool has the following defaults associated with it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "7f094f01-2e98-4947-acc4-0846963a96e0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: wiki-tool\n",
-      "Description: look up things in wikipedia\n",
-      "args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
-      "returns directly?: True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Name: {tool.name}\")\n",
-    "print(f\"Description: {tool.description}\")\n",
-    "print(f\"args schema: {tool.args}\")\n",
-    "print(f\"returns directly?: {tool.return_direct}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19eee1d5",
-   "metadata": {},
-   "source": [
-    "## Customizing Default Tools\n",
-    "We can also modify the built in name, description, and JSON schema of the arguments.\n",
-    "\n",
-    "When defining the JSON schema of the arguments, it is important that the inputs remain the same as the function, so you shouldn't change that. But you can define custom descriptions for each input easily."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "1365784c-e666-41c8-a1bb-e50f822b5936",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Page: LangChain\n",
-      "Summary: LangChain is a framework designed to simplify the creation of applications \n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.tools import WikipediaQueryRun\n",
-    "from langchain_community.utilities import WikipediaAPIWrapper\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class WikiInputs(BaseModel):\n",
-    "    \"\"\"Inputs to the wikipedia tool.\"\"\"\n",
-    "\n",
-    "    query: str = Field(\n",
-    "        description=\"query to look up in Wikipedia, should be 3 or less words\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "tool = WikipediaQueryRun(\n",
-    "    name=\"wiki-tool\",\n",
-    "    description=\"look up things in wikipedia\",\n",
-    "    args_schema=WikiInputs,\n",
-    "    api_wrapper=api_wrapper,\n",
-    "    return_direct=True,\n",
-    ")\n",
-    "\n",
-    "print(tool.run(\"langchain\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "6e8850d6-6840-443e-a2be-adf64b30975c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: wiki-tool\n",
-      "Description: look up things in wikipedia\n",
-      "args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
-      "returns directly?: True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Name: {tool.name}\")\n",
-    "print(f\"Description: {tool.description}\")\n",
-    "print(f\"args schema: {tool.args}\")\n",
-    "print(f\"returns directly?: {tool.return_direct}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "acf0c2f7-ddc6-4633-8cef-59f234321e5c",
-   "metadata": {},
-   "source": [
-    "## How to use built-in toolkits\n",
-    "\n",
-    "Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.\n",
-    "\n",
-    "For a complete list of available ready-made toolkits, visit [Integrations](/docs/integrations/toolkits/).\n",
-    "\n",
-    "All Toolkits expose a `get_tools` method which returns a list of tools.\n",
-    "\n",
-    "You're usually meant to use them this way:\n",
-    "\n",
-    "```python\n",
-    "# Initialize a toolkit\n",
-    "toolkit = ExampleTookit(...)\n",
-    "\n",
-    "# Get list of tools\n",
-    "tools = toolkit.get_tools()\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/how_to/tools_chain.ipynb

@@ -45,7 +45,7 @@
    "id": "36a9c6fc-8264-462f-b8d7-9c7bbec22ef9",
    "metadata": {},
    "source": [
-    "If you'd like to trace your runs in [LangSmith](https://docs.smith.langchain.com/) uncomment and set the following environment variables:"
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
    ]
   },
   {
@@ -335,7 +335,7 @@
    "id": "616f9714-5b18-4eed-b88a-d38e4cb1de99",
    "metadata": {},
    "source": [
-    "Agents are also great because they make it easy to use multiple tools."
+    "Agents are also great because they make it easy to use multiple tools. To learn how to build Chains that use multiple tools, check out the [Chains with multiple tools](/docs/how_to/tools_multiple) page."
    ]
   },
   {
@@ -457,6 +457,21 @@
    "source": [
     "Check out the [LangSmith trace here](https://smith.langchain.com/public/eeeb27a4-a2f8-4f06-a3af-9c983f76146c/r)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0e4b7f4-58ce-4ca0-a986-d05a436a7ccf",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "Here we've gone over the basic ways to use Tools with Chains and Agents. We recommend the following sections to explore next:\n",
+    "\n",
+    "- [Agents](/docs/tutorials/agents): Everything related to Agents.\n",
+    "- [Choosing between multiple tools](/docs/how_to/tools_multiple): How to make tool chains that select from multiple tools.\n",
+    "- [Prompting for tool use](/docs/how_to/tools_prompting): How to make tool chains that prompt models directly, without using function-calling APIs.\n",
+    "- [Parallel tool use](/docs/how_to/tools_parallel): How to make tool chains that invoke multiple tools at once."
+   ]
   }
  ],
  "metadata": {
@@ -475,7 +490,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_error.ipynb

@@ -37,7 +37,7 @@
    "id": "68107597-0c8c-4bb5-8c12-9992fabdf71a",
    "metadata": {},
    "source": [
-    "If you'd like to trace your runs in [LangSmith](https://docs.smith.langchain.com/) uncomment and set the following environment variables:"
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
    ]
   },
   {

docs/docs/how_to/tools_human.ipynb

@@ -7,16 +7,7 @@
    "source": [
     "# How to add a human-in-the-loop for tools\n",
     "\n",
-    "There are certain tools that we don't trust a model to execute on its own. One thing we can do in such situations is require human approval before the tool is invoked.\n",
-    "\n",
-    ":::{.callout-info}\n",
-    "\n",
-    "This how-to guide shows a simple way to add human-in-the-loop for code running in a jupyter notebook or in a terminal.\n",
-    "\n",
-    "To build a production application, you will need to do more work to keep track of application state appropriately.\n",
-    "\n",
-    "We recommend using `langgraph` for powering such a capability. For more details, please see this [guide](https://langchain-ai.github.io/langgraph/how-tos/human-in-the-loop/).\n",
-    ":::\n"
+    "There are certain tools that we don't trust a model to execute on its own. One thing we can do in such situations is require human approval before the tool is invoked."
    ]
   },
   {
@@ -49,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "2bed0ccf-20cc-4fd3-9947-55471dd8c4da",
    "metadata": {},
    "outputs": [],
@@ -64,19 +55,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ecd5d7e-7c3c-4180-8958-7db2c1e43564",
+   "id": "43721981-4595-4721-bea0-5c67696426d3",
    "metadata": {},
    "source": [
     "## Chain\n",
     "\n",
-    "Let's create a few simple (dummy) tools and a tool-calling chain:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43721981-4595-4721-bea0-5c67696426d3",
-   "metadata": {},
-   "source": [
+    "Suppose we have the following (dummy) tools and tool-calling chain:\n",
+    "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
@@ -86,13 +71,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "e0ff02ac-e750-493b-9b09-4578711a6726",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# | output: false\n",
     "# | echo: false\n",
+    "# | outout: false\n",
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
@@ -101,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "0221fdfd-2a18-4449-a123-e6b0b15bb3d9",
    "metadata": {},
    "outputs": [
@@ -110,16 +95,17 @@
       "text/plain": [
        "[{'name': 'count_emails',\n",
        "  'args': {'last_n_days': 5},\n",
-       "  'id': 'toolu_01QYZdJ4yPiqsdeENWHqioFW',\n",
+       "  'id': 'toolu_012VHuh7vk5dVNct5SgZj3gh',\n",
        "  'output': 10}]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "from operator import itemgetter\n",
     "from typing import Dict, List\n",
     "\n",
     "from langchain_core.messages import AIMessage\n",
@@ -163,14 +149,12 @@
    "source": [
     "## Adding human approval\n",
     "\n",
-    "Let's add a step in the chain that will ask a person to approve or reject the tall call request.\n",
-    "\n",
-    "On rejection, the step will raise an exception which will stop execution of the rest of the chain."
+    "We can add a simple human approval step to our tool_chain function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "341fb055-0315-47bc-8f72-ed6103d2981f",
    "metadata": {},
    "outputs": [],
@@ -178,35 +162,23 @@
     "import json\n",
     "\n",
     "\n",
-    "class NotApproved(Exception):\n",
-    "    \"\"\"Custom exception.\"\"\"\n",
-    "\n",
-    "\n",
-    "def human_approval(msg: AIMessage) -> AIMessage:\n",
-    "    \"\"\"Responsible for passing through its input or raising an exception.\n",
-    "\n",
-    "    Args:\n",
-    "        msg: output from the chat model\n",
-    "\n",
-    "    Returns:\n",
-    "        msg: original output from the msg\n",
-    "    \"\"\"\n",
+    "def human_approval(msg: AIMessage) -> Runnable:\n",
     "    tool_strs = \"\\n\\n\".join(\n",
     "        json.dumps(tool_call, indent=2) for tool_call in msg.tool_calls\n",
     "    )\n",
     "    input_msg = (\n",
     "        f\"Do you approve of the following tool invocations\\n\\n{tool_strs}\\n\\n\"\n",
-    "        \"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\\n >>>\"\n",
+    "        \"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\"\n",
     "    )\n",
     "    resp = input(input_msg)\n",
     "    if resp.lower() not in (\"yes\", \"y\"):\n",
-    "        raise NotApproved(f\"Tool invocations not approved:\\n\\n{tool_strs}\")\n",
+    "        raise ValueError(f\"Tool invocations not approved:\\n\\n{tool_strs}\")\n",
     "    return msg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "25dca07b-56ca-4b94-9955-d4f3e9895e03",
    "metadata": {},
    "outputs": [
@@ -221,11 +193,10 @@
       "  \"args\": {\n",
       "    \"last_n_days\": 5\n",
       "  },\n",
-      "  \"id\": \"toolu_01WbD8XeMoQaRFtsZezfsHor\"\n",
+      "  \"id\": \"toolu_01LCpjpFxrRspygDscnHYyPm\"\n",
       "}\n",
       "\n",
-      "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\n",
-      " >>> yes\n"
+      "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no. yes\n"
      ]
     },
     {
@@ -233,11 +204,11 @@
       "text/plain": [
        "[{'name': 'count_emails',\n",
        "  'args': {'last_n_days': 5},\n",
-       "  'id': 'toolu_01WbD8XeMoQaRFtsZezfsHor',\n",
+       "  'id': 'toolu_01LCpjpFxrRspygDscnHYyPm',\n",
        "  'output': 10}]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -249,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "f558f2cd-847b-4ef9-a770-3961082b540c",
    "metadata": {},
    "outputs": [
@@ -262,41 +233,45 @@
       "{\n",
       "  \"name\": \"send_email\",\n",
       "  \"args\": {\n",
-      "    \"recipient\": \"sally@gmail.com\",\n",
-      "    \"message\": \"What's up homie\"\n",
+      "    \"message\": \"What's up homie\",\n",
+      "    \"recipient\": \"sally@gmail.com\"\n",
       "  },\n",
-      "  \"id\": \"toolu_014XccHFzBiVcc9GV1harV9U\"\n",
+      "  \"id\": \"toolu_0158qJVd1AL32Y1xxYUAtNEy\"\n",
       "}\n",
       "\n",
-      "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\n",
-      " >>> no\n"
+      "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no. no\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Tool invocations not approved:\n",
-      "\n",
-      "{\n",
-      "  \"name\": \"send_email\",\n",
-      "  \"args\": {\n",
-      "    \"recipient\": \"sally@gmail.com\",\n",
-      "    \"message\": \"What's up homie\"\n",
-      "  },\n",
-      "  \"id\": \"toolu_014XccHFzBiVcc9GV1harV9U\"\n",
-      "}\n"
+     "ename": "ValueError",
+     "evalue": "Tool invocations not approved:\n\n{\n  \"name\": \"send_email\",\n  \"args\": {\n    \"message\": \"What's up homie\",\n    \"recipient\": \"sally@gmail.com\"\n  },\n  \"id\": \"toolu_0158qJVd1AL32Y1xxYUAtNEy\"\n}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSend sally@gmail.com an email saying \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mWhat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms up homie\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:2499\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   2497\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2498\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2499\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2500\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2501\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   2502\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2503\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2504\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2505\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2506\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:3961\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3959\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[1;32m   3960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 3961\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3962\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3963\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3964\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3965\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3966\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3967\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3968\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   3969\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot invoke a coroutine function synchronously.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3970\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3971\u001b[0m     )\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:1625\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m   1621\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m   1622\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[1;32m   1623\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m   1624\u001b[0m         Output,\n\u001b[0;32m-> 1625\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1626\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1627\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1628\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1629\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1630\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1631\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1632\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m   1633\u001b[0m     )\n\u001b[1;32m   1634\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1635\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/config.py:347\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    346\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:3835\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[0;34m(self, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m   3833\u001b[0m                 output \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m   3834\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3835\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3836\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   3837\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3838\u001b[0m \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[1;32m   3839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/config.py:347\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    346\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[9], line 14\u001b[0m, in \u001b[0;36mhuman_approval\u001b[0;34m(msg)\u001b[0m\n\u001b[1;32m     12\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(input_msg)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resp\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myes\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool invocations not approved:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtool_strs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg\n",
+      "\u001b[0;31mValueError\u001b[0m: Tool invocations not approved:\n\n{\n  \"name\": \"send_email\",\n  \"args\": {\n    \"message\": \"What's up homie\",\n    \"recipient\": \"sally@gmail.com\"\n  },\n  \"id\": \"toolu_0158qJVd1AL32Y1xxYUAtNEy\"\n}"
      ]
     }
    ],
    "source": [
-    "try:\n",
-    "    chain.invoke(\"Send sally@gmail.com an email saying 'What's up homie'\")\n",
-    "except NotApproved as e:\n",
-    "    print()\n",
-    "    print(e)"
+    "chain.invoke(\"Send sally@gmail.com an email saying 'What's up homie'\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e938d8f1-df93-4726-a465-78e596312246",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -315,7 +290,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_multiple.ipynb

@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "1ea1fe24-fe1e-463b-a52c-79f0ef02328e",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 2\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
+   "metadata": {},
+   "source": [
+    "# How to use an LLM to choose between multiple tools\n",
+    "\n",
+    "In our [Quickstart](/docs/how_to/tool_calling) we went over how to build a Chain that calls a single `multiply` tool. Now let's take a look at how we might augment this chain so that it can pick from a number of tools to call. We'll focus on Chains since [Agents](/docs/tutorials/agents) can route between multiple tools by default."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fafec38-443a-42ad-a913-5be7667e3734",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "We'll need to install the following packages for this guide:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langchain-core"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
+   "metadata": {},
+   "source": [
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4185e74b-0500-4cad-ace0-bac37de466ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
+   "metadata": {},
+   "source": [
+    "## Tools\n",
+    "\n",
+    "Recall we already had a `multiply` tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e13ec98c-8521-4d63-b521-caf92da87b70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def multiply(first_int: int, second_int: int) -> int:\n",
+    "    \"\"\"Multiply two integers together.\"\"\"\n",
+    "    return first_int * second_int"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3de233af-b3bd-4f0c-8b1a-83527143a8db",
+   "metadata": {},
+   "source": [
+    "And now we can add to it an `exponentiate` and `add` tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e93661cd-a2ba-4ada-91ad-baf1b60879ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool\n",
+    "def add(first_int: int, second_int: int) -> int:\n",
+    "    \"Add two integers.\"\n",
+    "    return first_int + second_int\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def exponentiate(base: int, exponent: int) -> int:\n",
+    "    \"Exponentiate the base to the exponent power.\"\n",
+    "    return base**exponent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbea4555-ed10-4a18-b802-e9a3071f132b",
+   "metadata": {},
+   "source": [
+    "The main difference between using one Tool and many is that we can't be sure which Tool the model will invoke upfront, so we cannot hardcode, like we did in the [Quickstart](/docs/how_to/tool_calling), a specific tool into our chain. Instead we'll add `call_tools`, a `RunnableLambda` that takes the output AI message with tools calls and routes to the correct tools.\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f00f0f3f-8530-4c1d-a26c-d20824e31faf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "from typing import Dict, List, Union\n",
+    "\n",
+    "from langchain_core.messages import AIMessage\n",
+    "from langchain_core.runnables import (\n",
+    "    Runnable,\n",
+    "    RunnableLambda,\n",
+    "    RunnableMap,\n",
+    "    RunnablePassthrough,\n",
+    ")\n",
+    "\n",
+    "tools = [multiply, exponentiate, add]\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
+    "tool_map = {tool.name: tool for tool in tools}\n",
+    "\n",
+    "\n",
+    "def call_tools(msg: AIMessage) -> Runnable:\n",
+    "    \"\"\"Simple sequential tool calling helper.\"\"\"\n",
+    "    tool_map = {tool.name: tool for tool in tools}\n",
+    "    tool_calls = msg.tool_calls.copy()\n",
+    "    for tool_call in tool_calls:\n",
+    "        tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
+    "    return tool_calls\n",
+    "\n",
+    "\n",
+    "chain = llm_with_tools | call_tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'multiply',\n",
+       "  'args': {'first_int': 23, 'second_int': 7},\n",
+       "  'id': 'toolu_01Wf8kUs36kxRKLDL8vs7G8q',\n",
+       "  'output': 161}]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"What's 23 times 7\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b1c6c0f8-6d04-40d4-a40e-8719ca7b27c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'add',\n",
+       "  'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
+       "  'id': 'toolu_012aK4xZBQg2sXARsFZnqxHh',\n",
+       "  'output': 1001000000}]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"add a million plus a billion\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ce76f299-1a4d-421c-afa4-a6346e34285c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'exponentiate',\n",
+       "  'args': {'base': 37, 'exponent': 3},\n",
+       "  'id': 'toolu_01VDU6X3ugDb9cpnnmCZFPbC',\n",
+       "  'output': 50653}]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"cube thirty-seven\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how_to/tools_parallel.ipynb

@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
+   "metadata": {},
+   "source": [
+    "# How to call tools in parallel\n",
+    "\n",
+    "In the [Chains with multiple tools](/docs/how_to/tools_multiple) guide we saw how to build function-calling chains that select between multiple tools. Some models, like the OpenAI models released in Fall 2023, also support parallel function calling, which allows you to invoke multiple functions (or the same function multiple times) in a single model call. Our previous chain from the multiple tools guides actually already supports this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fafec38-443a-42ad-a913-5be7667e3734",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "We'll need to install the following packages for this guide:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langchain-core"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
+   "metadata": {},
+   "source": [
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4185e74b-0500-4cad-ace0-bac37de466ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
+   "metadata": {},
+   "source": [
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e13ec98c-8521-4d63-b521-caf92da87b70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def multiply(first_int: int, second_int: int) -> int:\n",
+    "    \"\"\"Multiply two integers together.\"\"\"\n",
+    "    return first_int * second_int\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def add(first_int: int, second_int: int) -> int:\n",
+    "    \"Add two integers.\"\n",
+    "    return first_int + second_int\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def exponentiate(base: int, exponent: int) -> int:\n",
+    "    \"Exponentiate the base to the exponent power.\"\n",
+    "    return base**exponent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "119d419c-1c61-4e0d-834a-5dabb72f5514",
+   "metadata": {},
+   "source": [
+    "# Chain\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" hideGoogle=\"true\"/>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f67d91d8-cc38-4065-8f80-901e079954dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | echo: false\n",
+    "# | output: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "from typing import Dict, List, Union\n",
+    "\n",
+    "from langchain_core.messages import AIMessage\n",
+    "from langchain_core.runnables import (\n",
+    "    Runnable,\n",
+    "    RunnableLambda,\n",
+    "    RunnableMap,\n",
+    "    RunnablePassthrough,\n",
+    ")\n",
+    "\n",
+    "tools = [multiply, exponentiate, add]\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
+    "tool_map = {tool.name: tool for tool in tools}\n",
+    "\n",
+    "\n",
+    "def call_tools(msg: AIMessage) -> Runnable:\n",
+    "    \"\"\"Simple sequential tool calling helper.\"\"\"\n",
+    "    tool_map = {tool.name: tool for tool in tools}\n",
+    "    tool_calls = msg.tool_calls.copy()\n",
+    "    for tool_call in tool_calls:\n",
+    "        tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
+    "    return tool_calls\n",
+    "\n",
+    "\n",
+    "chain = llm_with_tools | call_tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'multiply',\n",
+       "  'args': {'first_int': 23, 'second_int': 7},\n",
+       "  'id': 'call_22tgOrsVLyLMsl2RLbUhtycw',\n",
+       "  'output': 161},\n",
+       " {'name': 'multiply',\n",
+       "  'args': {'first_int': 5, 'second_int': 18},\n",
+       "  'id': 'call_EbKHEG3TjqBhEwb7aoxUtgzf',\n",
+       "  'output': 90},\n",
+       " {'name': 'add',\n",
+       "  'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
+       "  'id': 'call_LUhu2IT3vINxlTc5fCVY6Nhi',\n",
+       "  'output': 1001000000},\n",
+       " {'name': 'exponentiate',\n",
+       "  'args': {'base': 37, 'exponent': 3},\n",
+       "  'id': 'call_bnCZIXelOKkmcyd4uGXId9Ct',\n",
+       "  'output': 50653}]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\n",
+    "    \"What's 23 times 7, and what's five times 18 and add a million plus a billion and cube thirty-seven\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how_to/tools_prompting.ipynb

@@ -15,30 +15,9 @@
    "id": "14b94240",
    "metadata": {},
    "source": [
-    "# How to add ad-hoc tool calling capability to LLMs and Chat Models\n",
+    "# How to use tools without function calling\n",
     "\n",
-    ":::{.callout-caution}\n",
-    "\n",
-    "Some models have been fine-tuned for tool calling and provide a dedicated API for tool calling. Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling. Please see the [how to use a chat model to call tools](/docs/how_to/tool_calling/) guide for more information.\n",
-    "\n",
-    ":::\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [Function/tool calling](https://python.langchain.com/v0.2/docs/concepts/#functiontool-calling)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LLMs](/docs/concepts/#llms)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "In this guide, we'll see how to add **ad-hoc** tool calling support to a chat model. This is an alternative method to invoke tools if you're using a model that does not natively support [tool calling](/docs/how_to/tool_calling/).\n",
-    "\n",
-    "We'll do this by simply writing a prompt that will get the model to invoke the appropriate tools. Here's a diagram of the logic:\n",
-    "\n",
-    "![chain](../../static/img/tool_chain.svg)"
+    "In this guide we'll build a Chain that does not rely on any special model APIs (like tool calling, which we showed in the [Quickstart](/docs/how_to/tool_calling)) and instead just prompts the model directly to invoke tools."
    ]
   },
   {
@@ -58,58 +37,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-community"
+    "%pip install --upgrade --quiet langchain langchain-openai"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "897bc01e-cc2b-4400-8a64-db4aa56085d3",
+   "id": "5e727d22-f861-4eee-882a-688f8efc885e",
    "metadata": {},
    "source": [
-    "If you'd like to use LangSmith, uncomment the below:"
+    "And set these environment variables:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "5efb4170-b95b-4d29-8f57-09509f3ba6df",
+   "execution_count": null,
+   "id": "527ef906-0104-4872-b4e5-f371cf73feba",
    "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "\n",
+    "# If you'd like to use LangSmith, uncomment the below:\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "7ec6409b-21e5-4d0a-8a46-c4ef0b055dd3",
-   "metadata": {},
-   "source": [
-    "You can select any of the given models for this how-to guide. Keep in mind that most of these models already [support native tool calling](/docs/integrations/chat/), so using the prompting strategy shown here doesn't make sense for these models, and instead you should follow the [how to use a chat model to call tools](/docs/how_to/tool_calling/) guide.\n",
-    "\n",
-    "```{=mdx}\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
-    "```\n",
-    "\n",
-    "To illustrate the idea, we'll use `phi3` via Ollama, which does **NOT** have native support for tool calling. If you'd like to use `Ollama` as well follow [these instructions](/docs/integrations/chat/ollama/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "424be968-2806-4d1a-a6aa-5499ae20fac5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import Ollama\n",
-    "\n",
-    "model = Ollama(model=\"phi3\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "68946881",
@@ -117,75 +72,66 @@
    "source": [
     "## Create a tool\n",
     "\n",
-    "First, let's create an `add` and `multiply` tools. For more information on creating custom tools, please see [this guide](/docs/how_to/custom_tools)."
+    "First, we need to create a tool to call. For this example, we will create a custom tool from a function. For more information on all details related to creating custom tools, please see [this guide](/docs/how_to/custom_tools)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "4548e6fa-0f9b-4d7a-8fa5-66cec0350e5f",
+   "execution_count": 1,
+   "id": "90187d07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def multiply(first_int: int, second_int: int) -> int:\n",
+    "    \"\"\"Multiply two integers together.\"\"\"\n",
+    "    return first_int * second_int"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d7009e1a",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--\n",
       "multiply\n",
-      "Multiply two numbers together.\n",
-      "{'x': {'title': 'X', 'type': 'number'}, 'y': {'title': 'Y', 'type': 'number'}}\n",
-      "--\n",
-      "add\n",
-      "Add two numbers.\n",
-      "{'x': {'title': 'X', 'type': 'integer'}, 'y': {'title': 'Y', 'type': 'integer'}}\n"
+      "multiply(first_int: int, second_int: int) -> int - Multiply two integers together.\n",
+      "{'first_int': {'title': 'First Int', 'type': 'integer'}, 'second_int': {'title': 'Second Int', 'type': 'integer'}}\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(x: float, y: float) -> float:\n",
-    "    \"\"\"Multiply two numbers together.\"\"\"\n",
-    "    return x * y\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def add(x: int, y: int) -> int:\n",
-    "    \"Add two numbers.\"\n",
-    "    return x + y\n",
-    "\n",
-    "\n",
-    "tools = [multiply, add]\n",
-    "\n",
-    "# Let's inspect the tools\n",
-    "for t in tools:\n",
-    "    print(\"--\")\n",
-    "    print(t.name)\n",
-    "    print(t.description)\n",
-    "    print(t.args)"
+    "print(multiply.name)\n",
+    "print(multiply.description)\n",
+    "print(multiply.args)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "be77e780",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "20.0"
+       "20"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "multiply.invoke({\"x\": 4, \"y\": 5})"
+    "multiply.invoke({\"first_int\": 4, \"second_int\": 5})"
    ]
   },
   {
@@ -200,85 +146,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "2063b564-25ca-4729-a45f-ba4633175b04",
+   "execution_count": 4,
+   "id": "c64818f0-9364-423c-922e-bdfb8f01e726",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "multiply(x: float, y: float) -> float - Multiply two numbers together.\n",
-      "add(x: int, y: int) -> int - Add two numbers.\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'multiply: multiply(first_int: int, second_int: int) -> int - Multiply two integers together.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_core.output_parsers import JsonOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.tools import render_text_description\n",
+    "from langchain.tools.render import render_text_description\n",
     "\n",
-    "rendered_tools = render_text_description(tools)\n",
-    "print(rendered_tools)"
+    "rendered_tools = render_text_description([multiply])\n",
+    "rendered_tools"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "f02f1dce-76e7-4ca9-9bac-5af496131fe1",
+   "execution_count": 6,
+   "id": "63552d4d-8bd6-4aca-8805-56e236f6552d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "system_prompt = f\"\"\"\\\n",
-    "You are an assistant that has access to the following set of tools. \n",
-    "Here are the names and descriptions for each tool:\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "system_prompt = f\"\"\"You are an assistant that has access to the following set of tools. Here are the names and descriptions for each tool:\n",
     "\n",
     "{rendered_tools}\n",
     "\n",
-    "Given the user input, return the name and input of the tool to use. \n",
-    "Return your response as a JSON blob with 'name' and 'arguments' keys.\n",
-    "\n",
-    "The `arguments` should be a dictionary, with keys corresponding \n",
-    "to the argument names and the values corresponding to the requested values.\n",
-    "\"\"\"\n",
+    "Given the user input, return the name and input of the tool to use. Return your response as a JSON blob with 'name' and 'arguments' keys.\"\"\"\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [(\"system\", system_prompt), (\"user\", \"{input}\")]\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "f8623e03-60eb-4439-b57b-ecbcebc61b58",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "    \"name\": \"add\",\n",
-      "    \"arguments\": {\n",
-      "        \"x\": 3,\n",
-      "        \"y\": 1132\n",
-      "    }\n",
-      "}\n"
-     ]
-    }
-   ],
-   "source": [
-    "chain = prompt | model\n",
-    "message = chain.invoke({\"input\": \"what's 3 plus 1132\"})\n",
-    "\n",
-    "# Let's take a look at the output from the model\n",
-    "# if the model is an LLM (not a chat model), the output will be a string.\n",
-    "if isinstance(message, str):\n",
-    "    print(message)\n",
-    "else:  # Otherwise it's a chat model\n",
-    "    print(message.content)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "14df2cd5-b6fa-4b10-892d-e8692c7931e5",
@@ -291,153 +200,156 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "id": "f129f5bd-127c-4c95-8f34-8f437da7ca8f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'name': 'multiply', 'arguments': {'x': 13.0, 'y': 4.0}}"
+       "{'name': 'multiply', 'arguments': {'first_int': 13, 'second_int': 4}}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from langchain_core.output_parsers import JsonOutputParser\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "chain = prompt | model | JsonOutputParser()\n",
     "chain.invoke({\"input\": \"what's thirteen times 4\"})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e1f08255-f146-4f4a-be43-5c21c1d3ae83",
-   "metadata": {},
-   "source": [
-    ":::{.callout-important}\n",
-    "\n",
-    "🎉 Amazing! 🎉 We now instructed our model on how to **request** that a tool be invoked.\n",
-    "\n",
-    "Now, let's create some logic to actually run the tool!\n",
-    ":::"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8e29dd4c-8eb5-457f-92d1-8add076404dc",
    "metadata": {},
    "source": [
-    "## Invoking the tool 🏃\n",
+    "## Invoking the tool\n",
     "\n",
-    "Now that the model can request that a tool be invoked, we need to write a function that can actually invoke \n",
-    "the tool.\n",
-    "\n",
-    "The function will select the appropriate tool by name, and pass to it the arguments chosen by the model."
+    "We can invoke the tool as part of the chain by passing along the model-generated \"arguments\" to it:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "faee95e0-4095-4310-991f-9e9465c6738e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Any, Dict, Optional, TypedDict\n",
-    "\n",
-    "from langchain_core.runnables import RunnableConfig\n",
-    "\n",
-    "\n",
-    "class ToolCallRequest(TypedDict):\n",
-    "    \"\"\"A typed dict that shows the inputs into the invoke_tool function.\"\"\"\n",
-    "\n",
-    "    name: str\n",
-    "    arguments: Dict[str, Any]\n",
-    "\n",
-    "\n",
-    "def invoke_tool(\n",
-    "    tool_call_request: ToolCallRequest, config: Optional[RunnableConfig] = None\n",
-    "):\n",
-    "    \"\"\"A function that we can use the perform a tool invocation.\n",
-    "\n",
-    "    Args:\n",
-    "        tool_call_request: a dict that contains the keys name and arguments.\n",
-    "            The name must match the name of a tool that exists.\n",
-    "            The arguments are the arguments to that tool.\n",
-    "        config: This is configuration information that LangChain uses that contains\n",
-    "            things like callbacks, metadata, etc.See LCEL documentation about RunnableConfig.\n",
-    "\n",
-    "    Returns:\n",
-    "        output from the requested tool\n",
-    "    \"\"\"\n",
-    "    tool_name_to_tool = {tool.name: tool for tool in tools}\n",
-    "    name = tool_call_request[\"name\"]\n",
-    "    requested_tool = tool_name_to_tool[name]\n",
-    "    return requested_tool.invoke(tool_call_request[\"arguments\"], config=config)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4957532-9e0c-47f6-bb62-0fd789ac1d3e",
-   "metadata": {},
-   "source": [
-    "Let's test this out 🧪!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "d0ea3b2a-8fb2-4016-83c8-a5d3e78fedbc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15.0"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "invoke_tool({\"name\": \"multiply\", \"arguments\": {\"x\": 3, \"y\": 5}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "715af6e1-935d-4bc0-a3d2-646ecf8a329b",
-   "metadata": {},
-   "source": [
-    "## Let's put it together\n",
-    "\n",
-    "Let's put it together into a chain that creates a calculator with add and multiplication capabilities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "id": "0555b384-fde6-4404-86e0-7ea199003d58",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "53.83784653"
+       "52"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain = prompt | model | JsonOutputParser() | invoke_tool\n",
-    "chain.invoke({\"input\": \"what's thirteen times 4.14137281\"})"
+    "from operator import itemgetter\n",
+    "\n",
+    "chain = prompt | model | JsonOutputParser() | itemgetter(\"arguments\") | multiply\n",
+    "chain.invoke({\"input\": \"what's thirteen times 4\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d60b2cb-6ce0-48fc-8d18-d2337161a53d",
+   "metadata": {},
+   "source": [
+    "## Choosing from multiple tools\n",
+    "\n",
+    "Suppose we have multiple tools we want the chain to be able to choose from:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "95c86d32-ee45-4c87-a28c-14eff19b49e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool\n",
+    "def add(first_int: int, second_int: int) -> int:\n",
+    "    \"Add two integers.\"\n",
+    "    return first_int + second_int\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def exponentiate(base: int, exponent: int) -> int:\n",
+    "    \"Exponentiate the base to the exponent power.\"\n",
+    "    return base**exponent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "748405ff-4c85-4bd7-82e1-30458b5a4106",
+   "metadata": {},
+   "source": [
+    "With function calling, we can do this like so:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb3aa89e-40e1-45ec-b1f3-ab28cfc8e42d",
+   "metadata": {},
+   "source": [
+    "If we want to run the model selected tool, we can do so using a function that returns the tool based on the model output. Specifically, our function will action return it's own subchain that gets the \"arguments\" part of the model output and passes it to the chosen tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "db254773-5b8e-43d0-aabe-c21566c154cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [add, exponentiate, multiply]\n",
+    "\n",
+    "\n",
+    "def tool_chain(model_output):\n",
+    "    tool_map = {tool.name: tool for tool in tools}\n",
+    "    chosen_tool = tool_map[model_output[\"name\"]]\n",
+    "    return itemgetter(\"arguments\") | chosen_tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ad9f5cff-b86a-45fc-9ce4-b0aa9025a378",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1135"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rendered_tools = render_text_description(tools)\n",
+    "system_prompt = f\"\"\"You are an assistant that has access to the following set of tools. Here are the names and descriptions for each tool:\n",
+    "\n",
+    "{rendered_tools}\n",
+    "\n",
+    "Given the user input, return the name and input of the tool to use. Return your response as a JSON blob with 'name' and 'arguments' keys.\"\"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [(\"system\", system_prompt), (\"user\", \"{input}\")]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | model | JsonOutputParser() | tool_chain\n",
+    "chain.invoke({\"input\": \"what's 3 plus 1132\"})"
    ]
   },
   {
@@ -452,19 +364,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "id": "45404406-859d-4caa-8b9d-5838162c80a0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'name': 'multiply',\n",
-       " 'arguments': {'x': 13, 'y': 4.14137281},\n",
-       " 'output': 53.83784653}"
+       "{'name': 'add',\n",
+       " 'arguments': {'first_int': 3, 'second_int': 1132},\n",
+       " 'output': 1135}"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -473,26 +385,9 @@
     "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "chain = (\n",
-    "    prompt | model | JsonOutputParser() | RunnablePassthrough.assign(output=invoke_tool)\n",
+    "    prompt | model | JsonOutputParser() | RunnablePassthrough.assign(output=tool_chain)\n",
     ")\n",
-    "chain.invoke({\"input\": \"what's thirteen times 4.14137281\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1797fe82-ea35-4cba-834a-1caf9740d184",
-   "metadata": {},
-   "source": [
-    "## What's next?\n",
-    "\n",
-    "This how-to guide shows the \"happy path\" when the model correctly outputs all the required tool information.\n",
-    "\n",
-    "In reality, if you're using more complex tools, you will start encountering errors from the model, especially for models that have not been fine tuned for tool calling and for less capable models.\n",
-    "\n",
-    "You will need to be prepared to add strategies to improve the output from the model; e.g.,\n",
-    "\n",
-    "1. Provide few shot examples.\n",
-    "2. Add error handling (e.g., catch the exception and feed it back to the LLM to ask it to correct its previous output)."
+    "chain.invoke({\"input\": \"what's 3 plus 1132\"})"
    ]
   }
  ],
@@ -512,7 +407,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/installation.mdx

@@ -41,7 +41,7 @@ pip install langchain-core
 ```
 
 ## LangChain community
-The `langchain-community` package contains third-party integrations. Install with:
+The `langchain-community` package contains third-party integrations. It is automatically installed by `langchain`, but can also be used separately. Install with:
 
 ```bash
 pip install langchain-community

docs/docs/integrations/callbacks/comet_tracing.ipynb

@@ -29,7 +29,6 @@
     "import os\n",
     "\n",
     "import comet_llm\n",
-    "from langchain_openai import OpenAI\n",
     "\n",
     "os.environ[\"LANGCHAIN_COMET_TRACING\"] = \"true\"\n",
     "\n",
@@ -41,7 +40,8 @@
     "# here we are configuring the comet project\n",
     "os.environ[\"COMET_PROJECT_NAME\"] = \"comet-example-langchain-tracing\"\n",
     "\n",
-    "from langchain.agents import AgentType, initialize_agent, load_tools"
+    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
+    "from langchain.llms import OpenAI"
    ]
   },
   {

docs/docs/integrations/callbacks/context.ipynb

@@ -114,7 +114,10 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain.schema import (\n",
+    "    HumanMessage,\n",
+    "    SystemMessage,\n",
+    ")\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "token = os.environ[\"CONTEXT_API_TOKEN\"]\n",

docs/docs/integrations/callbacks/promptlayer.ipynb

@@ -94,7 +94,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.messages import HumanMessage\n",
+    "from langchain.schema import (\n",
+    "    HumanMessage,\n",
+    ")\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "chat_llm = ChatOpenAI(\n",

docs/docs/integrations/callbacks/uptrain.ipynb

@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,69 +194,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup\n",
-    "\n",
-    "UpTrain provides you with:\n",
-    "1. Dashboards with advanced drill-down and filtering options\n",
-    "1. Insights and common topics among failing cases\n",
-    "1. Observability and real-time monitoring of production data\n",
-    "1. Regression testing via seamless integration with your CI/CD pipelines\n",
-    "\n",
-    "You can choose between the following options for evaluating using UpTrain:\n",
-    "### 1. **UpTrain's Open-Source Software (OSS)**: \n",
-    "You can use the open-source evaluation service to evaluate your model. In this case, you will need to provie an OpenAI API key. UpTrain uses the GPT models to evaluate the responses generated by the LLM. You can get yours [here](https://platform.openai.com/account/api-keys).\n",
-    "\n",
-    "In order to view your evaluations in the UpTrain dashboard, you will need to set it up by running the following commands in your terminal:\n",
-    "\n",
-    "```bash\n",
-    "git clone https://github.com/uptrain-ai/uptrain\n",
-    "cd uptrain\n",
-    "bash run_uptrain.sh\n",
-    "```\n",
-    "\n",
-    "This will start the UpTrain dashboard on your local machine. You can access it at `http://localhost:3000/dashboard`.\n",
-    "\n",
-    "Parameters:\n",
-    "- key_type=\"openai\"\n",
-    "- api_key=\"OPENAI_API_KEY\"\n",
-    "- project_name=\"PROJECT_NAME\"\n",
-    "\n",
-    "\n",
-    "### 2. **UpTrain Managed Service and Dashboards**:\n",
-    "Alternatively, you can use UpTrain's managed service to evaluate your model. You can create a free UpTrain account [here](https://uptrain.ai/) and get free trial credits. If you want more trial credits, [book a call with the maintainers of UpTrain here](https://calendly.com/uptrain-sourabh/30min).\n",
-    "\n",
-    "The benefits of using the managed service are:\n",
-    "1. No need to set up the UpTrain dashboard on your local machine.\n",
-    "1. Access to many LLMs without needing their API keys.\n",
-    "\n",
-    "Once you perform the evaluations, you can view them in the UpTrain dashboard at `https://dashboard.uptrain.ai/dashboard`\n",
-    "\n",
-    "Parameters:\n",
-    "- key_type=\"uptrain\"\n",
-    "- api_key=\"UPTRAIN_API_KEY\"\n",
-    "- project_name=\"PROJECT_NAME\"\n",
-    "\n",
-    "\n",
-    "**Note:** The `project_name` will be the project name under which the evaluations performed will be shown in the UpTrain dashboard."
+    "## Set the openai API key\n",
+    "This key is required to perform the evaluations. UpTrain uses the GPT models to evaluate the responses generated by the LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OPENAI_API_KEY = getpass()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Set the API key\n",
+    "## Setup\n",
     "\n",
-    "The notebook will prompt you to enter the API key. You can choose between the OpenAI API key or the UpTrain API key by changing the `key_type` parameter in the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "KEY_TYPE = \"openai\"  # or \"uptrain\"\n",
-    "API_KEY = getpass()"
+    "For each of the retrievers below, it is better to define the callback handler again to avoid interference. You can choose between the following options for evaluating using UpTrain:\n",
+    "\n",
+    "### 1. **UpTrain's Open-Source Software (OSS)**: \n",
+    "You can use the open-source evaluation service to evaluate your model.\n",
+    "In this case, you will need to provie an OpenAI API key. You can get yours [here](https://platform.openai.com/account/api-keys).\n",
+    "\n",
+    "Parameters:\n",
+    "- key_type=\"openai\"\n",
+    "- api_key=\"OPENAI_API_KEY\"\n",
+    "- project_name_prefix=\"PROJECT_NAME_PREFIX\"\n",
+    "\n",
+    "\n",
+    "### 2. **UpTrain Managed Service and Dashboards**: \n",
+    "You can create a free UpTrain account [here](https://uptrain.ai/) and get free trial credits. If you want more trial credits, [book a call with the maintainers of UpTrain here](https://calendly.com/uptrain-sourabh/30min).\n",
+    "\n",
+    "UpTrain Managed service provides:\n",
+    "1. Dashboards with advanced drill-down and filtering options\n",
+    "1. Insights and common topics among failing cases\n",
+    "1. Observability and real-time monitoring of production data\n",
+    "1. Regression testing via seamless integration with your CI/CD pipelines\n",
+    "\n",
+    "The notebook contains some screenshots of the dashboards and the insights that you can get from the UpTrain managed service.\n",
+    "\n",
+    "Parameters:\n",
+    "- key_type=\"uptrain\"\n",
+    "- api_key=\"UPTRAIN_API_KEY\"\n",
+    "- project_name_prefix=\"PROJECT_NAME_PREFIX\"\n",
+    "\n",
+    "\n",
+    "**Note:** The `project_name_prefix` will be used as prefix for the project names in the UpTrain dashboard. These will be different for different types of evals. For example, if you set project_name_prefix=\"langchain\" and perform the multi_query evaluation, the project name will be \"langchain_multi_query\"."
    ]
   },
   {
@@ -278,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -320,7 +306,7 @@
     ")\n",
     "\n",
     "# Create the uptrain callback handler\n",
-    "uptrain_callback = UpTrainCallbackHandler(key_type=KEY_TYPE, api_key=API_KEY)\n",
+    "uptrain_callback = UpTrainCallbackHandler(key_type=\"openai\", api_key=OPENAI_API_KEY)\n",
     "config = {\"callbacks\": [uptrain_callback]}\n",
     "\n",
     "# Invoke the chain with a query\n",
@@ -342,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -394,7 +380,7 @@
     "multi_query_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=llm)\n",
     "\n",
     "# Create the uptrain callback\n",
-    "uptrain_callback = UpTrainCallbackHandler(key_type=KEY_TYPE, api_key=API_KEY)\n",
+    "uptrain_callback = UpTrainCallbackHandler(key_type=\"openai\", api_key=OPENAI_API_KEY)\n",
     "config = {\"callbacks\": [uptrain_callback]}\n",
     "\n",
     "# Create the RAG prompt\n",
@@ -429,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -484,24 +470,13 @@
     "chain = RetrievalQA.from_chain_type(llm=llm, retriever=compression_retriever)\n",
     "\n",
     "# Create the uptrain callback\n",
-    "uptrain_callback = UpTrainCallbackHandler(key_type=KEY_TYPE, api_key=API_KEY)\n",
+    "uptrain_callback = UpTrainCallbackHandler(key_type=\"openai\", api_key=OPENAI_API_KEY)\n",
     "config = {\"callbacks\": [uptrain_callback]}\n",
     "\n",
     "# Invoke the chain with a query\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "result = chain.invoke(query, config=config)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# UpTrain's Dashboard and Insights\n",
-    "\n",
-    "Here's a short video showcasing the dashboard and the insights:\n",
-    "\n",
-    "![langchain_uptrain.gif](https://uptrain-assets.s3.ap-south-1.amazonaws.com/images/langchain/langchain_uptrain.gif)"
-   ]
   }
  ],
  "metadata": {

docs/docs/integrations/chat/ai21.ipynb

@@ -95,7 +95,7 @@
     "from langchain_ai21 import ChatAI21\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
-    "chat = ChatAI21(model=\"jamba-instruct\")\n",
+    "chat = ChatAI21(model=\"j2-ultra\")\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -107,6 +107,14 @@
     "chain = prompt | chat\n",
     "chain.invoke({\"english_text\": \"Hello, how are you?\"})"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c159a79f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/chat/anthropic.ipynb

@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "238bdbaa-526a-4130-89e9-523aa44bb196",
    "metadata": {},
    "outputs": [],
@@ -250,7 +250,16 @@
    "execution_count": 3,
    "id": "42f87466-cb8e-490d-a9f8-aa0f8e9b4217",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/bagatur/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: The function `bind_tools` is in beta. It is actively being worked on, so the API may change.\n",
+      "  warn_beta(\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
@@ -360,49 +369,13 @@
    "id": "90e015e0-c6e5-4ff5-8fb9-be0cd3c86395",
    "metadata": {},
    "source": [
-    ":::tip\n",
+    "::: {.callout-tip}\n",
     "\n",
     "ChatAnthropic model outputs are always a single AI message that can have either a single string or a list of content blocks. The content blocks can be text blocks or tool-duse blocks. There can be multiple of each and they can be interspersed.\n",
     "\n",
     ":::"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b5145dea-0183-4cab-b9e2-0e35fb8370cf",
-   "metadata": {},
-   "source": [
-    "### Forcing tool calls\n",
-    "\n",
-    "By default the model can choose whether to call any tools. To force the model to call at least one tool we can specify `bind_tools(..., tool_choice=\"any\")` and to force the model to call a specific tool we can pass in that tool name `bind_tools(..., tool_choice=\"GetWeather\")`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "05993626-060c-449f-8069-e52d31442977",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'GetWeather',\n",
-       "  'args': {'location': '<UNKNOWN>'},\n",
-       "  'id': 'toolu_01DwWjKzHPs6EHCUPxsGm9bN'}]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_with_force_tools = llm.bind_tools([GetWeather], tool_choice=\"GetWeather\")\n",
-    "# Notice the model will still return tool calls despite a message that\n",
-    "# doesn't have anything to do with the tools.\n",
-    "llm_with_force_tools.invoke(\"this doesn't really require tool use\").tool_calls"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8652ee98-814c-4ed6-9def-275eeaa9651e",
@@ -683,9 +656,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-2",
+   "display_name": ".venv",
    "language": "python",
-   "name": "poetry-venv-2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -697,7 +670,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -127,7 +127,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.callbacks import get_openai_callback"
+    "from langchain.callbacks import get_openai_callback"
    ]
   },
   {

docs/docs/integrations/chat/baichuan.ipynb

@@ -161,7 +161,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.4"
-  }
+  },
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/docs/integrations/chat/coze.ipynb

@@ -86,13 +86,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='为你找到关于coze的信息如下：\n",
-       "\n",
-       "Coze是一个由字节跳动推出的AI聊天机器人和应用程序编辑开发平台。\n",
-       "\n",
-       "用户无论是否有编程经验，都可以通过该平台快速创建各种类型的聊天机器人、智能体、AI应用和插件，并将其部署在社交平台和即时聊天应用程序中。\n",
-       "\n",
-       "国际版使用的模型比国内版更强大。')"
+       "AIMessage(content='为你找到关于coze的信息如下：\n\nCoze是一个由字节跳动推出的AI聊天机器人和应用程序编辑开发平台。\n\n用户无论是否有编程经验，都可以通过该平台快速创建各种类型的聊天机器人、智能体、AI应用和插件，并将其部署在社交平台和即时聊天应用程序中。\n\n国际版使用的模型比国内版更强大。')"
       ]
      },
      "execution_count": 3,
@@ -179,7 +173,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.4"
-  }
+  },
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/docs/integrations/chat/deepinfra.ipynb

@@ -67,7 +67,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.callbacks import StreamingStdOutCallbackHandler"
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
    ]
   },
   {

docs/docs/integrations/chat/ernie.ipynb

@@ -133,7 +133,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.4"
-  }
+  },
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/docs/integrations/chat/everlyai.ipynb

@@ -126,8 +126,8 @@
     }
    ],
    "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain_community.chat_models import ChatEverlyAI\n",
-    "from langchain_core.callbacks import StreamingStdOutCallbackHandler\n",
     "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
@@ -184,8 +184,8 @@
     }
    ],
    "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain_community.chat_models import ChatEverlyAI\n",
-    "from langchain_core.callbacks import StreamingStdOutCallbackHandler\n",
     "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",

docs/docs/integrations/chat/gpt_router.ipynb

@@ -143,7 +143,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler"
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
    ]
   },
   {

docs/docs/integrations/chat/kinetica.ipynb

@@ -54,9 +54,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "# Install Langchain community and core packages\n",
     "%pip install --upgrade --quiet langchain-core langchain-community\n",
@@ -114,37 +123,126 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         username             name sex  \\\n",
-      "id                                       \n",
-      "0       eduardo69       Haley Beck   F   \n",
-      "1        lbarrera  Joshua Stephens   M   \n",
-      "2         bburton     Paula Kaiser   F   \n",
-      "3       melissa49      Wendy Reese   F   \n",
-      "4   melissacarter      Manuel Rios   M   \n",
-      "\n",
-      "                                              address                    mail  \\\n",
-      "id                                                                              \n",
-      "0   59836 Carla Causeway Suite 939\\nPort Eugene, I...  meltondenise@yahoo.com   \n",
-      "1   3108 Christina Forges\\nPort Timothychester, KY...     erica80@hotmail.com   \n",
-      "2                    Unit 7405 Box 3052\\nDPO AE 09858  timothypotts@gmail.com   \n",
-      "3   6408 Christopher Hill Apt. 459\\nNew Benjamin, ...        dadams@gmail.com   \n",
-      "4    2241 Bell Gardens Suite 723\\nScottside, CA 38463  williamayala@gmail.com   \n",
-      "\n",
-      "    birthdate  \n",
-      "id             \n",
-      "0  1997-12-01  \n",
-      "1  1924-07-27  \n",
-      "2  1933-11-28  \n",
-      "3  1988-10-19  \n",
-      "4  1931-03-12  \n"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>username</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>address</th>\n",
+       "      <th>mail</th>\n",
+       "      <th>birthdate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>eduardo69</td>\n",
+       "      <td>Haley Beck</td>\n",
+       "      <td>F</td>\n",
+       "      <td>59836 Carla Causeway Suite 939\\nPort Eugene, I...</td>\n",
+       "      <td>meltondenise@yahoo.com</td>\n",
+       "      <td>1997-11-23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>lbarrera</td>\n",
+       "      <td>Joshua Stephens</td>\n",
+       "      <td>M</td>\n",
+       "      <td>3108 Christina Forges\\nPort Timothychester, KY...</td>\n",
+       "      <td>erica80@hotmail.com</td>\n",
+       "      <td>1924-07-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>bburton</td>\n",
+       "      <td>Paula Kaiser</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Unit 7405 Box 3052\\nDPO AE 09858</td>\n",
+       "      <td>timothypotts@gmail.com</td>\n",
+       "      <td>1933-11-20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>melissa49</td>\n",
+       "      <td>Wendy Reese</td>\n",
+       "      <td>F</td>\n",
+       "      <td>6408 Christopher Hill Apt. 459\\nNew Benjamin, ...</td>\n",
+       "      <td>dadams@gmail.com</td>\n",
+       "      <td>1988-10-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>melissacarter</td>\n",
+       "      <td>Manuel Rios</td>\n",
+       "      <td>M</td>\n",
+       "      <td>2241 Bell Gardens Suite 723\\nScottside, CA 38463</td>\n",
+       "      <td>williamayala@gmail.com</td>\n",
+       "      <td>1931-03-04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         username             name sex  \\\n",
+       "id                                       \n",
+       "0       eduardo69       Haley Beck   F   \n",
+       "1        lbarrera  Joshua Stephens   M   \n",
+       "2         bburton     Paula Kaiser   F   \n",
+       "3       melissa49      Wendy Reese   F   \n",
+       "4   melissacarter      Manuel Rios   M   \n",
+       "\n",
+       "                                              address                    mail  \\\n",
+       "id                                                                              \n",
+       "0   59836 Carla Causeway Suite 939\\nPort Eugene, I...  meltondenise@yahoo.com   \n",
+       "1   3108 Christina Forges\\nPort Timothychester, KY...     erica80@hotmail.com   \n",
+       "2                    Unit 7405 Box 3052\\nDPO AE 09858  timothypotts@gmail.com   \n",
+       "3   6408 Christopher Hill Apt. 459\\nNew Benjamin, ...        dadams@gmail.com   \n",
+       "4    2241 Bell Gardens Suite 723\\nScottside, CA 38463  williamayala@gmail.com   \n",
+       "\n",
+       "    birthdate  \n",
+       "id             \n",
+       "0  1997-11-23  \n",
+       "1  1924-07-19  \n",
+       "2  1933-11-20  \n",
+       "3  1988-10-11  \n",
+       "4  1931-03-04  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -165,7 +263,7 @@
     "\n",
     "\n",
     "load_df = pd.DataFrame.from_records(data=profile_gen(100), index=\"id\")\n",
-    "print(load_df.head())"
+    "load_df.head()"
    ]
   },
   {
@@ -181,17 +279,85 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        name    type   properties\n",
-      "0   username  string     [char32]\n",
-      "1       name  string     [char32]\n",
-      "2        sex  string      [char2]\n",
-      "3    address  string     [char64]\n",
-      "4       mail  string     [char32]\n",
-      "5  birthdate    long  [timestamp]\n"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>properties</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>username</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char32]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>name</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char32]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sex</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>address</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char64]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mail</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char32]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>birthdate</td>\n",
+       "      <td>long</td>\n",
+       "      <td>[timestamp]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        name    type   properties\n",
+       "0   username  string     [char32]\n",
+       "1       name  string     [char32]\n",
+       "2        sex  string      [char1]\n",
+       "3    address  string     [char64]\n",
+       "4       mail  string     [char32]\n",
+       "5  birthdate    long  [timestamp]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -206,7 +372,7 @@
     ")\n",
     "\n",
     "# See the Kinetica column types\n",
-    "print(gpudb_table.type_as_df())"
+    "gpudb_table.type_as_df()"
    ]
   },
   {
@@ -228,7 +394,10 @@
     {
      "data": {
       "text/plain": [
-       "1"
+       "{'status': 'OK',\n",
+       " 'message': '',\n",
+       " 'data_type': 'execute_sql_response',\n",
+       " 'response_time': 0.0148}"
       ]
      },
      "execution_count": 4,
@@ -239,23 +408,34 @@
    "source": [
     "# create an LLM context for the table.\n",
     "\n",
+    "from gpudb import GPUdbException\n",
+    "\n",
     "sql = f\"\"\"\n",
     "CREATE OR REPLACE CONTEXT {kinetica_ctx}\n",
     "(\n",
-    "    TABLE = {table_name}\n",
+    "    TABLE = demo.test_profiles\n",
     "    COMMENT = 'Contains user profiles.'\n",
     "),\n",
     "(\n",
     "    SAMPLES = (\n",
     "    'How many male users are there?' = \n",
     "    'select count(1) as num_users\n",
-    "    from {table_name}\n",
+    "    from demo.test_profiles\n",
     "    where sex = ''M'';')\n",
     ")\n",
     "\"\"\"\n",
     "\n",
-    "count_affected = kinetica_llm.kdbc.execute(sql)\n",
-    "count_affected"
+    "\n",
+    "def _check_error(response: dict) -> None:\n",
+    "    status = response[\"status_info\"][\"status\"]\n",
+    "    if status != \"OK\":\n",
+    "        message = response[\"status_info\"][\"message\"]\n",
+    "        raise GPUdbException(\"[%s]: %s\" % (status, message))\n",
+    "\n",
+    "\n",
+    "response = kinetica_llm.kdbc.execute_sql(sql)\n",
+    "_check_error(response)\n",
+    "response[\"status_info\"]"
    ]
   },
   {
@@ -282,16 +462,16 @@
      "text": [
       "================================\u001b[1m System Message \u001b[0m================================\n",
       "\n",
-      "CREATE TABLE demo.user_profiles AS\n",
+      "CREATE TABLE demo.test_profiles AS\n",
       "(\n",
       "   username VARCHAR (32) NOT NULL,\n",
       "   name VARCHAR (32) NOT NULL,\n",
-      "   sex VARCHAR (2) NOT NULL,\n",
+      "   sex VARCHAR (1) NOT NULL,\n",
       "   address VARCHAR (64) NOT NULL,\n",
       "   mail VARCHAR (32) NOT NULL,\n",
       "   birthdate TIMESTAMP NOT NULL\n",
       ");\n",
-      "COMMENT ON TABLE demo.user_profiles IS 'Contains user profiles.';\n",
+      "COMMENT ON TABLE demo.test_profiles IS 'Contains user profiles.';\n",
       "\n",
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
@@ -300,7 +480,7 @@
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
       "select count(1) as num_users\n",
-      "    from demo.user_profiles\n",
+      "    from demo.test_profiles\n",
       "    where sex = 'M';\n",
       "\n",
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
@@ -365,16 +545,78 @@
      "output_type": "stream",
      "text": [
       "SQL: SELECT username, name\n",
-      "    FROM demo.user_profiles\n",
+      "    FROM demo.test_profiles\n",
       "    WHERE sex = 'F'\n",
-      "    ORDER BY username;\n",
-      "      username               name\n",
-      "0  alexander40       Tina Ramirez\n",
-      "1      bburton       Paula Kaiser\n",
-      "2      brian12  Stefanie Williams\n",
-      "3    brownanna      Jennifer Rowe\n",
-      "4       carl19       Amanda Potts\n"
+      "    ORDER BY username;\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>username</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>alexander40</td>\n",
+       "      <td>Tina Ramirez</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>bburton</td>\n",
+       "      <td>Paula Kaiser</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>brian12</td>\n",
+       "      <td>Stefanie Williams</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>brownanna</td>\n",
+       "      <td>Jennifer Rowe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>carl19</td>\n",
+       "      <td>Amanda Potts</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      username               name\n",
+       "0  alexander40       Tina Ramirez\n",
+       "1      bburton       Paula Kaiser\n",
+       "2      brian12  Stefanie Williams\n",
+       "3    brownanna      Jennifer Rowe\n",
+       "4       carl19       Amanda Potts"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -384,7 +626,7 @@
     ")\n",
     "\n",
     "print(f\"SQL: {response.sql}\")\n",
-    "print(response.dataframe.head())"
+    "response.dataframe.head()"
    ]
   }
  ],
@@ -404,7 +646,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.19"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/litellm.ipynb

@@ -94,7 +94,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler"
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
    ]
   },
   {

docs/docs/integrations/chat/litellm_router.ipynb

@@ -122,7 +122,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler"
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
    ]
   },
   {

docs/docs/integrations/chat/maritalk.ipynb

@@ -173,8 +173,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import OnlinePDFLoader\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "from langchain.document_loaders import OnlinePDFLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "\n",
     "# Loading the COMVEST 2024 notice\n",
     "loader = OnlinePDFLoader(\n",
@@ -202,7 +202,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.retrievers import BM25Retriever\n",
+    "from langchain.retrievers import BM25Retriever\n",
     "\n",
     "retriever = BM25Retriever.from_documents(texts)"
    ]

docs/docs/integrations/chat/minimax.ipynb

@@ -71,7 +71,8 @@
  "metadata": {
   "language_info": {
    "name": "python"
-  }
+  },
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/docs/integrations/chat/mlx.ipynb

@@ -9,7 +9,7 @@
     "This notebook shows how to get started using `MLX` LLM's as chat models.\n",
     "\n",
     "In particular, we will:\n",
-    "1. Utilize the [MLXPipeline](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/llms/mlx_pipeline.py), \n",
+    "1. Utilize the [MLXPipeline](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/mlx_pipelines.py), \n",
     "2. Utilize the `ChatMLX` class to enable any of these LLMs to interface with LangChain's [Chat Messages](https://python.langchain.com/docs/modules/model_io/chat/#messages) abstraction.\n",
     "3. Demonstrate how to use an open-source LLM to power an `ChatAgent` pipeline\n"
    ]
@@ -66,8 +66,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.schema import (\n",
+    "    HumanMessage,\n",
+    ")\n",
     "from langchain_community.chat_models.mlx import ChatMLX\n",
-    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "messages = [\n",
     "    HumanMessage(\n",

docs/docs/integrations/chat/moonshot.ipynb

@@ -78,7 +78,8 @@
  "metadata": {
   "language_info": {
    "name": "python"
-  }
+  },
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/docs/integrations/chat/openai.ipynb

@@ -147,7 +147,7 @@
     "\n",
     "### ChatOpenAI.bind_tools()\n",
     "\n",
-    "With `ChatOpenAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
+    "With `ChatAnthropic.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an Anthropic tool schemas, which looks like:\n",
     "```\n",
     "{\n",
     "    \"name\": \"...\",\n",

docs/docs/integrations/chat/sparkllm.ipynb

@@ -34,9 +34,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AIMessage(content='Hello! How can I help you today?')"
-      ]
+      "text/plain": "AIMessage(content='Hello! How can I help you today?')"
      },
      "execution_count": 10,
      "metadata": {},

docs/docs/integrations/chat/tongyi.ipynb

@@ -26,22 +26,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Install the package\n",
     "%pip install --upgrade --quiet  dashscope"
@@ -56,7 +48,15 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
    "source": [
     "# Get a new token: https://help.aliyun.com/document_detail/611472.html?spm=a2c4g.2399481.0.0\n",
     "from getpass import getpass\n",
@@ -94,12 +94,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chat resp: content='Hello' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'\n",
-      "chat resp: content='!' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'\n",
-      "chat resp: content=' How' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'\n",
-      "chat resp: content=' can I assist you today' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'\n",
-      "chat resp: content='?' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'\n",
-      "chat resp: content='' response_metadata={'finish_reason': 'stop', 'request_id': '921db2c5-4d53-9a89-8e87-e4ad6a671237', 'token_usage': {'input_tokens': 20, 'output_tokens': 9, 'total_tokens': 29}} id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'\n"
+      "chat resp: content='Hello! How' additional_kwargs={} example=False\n",
+      "chat resp: content=' can I assist you today?' additional_kwargs={} example=False\n"
      ]
     }
    ],
@@ -120,18 +116,10 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/cheese/PARA/Projects/langchain-contribution/langchain/libs/core/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseChatModel.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.2.0. Use invoke instead.\n",
-      "  warn_deprecated(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'stop', 'request_id': 'ae725086-0ffa-9728-8c72-b204c7bc7eeb', 'token_usage': {'input_tokens': 36, 'output_tokens': 6, 'total_tokens': 42}}, id='run-060cc103-ef5f-4c8a-af40-792ac7f40c26-0')"
+       "AIMessageChunk(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
       ]
      },
      "execution_count": 5,
@@ -161,65 +149,18 @@
     "ChatTongyi supports tool calling API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use with `bind_tools`"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "content='' additional_kwargs={'tool_calls': [{'function': {'name': 'multiply', 'arguments': '{\"first_int\": 5, \"second_int\": 42}'}, 'id': '', 'type': 'function'}]} response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'tool_calls', 'request_id': '4acf0e36-44af-987a-a0c0-8b5c5eaa1a8b', 'token_usage': {'input_tokens': 200, 'output_tokens': 25, 'total_tokens': 225}} id='run-0ecd0f09-1d20-4e55-a4f3-f14d1f710ae7-0' tool_calls=[{'name': 'multiply', 'args': {'first_int': 5, 'second_int': 42}, 'id': ''}]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.chat_models.tongyi import ChatTongyi\n",
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(first_int: int, second_int: int) -> int:\n",
-    "    \"\"\"Multiply two integers together.\"\"\"\n",
-    "    return first_int * second_int\n",
-    "\n",
-    "\n",
-    "llm = ChatTongyi(model=\"qwen-turbo\")\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([multiply])\n",
-    "\n",
-    "msg = llm_with_tools.invoke(\"What's 5 times forty two\")\n",
-    "\n",
-    "print(msg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Construct args manually"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'name': 'get_current_weather', 'arguments': '{\"location\": \"San Francisco\"}'}, 'id': '', 'type': 'function'}]}, response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'tool_calls', 'request_id': '87ef33d2-5c6b-9457-91e2-39faad7120eb', 'token_usage': {'input_tokens': 229, 'output_tokens': 19, 'total_tokens': 248}}, id='run-7939ba7f-e3f7-46f8-980b-30499b52723c-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco'}, 'id': ''}])"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'name': 'get_current_weather', 'arguments': '{\"location\": \"San Francisco\"}'}, 'id': '', 'type': 'function'}]}, response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'tool_calls', 'request_id': 'dae79197-8780-9b7e-8c15-6a83e2a53534', 'token_usage': {'input_tokens': 229, 'output_tokens': 19, 'total_tokens': 248}}, id='run-9e06f837-582b-473b-bb1f-5e99a68ecc10-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco'}, 'id': ''}])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -265,50 +206,6 @@
     "ai_message = chatLLM.bind(**llm_kwargs).invoke(messages)\n",
     "ai_message"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tongyi With Vision\n",
-    "Qwen-VL(qwen-vl-plus/qwen-vl-max) are models that can process images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=[{'text': 'The image presents a flowchart of an artificial intelligence system. The system is divided into two main components: short-term memory and long-term memory, which are connected to the \"Memory\" box.\\n\\nFrom the \"Memory\" box, there are three branches leading to different functionalities:\\n\\n1. \"Tools\" - This branch represents various tools that the AI system can utilize, including \"Calendar()\", \"Calculator()\", \"CodeInterpreter()\", \"Search()\" and others not explicitly listed.\\n\\n2. \"Action\" - This branch represents the action taken by the AI system based on its processing of information. It\\'s connected to both the \"Tools\" and the \"Agent\" boxes.\\n\\n3. \"Planning\" - This branch represents the planning process of the AI system, which involves reflection, self-critics, chain of thoughts, subgoal decomposition, and other processes not shown.\\n\\nThe central component of the system is the \"Agent\" box, which seems to orchestrate the flow of information between the different components. The \"Agent\" interacts with the \"Tools\" and \"Memory\" boxes, suggesting it plays a crucial role in the AI\\'s decision-making process. \\n\\nOverall, the image depicts a complex and interconnected artificial intelligence system, where different components work together to process information, make decisions, and take actions.'}], response_metadata={'model_name': 'qwen-vl-max', 'finish_reason': 'stop', 'request_id': '6a2b9e90-7c3b-960d-8a10-6a0cf9991ae5', 'token_usage': {'input_tokens': 1262, 'output_tokens': 260, 'image_tokens': 1232}}, id='run-fd030661-c734-4580-b977-b77d42680742-0')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.chat_models import ChatTongyi\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "\n",
-    "chatLLM = ChatTongyi(model_name=\"qwen-vl-max\")\n",
-    "image_message = {\n",
-    "    \"image\": \"https://lilianweng.github.io/posts/2023-06-23-agent/agent-overview.png\",\n",
-    "}\n",
-    "text_message = {\n",
-    "    \"text\": \"summarize this picture\",\n",
-    "}\n",
-    "message = HumanMessage(content=[text_message, image_message])\n",
-    "chatLLM.invoke([message])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
@@ -327,7 +224,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,