modifying suffix to account for changes

_Thank you to the LangChain team for the great project and in advance
for your review. Let me know if I can provide any other additional
information or do things differently in the future to make your lives
easier 🙏 _

@hwchase17 please let me know if you're not the right person to review 😄

This PR enables LangChain to access the Konko API via the chat_models
API wrapper.

Konko API is a fully managed API designed to help application
developers:

1. Select the right LLM(s) for their application
2. Prototype with various open-source and proprietary LLMs
3. Move to production in-line with their security, privacy, throughput,
latency SLAs without infrastructure set-up or administration using Konko
AI's SOC 2 compliant infrastructure

_Note on integration tests:_ 
We added 14 integration tests. They will all fail unless you export the
right API keys. 13 will pass with a KONKO_API_KEY provided and the other
one will pass with a OPENAI_API_KEY provided. When both are provided,
all 14 integration tests pass. If you would like to test this yourself,
please let me know and I can provide some temporary keys.

### Installation and Setup

1. **First you'll need an API key**
2. **Install Konko AI's Python SDK**
    1. Enable a Python3.8+ environment
    
    `pip install konko`
    
3.  **Set API Keys**
    
          **Option 1:** Set Environment Variables
    
    You can set environment variables for
    
    1. KONKO_API_KEY (Required)
    2. OPENAI_API_KEY (Optional)
    
    In your current shell session, use the export command:
    
    `export KONKO_API_KEY={your_KONKO_API_KEY_here}`
    `export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional`
    
Alternatively, you can add the above lines directly to your shell
startup script (such as .bashrc or .bash_profile for Bash shell and
.zshrc for Zsh shell) to have them set automatically every time a new
shell session starts.
    
    **Option 2:** Set API Keys Programmatically
    
If you prefer to set your API keys directly within your Python script or
Jupyter notebook, you can use the following commands:
    
    ```python
    konko.set_api_key('your_KONKO_API_KEY_here')
    konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional
    
    ```
    

### Calling a model

Find a model on the [[Konko Introduction
page](https://docs.konko.ai/docs#available-models)](https://docs.konko.ai/docs#available-models)

For example, for this [[LLama 2
model](https://docs.konko.ai/docs/meta-llama-2-13b-chat)](https://docs.konko.ai/docs/meta-llama-2-13b-chat).
The model id would be: `"meta-llama/Llama-2-13b-chat-hf"`

Another way to find the list of models running on the Konko instance is
through this
[[endpoint](https://docs.konko.ai/reference/listmodels)](https://docs.konko.ai/reference/listmodels).

From here, we can initialize our model:

```python
chat_instance = ChatKonko(max_tokens=10, model = 'meta-llama/Llama-2-13b-chat-hf')

```

And run it:

```python
msg = HumanMessage(content="Hi")
chat_response = chat_instance([msg])

```

.github/actions/poetry_setup/action.yml

@@ -27,7 +27,7 @@ runs:
   using: composite
   steps:
     - uses: actions/setup-python@v4
-      name: Setup python $${ inputs.python-version }}
+      name: Setup python ${{ inputs.python-version }}
       with:
         python-version: ${{ inputs.python-version }}
 
@@ -39,10 +39,35 @@ runs:
       with:
         path: |
           /opt/pipx/venvs/poetry
-          /opt/pipx_bin/poetry
         # This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
         key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
 
+    - name: Refresh shell hashtable and fixup softlinks
+      if: steps.cache-bin-poetry.outputs.cache-hit == 'true'
+      shell: bash
+      env:
+        POETRY_VERSION: ${{ inputs.poetry-version }}
+        PYTHON_VERSION: ${{ inputs.python-version }}
+      run: |
+        set -eux
+
+        # Refresh the shell hashtable, to ensure correct `which` output.
+        hash -r
+
+        # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
+        # Delete and recreate the softlinks pipx expects to have.
+        rm /opt/pipx/venvs/poetry/bin/python
+        cd /opt/pipx/venvs/poetry/bin
+        ln -s "$(which "python$PYTHON_VERSION")" python
+        chmod +x python
+        cd /opt/pipx_bin/
+        ln -s /opt/pipx/venvs/poetry/bin/poetry poetry
+        chmod +x poetry
+
+        # Ensure everything got set up correctly.
+        /opt/pipx/venvs/poetry/bin/python --version
+        /opt/pipx_bin/poetry --version
+
     - name: Install poetry
       if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
       shell: bash

.github/workflows/_lint.yml

@@ -87,7 +87,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
-          cache-key: lint
+          cache-key: lint-with-extras
 
       - name: Check Poetry File
         shell: bash
@@ -102,9 +102,17 @@ jobs:
           poetry lock --check
 
       - name: Install dependencies
+        # Also installs dev/lint/test/typing dependencies, to ensure we have
+        # type hints for as many of our libraries as possible.
+        # This helps catch errors that require dependencies to be spotted, for example:
+        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
+        #
+        # If you change this configuration, make sure to change the `cache-key`
+        # in the `poetry_setup` action above to stop using the old cache.
+        # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry install
+          poetry install --with dev,lint,test,typing
 
       - name: Install langchain editable
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_pydantic_compatibility.yml

@@ -79,3 +79,15 @@ jobs:
       - name: Run pydantic compatibility tests
         shell: bash
         run: make test
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_test.yml

@@ -43,3 +43,15 @@ jobs:
       - name: Run core tests
         shell: bash
         run: make test
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/langchain_ci.yml

@@ -6,6 +6,8 @@ on:
     branches: [ master ]
   pull_request:
     paths:
+      - '.github/actions/poetry_setup/action.yml'
+      - '.github/tools/**'
       - '.github/workflows/_lint.yml'
       - '.github/workflows/_test.yml'
       - '.github/workflows/_pydantic_compatibility.yml'
@@ -81,3 +83,15 @@ jobs:
 
       - name: Run extended tests
         run: make extended_tests
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/langchain_experimental_ci.yml

@@ -6,6 +6,8 @@ on:
     branches: [ master ]
   pull_request:
     paths:
+      - '.github/actions/poetry_setup/action.yml'
+      - '.github/tools/**'
       - '.github/workflows/_lint.yml'
       - '.github/workflows/_test.yml'
       - '.github/workflows/langchain_experimental_ci.yml'
@@ -113,3 +115,15 @@ jobs:
 
       - name: Run extended tests
         run: make extended_tests
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/scheduled_test.yml

@@ -47,3 +47,15 @@ jobs:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: |
           make scheduled_tests
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'

docs/api_reference/guide_imports.json

@@ -317,7 +317,7 @@
         "Chatbots": "https://python.langchain.com/docs/use_cases/chatbots",
         "Summarization": "https://python.langchain.com/docs/use_cases/summarization",
         "Extraction": "https://python.langchain.com/docs/use_cases/extraction",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "Tagging": "https://python.langchain.com/docs/use_cases/tagging",
         "Code Understanding": "https://python.langchain.com/docs/use_cases/code_understanding",
         "AutoGPT": "https://python.langchain.com/docs/use_cases/autonomous_agents/autogpt",
@@ -338,6 +338,7 @@
         "Neptune Open Cypher QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/neptune_cypher_qa",
         "NebulaGraphQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_nebula_qa",
         "KuzuQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_kuzu_qa",
+        "FalkorDBQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa",
         "HugeGraph QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_hugegraph_qa",
         "GraphSparqlQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_sparql_qa",
         "ArangoDB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_arangodb_qa",
@@ -399,7 +400,7 @@
         "Summarization": "https://python.langchain.com/docs/use_cases/summarization",
         "Extraction": "https://python.langchain.com/docs/use_cases/extraction",
         "Interacting with APIs": "https://python.langchain.com/docs/use_cases/apis",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "QA over Documents": "https://python.langchain.com/docs/use_cases/question_answering/index",
         "Retrieve from vector stores directly": "https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_text_generation",
         "Improve document indexing with HyDE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/hyde",
@@ -640,7 +641,7 @@
         "Chatbots": "https://python.langchain.com/docs/use_cases/chatbots",
         "Extraction": "https://python.langchain.com/docs/use_cases/extraction",
         "Interacting with APIs": "https://python.langchain.com/docs/use_cases/apis",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "HuggingGPT": "https://python.langchain.com/docs/use_cases/autonomous_agents/hugginggpt",
         "Perform context-aware text splitting": "https://python.langchain.com/docs/use_cases/question_answering/how_to/document-context-aware-QA",
         "Retrieve from vector stores directly": "https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_text_generation",
@@ -1008,7 +1009,7 @@
         "LangSmith Walkthrough": "https://python.langchain.com/docs/guides/langsmith/walkthrough",
         "Comparing Chain Outputs": "https://python.langchain.com/docs/guides/evaluation/examples/comparisons",
         "Agent Trajectory": "https://python.langchain.com/docs/guides/evaluation/trajectory/trajectory_eval",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "Multi-modal outputs: Image & Text": "https://python.langchain.com/docs/use_cases/multi_modal/image_agent",
         "Agent Debates with Tools": "https://python.langchain.com/docs/use_cases/agent_simulations/two_agent_debate_tools",
         "Multiple callback handlers": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks",
@@ -1267,7 +1268,7 @@
         "SQL Database Agent": "https://python.langchain.com/docs/integrations/toolkits/sql_database",
         "JSON Agent": "https://python.langchain.com/docs/integrations/toolkits/json",
         "NIBittensorLLM": "https://python.langchain.com/docs/integrations/llms/bittensor",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "BabyAGI with Tools": "https://python.langchain.com/docs/use_cases/agents/baby_agi_with_agent",
         "Conversational Retrieval Agent": "https://python.langchain.com/docs/use_cases/question_answering/how_to/conversational_retrieval_agents",
         "Plug-and-Plai": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai",
@@ -1831,12 +1832,12 @@
     "create_sql_agent": {
         "CnosDB": "https://python.langchain.com/docs/integrations/providers/cnosdb",
         "SQL Database Agent": "https://python.langchain.com/docs/integrations/toolkits/sql_database",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql"
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql"
     },
     "SQLDatabaseToolkit": {
         "CnosDB": "https://python.langchain.com/docs/integrations/providers/cnosdb",
         "SQL Database Agent": "https://python.langchain.com/docs/integrations/toolkits/sql_database",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "Use ToolKits with OpenAI Functions": "https://python.langchain.com/docs/modules/agents/how_to/use_toolkits_with_openai_functions"
     },
     "SageMakerCallbackHandler": {
@@ -1898,7 +1899,7 @@
         "Rebuff": "https://python.langchain.com/docs/integrations/providers/rebuff",
         "SQL Database Agent": "https://python.langchain.com/docs/integrations/toolkits/sql_database",
         "Cookbook": "https://python.langchain.com/docs/guides/expression_language/cookbook",
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "Multiple Retrieval Sources": "https://python.langchain.com/docs/use_cases/question_answering/how_to/multiple_retrieval"
     },
     "Weaviate": {
@@ -3034,11 +3035,11 @@
         "Interacting with APIs": "https://python.langchain.com/docs/use_cases/apis"
     },
     "create_sql_query_chain": {
-        "SQL": "https://python.langchain.com/docs/use_cases/sql",
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql",
         "Multiple Retrieval Sources": "https://python.langchain.com/docs/use_cases/question_answering/how_to/multiple_retrieval"
     },
     "ElasticsearchDatabaseChain": {
-        "SQL": "https://python.langchain.com/docs/use_cases/sql"
+        "SQL": "https://python.langchain.com/docs/use_cases/qa_structured/sql"
     },
     "FileChatMessageHistory": {
         "AutoGPT": "https://python.langchain.com/docs/use_cases/autonomous_agents/autogpt"
@@ -3174,6 +3175,12 @@
     "KuzuQAChain": {
         "KuzuQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_kuzu_qa"
     },
+    "FalkorDBGraph": {
+        "KuzuQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa"
+    },
+    "FalkorDBQAChain": {
+        "FalkorDB QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa"
+    },
     "HugeGraphQAChain": {
         "HugeGraph QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_hugegraph_qa"
     },

docs/docs_skeleton/docs/get_started/quickstart.mdx

@@ -59,8 +59,8 @@ LangChain provides several objects to easily distinguish between different roles
 If none of those roles sound right, there is also a `ChatMessage` class where you can specify the role manually.
 For more information on how to use these different messages most effectively, see our prompting guide.
 
-LangChain exposes a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model.
-The standard interface that LangChain exposes has two methods:
+LangChain provides a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model.
+The standard interface that LangChain provides has two methods:
 - `predict`: Takes in a string, returns a string
 - `predict_messages`: Takes in a list of messages, returns a message.
 

docs/docs_skeleton/docs/guides/langsmith/index.md

@@ -2,11 +2,21 @@
 
 import DocCardList from "@theme/DocCardList";
 
-LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you
+[LangSmith](https://smith.langchain.com) helps you trace and evaluate your language model applications and intelligent agents to help you
 move from prototype to production.
 
 Check out the [interactive walkthrough](/docs/guides/langsmith/walkthrough) below to get started.
 
-For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/)
+For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/).
+
+For tutorials and other end-to-end examples demonstrating ways to integrate LangSmith in your workflow,
+check out the [LangSmith Cookbook](https://github.com/langchain-ai/langsmith-cookbook). Some of the guides therein include:
+
+- Leveraging user feedback in your JS application ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/feedback-examples/nextjs/README.md)).
+- Building an automated feedback pipeline ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/feedback-examples/algorithmic-feedback/algorithmic_feedback.ipynb)).
+- How to evaluate and audit your RAG workflows ([link](https://github.com/langchain-ai/langsmith-cookbook/tree/main/testing-examples/qa-correctness)).
+- How to fine-tune a LLM on real usage data ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/fine-tuning-examples/export-to-openai/fine-tuning-on-chat-runs.ipynb)).
+- How to use the [LangChain Hub](https://smith.langchain.com/hub) to version your prompts ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/hub-examples/retrieval-qa-chain/retrieval-qa.ipynb))
+
 
 <DocCardList />

docs/docs_skeleton/docs/guides/safety/index.mdx

@@ -4,4 +4,5 @@ One of the key concerns with using LLMs is that they may generate harmful or une
 
 - [Moderation chain](/docs/guides/safety/moderation): Explicitly check if any output text is harmful and flag it.
 - [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.
+- [Logical Fallacy chain](/docs/guides/safety/logical_fallacy_chain): Checks the model output against logical fallacies to correct any deviation.
 - [Amazon Comprehend moderation chain](/docs/guides/safety/amazon_comprehend_chain): Use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle PII and toxicity.

docs/docs_skeleton/docs/guides/safety/logical_fallacy_chain.mdx

@@ -0,0 +1,85 @@
+# Removing logical fallacies from model output
+Logical fallacies are flawed reasoning or false arguments that can undermine the validity of a model's outputs. Examples include circular reasoning, false
+dichotomies, ad hominem attacks, etc.  Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However, 
+optimizing for metrics alone does not guarantee logically sound reasoning.
+
+Language models can learn to exploit flaws in reasoning to generate plausible-sounding but logically invalid arguments.  When models rely on fallacies, their outputs become unreliable and untrustworthy, even if they achieve high scores on metrics. Users cannot depend on such outputs. Propagating logical fallacies can spread misinformation, confuse users, and lead to harmful real-world consequences when models are deployed in products or services.
+
+Monitoring and testing specifically for logical flaws is challenging unlike other quality issues. It requires reasoning about arguments rather than pattern matching.
+
+Therefore, it is crucial that model developers proactively address logical fallacies after optimizing metrics. Specialized techniques like causal modeling, robustness testing, and bias mitigation can help avoid flawed reasoning.  Overall, allowing logical flaws to persist makes models less safe and ethical. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks.
+
+
+
+```python
+# Imports
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains.llm import LLMChain
+from langchain_experimental.fallacy_removal.base import FallacyChain
+```
+
+```python
+# Example of a model output being returned with a logical fallacy
+misleading_prompt = PromptTemplate(
+    template="""You have to respond by using only logical fallacies inherent in your answer explanations.
+
+Question: {question}
+
+Bad answer:""",
+    input_variables=["question"],
+)
+
+llm = OpenAI(temperature=0)
+
+misleading_chain = LLMChain(llm=llm, prompt=misleading_prompt)
+
+misleading_chain.run(question="How do I know the earth is round?")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    'The earth is round because my professor said it is, and everyone believes my professor'
+```
+
+</CodeOutputBlock>
+
+
+```python
+fallacies = FallacyChain.get_fallacies(["correction"])
+fallacy_chain = FallacyChain.from_llm(
+    chain=misleading_chain,
+    logical_fallacies=fallacies,
+    llm=llm,
+    verbose=True,
+)
+
+fallacy_chain.run(question="How do I know the earth is round?")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+
+
+    > Entering new FallacyChain chain...
+    Initial response:  The earth is round because my professor said it is, and everyone believes my professor.
+
+    Applying correction...
+
+    Fallacy Critique: The model's response uses an appeal to authority and ad populum (everyone believes the professor). Fallacy Critique Needed.
+
+    Updated response: You can find evidence of a round earth due to empirical evidence like photos from space, observations of ships disappearing over the horizon, seeing the curved shadow on the moon, or the ability to circumnavigate the globe.
+
+
+    > Finished chain.
+
+
+
+
+
+    'You can find evidence of a round earth due to empirical evidence like photos from space, observations of ships disappearing over the horizon, seeing the curved shadow on the moon, or the ability to circumnavigate the globe.'
+```
+
+</CodeOutputBlock>

docs/docs_skeleton/docs/modules/chains/document/index.mdx

@@ -3,7 +3,7 @@ sidebar_position: 2
 ---
 # Documents
 
-These are the core chains for working with Documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more.
+These are the core chains for working with documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more.
 
 These chains all implement a common interface:
 

docs/docs_skeleton/docs/modules/chains/document/refine.mdx

@@ -3,10 +3,10 @@ sidebar_position: 1
 ---
 # Refine
 
-The refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
+The Refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
 
 Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context.
 The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain.
 There are also certain tasks which are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
 
-![refine_diagram](/img/refine.jpg)
+![refine_diagram](/img/refine.jpg)

docs/docs_skeleton/docs/modules/chains/foundational/llm_chain.mdx

@@ -1,11 +1,11 @@
 # LLM
 
-An LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.
+An `LLMChain` is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.
 
-An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
+An `LLMChain` consists of a `PromptTemplate` and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
 
 ## Get started
 
 import Example from "@snippets/modules/chains/foundational/llm_chain.mdx"
 
-<Example/>
+<Example/>

docs/docs_skeleton/docs/modules/chains/foundational/sequential_chains.mdx

@@ -4,7 +4,7 @@
 
 The next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another.
 
-In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario.. There are two types of sequential chains:
+In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario. There are two types of sequential chains:
 
 - `SimpleSequentialChain`: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.
 - `SequentialChain`: A more general form of sequential chains, allowing for multiple inputs/outputs.

docs/docs_skeleton/docs/modules/chains/index.mdx

@@ -30,4 +30,4 @@ Chains allow us to combine multiple components together to create a single, cohe
 
 import GetStarted from "@snippets/modules/chains/get_started.mdx"
 
-<GetStarted/>
+<GetStarted/>

docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.mdx

@@ -11,7 +11,7 @@ Use document loaders to load data from a source as `Document`'s. A `Document` is
 and associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text
 contents of any web page, or even for loading a transcript of a YouTube video.
 
-Document loaders expose a "load" method for loading data as documents from a configured source. They optionally
+Document loaders provide a "load" method for loading data as documents from a configured source. They optionally
 implement a "lazy load" as well for lazily loading data into memory.
 
 ## Get started

docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx

@@ -2,8 +2,8 @@
 
 This is the simplest method. This splits based on characters (by default "\n\n") and measure chunk length by number of characters.
 
-1. How the text is split: by single character
-2. How the chunk size is measured: by number of characters
+1. How the text is split: by single character.
+2. How the chunk size is measured: by number of characters.
 
 import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx"
 

docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx

@@ -1,6 +1,6 @@
 # Split code
 
-CodeTextSplitter allows you to split your code with multiple language support. Import enum `Language` and specify the language. 
+CodeTextSplitter allows you to split your code with multiple languages supported. Import enum `Language` and specify the language. 
 
 import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx"
 

docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx

@@ -2,8 +2,8 @@
 
 This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `["\n\n", "\n", " ", ""]`. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.
 
-1. How the text is split: by list of characters
-2. How the chunk size is measured: by number of characters
+1. How the text is split: by list of characters.
+2. How the chunk size is measured: by number of characters.
 
 import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx"
 

docs/docs_skeleton/docs/modules/data_connection/index.mdx

@@ -18,9 +18,9 @@ This encompasses several key modules.
 **[Document loaders](/docs/modules/data_connection/document_loaders/)**
 
 Load documents from many different sources.
-LangChain provides over a 100 different document loaders as well as integrations with other major providers in the space,
+LangChain provides over 100 different document loaders as well as integrations with other major providers in the space,
 like AirByte and Unstructured.
-We provide integrations to load all types of documents (html, PDF, code) from all types of locations (private s3 buckets, public websites).
+We provide integrations to load all types of documents (HTML, PDF, code) from all types of locations (private s3 buckets, public websites).
 
 **[Document transformers](/docs/modules/data_connection/document_transformers/)**
 
@@ -32,18 +32,18 @@ LangChain provides several different algorithms for doing this, as well as logic
 **[Text embedding models](/docs/modules/data_connection/text_embedding/)**
 
 Another key part of retrieval has become creating embeddings for documents.
-Embeddings capture the semantic meaning of text, allowing you to quickly and
+Embeddings capture the semantic meaning of the text, allowing you to quickly and
 efficiently find other pieces of text that are similar.
 LangChain provides integrations with over 25 different embedding providers and methods,
 from open-source to proprietary API,
 allowing you to choose the one best suited for your needs.
-LangChain exposes a standard interface, allowing you to easily swap between models.
+LangChain provides a standard interface, allowing you to easily swap between models.
 
 **[Vector stores](/docs/modules/data_connection/vectorstores/)**
 
 With the rise of embeddings, there has emerged a need for databases to support efficient storage and searching of these embeddings.
 LangChain provides integrations with over 50 different vectorstores, from open-source local ones to cloud-hosted proprietary ones,
-allowing you choose the one best suited for your needs.
+allowing you to choose the one best suited for your needs.
 LangChain exposes a standard interface, allowing you to easily swap between vector stores.
 
 **[Retrievers](/docs/modules/data_connection/retrievers/)**
@@ -55,7 +55,7 @@ However, we have also added a collection of algorithms on top of this to increas
 These include:
 
 - [Parent Document Retriever](/docs/modules/data_connection/retrievers/parent_document_retriever): This allows you to create multiple embeddings per parent document, allowing you to look up smaller chunks but return larger context.
-- [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query): User questions often contain reference to something that isn't just semantic, but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the *semantic* part of a query from other *metadata filters* present in the query
+- [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query): User questions often contain a reference to something that isn't just semantic but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the *semantic* part of a query from other *metadata filters* present in the query.
 - [Ensemble Retriever](/docs/modules/data_connection/retrievers/ensemble): Sometimes you may want to retrieve documents from multiple different sources, or using multiple different algorithms. The ensemble retriever allows you to easily do this.
 - And more!
 

docs/docs_skeleton/docs/modules/data_connection/retrievers/contextual_compression/index.mdx

@@ -5,10 +5,10 @@ One challenge with retrieval is that usually you don't know the specific queries
 Contextual compression is meant to fix this. The idea is simple: instead of immediately returning retrieved documents as-is, you can compress them using the context of the given query, so that only the relevant information is returned. “Compressing” here refers to both compressing the contents of an individual document and filtering out documents wholesale.
 
 To use the Contextual Compression Retriever, you'll need:
-- a base Retriever
+- a base retriever
 - a Document Compressor
 
-The Contextual Compression Retriever passes queries to the base Retriever, takes the initial documents and passes them through the Document Compressor. The Document Compressor takes a list of Documents and shortens it by reducing the contents of Documents or dropping Documents altogether.
+The Contextual Compression Retriever passes queries to the base retriever, takes the initial documents and passes them through the Document Compressor. The Document Compressor takes a list of documents and shortens it by reducing the contents of documents or dropping documents altogether.
 
 ![](https://drive.google.com/uc?id=1CtNgWODXZudxAWSRiWgSGEoTNrUFT98v)
 

docs/docs_skeleton/docs/modules/data_connection/retrievers/index.mdx

@@ -8,7 +8,7 @@ Head to [Integrations](/docs/integrations/retrievers/) for documentation on buil
 :::
 
 A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
-A retriever does not need to be able to store documents, only to return (or retrieve) it. Vector stores can be used
+A retriever does not need to be able to store documents, only to return (or retrieve) them. Vector stores can be used
 as the backbone of a retriever, but there are other types of retrievers as well.
 
 ## Get started

docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx

@@ -1,6 +1,6 @@
 # Self-querying
 
-A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filters.
+A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to its underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documents but to also extract filters from the user query on the metadata of stored documents and to execute those filters.
 
 ![](https://drive.google.com/uc?id=1OQUN-0MJcDUxmPXofgS7MqReEs720pqS)
 

docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx

@@ -8,7 +8,7 @@ The algorithm for scoring them is:
 semantic_similarity + (1.0 - decay_rate) ^ hours_passed
 ```
 
-Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh."
+Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh".
 
 import Example from "@snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx"
 

docs/docs_skeleton/docs/modules/data_connection/retrievers/vectorstore.mdx

@@ -1,9 +1,9 @@
 # Vector store-backed retriever
 
-A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the Vector Store class to make it conform to the Retriever interface.
+A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface.
 It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store.
 
-Once you construct a Vector store, it's very easy to construct a retriever. Let's walk through an example.
+Once you construct a vector store, it's very easy to construct a retriever. Let's walk through an example.
 
 import Example from "@snippets/modules/data_connection/retrievers/how_to/vectorstore.mdx"
 

docs/docs_skeleton/docs/modules/data_connection/text_embedding/index.mdx

@@ -11,7 +11,7 @@ The Embeddings class is a class designed for interfacing with text embedding mod
 
 Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.
 
-The base Embeddings class in LangChain exposes two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
+The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
 
 ## Get started
 

docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx

@@ -16,7 +16,7 @@ for you.
 
 ## Get started
 
-This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/data_connection/text_embedding/) interfaces before diving into this.
+This walkthrough showcases basic functionality related to vector stores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/data_connection/text_embedding/) interfaces before diving into this.
 
 import GetStarted from "@snippets/modules/data_connection/vectorstores/get_started.mdx"
 

docs/docs_skeleton/docs/modules/memory/chat_messages/index.mdx

@@ -8,10 +8,10 @@ Head to [Integrations](/docs/integrations/memory/) for documentation on built-in
 :::
 
 One of the core utility classes underpinning most (if not all) memory modules is the `ChatMessageHistory` class.
-This is a super lightweight wrapper which exposes convenience methods for saving Human messages, AI messages, and then fetching them all.
+This is a super lightweight wrapper which provides convenience methods for saving HumanMessages, AIMessages, and then fetching them all.
 
 You may want to use this class directly if you are managing memory outside of a chain.
 
 import GetStarted from "@snippets/modules/memory/chat_messages/get_started.mdx"
 
-<GetStarted/>
+<GetStarted/>

docs/docs_skeleton/docs/modules/memory/index.mdx

@@ -32,7 +32,7 @@ Even if these are not all used directly, they need to be stored in some form.
 One of the key parts of the LangChain memory module is a series of integrations for storing these chat messages,
 from in-memory lists to persistent databases.
 
-- [Chat message storage](/docs/modules/memory/chat_messages/): How to work with Chat Messages, and the various integrations offered
+- [Chat message storage](/docs/modules/memory/chat_messages/): How to work with Chat Messages, and the various integrations offered.
 
 ### Querying: Data structures and algorithms on top of chat messages
 Keeping a list of chat messages is fairly straight-forward.

docs/docs_skeleton/docs/modules/memory/types/buffer.mdx

@@ -1,6 +1,6 @@
 # Conversation Buffer
 
-This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing of messages and then extracts the messages in a variable.
+This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing messages and then extracts the messages in a variable.
 
 We can first extract it as a string.
 

docs/docs_skeleton/docs/modules/memory/types/buffer_window.mdx

@@ -1,6 +1,6 @@
 # Conversation Buffer Window
 
-`ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large
+`ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large.
 
 Let's first explore the basic functionality of this type of memory.
 

docs/docs_skeleton/docs/modules/memory/types/entity_summary_memory.mdx

@@ -1,6 +1,6 @@
 # Entity
 
-Entity Memory remembers given facts about specific entities in a conversation. It extracts information on entities (using an LLM) and builds up its knowledge about that entity over time (also using an LLM).
+Entity memory remembers given facts about specific entities in a conversation. It extracts information on entities (using an LLM) and builds up its knowledge about that entity over time (also using an LLM).
 
 Let's first walk through using this functionality.
 

docs/docs_skeleton/docs/modules/memory/types/index.mdx

@@ -1,7 +1,7 @@
 ---
 sidebar_position: 2
 ---
-# Memory Types
+# Memory types
 
 There are many different types of memory.
 Each has their own parameters, their own return types, and is useful in different scenarios.

docs/docs_skeleton/docs/modules/memory/types/vectorstore_retriever_memory.mdx

@@ -1,6 +1,6 @@
 # Backed by a Vector Store
 
-`VectorStoreRetrieverMemory` stores memories in a VectorDB and queries the top-K most "salient" docs every time it is called.
+`VectorStoreRetrieverMemory` stores memories in a vector store and queries the top-K most "salient" docs every time it is called.
 
 This differs from most of the other Memory classes in that it doesn't explicitly track the order of interactions.
 

docs/docs_skeleton/docs/modules/model_io/models/chat/chat_model_caching.mdx

@@ -1,5 +1,5 @@
 # Caching
-LangChain provides an optional caching layer for Chat Models. This is useful for two reasons:
+LangChain provides an optional caching layer for chat models. This is useful for two reasons:
 
 It can save you money by reducing the number of API calls you make to the LLM provider, if you're often requesting the same completion multiple times.
 It can speed up your application by reducing the number of API calls you make to the LLM provider.

docs/docs_skeleton/docs/modules/model_io/models/chat/index.mdx

@@ -8,8 +8,8 @@ Head to [Integrations](/docs/integrations/chat/) for documentation on built-in i
 :::
 
 Chat models are a variation on language models.
-While chat models use language models under the hood, the interface they expose is a bit different.
-Rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
+While chat models use language models under the hood, the interface they use is a bit different.
+Rather than using a "text in, text out" API, they use an interface where "chat messages" are the inputs and outputs.
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 

docs/docs_skeleton/docs/modules/model_io/models/chat/prompts.mdx

@@ -1,6 +1,6 @@
 # Prompts
 
-Prompts for Chat models are built around messages, instead of just plain text.
+Prompts for chat models are built around messages, instead of just plain text.
 
 import Prompts from "@snippets/modules/model_io/models/chat/how_to/prompts.mdx"
 

docs/docs_skeleton/docs/modules/model_io/models/chat/streaming.mdx

@@ -1,6 +1,6 @@
 # Streaming
 
-Some Chat models provide a streaming response. This means that instead of waiting for the entire response to be returned, you can start processing it as soon as it's available. This is useful if you want to display the response to the user as it's being generated, or if you want to process the response as it's being generated.
+Some chat models provide a streaming response. This means that instead of waiting for the entire response to be returned, you can start processing it as soon as it's available. This is useful if you want to display the response to the user as it's being generated, or if you want to process the response as it's being generated.
 
 import StreamingChatModel from "@snippets/modules/model_io/models/chat/how_to/streaming.mdx"
 

docs/docs_skeleton/docs/modules/model_io/models/index.mdx

@@ -8,16 +8,16 @@ LangChain provides interfaces and integrations for two types of models:
 - [LLMs](/docs/modules/model_io/models/llms/): Models that take a text string as input and return a text string
 - [Chat models](/docs/modules/model_io/models/chat/): Models that are backed by a language model but take a list of Chat Messages as input and return a Chat Message
 
-## LLMs vs Chat Models
+## LLMs vs chat models
 
-LLMs and Chat Models are subtly but importantly different. LLMs in LangChain refer to pure text completion models.
+LLMs and chat models are subtly but importantly different. LLMs in LangChain refer to pure text completion models.
 The APIs they wrap take a string prompt as input and output a string completion. OpenAI's GPT-3 is implemented as an LLM.
 Chat models are often backed by LLMs but tuned specifically for having conversations.
-And, crucially, their provider APIs expose a different interface than pure text completion models. Instead of a single string,
+And, crucially, their provider APIs use a different interface than pure text completion models. Instead of a single string,
 they take a list of chat messages as input. Usually these messages are labeled with the speaker (usually one of "System",
-"AI", and "Human"). And they return a ("AI") chat message as output. GPT-4 and Anthropic's Claude are both implemented as Chat Models.
+"AI", and "Human"). And they return an AI chat message as output. GPT-4 and Anthropic's Claude are both implemented as chat models.
 
-To make it possible to swap LLMs and Chat Models, both implement the Base Language Model interface. This exposes common
+To make it possible to swap LLMs and chat models, both implement the Base Language Model interface. This includes common
 methods "predict", which takes a string and returns a string, and "predict messages", which takes messages and returns a message.
-If you are using a specific model it's recommended you use the methods specific to that model class (i.e., "predict" for LLMs and "predict messages" for Chat Models),
+If you are using a specific model it's recommended you use the methods specific to that model class (i.e., "predict" for LLMs and "predict messages" for chat models),
 but if you're creating an application that should work with different types of models the shared interface can be helpful.

docs/docs_skeleton/docs/modules/model_io/output_parsers/index.mdx

@@ -12,7 +12,7 @@ Output parsers are classes that help structure language model responses. There a
 
 And then one optional one:
 
-- "Parse with prompt": A method which takes in a string (assumed to be the response from a language model) and a prompt (assumed to the prompt that generated such a response) and parses it into some structure. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.
+- "Parse with prompt": A method which takes in a string (assumed to be the response from a language model) and a prompt (assumed to be the prompt that generated such a response) and parses it into some structure. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.
 
 ## Get started
 

docs/docs_skeleton/docs/use_cases/question_answering/_category_.yml

@@ -0,0 +1,2 @@
+position: 0
+collapsed: false

docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx

@@ -5,7 +5,7 @@ sidebar_position: 2
 # Store and reference chat history
 The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.
 
-It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question answering chain to return a response.
+It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question-answering chain to return a response.
 
 To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings.
 

docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx

@@ -1,9 +0,0 @@
----
-sidebar_position: 3
----
-
-# Web Scraping
-
-Web scraping has historically been a challenging endeavor due to the ever-changing nature of website structures, making it tedious for developers to maintain their scraping scripts. Traditional methods often rely on specific HTML tags and patterns which, when altered, can disrupt data extraction processes.
-
-Enter the LLM-based method for parsing HTML: By leveraging the capabilities of LLMs, and especially OpenAI Functions in LangChain's extraction chain, developers can instruct the model to extract only the desired data in a specified format. This method not only streamlines the extraction process but also significantly reduces the time spent on manual debugging and script modifications. Its adaptability means that even if websites undergo significant design changes, the extraction remains consistent and robust. This level of resilience translates to reduced maintenance efforts, cost savings, and ensures a higher quality of extracted data. Compared to its predecessors, LLM-based approach wins out the web scraping domain by transforming a historically cumbersome task into a more automated and efficient process.

docs/docs_skeleton/vercel.json

@@ -1076,6 +1076,10 @@
       "source": "/docs/modules/agents/tools/integrations/zapier",
       "destination": "/docs/integrations/tools/zapier"
     },
+    {
+      "source": "/docs/integrations/tools/sqlite",
+      "destination": "/docs/use_cases/sql/sqlite"
+    },
     {
       "source": "/en/latest/modules/callbacks/filecallbackhandler.html",
       "destination": "/docs/modules/callbacks/how_to/filecallbackhandler"
@@ -2216,6 +2220,10 @@
       "source": "/docs/modules/data_connection/text_embedding/integrations/tensorflowhub",
       "destination": "/docs/integrations/text_embedding/tensorflowhub"
     },
+    {
+      "source": "/docs/integrations/text_embedding/Awa",
+      "destination": "/docs/integrations/text_embedding/awadb"
+    },
     {
       "source": "/en/latest/modules/indexes/vectorstores/examples/analyticdb.html",
       "destination": "/docs/integrations/vectorstores/analyticdb"
@@ -3178,7 +3186,11 @@
     },
     {
       "source": "/en/latest/use_cases/tabular.html",
-      "destination": "/docs/use_cases/tabular"
+      "destination": "/docs/use_cases/qa_structured"
+    },
+    {
+      "source": "/docs/use_cases/sql(/?)",
+      "destination": "/docs/use_cases/qa_structured/sql"
     },
     {
       "source": "/en/latest/youtube.html",
@@ -3370,7 +3382,7 @@
     },
     {
       "source": "/docs/modules/chains/popular/sqlite",
-      "destination": "/docs/use_cases/tabular/sqlite"
+      "destination": "/docs/use_cases/qa_structured/sql"
     },
     {
       "source": "/docs/modules/chains/popular/openai_functions",
@@ -3476,6 +3488,14 @@
       "source": "/docs/modules/chains/additional/graph_kuzu_qa",
       "destination": "/docs/use_cases/more/graph/graph_kuzu_qa"
     },
+    {
+      "source": "/docs/use_cases/graph/graph_falkordb_qa",
+      "destination": "/docs/use_cases/more/graph/graph_falkordb_qa"
+    },
+    {
+      "source": "/docs/modules/chains/additional/graph_falkordb_qa",
+      "destination": "/docs/use_cases/more/graph/graph_falkordb_qa"
+    },
     {
       "source": "/docs/use_cases/graph/graph_nebula_qa",
       "destination": "/docs/use_cases/more/graph/graph_nebula_qa"
@@ -3574,7 +3594,7 @@
     },
     {
       "source": "/docs/modules/chains/additional/elasticsearch_database",
-      "destination": "/docs/use_cases/tabular/elasticsearch_database"
+      "destination": "/docs/use_cases/qa_structured/integrations/elasticsearch"
     },
     {
       "source": "/docs/modules/chains/additional/tagging",

docs/extras/_templates/integration.mdx

@@ -47,7 +47,7 @@ from langchain.embeddings import integration_class_REPLACE_ME
 ```
 
 
-## Chat Models
+## Chat models
 
 See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME)
 

docs/extras/additional_resources/youtube.mdx

@@ -1,6 +1,6 @@
 # YouTube videos
 
-⛓ icon marks a new addition [last update 2023-06-20]
+⛓ icon marks a new addition [last update 2023-09-05]
 
 ### [Official LangChain YouTube channel](https://www.youtube.com/@LangChain)
 
@@ -86,20 +86,20 @@
 - [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
 - [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)
-- ⛓ [Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
-- ⛓ [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 [Toolfinder AI](https://www.youtube.com/@toolfinderai)
-- ⛓ [`PrivateGPT`: Chat to your FILES OFFLINE and FREE [Installation and Tutorial]](https://youtu.be/G7iLllmx4qc) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
-- ⛓ [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)
-
+- [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)
+- [Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
+- [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 [Toolfinder AI](https://www.youtube.com/@toolfinderai)
+- [`PrivateGPT`: Chat to your FILES OFFLINE and FREE [Installation and Tutorial]](https://youtu.be/G7iLllmx4qc) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
+- [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)
+- ⛓ [LangChain HowTo and Guides YouTube playlist](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai/)
 
 
 ### [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)

docs/extras/expression_language/cookbook/code_writing.ipynb

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f09fd305",
+   "metadata": {},
+   "source": [
+    "# Code writing\n",
+    "\n",
+    "Example of how to use LCEL to write Python code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bd7c259a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.utilities import PythonREPL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "73795d2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Write some python code to solve the user's problem. \n",
+    "\n",
+    "Return only python code in Markdown format, e.g.:\n",
+    "\n",
+    "```python\n",
+    "....\n",
+    "```\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [(\"system\", template), (\"human\", \"{input}\")]\n",
+    ")\n",
+    "\n",
+    "model = ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "42859e8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _sanitize_output(text: str):\n",
+    "    _, after = text.split(\"```python\")\n",
+    "    return after.split(\"```\")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5ded1a86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = prompt | model | StrOutputParser() | _sanitize_output | PythonREPL().run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "208c2b75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Python REPL can execute arbitrary code. Use with caution.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'4\\n'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"input\": \"whats 2 plus 2\"})"
+   ]
+  }
+ ],
+ "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/expression_language/cookbook/index.mdx

@@ -0,0 +1,11 @@
+---
+sidebar_position: 2
+---
+
+# Cookbook
+
+import DocCardList from "@theme/DocCardList";
+
+Example code for accomplishing common tasks with the LangChain Expression Language (LCEL). These examples show how to compose different Runnable (the core LCEL interface) components to achieve various tasks. If you're just getting acquainted with LCEL, the [Prompt + LLM](/docs/expression_language/cookbook/prompt_llm_parser) page is a good place to start.
+
+<DocCardList />

docs/extras/expression_language/cookbook/memory.ipynb

@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5062941a",
+   "metadata": {},
+   "source": [
+    "# Adding memory\n",
+    "\n",
+    "This shows how to add memory to an arbitrary chain. Right now, you can use the memory classes but need to hook it up manually"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7998efd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.schema.runnable import RunnableMap\n",
+    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "prompt = ChatPromptTemplate.from_messages([\n",
+    "    (\"system\", \"You are a helpful chatbot\"),\n",
+    "    MessagesPlaceholder(variable_name=\"history\"),\n",
+    "    (\"human\", \"{input}\")\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fa0087f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferMemory(return_messages=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06b531ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'history': []}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memory.load_memory_variables({})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d9437af6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = RunnableMap({\n",
+    "    \"input\": lambda x: x[\"input\"],\n",
+    "    \"memory\": memory.load_memory_variables\n",
+    "}) | {\n",
+    "    \"input\": lambda x: x[\"input\"],\n",
+    "    \"history\": lambda x: x[\"memory\"][\"history\"]\n",
+    "} | prompt | model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bed1e260",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = {\"input\": \"hi im bob\"}\n",
+    "response = chain.invoke(inputs)\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "890475b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory.save_context(inputs, {\"output\": response.content})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e8fcb77f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'history': [HumanMessage(content='hi im bob', additional_kwargs={}, example=False),\n",
+       "  AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, example=False)]}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memory.load_memory_variables({})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d837d5c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Your name is Bob.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = {\"input\": \"whats my name\"}\n",
+    "response = chain.invoke(inputs)\n",
+    "response"
+   ]
+  }
+ ],
+ "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/expression_language/cookbook/moderation.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4927a727-b4c8-453c-8c83-bd87b4fcac14",
+   "metadata": {},
+   "source": [
+    "# Adding moderation\n",
+    "\n",
+    "This shows how to add in moderation (or other safeguards) around your LLM application."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4f5f6449-940a-4f5c-97c0-39b71c3e2a68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import OpenAIModerationChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fcb8312b-7e7a-424f-a3ec-76738c9a9d21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moderate = OpenAIModerationChain()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b24b9148-f6b0-4091-8ea8-d3fb281bd950",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = OpenAI()\n",
+    "prompt = ChatPromptTemplate.from_messages([\n",
+    "    (\"system\", \"repeat after me: {input}\")\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1c8ed87c-9ca6-4559-bf60-d40e94a0af08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = prompt | model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5256b9bd-381a-42b0-bfa8-7e6d18f853cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nYou are stupid.'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"input\": \"you are stupid\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fe6e3b33-dc9a-49d5-b194-ba750c58a628",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moderated_chain = chain | moderate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d8ba0cbd-c739-4d23-be9f-6ae092bd5ffb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input': '\\n\\nYou are stupid',\n",
+       " 'output': \"Text was found that violates OpenAI's content policy.\"}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "moderated_chain.invoke({\"input\": \"you are stupid\"})"
+   ]
+  }
+ ],
+ "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/expression_language/cookbook/multiple_chains.ipynb

@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "877102d1-02ea-4fa3-8ec7-a08e242b95b3",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 2\n",
+    "title: Multiple chains\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f2bf8d3",
+   "metadata": {},
+   "source": [
+    "Runnables can easily be used to string together multiple Chains"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d65d4e9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'El país donde se encuentra la ciudad de Honolulu, donde nació Barack Obama, el 44º Presidente de los Estados Unidos, es Estados Unidos. Honolulu se encuentra en la isla de Oahu, en el estado de Hawái.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema import StrOutputParser\n",
+    "\n",
+    "prompt1 = ChatPromptTemplate.from_template(\"what is the city {person} is from?\")\n",
+    "prompt2 = ChatPromptTemplate.from_template(\"what country is the city {city} in? respond in {language}\")\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "\n",
+    "chain1 = prompt1 | model | StrOutputParser()\n",
+    "\n",
+    "chain2 = {\"city\": chain1, \"language\": itemgetter(\"language\")} | prompt2 | model | StrOutputParser()\n",
+    "\n",
+    "chain2.invoke({\"person\": \"obama\", \"language\": \"spanish\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "878f8176",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableMap, RunnablePassthrough\n",
+    "\n",
+    "prompt1 = ChatPromptTemplate.from_template(\"generate a {attribute} color. Return the name of the color and nothing else:\")\n",
+    "prompt2 = ChatPromptTemplate.from_template(\"what is a fruit of color: {color}. Return the name of the fruit and nothing else:\")\n",
+    "prompt3 = ChatPromptTemplate.from_template(\"what is a country with a flag that has the color: {color}. Return the name of the country and nothing else:\")\n",
+    "prompt4 = ChatPromptTemplate.from_template(\"What is the color of {fruit} and the flag of {country}?\")\n",
+    "\n",
+    "model_parser = model | StrOutputParser()\n",
+    "\n",
+    "color_generator = {\"attribute\": RunnablePassthrough()} | prompt1 | {\"color\": model_parser}\n",
+    "color_to_fruit = prompt2 | model_parser\n",
+    "color_to_country = prompt3 | model_parser\n",
+    "question_generator = color_generator | {\"fruit\": color_to_fruit, \"country\": color_to_country} | prompt4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d621a870",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ChatPromptValue(messages=[HumanMessage(content='What is the color of strawberry and the flag of China?', additional_kwargs={}, example=False)])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question_generator.invoke({\"warm\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b4a9812b-bead-4fd9-ae27-0b8be57e5dc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='The color of an apple is typically red or green. The flag of China is predominantly red with a large yellow star in the upper left corner and four smaller yellow stars surrounding it.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt = question_generator.invoke({\"warm\"})\n",
+    "model.invoke(prompt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d75a313-f1c8-4e94-9a17-24e0bf4a2bdc",
+   "metadata": {},
+   "source": [
+    "### Branching and Merging\n",
+    "\n",
+    "You may want the output of one component to be processed by 2 or more other components. [RunnableMaps](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.base.RunnableMap.html) let you split or fork the chain so multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:\n",
+    "\n",
+    "```text\n",
+    "     Input\n",
+    "      / \\\n",
+    "     /   \\\n",
+    " Branch1 Branch2\n",
+    "     \\   /\n",
+    "      \\ /\n",
+    "      Combine\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "247fa0bd-4596-4063-8cb3-1d7fc119d982",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "planner = (\n",
+    "    ChatPromptTemplate.from_template(\n",
+    "        \"Generate an argument about: {input}\"\n",
+    "    )\n",
+    "    | ChatOpenAI()\n",
+    "    | StrOutputParser()\n",
+    "    | {\"base_response\": RunnablePassthrough()}\n",
+    ")\n",
+    "\n",
+    "arguments_for = (\n",
+    "    ChatPromptTemplate.from_template(\n",
+    "        \"List the pros or positive aspects of {base_response}\"\n",
+    "    )\n",
+    "    | ChatOpenAI()\n",
+    "    | StrOutputParser()\n",
+    ")\n",
+    "arguments_against =  (\n",
+    "    ChatPromptTemplate.from_template(\n",
+    "        \"List the cons or negative aspects of {base_response}\"\n",
+    "    )\n",
+    "    | ChatOpenAI()\n",
+    "    | StrOutputParser()\n",
+    ")\n",
+    "\n",
+    "final_responder = (\n",
+    "    ChatPromptTemplate.from_messages(\n",
+    "        [\n",
+    "            (\"ai\", \"{original_response}\"),\n",
+    "            (\"human\", \"Pros:\\n{results_1}\\n\\nCons:\\n{results_2}\"),\n",
+    "            (\"system\", \"Generate a final response given the critique\"),\n",
+    "        ]\n",
+    "    )\n",
+    "    | ChatOpenAI()\n",
+    "    | StrOutputParser()\n",
+    ")\n",
+    "\n",
+    "chain = (\n",
+    "    planner \n",
+    "    | {\n",
+    "        \"results_1\": arguments_for,\n",
+    "        \"results_2\": arguments_against,\n",
+    "        \"original_response\": itemgetter(\"base_response\"),\n",
+    "    }\n",
+    "    | final_responder\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2564f310-0674-4bb1-9c4e-d7848ca73511",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'While Scrum has its potential cons and challenges, many organizations have successfully embraced and implemented this project management framework to great effect. The cons mentioned above can be mitigated or overcome with proper training, support, and a commitment to continuous improvement. It is also important to note that not all cons may be applicable to every organization or project.\\n\\nFor example, while Scrum may be complex initially, with proper training and guidance, teams can quickly grasp the concepts and practices. The lack of predictability can be mitigated by implementing techniques such as velocity tracking and release planning. The limited documentation can be addressed by maintaining a balance between lightweight documentation and clear communication among team members. The dependency on team collaboration can be improved through effective communication channels and regular team-building activities.\\n\\nScrum can be scaled and adapted to larger projects by using frameworks like Scrum of Scrums or LeSS (Large Scale Scrum). Concerns about speed versus quality can be addressed by incorporating quality assurance practices, such as continuous integration and automated testing, into the Scrum process. Scope creep can be managed by having a well-defined and prioritized product backlog, and a strong product owner can be developed through training and mentorship.\\n\\nResistance to change can be overcome by providing proper education and communication to stakeholders and involving them in the decision-making process. Ultimately, the cons of Scrum can be seen as opportunities for growth and improvement, and with the right mindset and support, they can be effectively managed.\\n\\nIn conclusion, while Scrum may have its challenges and potential cons, the benefits and advantages it offers in terms of collaboration, flexibility, adaptability, transparency, and customer satisfaction make it a widely adopted and successful project management framework. With proper implementation and continuous improvement, organizations can leverage Scrum to drive innovation, efficiency, and project success.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"input\": \"scrum\"})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv",
+   "language": "python",
+   "name": "poetry-venv"
+  },
+  "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/extras/expression_language/cookbook/prompt_llm_parser.ipynb

@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "abf7263d-3a62-4016-b5d5-b157f92f2070",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 0\n",
+    "title: Prompt + LLM\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a434f2b-9405-468c-9dfd-254d456b57a6",
+   "metadata": {},
+   "source": [
+    "The most common and valuable composition is taking:\n",
+    "\n",
+    "``PromptTemplate`` / ``ChatPromptTemplate`` -> ``LLM`` / ``ChatModel`` -> ``OutputParser``\n",
+    "\n",
+    "Almost any other chains you build will use this building block."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93aa2c87",
+   "metadata": {},
+   "source": [
+    "## PromptTemplate + LLM\n",
+    "\n",
+    "The simplest composition is just combing a prompt and model to create a chain that takes user input, adds it to a prompt, passes it to a model, and returns the raw model input.\n",
+    "\n",
+    "Note, you can mix and match PromptTemplate/ChatPromptTemplates and LLMs/ChatModels as you like here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "466b65b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {foo}\")\n",
+    "model = ChatOpenAI()\n",
+    "chain = prompt | model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e3d0a6cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"foo\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eb9ef50",
+   "metadata": {},
+   "source": [
+    "Often times we want to attach kwargs that'll be passed to each model call. Here's a few examples of that:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b1d8f88",
+   "metadata": {},
+   "source": [
+    "### Attaching Stop Sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "562a06bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = prompt | model.bind(stop=[\"\\n\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "43f5d04c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Why did the bear never wear shoes?', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"foo\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3eaf88a",
+   "metadata": {},
+   "source": [
+    "### Attaching Function Call information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f94b71b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions = [\n",
+    "    {\n",
+    "      \"name\": \"joke\",\n",
+    "      \"description\": \"A joke\",\n",
+    "      \"parameters\": {\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "          \"setup\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The setup for the joke\"\n",
+    "          },\n",
+    "          \"punchline\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The punchline for the joke\"\n",
+    "          }\n",
+    "        },\n",
+    "        \"required\": [\"setup\", \"punchline\"]\n",
+    "      }\n",
+    "    }\n",
+    "  ]\n",
+    "chain = prompt | model.bind(function_call= {\"name\": \"joke\"}, functions= functions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "decf7710",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'joke', 'arguments': '{\\n  \"setup\": \"Why don\\'t bears wear shoes?\",\\n  \"punchline\": \"Because they have bear feet!\"\\n}'}}, example=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"foo\": \"bears\"}, config={})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9098c5ed",
+   "metadata": {},
+   "source": [
+    "## PromptTemplate + LLM + OutputParser\n",
+    "\n",
+    "We can also add in an output parser to easily trasform the raw LLM/ChatModel output into a more workable format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cc194c78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "chain = prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77acf448",
+   "metadata": {},
+   "source": [
+    "Notice that this now returns a string - a much more workable format for downstream tasks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e3d69a18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"foo\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c01864e5",
+   "metadata": {},
+   "source": [
+    "### Functions Output Parser\n",
+    "\n",
+    "When you specify the function to return, you may just want to parse that directly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ad0dd88e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
+    "\n",
+    "chain = (\n",
+    "    prompt \n",
+    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
+    "    | JsonOutputFunctionsParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1e7aa8eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'setup': \"Why don't bears like fast food?\",\n",
+       " 'punchline': \"Because they can't catch it!\"}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"foo\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d4aa1a01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser\n",
+    "\n",
+    "chain = (\n",
+    "    prompt \n",
+    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
+    "    | JsonKeyOutputFunctionsParser(key_name=\"setup\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8b6df9ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Why don't bears wear shoes?\""
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"foo\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "023fbccb-ef7d-489e-a9ba-f98e17283d51",
+   "metadata": {},
+   "source": [
+    "## Simplifying input\n",
+    "\n",
+    "To make invocation even simpler, we can add a `RunnableMap` to take care of creating the prompt input dict for us:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9601c0f0-71f9-4bd4-a672-7bd04084b018",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableMap, RunnablePassthrough\n",
+    "\n",
+    "map_ = RunnableMap({\"foo\": RunnablePassthrough()})\n",
+    "chain = (\n",
+    "    map_ \n",
+    "    | prompt\n",
+    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
+    "    | JsonKeyOutputFunctionsParser(key_name=\"setup\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7ec4f154-fda5-4847-9220-41aa902fdc33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Why don't bears wear shoes?\""
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"bears\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "def00bfe-0f83-4805-8c8f-8a53f99fa8ea",
+   "metadata": {},
+   "source": [
+    "Since we're composing our map with another Runnable, we can even use some syntactic sugar and just use a dict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7bf3846a-02ee-41a3-ba1b-a708827d4f3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = (\n",
+    "    {\"foo\": RunnablePassthrough()} \n",
+    "    | prompt\n",
+    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
+    "    | JsonKeyOutputFunctionsParser(key_name=\"setup\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e566d6a1-538d-4cb5-a210-a63e082e4c74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Why don't bears like fast food?\""
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"bears\")"
+   ]
+  }
+ ],
+ "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/expression_language/cookbook/retrieval.ipynb

@@ -0,0 +1,461 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "abe47592-909c-4844-bf44-9e55c2fb4bfa",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 1\n",
+    "title: RAG\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91c5ef3d",
+   "metadata": {},
+   "source": [
+    "Let's look at adding in a retrieval step to a prompt and LLM, which adds up to a \"retrieval-augmented generation\" chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f25d9e9-d192-42e9-af50-5660a4bfb0d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain openai faiss-cpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "33be32af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain.vectorstores import FAISS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bfc47ec1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorstore = FAISS.from_texts([\"harrison worked at kensho\"], embedding=OpenAIEmbeddings())\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "\n",
+    "model = ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eae31755",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = (\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()} \n",
+    "    | prompt \n",
+    "    | model \n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f3040b0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Harrison worked at Kensho.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"where did harrison work?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e1d20c7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\n",
+    "Answer in the following language: {language}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "\n",
+    "chain = {\n",
+    "    \"context\": itemgetter(\"question\") | retriever, \n",
+    "    \"question\": itemgetter(\"question\"), \n",
+    "    \"language\": itemgetter(\"language\")\n",
+    "} | prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7ee8b2d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Harrison ha lavorato a Kensho.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"question\": \"where did harrison work\", \"language\": \"italian\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f007669c",
+   "metadata": {},
+   "source": [
+    "## Conversational Retrieval Chain\n",
+    "\n",
+    "We can easily add in conversation history. This primarily means adding in chat_message_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3f30c348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableMap\n",
+    "from langchain.schema import format_document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "64ab1dbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "_template = \"\"\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\n",
+    "\n",
+    "Chat History:\n",
+    "{chat_history}\n",
+    "Follow Up Input: {question}\n",
+    "Standalone question:\"\"\"\n",
+    "CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7d628c97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "ANSWER_PROMPT = ChatPromptTemplate.from_template(template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f60a5d0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template=\"{page_content}\")\n",
+    "def _combine_documents(docs, document_prompt = DEFAULT_DOCUMENT_PROMPT, document_separator=\"\\n\\n\"):\n",
+    "    doc_strings = [format_document(doc, document_prompt) for doc in docs]\n",
+    "    return document_separator.join(doc_strings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7d007db6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Tuple, List\n",
+    "def _format_chat_history(chat_history: List[Tuple]) -> str:\n",
+    "    buffer = \"\"\n",
+    "    for dialogue_turn in chat_history:\n",
+    "        human = \"Human: \" + dialogue_turn[0]\n",
+    "        ai = \"Assistant: \" + dialogue_turn[1]\n",
+    "        buffer += \"\\n\" + \"\\n\".join([human, ai])\n",
+    "    return buffer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5c32cc89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_inputs = RunnableMap(\n",
+    "    {\n",
+    "        \"standalone_question\": {\n",
+    "            \"question\": lambda x: x[\"question\"],\n",
+    "            \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
+    "        } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
+    "    }\n",
+    ")\n",
+    "_context = {\n",
+    "    \"context\": itemgetter(\"standalone_question\") | retriever | _combine_documents,\n",
+    "    \"question\": lambda x: x[\"standalone_question\"]\n",
+    "}\n",
+    "conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "135c8205",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversational_qa_chain.invoke({\n",
+    "    \"question\": \"where did harrison work?\",\n",
+    "    \"chat_history\": [],\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "424e7e7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Harrison worked at Kensho.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversational_qa_chain.invoke({\n",
+    "    \"question\": \"where did he work?\",\n",
+    "    \"chat_history\": [(\"Who wrote this notebook?\", \"Harrison\")],\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5543183",
+   "metadata": {},
+   "source": [
+    "### With Memory and returning source documents\n",
+    "\n",
+    "This shows how to use memory with the above. For memory, we need to manage that outside at the memory. For returning the retrieved documents, we just need to pass them through all the way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e31dd17c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import ConversationBufferMemory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d4bffe94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferMemory(return_messages=True, output_key=\"answer\", input_key=\"question\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "733be985",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we add a step to load memory\n",
+    "# This needs to be a RunnableMap because its the first input\n",
+    "loaded_memory = RunnableMap(\n",
+    "    {\n",
+    "        \"question\": itemgetter(\"question\"),\n",
+    "        \"memory\": memory.load_memory_variables,\n",
+    "    }\n",
+    ")\n",
+    "# Next we add a step to expand memory into the variables\n",
+    "expanded_memory = {\n",
+    "    \"question\": itemgetter(\"question\"),\n",
+    "    \"chat_history\": lambda x: x[\"memory\"][\"history\"]\n",
+    "}\n",
+    "\n",
+    "# Now we calculate the standalone question\n",
+    "standalone_question = {\n",
+    "    \"standalone_question\": {\n",
+    "        \"question\": lambda x: x[\"question\"],\n",
+    "        \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
+    "    } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
+    "}\n",
+    "# Now we retrieve the documents\n",
+    "retrieved_documents = {\n",
+    "    \"docs\": itemgetter(\"standalone_question\") | retriever,\n",
+    "    \"question\": lambda x: x[\"standalone_question\"]\n",
+    "}\n",
+    "# Now we construct the inputs for the final prompt\n",
+    "final_inputs = {\n",
+    "    \"context\": lambda x: _combine_documents(x[\"docs\"]),\n",
+    "    \"question\": itemgetter(\"question\")\n",
+    "}\n",
+    "# And finally, we do the part that returns the answers\n",
+    "answer = {\n",
+    "    \"answer\": final_inputs | ANSWER_PROMPT | ChatOpenAI(),\n",
+    "    \"docs\": itemgetter(\"docs\"),\n",
+    "}\n",
+    "# And now we put it all together!\n",
+    "final_chain = loaded_memory | expanded_memory | standalone_question | retrieved_documents | answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "806e390c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False),\n",
+       " 'docs': [Document(page_content='harrison worked at kensho', metadata={})]}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = {\"question\": \"where did harrison work?\"}\n",
+    "result = final_chain.invoke(inputs)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "977399fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that the memory does not save automatically\n",
+    "# This will be improved in the future\n",
+    "# For now you need to save it yourself\n",
+    "memory.save_context(inputs, {\"answer\": result[\"answer\"].content})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f94f7de4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'history': [HumanMessage(content='where did harrison work?', additional_kwargs={}, example=False),\n",
+       "  AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)]}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memory.load_memory_variables({})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv",
+   "language": "python",
+   "name": "poetry-venv"
+  },
+  "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/extras/expression_language/cookbook/sql_db.ipynb

@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "c14da114-1a4a-487d-9cff-e0e8c30ba366",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 3\n",
+    "title: Querying a SQL DB\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "506e9636",
+   "metadata": {},
+   "source": [
+    "We can replicate our SQLDatabaseChain with Runnables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7a927516",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "\n",
+    "template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
+    "{schema}\n",
+    "\n",
+    "Question: {question}\n",
+    "SQL Query:\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f51f386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import SQLDatabase"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c3449d6-684b-416e-ba16-90a035835a88",
+   "metadata": {},
+   "source": [
+    "We'll need the Chinook sample DB for this example. There's many places to download it from, e.g. https://database.guide/2-sample-databases-sqlite/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2ccca6fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(\"sqlite:///./Chinook.db\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "05ba88ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_schema(_):\n",
+    "    return db.get_table_info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a4eda902",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_query(query):\n",
+    "    return db.run(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5046cb17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnableLambda, RunnableMap\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "\n",
+    "inputs = {\n",
+    "    \"schema\": RunnableLambda(get_schema),\n",
+    "    \"question\": itemgetter(\"question\")\n",
+    "}\n",
+    "sql_response = (\n",
+    "        RunnableMap(inputs)\n",
+    "        | prompt\n",
+    "        | model.bind(stop=[\"\\nSQLResult:\"])\n",
+    "        | StrOutputParser()\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a5552039",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT COUNT(*) FROM Employee'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_response.invoke({\"question\": \"How many employees are there?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d6fee130",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
+    "{schema}\n",
+    "\n",
+    "Question: {question}\n",
+    "SQL Query: {query}\n",
+    "SQL Response: {response}\"\"\"\n",
+    "prompt_response = ChatPromptTemplate.from_template(template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "923aa634",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_chain = (\n",
+    "    RunnableMap({\n",
+    "        \"question\": itemgetter(\"question\"),\n",
+    "        \"query\": sql_response,\n",
+    "    }) \n",
+    "    | {\n",
+    "        \"schema\": RunnableLambda(get_schema),\n",
+    "        \"question\": itemgetter(\"question\"),\n",
+    "        \"query\": itemgetter(\"query\"),\n",
+    "        \"response\": lambda x: db.run(x[\"query\"])    \n",
+    "    } \n",
+    "    | prompt_response \n",
+    "    | model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e94963d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='There are 8 employees.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_chain.invoke({\"question\": \"How many employees are there?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f358d7b-a721-4db3-9f92-f06913428afc",
+   "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/expression_language/cookbook/tools.ipynb

@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "29781123",
+   "metadata": {},
+   "source": [
+    "# Using tools\n",
+    "\n",
+    "You can use any Tools with Runnables easily."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5c579dd-2e22-41b0-a789-346dfdecb5a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install duckduckgo-search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9232d2a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.tools import DuckDuckGoSearchRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a0c64d2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = DuckDuckGoSearchRun()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "391969b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"turn the following user input into a search query for a search engine:\n",
+    "\n",
+    "{input}\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "\n",
+    "model = ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e3d9d20d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = prompt | model | StrOutputParser() | search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "55f2967d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'What sports games are on TV today & tonight? Watch and stream live sports on TV today, tonight, tomorrow. Today\\'s 2023 sports TV schedule includes football, basketball, baseball, hockey, motorsports, soccer and more. Watch on TV or stream online on ESPN, FOX, FS1, CBS, NBC, ABC, Peacock, Paramount+, fuboTV, local channels and many other networks. MLB Games Tonight: How to Watch on TV, Streaming & Odds - Thursday, September 7. Seattle Mariners\\' Julio Rodriguez greets teammates in the dugout after scoring against the Oakland Athletics in a ... Circle - Country Music and Lifestyle. Live coverage of all the MLB action today is available to you, with the information provided below. The Brewers will look to pick up a road win at PNC Park against the Pirates on Wednesday at 12:35 PM ET. Check out the latest odds and with BetMGM Sportsbook. Use bonus code \"GNPLAY\" for special offers! MLB Games Tonight: How to Watch on TV, Streaming & Odds - Tuesday, September 5. Houston Astros\\' Kyle Tucker runs after hitting a double during the fourth inning of a baseball game against the Los Angeles Angels, Sunday, Aug. 13, 2023, in Houston. (AP Photo/Eric Christian Smith) (APMedia) The Houston Astros versus the Texas Rangers is one of ... The second half of tonight\\'s college football schedule still has some good games remaining to watch on your television.. We\\'ve already seen an exciting one when Colorado upset TCU. And we saw some ...'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"input\": \"I'd like to figure out what games are tonight\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a16949cf-00ea-43c6-a6aa-797ad4f6918d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv",
+   "language": "python",
+   "name": "poetry-venv"
+  },
+  "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/extras/expression_language/how_to/_category_.yml

@@ -0,0 +1,2 @@
+label: 'How to'
+position: 1

docs/extras/expression_language/how_to/functions.ipynb

@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbc4bf6e",
+   "metadata": {},
+   "source": [
+    "# Run arbitrary functions\n",
+    "\n",
+    "You can use arbitrary functions in the pipeline\n",
+    "\n",
+    "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "6bb221b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableLambda\n",
+    "\n",
+    "def length_function(text):\n",
+    "    return len(text)\n",
+    "\n",
+    "def _multiple_length_function(text1, text2):\n",
+    "    return len(text1) * len(text2)\n",
+    "\n",
+    "def multiple_length_function(_dict):\n",
+    "    return _multiple_length_function(_dict[\"text1\"], _dict[\"text2\"])\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\"what is {a} + {b}\")\n",
+    "\n",
+    "chain1 = prompt | model\n",
+    "\n",
+    "chain = {\n",
+    "    \"a\": itemgetter(\"foo\") | RunnableLambda(length_function),\n",
+    "    \"b\": {\"text1\": itemgetter(\"foo\"), \"text2\": itemgetter(\"bar\")} | RunnableLambda(multiple_length_function)\n",
+    "} | prompt | model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "5488ec85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='3 + 9 equals 12.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"foo\": \"bar\", \"bar\": \"gah\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4728ddd9-914d-42ce-ae9b-72c9ce8ec940",
+   "metadata": {},
+   "source": [
+    "## Accepting a Runnable Config\n",
+    "\n",
+    "Runnable lambdas can optionally accept a [RunnableConfig](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.config.RunnableConfig.html?highlight=runnableconfig#langchain.schema.runnable.config.RunnableConfig), which they can use to pass callbacks, tags, and other configuration information to nested runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "80b3b5f6-5d58-44b9-807e-cce9a46bf49f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "ff0daf0c-49dd-4d21-9772-e5fa133c5f36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "def parse_or_fix(text: str, config: RunnableConfig):\n",
+    "    fixing_chain = (\n",
+    "        ChatPromptTemplate.from_template(\n",
+    "            \"Fix the following text:\\n\\n```text\\n{input}\\n```\\nError: {error}\"\n",
+    "            \" Don't narrate, just respond with the fixed data.\"\n",
+    "        )\n",
+    "        | ChatOpenAI()\n",
+    "        | StrOutputParser()\n",
+    "    )\n",
+    "    for _ in range(3):\n",
+    "        try:\n",
+    "            return json.loads(text)\n",
+    "        except Exception as e:\n",
+    "            text = fixing_chain.invoke({\"input\": text, \"error\": e}, config)\n",
+    "    return \"Failed to parse\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "1a5e709e-9d75-48c7-bb9c-503251990505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tokens Used: 65\n",
+      "\tPrompt Tokens: 56\n",
+      "\tCompletion Tokens: 9\n",
+      "Successful Requests: 1\n",
+      "Total Cost (USD): $0.00010200000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.callbacks import get_openai_callback\n",
+    "\n",
+    "with get_openai_callback() as cb:\n",
+    "    RunnableLambda(parse_or_fix).invoke(\"{foo: bar}\", {\"tags\": [\"my-tag\"], \"callbacks\": [cb]})\n",
+    "    print(cb)"
+   ]
+  }
+ ],
+ "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/expression_language/interface.ipynb

@@ -1,12 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "366a0e68-fd67-4fe5-a292-5c33733339ea",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 0\n",
+    "title: Interface\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9a9acd2e",
    "metadata": {},
    "source": [
-    "# Interface\n",
-    "\n",
     "In an effort to make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.Runnable.html#langchain.schema.runnable.Runnable) protocol that most components implement. This is a standard interface with a few different methods, which makes it easy to define custom chains as well as making it possible to invoke them in a standard way. The standard interface exposed includes:\n",
     "\n",
     "- `stream`: stream back chunks of the response\n",
@@ -429,7 +438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/extras/guides/debugging.md

@@ -2,7 +2,7 @@
 
 If you're building with LLMs, at some point something will break, and you'll need to debug. A model call will fail, or the model output will be misformatted, or there will be some nested model calls and it won't be clear where along the way an incorrect output was created.
 
-Here's a few different tools and functionalities to aid in debugging.
+Here are a few different tools and functionalities to aid in debugging.
 
 
 
@@ -18,9 +18,9 @@ For anyone building production-grade LLM applications, we highly recommend using
 
 If you're prototyping in Jupyter Notebooks or running Python scripts, it can be helpful to print out the intermediate steps of a Chain run. 
 
-There's a number of ways to enable printing at varying degrees of verbosity.
+There are a number of ways to enable printing at varying degrees of verbosity.
 
-Let's suppose we have a simple agent and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see:
+Let's suppose we have a simple agent, and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see:
 
 
 ```python

docs/extras/guides/deployments/template_repos.mdx

@@ -14,7 +14,7 @@ It also contains instructions for how to deploy this app on the Streamlit platfo
 
 ## [Gradio (on Hugging Face)](https://github.com/hwchase17/langchain-gradio-template)
 
-This repo serves as a template for how deploy a LangChain with Gradio.
+This repo serves as a template for how to deploy a LangChain with Gradio.
 It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills).
 It also contains instructions for how to deploy this app on the Hugging Face platform.
 This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
@@ -27,7 +27,7 @@ Chainlit [doc](https://docs.chainlit.io/langchain) on the integration with LangC
 
 ## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
 
-This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
+This repo serves as a template for how to deploy a LangChain with [Beam](https://beam.cloud).
 
 It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API.
 
@@ -49,7 +49,7 @@ A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using
 
 ## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
 
-A minimal example on how to deploy LangChain to DigitalOcean App Platform.
+A minimal example of how to deploy LangChain to DigitalOcean App Platform.
 
 ## [CI/CD Google Cloud Build + Dockerfile + Serverless Google Cloud Run](https://github.com/g-emarco/github-assistant)
 
@@ -57,7 +57,7 @@ Boilerplate LangChain project on how to deploy to Google Cloud Run using Docker
 
 ## [Google Cloud Run](https://github.com/homanp/gcp-langchain)
 
-A minimal example on how to deploy LangChain to Google Cloud Run.
+A minimal example of how to deploy LangChain to Google Cloud Run.
 
 ## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
 
@@ -82,4 +82,4 @@ These templates serve as examples of how to build, deploy, and share LangChain a
 
 ## [AzureML Online Endpoint](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/llm/langchain/1_langchain_basic_deploy.ipynb)
 
-A minimal example of how to deploy LangChain to an Azure Machine Learning Online Endpoint. 
+A minimal example of how to deploy LangChain to an Azure Machine Learning Online Endpoint. 

docs/extras/guides/local_llms.ipynb

@@ -146,7 +146,7 @@
    "source": [
     "## Environment\n",
     "\n",
-    "Inference speed is a chllenge when running models locally (see above).\n",
+    "Inference speed is a challenge when running models locally (see above).\n",
     "\n",
     "To minimize latency, it is desiable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n",
     "\n",
@@ -264,88 +264,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pip install llama-cpp-python"
+    "CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dirclear"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "9d5f94b5",
+   "execution_count": null,
+   "id": "a88bf0c8-e989-4bcd-bcb7-4d7757e684f2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "objc[10142]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x2a0c4c208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x2c28bc208). One of the two will be used. Which one is undefined.\n",
-      "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
-      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
-      "llama_model_load_internal: n_vocab    = 32000\n",
-      "llama_model_load_internal: n_ctx      = 2048\n",
-      "llama_model_load_internal: n_embd     = 5120\n",
-      "llama_model_load_internal: n_mult     = 256\n",
-      "llama_model_load_internal: n_head     = 40\n",
-      "llama_model_load_internal: n_layer    = 40\n",
-      "llama_model_load_internal: n_rot      = 128\n",
-      "llama_model_load_internal: freq_base  = 10000.0\n",
-      "llama_model_load_internal: freq_scale = 1\n",
-      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
-      "llama_model_load_internal: n_ff       = 13824\n",
-      "llama_model_load_internal: model size = 13B\n",
-      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
-      "llama_model_load_internal: mem required  = 8953.71 MB (+ 1608.00 MB per state)\n",
-      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
-      "ggml_metal_init: allocating\n",
-      "ggml_metal_init: using MPS\n",
-      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
-      "ggml_metal_init: loaded kernel_add                            0x47774af60\n",
-      "ggml_metal_init: loaded kernel_mul                            0x47774bc00\n",
-      "ggml_metal_init: loaded kernel_mul_row                        0x47774c230\n",
-      "ggml_metal_init: loaded kernel_scale                          0x47774c890\n",
-      "ggml_metal_init: loaded kernel_silu                           0x47774cef0\n",
-      "ggml_metal_init: loaded kernel_relu                           0x10e33e500\n",
-      "ggml_metal_init: loaded kernel_gelu                           0x47774b2f0\n",
-      "ggml_metal_init: loaded kernel_soft_max                       0x47771a580\n",
-      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x47774dab0\n",
-      "ggml_metal_init: loaded kernel_get_rows_f16                   0x47774e110\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x47774e7d0\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x13efd7170\n",
-      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x13efd73d0\n",
-      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x13efd7630\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x13efd7890\n",
-      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x4744c9740\n",
-      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x4744ca6b0\n",
-      "ggml_metal_init: loaded kernel_rms_norm                       0x4744cb250\n",
-      "ggml_metal_init: loaded kernel_norm                           0x4744cb970\n",
-      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x10e33f700\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x10e33fcd0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x4744cc2d0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x4744cc6f0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x4744cd6b0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x4744cde20\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x10e33ff30\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x10e340190\n",
-      "ggml_metal_init: loaded kernel_rope                           0x10e3403f0\n",
-      "ggml_metal_init: loaded kernel_alibi_f32                      0x10e340de0\n",
-      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x10e3416d0\n",
-      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x10e342080\n",
-      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x10e342ca0\n",
-      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
-      "ggml_metal_init: hasUnifiedMemory             = true\n",
-      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
-      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, ( 6986.19 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1032.00 MB, ( 8018.19 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, ( 9620.19 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   426.00 MB, (10046.19 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (10558.19 / 21845.34)\n",
-      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain.llms import LlamaCpp\n",
     "llm = LlamaCpp(\n",
-    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",
     "    n_gpu_layers=1,\n",
     "    n_batch=512,\n",
     "    n_ctx=2048,\n",
@@ -448,87 +379,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "b55a2147",
+   "execution_count": null,
+   "id": "915ecd4c-8f6b-4de3-a787-b64cb7c682b4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found model file at  /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
-      "llama_new_context_with_model: max tensor size =    87.89 MB\n",
-      "llama_new_context_with_model: max tensor size =    87.89 MB\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "llama.cpp: using Metal\n",
-      "llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
-      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
-      "llama_model_load_internal: n_vocab    = 32001\n",
-      "llama_model_load_internal: n_ctx      = 2048\n",
-      "llama_model_load_internal: n_embd     = 5120\n",
-      "llama_model_load_internal: n_mult     = 256\n",
-      "llama_model_load_internal: n_head     = 40\n",
-      "llama_model_load_internal: n_layer    = 40\n",
-      "llama_model_load_internal: n_rot      = 128\n",
-      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
-      "llama_model_load_internal: n_ff       = 13824\n",
-      "llama_model_load_internal: n_parts    = 1\n",
-      "llama_model_load_internal: model size = 13B\n",
-      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
-      "llama_model_load_internal: mem required  = 9031.71 MB (+ 1608.00 MB per state)\n",
-      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
-      "ggml_metal_init: allocating\n",
-      "ggml_metal_init: using MPS\n",
-      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/ggml-metal.metal'\n",
-      "ggml_metal_init: loaded kernel_add                            0x37944d850\n",
-      "ggml_metal_init: loaded kernel_mul                            0x37944f350\n",
-      "ggml_metal_init: loaded kernel_mul_row                        0x37944fdd0\n",
-      "ggml_metal_init: loaded kernel_scale                          0x3794505a0\n",
-      "ggml_metal_init: loaded kernel_silu                           0x379450800\n",
-      "ggml_metal_init: loaded kernel_relu                           0x379450a60\n",
-      "ggml_metal_init: loaded kernel_gelu                           0x379450cc0\n",
-      "ggml_metal_init: loaded kernel_soft_max                       0x379450ff0\n",
-      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x379451250\n",
-      "ggml_metal_init: loaded kernel_get_rows_f16                   0x3794514b0\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x379451710\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x379451970\n",
-      "ggml_metal_init: loaded kernel_get_rows_q2_k                  0x379451bd0\n",
-      "ggml_metal_init: loaded kernel_get_rows_q3_k                  0x379451e30\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_k                  0x379452090\n",
-      "ggml_metal_init: loaded kernel_get_rows_q5_k                  0x3794522f0\n",
-      "ggml_metal_init: loaded kernel_get_rows_q6_k                  0x379452550\n",
-      "ggml_metal_init: loaded kernel_rms_norm                       0x3794527b0\n",
-      "ggml_metal_init: loaded kernel_norm                           0x379452a10\n",
-      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x379452c70\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x379452ed0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x379453130\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q2_k_f32               0x379453390\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q3_k_f32               0x3794535f0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_k_f32               0x379453850\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q5_k_f32               0x379453ab0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q6_k_f32               0x379453d10\n",
-      "ggml_metal_init: loaded kernel_rope                           0x379453f70\n",
-      "ggml_metal_init: loaded kernel_alibi_f32                      0x3794541d0\n",
-      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x379454430\n",
-      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x379454690\n",
-      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x3794548f0\n",
-      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
-      "ggml_metal_init: hasUnifiedMemory             = true\n",
-      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
-      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, (17542.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1024.00 MB, (18566.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, (20168.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   512.00 MB, (20680.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (21192.94 / 21845.34)\n",
-      "ggml_metal_free: deallocating\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain.llms import GPT4All\n",
     "llm = GPT4All(model=\"/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\")"
@@ -564,89 +418,21 @@
     "\n",
     "Some LLMs will benefit from specific prompts.\n",
     "\n",
-    "For example, llama2 can use [special tokens](https://twitter.com/RLanceMartin/status/1681879318493003776?s=20).\n",
+    "For example, LLaMA will use [special tokens](https://twitter.com/RLanceMartin/status/1681879318493003776?s=20).\n",
     "\n",
     "We can use `ConditionalPromptSelector` to set prompt based on the model type."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "d082b10a",
+   "execution_count": null,
+   "id": "16759b7c-7903-4269-b7b4-f83b313d8091",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
-      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
-      "llama_model_load_internal: n_vocab    = 32000\n",
-      "llama_model_load_internal: n_ctx      = 2048\n",
-      "llama_model_load_internal: n_embd     = 5120\n",
-      "llama_model_load_internal: n_mult     = 256\n",
-      "llama_model_load_internal: n_head     = 40\n",
-      "llama_model_load_internal: n_layer    = 40\n",
-      "llama_model_load_internal: n_rot      = 128\n",
-      "llama_model_load_internal: freq_base  = 10000.0\n",
-      "llama_model_load_internal: freq_scale = 1\n",
-      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
-      "llama_model_load_internal: n_ff       = 13824\n",
-      "llama_model_load_internal: model size = 13B\n",
-      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
-      "llama_model_load_internal: mem required  = 8953.71 MB (+ 1608.00 MB per state)\n",
-      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
-      "ggml_metal_init: allocating\n",
-      "ggml_metal_init: using MPS\n",
-      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
-      "ggml_metal_init: loaded kernel_add                            0x4744d09d0\n",
-      "ggml_metal_init: loaded kernel_mul                            0x3781cb3d0\n",
-      "ggml_metal_init: loaded kernel_mul_row                        0x37813bb60\n",
-      "ggml_metal_init: loaded kernel_scale                          0x474481080\n",
-      "ggml_metal_init: loaded kernel_silu                           0x4744d29f0\n",
-      "ggml_metal_init: loaded kernel_relu                           0x3781254c0\n",
-      "ggml_metal_init: loaded kernel_gelu                           0x47447f280\n",
-      "ggml_metal_init: loaded kernel_soft_max                       0x4744cf470\n",
-      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x4744cf6d0\n",
-      "ggml_metal_init: loaded kernel_get_rows_f16                   0x4744cf930\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x4744cfb90\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x4744cfdf0\n",
-      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x4744d0050\n",
-      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x4744ce980\n",
-      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x4744cebe0\n",
-      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x4744cee40\n",
-      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x4744cf0a0\n",
-      "ggml_metal_init: loaded kernel_rms_norm                       0x474482450\n",
-      "ggml_metal_init: loaded kernel_norm                           0x4744826b0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x474482910\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x474482b70\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x474482dd0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x474483030\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x474483290\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x4744834f0\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x474483750\n",
-      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x4744839b0\n",
-      "ggml_metal_init: loaded kernel_rope                           0x474483c10\n",
-      "ggml_metal_init: loaded kernel_alibi_f32                      0x474483e70\n",
-      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x4744840d0\n",
-      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x474484330\n",
-      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x474484590\n",
-      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
-      "ggml_metal_init: hasUnifiedMemory             = true\n",
-      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
-      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, ( 6986.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1032.00 MB, ( 8018.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, ( 9620.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   426.00 MB, (10046.94 / 21845.34)\n",
-      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (10558.94 / 21845.34)\n",
-      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Set our LLM\n",
     "llm = LlamaCpp(\n",
-    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",
     "    n_gpu_layers=1,\n",
     "    n_batch=512,\n",
     "    n_ctx=2048,\n",
@@ -661,7 +447,7 @@
    "id": "66656084",
    "metadata": {},
    "source": [
-    "Set the associated prompt."
+    "Set the associated prompt based upon the model version."
    ]
   },
   {
@@ -759,6 +545,18 @@
     "llm_chain.run({\"question\":question})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6e0d37e7-f1d9-4848-bf2c-c22392ee141f",
+   "metadata": {},
+   "source": [
+    "We also can use the LangChain Prompt Hub to fetch and / or store prompts that are model specific.\n",
+    "\n",
+    "This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n",
+    "\n",
+    "For example, [here](https://smith.langchain.com/hub/rlm/rag-prompt-llama) is a prompt for RAG with LLaMA-specific tokens."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6ba66260",
@@ -770,16 +568,12 @@
     "\n",
     "For example, here is a guide to [RAG](docs/use_cases/question_answering/how_to/local_retrieval_qa) with local LLMs.\n",
     "\n",
-    "In general, use cases for local model can be driven by at least two factors:\n",
+    "In general, use cases for local LLMs can be driven by at least two factors:\n",
     "\n",
     "* `Privacy`: private data (e.g., journals, etc) that a user does not want to share \n",
     "* `Cost`: text preprocessing (extraction/tagging), summarization, and agent simulations are token-use-intensive tasks\n",
     "\n",
-    "There are a few approach to support specific use-cases: \n",
-    "\n",
-    "* Fine-tuning (e.g., [gpt-llm-trainer](https://github.com/mshumer/gpt-llm-trainer), [Anyscale](https://www.anyscale.com/blog/fine-tuning-llama-2-a-comprehensive-case-study-for-tailoring-models-to-unique-applications)) \n",
-    "* [Function-calling](https://github.com/MeetKai/functionary/tree/main) for use-cases like extraction or tagging\n",
-    "\n"
+    "In addition, [here](https://blog.langchain.dev/using-langsmith-to-support-fine-tuning-of-open-source-llms/) is an overview on fine-tuning, which can utilize open source LLMs."
    ]
   }
  ],

docs/extras/guides/privacy/presidio_data_anonymization/index.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Data anonymization with Microsoft Presidio\n",
     "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/guides/privacy/presidio_data_anonymization.ipynb)\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/guides/privacy/presidio_data_anonymization/index.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,16 +47,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'My name is Mrs. Rachel Chen DDS, call me at 849-829-7628x073 or email me at christopherfrey@example.org'"
+       "'My name is Laura Ruiz, call me at +1-412-982-8374x13414 or email me at javierwatkins@example.net'"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,35 +94,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = f\"\"\"Slim Shady recently lost his wallet. \n",
+    "Inside is some cash and his credit card with the number 4916 0387 9536 0861. \n",
+    "If you would find it, please call at 313-666-7440 or write an email here: real.slim.shady@gmail.com.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='You can find our super secret data at https://www.ross.com/', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dear Sir/Madam,\n",
+      "\n",
+      "We regret to inform you that Richard Fields has recently misplaced his wallet, which contains a sum of cash and his credit card bearing the number 30479847307774. \n",
+      "\n",
+      "Should you happen to come across it, we kindly request that you contact us immediately at 6439182672 or via email at frank45@example.com.\n",
+      "\n",
+      "Thank you for your attention to this matter.\n",
+      "\n",
+      "Yours faithfully,\n",
+      "\n",
+      "[Your Name]\n"
+     ]
     }
    ],
    "source": [
     "from langchain.prompts.prompt import PromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
     "\n",
-    "template = \"\"\"According to this text, where can you find our super secret data?\n",
+    "anonymizer = PresidioAnonymizer()\n",
     "\n",
-    "{anonymized_text}\n",
+    "template = \"\"\"Rewrite this text into an official, short email:\n",
     "\n",
-    "Answer:\"\"\"\n",
+    "{anonymized_text}\"\"\"\n",
     "prompt = PromptTemplate.from_template(template)\n",
-    "llm = ChatOpenAI()\n",
+    "llm = ChatOpenAI(temperature=0)\n",
     "\n",
     "chain = {\"anonymized_text\": anonymizer.anonymize} | prompt | llm\n",
-    "chain.invoke(\"You can find our super secret data at https://supersecretdata.com\")"
+    "response = chain.invoke(text)\n",
+    "print(response.content)"
    ]
   },
   {
@@ -135,16 +153,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'My name is Gabrielle Edwards, call me at 313-666-7440 or email me at real.slim.shady@gmail.com'"
+       "'My name is Adrian Fleming, call me at 313-666-7440 or email me at real.slim.shady@gmail.com'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -166,16 +184,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'My name is Victoria Mckinney, call me at 713-549-8623 or email me at real.slim.shady@gmail.com'"
+       "'My name is Justin Miller, call me at 761-824-1889 or email me at real.slim.shady@gmail.com'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -201,16 +219,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'My name is Billy Russo, call me at 970-996-9453x038 or email me at jamie80@example.org'"
+       "'My name is Dr. Jennifer Baker, call me at (508)839-9329x232 or email me at ehamilton@example.com'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -232,16 +250,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'My polish phone number is EVIA70648911396944'"
+       "'My polish phone number is NRGN41434238921378'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -261,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -337,16 +355,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'+48 533 220 543'"
+       "'511 622 683'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -374,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,16 +416,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'My polish phone number is +48 692 715 636'"
+       "'My polish phone number is +48 734 630 977'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -421,8 +439,6 @@
    "metadata": {},
    "source": [
     "## Future works\n",
-    "\n",
-    "- **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data.\n",
     "- **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object."
    ]
   }

docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb

@@ -0,0 +1,520 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mutli-language data anonymization with Microsoft Presidio\n",
+    "\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb)\n",
+    "\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "Multi-language support in data pseudonymization is essential due to differences in language structures and cultural contexts. Different languages may have varying formats for personal identifiers. For example, the structure of names, locations and dates can differ greatly between languages and regions. Furthermore, non-alphanumeric characters, accents, and the direction of writing can impact pseudonymization processes. Without multi-language support, data could remain identifiable or be misinterpreted, compromising data privacy and accuracy. Hence, it enables effective and precise pseudonymization suited for global operations.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "PII detection in Microsoft Presidio relies on several components - in addition to the usual pattern matching (e.g. using regex), the analyser uses a model for Named Entity Recognition (NER) to extract entities such as:\n",
+    "- `PERSON`\n",
+    "- `LOCATION`\n",
+    "- `DATE_TIME`\n",
+    "- `NRP`\n",
+    "- `ORGANIZATION`\n",
+    "\n",
+    "[[Source]](https://github.com/microsoft/presidio/blob/main/presidio-analyzer/presidio_analyzer/predefined_recognizers/spacy_recognizer.py)\n",
+    "\n",
+    "To handle NER in specific languages, we utilize unique models from the `spaCy` library, recognized for its extensive selection covering multiple languages and sizes. However, it's not restrictive, allowing for integration of alternative frameworks such as [Stanza](https://microsoft.github.io/presidio/analyzer/nlp_engines/spacy_stanza/) or [transformers](https://microsoft.github.io/presidio/analyzer/nlp_engines/transformers/) when necessary.\n",
+    "\n",
+    "\n",
+    "## Quickstart\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install necessary packages\n",
+    "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
+    "# ! python -m spacy download en_core_web_lg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer\n",
+    "\n",
+    "anonymizer = PresidioReversibleAnonymizer(\n",
+    "    analyzed_fields=[\"PERSON\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, `PresidioAnonymizer` and `PresidioReversibleAnonymizer` use a model trained on English texts, so they handle other languages moderately well. \n",
+    "\n",
+    "For example, here the model did not detect the person:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Me llamo Sofía'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer.anonymize(\"Me llamo Sofía\")  # \"My name is Sofía\" in Spanish"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They may also take words from another language as actual entities. Here, both the word *'Yo'* (*'I'* in Spanish) and *Sofía* have been classified as `PERSON`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Bridget Kirk soy Sally Knight'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer.anonymize(\"Yo soy Sofía\")  # \"I am Sofía\" in Spanish"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to anonymise texts from other languages, you need to download other models and add them to the anonymiser configuration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download the models for the languages you want to use\n",
+    "# ! python -m spacy download en_core_web_md\n",
+    "# ! python -m spacy download es_core_news_md"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nlp_config = {\n",
+    "    \"nlp_engine_name\": \"spacy\",\n",
+    "    \"models\": [\n",
+    "        {\"lang_code\": \"en\", \"model_name\": \"en_core_web_md\"},\n",
+    "        {\"lang_code\": \"es\", \"model_name\": \"es_core_news_md\"},\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have therefore added a Spanish language model. Note also that we have downloaded an alternative model for English as well - in this case we have replaced the large model `en_core_web_lg` (560MB) with its smaller version `en_core_web_md` (40MB) - the size is therefore reduced by 14 times! If you care about the speed of anonymisation, it is worth considering it.\n",
+    "\n",
+    "All models for the different languages can be found in the [spaCy documentation](https://spacy.io/usage/models).\n",
+    "\n",
+    "Now pass the configuration as the `languages_config` parameter to Anonymiser. As you can see, both previous examples work flawlessly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Me llamo Michelle Smith\n",
+      "Yo soy Rachel Wright\n"
+     ]
+    }
+   ],
+   "source": [
+    "anonymizer = PresidioReversibleAnonymizer(\n",
+    "    analyzed_fields=[\"PERSON\"],\n",
+    "    languages_config=nlp_config,\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    anonymizer.anonymize(\"Me llamo Sofía\", language=\"es\")\n",
+    ")  # \"My name is Sofía\" in Spanish\n",
+    "print(anonymizer.anonymize(\"Yo soy Sofía\", language=\"es\"))  # \"I am Sofía\" in Spanish"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the language indicated first in the configuration will be used when anonymising text (in this case English):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is Ronnie Ayala\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(anonymizer.anonymize(\"My name is John\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced usage\n",
+    "\n",
+    "### Custom labels in NER model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It may be that the spaCy model has different class names than those supported by the Microsoft Presidio by default. Take Polish, for example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Text: Wiktoria, Start: 12, End: 20, Label: persName\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ! python -m spacy download pl_core_news_md\n",
+    "\n",
+    "import spacy\n",
+    "\n",
+    "nlp = spacy.load(\"pl_core_news_md\")\n",
+    "doc = nlp(\"Nazywam się Wiktoria\")  # \"My name is Wiktoria\" in Polish\n",
+    "\n",
+    "for ent in doc.ents:\n",
+    "    print(\n",
+    "        f\"Text: {ent.text}, Start: {ent.start_char}, End: {ent.end_char}, Label: {ent.label_}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The name *Victoria* was classified as `persName`, which does not correspond to the default class names `PERSON`/`PER` implemented in Microsoft Presidio (look for `CHECK_LABEL_GROUPS` in [SpacyRecognizer implementation](https://github.com/microsoft/presidio/blob/main/presidio-analyzer/presidio_analyzer/predefined_recognizers/spacy_recognizer.py)). \n",
+    "\n",
+    "You can find out more about custom labels in spaCy models (including your own, trained ones) in [this thread](https://github.com/microsoft/presidio/issues/851).\n",
+    "\n",
+    "That's why our sentence will not be anonymized:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nazywam się Wiktoria\n"
+     ]
+    }
+   ],
+   "source": [
+    "nlp_config = {\n",
+    "    \"nlp_engine_name\": \"spacy\",\n",
+    "    \"models\": [\n",
+    "        {\"lang_code\": \"en\", \"model_name\": \"en_core_web_md\"},\n",
+    "        {\"lang_code\": \"es\", \"model_name\": \"es_core_news_md\"},\n",
+    "        {\"lang_code\": \"pl\", \"model_name\": \"pl_core_news_md\"},\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "anonymizer = PresidioReversibleAnonymizer(\n",
+    "    analyzed_fields=[\"PERSON\", \"LOCATION\", \"DATE_TIME\"],\n",
+    "    languages_config=nlp_config,\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    anonymizer.anonymize(\"Nazywam się Wiktoria\", language=\"pl\")\n",
+    ")  # \"My name is Wiktoria\" in Polish"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To address this, create your own `SpacyRecognizer` with your own class mapping and add it to the anonymizer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from presidio_analyzer.predefined_recognizers import SpacyRecognizer\n",
+    "\n",
+    "polish_check_label_groups = [\n",
+    "    ({\"LOCATION\"}, {\"placeName\", \"geogName\"}),\n",
+    "    ({\"PERSON\"}, {\"persName\"}),\n",
+    "    ({\"DATE_TIME\"}, {\"date\", \"time\"}),\n",
+    "]\n",
+    "\n",
+    "spacy_recognizer = SpacyRecognizer(\n",
+    "    supported_language=\"pl\",\n",
+    "    check_label_groups=polish_check_label_groups,\n",
+    ")\n",
+    "\n",
+    "anonymizer.add_recognizer(spacy_recognizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now everything works smoothly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nazywam się Morgan Walters\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    anonymizer.anonymize(\"Nazywam się Wiktoria\", language=\"pl\")\n",
+    ")  # \"My name is Wiktoria\" in Polish"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try on more complex example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nazywam się Ernest Liu. New Taylorburgh to moje miasto rodzinne. Urodziłam się 1987-01-19\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    anonymizer.anonymize(\n",
+    "        \"Nazywam się Wiktoria. Płock to moje miasto rodzinne. Urodziłam się dnia 6 kwietnia 2001 roku\",\n",
+    "        language=\"pl\",\n",
+    "    )\n",
+    ")  # \"My name is Wiktoria. Płock is my home town. I was born on 6 April 2001\" in Polish"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, thanks to class mapping, the anonymiser can cope with different types of entities. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Custom language-specific operators\n",
+    "\n",
+    "In the example above, the sentence has been anonymised correctly, but the fake data does not fit the Polish language at all. Custom operators can therefore be added, which will resolve the issue:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from faker import Faker\n",
+    "from presidio_anonymizer.entities import OperatorConfig\n",
+    "\n",
+    "fake = Faker(locale=\"pl_PL\")  # Setting faker to provide Polish data\n",
+    "\n",
+    "new_operators = {\n",
+    "    \"PERSON\": OperatorConfig(\"custom\", {\"lambda\": lambda _: fake.first_name_female()}),\n",
+    "    \"LOCATION\": OperatorConfig(\"custom\", {\"lambda\": lambda _: fake.city()}),\n",
+    "}\n",
+    "\n",
+    "anonymizer.add_operators(new_operators)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nazywam się Marianna. Szczecin to moje miasto rodzinne. Urodziłam się 1976-11-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    anonymizer.anonymize(\n",
+    "        \"Nazywam się Wiktoria. Płock to moje miasto rodzinne. Urodziłam się dnia 6 kwietnia 2001 roku\",\n",
+    "        language=\"pl\",\n",
+    "    )\n",
+    ")  # \"My name is Wiktoria. Płock is my home town. I was born on 6 April 2001\" in Polish"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Limitations\n",
+    "\n",
+    "Remember - results are as good as your recognizers and as your NER models!\n",
+    "\n",
+    "Look at the example below - we downloaded the small model for Spanish (12MB) and it no longer performs as well as the medium version (40MB):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: es_core_news_sm. Result: Me llamo Sofía\n",
+      "Model: es_core_news_md. Result: Me llamo Lawrence Davis\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ! python -m spacy download es_core_news_sm\n",
+    "\n",
+    "for model in [\"es_core_news_sm\", \"es_core_news_md\"]:\n",
+    "    nlp_config = {\n",
+    "        \"nlp_engine_name\": \"spacy\",\n",
+    "        \"models\": [\n",
+    "            {\"lang_code\": \"es\", \"model_name\": model},\n",
+    "        ],\n",
+    "    }\n",
+    "\n",
+    "    anonymizer = PresidioReversibleAnonymizer(\n",
+    "        analyzed_fields=[\"PERSON\"],\n",
+    "        languages_config=nlp_config,\n",
+    "    )\n",
+    "\n",
+    "    print(\n",
+    "        f\"Model: {model}. Result: {anonymizer.anonymize('Me llamo Sofía', language='es')}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many cases, even the larger models from spaCy will not be sufficient - there are already other, more complex and better methods of detecting named entities, based on transformers. You can read more about this [here](https://microsoft.github.io/presidio/analyzer/nlp_engines/transformers/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Future works\n",
+    "\n",
+    "- **automatic language detection** - instead of passing the language as a parameter in `anonymizer.anonymize`, we could detect the language/s beforehand and then use the corresponding NER model."
+   ]
+  }
+ ],
+ "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/guides/privacy/presidio_data_anonymization/reversible.ipynb

@@ -0,0 +1,461 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reversible data anonymization with Microsoft Presidio\n",
+    "\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/guides/privacy/presidio_data_anonymization/reversible.ipynb)\n",
+    "\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "We have already written about the importance of anonymizing sensitive data in the previous section. **Reversible Anonymization** is an equally essential technology while sharing information with language models, as it balances data protection with data usability. This technique involves masking sensitive personally identifiable information (PII), yet it can be reversed and original data can be restored when authorized users need it. Its main advantage lies in the fact that while it conceals individual identities to prevent misuse, it also allows the concealed data to be accurately unmasked should it be necessary for legal or compliance purposes. \n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "We implemented the `PresidioReversibleAnonymizer`, which consists of two parts:\n",
+    "\n",
+    "1. anonymization - it works the same way as `PresidioAnonymizer`, plus the object itself stores a mapping of made-up values to original ones, for example:\n",
+    "```\n",
+    "    {\n",
+    "        \"PERSON\": {\n",
+    "            \"<anonymized>\": \"<original>\",\n",
+    "            \"John Doe\": \"Slim Shady\"\n",
+    "        },\n",
+    "        \"PHONE_NUMBER\": {\n",
+    "            \"111-111-1111\": \"555-555-5555\"\n",
+    "        }\n",
+    "        ...\n",
+    "    }\n",
+    "```\n",
+    "\n",
+    "2. deanonymization - using the mapping described above, it matches fake data with original data and then substitutes it.\n",
+    "\n",
+    "Between anonymization and deanonymization user can perform different operations, for example, passing the output to LLM.\n",
+    "\n",
+    "## Quickstart\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install necessary packages\n",
+    "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
+    "# ! python -m spacy download en_core_web_lg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`PresidioReversibleAnonymizer` is not significantly different from its predecessor (`PresidioAnonymizer`) in terms of anonymization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Maria Lynch, call me at 7344131647 or email me at jamesmichael@example.com. By the way, my card number is: 4838637940262'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer\n",
+    "\n",
+    "anonymizer = PresidioReversibleAnonymizer(\n",
+    "    analyzed_fields=[\"PERSON\", \"PHONE_NUMBER\", \"EMAIL_ADDRESS\", \"CREDIT_CARD\"],\n",
+    "    # Faker seed is used here to make sure the same fake data is generated for the test purposes\n",
+    "    # In production, it is recommended to remove the faker_seed parameter (it will default to None)\n",
+    "    faker_seed=42,\n",
+    ")\n",
+    "\n",
+    "anonymizer.anonymize(\n",
+    "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com. \"\n",
+    "    \"By the way, my card number is: 4916 0387 9536 0861\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is what the full string we want to deanonymize looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maria Lynch recently lost his wallet. \n",
+      "Inside is some cash and his credit card with the number 4838637940262. \n",
+      "If you would find it, please call at 7344131647 or write an email here: jamesmichael@example.com.\n",
+      "Maria Lynch would be very grateful!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We know this data, as we set the faker_seed parameter\n",
+    "fake_name = \"Maria Lynch\"\n",
+    "fake_phone = \"7344131647\"\n",
+    "fake_email = \"jamesmichael@example.com\"\n",
+    "fake_credit_card = \"4838637940262\"\n",
+    "\n",
+    "anonymized_text = f\"\"\"{fake_name} recently lost his wallet. \n",
+    "Inside is some cash and his credit card with the number {fake_credit_card}. \n",
+    "If you would find it, please call at {fake_phone} or write an email here: {fake_email}.\n",
+    "{fake_name} would be very grateful!\"\"\"\n",
+    "\n",
+    "print(anonymized_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now, using the `deanonymize` method, we can reverse the process:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Slim Shady recently lost his wallet. \n",
+      "Inside is some cash and his credit card with the number 4916 0387 9536 0861. \n",
+      "If you would find it, please call at 313-666-7440 or write an email here: real.slim.shady@gmail.com.\n",
+      "Slim Shady would be very grateful!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(anonymizer.deanonymize(anonymized_text))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using with LangChain Expression Language\n",
+    "\n",
+    "With LCEL we can easily chain together anonymization and deanonymization with the rest of our application. This is an example of using the anonymization mechanism with a query to LLM (without deanonymization for now):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = f\"\"\"Slim Shady recently lost his wallet. \n",
+    "Inside is some cash and his credit card with the number 4916 0387 9536 0861. \n",
+    "If you would find it, please call at 313-666-7440 or write an email here: real.slim.shady@gmail.com.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dear Sir/Madam,\n",
+      "\n",
+      "We regret to inform you that Mr. Dana Rhodes has reported the loss of his wallet. The wallet contains a sum of cash and his credit card, bearing the number 4397528473885757. \n",
+      "\n",
+      "If you happen to come across the aforementioned wallet, we kindly request that you contact us immediately at 258-481-7074x714 or via email at laurengoodman@example.com.\n",
+      "\n",
+      "Your prompt assistance in this matter would be greatly appreciated.\n",
+      "\n",
+      "Yours faithfully,\n",
+      "\n",
+      "[Your Name]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "anonymizer = PresidioReversibleAnonymizer()\n",
+    "\n",
+    "template = \"\"\"Rewrite this text into an official, short email:\n",
+    "\n",
+    "{anonymized_text}\"\"\"\n",
+    "prompt = PromptTemplate.from_template(template)\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "\n",
+    "chain = {\"anonymized_text\": anonymizer.anonymize} | prompt | llm\n",
+    "response = chain.invoke(text)\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's add **deanonymization step** to our sequence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dear Sir/Madam,\n",
+      "\n",
+      "We regret to inform you that Mr. Slim Shady has recently misplaced his wallet. The wallet contains a sum of cash and his credit card, bearing the number 4916 0387 9536 0861. \n",
+      "\n",
+      "If by any chance you come across the lost wallet, kindly contact us immediately at 313-666-7440 or send an email to real.slim.shady@gmail.com.\n",
+      "\n",
+      "Your prompt assistance in this matter would be greatly appreciated.\n",
+      "\n",
+      "Yours faithfully,\n",
+      "\n",
+      "[Your Name]\n"
+     ]
+    }
+   ],
+   "source": [
+    "chain = chain | (lambda ai_message: anonymizer.deanonymize(ai_message.content))\n",
+    "response = chain.invoke(text)\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Anonymized data was given to the model itself, and therefore it was protected from being leaked to the outside world. Then, the model's response was processed, and the factual value was replaced with the real one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extra knowledge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`PresidioReversibleAnonymizer` stores the mapping of the fake values to the original values in the `deanonymizer_mapping` parameter, where key is fake PII and value is the original one: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PERSON': {'Maria Lynch': 'Slim Shady'},\n",
+       " 'PHONE_NUMBER': {'7344131647': '313-666-7440'},\n",
+       " 'EMAIL_ADDRESS': {'jamesmichael@example.com': 'real.slim.shady@gmail.com'},\n",
+       " 'CREDIT_CARD': {'4838637940262': '4916 0387 9536 0861'}}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer\n",
+    "\n",
+    "anonymizer = PresidioReversibleAnonymizer(\n",
+    "    analyzed_fields=[\"PERSON\", \"PHONE_NUMBER\", \"EMAIL_ADDRESS\", \"CREDIT_CARD\"],\n",
+    "    # Faker seed is used here to make sure the same fake data is generated for the test purposes\n",
+    "    # In production, it is recommended to remove the faker_seed parameter (it will default to None)\n",
+    "    faker_seed=42,\n",
+    ")\n",
+    "\n",
+    "anonymizer.anonymize(\n",
+    "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com. \"\n",
+    "    \"By the way, my card number is: 4916 0387 9536 0861\"\n",
+    ")\n",
+    "\n",
+    "anonymizer.deanonymizer_mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Anonymizing more texts will result in new mapping entries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Do you have his VISA card number? Yep, it's 3537672423884966. I'm William Bowman by the way.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'PERSON': {'Maria Lynch': 'Slim Shady', 'William Bowman': 'John Doe'},\n",
+       " 'PHONE_NUMBER': {'7344131647': '313-666-7440'},\n",
+       " 'EMAIL_ADDRESS': {'jamesmichael@example.com': 'real.slim.shady@gmail.com'},\n",
+       " 'CREDIT_CARD': {'4838637940262': '4916 0387 9536 0861',\n",
+       "  '3537672423884966': '4001 9192 5753 7193'}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    anonymizer.anonymize(\n",
+    "        \"Do you have his VISA card number? Yep, it's 4001 9192 5753 7193. I'm John Doe by the way.\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "anonymizer.deanonymizer_mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can save the mapping itself to a file for future use: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can save the deanonymizer mapping as a JSON or YAML file\n",
+    "\n",
+    "anonymizer.save_deanonymizer_mapping(\"deanonymizer_mapping.json\")\n",
+    "# anonymizer.save_deanonymizer_mapping(\"deanonymizer_mapping.yaml\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then, load it in another `PresidioReversibleAnonymizer` instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer = PresidioReversibleAnonymizer()\n",
+    "\n",
+    "anonymizer.deanonymizer_mapping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PERSON': {'Maria Lynch': 'Slim Shady', 'William Bowman': 'John Doe'},\n",
+       " 'PHONE_NUMBER': {'7344131647': '313-666-7440'},\n",
+       " 'EMAIL_ADDRESS': {'jamesmichael@example.com': 'real.slim.shady@gmail.com'},\n",
+       " 'CREDIT_CARD': {'4838637940262': '4916 0387 9536 0861',\n",
+       "  '3537672423884966': '4001 9192 5753 7193'}}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer.load_deanonymizer_mapping(\"deanonymizer_mapping.json\")\n",
+    "\n",
+    "anonymizer.deanonymizer_mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Future works\n",
+    "\n",
+    "- **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object.\n",
+    "- **better matching and substitution of fake values for real ones** - currently the strategy is based on matching full strings and then substituting them. Due to the indeterminism of language models, it may happen that the value in the answer is slightly changed (e.g. *John Doe* -> *John* or *Main St, New York* -> *New York*) and such a substitution is then no longer possible. Therefore, it is worth adjusting the matching for your needs."
+   ]
+  }
+ ],
+ "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/guides/safety/amazon_comprehend_chain.ipynb

@@ -512,9 +512,9 @@
     "# Examples\n",
     "---\n",
     "\n",
-    "## With HuggingFace Hub Models\n",
+    "## With Hugging Face Hub Models\n",
     "\n",
-    "Get your API Key from Huggingface hub - https://huggingface.co/docs/api-inference/quicktour#get-your-api-token"
+    "Get your API Key from Hugging Face hub - https://huggingface.co/docs/api-inference/quicktour#get-your-api-token"
    ]
   },
   {

docs/extras/integrations/callbacks/context.ipynb

@@ -93,7 +93,7 @@
    "metadata": {},
    "source": [
     "## Usage\n",
-    "### Using the Context callback within a Chat Model\n",
+    "### Using the Context callback within a chat model\n",
     "\n",
     "The Context callback handler can be used to directly record transcripts between users and AI assistants.\n",
     "\n",

docs/extras/integrations/chat/konko.ipynb

@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Konko\n",
+    "\n",
+    ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n",
+    "\n",
+    "Konko API is a fully managed API designed to help application developers:\n",
+    "\n",
+    "1. Select the right LLM(s) for their application\n",
+    "2. Prototype with various open-source and proprietary LLMs\n",
+    "3. Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructure\n",
+    "\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with `Konko` [models](https://docs.konko.ai/docs/overview)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this notebook, you'll need Konko API key. You can request it by messaging support@konko.ai."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatKonko\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    AIMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.schema import AIMessage, HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Set API Keys\n",
+    "\n",
+    "<br />\n",
+    "\n",
+    "### Option 1: Set Environment Variables\n",
+    "\n",
+    "1. You can set environment variables for \n",
+    "   1. KONKO_API_KEY (Required)\n",
+    "   2. OPENAI_API_KEY (Optional)\n",
+    "2. In your current shell session, use the export command:\n",
+    "\n",
+    "```shell\n",
+    "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n",
+    "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n",
+    "```\n",
+    "\n",
+    "Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.\n",
+    "\n",
+    "### Option 2: Set API Keys Programmatically\n",
+    "\n",
+    "If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:\n",
+    "\n",
+    "```python\n",
+    "konko.set_api_key('your_KONKO_API_KEY_here')  \n",
+    "konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calling a model\n",
+    "\n",
+    "Find a model on the [Konko overview page](https://docs.konko.ai/docs/overview)\n",
+    "\n",
+    "For example, for this [LLama 2 model](https://docs.konko.ai/docs/meta-llama-2-13b-chat). The model id would be: `\"meta-llama/Llama-2-13b-chat-hf\"`\n",
+    "\n",
+    "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/listmodels).\n",
+    "\n",
+    "From here, we can initialize our model:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ChatKonko(max_tokens=400, model = 'meta-llama/Llama-2-13b-chat-hf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" Sure, I'd be happy to explain the Big Bang Theory briefly!\\n\\nThe Big Bang Theory is the leading explanation for the origin and evolution of the universe, based on a vast amount of observational evidence from many fields of science. In essence, the theory posits that the universe began as an infinitely hot and dense point, known as a singularity, around 13.8 billion years ago. This singularity expanded rapidly, and as it did, it cooled and formed subatomic particles, which eventually coalesced into the first atoms, and later into the stars and galaxies we see today.\\n\\nThe theory gets its name from the idea that the universe began in a state of incredibly high energy and temperature, and has been expanding and cooling ever since. This expansion is thought to have been driven by a mysterious force known as dark energy, which is thought to be responsible for the accelerating expansion of the universe.\\n\\nOne of the key predictions of the Big Bang Theory is that the universe should be homogeneous and isotropic on large scales, meaning that it should look the same in all directions and have the same properties everywhere. This prediction has been confirmed by a wealth of observational evidence, including the cosmic microwave background radiation, which is thought to be a remnant of the early universe.\\n\\nOverall, the Big Bang Theory is a well-established and widely accepted explanation for the origins of the universe, and it has been supported by a vast amount of observational evidence from many fields of science.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful assistant.\"\n",
+    "    ),\n",
+    "    HumanMessage(\n",
+    "        content=\"Explain Big Bang Theory briefly\"\n",
+    "    ),\n",
+    "]\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.11.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/integrations/document_loaders/aws_s3_directory.ipynb

@@ -102,13 +102,34 @@
     "loader.load()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Configuring the AWS Boto3 client\n",
+    "You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
+    "named arguments when creating the S3DirectoryLoader.\n",
+    "This is useful for instance when AWS credentials can't be set as environment variables.\n",
+    "See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "885dc280",
-   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "loader = S3DirectoryLoader(\"testing-hwc\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\")"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {

docs/extras/integrations/document_loaders/aws_s3_file.ipynb

@@ -66,12 +66,34 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "id": "93689594",
    "metadata": {},
+   "source": [
+    "## Configuring the AWS Boto3 client\n",
+    "You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
+    "named arguments when creating the S3DirectoryLoader.\n",
+    "This is useful for instance when AWS credentials can't be set as environment variables.\n",
+    "See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "outputs": [],
-   "source": []
+   "source": [
+    "loader = S3FileLoader(\"testing-hwc\", \"fake.docx\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\")"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {

docs/extras/integrations/document_loaders/azure_document_intelligence.ipynb

@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Azure Document Intelligence"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning \n",
+    "based service that extracts text (including handwriting), tables or key-value-pairs from\n",
+    "scanned documents or images.\n",
+    "\n",
+    "This current implementation of a loader using Document Intelligence is able to incorporate content page-wise and turn it into LangChain documents.\n",
+    "\n",
+    "Document Intelligence supports PDF, JPEG, PNG, BMP, or TIFF.\n",
+    "\n",
+    "Further documentation is available at https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/?view=doc-intel-3.1.0.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install langchain azure-ai-formrecognizer -q"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first example uses a local file which will be sent to Azure Document Intelligence.\n",
+    "\n",
+    "First, an instance of a DocumentAnalysisClient is created with endpoint and key for the Azure service.    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azure.ai.formrecognizer import DocumentAnalysisClient\n",
+    "from azure.core.credentials import AzureKeyCredential\n",
+    "\n",
+    "document_analysis_client = DocumentAnalysisClient(\n",
+    "                endpoint=\"<service_endpoint>\", credential=AzureKeyCredential(\"<service_key>\")\n",
+    "            )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the initialized document analysis client, we can proceed to create an instance of the DocumentIntelligenceLoader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.pdf import DocumentIntelligenceLoader\n",
+    "loader = DocumentIntelligenceLoader(\n",
+    "    \"<Local_filename>\",\n",
+    "    client=document_analysis_client,\n",
+    "    model=\"<model_name>\") # e.g. prebuilt-document\n",
+    "\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output contains each page of the source document as a LangChain document: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='...', metadata={'source': '...', 'page': 1})]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/integrations/document_loaders/google_drive.ipynb

@@ -210,7 +210,7 @@
    "id": "83ac576b-48c9-4aad-a35e-e978ea32f746",
    "metadata": {},
    "source": [
-    "# Extended usage\n",
+    "## Extended usage\n",
     "An external component can manage the complexity of Google Drive : `langchain-googledrive`\n",
     "It's compatible with the ̀`langchain.document_loaders.GoogleDriveLoader` and can be used\n",
     "in its place.\n",
@@ -319,7 +319,7 @@
    "id": "cd13d7d1-db7a-498d-ac98-76ccd9ad9019",
    "metadata": {},
    "source": [
-    "## Customize the search pattern\n",
+    "### Customize the search pattern\n",
     "\n",
     "All parameter compatible with Google [`list()`](https://developers.google.com/drive/api/v3/reference/files/list)\n",
     "API can be set.\n",
@@ -398,7 +398,7 @@
    "id": "375bb465-8f69-407b-94bd-ffa3718ef500",
    "metadata": {},
    "source": [
-    "### Modes for GSlide and GSheet\n",
+    "#### Modes for GSlide and GSheet\n",
     "The parameter mode accepts different values:\n",
     "\n",
     "- \"document\": return the body of each document\n",
@@ -469,7 +469,7 @@
    "id": "09acb864-e919-4add-9e06-deba6f7f0cd8",
    "metadata": {},
    "source": [
-    "## Advanced usage\n",
+    "### Advanced usage\n",
     "All Google File have a 'description' in the metadata. This field can be used to memorize a summary of the document or others indexed tags (See method `lazy_update_description_with_summary()`).\n",
     "\n",
     "If you use the `mode=\"snippet\"`, only the description will be used for the body. Else, the `metadata['summary']` has the field.\n",
@@ -525,7 +525,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/document_transformers/nuclia_transformer.ipynb

@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,8 +93,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "langchain",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "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"
   },
   "orig_nbformat": 4
  },

docs/extras/integrations/llms/banana.ipynb

@@ -31,11 +31,16 @@
    "outputs": [],
    "source": [
     "# get new tokens: https://app.banana.dev/\n",
-    "# We need two tokens, not just an `api_key`: `BANANA_API_KEY` and `YOUR_MODEL_KEY`\n",
+    "# We need three parameters to make a Banana.dev API call:\n",
+    "# * a team api key\n",
+    "# * the model's unique key\n",
+    "# * the model's url slug\n",
     "\n",
     "import os\n",
     "from getpass import getpass\n",
     "\n",
+    "# You can get this from the main dashboard\n",
+    "# at https://app.banana.dev\n",
     "os.environ[\"BANANA_API_KEY\"] = \"YOUR_API_KEY\"\n",
     "# OR\n",
     "# BANANA_API_KEY = getpass()"
@@ -70,7 +75,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = Banana(model_key=\"YOUR_MODEL_KEY\")"
+    "# Both of these are found in your model's \n",
+    "# detail page in https://app.banana.dev\n",
+    "llm = Banana(model_key=\"YOUR_MODEL_KEY\", model_url_slug=\"YOUR_MODEL_URL_SLUG\")"
    ]
   },
   {

docs/extras/integrations/llms/bittensor.ipynb

@@ -4,11 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# NIBittensorLLM\n",
+    "# Bittensor\n",
     "\n",
-    "NIBittensorLLM is developed by [Neural Internet](https://neuralinternet.ai/), powered by [Bittensor](https://bittensor.com/).\n",
+    ">[Bittensor](https://bittensor.com/) is a mining network, similar to Bitcoin, that includes built-in incentives designed to encourage miners to contribute compute + knowledge.\n",
+    ">\n",
+    ">`NIBittensorLLM` is developed by [Neural Internet](https://neuralinternet.ai/), powered by `Bittensor`.\n",
     "\n",
-    "This LLM showcases true potential of decentralized AI by giving you the best response(s) from the Bittensor protocol, which consist of various AI models such as OpenAI, LLaMA2 etc.\n",
+    ">This LLM showcases true potential of decentralized AI by giving you the best response(s) from the `Bittensor protocol`, which consist of various AI models such as `OpenAI`, `LLaMA2` etc.\n",
     "\n",
     "Users can view their logs, requests, and API keys on the [Validator Endpoint Frontend](https://api.neuralinternet.ai/). However, changes to the configuration are currently prohibited; otherwise, the user's queries will be blocked.\n",
     "\n",
@@ -157,11 +159,24 @@
   }
  ],
  "metadata": {
-  "language_info": {
-   "name": "python"
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "orig_nbformat": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/llms/google_vertex_ai_palm.ipynb

@@ -236,7 +236,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm_oss = VertexAIModelGarden(\n",
+    "llm = VertexAIModelGarden(\n",
     "    project=\"YOUR PROJECT\",\n",
     "    endpoint_id=\"YOUR ENDPOINT_ID\"\n",
     ")"
@@ -248,14 +248,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm_oss(\"What is the meaning of life?\")"
+    "llm(\"What is the meaning of life?\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also use it as a chain:"
+    "Like all LLMs, we can then compose it with other components:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")"
    ]
   },
   {
@@ -264,17 +275,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm_oss_chain = LLMChain(prompt=prompt, llm=llm_oss(\"What is the meaning of life?\")\n",
-    ")\n",
-    "llm_oss_chain.run(question)"
+    "llm_oss_chain = prompt | llm\n",
+    "\n",
+    "llm_oss_chain.invoke({\"thing\": \"life\"})"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -286,7 +297,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/llms/titan_takeoff.ipynb

@@ -42,7 +42,7 @@
    "metadata": {},
    "source": [
     "## Choose a Model\n",
-    "Iris Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n",
+    "Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n",
     "\n",
     "Going forward in this demo we will be using the falcon 7B instruct model. This is a good open source model that is trained to follow instructions, and is small enough to easily inference even on CPUs.\n",
     "\n",
@@ -64,8 +64,7 @@
    "source": [
     "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu\n",
     "iris takeoff --model tiiuae/falcon-7b-instruct --device cuda # Nvidia GPU required\n",
-    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu --port 5000 # run on port 5000 (default: 8000)\n",
-    "```"
+    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu --port 5000 # run on port 5000 (default: 8000)"
    ]
   },
   {
@@ -73,8 +72,29 @@
    "metadata": {},
    "source": [
     "You will then be directed to a login page, where you will need to create an account to proceed.\n",
-    "After logging in, run the command onscreen to check whether the server is ready. When it is ready, you can start using the Takeoff integration\n",
+    "After logging in, run the command onscreen to check whether the server is ready. When it is ready, you can start using the Takeoff integration.\n",
     "\n",
+    "To shutdown the server, run the following command. You will be presented with options on which Takeoff server to shut down, in case you have multiple running servers.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iris takeoff --shutdown # shutdown the server"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Inferencing your model\n",
     "To access your LLM, use the TitanTakeoff LLM wrapper:"
    ]
@@ -88,7 +108,7 @@
     "from langchain.llms import TitanTakeoff\n",
     "\n",
     "llm = TitanTakeoff(\n",
-    "    port=8000,\n",
+    "    baseURL=\"http://localhost:8000\",\n",
     "    generate_max_length=128,\n",
     "    temperature=1.0\n",
     ")\n",
@@ -102,7 +122,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "No parameters are needed by default, but a port can be specified and [generation parameters](https://docs.titanml.co/docs/titan-takeoff/Advanced/generation-parameters) can be supplied.\n",
+    "No parameters are needed by default, but a baseURL that points to your desired URL where Takeoff is running can be specified and [generation parameters](https://docs.titanml.co/docs/titan-takeoff/Advanced/generation-parameters) can be supplied.\n",
     "\n",
     "### Streaming\n",
     "Streaming is also supported via the streaming flag:"
@@ -117,7 +137,7 @@
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain.callbacks.manager import CallbackManager\n",
     "\n",
-    "llm = TitanTakeoff(port=8000, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), streaming=True)\n",
+    "llm = TitanTakeoff(callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), streaming=True)\n",
     "\n",
     "prompt = \"What is the capital of France?\"\n",
     "\n",

docs/extras/integrations/memory/dynamodb_chat_message_history.ipynb

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "id": "93ce1811",
    "metadata": {},
    "outputs": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "id": "d15e3302",
    "metadata": {},
    "outputs": [],
@@ -87,18 +87,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "id": "64fc465e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n",
-       " AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
-      ]
+      "text/plain": "[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n AIMessage(content='whats up?', additional_kwargs={}, example=False),\n HumanMessage(content='hi!', additional_kwargs={}, example=False),\n AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
      },
-     "execution_count": 3,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "225713c8",
    "metadata": {},
    "outputs": [],
@@ -133,6 +130,81 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## DynamoDBChatMessageHistory With Different Keys Composite Keys\n",
+    "The default key for DynamoDBChatMessageHistory is ```{\"SessionId\": self.session_id}```, but you can modify this to match your table design.\n",
+    "\n",
+    "### Primary Key Name\n",
+    "You may modify the primary key by passing in a primary_key_name value in the constructor, resulting in the following:\n",
+    "```{self.primary_key_name: self.session_id}```\n",
+    "\n",
+    "### Composite Keys\n",
+    "When using an existing DynamoDB table, you may need to modify the key structure from the default of to something including a Sort Key. To do this you may use the ```key``` parameter.\n",
+    "\n",
+    "Passing a value for key will override the primary_key parameter, and the resulting key structure will be the passed value.\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "[HumanMessage(content='hello, composite dynamodb table!', additional_kwargs={}, example=False)]"
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory\n",
+    "\n",
+    "composite_table = dynamodb.create_table(\n",
+    "    TableName=\"CompositeTable\",\n",
+    "    KeySchema=[{\"AttributeName\": \"PK\", \"KeyType\": \"HASH\"}, {\"AttributeName\": \"SK\", \"KeyType\": \"RANGE\"}],\n",
+    "    AttributeDefinitions=[{\"AttributeName\": \"PK\", \"AttributeType\": \"S\"}, {\"AttributeName\": \"SK\", \"AttributeType\": \"S\"}],\n",
+    "    BillingMode=\"PAY_PER_REQUEST\",\n",
+    ")\n",
+    "\n",
+    "# Wait until the table exists.\n",
+    "composite_table.meta.client.get_waiter(\"table_exists\").wait(TableName=\"CompositeTable\")\n",
+    "\n",
+    "# Print out some data about the table.\n",
+    "print(composite_table.item_count)\n",
+    "\n",
+    "my_key = {\n",
+    "    \"PK\": \"session_id::0\",\n",
+    "    \"SK\":  \"langchain_history\",\n",
+    "}\n",
+    "\n",
+    "composite_key_history = DynamoDBChatMessageHistory(\n",
+    "    table_name=\"CompositeTable\",\n",
+    "    session_id=\"0\",\n",
+    "    endpoint_url=\"http://localhost.localstack.cloud:4566\",\n",
+    "    key=my_key,\n",
+    ")\n",
+    "\n",
+    "composite_key_history.add_user_message(\"hello, composite dynamodb table!\")\n",
+    "\n",
+    "composite_key_history.messages"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -144,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "f92d9499",
    "metadata": {},
    "outputs": [],
@@ -165,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "id": "1167eeba",
    "metadata": {},
    "outputs": [],
@@ -184,10 +256,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "id": "fce085c5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValidationError",
+     "evalue": "1 validation error for ChatOpenAI\n__root__\n  Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass  `openai_api_key` as a named parameter. (type=value_error)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mValidationError\u001B[0m                           Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[17], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m llm \u001B[38;5;241m=\u001B[39m \u001B[43mChatOpenAI\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtemperature\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m      2\u001B[0m agent_chain \u001B[38;5;241m=\u001B[39m initialize_agent(\n\u001B[1;32m      3\u001B[0m     tools,\n\u001B[1;32m      4\u001B[0m     llm,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m      7\u001B[0m     memory\u001B[38;5;241m=\u001B[39mmemory,\n\u001B[1;32m      8\u001B[0m )\n",
+      "File \u001B[0;32m~/Documents/projects/langchain/libs/langchain/langchain/load/serializable.py:74\u001B[0m, in \u001B[0;36mSerializable.__init__\u001B[0;34m(self, **kwargs)\u001B[0m\n\u001B[1;32m     73\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs: Any) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m---> 74\u001B[0m     \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;21;43m__init__\u001B[39;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     75\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lc_kwargs \u001B[38;5;241m=\u001B[39m kwargs\n",
+      "File \u001B[0;32m~/Documents/projects/langchain/.venv/lib/python3.9/site-packages/pydantic/main.py:341\u001B[0m, in \u001B[0;36mpydantic.main.BaseModel.__init__\u001B[0;34m()\u001B[0m\n",
+      "\u001B[0;31mValidationError\u001B[0m: 1 validation error for ChatOpenAI\n__root__\n  Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass  `openai_api_key` as a named parameter. (type=value_error)"
+     ]
+    }
+   ],
    "source": [
     "llm = ChatOpenAI(temperature=0)\n",
     "agent_chain = initialize_agent(\n",
@@ -201,152 +287,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "952a3103",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Hello! How can I assist you today?\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Hello! How can I assist you today?'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "agent_chain.run(input=\"Hello!\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "54c4aaf4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"python_repl\",\n",
-      "    \"action_input\": \"import requests\\nfrom bs4 import BeautifulSoup\\n\\nurl = 'https://en.wikipedia.org/wiki/Twitter'\\nresponse = requests.get(url)\\nsoup = BeautifulSoup(response.content, 'html.parser')\\nowner = soup.find('th', text='Owner').find_next_sibling('td').text.strip()\\nprint(owner)\"\n",
-      "}\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mX Corp. (2023–present)Twitter, Inc. (2006–2023)\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"X Corp. (2023–present)Twitter, Inc. (2006–2023)\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'X Corp. (2023–present)Twitter, Inc. (2006–2023)'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "agent_chain.run(input=\"Who owns Twitter?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "f9013118",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Hello Bob! How can I assist you today?'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "agent_chain.run(input=\"My name is Bob.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "405e5315",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Your name is Bob.\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Your name is Bob.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "agent_chain.run(input=\"Who am I?\")"
+    "agent_chain.run(input=\"Who am I?\")\n"
    ]
   }
  ],

docs/extras/integrations/memory/sql_chat_message_history.ipynb

@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# SQL Chat Message History\n",
+    "\n",
+    "This notebook goes over a **SQLChatMessageHistory** class that allows to store chat history in any database supported by SQLAlchemy.\n",
+    "\n",
+    "Please note that to use it with databases other than SQLite, you will need to install the corresponding database driver."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "f22eab3f84cbeb37"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Basic Usage\n",
+    "\n",
+    "To use the storage you need to provide only 2 things:\n",
+    "\n",
+    "1. Session Id - a unique identifier of the session, like user name, email, chat id etc.\n",
+    "2. Connection string - a string that specifies the database connection. It will be passed to SQLAlchemy create_engine function."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "f8f2830ee9ca1e01"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "from langchain.memory.chat_message_histories import SQLChatMessageHistory\n",
+    "\n",
+    "chat_message_history = SQLChatMessageHistory(\n",
+    "\tsession_id='test_session',\n",
+    "\tconnection_string='sqlite:///sqlite.db'\n",
+    ")\n",
+    "\n",
+    "chat_message_history.add_user_message('Hello')\n",
+    "chat_message_history.add_ai_message('Hi')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:38.077748Z",
+     "start_time": "2023-08-28T10:04:36.105894Z"
+    }
+   },
+   "id": "4576e914a866fb40"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]"
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_message_history.messages"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:38.929396Z",
+     "start_time": "2023-08-28T10:04:38.915727Z"
+    }
+   },
+   "id": "b476688cbb32ba90"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Custom Storage Format\n",
+    "\n",
+    "By default, only the session id and message dictionary are stored in the table.\n",
+    "\n",
+    "However, sometimes you might want to store some additional information, like message date, author, language etc.\n",
+    "\n",
+    "To do that, you can create a custom message converter, by implementing **BaseMessageConverter** interface."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "2e5337719d5614fd"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "from langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessage\n",
+    "from typing import Any\n",
+    "from sqlalchemy import Column, Integer, Text, DateTime\n",
+    "from sqlalchemy.orm import declarative_base\n",
+    "from langchain.memory.chat_message_histories.sql import BaseMessageConverter\n",
+    "\n",
+    "\n",
+    "Base = declarative_base()\n",
+    "\n",
+    "\n",
+    "class CustomMessage(Base):\n",
+    "\t__tablename__ = 'custom_message_store'\n",
+    "\n",
+    "\tid = Column(Integer, primary_key=True)\n",
+    "\tsession_id = Column(Text)\n",
+    "\ttype = Column(Text)\n",
+    "\tcontent = Column(Text)\n",
+    "\tcreated_at = Column(DateTime)\n",
+    "\tauthor_email = Column(Text)\n",
+    "\n",
+    "\n",
+    "class CustomMessageConverter(BaseMessageConverter):\n",
+    "\tdef __init__(self, author_email: str):\n",
+    "\t\tself.author_email = author_email\n",
+    "\t\n",
+    "\tdef from_sql_model(self, sql_message: Any) -> BaseMessage:\n",
+    "\t\tif sql_message.type == 'human':\n",
+    "\t\t\treturn HumanMessage(\n",
+    "\t\t\t\tcontent=sql_message.content,\n",
+    "\t\t\t)\n",
+    "\t\telif sql_message.type == 'ai':\n",
+    "\t\t\treturn AIMessage(\n",
+    "\t\t\t\tcontent=sql_message.content,\n",
+    "\t\t\t)\n",
+    "\t\telif sql_message.type == 'system':\n",
+    "\t\t\treturn SystemMessage(\n",
+    "\t\t\t\tcontent=sql_message.content,\n",
+    "\t\t\t)\n",
+    "\t\telse:\n",
+    "\t\t\traise ValueError(f'Unknown message type: {sql_message.type}')\n",
+    "\t\n",
+    "\tdef to_sql_model(self, message: BaseMessage, session_id: str) -> Any:\n",
+    "\t\tnow = datetime.now()\n",
+    "\t\treturn CustomMessage(\n",
+    "\t\t\tsession_id=session_id,\n",
+    "\t\t\ttype=message.type,\n",
+    "\t\t\tcontent=message.content,\n",
+    "\t\t\tcreated_at=now,\n",
+    "\t\t\tauthor_email=self.author_email\n",
+    "\t\t)\n",
+    "\t\n",
+    "\tdef get_sql_model_class(self) -> Any:\n",
+    "\t\treturn CustomMessage\n",
+    "\n",
+    "\n",
+    "chat_message_history = SQLChatMessageHistory(\n",
+    "\tsession_id='test_session',\n",
+    "\tconnection_string='sqlite:///sqlite.db',\n",
+    "\tcustom_message_converter=CustomMessageConverter(\n",
+    "\t\tauthor_email='test@example.com'\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "chat_message_history.add_user_message('Hello')\n",
+    "chat_message_history.add_ai_message('Hi')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:41.510498Z",
+     "start_time": "2023-08-28T10:04:41.494912Z"
+    }
+   },
+   "id": "fdfde84c07d071bb"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_message_history.messages"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:43.497990Z",
+     "start_time": "2023-08-28T10:04:43.492517Z"
+    }
+   },
+   "id": "4a6a54d8a9e2856f"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "You also might want to change the name of session_id column. In this case you'll need to specify `session_id_field_name` parameter."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "622aded629a1adeb"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/providers/arangodb.mdx

@@ -11,7 +11,7 @@ pip install python-arango
 
 ## Graph QA Chain
 
-Connect your ArangoDB Database with a Chat Model to get insights on your data. 
+Connect your ArangoDB Database with a chat model to get insights on your data. 
 
 See the notebook example [here](/docs/use_cases/more/graph/graph_arangodb_qa.html).
 

docs/extras/integrations/providers/awadb.md

@@ -9,13 +9,20 @@ pip install awadb
 ```
 
 
-## VectorStore
+## Vector Store
 
-There exists a wrapper around AwaDB vector databases, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
 
 ```python
 from langchain.vectorstores import AwaDB
 ```
 
-For a more detailed walkthrough of the AwaDB wrapper, see [here](/docs/integrations/vectorstores/awadb.html).
+See a [usage example](/docs/integrations/vectorstores/awadb).
+
+
+## Text Embedding Model
+
+```python
+from langchain.embeddings import AwaEmbeddings
+```
+
+See a [usage example](/docs/integrations/text_embedding/awadb).

docs/extras/integrations/providers/bananadev.mdx

@@ -1,79 +1,72 @@
 # Banana
 
-This page covers how to use the Banana ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
+Banana provided serverless GPU inference for AI models, including a CI/CD build pipeline and a simple Python framework (Potassium) to server your models.
+
+This page covers how to use the [Banana](https://www.banana.dev) ecosystem within LangChain.
+
+It is broken into two parts: 
+* installation and setup, 
+* and then references to specific Banana wrappers.
 
 ## Installation and Setup
 
 - Install with `pip install banana-dev`
-- Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
+- Get an Banana api key from the [Banana.dev dashboard](https://app.banana.dev) and set it as an environment variable (`BANANA_API_KEY`)
+- Get your model's key and url slug from the model's details page
 
 ## Define your Banana Template
 
-If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base).
-This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/).
-You can check out an example Banana repository [here](https://github.com/conceptofmind/serverless-template-palmyra-base).
+You'll need to set up a Github repo for your Banana app. You can get started in 5 minutes using [this guide](https://docs.banana.dev/banana-docs/).
+
+Alternatively, for a ready-to-go LLM example, you can check out Banana's [CodeLlama-7B-Instruct-GPTQ](https://github.com/bananaml/demo-codellama-7b-instruct-gptq) GitHub repository. Just fork it and deploy it within Banana.
+
+Other starter repos are available [here](https://github.com/orgs/bananaml/repositories?q=demo-&type=all&language=&sort=).
 
 ## Build the Banana app
 
-Banana Apps must include the "output" key in the return json. 
-There is a rigid response structure.
+To use Banana apps within Langchain, they must include the `outputs` key 
+in the returned json, and the value must be a string.
 
 ```python
 # Return the results as a dictionary
-result = {'output': result}
+result = {'outputs': result}
 ```
 
 An example inference function would be:
 
 ```python
-def inference(model_inputs:dict) -> dict:
-    global model
-    global tokenizer
-
-    # Parse out your arguments
-    prompt = model_inputs.get('prompt', None)
-    if prompt == None:
-        return {'message': "No prompt provided"}
-
-    # Run the model
-    input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()
-    output = model.generate(
-        input_ids,
-        max_length=100,
-        do_sample=True,
-        top_k=50,
-        top_p=0.95,
-        num_return_sequences=1,
-        temperature=0.9,
-        early_stopping=True,
-        no_repeat_ngram_size=3,
-        num_beams=5,
-        length_penalty=1.5,
-        repetition_penalty=1.5,
-        bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
-        )
-
-    result = tokenizer.decode(output[0], skip_special_tokens=True)
-    # Return the results as a dictionary
-    result = {'output': result}
-    return result
+@app.handler("/")
+def handler(context: dict, request: Request) -> Response:
+    """Handle a request to generate code from a prompt."""
+    model = context.get("model")
+    tokenizer = context.get("tokenizer")
+    max_new_tokens = request.json.get("max_new_tokens", 512)
+    temperature = request.json.get("temperature", 0.7)
+    prompt = request.json.get("prompt")
+    prompt_template=f'''[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
+    {prompt}
+    [/INST]
+    '''
+    input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+    output = model.generate(inputs=input_ids, temperature=temperature, max_new_tokens=max_new_tokens)
+    result = tokenizer.decode(output[0])
+    return Response(json={"outputs": result}, status=200)
 ```
 
-You can find a full example of a Banana app [here](https://github.com/conceptofmind/serverless-template-palmyra-base/blob/main/app.py).
+This example is from the `app.py` file in [CodeLlama-7B-Instruct-GPTQ](https://github.com/bananaml/demo-codellama-7b-instruct-gptq).
 
 ## Wrappers
 
 ### LLM
 
-There exists an Banana LLM wrapper, which you can access with
+Within Langchain, there exists a Banana LLM wrapper, which you can access with
 
 ```python
 from langchain.llms import Banana
 ```
 
-You need to provide a model key located in the dashboard:
+You need to provide a model key and model url slug, which you can get from the model's details page in the [Banana.dev dashboard](https://app.banana.dev).
 
 ```python
-llm = Banana(model_key="YOUR_MODEL_KEY")
+llm = Banana(model_key="YOUR_MODEL_KEY", model_url_slug="YOUR_MODEL_URL_SLUG")
 ```

docs/extras/integrations/providers/bittensor.mdx

@@ -20,7 +20,7 @@ from langchain.llms import NIBittensorLLM
 It provides a unified interface for all models:
 
 ```python
-llm = NIBittensorLLM(system_prompt="Your task is to provide consice and accurate response based on user prompt")
+llm = NIBittensorLLM(system_prompt="Your task is to provide concise and accurate response based on user prompt")
 
 print(llm('Write a fibonacci function in python with golder ratio'))
 ```

docs/extras/integrations/providers/datadog.mdx

@@ -4,12 +4,12 @@
 
 Key features of the ddtrace integration for LangChain:
 - Traces: Capture LangChain requests, parameters, prompt-completions, and help visualize LangChain operations.
-- Metrics: Capture LangChain request latency, errors, and token/cost usage (for OpenAI LLMs and Chat Models).
+- Metrics: Capture LangChain request latency, errors, and token/cost usage (for OpenAI LLMs and chat models).
 - Logs: Store prompt completion data for each LangChain operation.
 - Dashboard: Combine metrics, logs, and trace data into a single plane to monitor LangChain requests.
 - Monitors: Provide alerts in response to spikes in LangChain request latency or error rate.
 
-Note: The ddtrace LangChain integration currently provides tracing for LLMs, Chat Models, Text Embedding Models, Chains, and Vectorstores.
+Note: The ddtrace LangChain integration currently provides tracing for LLMs, chat models, Text Embedding Models, Chains, and Vectorstores.
 
 ## Installation and Setup
 

docs/extras/integrations/providers/konko.mdx

@@ -0,0 +1,80 @@
+# Konko
+This page covers how to run models on Konko within LangChain.
+
+Konko API is a fully managed API designed to help application developers:
+
+Select the right LLM(s) for their application
+Prototype with various open-source and proprietary LLMs
+Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructure
+
+## Installation and Setup
+
+### First you'll need an API key
+You can request it by messaging [support@konko.ai](mailto:support@konko.ai) 
+
+### Install Konko AI's Python SDK
+
+#### 1. Enable a Python3.8+ environment
+
+#### 2. Set API Keys
+
+##### Option 1: Set Environment Variables
+
+1. You can set environment variables for 
+   1. KONKO_API_KEY (Required)
+   2. OPENAI_API_KEY (Optional)
+
+2. In your current shell session, use the export command:
+
+```shell
+export KONKO_API_KEY={your_KONKO_API_KEY_here}
+export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional
+```
+
+Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.
+
+##### Option 2: Set API Keys Programmatically
+
+If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:
+
+```python
+konko.set_api_key('your_KONKO_API_KEY_here')  
+konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional
+```
+
+#### 3. Install the SDK
+
+
+```shell
+pip install konko
+```
+
+#### 4. Verify Installation & Authentication
+
+```python
+#Confirm konko has installed successfully
+import konko
+#Confirm API keys from Konko and OpenAI are set properly
+konko.Model.list()
+```
+
+## Calling a model
+
+Find a model on the [Konko Introduction page](https://docs.konko.ai/docs#available-models)
+
+For example, for this [LLama 2 model](https://docs.konko.ai/docs/meta-llama-2-13b-chat). The model id would be: `"meta-llama/Llama-2-13b-chat-hf"`
+
+Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/listmodels).
+
+From here, we can initialize our model:
+
+```python
+chat_instance = ChatKonko(max_tokens=10, model = 'meta-llama/Llama-2-13b-chat-hf')
+```
+
+And run it:
+
+```python
+msg = HumanMessage(content="Hi")
+chat_response = chat_instance([msg])
+```

docs/extras/integrations/providers/mlflow_ai_gateway.mdx

@@ -1,6 +1,9 @@
 # MLflow AI Gateway
 
->`The MLflow AI Gateway` service is a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index.html) for more details.
+>[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/gateway/index.html) service is a powerful tool designed to streamline the usage and management of various large 
+> language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface 
+> that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests.
+> See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index.html) for more details.
 
 ## Installation and Setup
 
@@ -43,6 +46,16 @@ Start the Gateway server:
 mlflow gateway start --config-path /path/to/config.yaml
 ```
 
+## Example provided by `MLflow`
+
+>The `mlflow.langchain` module provides an API for logging and loading `LangChain` models. 
+> This module exports multivariate LangChain models in the langchain flavor and univariate LangChain 
+> models in the pyfunc flavor.
+ 
+See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain.html).
+
+
+
 ## Completions Example
 
 ```python

docs/extras/integrations/providers/modelscope.mdx

@@ -1,20 +1,24 @@
 # ModelScope
 
+>[ModelScope](https://www.modelscope.cn/home) is a big repository of the models and datasets.
+
 This page covers how to use the modelscope ecosystem within LangChain.
 It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.
 
 ## Installation and Setup
 
-* Install the Python SDK with `pip install modelscope`
+Install the `modelscope` package.
+ 
+```bash
+pip install modelscope
+```
 
-## Wrappers
 
-### Embeddings
+## Text Embedding Models
 
-There exists a modelscope Embeddings wrapper, which you can access with 
 
 ```python
 from langchain.embeddings import ModelScopeEmbeddings
 ```
 
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/modelscope_hub.html)
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/modelscope_hub)

docs/extras/integrations/providers/nlpcloud.mdx

@@ -1,17 +1,31 @@
 # NLPCloud
 
-This page covers how to use the NLPCloud ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers.
+>[NLP Cloud](https://docs.nlpcloud.com/#introduction) is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. 
+
 
 ## Installation and Setup
-- Install the Python SDK with `pip install nlpcloud`
+
+- Install the `nlpcloud` package.
+
+```bash
+pip install nlpcloud
+```
+
 - Get an NLPCloud api key and set it as an environment variable (`NLPCLOUD_API_KEY`)
 
-## Wrappers
 
-### LLM
+## LLM
+
+See a [usage example](/docs/integrations/llms/nlpcloud).
 
-There exists an NLPCloud LLM wrapper, which you can access with 
 ```python
 from langchain.llms import NLPCloud
 ```
+
+## Text Embedding Models
+
+See a [usage example](/docs/integrations/text_embedding/nlp_cloud)
+
+```python
+from langchain.embeddings import NLPCloudEmbeddings
+```

docs/extras/integrations/providers/portkey/index.md

@@ -1,4 +1,10 @@
 # Portkey
+
+>[Portkey](https://docs.portkey.ai/overview/introduction) is a platform designed to streamline the deployment 
+> and management of Generative AI applications. 
+> It provides comprehensive features for monitoring, managing models,
+> and improving the performance of your AI applications.
+
 ## LLMOps for Langchain
 
 Portkey brings production readiness to Langchain. With Portkey, you can 

docs/extras/integrations/providers/portkey/logging_tracing_portkey.ipynb

@@ -1,19 +1,14 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Log, Trace, and Monitor Langchain LLM Calls\n",
+    "# Log, Trace, and Monitor\n",
     "\n",
     "When building apps or agents using Langchain, you end up making multiple API calls to fulfill a single user request. However, these requests are not chained when you want to analyse them. With [**Portkey**](/docs/ecosystem/integrations/portkey), all the embeddings, completion, and other requests from a single user request will get logged and traced to a common ID, enabling you to gain full visibility of user interactions.\n",
     "\n",
-    "This notebook serves as a step-by-step guide on how to integrate and use Portkey in your Langchain app."
+    "This notebook serves as a step-by-step guide on how to log, trace, and monitor Langchain LLM calls using `Portkey` in your Langchain app."
    ]
   },
   {
@@ -234,9 +229,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/providers/spacy.mdx

@@ -18,3 +18,11 @@ See a [usage example](/docs/modules/data_connection/document_transformers/text_s
 ```python
 from langchain.text_splitter import SpacyTextSplitter
 ```
+
+## Text Embedding Models
+
+See a [usage example](/docs/integrations/text_embedding/spacy_embedding)
+
+```python
+from langchain.embeddings.spacy_embeddings import SpacyEmbeddings
+```

docs/extras/integrations/providers/vectara/index.mdx

@@ -11,9 +11,10 @@ What is Vectara?
 - You can use Vectara's integration with LangChain as a Vector store or using the Retriever abstraction.
 
 ## Installation and Setup
-To use Vectara with LangChain no special installation steps are required. You just have to provide your customer_id, corpus ID, and an API key created within the Vectara console to enable indexing and searching.
+To use Vectara with LangChain no special installation steps are required. 
+To get started, follow our [quickstart](https://docs.vectara.com/docs/quickstart) guide to create an account, a corpus and an API key. 
+Once you have these, you can provide them as arguments to the Vectara vectorstore, or you can set them as environment variables.
 
-Alternatively these can be provided as environment variables
 - export `VECTARA_CUSTOMER_ID`="your_customer_id"
 - export `VECTARA_CORPUS_ID`="your_corpus_id"
 - export `VECTARA_API_KEY`="your-vectara-api-key"

docs/extras/integrations/retrievers/google_drive.ipynb

@@ -59,7 +59,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.retrievers import GoogleDriveRetriever"
+    "from langchain_googledrive.retrievers import GoogleDriveRetriever"
    ]
   },
   {

docs/extras/integrations/text_embedding/awadb.ipynb

@@ -5,9 +5,11 @@
    "id": "b14a24db",
    "metadata": {},
    "source": [
-    "# AwaEmbedding\n",
+    "# AwaDB\n",
     "\n",
-    "This notebook explains how to use AwaEmbedding, which is included in [awadb](https://github.com/awa-ai/awadb), to embedding texts in langchain."
+    ">[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.\n",
+    "\n",
+    "This notebook explains how to use `AwaEmbeddings` in LangChain."
    ]
   },
   {
@@ -101,7 +103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/text_embedding/bedrock.ipynb

@@ -5,7 +5,9 @@
    "id": "75e378f5-55d7-44b6-8e2e-6d7b8b171ec4",
    "metadata": {},
    "source": [
-    "# Bedrock Embeddings"
+    "# Bedrock\n",
+    "\n",
+    ">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.\n"
    ]
   },
   {
@@ -91,7 +93,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/text_embedding/bge_huggingface.ipynb

@@ -5,26 +5,29 @@
    "id": "719619d3",
    "metadata": {},
    "source": [
-    "# BGE Hugging Face Embeddings\n",
+    "# BGE on Hugging Face\n",
     "\n",
-    "This notebook shows how to use BGE Embeddings through Hugging Face"
+    ">[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).\n",
+    ">BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://www.baai.ac.cn/english.html). `BAAI` is a private non-profit organization engaged in AI research and development.\n",
+    "\n",
+    "This notebook shows how to use `BGE Embeddings` through `Hugging Face`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "f7a54279",
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# !pip install sentence_transformers"
+    "#!pip install sentence_transformers"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "9e1d5b6b",
    "metadata": {},
    "outputs": [],
@@ -43,12 +46,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "e59d1a89",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "384"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "embedding = hf.embed_query(\"hi this is harrison\")"
+    "embedding = hf.embed_query(\"hi this is harrison\")\n",
+    "len(embedding)"
    ]
   },
   {
@@ -76,7 +91,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/text_embedding/google_vertex_ai_palm.ipynb

@@ -1,13 +1,14 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Google Cloud Platform Vertex AI PaLM \n",
+    "# Google Vertex AI PaLM \n",
     "\n",
-    "Note: This is seperate from the Google PaLM integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on Google Cloud. \n",
+    ">[Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) is a service on Google Cloud exposing the embedding models. \n",
+    "\n",
+    "Note: This integration is seperate from the Google PaLM integration.\n",
     "\n",
     "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
     "\n",
@@ -96,7 +97,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {