langchain[patch]: Release 0.2.9 (#24327)

**Description:** At the moment neo4j wrapper is using setVectorProperty,
which is deprecated
([link](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_create_setVectorProperty)).
I replaced with the non-deprecated version.

Neo4j recently introduced a new cypher method to associate embeddings
into relations using "setRelationshipVectorProperty" method. In this PR
I also implemented a new method to perform this association maintaining
the same format used in the "add_embeddings" method which is used to
associate embeddings into Nodes.
I also included a test case for this new method.

.devcontainer/docker-compose.yaml

@@ -5,10 +5,10 @@ services:
       dockerfile: libs/langchain/dev.Dockerfile
       context: ..
     volumes:
-   # Update this to wherever you want VS Code to mount the folder of your project
+      # Update this to wherever you want VS Code to mount the folder of your project
       - ..:/workspaces/langchain:cached
     networks:
-      - langchain-network 
+      - langchain-network
   #   environment:
   #     MONGO_ROOT_USERNAME: root
   #     MONGO_ROOT_PASSWORD: example123
@@ -28,5 +28,3 @@ services:
 networks:
   langchain-network:
     driver: bridge
-    
-    

.github/scripts/check_diff.py

@@ -6,6 +6,7 @@ import sys
 import tomllib
 from collections import defaultdict
 from typing import Dict, List, Set
+from pathlib import Path
 
 
 LANGCHAIN_DIRS = [
@@ -26,17 +27,48 @@ def all_package_dirs() -> Set[str]:
 
 
 def dependents_graph() -> dict:
+    """
+    Construct a mapping of package -> dependents, such that we can
+    run tests on all dependents of a package when a change is made.
+    """
     dependents = defaultdict(set)
 
     for path in glob.glob("./libs/**/pyproject.toml", recursive=True):
         if "template" in path:
             continue
+
+        # load regular and test deps from pyproject.toml
         with open(path, "rb") as f:
             pyproject = tomllib.load(f)["tool"]["poetry"]
+
         pkg_dir = "libs" + "/".join(path.split("libs")[1].split("/")[:-1])
-        for dep in pyproject["dependencies"]:
+        for dep in [
+            *pyproject["dependencies"].keys(),
+            *pyproject["group"]["test"]["dependencies"].keys(),
+        ]:
             if "langchain" in dep:
                 dependents[dep].add(pkg_dir)
+                continue
+
+        # load extended deps from extended_testing_deps.txt
+        package_path = Path(path).parent
+        extended_requirement_path = package_path / "extended_testing_deps.txt"
+        if extended_requirement_path.exists():
+            with open(extended_requirement_path, "r") as f:
+                extended_deps = f.read().splitlines()
+                for depline in extended_deps:
+                    if depline.startswith("-e "):
+                        # editable dependency
+                        assert depline.startswith(
+                            "-e ../partners/"
+                        ), "Extended test deps should only editable install partner packages"
+                        partner = depline.split("partners/")[1]
+                        dep = f"langchain-{partner}"
+                    else:
+                        dep = depline.split("==")[0]
+
+                    if "langchain" in dep:
+                        dependents[dep].add(pkg_dir)
     return dependents
 
 

.github/scripts/check_prerelease_dependencies.py

@@ -0,0 +1,35 @@
+import sys
+import tomllib
+
+if __name__ == "__main__":
+    # Get the TOML file path from the command line argument
+    toml_file = sys.argv[1]
+
+    # read toml file
+    with open(toml_file, "rb") as file:
+        toml_data = tomllib.load(file)
+
+    # see if we're releasing an rc
+    version = toml_data["tool"]["poetry"]["version"]
+    releasing_rc = "rc" in version
+
+    # if not, iterate through dependencies and make sure none allow prereleases
+    if not releasing_rc:
+        dependencies = toml_data["tool"]["poetry"]["dependencies"]
+        for lib in dependencies:
+            dep_version = dependencies[lib]
+            dep_version_string = (
+                dep_version["version"] if isinstance(dep_version, dict) else dep_version
+            )
+
+            if "rc" in dep_version_string:
+                raise ValueError(
+                    f"Dependency {lib} has a prerelease version. Please remove this."
+                )
+
+            if isinstance(dep_version, dict) and dep_version.get(
+                "allow-prereleases", False
+            ):
+                raise ValueError(
+                    f"Dependency {lib} has allow-prereleases set to true. Please remove this."
+                )

.github/scripts/get_min_versions.py

@@ -9,6 +9,7 @@ MIN_VERSION_LIBS = [
     "langchain-community",
     "langchain",
     "langchain-text-splitters",
+    "SQLAlchemy",
 ]
 
 

.github/workflows/_compile_integration_test.yml

@@ -21,14 +21,6 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.8"
-          - "3.9"
-          - "3.10"
-          - "3.11"
-          - "3.12"
     name: "poetry run pytest -m compile tests/integration_tests #${{ inputs.python-version }}"
     steps:
       - uses: actions/checkout@v4

.github/workflows/_release.yml

@@ -221,6 +221,11 @@ jobs:
         run: make tests
         working-directory: ${{ inputs.working-directory }}
 
+      - name: Check for prerelease versions
+        working-directory: ${{ inputs.working-directory }}
+        run: |
+          poetry run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
+
       - name: Get minimum versions
         working-directory: ${{ inputs.working-directory }}
         id: min-version

.github/workflows/_test_doc_imports.yml

@@ -14,10 +14,6 @@ env:
 jobs:
   build:
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.12"
     name: "check doc imports #${{ inputs.python-version }}"
     steps:
       - uses: actions/checkout@v4

cookbook/README.md

@@ -57,4 +57,5 @@ Notebook | Description
 [two_agent_debate_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_agent_debate_tools.ipynb) | Simulate multi-agent dialogues where the agents can utilize various tools.
 [two_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_player_dnd.ipynb) | Simulate a two-player dungeons & dragons game, where a dialogue simulator class is used to coordinate the dialogue between the protagonist and the dungeon master.
 [wikibase_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/wikibase_agent.ipynb) | Create a simple wikibase agent that utilizes sparql generation, with testing done on http://wikidata.org.
-[oracleai_demo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/oracleai_demo.ipynb) | This guide outlines how to utilize Oracle AI Vector Search alongside Langchain for an end-to-end RAG pipeline, providing step-by-step examples. The process includes loading documents from various sources using OracleDocLoader, summarizing them either within or outside the database with OracleSummary, and generating embeddings similarly through OracleEmbeddings. It also covers chunking documents according to specific requirements using Advanced Oracle Capabilities from OracleTextSplitter, and finally, storing and indexing these documents in a Vector Store for querying with OracleVS.
+[oracleai_demo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/oracleai_demo.ipynb) | This guide outlines how to utilize Oracle AI Vector Search alongside Langchain for an end-to-end RAG pipeline, providing step-by-step examples. The process includes loading documents from various sources using OracleDocLoader, summarizing them either within or outside the database with OracleSummary, and generating embeddings similarly through OracleEmbeddings. It also covers chunking documents according to specific requirements using Advanced Oracle Capabilities from OracleTextSplitter, and finally, storing and indexing these documents in a Vector Store for querying with OracleVS.
+[rag-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag-locally-on-intel-cpu.ipynb) | Perform Retrieval-Augmented-Generation (RAG) on locally downloaded open-source models using langchain and open source tools and execute it on Intel Xeon CPU. We showed an example of how to apply RAG on Llama 2 model and enable it to answer the queries related to Intel Q1 2024 earnings release.

cookbook/rag-locally-on-intel-cpu.ipynb

@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "10f50955-be55-422f-8c62-3a32f8cf02ed",
+   "metadata": {},
+   "source": [
+    "# RAG application running locally on Intel Xeon CPU using langchain and open-source models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48113be6-44bb-4aac-aed3-76a1365b9561",
+   "metadata": {},
+   "source": [
+    "Author - Pratool Bharti (pratool.bharti@intel.com)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b10b54b-1572-4ea1-9c1e-1d29fcc3dcd9",
+   "metadata": {},
+   "source": [
+    "In this cookbook, we use langchain tools and open source models to execute locally on CPU. This notebook has been validated to run on Intel Xeon 8480+ CPU. Here we implement a RAG pipeline for Llama2 model to answer questions about Intel Q1 2024 earnings release."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acadbcec-3468-4926-8ce5-03b678041c0a",
+   "metadata": {},
+   "source": [
+    "**Create a conda or virtualenv environment with python >=3.10 and install following libraries**\n",
+    "<br>\n",
+    "\n",
+    "`pip install --upgrade langchain langchain-community langchainhub langchain-chroma bs4 gpt4all pypdf pysqlite3-binary` <br>\n",
+    "`pip install llama-cpp-python   --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c392c8-700a-42ec-8e94-806597f22e43",
+   "metadata": {},
+   "source": [
+    "**Load pysqlite3 in sys modules since ChromaDB requires sqlite3.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "145cd491-b388-4ea7-bdc8-2f4995cac6fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__import__(\"pysqlite3\")\n",
+    "import sys\n",
+    "\n",
+    "sys.modules[\"sqlite3\"] = sys.modules.pop(\"pysqlite3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14dde7e2-b236-49b9-b3a0-08c06410418c",
+   "metadata": {},
+   "source": [
+    "**Import essential components from langchain to load and split data**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "887643ba-249e-48d6-9aa7-d25087e8dfbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import PyPDFLoader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "922c0eba-8736-4de5-bd2f-3d0f00b16e43",
+   "metadata": {},
+   "source": [
+    "**Download Intel Q1 2024 earnings release**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2d6a2419-5338-4188-8615-a40a65ff8019",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2024-07-15 15:04:43--  https://d1io3yog0oux5.cloudfront.net/_11d435a500963f99155ee058df09f574/intel/db/887/9014/earnings_release/Q1+24_EarningsRelease_FINAL.pdf\n",
+      "Resolving proxy-dmz.intel.com (proxy-dmz.intel.com)... 10.7.211.16\n",
+      "Connecting to proxy-dmz.intel.com (proxy-dmz.intel.com)|10.7.211.16|:912... connected.\n",
+      "Proxy request sent, awaiting response... 200 OK\n",
+      "Length: 133510 (130K) [application/pdf]\n",
+      "Saving to: ‘intel_q1_2024_earnings.pdf’\n",
+      "\n",
+      "intel_q1_2024_earni 100%[===================>] 130.38K  --.-KB/s    in 0.005s  \n",
+      "\n",
+      "2024-07-15 15:04:44 (24.6 MB/s) - ‘intel_q1_2024_earnings.pdf’ saved [133510/133510]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget  'https://d1io3yog0oux5.cloudfront.net/_11d435a500963f99155ee058df09f574/intel/db/887/9014/earnings_release/Q1+24_EarningsRelease_FINAL.pdf' -O intel_q1_2024_earnings.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3612627-e105-453d-8a50-bbd6e39dedb5",
+   "metadata": {},
+   "source": [
+    "**Loading earning release pdf document through PyPDFLoader**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cac6278e-ebad-4224-a062-bf6daca24cb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyPDFLoader(\"intel_q1_2024_earnings.pdf\")\n",
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7dca43b-1c62-41df-90c7-6ed2904f823d",
+   "metadata": {},
+   "source": [
+    "**Splitting entire document in several chunks with each chunk size is 500 tokens**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4486adbe-0d0e-4685-8c08-c1774ed6e993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
+    "all_splits = text_splitter.split_documents(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af142346-e793-4a52-9a56-63e3be416b3d",
+   "metadata": {},
+   "source": [
+    "**Looking at the first split of the document**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e4240fd1-898e-4bfc-a377-02c9bc25b56e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': 'intel_q1_2024_earnings.pdf', 'page': 0}, page_content='Intel Corporation\\n2200 Mission College Blvd.\\nSanta Clara, CA 95054-1549\\n                                                         \\nNews Release\\n Intel Reports First -Quarter 2024  Financial Results\\nNEWS SUMMARY\\n▪First-quarter revenue of $12.7 billion , up 9%  year over year (YoY).\\n▪First-quarter GAAP earnings (loss) per share (EPS) attributable to Intel was $(0.09) ; non-GAAP EPS \\nattributable to Intel was $0.18 .')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_splits[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b88d2632-7c1b-49ef-a691-c0eb67d23e6a",
+   "metadata": {},
+   "source": [
+    "**One of the major step in RAG is to convert each split of document into embeddings and store in a vector database such that searching relevant documents are efficient.** <br>\n",
+    "**For that, importing Chroma vector database from langchain. Also, importing open source GPT4All for embedding models**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9ff99dd7-9d47-4239-ba0a-d775792334ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_chroma import Chroma\n",
+    "from langchain_community.embeddings import GPT4AllEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5d1f4dd-dd8d-4a20-95d1-2dbdd204375a",
+   "metadata": {},
+   "source": [
+    "**In next step, we will download one of the most popular embedding model \"all-MiniLM-L6-v2\". Find more details of the model at this link https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "05db3494-5d8e-4a13-9941-26330a86f5e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = \"all-MiniLM-L6-v2.gguf2.f16.gguf\"\n",
+    "gpt4all_kwargs = {\"allow_download\": \"True\"}\n",
+    "embeddings = GPT4AllEmbeddings(model_name=model_name, gpt4all_kwargs=gpt4all_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e53999e-1983-46ac-8039-2783e194c3ae",
+   "metadata": {},
+   "source": [
+    "**Store all the embeddings in the Chroma database**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0922951a-9ddf-4761-973d-8e9a86f61284",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorstore = Chroma.from_documents(documents=all_splits, embedding=embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29f94fa0-6c75-4a65-a1a3-debc75422479",
+   "metadata": {},
+   "source": [
+    "**Now, let's find relevant splits from the documents related to the question**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "88c8152d-ec7a-4f0b-9d86-877789407537",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"What is Intel CCG revenue in Q1 2024\"\n",
+    "docs = vectorstore.similarity_search(question)\n",
+    "print(len(docs))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53330c6b-cb0f-43f9-b379-2e57ac1e5335",
+   "metadata": {},
+   "source": [
+    "**Look at the first retrieved document from the vector database**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "43a6d94f-b5c4-47b0-a353-2db4c3d24d9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'page': 1, 'source': 'intel_q1_2024_earnings.pdf'}, page_content='Client Computing Group (CCG) $7.5 billion up31%\\nData Center and AI (DCAI) $3.0 billion up5%\\nNetwork and Edge (NEX) $1.4 billion down 8%\\nTotal Intel Products revenue $11.9 billion up17%\\nIntel Foundry $4.4 billion down 10%\\nAll other:\\nAltera $342 million down 58%\\nMobileye $239 million down 48%\\nOther $194 million up17%\\nTotal all other revenue $775 million down 46%\\nIntersegment eliminations $(4.4) billion\\nTotal net revenue $12.7 billion up9%\\nIntel Products Highlights')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64ba074f-4b36-442e-b7e2-b26d6e2815c3",
+   "metadata": {},
+   "source": [
+    "**Download Lllama-2 model from Huggingface and store locally** <br>\n",
+    "**You can download different quantization variant of Lllama-2 model from the link below. We are using Q8 version here (7.16GB).** <br>\n",
+    "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8dd0811-6f43-4bc6-b854-2ab377639c9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!huggingface-cli download TheBloke/Llama-2-7b-Chat-GGUF llama-2-7b-chat.Q8_0.gguf --local-dir . --local-dir-use-symlinks False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3895b1f5-f51d-4539-abf0-af33d7ca48ea",
+   "metadata": {},
+   "source": [
+    "**Import langchain components required to load downloaded LLMs model**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "fb087088-aa62-44c0-8356-061e9b9f1186",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain_community.llms import LlamaCpp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a8a111e-2614-4b70-b034-85cd3e7304cb",
+   "metadata": {},
+   "source": [
+    "**Loading the local Lllama-2 model using Llama-cpp library**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fb917da2-c0d7-4995-b56d-26254276e0da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from llama-2-7b-chat.Q8_0.gguf (version GGUF V2)\n",
+      "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
+      "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
+      "llama_model_loader: - kv   1:                               general.name str              = LLaMA v2\n",
+      "llama_model_loader: - kv   2:                       llama.context_length u32              = 4096\n",
+      "llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096\n",
+      "llama_model_loader: - kv   4:                          llama.block_count u32              = 32\n",
+      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 11008\n",
+      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128\n",
+      "llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32\n",
+      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 32\n",
+      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000001\n",
+      "llama_model_loader: - kv  10:                          general.file_type u32              = 7\n",
+      "llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama\n",
+      "llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr[str,32000]   = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
+      "llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...\n",
+      "llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
+      "llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 1\n",
+      "llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 2\n",
+      "llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32              = 0\n",
+      "llama_model_loader: - kv  18:               general.quantization_version u32              = 2\n",
+      "llama_model_loader: - type  f32:   65 tensors\n",
+      "llama_model_loader: - type q8_0:  226 tensors\n",
+      "llm_load_vocab: special tokens cache size = 259\n",
+      "llm_load_vocab: token to piece cache size = 0.1684 MB\n",
+      "llm_load_print_meta: format           = GGUF V2\n",
+      "llm_load_print_meta: arch             = llama\n",
+      "llm_load_print_meta: vocab type       = SPM\n",
+      "llm_load_print_meta: n_vocab          = 32000\n",
+      "llm_load_print_meta: n_merges         = 0\n",
+      "llm_load_print_meta: vocab_only       = 0\n",
+      "llm_load_print_meta: n_ctx_train      = 4096\n",
+      "llm_load_print_meta: n_embd           = 4096\n",
+      "llm_load_print_meta: n_layer          = 32\n",
+      "llm_load_print_meta: n_head           = 32\n",
+      "llm_load_print_meta: n_head_kv        = 32\n",
+      "llm_load_print_meta: n_rot            = 128\n",
+      "llm_load_print_meta: n_swa            = 0\n",
+      "llm_load_print_meta: n_embd_head_k    = 128\n",
+      "llm_load_print_meta: n_embd_head_v    = 128\n",
+      "llm_load_print_meta: n_gqa            = 1\n",
+      "llm_load_print_meta: n_embd_k_gqa     = 4096\n",
+      "llm_load_print_meta: n_embd_v_gqa     = 4096\n",
+      "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
+      "llm_load_print_meta: f_norm_rms_eps   = 1.0e-06\n",
+      "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
+      "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
+      "llm_load_print_meta: f_logit_scale    = 0.0e+00\n",
+      "llm_load_print_meta: n_ff             = 11008\n",
+      "llm_load_print_meta: n_expert         = 0\n",
+      "llm_load_print_meta: n_expert_used    = 0\n",
+      "llm_load_print_meta: causal attn      = 1\n",
+      "llm_load_print_meta: pooling type     = 0\n",
+      "llm_load_print_meta: rope type        = 0\n",
+      "llm_load_print_meta: rope scaling     = linear\n",
+      "llm_load_print_meta: freq_base_train  = 10000.0\n",
+      "llm_load_print_meta: freq_scale_train = 1\n",
+      "llm_load_print_meta: n_ctx_orig_yarn  = 4096\n",
+      "llm_load_print_meta: rope_finetuned   = unknown\n",
+      "llm_load_print_meta: ssm_d_conv       = 0\n",
+      "llm_load_print_meta: ssm_d_inner      = 0\n",
+      "llm_load_print_meta: ssm_d_state      = 0\n",
+      "llm_load_print_meta: ssm_dt_rank      = 0\n",
+      "llm_load_print_meta: model type       = 7B\n",
+      "llm_load_print_meta: model ftype      = Q8_0\n",
+      "llm_load_print_meta: model params     = 6.74 B\n",
+      "llm_load_print_meta: model size       = 6.67 GiB (8.50 BPW) \n",
+      "llm_load_print_meta: general.name     = LLaMA v2\n",
+      "llm_load_print_meta: BOS token        = 1 '<s>'\n",
+      "llm_load_print_meta: EOS token        = 2 '</s>'\n",
+      "llm_load_print_meta: UNK token        = 0 '<unk>'\n",
+      "llm_load_print_meta: LF token         = 13 '<0x0A>'\n",
+      "llm_load_print_meta: max token length = 48\n",
+      "llm_load_tensors: ggml ctx size =    0.14 MiB\n",
+      "llm_load_tensors:        CPU buffer size =  6828.64 MiB\n",
+      "...................................................................................................\n",
+      "llama_new_context_with_model: n_ctx      = 2048\n",
+      "llama_new_context_with_model: n_batch    = 512\n",
+      "llama_new_context_with_model: n_ubatch   = 512\n",
+      "llama_new_context_with_model: flash_attn = 0\n",
+      "llama_new_context_with_model: freq_base  = 10000.0\n",
+      "llama_new_context_with_model: freq_scale = 1\n",
+      "llama_kv_cache_init:        CPU KV buffer size =  1024.00 MiB\n",
+      "llama_new_context_with_model: KV self size  = 1024.00 MiB, K (f16):  512.00 MiB, V (f16):  512.00 MiB\n",
+      "llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB\n",
+      "llama_new_context_with_model:        CPU compute buffer size =   164.01 MiB\n",
+      "llama_new_context_with_model: graph nodes  = 1030\n",
+      "llama_new_context_with_model: graph splits = 1\n",
+      "AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | \n",
+      "Model metadata: {'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.context_length': '4096', 'general.name': 'LLaMA v2', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '11008', 'llama.attention.layer_norm_rms_epsilon': '0.000001', 'llama.rope.dimension_count': '128', 'llama.attention.head_count': '32', 'tokenizer.ggml.bos_token_id': '1', 'llama.block_count': '32', 'llama.attention.head_count_kv': '32', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '7'}\n",
+      "Using fallback chat format: llama-2\n"
+     ]
+    }
+   ],
+   "source": [
+    "llm = LlamaCpp(\n",
+    "    model_path=\"llama-2-7b-chat.Q8_0.gguf\",\n",
+    "    n_gpu_layers=-1,\n",
+    "    n_batch=512,\n",
+    "    n_ctx=2048,\n",
+    "    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls\n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43e06f56-ef97-451b-87d9-8465ea442aed",
+   "metadata": {},
+   "source": [
+    "**Now let's ask the same question to Llama model without showing them the earnings release.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1033dd82-5532-437d-a548-27695e109589",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "?\n",
+      "(NASDAQ:INTC)\n",
+      "Intel's CCG (Client Computing Group) revenue for Q1 2024 was $9.6 billion, a decrease of 35% from the previous quarter and a decrease of 42% from the same period last year."
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =     131.20 ms\n",
+      "llama_print_timings:      sample time =      16.05 ms /    68 runs   (    0.24 ms per token,  4236.76 tokens per second)\n",
+      "llama_print_timings: prompt eval time =     131.14 ms /    16 tokens (    8.20 ms per token,   122.01 tokens per second)\n",
+      "llama_print_timings:        eval time =    3225.00 ms /    67 runs   (   48.13 ms per token,    20.78 tokens per second)\n",
+      "llama_print_timings:       total time =    3466.40 ms /    83 tokens\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"?\\n(NASDAQ:INTC)\\nIntel's CCG (Client Computing Group) revenue for Q1 2024 was $9.6 billion, a decrease of 35% from the previous quarter and a decrease of 42% from the same period last year.\""
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm.invoke(question)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75f5cb10-746f-4e37-9386-b85a4d2b84ef",
+   "metadata": {},
+   "source": [
+    "**As you can see, model is giving wrong information. Correct asnwer is CCG revenue in Q1 2024 is $7.5B. Now let's apply RAG using the earning release document**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f4150ec-5692-4756-b11a-22feb7ab88ff",
+   "metadata": {},
+   "source": [
+    "**in RAG, we modify the input prompt by adding relevent documents with the question. Here, we use one of the popular RAG prompt**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "226c14b0-f43e-4a1f-a1e4-04731d467ec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: {question} \\nContext: {context} \\nAnswer:\"))]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import hub\n",
+    "\n",
+    "rag_prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "rag_prompt.messages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77deb6a0-0950-450a-916a-f2a029676c20",
+   "metadata": {},
+   "source": [
+    "**Appending all retreived documents in a single document**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2dbc3327-6ef3-4c1f-8797-0c71964b0921",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e2d9f18-49d0-43a3-bea8-78746ffa86b7",
+   "metadata": {},
+   "source": [
+    "**The last step is to create a chain using langchain tool that will create an e2e pipeline. It will take question and context as an input.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "427379c2-51ff-4e0f-8278-a45221363299",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough, RunnablePick\n",
+    "\n",
+    "# Chain\n",
+    "chain = (\n",
+    "    RunnablePassthrough.assign(context=RunnablePick(\"context\") | format_docs)\n",
+    "    | rag_prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "095d6280-c949-4d00-8e32-8895a82d245f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Llama.generate: prefix-match hit\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Based on the provided context, Intel CCG revenue in Q1 2024 was $7.5 billion up 31%."
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =     131.20 ms\n",
+      "llama_print_timings:      sample time =       7.74 ms /    31 runs   (    0.25 ms per token,  4004.13 tokens per second)\n",
+      "llama_print_timings: prompt eval time =    2529.41 ms /   674 tokens (    3.75 ms per token,   266.46 tokens per second)\n",
+      "llama_print_timings:        eval time =    1542.94 ms /    30 runs   (   51.43 ms per token,    19.44 tokens per second)\n",
+      "llama_print_timings:       total time =    4123.68 ms /   704 tokens\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Based on the provided context, Intel CCG revenue in Q1 2024 was $7.5 billion up 31%.'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"context\": docs, \"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "638364b2-6bd2-4471-9961-d3a1d1b9d4ee",
+   "metadata": {},
+   "source": [
+    "**Now we see the results are correct as it is mentioned in earnings release.** <br>\n",
+    "**To further automate, we will create a chain that will take input as question and retriever so that we don't need to retrieve documents seperately**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4654e5b7-635f-4767-8b31-4c430164cdd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = vectorstore.as_retriever()\n",
+    "qa_chain = (\n",
+    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+    "    | rag_prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0979f393-fd0a-4e82-b844-68371c6ad68f",
+   "metadata": {},
+   "source": [
+    "**Now we only need to pass the question to the chain and it will fetch the contexts directly from the vector database to generate the answer**\n",
+    "<br>\n",
+    "**Let's try with another question**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "3ea07b82-e6ec-4084-85f4-191373530172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Llama.generate: prefix-match hit\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " According to the provided context, Intel DCAI revenue in Q1 2024 was $3.0 billion up 5%."
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =     131.20 ms\n",
+      "llama_print_timings:      sample time =       6.28 ms /    31 runs   (    0.20 ms per token,  4937.88 tokens per second)\n",
+      "llama_print_timings: prompt eval time =    2681.93 ms /   730 tokens (    3.67 ms per token,   272.19 tokens per second)\n",
+      "llama_print_timings:        eval time =    1471.07 ms /    30 runs   (   49.04 ms per token,    20.39 tokens per second)\n",
+      "llama_print_timings:       total time =    4206.77 ms /   760 tokens\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' According to the provided context, Intel DCAI revenue in Q1 2024 was $3.0 billion up 5%.'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qa_chain.invoke(\"what is Intel DCAI revenue in Q1 2024?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9407f2a0-4a35-4315-8e96-02fcb80f210c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rag-on-intel",
+   "language": "python",
+   "name": "rag-on-intel"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/Makefile

@@ -13,7 +13,7 @@ OUTPUT_NEW_DOCS_DIR = $(OUTPUT_NEW_DIR)/docs
 
 PYTHON = .venv/bin/python
 
-PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec test -e "{}/pyproject.toml" \; -print | grep -vE "airbyte|ibm" | tr '\n' ' ')
+PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec test -e "{}/pyproject.toml" \; -print | grep -vE "airbyte|ibm|couchbase" | tr '\n' ' ')
 
 PORT ?= 3001
 

docs/api_reference/conf.py

@@ -178,3 +178,10 @@ autosummary_generate = True
 
 html_copy_source = False
 html_show_sourcelink = False
+
+# Set canonical URL from the Read the Docs Domain
+html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "")
+
+# Tell Jinja2 templates the build is running on Read the Docs
+if os.environ.get("READTHEDOCS", "") == "True":
+    html_context["READTHEDOCS"] = True

docs/api_reference/create_api_rst.py

@@ -78,7 +78,7 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
             continue
 
         if inspect.isclass(type_):
-            # The clasification of the class is used to select a template
+            # The type of the class is used to select a template
             # for the object when rendering the documentation.
             # See `templates` directory for defined templates.
             # This is a hacky solution to distinguish between different

docs/docs/concepts.mdx

@@ -55,6 +55,7 @@ A developer platform that lets you debug, test, evaluate, and monitor LLM applic
     dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
   }}
   title="LangChain Framework Overview"
+  style={{ width: "100%" }}
 />
 
 ## LangChain Expression Language (LCEL)
@@ -521,7 +522,7 @@ Generally, when designing tools to be used by a chat model or LLM, it is importa
 
 For specifics on how to use tools, see the [relevant how-to guides here](/docs/how_to/#tools).
 
-To use an existing pre-built tool, see [here](docs/integrations/tools/) for a list of pre-built tools. 
+To use an existing pre-built tool, see [here](/docs/integrations/tools/) for a list of pre-built tools.
 
 ### Toolkits
 
@@ -821,7 +822,7 @@ We recommend this method as a starting point when working with structured output
 - If multiple underlying techniques are supported, you can supply a `method` parameter to
 [toggle which one is used](/docs/how_to/structured_output/#advanced-specifying-the-method-for-structuring-outputs).
 
-You may want or need to use other techiniques if:
+You may want or need to use other techniques if:
 
 - The chat model you are using does not support tool calling.
 - You are working with very complex schemas and the model is having trouble generating outputs that conform.
@@ -1130,7 +1131,7 @@ Table columns:
 | Token    | [many classes](/docs/how_to/split_by_token/)                                                                                                                                  | Tokens                                                                                                          |               | Splits text on tokens. There exist a few different ways to measure tokens.                                                                                                                                                                                                   |
 | Character  | [CharacterTextSplitter](/docs/how_to/character_text_splitter/)                                                                                                                | A user defined character                                                                                        |               | Splits text based on a user defined character. One of the simpler methods.                                                                                                                                                                                                   |
 | Semantic Chunker (Experimental) | [SemanticChunker](/docs/how_to/semantic-chunker/)                                                                                                                             | Sentences                                                                                                       |               | First splits on sentences. Then combines ones next to each other if they are semantically similar enough. Taken from [Greg Kamradt](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb) |
-| Integration: AI21 Semantic | [AI21SemanticTextSplitter](/docs/integrations/document_transformers/ai21_semantic_text_splitter/)                                                                                                                    |    ✅           | Identifies distinct topics that form coherent pieces of text and splits along those.                                                                                                                                                                                         |
+| Integration: AI21 Semantic | [AI21SemanticTextSplitter](/docs/integrations/document_transformers/ai21_semantic_text_splitter/)                                                                                                 |                  |    ✅           | Identifies distinct topics that form coherent pieces of text and splits along those.                                                                                                                                                                                         |
 
 ### Evaluation
 <span data-heading-keywords="evaluation,evaluate"></span>

docs/docs/contributing/documentation/style_guide.mdx

@@ -33,6 +33,8 @@ Some examples include:
 
 - [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain/)
 - [Build a Retrieval Augmented Generation (RAG) App](/docs/tutorials/rag/)
+
+A good structural rule of thumb is to follow the structure of this [example from Numpy](https://numpy.org/numpy-tutorials/content/tutorial-svd.html).
   
 Here are some high-level tips on writing a good tutorial:
 

docs/docs/contributing/integrations.mdx

@@ -11,7 +11,7 @@ There are a few different places you can contribute integrations for LangChain:
 - **Community**: For lighter-weight integrations that are primarily maintained by LangChain and the Open Source Community.
 - **Partner Packages**: For independent packages that are co-maintained by LangChain and a partner.
 
-For the most part, new integrations should be added to the Community package. Partner packages require more maintenance as separate packages, so please confirm with the LangChain team before creating a new partner package.
+For the most part, **new integrations should be added to the Community package**. Partner packages require more maintenance as separate packages, so please confirm with the LangChain team before creating a new partner package.
 
 In the following sections, we'll walk through how to contribute to each of these packages from a fake company, `Parrot Link AI`.
 
@@ -60,6 +60,10 @@ And add documentation to:
 
 ## Partner package in LangChain repo
 
+:::caution
+Before starting a **partner** package, please confirm your intent with the LangChain team. Partner packages require more maintenance as separate packages, so we will close PRs that add new partner packages without prior discussion. See the above section for how to add a community integration.
+:::
+
 Partner packages can be hosted in the `LangChain` monorepo or in an external repo.
 
 Partner package in the `LangChain` repo is placed in `libs/partners/{partner}` 

docs/docs/how_to/callbacks_custom_events.ipynb

@@ -0,0 +1,342 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to dispatch custom callback events\n",
+    "\n",
+    ":::info Prerequisites\n",
+    "\n",
+    "This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "- [Callbacks](/docs/concepts/#callbacks)\n",
+    "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
+    "- [Astream Events API](/docs/concepts/#astream_events) the `astream_events` method will surface custom callback events.\n",
+    ":::\n",
+    "\n",
+    "In some situations, you may want to dipsatch a custom callback event from within a [Runnable](/docs/concepts/#runnable-interface) so it can be surfaced\n",
+    "in a custom callback handler or via the [Astream Events API](/docs/concepts/#astream_events).\n",
+    "\n",
+    "For example, if you have a long running tool with multiple steps, you can dispatch custom events between the steps and use these custom events to monitor progress.\n",
+    "You could also surface these custom events to an end user of your application to show them how the current task is progressing.\n",
+    "\n",
+    "To dispatch a custom event you need to decide on two attributes for the event: the `name` and the `data`.\n",
+    "\n",
+    "| Attribute | Type | Description                                                                                              |\n",
+    "|-----------|------|----------------------------------------------------------------------------------------------------------|\n",
+    "| name      | str  | A user defined name for the event.                                                                       |\n",
+    "| data      | Any  | The data associated with the event. This can be anything, though we suggest making it JSON serializable. |\n",
+    "\n",
+    "\n",
+    ":::{.callout-important}\n",
+    "* Dispatching custom callback events requires `langchain-core>=0.2.15`.\n",
+    "* Custom callback events can only be dispatched from within an existing `Runnable`.\n",
+    "* If using `astream_events`, you must use `version='v2'` to see custom events.\n",
+    "* Sending or rendering custom callbacks events in LangSmith is not yet supported.\n",
+    ":::\n",
+    "\n",
+    "\n",
+    ":::caution COMPATIBILITY\n",
+    "LangChain cannot automatically propagate configuration, including callbacks necessary for astream_events(), to child runnables if you are running async code in python<=3.10. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "\n",
+    "If you are running python<=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
+    "\n",
+    "If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in other Python versions.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "\n",
+    "%pip install -qU langchain-core"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Astream Events API\n",
+    "\n",
+    "The most useful way to consume custom events is via the [Astream Events API](/docs/concepts/#astream_events).\n",
+    "\n",
+    "We can use the `async` `adispatch_custom_event` API to emit custom events in an async setting. \n",
+    "\n",
+    "\n",
+    ":::{.callout-important}\n",
+    "\n",
+    "To see custom events via the astream events API, you need to use the newer `v2` API of `astream_events`.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'event': 'on_chain_start', 'data': {'input': 'hello world'}, 'name': 'foo', 'tags': [], 'run_id': 'f354ffe8-4c22-4881-890a-c1cad038a9a6', 'metadata': {}, 'parent_ids': []}\n",
+      "{'event': 'on_custom_event', 'run_id': 'f354ffe8-4c22-4881-890a-c1cad038a9a6', 'name': 'event1', 'tags': [], 'metadata': {}, 'data': {'x': 'hello world'}, 'parent_ids': []}\n",
+      "{'event': 'on_custom_event', 'run_id': 'f354ffe8-4c22-4881-890a-c1cad038a9a6', 'name': 'event2', 'tags': [], 'metadata': {}, 'data': 5, 'parent_ids': []}\n",
+      "{'event': 'on_chain_stream', 'run_id': 'f354ffe8-4c22-4881-890a-c1cad038a9a6', 'name': 'foo', 'tags': [], 'metadata': {}, 'data': {'chunk': 'hello world'}, 'parent_ids': []}\n",
+      "{'event': 'on_chain_end', 'data': {'output': 'hello world'}, 'run_id': 'f354ffe8-4c22-4881-890a-c1cad038a9a6', 'name': 'foo', 'tags': [], 'metadata': {}, 'parent_ids': []}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.callbacks.manager import (\n",
+    "    adispatch_custom_event,\n",
+    ")\n",
+    "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_core.runnables.config import RunnableConfig\n",
+    "\n",
+    "\n",
+    "@RunnableLambda\n",
+    "async def foo(x: str) -> str:\n",
+    "    await adispatch_custom_event(\"event1\", {\"x\": x})\n",
+    "    await adispatch_custom_event(\"event2\", 5)\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "async for event in foo.astream_events(\"hello world\", version=\"v2\"):\n",
+    "    print(event)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In python <= 3.10, you must propagate the config manually!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'event': 'on_chain_start', 'data': {'input': 'hello world'}, 'name': 'bar', 'tags': [], 'run_id': 'c787b09d-698a-41b9-8290-92aaa656f3e7', 'metadata': {}, 'parent_ids': []}\n",
+      "{'event': 'on_custom_event', 'run_id': 'c787b09d-698a-41b9-8290-92aaa656f3e7', 'name': 'event1', 'tags': [], 'metadata': {}, 'data': {'x': 'hello world'}, 'parent_ids': []}\n",
+      "{'event': 'on_custom_event', 'run_id': 'c787b09d-698a-41b9-8290-92aaa656f3e7', 'name': 'event2', 'tags': [], 'metadata': {}, 'data': 5, 'parent_ids': []}\n",
+      "{'event': 'on_chain_stream', 'run_id': 'c787b09d-698a-41b9-8290-92aaa656f3e7', 'name': 'bar', 'tags': [], 'metadata': {}, 'data': {'chunk': 'hello world'}, 'parent_ids': []}\n",
+      "{'event': 'on_chain_end', 'data': {'output': 'hello world'}, 'run_id': 'c787b09d-698a-41b9-8290-92aaa656f3e7', 'name': 'bar', 'tags': [], 'metadata': {}, 'parent_ids': []}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.callbacks.manager import (\n",
+    "    adispatch_custom_event,\n",
+    ")\n",
+    "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_core.runnables.config import RunnableConfig\n",
+    "\n",
+    "\n",
+    "@RunnableLambda\n",
+    "async def bar(x: str, config: RunnableConfig) -> str:\n",
+    "    \"\"\"An example that shows how to manually propagate config.\n",
+    "\n",
+    "    You must do this if you're running python<=3.10.\n",
+    "    \"\"\"\n",
+    "    await adispatch_custom_event(\"event1\", {\"x\": x}, config=config)\n",
+    "    await adispatch_custom_event(\"event2\", 5, config=config)\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "async for event in bar.astream_events(\"hello world\", version=\"v2\"):\n",
+    "    print(event)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Async Callback Handler\n",
+    "\n",
+    "You can also consume the dispatched event via an async callback handler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Received event event1 with data: {'x': 1}, with tags: ['foo', 'bar'], with metadata: {} and run_id: a62b84be-7afd-4829-9947-7165df1f37d9\n",
+      "Received event event2 with data: 5, with tags: ['foo', 'bar'], with metadata: {} and run_id: a62b84be-7afd-4829-9947-7165df1f37d9\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Any, Dict, List, Optional\n",
+    "from uuid import UUID\n",
+    "\n",
+    "from langchain_core.callbacks import AsyncCallbackHandler\n",
+    "from langchain_core.callbacks.manager import (\n",
+    "    adispatch_custom_event,\n",
+    ")\n",
+    "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_core.runnables.config import RunnableConfig\n",
+    "\n",
+    "\n",
+    "class AsyncCustomCallbackHandler(AsyncCallbackHandler):\n",
+    "    async def on_custom_event(\n",
+    "        self,\n",
+    "        name: str,\n",
+    "        data: Any,\n",
+    "        *,\n",
+    "        run_id: UUID,\n",
+    "        tags: Optional[List[str]] = None,\n",
+    "        metadata: Optional[Dict[str, Any]] = None,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> None:\n",
+    "        print(\n",
+    "            f\"Received event {name} with data: {data}, with tags: {tags}, with metadata: {metadata} and run_id: {run_id}\"\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "@RunnableLambda\n",
+    "async def bar(x: str, config: RunnableConfig) -> str:\n",
+    "    \"\"\"An example that shows how to manually propagate config.\n",
+    "\n",
+    "    You must do this if you're running python<=3.10.\n",
+    "    \"\"\"\n",
+    "    await adispatch_custom_event(\"event1\", {\"x\": x}, config=config)\n",
+    "    await adispatch_custom_event(\"event2\", 5, config=config)\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "async_handler = AsyncCustomCallbackHandler()\n",
+    "await foo.ainvoke(1, {\"callbacks\": [async_handler], \"tags\": [\"foo\", \"bar\"]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sync Callback Handler\n",
+    "\n",
+    "Let's see how to emit custom events in a sync environment using `dispatch_custom_event`.\n",
+    "\n",
+    "You **must** call `dispatch_custom_event` from within an existing `Runnable`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Received event event1 with data: {'x': 1}, with tags: ['foo', 'bar'], with metadata: {} and run_id: 27b5ce33-dc26-4b34-92dd-08a89cb22268\n",
+      "Received event event2 with data: {'x': 1}, with tags: ['foo', 'bar'], with metadata: {} and run_id: 27b5ce33-dc26-4b34-92dd-08a89cb22268\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Any, Dict, List, Optional\n",
+    "from uuid import UUID\n",
+    "\n",
+    "from langchain_core.callbacks import BaseCallbackHandler\n",
+    "from langchain_core.callbacks.manager import (\n",
+    "    dispatch_custom_event,\n",
+    ")\n",
+    "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_core.runnables.config import RunnableConfig\n",
+    "\n",
+    "\n",
+    "class CustomHandler(BaseCallbackHandler):\n",
+    "    def on_custom_event(\n",
+    "        self,\n",
+    "        name: str,\n",
+    "        data: Any,\n",
+    "        *,\n",
+    "        run_id: UUID,\n",
+    "        tags: Optional[List[str]] = None,\n",
+    "        metadata: Optional[Dict[str, Any]] = None,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> None:\n",
+    "        print(\n",
+    "            f\"Received event {name} with data: {data}, with tags: {tags}, with metadata: {metadata} and run_id: {run_id}\"\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "@RunnableLambda\n",
+    "def foo(x: int, config: RunnableConfig) -> int:\n",
+    "    dispatch_custom_event(\"event1\", {\"x\": x})\n",
+    "    dispatch_custom_event(\"event2\", {\"x\": x})\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "handler = CustomHandler()\n",
+    "foo.invoke(1, {\"callbacks\": [handler], \"tags\": [\"foo\", \"bar\"]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "You've seen how to emit custom events, you can check out the more in depth guide for [astream events](/docs/how_to/streaming/#using-stream-events) which is the easiest way to leverage custom events."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/how_to/chat_models_universal_init.ipynb

@@ -15,6 +15,12 @@
     "\n",
     "Make sure you have the integration packages installed for any model providers you want to support. E.g. you should have `langchain-openai` installed to init an OpenAI model.\n",
     "\n",
+    ":::\n",
+    "\n",
+    ":::info Requires ``langchain >= 0.2.8``\n",
+    "\n",
+    "This functionality was added in ``langchain-core == 0.2.8``. Please make sure your package is up to date.\n",
+    "\n",
     ":::"
    ]
   },
@@ -25,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain langchain-openai langchain-anthropic langchain-google-vertexai"
+    "%pip install -qU langchain>=0.2.8 langchain-openai langchain-anthropic langchain-google-vertexai"
    ]
   },
   {
@@ -76,32 +82,6 @@
     "print(\"Gemini 1.5: \" + gemini_15.invoke(\"what's your name\").content + \"\\n\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "fff9a4c8-b6ee-4a1a-8d3d-0ecaa312d4ed",
-   "metadata": {},
-   "source": [
-    "## Simple config example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "75c25d39-bf47-4b51-a6c6-64d9c572bfd6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "user_config = {\n",
-    "    \"model\": \"...user-specified...\",\n",
-    "    \"model_provider\": \"...user-specified...\",\n",
-    "    \"temperature\": 0,\n",
-    "    \"max_tokens\": 1000,\n",
-    "}\n",
-    "\n",
-    "llm = init_chat_model(**user_config)\n",
-    "llm.invoke(\"what's your name\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f811f219-5e78-4b62-b495-915d52a22532",
@@ -125,12 +105,215 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "da07b5c0-d2e6-42e4-bfcd-2efcfaae6221",
+   "cell_type": "markdown",
+   "id": "476a44db-c50d-4846-951d-0f1c9ba8bbaa",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Creating a configurable model\n",
+    "\n",
+    "You can also create a runtime-configurable model by specifying `configurable_fields`. If you don't specify a `model` value, then \"model\" and \"model_provider\" be configurable by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6c037f27-12d7-4e83-811e-4245c0e3ba58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 37, 'prompt_tokens': 11, 'total_tokens': 48}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d576307f90', 'finish_reason': 'stop', 'logprobs': None}, id='run-5428ab5c-b5c0-46de-9946-5d4ca40dbdc8-0', usage_metadata={'input_tokens': 11, 'output_tokens': 37, 'total_tokens': 48})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "configurable_model = init_chat_model(temperature=0)\n",
+    "\n",
+    "configurable_model.invoke(\n",
+    "    \"what's your name\", config={\"configurable\": {\"model\": \"gpt-4o\"}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "321e3036-abd2-4e1f-bcc6-606efd036954",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", response_metadata={'id': 'msg_012XvotUJ3kGLXJUWKBVxJUi', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-1ad1eefe-f1c6-4244-8bc6-90e2cb7ee554-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "configurable_model.invoke(\n",
+    "    \"what's your name\", config={\"configurable\": {\"model\": \"claude-3-5-sonnet-20240620\"}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f3b3d4a-4066-45e4-8297-ea81ac8e70b7",
+   "metadata": {},
+   "source": [
+    "### Configurable model with default values\n",
+    "\n",
+    "We can create a configurable model with default model values, specify which parameters are configurable, and add prefixes to configurable params:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "814a2289-d0db-401e-b555-d5116112b413",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 37, 'prompt_tokens': 11, 'total_tokens': 48}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_ce0793330f', 'finish_reason': 'stop', 'logprobs': None}, id='run-3923e328-7715-4cd6-b215-98e4b6bf7c9d-0', usage_metadata={'input_tokens': 11, 'output_tokens': 37, 'total_tokens': 48})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_llm = init_chat_model(\n",
+    "    model=\"gpt-4o\",\n",
+    "    temperature=0,\n",
+    "    configurable_fields=(\"model\", \"model_provider\", \"temperature\", \"max_tokens\"),\n",
+    "    config_prefix=\"first\",  # useful when you have a chain with multiple models\n",
+    ")\n",
+    "\n",
+    "first_llm.invoke(\"what's your name\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6c8755ba-c001-4f5a-a497-be3f1db83244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", response_metadata={'id': 'msg_01RyYR64DoMPNCfHeNnroMXm', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-22446159-3723-43e6-88df-b84797e7751d-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_llm.invoke(\n",
+    "    \"what's your name\",\n",
+    "    config={\n",
+    "        \"configurable\": {\n",
+    "            \"first_model\": \"claude-3-5-sonnet-20240620\",\n",
+    "            \"first_temperature\": 0.5,\n",
+    "            \"first_max_tokens\": 100,\n",
+    "        }\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0072b1a3-7e44-4b4e-8b07-efe1ba91a689",
+   "metadata": {},
+   "source": [
+    "### Using a configurable model declaratively\n",
+    "\n",
+    "We can call declarative operations like `bind_tools`, `with_structured_output`, `with_configurable`, etc. on a configurable model and chain a configurable model in the same way that we would a regularly instantiated chat model object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "067dabee-1050-4110-ae24-c48eba01e13b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'GetPopulation',\n",
+       "  'args': {'location': 'Los Angeles, CA'},\n",
+       "  'id': 'call_sYT3PFMufHGWJD32Hi2CTNUP'},\n",
+       " {'name': 'GetPopulation',\n",
+       "  'args': {'location': 'New York, NY'},\n",
+       "  'id': 'call_j1qjhxRnD3ffQmRyqjlI1Lnk'}]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class GetWeather(BaseModel):\n",
+    "    \"\"\"Get the current weather in a given location\"\"\"\n",
+    "\n",
+    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+    "\n",
+    "\n",
+    "class GetPopulation(BaseModel):\n",
+    "    \"\"\"Get the current population in a given location\"\"\"\n",
+    "\n",
+    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+    "\n",
+    "\n",
+    "llm = init_chat_model(temperature=0)\n",
+    "llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])\n",
+    "\n",
+    "llm_with_tools.invoke(\n",
+    "    \"what's bigger in 2024 LA or NYC\", config={\"configurable\": {\"model\": \"gpt-4o\"}}\n",
+    ").tool_calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e57dfe9f-cd24-4e37-9ce9-ccf8daf78f89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'GetPopulation',\n",
+       "  'args': {'location': 'Los Angeles, CA'},\n",
+       "  'id': 'toolu_01CxEHxKtVbLBrvzFS7GQ5xR'},\n",
+       " {'name': 'GetPopulation',\n",
+       "  'args': {'location': 'New York City, NY'},\n",
+       "  'id': 'toolu_013A79qt5toWSsKunFBDZd5S'}]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_with_tools.invoke(\n",
+    "    \"what's bigger in 2024 LA or NYC\",\n",
+    "    config={\"configurable\": {\"model\": \"claude-3-5-sonnet-20240620\"}},\n",
+    ").tool_calls"
+   ]
   }
  ],
  "metadata": {
@@ -149,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/code_splitter.ipynb

@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "ac9295d3",
    "metadata": {},
    "outputs": [],
@@ -312,10 +312,8 @@
     "\n",
     "## Quick Install\n",
     "\n",
-    "```bash\n",
     "# Hopefully this code block isn't split\n",
     "pip install langchain\n",
-    "```\n",
     "\n",
     "As an open-source project in a rapidly developing field, we are extremely open to contributions.\n",
     "\"\"\""
@@ -323,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "3a0cb17a",
    "metadata": {},
    "outputs": [
@@ -332,15 +330,14 @@
       "text/plain": [
        "[Document(page_content='# 🦜️🔗 LangChain'),\n",
        " Document(page_content='⚡ Building applications with LLMs through composability ⚡'),\n",
-       " Document(page_content='## Quick Install\\n\\n```bash'),\n",
+       " Document(page_content='## Quick Install'),\n",
        " Document(page_content=\"# Hopefully this code block isn't split\"),\n",
        " Document(page_content='pip install langchain'),\n",
-       " Document(page_content='```'),\n",
        " Document(page_content='As an open-source project in a rapidly developing field, we'),\n",
        " Document(page_content='are extremely open to contributions.')]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -742,7 +739,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/how_to/configure.ipynb

@@ -48,20 +48,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "40ed76a2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.0 is available.\n",
-      "You should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
-      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install --upgrade --quiet langchain langchain-openai\n",
     "\n",

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -180,7 +180,7 @@
    "id": "32b1a992-8997-4c98-8eb2-c9fe9431b799",
    "metadata": {},
    "source": [
-    "Alternatively, we can add typing information via [Runnable.with_types](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_types):"
+    "Alternatively, the schema can be fully specified by directly passing the desired [args_schema](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool.args_schema) for the tool:"
    ]
   },
   {
@@ -190,10 +190,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "as_tool = runnable.with_types(input_type=Args).as_tool(\n",
-    "    name=\"My tool\",\n",
-    "    description=\"Explanation of when to use tool.\",\n",
-    ")"
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class GSchema(BaseModel):\n",
+    "    \"\"\"Apply a function to an integer and list of integers.\"\"\"\n",
+    "\n",
+    "    a: int = Field(..., description=\"Integer\")\n",
+    "    b: List[int] = Field(..., description=\"List of ints\")\n",
+    "\n",
+    "\n",
+    "runnable = RunnableLambda(g)\n",
+    "as_tool = runnable.as_tool(GSchema)"
    ]
   },
   {

docs/docs/how_to/custom_chat_model.ipynb

@@ -131,7 +131,7 @@
    "source": [
     "## Base Chat Model\n",
     "\n",
-    "Let's implement a chat model that echoes back the first `n` characetrs of the last message in the prompt!\n",
+    "Let's implement a chat model that echoes back the first `n` characters of the last message in the prompt!\n",
     "\n",
     "To do so, we will inherit from `BaseChatModel` and we'll need to implement the following:\n",
     "\n",

docs/docs/how_to/custom_tools.ipynb

@@ -16,13 +16,15 @@
     "| args_schema   | Pydantic BaseModel      | Optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters |\n",
     "| return_direct   | boolean      | Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user.  |\n",
     "\n",
-    "LangChain provides 3 ways to create tools:\n",
+    "LangChain supports the creation of tools from:\n",
     "\n",
-    "1. Using [@tool decorator](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.tool.html#langchain_core.tools.tool) -- the simplest way to define a custom tool.\n",
-    "2. Using [StructuredTool.from_function](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method -- this is similar to the `@tool` decorator, but allows more configuration and specification of both sync and async implementations.\n",
+    "1. Functions;\n",
+    "2. LangChain [Runnables](/docs/concepts#runnable-interface);\n",
     "3. By sub-classing from [BaseTool](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
     "\n",
-    "The `@tool` or the `StructuredTool.from_function` class method should be sufficient for most use cases.\n",
+    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method.\n",
+    "\n",
+    "In this guide we provide an overview of these methods.\n",
     "\n",
     ":::{.callout-tip}\n",
     "\n",
@@ -35,7 +37,9 @@
    "id": "c7326b23",
    "metadata": {},
    "source": [
-    "## @tool decorator\n",
+    "## Creating tools from functions\n",
+    "\n",
+    "### @tool decorator\n",
     "\n",
     "This `@tool` decorator is the simplest way to define a custom tool. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function's docstring as the tool's description - so a docstring MUST be provided. "
    ]
@@ -51,7 +55,7 @@
      "output_type": "stream",
      "text": [
       "multiply\n",
-      "multiply(a: int, b: int) -> int - Multiply two numbers.\n",
+      "Multiply two numbers.\n",
       "{'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}\n"
      ]
     }
@@ -96,6 +100,57 @@
     "    return a * b"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8f0edc51-c586-414c-8941-c8abe779943f",
+   "metadata": {},
+   "source": [
+    "Note that `@tool` supports parsing of annotations, nested schemas, and other features:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5626423f-053e-4a66-adca-1d794d835397",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'title': 'multiply_by_maxSchema',\n",
+       " 'description': 'Multiply a by the maximum of b.',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'a': {'title': 'A',\n",
+       "   'description': 'scale factor',\n",
+       "   'type': 'string'},\n",
+       "  'b': {'title': 'B',\n",
+       "   'description': 'list of ints over which to take maximum',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'integer'}}},\n",
+       " 'required': ['a', 'b']}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Annotated, List\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def multiply_by_max(\n",
+    "    a: Annotated[str, \"scale factor\"],\n",
+    "    b: Annotated[List[int], \"list of ints over which to take maximum\"],\n",
+    ") -> int:\n",
+    "    \"\"\"Multiply a by the maximum of b.\"\"\"\n",
+    "    return a * max(b)\n",
+    "\n",
+    "\n",
+    "multiply_by_max.args_schema.schema()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "98d6eee9",
@@ -106,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "9216d03a-f6ea-4216-b7e1-0661823a4c0b",
    "metadata": {},
    "outputs": [
@@ -115,7 +170,7 @@
      "output_type": "stream",
      "text": [
       "multiplication-tool\n",
-      "multiplication-tool(a: int, b: int) -> int - Multiply two numbers.\n",
+      "Multiply two numbers.\n",
       "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n",
       "True\n"
      ]
@@ -143,19 +198,84 @@
     "print(multiply.return_direct)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "33a9e94d-0b60-48f3-a4c2-247dce096e66",
+   "metadata": {},
+   "source": [
+    "#### Docstring parsing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d0cb586-93d4-4ff1-9779-71df7853cb68",
+   "metadata": {},
+   "source": [
+    "`@tool` can optionally parse [Google Style docstrings](https://google.github.io/styleguide/pyguide.html#383-functions-and-methods) and associate the docstring components (such as arg descriptions) to the relevant parts of the tool schema. To toggle this behavior, specify `parse_docstring`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "336f5538-956e-47d5-9bde-b732559f9e61",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'title': 'fooSchema',\n",
+       " 'description': 'The foo.',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'bar': {'title': 'Bar',\n",
+       "   'description': 'The bar.',\n",
+       "   'type': 'string'},\n",
+       "  'baz': {'title': 'Baz', 'description': 'The baz.', 'type': 'integer'}},\n",
+       " 'required': ['bar', 'baz']}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "@tool(parse_docstring=True)\n",
+    "def foo(bar: str, baz: int) -> str:\n",
+    "    \"\"\"The foo.\n",
+    "\n",
+    "    Args:\n",
+    "        bar: The bar.\n",
+    "        baz: The baz.\n",
+    "    \"\"\"\n",
+    "    return bar\n",
+    "\n",
+    "\n",
+    "foo.args_schema.schema()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f18a2503-5393-421b-99fa-4a01dd824d0e",
+   "metadata": {},
+   "source": [
+    ":::{.callout-caution}\n",
+    "By default, `@tool(parse_docstring=True)` will raise `ValueError` if the docstring does not parse correctly. See [API Reference](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.tool.html) for detail and examples.\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b63fcc3b",
    "metadata": {},
    "source": [
-    "## StructuredTool\n",
+    "### StructuredTool\n",
     "\n",
     "The `StrurcturedTool.from_function` class method provides a bit more configurability than the `@tool` decorator, without requiring much additional code."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "564fbe6f-11df-402d-b135-ef6ff25e1e63",
    "metadata": {},
    "outputs": [
@@ -198,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "6bc055d4-1fbe-4db5-8881-9c382eba6b1b",
    "metadata": {},
    "outputs": [
@@ -208,7 +328,7 @@
      "text": [
       "6\n",
       "Calculator\n",
-      "Calculator(a: int, b: int) -> int - multiply numbers\n",
+      "multiply numbers\n",
       "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n"
      ]
     }
@@ -239,6 +359,63 @@
     "print(calculator.args)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5517995d-54e3-449b-8fdb-03561f5e4647",
+   "metadata": {},
+   "source": [
+    "## Creating tools from Runnables\n",
+    "\n",
+    "LangChain [Runnables](/docs/concepts#runnable-interface) that accept string or `dict` input can be converted to tools using the [as_tool](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.as_tool) method, which allows for the specification of names, descriptions, and additional schema information for arguments.\n",
+    "\n",
+    "Example usage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8ef593c5-cf72-4c10-bfc9-7d21874a0c24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer_style': {'title': 'Answer Style', 'type': 'string'}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.language_models import GenericFakeChatModel\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [(\"human\", \"Hello. Please respond in the style of {answer_style}.\")]\n",
+    ")\n",
+    "\n",
+    "# Placeholder LLM\n",
+    "llm = GenericFakeChatModel(messages=iter([\"hello matey\"]))\n",
+    "\n",
+    "chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "as_tool = chain.as_tool(\n",
+    "    name=\"Style responder\", description=\"Description of when to use tool.\"\n",
+    ")\n",
+    "as_tool.args"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0521b787-a146-45a6-8ace-ae1ac4669dd7",
+   "metadata": {},
+   "source": [
+    "See [this guide](/docs/how_to/convert_runnable_to_tool) for more detail."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b840074b-9c10-4ca0-aed8-626c52b2398f",
@@ -251,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "1dad8f8e",
    "metadata": {},
    "outputs": [],
@@ -300,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "bb551c33",
    "metadata": {},
    "outputs": [
@@ -351,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "6615cb77-fd4c-4676-8965-f92cc71d4944",
    "metadata": {},
    "outputs": [
@@ -383,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "bb2af583-eadd-41f4-a645-bf8748bd3dcd",
    "metadata": {},
    "outputs": [
@@ -428,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "id": "4ad0932c-8610-4278-8c57-f9218f654c8a",
    "metadata": {},
    "outputs": [
@@ -473,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "id": "7094c0e8-6192-4870-a942-aad5b5ae48fd",
    "metadata": {},
    "outputs": [],
@@ -496,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "b4d22022-b105-4ccc-a15b-412cb9ea3097",
    "metadata": {},
    "outputs": [
@@ -506,7 +683,7 @@
        "'Error: There is no city by the name of foobar.'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -530,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "3fad1728-d367-4e1b-9b54-3172981271cf",
    "metadata": {},
    "outputs": [
@@ -540,7 +717,7 @@
        "\"There is no such city, but it's probably above 0K there!\""
       ]
      },
-     "execution_count": 13,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "id": "ebfe7c1f-318d-4e58-99e1-f31e69473c46",
    "metadata": {},
    "outputs": [
@@ -574,7 +751,7 @@
        "'The following errors occurred during tool execution: `Error: There is no city by the name of foobar.`'"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -591,13 +768,189 @@
     "\n",
     "get_weather_tool.invoke({\"city\": \"foobar\"})"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a8d8383-11b3-445e-956f-df4e96995e00",
+   "metadata": {},
+   "source": [
+    "## Returning artifacts of Tool execution\n",
+    "\n",
+    "Sometimes there are artifacts of a tool's execution that we want to make accessible to downstream components in our chain or agent, but that we don't want to expose to the model itself. For example if a tool returns custom objects like Documents, we may want to pass some view or metadata about this output to the model without passing the raw output to the model. At the same time, we may want to be able to access this full output elsewhere, for example in downstream tools.\n",
+    "\n",
+    "The Tool and [ToolMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
+    "\n",
+    ":::info Requires ``langchain-core >= 0.2.19``\n",
+    "\n",
+    "This functionality was added in ``langchain-core == 0.2.19``. Please make sure your package is up to date.\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "If we want our tool to distinguish between message content and other artifacts, we need to specify `response_format=\"content_and_artifact\"` when defining our tool and make sure that we return a tuple of (content, artifact):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "14905425-0334-43a0-9de9-5bcf622ede0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "from typing import List, Tuple\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool(response_format=\"content_and_artifact\")\n",
+    "def generate_random_ints(min: int, max: int, size: int) -> Tuple[str, List[int]]:\n",
+    "    \"\"\"Generate size random ints in the range [min, max].\"\"\"\n",
+    "    array = [random.randint(min, max) for _ in range(size)]\n",
+    "    content = f\"Successfully generated array of {size} random ints in [{min}, {max}].\"\n",
+    "    return content, array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49f057a6-8938-43ea-8faf-ae41e797ceb8",
+   "metadata": {},
+   "source": [
+    "If we invoke our tool directly with the tool arguments, we'll get back just the content part of the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0f2e1528-404b-46e6-b87c-f0957c4b9217",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Successfully generated array of 10 random ints in [0, 9].'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generate_random_ints.invoke({\"min\": 0, \"max\": 9, \"size\": 10})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e62ebba-1737-4b97-b61a-7313ade4e8c2",
+   "metadata": {},
+   "source": [
+    "If we invoke our tool with a ToolCall (like the ones generated by tool-calling models), we'll get back a ToolMessage that contains both the content and artifact generated by the Tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cc197777-26eb-46b3-a83b-c2ce116c6311",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[1, 4, 2, 5, 3, 9, 0, 4, 7, 7])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generate_random_ints.invoke(\n",
+    "    {\n",
+    "        \"name\": \"generate_random_ints\",\n",
+    "        \"args\": {\"min\": 0, \"max\": 9, \"size\": 10},\n",
+    "        \"id\": \"123\",  # required\n",
+    "        \"type\": \"tool_call\",  # required\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfdc1040-bf25-4790-b4c3-59452db84e11",
+   "metadata": {},
+   "source": [
+    "We can do the same when subclassing BaseTool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fe1a09d1-378b-4b91-bb5e-0697c3d7eb92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.tools import BaseTool\n",
+    "\n",
+    "\n",
+    "class GenerateRandomFloats(BaseTool):\n",
+    "    name: str = \"generate_random_floats\"\n",
+    "    description: str = \"Generate size random floats in the range [min, max].\"\n",
+    "    response_format: str = \"content_and_artifact\"\n",
+    "\n",
+    "    ndigits: int = 2\n",
+    "\n",
+    "    def _run(self, min: float, max: float, size: int) -> Tuple[str, List[float]]:\n",
+    "        range_ = max - min\n",
+    "        array = [\n",
+    "            round(min + (range_ * random.random()), ndigits=self.ndigits)\n",
+    "            for _ in range(size)\n",
+    "        ]\n",
+    "        content = f\"Generated {size} floats in [{min}, {max}], rounded to {self.ndigits} decimals.\"\n",
+    "        return content, array\n",
+    "\n",
+    "    # Optionally define an equivalent async method\n",
+    "\n",
+    "    # async def _arun(self, min: float, max: float, size: int) -> Tuple[str, List[float]]:\n",
+    "    #     ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8c3d16f6-1c4a-48ab-b05a-38547c592e79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.4277, 0.7578, 2.4871])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rand_gen = GenerateRandomFloats(ndigits=4)\n",
+    "\n",
+    "rand_gen.invoke(\n",
+    "    {\n",
+    "        \"name\": \"generate_random_floats\",\n",
+    "        \"args\": {\"min\": 0.1, \"max\": 3.3333, \"size\": 3},\n",
+    "        \"id\": \"123\",\n",
+    "        \"type\": \"tool_call\",\n",
+    "    }\n",
+    ")"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -609,7 +962,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   },
   "vscode": {
    "interpreter": {

docs/docs/how_to/embed_text.mdx

@@ -67,15 +67,16 @@ If you'd prefer not to set an environment variable you can pass the key in direc
 ```python
 from langchain_cohere import CohereEmbeddings
 
-embeddings_model = CohereEmbeddings(cohere_api_key="...")
+embeddings_model = CohereEmbeddings(cohere_api_key="...", model='embed-english-v3.0')
 ```
 
-Otherwise you can initialize without any params:
+Otherwise you can initialize simply as shown below:
 ```python
 from langchain_cohere import CohereEmbeddings
 
-embeddings_model = CohereEmbeddings()
+embeddings_model = CohereEmbeddings(model='embed-english-v3.0')
 ```
+Do note that it is mandatory to pass the model parameter while initializing the CohereEmbeddings class.
 
   </TabItem>
   <TabItem value="huggingface" label="Hugging Face">

docs/docs/how_to/hybrid.ipynb

@@ -9,11 +9,13 @@
    "source": [
     "# Hybrid Search\n",
     "\n",
-    "The standard search in LangChain is done by vector similarity. However, a number of vectorstores implementations (Astra DB, ElasticSearch, Neo4J, AzureSearch, ...) also support more advanced search combining vector similarity search and other search techniques (full-text, BM25, and so on). This is generally referred to as \"Hybrid\" search.\n",
+    "The standard search in LangChain is done by vector similarity. However, a number of vectorstores implementations (Astra DB, ElasticSearch, Neo4J, AzureSearch, Qdrant...) also support more advanced search combining vector similarity search and other search techniques (full-text, BM25, and so on). This is generally referred to as \"Hybrid\" search.\n",
     "\n",
     "**Step 1: Make sure the vectorstore you are using supports hybrid search**\n",
     "\n",
-    "At the moment, there is no unified way to perform hybrid search in LangChain. Each vectorstore may have their own way to do it. This is generally exposed as a keyword argument that is passed in during `similarity_search`. By reading the documentation or source code, figure out whether the vectorstore you are using supports hybrid search, and, if so, how to use it.\n",
+    "At the moment, there is no unified way to perform hybrid search in LangChain. Each vectorstore may have their own way to do it. This is generally exposed as a keyword argument that is passed in during `similarity_search`.\n",
+    "\n",
+    "By reading the documentation or source code, figure out whether the vectorstore you are using supports hybrid search, and, if so, how to use it.\n",
     "\n",
     "**Step 2: Add that parameter as a configurable field for the chain**\n",
     "\n",

docs/docs/how_to/index.mdx

@@ -84,7 +84,7 @@ These are the core building blocks you can use when building applications.
 - [How to: use chat model to call tools](/docs/how_to/tool_calling)
 - [How to: stream tool calls](/docs/how_to/tool_streaming)
 - [How to: few shot prompt tool behavior](/docs/how_to/tools_few_shot)
-- [How to: bind model-specific formated tools](/docs/how_to/tools_model_specific)
+- [How to: bind model-specific formatted tools](/docs/how_to/tools_model_specific)
 - [How to: force a specific tool call](/docs/how_to/tool_choice)
 - [How to: init any model in one line](/docs/how_to/chat_models_universal_init/)
 
@@ -195,6 +195,9 @@ LangChain [Tools](/docs/concepts/#tools) contain a description of the tool (to p
 - [How to: add a human in the loop to tool usage](/docs/how_to/tools_human)
 - [How to: handle errors when calling tools](/docs/how_to/tools_error)
 - [How to: disable parallel tool calling](/docs/how_to/tool_choice)
+- [How to: access the `RunnableConfig` object within a custom tool](/docs/how_to/tool_configure)
+- [How to: stream events from child runs within a custom tool](/docs/how_to/tool_stream_events)
+- [How to: return extra artifacts from a tool](/docs/how_to/tool_artifacts/)
 
 ### Multimodal
 
@@ -222,6 +225,7 @@ For in depth how-to guides for agents, please check out [LangGraph](https://lang
 - [How to: pass callbacks into a module constructor](/docs/how_to/callbacks_constructor)
 - [How to: create custom callback handlers](/docs/how_to/custom_callbacks)
 - [How to: use callbacks in async environments](/docs/how_to/callbacks_async)
+- [How to: dispatch custom callback events](/docs/how_to/callbacks_custom_events)
 
 ### Custom
 
@@ -234,6 +238,7 @@ All of LangChain components can easily be extended to support your own versions.
 - [How to: write a custom output parser class](/docs/how_to/output_parser_custom)
 - [How to: create custom callback handlers](/docs/how_to/custom_callbacks)
 - [How to: define a custom tool](/docs/how_to/custom_tools)
+- [How to: dispatch custom callback events](/docs/how_to/callbacks_custom_events)
 
 ### Serialization
 - [How to: save and load LangChain objects](/docs/how_to/serialization)

docs/docs/how_to/merge_message_runs.ipynb

@@ -63,6 +63,38 @@
     "Notice that if the contents of one of the messages to merge is a list of content blocks then the merged message will have a list of content blocks. And if both messages to merge have string contents then those are concatenated with a newline character."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "11f7e8d3",
+   "metadata": {},
+   "source": [
+    "The `merge_message_runs` utility also works with messages composed together using the overloaded `+` operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b51855c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = (\n",
+    "    SystemMessage(\"you're a good assistant.\")\n",
+    "    + SystemMessage(\"you always respond with a joke.\")\n",
+    "    + HumanMessage([{\"type\": \"text\", \"text\": \"i wonder why it's called langchain\"}])\n",
+    "    + HumanMessage(\"and who is harrison chasing anyways\")\n",
+    "    + AIMessage(\n",
+    "        'Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!'\n",
+    "    )\n",
+    "    + AIMessage(\n",
+    "        \"Why, he's probably chasing after the last cup of coffee in the office!\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "merged = merge_message_runs(messages)\n",
+    "print(\"\\n\\n\".join([repr(x) for x in merged]))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1b2eee74-71c8-4168-b968-bca580c25d18",

docs/docs/how_to/tool_artifacts.ipynb

@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "503e36ae-ca62-4f8a-880c-4fe78ff5df93",
+   "metadata": {},
+   "source": [
+    "# How to return extra artifacts from a tool\n",
+    "\n",
+    ":::info Prerequisites\n",
+    "This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "- [Tools](/docs/concepts/#tools)\n",
+    "- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "Tools are utilities that can be called by a model, and whose outputs are designed to be fed back to a model. Sometimes, however, there are artifacts of a tool's execution that we want to make accessible to downstream components in our chain or agent, but that we don't want to expose to the model itself. For example if a tool returns a custom object, a dataframe or an image, we may want to pass some metadata about this output to the model without passing the actual output to the model. At the same time, we may want to be able to access this full output elsewhere, for example in downstream tools.\n",
+    "\n",
+    "The Tool and [ToolMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
+    "\n",
+    ":::info Requires ``langchain-core >= 0.2.19``\n",
+    "\n",
+    "This functionality was added in ``langchain-core == 0.2.19``. Please make sure your package is up to date.\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "## Defining the tool\n",
+    "\n",
+    "If we want our tool to distinguish between message content and other artifacts, we need to specify `response_format=\"content_and_artifact\"` when defining our tool and make sure that we return a tuple of (content, artifact):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "762b9199-885f-4946-9c98-cc54d72b0d76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU \"langchain-core>=0.2.19\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b9eb179d-1f41-4748-9866-b3d3e8c73cd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "from typing import List, Tuple\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool(response_format=\"content_and_artifact\")\n",
+    "def generate_random_ints(min: int, max: int, size: int) -> Tuple[str, List[int]]:\n",
+    "    \"\"\"Generate size random ints in the range [min, max].\"\"\"\n",
+    "    array = [random.randint(min, max) for _ in range(size)]\n",
+    "    content = f\"Successfully generated array of {size} random ints in [{min}, {max}].\"\n",
+    "    return content, array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab05d25-af4a-4e5a-afe2-f090416d7ee7",
+   "metadata": {},
+   "source": [
+    "## Invoking the tool with ToolCall\n",
+    "\n",
+    "If we directly invoke our tool with just the tool arguments, you'll notice that we only get back the content part of the Tool output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5e7d5e77-3102-4a59-8ade-e4e699dd1817",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Successfully generated array of 10 random ints in [0, 9].'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to batch ingest runs: LangSmithRateLimitError('Rate limit exceeded for https://api.smith.langchain.com/runs/batch. HTTPError(\\'429 Client Error: Too Many Requests for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Monthly unique traces usage limit exceeded\"}\\')')\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_random_ints.invoke({\"min\": 0, \"max\": 9, \"size\": 10})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30db7228-f04c-489e-afda-9a572eaa90a1",
+   "metadata": {},
+   "source": [
+    "In order to get back both the content and the artifact, we need to invoke our model with a ToolCall (which is just a dictionary with \"name\", \"args\", \"id\" and \"type\" keys), which has additional info needed to generate a ToolMessage like the tool call ID:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "da1d939d-a900-4b01-92aa-d19011a6b034",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[2, 8, 0, 6, 0, 0, 1, 5, 0, 0])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generate_random_ints.invoke(\n",
+    "    {\n",
+    "        \"name\": \"generate_random_ints\",\n",
+    "        \"args\": {\"min\": 0, \"max\": 9, \"size\": 10},\n",
+    "        \"id\": \"123\",  # required\n",
+    "        \"type\": \"tool_call\",  # required\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3cfc03d-020b-42c7-b0f8-c824af19e45e",
+   "metadata": {},
+   "source": [
+    "## Using with a model\n",
+    "\n",
+    "With a [tool-calling model](/docs/how_to/tool_calling/), we can easily use a model to call our Tool and generate ToolMessages:\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs\n",
+    "  customVarName=\"llm\"\n",
+    "/>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "74de0286-b003-4b48-9cdd-ecab435515ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | echo: false\n",
+    "# | output: false\n",
+    "\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8a67424b-d19c-43df-ac7b-690bca42146c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'generate_random_ints',\n",
+       "  'args': {'min': 1, 'max': 24, 'size': 6},\n",
+       "  'id': 'toolu_01EtALY3Wz1DVYhv1TLvZGvE',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_with_tools = llm.bind_tools([generate_random_ints])\n",
+    "\n",
+    "ai_msg = llm_with_tools.invoke(\"generate 6 positive ints less than 25\")\n",
+    "ai_msg.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "00c4e906-3ca8-41e8-a0be-65cb0db7d574",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ToolMessage(content='Successfully generated array of 6 random ints in [1, 24].', name='generate_random_ints', tool_call_id='toolu_01EtALY3Wz1DVYhv1TLvZGvE', artifact=[2, 20, 23, 8, 1, 15])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generate_random_ints.invoke(ai_msg.tool_calls[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddef2690-70de-4542-ab20-2337f77f3e46",
+   "metadata": {},
+   "source": [
+    "If we just pass in the tool call args, we'll only get back the content:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f4a6c9a6-0ffc-4b0e-a59f-f3c3d69d824d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Successfully generated array of 6 random ints in [1, 24].'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generate_random_ints.invoke(ai_msg.tool_calls[0][\"args\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98d6443b-ff41-4d91-8523-b6274fc74ee5",
+   "metadata": {},
+   "source": [
+    "If we wanted to declaratively create a chain, we could do this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eb55ec23-95a4-464e-b886-d9679bf3aaa2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[ToolMessage(content='Successfully generated array of 1 random ints in [1, 5].', name='generate_random_ints', tool_call_id='toolu_01FwYhnkwDPJPbKdGq4ng6uD', artifact=[5])]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from operator import attrgetter\n",
+    "\n",
+    "chain = llm_with_tools | attrgetter(\"tool_calls\") | generate_random_ints.map()\n",
+    "\n",
+    "chain.invoke(\"give me a random number between 1 and 5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4df46be2-babb-4bfe-a641-91cd3d03ffaf",
+   "metadata": {},
+   "source": [
+    "## Creating from BaseTool class\n",
+    "\n",
+    "If you want to create a BaseTool object directly, instead of decorating a function with `@tool`, you can do so like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9a9129e1-6aee-4a10-ad57-62ef3bf0276c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.tools import BaseTool\n",
+    "\n",
+    "\n",
+    "class GenerateRandomFloats(BaseTool):\n",
+    "    name: str = \"generate_random_floats\"\n",
+    "    description: str = \"Generate size random floats in the range [min, max].\"\n",
+    "    response_format: str = \"content_and_artifact\"\n",
+    "\n",
+    "    ndigits: int = 2\n",
+    "\n",
+    "    def _run(self, min: float, max: float, size: int) -> Tuple[str, List[float]]:\n",
+    "        range_ = max - min\n",
+    "        array = [\n",
+    "            round(min + (range_ * random.random()), ndigits=self.ndigits)\n",
+    "            for _ in range(size)\n",
+    "        ]\n",
+    "        content = f\"Generated {size} floats in [{min}, {max}], rounded to {self.ndigits} decimals.\"\n",
+    "        return content, array\n",
+    "\n",
+    "    # Optionally define an equivalent async method\n",
+    "\n",
+    "    # async def _arun(self, min: float, max: float, size: int) -> Tuple[str, List[float]]:\n",
+    "    #     ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d7322619-f420-4b29-8ee5-023e693d0179",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rand_gen = GenerateRandomFloats(ndigits=4)\n",
+    "rand_gen.invoke({\"min\": 0.1, \"max\": 3.3333, \"size\": 3})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0892f277-23a6-4bb8-a0e9-59f533ac9750",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.5789, 2.464, 2.2719])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rand_gen.invoke(\n",
+    "    {\n",
+    "        \"name\": \"generate_random_floats\",\n",
+    "        \"args\": {\"min\": 0.1, \"max\": 3.3333, \"size\": 3},\n",
+    "        \"id\": \"123\",\n",
+    "        \"type\": \"tool_call\",\n",
+    "    }\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv-311",
+   "language": "python",
+   "name": "poetry-venv-311"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how_to/tool_configure.ipynb

@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to access the RunnableConfig object within a custom tool\n",
+    "\n",
+    ":::info Prerequisites\n",
+    "\n",
+    "This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [Custom tools](/docs/how_to/custom_tools)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language-lcel)\n",
+    "- [Configuring runnable behavior](/docs/how_to/configure/)\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "If you have a tool  that call chat models, retrievers, or other runnables, you may want to access internal events from those runnables or configure them with additional properties. This guide shows you how to manually pass parameters properly so that you can do this using the `astream_events()` method.\n",
+    "\n",
+    "Tools are runnables, and you can treat them the same way as any other runnable at the interface level - you can call `invoke()`, `batch()`, and `stream()` on them as normal. However, when writing custom tools, you may want to invoke other runnables like chat models or retrievers. In order to properly trace and configure those sub-invocations, you'll need to manually access and pass in the tool's current [`RunnableConfig`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html) object. This guide show you some examples of how to do that.\n",
+    "\n",
+    ":::caution Compatibility\n",
+    "\n",
+    "This guide requires `langchain-core>=0.2.16`.\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "## Inferring by parameter type\n",
+    "\n",
+    "To access reference the active config object from your custom tool, you'll need to add a parameter to your tool's signature typed as `RunnableConfig`. When you invoke your tool, LangChain will inspect your tool's signature, look for a parameter typed as `RunnableConfig`, and if it exists, populate that parameter with the correct value.\n",
+    "\n",
+    "**Note:** The actual name of the parameter doesn't matter, only the typing.\n",
+    "\n",
+    "To illustrate this, define a custom tool that takes a two parameters - one typed as a string, the other typed as `RunnableConfig`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_core"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnableConfig\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "async def reverse_tool(text: str, special_config_param: RunnableConfig) -> str:\n",
+    "    \"\"\"A test tool that combines input text with a configurable parameter.\"\"\"\n",
+    "    return (text + special_config_param[\"configurable\"][\"additional_field\"])[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, if we invoke the tool with a `config` containing a `configurable` field, we can see that `additional_field` is passed through correctly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'321cba'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await reverse_tool.ainvoke(\n",
+    "    {\"text\": \"abc\"}, config={\"configurable\": {\"additional_field\": \"123\"}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "You've now seen how to configure and stream events from within a tool. Next, check out the following guides for more on using tools:\n",
+    "\n",
+    "- [Stream events from child runs within a custom tool](/docs/how_to/tool_stream_events/)\n",
+    "- Pass [tool results back to a model](/docs/how_to/tool_results_pass_to_model)\n",
+    "\n",
+    "You can also check out some more specific uses of tool calling:\n",
+    "\n",
+    "- Building [tool-using chains and agents](/docs/how_to#tools)\n",
+    "- Getting [structured outputs](/docs/how_to/structured_output/) from models"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -6,12 +6,20 @@
    "source": [
     "# How to pass tool outputs to the model\n",
     "\n",
-    "If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s. First, let's define our tools and our model."
+    ":::info Prerequisites\n",
+    "This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "- [Tools](/docs/concepts/#tools)\n",
+    "- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s and `ToolCall`s. First, let's define our tools and our model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,25 +62,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can use ``ToolMessage`` to pass back the output of the tool calls to the model."
+    "The nice thing about Tools is that if we invoke them with a ToolCall, we'll automatically get back a ToolMessage that can be fed back to the model: \n",
+    "\n",
+    ":::info Requires ``langchain-core >= 0.2.19``\n",
+    "\n",
+    "This functionality was added in ``langchain-core == 0.2.19``. Please make sure your package is up to date.\n",
+    "\n",
+    ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[HumanMessage(content='What is 3 * 12? Also, what is 11 + 49?'),\n",
-       " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_svc2GLSxNFALbaCAbSjMI9J8', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'Multiply'}, 'type': 'function'}, {'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 105, 'total_tokens': 155}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a79ad1dd-95f1-4a46-b688-4c83f327a7b3-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_svc2GLSxNFALbaCAbSjMI9J8'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh'}]),\n",
-       " ToolMessage(content='36', tool_call_id='call_svc2GLSxNFALbaCAbSjMI9J8'),\n",
-       " ToolMessage(content='60', tool_call_id='call_r8jxte3zW6h3MEGV3zH2qzFh')]"
+       " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Smg3NHJNxrKfAmd4f9GkaYn3', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'multiply'}, 'type': 'function'}, {'id': 'call_55K1C0DmH6U5qh810gW34xZ0', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 49, 'prompt_tokens': 88, 'total_tokens': 137}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-56657feb-96dd-456c-ab8e-1857eab2ade0-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_Smg3NHJNxrKfAmd4f9GkaYn3', 'type': 'tool_call'}, {'name': 'add', 'args': {'a': 11, 'b': 49}, 'id': 'call_55K1C0DmH6U5qh810gW34xZ0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 88, 'output_tokens': 49, 'total_tokens': 137}),\n",
+       " ToolMessage(content='36', name='multiply', tool_call_id='call_Smg3NHJNxrKfAmd4f9GkaYn3'),\n",
+       " ToolMessage(content='60', name='add', tool_call_id='call_55K1C0DmH6U5qh810gW34xZ0')]"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -85,24 +100,25 @@
     "messages.append(ai_msg)\n",
     "for tool_call in ai_msg.tool_calls:\n",
     "    selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
-    "    tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
-    "    messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
+    "    tool_msg = selected_tool.invoke(tool_call)\n",
+    "    messages.append(tool_msg)\n",
     "messages"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 171, 'total_tokens': 189}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'stop', 'logprobs': None}, id='run-20b52149-e00d-48ea-97cf-f8de7a255f8c-0')"
+       "AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 153, 'total_tokens': 171}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ba5032f0-f773-406d-a408-8314e66511d0-0', usage_metadata={'input_tokens': 153, 'output_tokens': 18, 'total_tokens': 171})"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -118,10 +134,24 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv-311",
+   "language": "python",
+   "name": "poetry-venv-311"
+  },
   "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": "3.11.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/how_to/tool_stream_events.ipynb

@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to stream events from child runs within a custom tool\n",
+    "\n",
+    ":::info Prerequisites\n",
+    "\n",
+    "This guide assumes familiarity with the following concepts:\n",
+    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [Custom tools](/docs/how_to/custom_tools)\n",
+    "- [Using stream events](/docs/how_to/streaming/#using-stream-events)\n",
+    "- [Accessing RunnableConfig within a custom tool](/docs/how_to/tool_configure/)\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "If you have tools that call chat models, retrievers, or other runnables, you may want to access internal events from those runnables or configure them with additional properties. This guide shows you how to manually pass parameters properly so that you can do this using the `astream_events()` method.\n",
+    "\n",
+    ":::caution Compatibility\n",
+    "\n",
+    "LangChain cannot automatically propagate configuration, including callbacks necessary for `astream_events()`, to child runnables if you are running `async` code in `python<=3.10`. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "\n",
+    "If you are running python<=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
+    "\n",
+    "If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in older Python versions.\n",
+    "\n",
+    "This guide also requires `langchain-core>=0.2.16`.\n",
+    ":::\n",
+    "\n",
+    "Say you have a custom tool that calls a chain that condenses its input by prompting a chat model to return only 10 words, then reversing the output. First, define it in a naive way:\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"model\" />\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "\n",
+    "%pip install -qU langchain langchain_anthropic langchain_core\n",
+    "\n",
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
+    "\n",
+    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "async def special_summarization_tool(long_text: str) -> str:\n",
+    "    \"\"\"A tool that summarizes input text using advanced techniques.\"\"\"\n",
+    "    prompt = ChatPromptTemplate.from_template(\n",
+    "        \"You are an expert writer. Summarize the following text in 10 words or less:\\n\\n{long_text}\"\n",
+    "    )\n",
+    "\n",
+    "    def reverse(x: str):\n",
+    "        return x[::-1]\n",
+    "\n",
+    "    chain = prompt | model | StrOutputParser() | reverse\n",
+    "    summary = await chain.ainvoke({\"long_text\": long_text})\n",
+    "    return summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Invoking the tool directly works just fine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'.yad noitaudarg rof tiftuo sesoohc yrraB ;scisyhp seifed eeB'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LONG_TEXT = \"\"\"\n",
+    "NARRATOR:\n",
+    "(Black screen with text; The sound of buzzing bees can be heard)\n",
+    "According to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.\n",
+    "BARRY BENSON:\n",
+    "(Barry is picking out a shirt)\n",
+    "Yellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.\n",
+    "JANET BENSON:\n",
+    "Barry! Breakfast is ready!\n",
+    "BARRY:\n",
+    "Coming! Hang on a second.\n",
+    "\"\"\"\n",
+    "\n",
+    "await special_summarization_tool.ainvoke({\"long_text\": LONG_TEXT})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you wanted to access the raw output from the chat model rather than the full tool, you might try to use the [`astream_events()`](/docs/how_to/streaming/#using-stream-events) method and look for an `on_chat_model_end` event. Here's what happens:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stream = special_summarization_tool.astream_events(\n",
+    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
+    ")\n",
+    "\n",
+    "async for event in stream:\n",
+    "    if event[\"event\"] == \"on_chat_model_end\":\n",
+    "        # Never triggers in python<=3.10!\n",
+    "        print(event)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You'll notice (unless you're running through this guide in `python>=3.11`) that there are no chat model events emitted from the child run!\n",
+    "\n",
+    "This is because the example above does not pass the tool's config object into the internal chain. To fix this, redefine your tool to take a special parameter typed as `RunnableConfig` (see [this guide](/docs/how_to/tool_configure) for more details). You'll also need to pass that parameter through into the internal chain when executing it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnableConfig\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "async def special_summarization_tool_with_config(\n",
+    "    long_text: str, config: RunnableConfig\n",
+    ") -> str:\n",
+    "    \"\"\"A tool that summarizes input text using advanced techniques.\"\"\"\n",
+    "    prompt = ChatPromptTemplate.from_template(\n",
+    "        \"You are an expert writer. Summarize the following text in 10 words or less:\\n\\n{long_text}\"\n",
+    "    )\n",
+    "\n",
+    "    def reverse(x: str):\n",
+    "        return x[::-1]\n",
+    "\n",
+    "    chain = prompt | model | StrOutputParser() | reverse\n",
+    "    # Pass the \"config\" object as an argument to any executed runnables\n",
+    "    summary = await chain.ainvoke({\"long_text\": long_text}, config=config)\n",
+    "    return summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now try the same `astream_events()` call as before with your new tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'event': 'on_chat_model_end', 'data': {'output': AIMessage(content='Bee defies physics; Barry chooses outfit for graduation day.', response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-d23abc80-0dce-4f74-9d7b-fb98ca4f2a9e', usage_metadata={'input_tokens': 182, 'output_tokens': 16, 'total_tokens': 198}), 'input': {'messages': [[HumanMessage(content=\"You are an expert writer. Summarize the following text in 10 words or less:\\n\\n\\nNARRATOR:\\n(Black screen with text; The sound of buzzing bees can be heard)\\nAccording to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.\\nBARRY BENSON:\\n(Barry is picking out a shirt)\\nYellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.\\nJANET BENSON:\\nBarry! Breakfast is ready!\\nBARRY:\\nComing! Hang on a second.\\n\")]]}}, 'run_id': 'd23abc80-0dce-4f74-9d7b-fb98ca4f2a9e', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['f25c41fe-8972-4893-bc40-cecf3922c1fa']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "stream = special_summarization_tool_with_config.astream_events(\n",
+    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
+    ")\n",
+    "\n",
+    "async for event in stream:\n",
+    "    if event[\"event\"] == \"on_chat_model_end\":\n",
+    "        print(event)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Awesome! This time there's an event emitted.\n",
+    "\n",
+    "For streaming, `astream_events()` automatically calls internal runnables in a chain with streaming enabled if possible, so if you wanted to a stream of tokens as they are generated from the chat model, you could simply filter to look for `on_chat_model_stream` events with no other changes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42', usage_metadata={'input_tokens': 182, 'output_tokens': 0, 'total_tokens': 182})}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='Bee', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' def', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='ies physics', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=';', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Barry', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' cho', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='oses outfit', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' for', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' graduation', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' day', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='.', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42', usage_metadata={'input_tokens': 0, 'output_tokens': 16, 'total_tokens': 16})}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "stream = special_summarization_tool_with_config.astream_events(\n",
+    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
+    ")\n",
+    "\n",
+    "async for event in stream:\n",
+    "    if event[\"event\"] == \"on_chat_model_stream\":\n",
+    "        print(event)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "You've now seen how to stream events from within a tool. Next, check out the following guides for more on using tools:\n",
+    "\n",
+    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
+    "- Pass [tool results back to a model](/docs/how_to/tool_results_pass_to_model)\n",
+    "- [Dispatch custom callback events](/docs/how_to/callbacks_custom_events)\n",
+    "\n",
+    "You can also check out some more specific uses of tool calling:\n",
+    "\n",
+    "- Building [tool-using chains and agents](/docs/how_to#tools)\n",
+    "- Getting [structured outputs](/docs/how_to/structured_output/) from models"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/how_to/tools_chain.ipynb

@@ -419,13 +419,13 @@
       "Invoking: `exponentiate` with `{'base': 405, 'exponent': 2}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[38;5;200m\u001b[1;3m164025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of taking 3 to the fifth power is 243. \n",
+      "\u001b[0m\u001b[38;5;200m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of taking 3 to the fifth power is 243. \n",
       "\n",
       "The sum of twelve and three is 15. \n",
       "\n",
       "Multiplying 243 by 15 gives 3645. \n",
       "\n",
-      "Finally, squaring 3645 gives 164025.\u001b[0m\n",
+      "Finally, squaring 3645 gives 13286025.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -434,7 +434,7 @@
      "data": {
       "text/plain": [
        "{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',\n",
-       " 'output': 'The result of taking 3 to the fifth power is 243. \\n\\nThe sum of twelve and three is 15. \\n\\nMultiplying 243 by 15 gives 3645. \\n\\nFinally, squaring 3645 gives 164025.'}"
+       " 'output': 'The result of taking 3 to the fifth power is 243. \\n\\nThe sum of twelve and three is 15. \\n\\nMultiplying 243 by 15 gives 3645. \\n\\nFinally, squaring 3645 gives 13286025.'}"
       ]
      },
      "execution_count": 18,

docs/docs/how_to/tools_error.ipynb

@@ -7,9 +7,18 @@
    "source": [
     "# How to handle tool errors\n",
     "\n",
-    "Using a model to invoke a tool has some obvious potential failure modes. Firstly, the model needs to return a output that can be parsed at all. Secondly, the model needs to return tool arguments that are valid.\n",
+    ":::info Prerequisites\n",
     "\n",
-    "We can build error handling into our chains to mitigate these failure modes."
+    "This guide assumes familiarity with the following concepts:\n",
+    "- [Chat models](/docs/concepts/#chat-models)\n",
+    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [How to use a model to call tools](/docs/how_to/tool_calling)\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "Calling tools with an LLM is generally more reliable than pure prompting, but it isn't perfect. The model may try to call a tool that doesn't exist or fail to return arguments that match the requested schema. Strategies like keeping schemas simple, reducing the number of tools you pass at once, and having good names and descriptions can help mitigate this risk, but aren't foolproof.\n",
+    "\n",
+    "This guide covers some ways to build error handling into your chains to mitigate these failure modes."
    ]
   },
   {
@@ -42,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "08785b6d-722d-4620-b6ec-36deb3842c69",
    "metadata": {},
    "outputs": [],
@@ -72,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "86258950-5e61-4340-81b9-84a5d26e8773",
    "metadata": {},
    "outputs": [],
@@ -82,12 +91,14 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "1d20604e-c4d1-4d21-841b-23e4f61aec36",
    "metadata": {},
    "outputs": [],
@@ -99,28 +110,13 @@
     "@tool\n",
     "def complex_tool(int_arg: int, float_arg: float, dict_arg: dict) -> int:\n",
     "    \"\"\"Do something complex with a complex tool.\"\"\"\n",
-    "    return int_arg * float_arg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "553c2c13-28c8-4451-8a3a-6c31d52dc31d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "    return int_arg * float_arg\n",
+    "\n",
+    "\n",
     "llm_with_tools = llm.bind_tools(\n",
     "    [complex_tool],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "802b2eca-9f79-4d6c-8257-85139ca5c752",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    ")\n",
+    "\n",
     "# Define chain\n",
     "chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool"
    ]
@@ -135,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "d354664c-ac44-4967-a35f-8912b3ad9477",
    "metadata": {},
    "outputs": [
@@ -146,14 +142,14 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:2499\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   2497\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2498\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2499\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2500\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2501\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   2502\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2503\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2504\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2505\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2506\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:241\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    236\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[1;32m    237\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    238\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    239\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    240\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m--> 241\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    242\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    243\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    244\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    249\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:387\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error:\n\u001b[0;32m--> 387\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error, \u001b[38;5;28mbool\u001b[39m):\n\u001b[1;32m    389\u001b[0m         observation \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool input validation error\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:378\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    364\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_tool_start(\n\u001b[1;32m    365\u001b[0m     {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdescription\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdescription},\n\u001b[1;32m    366\u001b[0m     tool_input \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tool_input, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(tool_input),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    375\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    376\u001b[0m )\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 378\u001b[0m     parsed_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    379\u001b[0m     tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_args_and_kwargs(parsed_input)\n\u001b[1;32m    380\u001b[0m     observation \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    381\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run(\u001b[38;5;241m*\u001b[39mtool_args, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    382\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    383\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run(\u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    384\u001b[0m     )\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:283\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    282\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 283\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    284\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    285\u001b[0m             k: \u001b[38;5;28mgetattr\u001b[39m(result, k)\n\u001b[1;32m    286\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdict()\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    287\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m tool_input\n\u001b[1;32m    288\u001b[0m         }\n\u001b[1;32m    289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_input\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.9/site-packages/pydantic/v1/main.py:526\u001b[0m, in \u001b[0;36mBaseModel.parse_obj\u001b[0;34m(cls, obj)\u001b[0m\n\u001b[1;32m    524\u001b[0m         exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m expected dict not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mobj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    525\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ValidationError([ErrorWrapper(exc, loc\u001b[38;5;241m=\u001b[39mROOT_KEY)], \u001b[38;5;28mcls\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m--> 526\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.9/site-packages/pydantic/v1/main.py:341\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(__pydantic_self__, **data)\u001b[0m\n\u001b[1;32m    339\u001b[0m values, fields_set, validation_error \u001b[38;5;241m=\u001b[39m validate_model(__pydantic_self__\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, data)\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validation_error:\n\u001b[0;32m--> 341\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m validation_error\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    343\u001b[0m     object_setattr(__pydantic_self__, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__dict__\u001b[39m\u001b[38;5;124m'\u001b[39m, values)\n",
+      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/runnables/base.py:2572\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2570\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m step\u001b[38;5;241m.\u001b[39minvoke(\u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2571\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2572\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2573\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2574\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:380\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    375\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[1;32m    376\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    377\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    378\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    379\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m--> 380\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    385\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    388\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    389\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:537\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)\u001b[0m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    536\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error:\n\u001b[0;32m--> 537\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    538\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error, \u001b[38;5;28mbool\u001b[39m):\n\u001b[1;32m    539\u001b[0m         observation \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool input validation error\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:526\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)\u001b[0m\n\u001b[1;32m    524\u001b[0m context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    525\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[0;32m--> 526\u001b[0m parsed_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    527\u001b[0m tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_args_and_kwargs(parsed_input)\n\u001b[1;32m    528\u001b[0m observation \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    529\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(\n\u001b[1;32m    530\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run, \u001b[38;5;241m*\u001b[39mtool_args, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m context\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run, \u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    534\u001b[0m )\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:424\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    423\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 424\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    425\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    426\u001b[0m             k: \u001b[38;5;28mgetattr\u001b[39m(result, k)\n\u001b[1;32m    427\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdict()\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    428\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m tool_input\n\u001b[1;32m    429\u001b[0m         }\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_input\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/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 complex_toolSchema\ndict_arg\n  field required (type=value_error.missing)"
      ]
     }
@@ -176,10 +172,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "8fedb550-683d-45ae-8876-ae7acb332019",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calling tool with arguments:\n",
+      "\n",
+      "{'int_arg': 5, 'float_arg': 2.1}\n",
+      "\n",
+      "raised the following error:\n",
+      "\n",
+      "<class 'pydantic.error_wrappers.ValidationError'>: 1 validation error for complex_toolSchema\n",
+      "dict_arg\n",
+      "  field required (type=value_error.missing)\n"
+     ]
+    }
+   ],
    "source": [
     "from typing import Any\n",
     "\n",
@@ -193,32 +205,8 @@
     "        return f\"Calling tool with arguments:\\n\\n{tool_args}\\n\\nraised the following error:\\n\\n{type(e)}: {e}\"\n",
     "\n",
     "\n",
-    "chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | try_except_tool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "71a2c98d-c0be-4c0a-bb3d-41ad4596526c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calling tool with arguments:\n",
-      "\n",
-      "{'int_arg': 5, 'float_arg': 2.1}\n",
-      "\n",
-      "raised the following error:\n",
-      "\n",
-      "<class 'pydantic.v1.error_wrappers.ValidationError'>: 1 validation error for complex_toolSchema\n",
-      "dict_arg\n",
-      "  field required (type=value_error.missing)\n"
-     ]
-    }
-   ],
-   "source": [
+    "chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | try_except_tool\n",
+    "\n",
     "print(\n",
     "    chain.invoke(\n",
     "        \"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg\"\n",
@@ -238,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "id": "02cc4223-35fa-4240-976a-012299ca703c",
    "metadata": {},
    "outputs": [
@@ -248,19 +236,22 @@
        "10.5"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool\n",
+    "\n",
     "better_model = ChatOpenAI(model=\"gpt-4-1106-preview\", temperature=0).bind_tools(\n",
     "    [complex_tool], tool_choice=\"complex_tool\"\n",
     ")\n",
+    "\n",
     "better_chain = better_model | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool\n",
     "\n",
     "chain_with_fallback = chain.with_fallbacks([better_chain])\n",
+    "\n",
     "chain_with_fallback.invoke(\n",
     "    \"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg\"\n",
     ")"
@@ -271,7 +262,7 @@
    "id": "412f8c4e-cc83-4d87-84a1-5ba2f8edb1e9",
    "metadata": {},
    "source": [
-    "Looking at the [Langsmith trace](https://smith.langchain.com/public/00e91fc2-e1a4-4b0f-a82e-e6b3119d196c/r) for this chain run, we can see that the first chain call fails as expected and it's the fallback that succeeds."
+    "Looking at the [LangSmith trace](https://smith.langchain.com/public/00e91fc2-e1a4-4b0f-a82e-e6b3119d196c/r) for this chain run, we can see that the first chain call fails as expected and it's the fallback that succeeds."
    ]
   },
   {
@@ -286,17 +277,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "b5659956-9454-468a-9753-a3ff9052b8f5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import json\n",
-    "from typing import Any\n",
-    "\n",
     "from langchain_core.messages import AIMessage, HumanMessage, ToolCall, ToolMessage\n",
-    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
     "\n",
     "class CustomToolException(Exception):\n",
@@ -336,7 +323,7 @@
     "# affect the prompt at all, but gives us the option to insert an arbitrary list of Messages\n",
     "# into the prompt if needed. We'll use this on retries to insert the error message.\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"human\", \"{input}\"), MessagesPlaceholder(\"last_output\", optional=True)]\n",
+    "    [(\"human\", \"{input}\"), (\"placeholder\", \"{last_output}\")]\n",
     ")\n",
     "chain = prompt | llm_with_tools | tool_custom_exception\n",
     "\n",
@@ -348,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "4c45f5bd-cbb4-47d5-b4b6-aec50673c750",
    "metadata": {},
    "outputs": [
@@ -358,7 +345,7 @@
        "10.5"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,6 +365,24 @@
    "source": [
     "And our chain succeeds! Looking at the [LangSmith trace](https://smith.langchain.com/public/c11e804c-e14f-4059-bd09-64766f999c14/r), we can see that indeed our initial chain still fails, and it's only on retrying that the chain succeeds."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b97af9f",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "Now you've seen some strategies how to handle tool calling errors. Next, you can learn more about how to use tools:\n",
+    "\n",
+    "- Few shot prompting [with tools](/docs/how_to/tools_few_shot/)\n",
+    "- Stream [tool calls](/docs/how_to/tool_streaming/)\n",
+    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
+    "\n",
+    "You can also check out some more specific uses of tool calling:\n",
+    "\n",
+    "- Getting [structured outputs](/docs/how_to/structured_output/) from models"
+   ]
   }
  ],
  "metadata": {
@@ -396,7 +401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/scrapfly.ipynb

@@ -37,7 +37,7 @@
     "scrapfly_loader = ScrapflyLoader(\n",
     "    [\"https://web-scraping.dev/products\"],\n",
     "    api_key=\"Your ScrapFly API key\",  # Get your API key from https://www.scrapfly.io/\n",
-    "    ignore_scrape_failures=True,  # Ignore unprocessable web pages and log their exceptions\n",
+    "    continue_on_failure=True,  # Ignore unprocessable web pages and log their exceptions\n",
     ")\n",
     "\n",
     "# Load documents from URLs as markdown\n",
@@ -72,7 +72,7 @@
     "scrapfly_loader = ScrapflyLoader(\n",
     "    [\"https://web-scraping.dev/products\"],\n",
     "    api_key=\"Your ScrapFly API key\",  # Get your API key from https://www.scrapfly.io/\n",
-    "    ignore_scrape_failures=True,  # Ignore unprocessable web pages and log their exceptions\n",
+    "    continue_on_failure=True,  # Ignore unprocessable web pages and log their exceptions\n",
     "    scrape_config=scrapfly_scrape_config,  # Pass the scrape_config object\n",
     "    scrape_format=\"markdown\",  # The scrape result format, either `markdown`(default) or `text`\n",
     ")\n",

docs/docs/integrations/llm_caching.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "id": "10ad9224",
    "metadata": {
     "ExecuteTime": {
@@ -1809,7 +1809,6 @@
    "cell_type": "markdown",
    "id": "0c69d84d",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -1891,7 +1890,6 @@
    "cell_type": "markdown",
    "id": "5da41b77",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -2149,6 +2147,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2ac1a8c7",
    "metadata": {},
    "source": [
     "## SingleStoreDB Semantic Cache\n",
@@ -2173,6 +2172,353 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7019c991-0101-4f9c-b212-5729a5471293",
+   "metadata": {},
+   "source": [
+    "## Couchbase Caches\n",
+    "\n",
+    "Use [Couchbase](https://couchbase.com/) as a cache for prompts and responses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6aac680-ba32-4c19-8864-6471cf0e7d5a",
+   "metadata": {},
+   "source": [
+    "### Couchbase Cache\n",
+    "\n",
+    "The standard cache that looks for an exact match of the user prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9b4764e4-c75f-4185-b326-524287a826be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create couchbase connection object\n",
+    "from datetime import timedelta\n",
+    "\n",
+    "from couchbase.auth import PasswordAuthenticator\n",
+    "from couchbase.cluster import Cluster\n",
+    "from couchbase.options import ClusterOptions\n",
+    "from langchain_couchbase.cache import CouchbaseCache\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "COUCHBASE_CONNECTION_STRING = (\n",
+    "    \"couchbase://localhost\"  # or \"couchbases://localhost\" if using TLS\n",
+    ")\n",
+    "DB_USERNAME = \"Administrator\"\n",
+    "DB_PASSWORD = \"Password\"\n",
+    "\n",
+    "auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)\n",
+    "options = ClusterOptions(auth)\n",
+    "cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)\n",
+    "\n",
+    "# Wait until the cluster is ready for use.\n",
+    "cluster.wait_until_ready(timedelta(seconds=5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4b5e73c5-92c1-4eab-84e2-77924ea9c123",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the bucket, scope and collection to store the cached documents\n",
+    "BUCKET_NAME = \"langchain-testing\"\n",
+    "SCOPE_NAME = \"_default\"\n",
+    "COLLECTION_NAME = \"_default\"\n",
+    "\n",
+    "set_llm_cache(\n",
+    "    CouchbaseCache(\n",
+    "        cluster=cluster,\n",
+    "        bucket_name=BUCKET_NAME,\n",
+    "        scope_name=SCOPE_NAME,\n",
+    "        collection_name=COLLECTION_NAME,\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "db8d28cc-8d93-47b4-8326-57a29a06fb3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 22.2 ms, sys: 14 ms, total: 36.2 ms\n",
+      "Wall time: 938 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was two-tired!\""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The first time, it is not yet in the cache, so it should take longer\n",
+    "llm.invoke(\"Tell me a joke\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b470dc81-2e7f-4743-9435-ce9071394eea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 53 ms, sys: 29 ms, total: 82 ms\n",
+      "Wall time: 84.2 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was two-tired!\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The second time, it is in the cache, so it should be much faster\n",
+    "llm.invoke(\"Tell me a joke\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43626f33-d184-4260-b641-c9341cef5842",
+   "metadata": {},
+   "source": [
+    "### Couchbase Semantic Cache\n",
+    "Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore. This needs an appropriate Vector Search Index defined to work. Please look at the usage example on how to set up the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6b470c03-d7fe-4270-89e1-638251619a53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Couchbase connection object\n",
+    "from datetime import timedelta\n",
+    "\n",
+    "from couchbase.auth import PasswordAuthenticator\n",
+    "from couchbase.cluster import Cluster\n",
+    "from couchbase.options import ClusterOptions\n",
+    "from langchain_couchbase.cache import CouchbaseSemanticCache\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
+    "\n",
+    "COUCHBASE_CONNECTION_STRING = (\n",
+    "    \"couchbase://localhost\"  # or \"couchbases://localhost\" if using TLS\n",
+    ")\n",
+    "DB_USERNAME = \"Administrator\"\n",
+    "DB_PASSWORD = \"Password\"\n",
+    "\n",
+    "auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)\n",
+    "options = ClusterOptions(auth)\n",
+    "cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)\n",
+    "\n",
+    "# Wait until the cluster is ready for use.\n",
+    "cluster.wait_until_ready(timedelta(seconds=5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f831bc4c-f330-4bd7-9b80-76771d91827e",
+   "metadata": {},
+   "source": [
+    "Notes:\n",
+    "- The search index for the semantic cache needs to be defined before using the semantic cache. \n",
+    "- The optional parameter, `score_threshold` in the Semantic Cache that you can use to tune the results of the semantic search.\n",
+    "\n",
+    "### How to Import an Index to the Full Text Search service?\n",
+    " - [Couchbase Server](https://docs.couchbase.com/server/current/search/import-search-index.html)\n",
+    "     - Click on Search -> Add Index -> Import\n",
+    "     - Copy the following Index definition in the Import screen\n",
+    "     - Click on Create Index to create the index.\n",
+    " - [Couchbase Capella](https://docs.couchbase.com/cloud/search/import-search-index.html)\n",
+    "     - Copy the index definition to a new file `index.json`\n",
+    "     - Import the file in Capella using the instructions in the documentation.\n",
+    "     - Click on Create Index to create the index.\n",
+    "\n",
+    "#### Example index for the vector search. \n",
+    "  ```\n",
+    "  {\n",
+    "    \"type\": \"fulltext-index\",\n",
+    "    \"name\": \"langchain-testing._default.semantic-cache-index\",\n",
+    "    \"sourceType\": \"gocbcore\",\n",
+    "    \"sourceName\": \"langchain-testing\",\n",
+    "    \"planParams\": {\n",
+    "      \"maxPartitionsPerPIndex\": 1024,\n",
+    "      \"indexPartitions\": 16\n",
+    "    },\n",
+    "    \"params\": {\n",
+    "      \"doc_config\": {\n",
+    "        \"docid_prefix_delim\": \"\",\n",
+    "        \"docid_regexp\": \"\",\n",
+    "        \"mode\": \"scope.collection.type_field\",\n",
+    "        \"type_field\": \"type\"\n",
+    "      },\n",
+    "      \"mapping\": {\n",
+    "        \"analysis\": {},\n",
+    "        \"default_analyzer\": \"standard\",\n",
+    "        \"default_datetime_parser\": \"dateTimeOptional\",\n",
+    "        \"default_field\": \"_all\",\n",
+    "        \"default_mapping\": {\n",
+    "          \"dynamic\": true,\n",
+    "          \"enabled\": false\n",
+    "        },\n",
+    "        \"default_type\": \"_default\",\n",
+    "        \"docvalues_dynamic\": false,\n",
+    "        \"index_dynamic\": true,\n",
+    "        \"store_dynamic\": true,\n",
+    "        \"type_field\": \"_type\",\n",
+    "        \"types\": {\n",
+    "          \"_default.semantic-cache\": {\n",
+    "            \"dynamic\": false,\n",
+    "            \"enabled\": true,\n",
+    "            \"properties\": {\n",
+    "              \"embedding\": {\n",
+    "                \"dynamic\": false,\n",
+    "                \"enabled\": true,\n",
+    "                \"fields\": [\n",
+    "                  {\n",
+    "                    \"dims\": 1536,\n",
+    "                    \"index\": true,\n",
+    "                    \"name\": \"embedding\",\n",
+    "                    \"similarity\": \"dot_product\",\n",
+    "                    \"type\": \"vector\",\n",
+    "                    \"vector_index_optimized_for\": \"recall\"\n",
+    "                  }\n",
+    "                ]\n",
+    "              },\n",
+    "              \"metadata\": {\n",
+    "                \"dynamic\": true,\n",
+    "                \"enabled\": true\n",
+    "              },\n",
+    "              \"text\": {\n",
+    "                \"dynamic\": false,\n",
+    "                \"enabled\": true,\n",
+    "                \"fields\": [\n",
+    "                  {\n",
+    "                    \"index\": true,\n",
+    "                    \"name\": \"text\",\n",
+    "                    \"store\": true,\n",
+    "                    \"type\": \"text\"\n",
+    "                  }\n",
+    "                ]\n",
+    "              }\n",
+    "            }\n",
+    "          }\n",
+    "        }\n",
+    "      },\n",
+    "      \"store\": {\n",
+    "        \"indexType\": \"scorch\",\n",
+    "        \"segmentVersion\": 16\n",
+    "      }\n",
+    "    },\n",
+    "    \"sourceParams\": {}\n",
+    "  }\n",
+    "  ```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ae0766c8-ea34-4604-b0dc-cf2bbe8077f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BUCKET_NAME = \"langchain-testing\"\n",
+    "SCOPE_NAME = \"_default\"\n",
+    "COLLECTION_NAME = \"semantic-cache\"\n",
+    "INDEX_NAME = \"semantic-cache-index\"\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "cache = CouchbaseSemanticCache(\n",
+    "    cluster=cluster,\n",
+    "    embedding=embeddings,\n",
+    "    bucket_name=BUCKET_NAME,\n",
+    "    scope_name=SCOPE_NAME,\n",
+    "    collection_name=COLLECTION_NAME,\n",
+    "    index_name=INDEX_NAME,\n",
+    "    score_threshold=0.8,\n",
+    ")\n",
+    "\n",
+    "set_llm_cache(cache)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2e82743-10ea-4319-b43e-193475ae5449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "The average lifespan of a dog is around 12 years, but this can vary depending on the breed, size, and overall health of the individual dog. Some smaller breeds may live longer, while larger breeds may have shorter lifespans. Proper care, diet, and exercise can also play a role in extending a dog's lifespan.\n",
+      "CPU times: user 826 ms, sys: 2.46 s, total: 3.28 s\n",
+      "Wall time: 2.87 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The first time, it is not yet in the cache, so it should take longer\n",
+    "print(llm.invoke(\"How long do dogs live?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c36f4e29-d872-4334-a1f1-0e6d10c5d9f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "The average lifespan of a dog is around 12 years, but this can vary depending on the breed, size, and overall health of the individual dog. Some smaller breeds may live longer, while larger breeds may have shorter lifespans. Proper care, diet, and exercise can also play a role in extending a dog's lifespan.\n",
+      "CPU times: user 9.82 ms, sys: 2.61 ms, total: 12.4 ms\n",
+      "Wall time: 311 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The second time, it is in the cache, so it should be much faster\n",
+    "print(llm.invoke(\"What is the expected lifespan of a dog?\"))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ae1f5e1c-085e-4998-9f2d-b5867d2c3d5b",
@@ -2228,7 +2574,9 @@
     "| langchain_core.caches | [InMemoryCache](https://api.python.langchain.com/en/latest/caches/langchain_core.caches.InMemoryCache.html) |\n",
     "| langchain_elasticsearch.cache | [ElasticsearchCache](https://api.python.langchain.com/en/latest/cache/langchain_elasticsearch.cache.ElasticsearchCache.html) |\n",
     "| langchain_mongodb.cache | [MongoDBAtlasSemanticCache](https://api.python.langchain.com/en/latest/cache/langchain_mongodb.cache.MongoDBAtlasSemanticCache.html) |\n",
-    "| langchain_mongodb.cache | [MongoDBCache](https://api.python.langchain.com/en/latest/cache/langchain_mongodb.cache.MongoDBCache.html) |\n"
+    "| langchain_mongodb.cache | [MongoDBCache](https://api.python.langchain.com/en/latest/cache/langchain_mongodb.cache.MongoDBCache.html) |\n",
+    "| langchain_couchbase.cache | [CouchbaseCache](https://api.python.langchain.com/en/latest/cache/langchain_couchbase.cache.CouchbaseCache.html) |\n",
+    "| langchain_couchbase.cache | [CouchbaseSemanticCache](https://api.python.langchain.com/en/latest/cache/langchain_couchbase.cache.CouchbaseSemanticCache.html) |\n"
    ]
   },
   {
@@ -2256,7 +2604,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/pipelineai.ipynb

@@ -50,8 +50,8 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain.chains import LLMChain\n",
     "from langchain_community.llms import PipelineAI\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import PromptTemplate"
    ]
   },
@@ -123,7 +123,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+    "llm_chain = prompt | llm | StrOutputParser()"
    ]
   },
   {
@@ -142,7 +142,7 @@
    "source": [
     "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
     "\n",
-    "llm_chain.run(question)"
+    "llm_chain.invoke(question)"
    ]
   }
  ],

docs/docs/integrations/llms/tongyi.ipynb

@@ -27,7 +27,7 @@
    "outputs": [],
    "source": [
     "# Install the package\n",
-    "%pip install --upgrade --quiet  dashscope"
+    "%pip install --upgrade --quiet  langchain-community dashscope"
    ]
   },
   {

docs/docs/integrations/providers/couchbase.mdx

@@ -27,3 +27,65 @@ See a [usage example](/docs/integrations/document_loaders/couchbase).
 ```python
 from langchain_community.document_loaders.couchbase import CouchbaseLoader
 ```
+
+## LLM Caches
+
+### CouchbaseCache
+Use Couchbase as a cache for prompts and responses.
+
+See a [usage example](/docs/integrations/llm_caching/#couchbase-cache).
+
+To import this cache:
+```python
+from langchain_couchbase.cache import CouchbaseCache
+```
+
+To use this cache with your LLMs:
+```python
+from langchain_core.globals import set_llm_cache
+
+cluster = couchbase_cluster_connection_object
+
+set_llm_cache(
+    CouchbaseCache(
+        cluster=cluster,
+        bucket_name=BUCKET_NAME,
+        scope_name=SCOPE_NAME,
+        collection_name=COLLECTION_NAME,
+    )
+)
+```
+
+
+### CouchbaseSemanticCache
+Semantic caching allows users to retrieve cached prompts based on the semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore.
+The CouchbaseSemanticCache needs a Search Index defined to work. Please look at the [usage example](/docs/integrations/vectorstores/couchbase) on how to set up the index.
+
+See a [usage example](/docs/integrations/llm_caching/#couchbase-semantic-cache).
+
+To import this cache:
+```python
+from langchain_couchbase.cache import CouchbaseSemanticCache
+```
+
+To use this cache with your LLMs:
+```python
+from langchain_core.globals import set_llm_cache
+
+# use any embedding provider...
+from langchain_openai.Embeddings import OpenAIEmbeddings
+
+embeddings = OpenAIEmbeddings()
+cluster = couchbase_cluster_connection_object
+
+set_llm_cache(
+    CouchbaseSemanticCache(
+        cluster=cluster,
+        embedding = embeddings,
+        bucket_name=BUCKET_NAME,
+        scope_name=SCOPE_NAME,
+        collection_name=COLLECTION_NAME,
+        index_name=INDEX_NAME,
+    )
+)
+```

docs/docs/integrations/providers/nvidia.mdx

@@ -61,7 +61,7 @@ When ready to deploy, you can self-host models with NVIDIA NIM—which is includ
 ```python
 from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings, NVIDIARerank
 
-# connect to an chat NIM running at localhost:8000, specifyig a specific model
+# connect to a chat NIM running at localhost:8000, specifying a model
 llm = ChatNVIDIA(base_url="http://localhost:8000/v1", model="meta/llama3-8b-instruct")
 
 # connect to an embedding NIM running at localhost:8080

docs/docs/integrations/providers/premai.md

@@ -202,7 +202,7 @@ Prem Templates are also available for Streaming too.
 
 ## Prem Embeddings
 
-In this section we are going to dicuss how we can get access to different embedding model using `PremEmbeddings` with LangChain. Lets start by importing our modules and setting our API Key. 
+In this section we cover how we can get access to different embedding models using `PremEmbeddings` with LangChain. Let's start by importing our modules and setting our API Key.
 
 ```python
 import os

docs/docs/integrations/providers/qdrant.mdx

@@ -21,7 +21,7 @@ whether for semantic search or example selection.
 
 To import this vectorstore:
 ```python
-from langchain_qdrant import Qdrant
+from langchain_qdrant import QdrantVectorStore
 ```
 
 For a more detailed walkthrough of the Qdrant wrapper, see [this notebook](/docs/integrations/vectorstores/qdrant)

docs/docs/integrations/providers/shaleprotocol.md

@@ -21,7 +21,7 @@ For example
 ```python
 from langchain_openai import OpenAI
 from langchain_core.prompts import PromptTemplate
-from langchain.chains import LLMChain
+from langchain_core.output_parsers import StrOutputParser
 
 import os
 os.environ['OPENAI_API_BASE'] = "https://shale.live/v1"
@@ -35,10 +35,11 @@ template = """Question: {question}
 
 prompt = PromptTemplate.from_template(template)
 
-llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+llm_chain = prompt | llm | StrOutputParser()
 
 question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
 
-llm_chain.run(question)
+llm_chain.invoke(question)
 
 ```

docs/docs/integrations/retrievers/cohere-reranker.ipynb

@@ -309,9 +309,9 @@
     "documents = TextLoader(\"../../how_to/state_of_the_union.txt\").load()\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
     "texts = text_splitter.split_documents(documents)\n",
-    "retriever = FAISS.from_documents(texts, CohereEmbeddings()).as_retriever(\n",
-    "    search_kwargs={\"k\": 20}\n",
-    ")\n",
+    "retriever = FAISS.from_documents(\n",
+    "    texts, CohereEmbeddings(model=\"embed-english-v3.0\")\n",
+    ").as_retriever(search_kwargs={\"k\": 20})\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "docs = retriever.invoke(query)\n",
@@ -324,7 +324,8 @@
    "metadata": {},
    "source": [
     "## Doing reranking with CohereRerank\n",
-    "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `CohereRerank`, uses the Cohere rerank endpoint to rerank the returned results."
+    "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `CohereRerank`, uses the Cohere rerank endpoint to rerank the returned results.\n",
+    "Do note that it is mandatory to specify the model name in CohereRerank!"
    ]
   },
   {
@@ -339,7 +340,7 @@
     "from langchain_community.llms import Cohere\n",
     "\n",
     "llm = Cohere(temperature=0)\n",
-    "compressor = CohereRerank()\n",
+    "compressor = CohereRerank(model=\"rerank-english-v3.0\")\n",
     "compression_retriever = ContextualCompressionRetriever(\n",
     "    base_compressor=compressor, base_retriever=retriever\n",
     ")\n",

docs/docs/integrations/text_embedding/cohere.ipynb

@@ -40,7 +40,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = CohereEmbeddings(model=\"embed-english-light-v3.0\")"
+    "embeddings = CohereEmbeddings(\n",
+    "    model=\"embed-english-light-v3.0\"\n",
+    ")  # It is mandatory to pass a model parameter to initialize the CohereEmbeddings object"
    ]
   },
   {

docs/docs/integrations/tools/infobip.ipynb

@@ -22,7 +22,7 @@
     "`InfobipAPIWrapper` uses name parameters where you can provide credentials:\n",
     "\n",
     "- `infobip_api_key` - [API Key](https://www.infobip.com/docs/essentials/api-authentication#api-key-header) that you can find in your [developer tools](https://portal.infobip.com/dev/api-keys)\n",
-    "- `infobip_base_url` - [Base url](https://www.infobip.com/docs/essentials/base-url) for Infobip API. You can use default value `https://api.infobip.com/`.\n",
+    "- `infobip_base_url` - [Base url](https://www.infobip.com/docs/essentials/base-url) for Infobip API. You can use the default value `https://api.infobip.com/`.\n",
     "\n",
     "You can also provide `infobip_api_key` and `infobip_base_url` as environment variables `INFOBIP_API_KEY` and `INFOBIP_BASE_URL`."
    ]
@@ -60,7 +60,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Sending a Email"
+    "## Sending an Email"
    ]
   },
   {

docs/docs/integrations/vectorstores/aperturedb.ipynb

@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# ApertureDB\n",
+    "\n",
+    "[ApertureDB](https://docs.aperturedata.io) is a database that stores, indexes, and manages multi-modal data like text, images, videos, bounding boxes, and embeddings, together with their associated metadata.\n",
+    "\n",
+    "This notebook explains how to use the embeddings functionality of ApertureDB."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7393beb",
+   "metadata": {},
+   "source": [
+    "## Install ApertureDB Python SDK\n",
+    "\n",
+    "This installs the [Python SDK](https://docs.aperturedata.io/category/aperturedb-python-sdk) used to write client code for ApertureDB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a62cff8a-bcf7-4e33-bbbc-76999c2e3e20",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install --upgrade --quiet aperturedb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fe12f77",
+   "metadata": {},
+   "source": [
+    "## Run an ApertureDB instance\n",
+    "\n",
+    "To continue, you should have an [ApertureDB instance up and running](https://docs.aperturedata.io/HowToGuides/start/Setup) and configure your environment to use it.  \n",
+    "There are various ways to do that, for example:\n",
+    "\n",
+    "```bash\n",
+    "docker run --publish 55555:55555 aperturedata/aperturedb-standalone\n",
+    "adb config create local --active --no-interactive\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "667eabca",
+   "metadata": {},
+   "source": [
+    "## Download some web documents\n",
+    "We're going to do a mini-crawl here of one web page."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0798dfdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For loading documents from web\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "\n",
+    "loader = WebBaseLoader(\"https://docs.aperturedata.io\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f077d11",
+   "metadata": {},
+   "source": [
+    "## Select embeddings model\n",
+    "\n",
+    "We want to use OllamaEmbeddings so we have to import the necessary modules.\n",
+    "\n",
+    "Ollama can be set up as a docker container as described in the [documentation](https://hub.docker.com/r/ollama/ollama), for example:\n",
+    "```bash\n",
+    "# Run server\n",
+    "docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama\n",
+    "# Tell server to load a specific model\n",
+    "docker exec ollama ollama run llama2\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8b6ed9cd-81b9-46e5-9c20-5aafca2844d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.embeddings import OllamaEmbeddings\n",
+    "\n",
+    "embeddings = OllamaEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7b313e6",
+   "metadata": {},
+   "source": [
+    "## Split documents into segments\n",
+    "\n",
+    "We want to turn our single document into multiple segments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3c4b7b31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter()\n",
+    "documents = text_splitter.split_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46339d32",
+   "metadata": {},
+   "source": [
+    "## Create vectorstore from documents and embeddings\n",
+    "\n",
+    "This code creates a vectorstore in the ApertureDB instance.\n",
+    "Within the instance, this vectorstore is represented as a \"[descriptor set](https://docs.aperturedata.io/category/descriptorset-commands)\".\n",
+    "By default, the descriptor set is named `langchain`.  The following code will generate embeddings for each document and store them in ApertureDB as descriptors.  This will take a few seconds as the embeddings are bring generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dcf88bdf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.vectorstores import ApertureDB\n",
+    "\n",
+    "vector_db = ApertureDB.from_documents(documents, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7672877b",
+   "metadata": {},
+   "source": [
+    "## Select a large language model\n",
+    "\n",
+    "Again, we use the Ollama server we set up for local processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9a005e4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import Ollama\n",
+    "\n",
+    "llm = Ollama(model=\"llama2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd54f2ad",
+   "metadata": {},
+   "source": [
+    "## Build a RAG chain\n",
+    "\n",
+    "Now we have all the components we need to create a RAG (Retrieval-Augmented Generation) chain.  This chain does the following:\n",
+    "1. Generate embedding descriptor for user query\n",
+    "2. Find text segments that are similar to the user query using the vector store\n",
+    "3. Pass user query and context documents to the LLM using a prompt template\n",
+    "4. Return the LLM's answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a8c513ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Based on the provided context, ApertureDB can store images. In fact, it is specifically designed to manage multimodal data such as images, videos, documents, embeddings, and associated metadata including annotations. So, ApertureDB has the capability to store and manage images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create prompt\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\"\"\"Answer the following question based only on the provided context:\n",
+    "\n",
+    "<context>\n",
+    "{context}\n",
+    "</context>\n",
+    "\n",
+    "Question: {input}\"\"\")\n",
+    "\n",
+    "\n",
+    "# Create a chain that passes documents to an LLM\n",
+    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
+    "\n",
+    "document_chain = create_stuff_documents_chain(llm, prompt)\n",
+    "\n",
+    "\n",
+    "# Treat the vectorstore as a document retriever\n",
+    "retriever = vector_db.as_retriever()\n",
+    "\n",
+    "\n",
+    "# Create a RAG chain that connects the retriever to the LLM\n",
+    "from langchain.chains import create_retrieval_chain\n",
+    "\n",
+    "retrieval_chain = create_retrieval_chain(retriever, document_chain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bc6a882",
+   "metadata": {},
+   "source": [
+    "## Run the RAG chain\n",
+    "\n",
+    "Finally we pass a question to the chain and get our answer.  This will take a few seconds to run as the LLM generates an answer from the query and context documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "020f29f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Based on the provided context, ApertureDB can store images in several ways:\n",
+      "\n",
+      "1. Multimodal data management: ApertureDB offers a unified interface to manage multimodal data such as images, videos, documents, embeddings, and associated metadata including annotations. This means that images can be stored along with other types of data in a single database instance.\n",
+      "2. Image storage: ApertureDB provides image storage capabilities through its integration with the public cloud providers or on-premise installations. This allows customers to host their own ApertureDB instances and store images on their preferred cloud provider or on-premise infrastructure.\n",
+      "3. Vector database: ApertureDB also offers a vector database that enables efficient similarity search and classification of images based on their semantic meaning. This can be useful for applications where image search and classification are important, such as in computer vision or machine learning workflows.\n",
+      "\n",
+      "Overall, ApertureDB provides flexible and scalable storage options for images, allowing customers to choose the deployment model that best suits their needs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_query = \"How can ApertureDB store images?\"\n",
+    "response = retrieval_chain.invoke({\"input\": user_query})\n",
+    "print(response[\"answer\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/vectorstores/azuresearch.ipynb

@@ -169,6 +169,23 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify additional properties for the Azure client such as the following https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md#configurations\n",
+    "vector_store: AzureSearch = AzureSearch(\n",
+    "    azure_search_endpoint=vector_store_address,\n",
+    "    azure_search_key=vector_store_password,\n",
+    "    index_name=index_name,\n",
+    "    embedding_function=embeddings.embed_query,\n",
+    "    # Configure max retries for the Azure client\n",
+    "    additional_search_client_options={\"retry_total\": 4},\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/docs/integrations/vectorstores/databricks_vector_search.ipynb

@@ -174,7 +174,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Similarity search"
+    "## Similarity search\n",
+    "Optional keyword arguments to similarity_search include specifying k number of documents to retrive, \n",
+    "a filters dictionary for metadata filtering based on [this syntax](https://docs.databricks.com/en/generative-ai/create-query-vector-search.html#use-filters-on-queries),\n",
+    "as well as the [query_type](https://api-docs.databricks.com/python/vector-search/databricks.vector_search.html#databricks.vector_search.index.VectorSearchIndex.similarity_search) which can be ANN or HYBRID "
    ]
   },
   {

docs/docs/integrations/vectorstores/pgvector.ipynb

@@ -78,7 +78,7 @@
     "# See docker command above to launch a postgres instance with pgvector enabled.\n",
     "connection = \"postgresql+psycopg://langchain:langchain@localhost:6024/langchain\"  # Uses psycopg3!\n",
     "collection_name = \"my_docs\"\n",
-    "embeddings = CohereEmbeddings()\n",
+    "embeddings = CohereEmbeddings(model=\"embed-english-v3.0\")\n",
     "\n",
     "vectorstore = PGVector(\n",
     "    embeddings=embeddings,\n",

docs/docs/integrations/vectorstores/qdrant.ipynb

@@ -8,14 +8,15 @@
    "source": [
     "# Qdrant\n",
     "\n",
-    ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.\n",
+    ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.\n",
     "\n",
+    "This documentation demonstrates how to use Qdrant with Langchain for dense/sparse and hybrid retrieval.\n",
     "\n",
-    "This notebook shows how to use functionality related to the `Qdrant` vector database. \n",
+    "> This page documents the `QdrantVectorStore` class that supports multiple retrieval modes via Qdrant's new [Query API](https://qdrant.tech/blog/qdrant-1.10.x/). It requires you to run Qdrant v1.10.0 or above.\n",
     "\n",
     "There are various modes of how to run `Qdrant`, and depending on the chosen one, there will be some subtle differences. The options include:\n",
     "- Local mode, no server required\n",
-    "- On-premise server deployment\n",
+    "- Docker deployments\n",
     "- Qdrant Cloud\n",
     "\n",
     "See the [installation instructions](https://qdrant.tech/documentation/install/)."
@@ -30,7 +31,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain-qdrant langchain-openai langchain langchain-community"
+    "%pip install langchain-qdrant langchain-openai langchain"
    ]
   },
   {
@@ -39,30 +40,7 @@
    "id": "7b2f111b-357a-4f42-9730-ef0603bdc1b5",
    "metadata": {},
    "source": [
-    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "082e7e8b-ac52-430c-98d6-8f0924457642",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OpenAI API Key: ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+    "We will use `OpenAIEmbeddings` for demonstration."
    ]
   },
   {
@@ -80,7 +58,7 @@
    "source": [
     "from langchain_community.document_loaders import TextLoader\n",
     "from langchain_openai import OpenAIEmbeddings\n",
-    "from langchain_qdrant import Qdrant\n",
+    "from langchain_qdrant import QdrantVectorStore\n",
     "from langchain_text_splitters import CharacterTextSplitter"
    ]
   },
@@ -97,7 +75,7 @@
    },
    "outputs": [],
    "source": [
-    "loader = TextLoader(\"../../how_to/state_of_the_union.txt\")\n",
+    "loader = TextLoader(\"some-file.txt\")\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "docs = text_splitter.split_documents(documents)\n",
@@ -115,7 +93,7 @@
     "\n",
     "### Local mode\n",
     "\n",
-    "Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kepy in memory or persisted on disk.\n",
+    "Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things out and debugging or storing just a small amount of vectors. The embeddings might be fully kept in memory or persisted on disk.\n",
     "\n",
     "#### In-memory\n",
     "\n",
@@ -135,7 +113,7 @@
    },
    "outputs": [],
    "source": [
-    "qdrant = Qdrant.from_documents(\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    location=\":memory:\",  # Local mode with in-memory storage only\n",
@@ -151,7 +129,7 @@
    "source": [
     "#### On-disk storage\n",
     "\n",
-    "Local mode, without using the Qdrant server, may also store your vectors on disk so they're persisted between runs."
+    "Local mode, without using the Qdrant server, may also store your vectors on disk so they persist between runs."
    ]
   },
   {
@@ -167,7 +145,7 @@
    },
    "outputs": [],
    "source": [
-    "qdrant = Qdrant.from_documents(\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    path=\"/tmp/local_qdrant\",\n",
@@ -199,7 +177,7 @@
    "outputs": [],
    "source": [
     "url = \"<---qdrant url here --->\"\n",
-    "qdrant = Qdrant.from_documents(\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    url=url,\n",
@@ -233,7 +211,7 @@
    "source": [
     "url = \"<---qdrant cloud cluster url here --->\"\n",
     "api_key = \"<---api key here--->\"\n",
-    "qdrant = Qdrant.from_documents(\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    url=url,\n",
@@ -266,7 +244,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qdrant = Qdrant.from_existing_collection(\n",
+    "qdrant = QdrantVectorStore.from_existing_collection(\n",
     "    embeddings=embeddings,\n",
     "    collection_name=\"my_documents\",\n",
     "    url=\"http://localhost:6333\",\n",
@@ -297,7 +275,7 @@
    "outputs": [],
    "source": [
     "url = \"<---qdrant url here --->\"\n",
-    "qdrant = Qdrant.from_documents(\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    url=url,\n",
@@ -320,12 +298,31 @@
    "source": [
     "## Similarity search\n",
     "\n",
-    "The simplest scenario for using Qdrant vector store is to perform a similarity search. Under the hood, our query will be encoded with the `embedding_function` and used to find similar documents in Qdrant collection."
+    "The simplest scenario for using Qdrant vector store is to perform a similarity search. Under the hood, our query will be encoded into vector embeddings and used to find similar documents in Qdrant collection.\n",
+    "\n",
+    "`QdrantVectorStore` supports 3 modes for similarity searches. They can be configured using the `retrieval_mode` parameter when setting up the class.\n",
+    "\n",
+    "- Dense Vector Search(Default)\n",
+    "- Sparse Vector Search\n",
+    "- Hybrid Search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3a78d46",
+   "metadata": {},
+   "source": [
+    "### Dense Vector Search\n",
+    "\n",
+    "To search with only dense vectors,\n",
+    "\n",
+    "- The `retrieval_mode` parameter should be set to `RetrievalMode.DENSE`(default).\n",
+    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided for the `embedding` parameter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "a8c513ab",
    "metadata": {
     "ExecuteTime": {
@@ -336,38 +333,108 @@
    },
    "outputs": [],
    "source": [
+    "from langchain_qdrant import RetrievalMode\n",
+    "\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
+    "    docs,\n",
+    "    embedding=embeddings,\n",
+    "    location=\":memory:\",\n",
+    "    collection_name=\"my_documents\",\n",
+    "    retrieval_mode=RetrievalMode.DENSE,\n",
+    ")\n",
+    "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "found_docs = qdrant.similarity_search(query)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "fc516993",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-04-04T10:51:25.220984Z",
-     "start_time": "2023-04-04T10:51:25.213943Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
-     ]
-    }
-   ],
+   "cell_type": "markdown",
+   "id": "dbd93d85",
+   "metadata": {},
    "source": [
-    "print(found_docs[0].page_content)"
+    "### Sparse Vector Search\n",
+    "\n",
+    "To search with only sparse vectors,\n",
+    "\n",
+    "- The `retrieval_mode` parameter should be set to `RetrievalMode.SPARSE`.\n",
+    "- An implementation of the [`SparseEmbeddings`](https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py) interface using any sparse embeddings provider has to be provided as value to the `sparse_embedding` parameter.\n",
+    "\n",
+    "The `langchain-qdrant` package provides a [FastEmbed](https://github.com/qdrant/fastembed) based implementation out of the box.\n",
+    "\n",
+    "To use it, install the FastEmbed package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceb493a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install fastembed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "052e3412",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_qdrant import FastEmbedSparse, RetrievalMode\n",
+    "\n",
+    "sparse_embeddings = FastEmbedSparse(model_name=\"Qdrant/BM25\")\n",
+    "\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
+    "    docs,\n",
+    "    sparse_embedding=sparse_embeddings,\n",
+    "    location=\":memory:\",\n",
+    "    collection_name=\"my_documents\",\n",
+    "    retrieval_mode=RetrievalMode.SPARSE,\n",
+    ")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "found_docs = qdrant.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4b6c456",
+   "metadata": {},
+   "source": [
+    "### Hybrid Vector Search\n",
+    "\n",
+    "To perform a hybrid search using dense and sparse vectors with score fusion,\n",
+    "\n",
+    "- The `retrieval_mode` parameter should be set to `RetrievalMode.HYBRID`.\n",
+    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided for the `embedding` parameter.\n",
+    "- An implementation of the [`SparseEmbeddings`](https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py) interface using any sparse embeddings provider has to be provided as value to the `sparse_embedding` parameter.\n",
+    "\n",
+    "Note that if you've added documents with the `HYBRID` mode, you can switch to any retrieval mode when searching. Since both the dense and sparse vectors are available in the collection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce56f6e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_qdrant import FastEmbedSparse, RetrievalMode\n",
+    "\n",
+    "sparse_embeddings = FastEmbedSparse(model_name=\"Qdrant/BM25\")\n",
+    "\n",
+    "qdrant = QdrantVectorStore.from_documents(\n",
+    "    docs,\n",
+    "    embedding=embeddings,\n",
+    "    sparse_embedding=sparse_embeddings,\n",
+    "    location=\":memory:\",\n",
+    "    collection_name=\"my_documents\",\n",
+    "    retrieval_mode=RetrievalMode.HYBRID,\n",
+    ")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "found_docs = qdrant.similarity_search(query)"
    ]
   },
   {
@@ -400,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "756a6887",
    "metadata": {
     "ExecuteTime": {
@@ -408,23 +475,7 @@
      "start_time": "2023-04-04T10:51:25.635947Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "\n",
-      "Score: 0.8153784913324512\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "document, score = found_docs[0]\n",
     "print(document.page_content)\n",
@@ -449,10 +500,10 @@
    "metadata": {},
    "source": [
     "```python\n",
-    "from qdrant_client.http import models as rest\n",
+    "from qdrant_client.http import models\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "found_docs = qdrant.similarity_search_with_score(query, filter=rest.Filter(...))\n",
+    "found_docs = qdrant.similarity_search_with_score(query, filter=models.Filter(...))\n",
     "```"
    ]
   },
@@ -469,7 +520,9 @@
    "source": [
     "## Maximum marginal relevance search (MMR)\n",
     "\n",
-    "If you'd like to look up for some similar documents, but you'd also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents."
+    "If you'd like to look up some similar documents, but you'd also like to receive diverse results, MMR is the method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.\n",
+    "\n",
+    "Note that MMR search is only available if you've added documents with `DENSE` or `HYBRID` modes. Since it requires dense vectors."
    ]
   },
   {
@@ -490,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "80c6db11",
    "metadata": {
     "ExecuteTime": {
@@ -498,40 +551,7 @@
      "start_time": "2023-04-04T10:51:26.013329Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
-      "\n",
-      "2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n",
-      "\n",
-      "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n",
-      "\n",
-      "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n",
-      "\n",
-      "Officer Mora was 27 years old. \n",
-      "\n",
-      "Officer Rivera was 22. \n",
-      "\n",
-      "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n",
-      "\n",
-      "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
-      "\n",
-      "I’ve worked on these issues a long time. \n",
-      "\n",
-      "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for i, doc in enumerate(found_docs):\n",
     "    print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
@@ -545,7 +565,7 @@
    "source": [
     "## Qdrant as a Retriever\n",
     "\n",
-    "Qdrant, as all the other vector stores, is a LangChain Retriever, by using cosine similarity. "
+    "Qdrant, as all the other vector stores, is a LangChain Retriever. "
    ]
   },
   {
@@ -589,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "f3c70c31",
    "metadata": {
     "ExecuteTime": {
@@ -597,18 +617,7 @@
      "start_time": "2023-04-04T10:51:26.046407Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "retriever.invoke(query)[0]"
@@ -622,11 +631,11 @@
    "source": [
     "## Customizing Qdrant\n",
     "\n",
-    "There are some options to use an existing Qdrant collection within your Langchain application. In such cases you may need to define how to map Qdrant point into the Langchain `Document`.\n",
+    "There are options to use an existing Qdrant collection within your Langchain application. In such cases, you may need to define how to map Qdrant point into the Langchain `Document`.\n",
     "\n",
     "### Named vectors\n",
     "\n",
-    "Qdrant supports [multiple vectors per point](https://qdrant.tech/documentation/concepts/collections/#collection-with-multiple-vectors) by named vectors. Langchain requires just a single embedding per document and, by default, uses a single vector. However, if you work with a collection created externally or want to have the named vector used, you can configure it by providing its name.\n"
+    "Qdrant supports [multiple vectors per point](https://qdrant.tech/documentation/concepts/collections/#collection-with-multiple-vectors) by named vectors. If you work with a collection created externally or want to have the differently named vector used, you can configure it by providing its name.\n"
    ]
   },
   {
@@ -638,25 +647,18 @@
    },
    "outputs": [],
    "source": [
-    "Qdrant.from_documents(\n",
+    "QdrantVectorStore.from_documents(\n",
     "    docs,\n",
-    "    embeddings,\n",
+    "    embedding=embeddings,\n",
+    "    sparse_embedding=sparse_embeddings,\n",
     "    location=\":memory:\",\n",
     "    collection_name=\"my_documents_2\",\n",
+    "    retrieval_mode=RetrievalMode.HYBRID,\n",
     "    vector_name=\"custom_vector\",\n",
+    "    sparse_vector_name=\"custom_sparse_vector\",\n",
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b34f5230",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "As a Langchain user, you won't see any difference whether you use named vectors or not. Qdrant integration will handle the conversion under the hood."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "b2350093",
@@ -694,7 +696,7 @@
    },
    "outputs": [],
    "source": [
-    "Qdrant.from_documents(\n",
+    "QdrantVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    location=\":memory:\",\n",
@@ -729,7 +731,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

docs/docs/introduction.mdx

@@ -22,6 +22,7 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
     light: useBaseUrl('/svg/langchain_stack_062024.svg'),
     dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
   }}
+  style={{ width: "100%" }}
   title="LangChain Framework Overview"
 />
 

docs/docs/tutorials/rag.ipynb

@@ -107,7 +107,7 @@
     "```\n",
     "## Preview\n",
     "\n",
-    "In this guide we’ll build a QA app over as website. The specific website we will use is the [LLM Powered Autonomous\n",
+    "In this guide we’ll build an app that answers questions about the content of a website. The specific website we will use is the [LLM Powered Autonomous\n",
     "Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post\n",
     "by Lilian Weng, which allows us to ask questions about the contents of\n",
     "the post.\n",

docs/docs/tutorials/retrievers.ipynb

@@ -269,7 +269,7 @@
    "id": "b4991642-7275-40a9-b11a-e3beccbf2614",
    "metadata": {},
    "source": [
-    "Return documents based on similarity to a embedded query:"
+    "Return documents based on similarity to an embedded query:"
    ]
   },
   {

libs/community/Makefile

@@ -46,7 +46,7 @@ lint_tests: MYPY_CACHE=.mypy_cache_test
 
 lint lint_diff lint_package lint_tests:
 	./scripts/check_pydantic.sh .
-	./scripts/lint_imports.sh
+	./scripts/lint_imports.sh .
 	./scripts/check_pickle.sh .
 	poetry run ruff check .
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff

libs/community/extended_testing_deps.txt

@@ -72,6 +72,7 @@ rspace_client>=2.5.0,<3
 scikit-learn>=1.2.2,<2
 simsimd>=4.3.1,<5
 sqlite-vss>=0.1.2,<0.2
+sseclient-py>=1.8.0,<2
 streamlit>=1.18.0,<2
 sympy>=1.12,<2
 telethon>=1.28.5,<2

libs/community/langchain_community/chat_message_histories/sql.py

@@ -113,7 +113,33 @@ _warned_once_already = False
 
 
 class SQLChatMessageHistory(BaseChatMessageHistory):
-    """Chat message history stored in an SQL database."""
+    """Chat message history stored in an SQL database.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_core.messages import HumanMessage
+
+            from langchain_community.chat_message_histories import SQLChatMessageHistory
+
+            # create sync sql message history by connection_string
+            message_history = SQLChatMessageHistory(
+                session_id='foo', connection_string='sqlite///:memory.db'
+            )
+            message_history.add_message(HumanMessage("hello"))
+            message_history.message
+
+            # create async sql message history using aiosqlite
+            # from sqlalchemy.ext.asyncio import create_async_engine
+            #
+            # async_engine = create_async_engine("sqlite+aiosqlite:///memory.db")
+            # async_message_history = SQLChatMessageHistory(
+            #     session_id='foo', connection=async_engine,
+            # )
+            # await async_message_history.aadd_message(HumanMessage("hello"))
+            # await async_message_history.aget_messages()
+
+    """
 
     @property
     @deprecated("0.2.2", removal="0.3.0", alternative="session_maker")
@@ -131,6 +157,21 @@ class SQLChatMessageHistory(BaseChatMessageHistory):
         engine_args: Optional[Dict[str, Any]] = None,
         async_mode: Optional[bool] = None,  # Use only if connection is a string
     ):
+        """Initialize with a SQLChatMessageHistory instance.
+
+        Args:
+            session_id: Indicates the id of the same session.
+            connection_string: String parameter configuration for connecting
+                to the database.
+            table_name: Table name used to save data.
+            session_id_field_name: The name of field of `session_id`.
+            custom_message_converter: Custom message converter for converting
+                database data and `BaseMessage`
+            connection: Database connection object, which can be a string containing
+                connection configuration, Engine object or AsyncEngine object.
+            engine_args: Additional configuration for creating database engines.
+            async_mode: Whether it is an asynchronous connection.
+        """
         assert not (
             connection_string and connection
         ), "connection_string and connection are mutually exclusive"

libs/community/langchain_community/chat_models/anyscale.py

@@ -25,7 +25,7 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 DEFAULT_API_BASE = "https://api.endpoints.anyscale.com/v1"
-DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"
+DEFAULT_MODEL = "meta-llama/Meta-Llama-3-8B-Instruct"
 
 
 class ChatAnyscale(ChatOpenAI):

libs/community/langchain_community/chat_models/azureml_endpoint.py

@@ -141,9 +141,8 @@ class CustomOpenAIChatContentFormatter(ContentFormatterBase):
             except (KeyError, IndexError, TypeError) as e:
                 raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
             return ChatGeneration(
-                message=BaseMessage(
+                message=AIMessage(
                     content=choice.strip(),
-                    type="assistant",
                 ),
                 generation_info=None,
             )
@@ -158,7 +157,9 @@ class CustomOpenAIChatContentFormatter(ContentFormatterBase):
             except (KeyError, IndexError, TypeError) as e:
                 raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
             return ChatGeneration(
-                message=BaseMessage(
+                message=AIMessage(content=choice["message"]["content"].strip())
+                if choice["message"]["role"] == "assistant"
+                else BaseMessage(
                     content=choice["message"]["content"].strip(),
                     type=choice["message"]["role"],
                 ),

libs/community/langchain_community/chat_models/baichuan.py

@@ -22,6 +22,8 @@ from langchain_core.messages import (
     ChatMessageChunk,
     HumanMessage,
     HumanMessageChunk,
+    SystemMessage,
+    SystemMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
@@ -44,6 +46,8 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
         message_dict = {"role": "user", "content": message.content}
     elif isinstance(message, AIMessage):
         message_dict = {"role": "assistant", "content": message.content}
+    elif isinstance(message, SystemMessage):
+        message_dict = {"role": "system", "content": message.content}
     else:
         raise TypeError(f"Got unknown type {message}")
 
@@ -56,6 +60,8 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
         return HumanMessage(content=_dict["content"])
     elif role == "assistant":
         return AIMessage(content=_dict.get("content", "") or "")
+    elif role == "system":
+        return SystemMessage(content=_dict.get("content", ""))
     else:
         return ChatMessage(content=_dict["content"], role=role)
 
@@ -70,6 +76,8 @@ def _convert_delta_to_message_chunk(
         return HumanMessageChunk(content=content)
     elif role == "assistant" or default_class == AIMessageChunk:
         return AIMessageChunk(content=content)
+    elif role == "system" or default_class == SystemMessageChunk:
+        return SystemMessageChunk(content=content)
     elif role or default_class == ChatMessageChunk:
         return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
@@ -98,10 +106,145 @@ async def aconnect_httpx_sse(
 
 
 class ChatBaichuan(BaseChatModel):
-    """Baichuan chat models API by Baichuan Intelligent Technology.
+    """Baichuan chat model integration.
 
-    For more information, see https://platform.baichuan-ai.com/docs/api
-    """
+    Setup:
+        To use, you should have the environment variable``BAICHUAN_API_KEY`` set with
+    your API KEY.
+
+        .. code-block:: bash
+
+            export BAICHUAN_API_KEY="your-api-key"
+
+    Key init args — completion params:
+        model: Optional[str]
+            Name of Baichuan model to use.
+        max_tokens: Optional[int]
+            Max number of tokens to generate.
+        streaming: Optional[bool]
+            Whether to stream the results or not.
+        temperature: Optional[float]
+            Sampling temperature.
+        top_p: Optional[float]
+            What probability mass to use.
+        top_k: Optional[int]
+            What search sampling control to use.
+
+    Key init args — client params:
+        api_key: Optional[str]
+            MiniMax API key. If not passed in will be read from env var BAICHUAN_API_KEY.
+        base_url: Optional[str]
+            Base URL for API requests.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.chat_models import ChatBaichuan
+
+            chat = ChatBaichuan(
+                api_key=api_key,
+                model='Baichuan4',
+                # temperature=...,
+                # other params...
+            )
+
+    Invoke:
+        .. code-block:: python
+
+            messages = [
+                ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
+                ("human", "我喜欢编程。"),
+            ]
+            chat.invoke(messages)
+
+        .. code-block:: python
+
+            AIMessage(
+                content='I enjoy programming.',
+                response_metadata={
+                    'token_usage': {
+                        'prompt_tokens': 93,
+                        'completion_tokens': 5,
+                        'total_tokens': 98
+                    },
+                    'model': 'Baichuan4'
+                },
+                id='run-944ff552-6a93-44cf-a861-4e4d849746f9-0'
+            )
+
+    Stream:
+        .. code-block:: python
+
+            for chunk in chat.stream(messages):
+                print(chunk)
+
+        .. code-block:: python
+
+            content='I' id='run-f99fcd6f-dd31-46d5-be8f-0b6a22bf77d8'
+            content=' enjoy programming.' id='run-f99fcd6f-dd31-46d5-be8f-0b6a22bf77d8
+
+        .. code-block:: python
+
+            stream = chat.stream(messages)
+            full = next(stream)
+            for chunk in stream:
+                full += chunk
+            full
+
+        .. code-block:: python
+
+            AIMessageChunk(
+                content='I like programming.',
+                id='run-74689970-dc31-461d-b729-3b6aa93508d2'
+            )
+
+    Async:
+        .. code-block:: python
+
+            await chat.ainvoke(messages)
+
+            # stream
+            # async for chunk in chat.astream(messages):
+            #     print(chunk)
+
+            # batch
+            # await chat.abatch([messages])
+
+        .. code-block:: python
+
+            AIMessage(
+                content='I enjoy programming.',
+                response_metadata={
+                    'token_usage': {
+                        'prompt_tokens': 93,
+                        'completion_tokens': 5,
+                        'total_tokens': 98
+                    },
+                    'model': 'Baichuan4'
+                },
+                id='run-952509ed-9154-4ff9-b187-e616d7ddfbba-0'
+            )
+
+    Response metadata
+        .. code-block:: python
+
+            ai_msg = chat.invoke(messages)
+            ai_msg.response_metadata
+
+        .. code-block:: python
+
+            {
+                'token_usage': {
+                    'prompt_tokens': 93,
+                    'completion_tokens': 5,
+                    'total_tokens': 98
+                },
+                'model': 'Baichuan4'
+            }
+
+    """  # noqa: E501
 
     @property
     def lc_secrets(self) -> Dict[str, str]:
@@ -113,7 +256,7 @@ class ChatBaichuan(BaseChatModel):
     def lc_serializable(self) -> bool:
         return True
 
-    baichuan_api_base: str = Field(default=DEFAULT_API_BASE)
+    baichuan_api_base: str = Field(default=DEFAULT_API_BASE, alias="base_url")
     """Baichuan custom endpoints"""
     baichuan_api_key: SecretStr = Field(alias="api_key")
     """Baichuan API Key"""
@@ -121,6 +264,8 @@ class ChatBaichuan(BaseChatModel):
     """[DEPRECATED, keeping it for for backward compatibility] Baichuan Secret Key"""
     streaming: bool = False
     """Whether to stream the results or not."""
+    max_tokens: Optional[int] = None
+    """Maximum number of tokens to generate."""
     request_timeout: int = Field(default=60, alias="timeout")
     """request timeout for chat http requests"""
     model: str = "Baichuan2-Turbo-192K"
@@ -133,7 +278,8 @@ class ChatBaichuan(BaseChatModel):
     top_p: float = 0.85
     """What probability mass to use."""
     with_search_enhance: bool = False
-    """Whether to use search enhance, default is False."""
+    """[DEPRECATED, keeping it for for backward compatibility], 
+    Whether to use search enhance, default is False."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for API call not explicitly specified."""
 
@@ -193,8 +339,8 @@ class ChatBaichuan(BaseChatModel):
             "temperature": self.temperature,
             "top_p": self.top_p,
             "top_k": self.top_k,
-            "with_search_enhance": self.with_search_enhance,
             "stream": self.streaming,
+            "max_tokens": self.max_tokens,
         }
 
         return {**normal_params, **self.model_kwargs}

libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py

@@ -32,6 +32,7 @@ from langchain_core.messages import (
     SystemMessage,
     ToolMessage,
 )
+from langchain_core.messages.ai import UsageMetadata
 from langchain_core.output_parsers.base import OutputParserLike
 from langchain_core.output_parsers.openai_tools import (
     JsonOutputKeyToolsParser,
@@ -88,6 +89,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
         request_id=additional_kwargs["id"],
         object=additional_kwargs.get("object", ""),
         search_info=additional_kwargs.get("search_info", []),
+        usage=additional_kwargs.get("usage", None),
     )
 
     if additional_kwargs.get("function_call", {}):
@@ -102,6 +104,17 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
             }
         ]
 
+    if usage := additional_kwargs.get("usage", None):
+        return AIMessage(
+            content=content,
+            additional_kwargs=msg_additional_kwargs,
+            usage_metadata=UsageMetadata(
+                input_tokens=usage.prompt_tokens,
+                output_tokens=usage.completion_tokens,
+                total_tokens=usage.total_tokens,
+            ),
+        )
+
     return AIMessage(
         content=content,
         additional_kwargs=msg_additional_kwargs,
@@ -578,6 +591,7 @@ class QianfanChatEndpoint(BaseChatModel):
                         content=msg.content,
                         role="assistant",
                         additional_kwargs=additional_kwargs,
+                        usage_metadata=msg.usage_metadata,
                     ),
                     generation_info=msg.additional_kwargs,
                 )
@@ -605,6 +619,7 @@ class QianfanChatEndpoint(BaseChatModel):
                         content=msg.content,
                         role="assistant",
                         additional_kwargs=additional_kwargs,
+                        usage_metadata=msg.usage_metadata,
                     ),
                     generation_info=msg.additional_kwargs,
                 )

libs/community/langchain_community/chat_models/deepinfra.py

@@ -48,6 +48,7 @@ from langchain_core.messages import (
     ToolMessage,
 )
 from langchain_core.messages.tool import ToolCall
+from langchain_core.messages.tool import tool_call as create_tool_call
 from langchain_core.outputs import (
     ChatGeneration,
     ChatGenerationChunk,
@@ -96,7 +97,7 @@ def _parse_tool_calling(tool_call: dict) -> ToolCall:
     name = tool_call["function"].get("name", "")
     args = json.loads(tool_call["function"]["arguments"])
     id = tool_call.get("id")
-    return ToolCall(name=name, args=args, id=id)
+    return create_tool_call(name=name, args=args, id=id)
 
 
 def _convert_to_tool_calling(tool_call: ToolCall) -> Dict[str, Any]:

libs/community/langchain_community/chat_models/edenai.py

@@ -36,9 +36,11 @@ from langchain_core.messages import (
     InvalidToolCall,
     SystemMessage,
     ToolCall,
-    ToolCallChunk,
     ToolMessage,
 )
+from langchain_core.messages.tool import invalid_tool_call as create_invalid_tool_call
+from langchain_core.messages.tool import tool_call as create_tool_call
+from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
 from langchain_core.output_parsers.base import OutputParserLike
 from langchain_core.output_parsers.openai_tools import (
     JsonOutputKeyToolsParser,
@@ -63,7 +65,7 @@ def _result_to_chunked_message(generated_result: ChatResult) -> ChatGenerationCh
     message = generated_result.generations[0].message
     if isinstance(message, AIMessage) and message.tool_calls is not None:
         tool_call_chunks = [
-            ToolCallChunk(
+            create_tool_call_chunk(
                 name=tool_call["name"],
                 args=json.dumps(tool_call["args"]),
                 id=tool_call["id"],
@@ -189,7 +191,7 @@ def _extract_tool_calls_from_edenai_response(
         for raw_tool_call in raw_tool_calls:
             try:
                 tool_calls.append(
-                    ToolCall(
+                    create_tool_call(
                         name=raw_tool_call["name"],
                         args=json.loads(raw_tool_call["arguments"]),
                         id=raw_tool_call["id"],
@@ -197,7 +199,7 @@ def _extract_tool_calls_from_edenai_response(
                 )
             except json.JSONDecodeError as exc:
                 invalid_tool_calls.append(
-                    InvalidToolCall(
+                    create_invalid_tool_call(
                         name=raw_tool_call.get("name"),
                         args=raw_tool_call.get("arguments"),
                         id=raw_tool_call.get("id"),

libs/community/langchain_community/chat_models/minimax.py

@@ -116,18 +116,133 @@ def _convert_delta_to_message_chunk(
 
 
 class MiniMaxChat(BaseChatModel):
-    """MiniMax large language models.
+    """MiniMax chat model integration.
 
-    To use, you should have the environment variable``MINIMAX_API_KEY`` set with
-    your API token, or pass it as a named parameter to the constructor.
+    Setup:
+        To use, you should have the environment variable``MINIMAX_API_KEY`` set with
+    your API KEY.
 
-    Example:
+        .. code-block:: bash
+
+            export MINIMAX_API_KEY="your-api-key"
+
+    Key init args — completion params:
+        model: Optional[str]
+            Name of MiniMax model to use.
+        max_tokens: Optional[int]
+            Max number of tokens to generate.
+        temperature: Optional[float]
+            Sampling temperature.
+        top_p: Optional[float]
+            Total probability mass of tokens to consider at each step.
+        streaming: Optional[bool]
+             Whether to stream the results or not.
+
+    Key init args — client params:
+        api_key: Optional[str]
+            MiniMax API key. If not passed in will be read from env var MINIMAX_API_KEY.
+        base_url: Optional[str]
+            Base URL for API requests.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
         .. code-block:: python
 
             from langchain_community.chat_models import MiniMaxChat
-            llm = MiniMaxChat(model="abab5-chat")
 
-    """
+            chat = MiniMaxChat(
+                api_key=api_key,
+                model='abab6.5-chat',
+                # temperature=...,
+                # other params...
+            )
+
+    Invoke:
+        .. code-block:: python
+
+            messages = [
+                ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
+                ("human", "我喜欢编程。"),
+            ]
+            chat.invoke(messages)
+
+        .. code-block:: python
+
+            AIMessage(
+                content='I enjoy programming.',
+                response_metadata={
+                    'token_usage': {'total_tokens': 48},
+                    'model_name': 'abab6.5-chat',
+                    'finish_reason': 'stop'
+                },
+                id='run-42d62ba6-5dc1-4e16-98dc-f72708a4162d-0'
+            )
+
+    Stream:
+        .. code-block:: python
+
+            for chunk in chat.stream(messages):
+                print(chunk)
+
+        .. code-block:: python
+
+            content='I' id='run-a5837c45-4aaa-4f64-9ab4-2679bbd55522'
+            content=' enjoy programming.' response_metadata={'finish_reason': 'stop'} id='run-a5837c45-4aaa-4f64-9ab4-2679bbd55522'
+
+        .. code-block:: python
+
+            stream = chat.stream(messages)
+            full = next(stream)
+            for chunk in stream:
+                full += chunk
+            full
+
+        .. code-block:: python
+
+            AIMessageChunk(
+                content='I enjoy programming.',
+                response_metadata={'finish_reason': 'stop'},
+                id='run-01aed0a0-61c4-4709-be22-c6d8b17155d6'
+            )
+
+    Async:
+        .. code-block:: python
+
+            await chat.ainvoke(messages)
+
+            # stream
+            # async for chunk in chat.astream(messages):
+            #     print(chunk)
+
+            # batch
+            # await chat.abatch([messages])
+
+        .. code-block:: python
+
+            AIMessage(
+                content='I enjoy programming.',
+                response_metadata={
+                    'token_usage': {'total_tokens': 48},
+                    'model_name': 'abab6.5-chat',
+                    'finish_reason': 'stop'
+                },
+                id='run-c263b6f1-1736-4ece-a895-055c26b3436f-0'
+            )
+
+    Response metadata
+        .. code-block:: python
+
+            ai_msg = chat.invoke(messages)
+            ai_msg.response_metadata
+
+        .. code-block:: python
+
+            {'token_usage': {'total_tokens': 48},
+             'model_name': 'abab6.5-chat',
+             'finish_reason': 'stop'}
+
+    """  # noqa: E501conj
 
     @property
     def _identifying_params(self) -> Dict[str, Any]:

libs/community/langchain_community/chat_models/ollama.py

@@ -144,7 +144,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
                         elif (
                             isinstance(temp_image_url, dict) and "url" in temp_image_url
                         ):
-                            image_url = temp_image_url
+                            image_url = temp_image_url["url"]
                         else:
                             raise ValueError(
                                 "Only string image_url or dict with string 'url' "

libs/community/langchain_community/chat_models/tongyi.py

@@ -4,6 +4,7 @@ import asyncio
 import functools
 import json
 import logging
+from operator import itemgetter
 from typing import (
     Any,
     AsyncIterator,
@@ -40,7 +41,10 @@ from langchain_core.messages import (
     ToolMessage,
     ToolMessageChunk,
 )
+from langchain_core.output_parsers.base import OutputParserLike
 from langchain_core.output_parsers.openai_tools import (
+    JsonOutputKeyToolsParser,
+    PydanticToolsParser,
     make_invalid_tool_call,
     parse_tool_call,
 )
@@ -50,7 +54,7 @@ from langchain_core.outputs import (
     ChatResult,
 )
 from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
-from langchain_core.runnables import Runnable
+from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
 from langchain_core.utils.function_calling import convert_to_openai_tool
@@ -372,6 +376,33 @@ class ChatTongyi(BaseChatModel):
                 }
             ]
 
+    Structured output:
+        .. code-block:: python
+
+            from typing import Optional
+
+            from langchain_core.pydantic_v1 import BaseModel, Field
+
+
+            class Joke(BaseModel):
+                '''Joke to tell user.'''
+
+                setup: str = Field(description="The setup of the joke")
+                punchline: str = Field(description="The punchline to the joke")
+                rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
+
+
+            structured_chat = tongyi_chat.with_structured_output(Joke)
+            structured_chat.invoke("Tell me a joke about cats")
+
+        .. code-block:: python
+
+            Joke(
+                setup='Why did the cat join the band?',
+                punchline='Because it wanted to be a solo purr-sonality!',
+                rating=None
+            )
+
     Response metadata
         .. code-block:: python
 
@@ -791,3 +822,70 @@ class ChatTongyi(BaseChatModel):
 
         formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
         return super().bind(tools=formatted_tools, **kwargs)
+
+    def with_structured_output(
+        self,
+        schema: Union[Dict, Type[BaseModel]],
+        *,
+        include_raw: bool = False,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
+        """Model wrapper that returns outputs formatted to match the given schema.
+
+        Args:
+            schema: The output schema as a dict or a Pydantic class. If a Pydantic class
+                then the model output will be an object of that class. If a dict then
+                the model output will be a dict. With a Pydantic class the returned
+                attributes will be validated, whereas with a dict they will not be. If
+                `method` is "function_calling" and `schema` is a dict, then the dict
+                must match the OpenAI function-calling spec.
+            include_raw: If False then only the parsed structured output is returned. If
+                an error occurs during model output parsing it will be raised. If True
+                then both the raw model response (a BaseMessage) and the parsed model
+                response will be returned. If an error occurs during output parsing it
+                will be caught and returned as well. The final output is always a dict
+                with keys "raw", "parsed", and "parsing_error".
+
+        Returns:
+            A Runnable that takes any ChatModel input and returns as output:
+
+                If include_raw is True then a dict with keys:
+                    raw: BaseMessage
+                    parsed: Optional[_DictOrPydantic]
+                    parsing_error: Optional[BaseException]
+
+                If include_raw is False then just _DictOrPydantic is returned,
+                where _DictOrPydantic depends on the schema:
+
+                If schema is a Pydantic class then _DictOrPydantic is the Pydantic
+                    class.
+
+                If schema is a dict then _DictOrPydantic is a dict.
+
+        """
+        if kwargs:
+            raise ValueError(f"Received unsupported arguments {kwargs}")
+        is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
+        llm = self.bind_tools([schema])
+        if is_pydantic_schema:
+            output_parser: OutputParserLike = PydanticToolsParser(
+                tools=[schema],  # type: ignore[list-item]
+                first_tool_only=True,  # type: ignore[list-item]
+            )
+        else:
+            key_name = convert_to_openai_tool(schema)["function"]["name"]
+            output_parser = JsonOutputKeyToolsParser(
+                key_name=key_name, first_tool_only=True
+            )
+
+        if include_raw:
+            parser_assign = RunnablePassthrough.assign(
+                parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
+            )
+            parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
+            parser_with_fallback = parser_assign.with_fallbacks(
+                [parser_none], exception_key="parsing_error"
+            )
+            return RunnableMap(raw=llm) | parser_with_fallback
+        else:
+            return llm | output_parser

libs/community/langchain_community/cross_encoders/huggingface.py

@@ -60,7 +60,7 @@ class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
             List of scores, one for each pair.
         """
         scores = self.client.predict(text_pairs)
-        # Somes models e.g bert-multilingual-passage-reranking-msmarco
+        # Some models e.g bert-multilingual-passage-reranking-msmarco
         # gives two score not_relevant and relevant as compare with the query.
         if len(scores.shape) > 1:  # we are going to get the relevant scores
             scores = map(lambda x: x[1], scores)

libs/community/langchain_community/embeddings/ascend.py

@@ -60,7 +60,7 @@ class AscendEmbeddings(Embeddings, BaseModel):
             raise ValueError("model_path is required")
         if not os.access(values["model_path"], os.F_OK):
             raise FileNotFoundError(
-                f"Unabled to find valid model path in [{values['model_path']}]"
+                f"Unable to find valid model path in [{values['model_path']}]"
             )
         try:
             import torch_npu

libs/community/langchain_community/embeddings/azure_openai.py

@@ -61,7 +61,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
         # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
         # other forms of azure credentials.
         values["openai_api_key"] = (
-            values["openai_api_key"]
+            values.get("openai_api_key")
             or os.getenv("AZURE_OPENAI_API_KEY")
             or os.getenv("OPENAI_API_KEY")
         )
@@ -75,7 +75,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
             values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
         )
         values["openai_organization"] = (
-            values["openai_organization"]
+            values.get("openai_organization")
             or os.getenv("OPENAI_ORG_ID")
             or os.getenv("OPENAI_ORGANIZATION")
         )
@@ -85,10 +85,10 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
             "OPENAI_PROXY",
             default="",
         )
-        values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
+        values["azure_endpoint"] = values.get("azure_endpoint") or os.getenv(
             "AZURE_OPENAI_ENDPOINT"
         )
-        values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
+        values["azure_ad_token"] = values.get("azure_ad_token") or os.getenv(
             "AZURE_OPENAI_AD_TOKEN"
         )
         # Azure OpenAI embedding models allow a maximum of 16 texts

libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py

@@ -4,7 +4,7 @@ import logging
 from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
 
 logger = logging.getLogger(__name__)
@@ -13,10 +13,10 @@ logger = logging.getLogger(__name__)
 class QianfanEmbeddingsEndpoint(BaseModel, Embeddings):
     """`Baidu Qianfan Embeddings` embedding models."""
 
-    qianfan_ak: Optional[str] = None
+    qianfan_ak: Optional[SecretStr] = None
     """Qianfan application apikey"""
 
-    qianfan_sk: Optional[str] = None
+    qianfan_sk: Optional[SecretStr] = None
     """Qianfan application secretkey"""
 
     chunk_size: int = 16

libs/community/langchain_community/graph_vectorstores/extractors/__init__.py

@@ -0,0 +1,17 @@
+from langchain_community.graph_vectorstores.extractors.html_link_extractor import (
+    HtmlInput,
+    HtmlLinkExtractor,
+)
+from langchain_community.graph_vectorstores.extractors.link_extractor import (
+    LinkExtractor,
+)
+from langchain_community.graph_vectorstores.extractors.link_extractor_adapter import (
+    LinkExtractorAdapter,
+)
+
+__all__ = [
+    "LinkExtractor",
+    "LinkExtractorAdapter",
+    "HtmlInput",
+    "HtmlLinkExtractor",
+]

libs/community/langchain_community/graph_vectorstores/extractors/html_link_extractor.py

@@ -0,0 +1,124 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Set, Union
+from urllib.parse import urldefrag, urljoin, urlparse
+
+from langchain_core.documents import Document
+from langchain_core.graph_vectorstores import Link
+
+from langchain_community.graph_vectorstores.extractors.link_extractor import (
+    LinkExtractor,
+)
+from langchain_community.graph_vectorstores.extractors.link_extractor_adapter import (
+    LinkExtractorAdapter,
+)
+
+if TYPE_CHECKING:
+    from bs4 import BeautifulSoup
+    from bs4.element import Tag
+
+
+def _parse_url(link: Tag, page_url: str, drop_fragments: bool = True) -> Optional[str]:
+    href = link.get("href")
+    if href is None:
+        return None
+    url = urlparse(href)
+    if url.scheme not in ["http", "https", ""]:
+        return None
+
+    # Join the HREF with the page_url to convert relative paths to absolute.
+    url = str(urljoin(page_url, href))
+
+    # Fragments would be useful if we chunked a page based on section.
+    # Then, each chunk would have a different URL based on the fragment.
+    # Since we aren't doing that yet, they just "break" links. So, drop
+    # the fragment.
+    if drop_fragments:
+        return urldefrag(url).url
+    return url
+
+
+def _parse_hrefs(
+    soup: BeautifulSoup, url: str, drop_fragments: bool = True
+) -> Set[str]:
+    soup_links: List[Tag] = soup.find_all("a")
+    links: Set[str] = set()
+
+    for link in soup_links:
+        parse_url = _parse_url(link, page_url=url, drop_fragments=drop_fragments)
+        # Remove self links and entries for any 'a' tag that failed to parse
+        # (didn't have href, or invalid domain, etc.)
+        if parse_url and parse_url != url:
+            links.add(parse_url)
+
+    return links
+
+
+@dataclass
+class HtmlInput:
+    content: Union[str, BeautifulSoup]
+    base_url: str
+
+
+class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
+    def __init__(self, *, kind: str = "hyperlink", drop_fragments: bool = True):
+        """Extract hyperlinks from HTML content.
+
+        Expects the input to be an HTML string or a `BeautifulSoup` object.
+
+        Args:
+            kind: The kind of edge to extract. Defaults to "hyperlink".
+            drop_fragments: Whether fragments in URLs and links should be
+                dropped. Defaults to `True`.
+        """
+        try:
+            import bs4  # noqa:F401
+        except ImportError as e:
+            raise ImportError(
+                "BeautifulSoup4 is required for HtmlLinkExtractor. "
+                "Please install it with `pip install beautifulsoup4`."
+            ) from e
+
+        self._kind = kind
+        self.drop_fragments = drop_fragments
+
+    def as_document_extractor(
+        self, url_metadata_key: str = "source"
+    ) -> LinkExtractor[Document]:
+        """Return a LinkExtractor that applies to documents.
+
+        NOTE: Since the HtmlLinkExtractor parses HTML, if you use with other similar
+        link extractors it may be more efficient to call the link extractors directly
+        on the parsed BeautifulSoup object.
+
+        Args:
+            url_metadata_key: The name of the filed in document metadata with the URL of
+                the document.
+        """
+        return LinkExtractorAdapter(
+            underlying=self,
+            transform=lambda doc: HtmlInput(
+                doc.page_content, doc.metadata[url_metadata_key]
+            ),
+        )
+
+    def extract_one(
+        self,
+        input: HtmlInput,  # noqa: A002
+    ) -> Set[Link]:
+        content = input.content
+        if isinstance(content, str):
+            from bs4 import BeautifulSoup
+
+            content = BeautifulSoup(content, "html.parser")
+
+        base_url = input.base_url
+        if self.drop_fragments:
+            base_url = urldefrag(base_url).url
+
+        hrefs = _parse_hrefs(content, base_url, self.drop_fragments)
+
+        links = {Link.outgoing(kind=self._kind, tag=url) for url in hrefs}
+        links.add(Link.incoming(kind=self._kind, tag=base_url))
+        return links

libs/community/langchain_community/graph_vectorstores/extractors/link_extractor.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Generic, Iterable, Set, TypeVar
+
+from langchain_core.graph_vectorstores import Link
+
+InputT = TypeVar("InputT")
+
+METADATA_LINKS_KEY = "links"
+
+
+class LinkExtractor(ABC, Generic[InputT]):
+    """Interface for extracting links (incoming, outgoing, bidirectional)."""
+
+    @abstractmethod
+    def extract_one(self, input: InputT) -> set[Link]:  # noqa: A002
+        """Add edges from each `input` to the corresponding documents.
+
+        Args:
+            input: The input content to extract edges from.
+
+        Returns:
+            Set of links extracted from the input.
+        """
+
+    def extract_many(self, inputs: Iterable[InputT]) -> Iterable[Set[Link]]:
+        """Add edges from each `input` to the corresponding documents.
+
+        Args:
+            inputs: The input content to extract edges from.
+
+        Returns:
+            Iterable over the set of links extracted from the input.
+        """
+        return map(self.extract_one, inputs)

libs/community/langchain_community/graph_vectorstores/extractors/link_extractor_adapter.py

@@ -0,0 +1,27 @@
+from typing import Callable, Iterable, Set, TypeVar
+
+from langchain_core.graph_vectorstores import Link
+
+from langchain_community.graph_vectorstores.extractors.link_extractor import (
+    LinkExtractor,
+)
+
+InputT = TypeVar("InputT")
+UnderlyingInputT = TypeVar("UnderlyingInputT")
+
+
+class LinkExtractorAdapter(LinkExtractor[InputT]):
+    def __init__(
+        self,
+        underlying: LinkExtractor[UnderlyingInputT],
+        transform: Callable[[InputT], UnderlyingInputT],
+    ) -> None:
+        self._underlying = underlying
+        self._transform = transform
+
+    def extract_one(self, input: InputT) -> Set[Link]:  # noqa: A002
+        return self._underlying.extract_one(self._transform(input))
+
+    def extract_many(self, inputs: Iterable[InputT]) -> Iterable[Set[Link]]:
+        underlying_inputs = map(self._transform, inputs)
+        return self._underlying.extract_many(underlying_inputs)

libs/community/langchain_community/graphs/neo4j_graph.py

@@ -555,10 +555,11 @@ class Neo4jGraph(GraphStore):
                     el["labelsOrTypes"] == [BASE_ENTITY_LABEL]
                     and el["properties"] == ["id"]
                     for el in self.structured_schema.get("metadata", {}).get(
-                        "constraint"
+                        "constraint", []
                     )
                 ]
             )
+
             if not constraint_exists:
                 # Create constraint
                 self.query(

libs/community/langchain_community/llms/__init__.py

@@ -640,6 +640,12 @@ def _import_yuan2() -> Type[BaseLLM]:
     return Yuan2
 
 
+def _import_you() -> Type[BaseLLM]:
+    from langchain_community.llms.you import You
+
+    return You
+
+
 def _import_volcengine_maas() -> Type[BaseLLM]:
     from langchain_community.llms.volcengine_maas import VolcEngineMaasLLM
 
@@ -847,6 +853,8 @@ def __getattr__(name: str) -> Any:
         return _import_yandex_gpt()
     elif name == "Yuan2":
         return _import_yuan2()
+    elif name == "You":
+        return _import_you()
     elif name == "VolcEngineMaasLLM":
         return _import_volcengine_maas()
     elif name == "type_to_cls_dict":
@@ -959,6 +967,7 @@ __all__ = [
     "Writer",
     "Xinference",
     "YandexGPT",
+    "You",
     "Yuan2",
 ]
 
@@ -1056,6 +1065,7 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
         "qianfan_endpoint": _import_baidu_qianfan_endpoint,
         "yandex_gpt": _import_yandex_gpt,
         "yuan2": _import_yuan2,
+        "you": _import_you,
         "VolcEngineMaasLLM": _import_volcengine_maas,
         "SparkLLM": _import_sparkllm,
     }

libs/community/langchain_community/llms/baidu_qianfan_endpoint.py

@@ -16,7 +16,7 @@ from langchain_core.callbacks import (
 )
 from langchain_core.language_models.llms import LLM
 from langchain_core.outputs import GenerationChunk
-from langchain_core.pydantic_v1 import Field
+from langchain_core.pydantic_v1 import Field, SecretStr
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
 
 logger = logging.getLogger(__name__)
@@ -49,8 +49,8 @@ class QianfanLLMEndpoint(LLM):
 
     client: Any
 
-    qianfan_ak: Optional[str] = None
-    qianfan_sk: Optional[str] = None
+    qianfan_ak: Optional[SecretStr] = None
+    qianfan_sk: Optional[SecretStr] = None
 
     streaming: Optional[bool] = False
     """Whether to stream the results or not."""

libs/community/langchain_community/llms/huggingface_endpoint.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import os
 from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional
 
 from langchain_core._api.deprecation import deprecated
@@ -11,7 +12,6 @@ from langchain_core.language_models.llms import LLM
 from langchain_core.outputs import GenerationChunk
 from langchain_core.pydantic_v1 import Extra, Field, root_validator
 from langchain_core.utils import (
-    get_from_dict_or_env,
     get_pydantic_field_names,
     pre_init,
 )
@@ -177,16 +177,17 @@ class HuggingFaceEndpoint(LLM):
                 "Could not import huggingface_hub python package. "
                 "Please install it with `pip install huggingface_hub`."
             )
-        try:
-            huggingfacehub_api_token = get_from_dict_or_env(
-                values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
-            )
-            login(token=huggingfacehub_api_token)
-        except Exception as e:
-            raise ValueError(
-                "Could not authenticate with huggingface_hub. "
-                "Please check your API token."
-            ) from e
+        huggingfacehub_api_token = values["huggingfacehub_api_token"] or os.getenv(
+            "HUGGINGFACEHUB_API_TOKEN"
+        )
+        if huggingfacehub_api_token is not None:
+            try:
+                login(token=huggingfacehub_api_token)
+            except Exception as e:
+                raise ValueError(
+                    "Could not authenticate with huggingface_hub. "
+                    "Please check your API token."
+                ) from e
 
         from huggingface_hub import AsyncInferenceClient, InferenceClient
 

libs/community/langchain_community/llms/ollama.py

@@ -112,18 +112,20 @@ class _OllamaCommon(BaseLanguageModel):
     """Timeout for the request stream"""
 
     keep_alive: Optional[Union[int, str]] = None
-    """How long the model will stay loaded into memory."""
+    """How long the model will stay loaded into memory.
 
-    raw: Optional[bool] = None
-    """raw or not.""
     The parameter (Default: 5 minutes) can be set to:
     1. a duration string in Golang (such as "10m" or "24h");
     2. a number in seconds (such as 3600);
     3. any negative number which will keep the model loaded \
         in memory (e.g. -1 or "-1m");
     4. 0 which will unload the model immediately after generating a response;
+
     See the [Ollama documents](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-keep-a-model-loaded-in-memory-or-make-it-unload-immediately)"""
 
+    raw: Optional[bool] = None
+    """raw or not."""
+
     headers: Optional[dict] = None
     """Additional headers to pass to endpoint (e.g. Authorization, Referer).
     This is useful when Ollama is hosted on cloud services that require

libs/community/langchain_community/llms/you.py

@@ -0,0 +1,140 @@
+import os
+from typing import Any, Dict, Generator, Iterator, List, Literal, Optional
+
+import requests
+from langchain_core.callbacks.manager import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.outputs import GenerationChunk
+from langchain_core.pydantic_v1 import Field
+
+SMART_ENDPOINT = "https://chat-api.you.com/smart"
+RESEARCH_ENDPOINT = "https://chat-api.you.com/research"
+
+
+def _request(base_url: str, api_key: str, **kwargs: Any) -> Dict[str, Any]:
+    """
+    NOTE: This function can be replaced by a OpenAPI-generated Python SDK in the future,
+    for better input/output typing support.
+    """
+    headers = {"x-api-key": api_key}
+    response = requests.post(base_url, headers=headers, json=kwargs)
+    response.raise_for_status()
+    return response.json()
+
+
+def _request_stream(
+    base_url: str, api_key: str, **kwargs: Any
+) -> Generator[str, None, None]:
+    headers = {"x-api-key": api_key}
+    params = dict(**kwargs, stream=True)
+    response = requests.post(base_url, headers=headers, stream=True, json=params)
+    response.raise_for_status()
+
+    # Explicitly coercing the response to a generator to satisfy mypy
+    event_source = (bytestring for bytestring in response)
+
+    try:
+        import sseclient
+
+        client = sseclient.SSEClient(event_source)
+    except ImportError:
+        raise ImportError(
+            (
+                "Could not import `sseclient`. "
+                "Please install it with `pip install sseclient-py`."
+            )
+        )
+
+    for event in client.events():
+        if event.event in ("search_results", "done"):
+            pass
+        elif event.event == "token":
+            yield event.data
+        elif event.event == "error":
+            raise ValueError(f"Error in response: {event.data}")
+        else:
+            raise NotImplementedError(f"Unknown event type {event.event}")
+
+
+class You(LLM):
+    """Wrapper around You.com's conversational Smart and Research APIs.
+
+    Each API endpoint is designed to generate conversational
+    responses to a variety of query types, including inline citations
+    and web results when relevant.
+
+    Smart Endpoint:
+    - Quick, reliable answers for a variety of questions
+    - Cites the entire web page URL
+
+    Research Endpoint:
+    - In-depth answers with extensive citations for a variety of questions
+    - Cites the specific web page snippet relevant to the claim
+
+    To connect to the You.com api requires an API key which
+    you can get at https://api.you.com.
+
+    For more information, check out the documentations at
+    https://documentation.you.com/api-reference/.
+
+    Args:
+        endpoint: You.com conversational endpoints. Choose from "smart" or "research"
+        ydc_api_key: You.com API key, if `YDC_API_KEY` is not set in the environment
+    """
+
+    endpoint: Literal["smart", "research"] = Field(
+        "smart",
+        description=(
+            'You.com conversational endpoints. Choose from "smart" or "research"'
+        ),
+    )
+    ydc_api_key: Optional[str] = Field(
+        None,
+        description="You.com API key, if `YDC_API_KEY` is not set in the envrioment",
+    )
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        if stop:
+            raise NotImplementedError(
+                "Stop words are not implemented for You.com endpoints."
+            )
+        params = {"query": prompt}
+        response = _request(self._request_endpoint, api_key=self._api_key, **params)
+        return response["answer"]
+
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        if stop:
+            raise NotImplementedError(
+                "Stop words are not implemented for You.com endpoints."
+            )
+        params = {"query": prompt}
+        for token in _request_stream(
+            self._request_endpoint, api_key=self._api_key, **params
+        ):
+            yield GenerationChunk(text=token)
+
+    @property
+    def _request_endpoint(self) -> str:
+        if self.endpoint == "smart":
+            return SMART_ENDPOINT
+        return RESEARCH_ENDPOINT
+
+    @property
+    def _api_key(self) -> str:
+        return self.ydc_api_key or os.environ["YDC_API_KEY"]
+
+    @property
+    def _llm_type(self) -> str:
+        return "you.com"

libs/community/langchain_community/retrievers/tavily_search_api.py

@@ -31,14 +31,18 @@ class TavilySearchAPIRetriever(BaseRetriever):
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:
         try:
-            from tavily import Client
+            try:
+                from tavily import TavilyClient
+            except ImportError:
+                # Older of tavily used Client
+                from tavily import Client as TavilyClient
         except ImportError:
             raise ImportError(
                 "Tavily python package not found. "
                 "Please install it with `pip install tavily-python`."
             )
 
-        tavily = Client(api_key=self.api_key or os.environ["TAVILY_API_KEY"])
+        tavily = TavilyClient(api_key=self.api_key or os.environ["TAVILY_API_KEY"])
         max_results = self.k if not self.include_generated_answer else self.k - 1
         response = tavily.search(
             query=query,

libs/community/langchain_community/storage/sql.py

@@ -72,7 +72,7 @@ class SQLStore(BaseStore[str, bytes]):
             from langchain_rag.storage import SQLStore
 
             # Instantiate the SQLStore with the root path
-            sql_store = SQLStore(namespace="test", db_url="sqllite://:memory:")
+            sql_store = SQLStore(namespace="test", db_url="sqlite://:memory:")
 
             # Set values for keys
             sql_store.mset([("key1", b"value1"), ("key2", b"value2")])

libs/community/langchain_community/utilities/semanticscholar.py

@@ -80,7 +80,7 @@ class SemanticScholarAPIWrapper(BaseModel):
                 f"Published year: {getattr(item, 'year', None)}\n"
                 f"Title: {getattr(item, 'title', None)}\n"
                 f"Authors: {authors}\n"
-                f"Astract: {getattr(item, 'abstract', None)}\n"
+                f"Abstract: {getattr(item, 'abstract', None)}\n"
             )
 
         if documents:

libs/community/langchain_community/vectorstores/__init__.py

@@ -43,6 +43,9 @@ if TYPE_CHECKING:
     from langchain_community.vectorstores.apache_doris import (
         ApacheDoris,
     )
+    from langchain_community.vectorstores.aperturedb import (
+        ApertureDB,
+    )
     from langchain_community.vectorstores.astradb import (
         AstraDB,
     )
@@ -311,6 +314,7 @@ __all__ = [
     "AnalyticDB",
     "Annoy",
     "ApacheDoris",
+    "ApertureDB",
     "AstraDB",
     "AtlasDB",
     "AwaDB",
@@ -413,6 +417,7 @@ _module_lookup = {
     "AnalyticDB": "langchain_community.vectorstores.analyticdb",
     "Annoy": "langchain_community.vectorstores.annoy",
     "ApacheDoris": "langchain_community.vectorstores.apache_doris",
+    "ApertureDB": "langchain_community.vectorstores.aperturedb",
     "AstraDB": "langchain_community.vectorstores.astradb",
     "AtlasDB": "langchain_community.vectorstores.atlas",
     "AwaDB": "langchain_community.vectorstores.awadb",

libs/community/langchain_community/vectorstores/aperturedb.py

@@ -0,0 +1,516 @@
+# System imports
+from __future__ import annotations
+
+import logging
+import time
+import uuid
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
+
+# Third-party imports
+import numpy as np
+
+# Local imports
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from langchain_core.indexing.base import UpsertResponse
+from langchain_core.vectorstores import VectorStore
+from typing_extensions import override
+
+# Configure some defaults
+ENGINE = "HNSW"
+METRIC = "CS"
+DESCRIPTOR_SET = "langchain"
+BATCHSIZE = 1000
+PROPERTY_PREFIX = "lc_"  # Prefix for properties that are in the client metadata
+TEXT_PROPERTY = "text"  # Property name for the text
+UNIQUEID_PROPERTY = "uniqueid"  # Property name for the unique id
+
+
+class ApertureDB(VectorStore):
+    @override
+    def __init__(
+        self,
+        embeddings: Embeddings,
+        descriptor_set: str = DESCRIPTOR_SET,
+        dimensions: Optional[int] = None,
+        engine: Optional[str] = None,
+        metric: Optional[str] = None,
+        log_level: int = logging.WARN,
+        properties: Optional[Dict] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Create a vectorstore backed by ApertureDB
+
+        A single ApertureDB instance can support many vectorstores,
+        distinguished by 'descriptor_set' name.  The descriptor set is created
+        if it does not exist.  Different descriptor sets can use different
+        engines and metrics, be supplied by different embedding models, and have
+        different dimensions.
+
+        See ApertureDB documentation on `AddDescriptorSet`
+        https://docs.aperturedata.io/query_language/Reference/descriptor_commands/desc_set_commands/AddDescriptorSet
+        for more information on the engine and metric options.
+
+        Args:
+            embeddings (Embeddings): Embeddings object
+            descriptor_set (str, optional): Descriptor set name. Defaults to
+                "langchain".
+            dimensions (Optional[int], optional): Number of dimensions of the
+                embeddings. Defaults to None.
+            engine (str, optional): Engine to use. Defaults to "HNSW" for new
+                descriptorsets.
+            metric (str, optional): Metric to use. Defaults to "CS" for new
+                descriptorsets.
+            log_level (int, optional): Logging level. Defaults to logging.WARN.
+        """
+        # ApertureDB imports
+        try:
+            from aperturedb.Utils import Utils, create_connector
+        except ImportError:
+            raise ImportError(
+                "ApertureDB is not installed. Please install it using "
+                "'pip install aperturedb'"
+            )
+
+        super().__init__(**kwargs)
+        self.logger = logging.getLogger(__name__)
+        self.logger.setLevel(log_level)
+        self.descriptor_set = descriptor_set
+
+        self.embedding_function = embeddings
+        self.dimensions = dimensions
+        self.engine = engine
+        self.metric = metric
+        self.properties = properties
+        if embeddings is None:
+            self.logger.fatal("No embedding function provided.")
+            raise ValueError("No embedding function provided.")
+
+        try:
+            from aperturedb.Utils import Utils, create_connector
+        except ImportError:
+            self.logger.exception(
+                "ApertureDB is not installed. Please install it using "
+                "'pip install aperturedb'"
+            )
+            raise
+
+        self.connection = create_connector()
+        self.utils = Utils(self.connection)
+        try:
+            self.utils.status()
+        except Exception:
+            self.logger.exception("Failed to connect to ApertureDB")
+            raise
+
+        self._find_or_add_descriptor_set()
+
+    def _find_or_add_descriptor_set(self) -> None:
+        descriptor_set = self.descriptor_set
+        """Checks if the descriptor set exists, if not, creates it"""
+        find_ds_query = [
+            {
+                "FindDescriptorSet": {
+                    "with_name": descriptor_set,
+                    "engines": True,
+                    "metrics": True,
+                    "dimensions": True,
+                    "results": {"all_properties": True},
+                }
+            }
+        ]
+        r, b = self.connection.query(find_ds_query)
+        assert self.connection.last_query_ok(), r
+        n_entities = (
+            len(r[0]["FindDescriptorSet"]["entities"])
+            if "entities" in r[0]["FindDescriptorSet"]
+            else 0
+        )
+        assert n_entities <= 1, "Multiple descriptor sets with the same name"
+
+        if n_entities == 1:  # Descriptor set exists already
+            e = r[0]["FindDescriptorSet"]["entities"][0]
+            self.logger.info(f"Descriptor set {descriptor_set} already exists")
+
+            engines = e["_engines"]
+            assert len(engines) == 1, "Only one engine is supported"
+
+            if self.engine is None:
+                self.engine = engines[0]
+            elif self.engine != engines[0]:
+                self.logger.error(f"Engine mismatch: {self.engine} != {engines[0]}")
+
+            metrics = e["_metrics"]
+            assert len(metrics) == 1, "Only one metric is supported"
+            if self.metric is None:
+                self.metric = metrics[0]
+            elif self.metric != metrics[0]:
+                self.logger.error(f"Metric mismatch: {self.metric} != {metrics[0]}")
+
+            dimensions = e["_dimensions"]
+            if self.dimensions is None:
+                self.dimensions = dimensions
+            elif self.dimensions != dimensions:
+                self.logger.error(
+                    f"Dimensions mismatch: {self.dimensions} != {dimensions}"
+                )
+
+            self.properties = {
+                k[len(PROPERTY_PREFIX) :]: v
+                for k, v in e.items()
+                if k.startswith(PROPERTY_PREFIX)
+            }
+
+        else:
+            self.logger.info(
+                f"Descriptor set {descriptor_set} does not exist. Creating it"
+            )
+            if self.engine is None:
+                self.engine = ENGINE
+            if self.metric is None:
+                self.metric = METRIC
+            if self.dimensions is None:
+                self.dimensions = len(self.embedding_function.embed_query("test"))
+
+            properties = (
+                {PROPERTY_PREFIX + k: v for k, v in self.properties.items()}
+                if self.properties is not None
+                else None
+            )
+
+            self.utils.add_descriptorset(
+                name=descriptor_set,
+                dim=self.dimensions,
+                engine=self.engine,
+                metric=self.metric,
+                properties=properties,
+            )
+
+            # Create indexes
+            self.utils.create_entity_index("_Descriptor", "_create_txn")
+            self.utils.create_entity_index("_DescriptorSet", "_name")
+            self.utils.create_entity_index("_Descriptor", UNIQUEID_PROPERTY)
+
+    @override
+    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
+        """Delete documents from the vectorstore by id.
+
+        Args:
+            ids: List of ids to delete from the vectorstore.
+
+        Returns:
+            True if the deletion was successful, False otherwise
+        """
+        assert ids is not None, "ids must be provided"
+        query = [
+            {
+                "DeleteDescriptor": {
+                    "set": self.descriptor_set,
+                    "constraints": {UNIQUEID_PROPERTY: ["in", ids]},
+                }
+            }
+        ]
+
+        result, _ = self.utils.execute(query)
+        return result
+
+    @override
+    def get_by_ids(self, ids: Sequence[str], /) -> List[Document]:
+        """Find documents in the vectorstore by id.
+
+        Args:
+            ids: List of ids to find in the vectorstore.
+
+        Returns:
+            documents: List of Document objects found in the vectorstore.
+        """
+        query = [
+            {
+                "FindDescriptor": {
+                    "set": self.descriptor_set,
+                    "constraints": {UNIQUEID_PROPERTY: ["in", ids]},
+                    "results": {"all_properties": True},
+                }
+            }
+        ]
+
+        results, _ = self.utils.execute(query)
+        docs = [
+            self._descriptor_to_document(d)
+            for d in results[0]["FindDescriptor"].get("entities", [])
+        ]
+        return docs
+
+    @override
+    def similarity_search(
+        self, query: str, k: int = 4, *args: Any, **kwargs: Any
+    ) -> List[Document]:
+        """Search for documents similar to the query using the vectorstore
+
+        Args:
+            query: Query string to search for.
+            k: Number of results to return.
+
+        Returns:
+            List of Document objects ordered by decreasing similarity to the query.
+        """
+        assert self.embedding_function is not None, "Embedding function is not set"
+        embedding = self.embedding_function.embed_query(query)
+        return self.similarity_search_by_vector(embedding, k, *args, **kwargs)
+
+    @override
+    def similarity_search_with_score(
+        self, query: str, *args: Any, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        embedding = self.embedding_function.embed_query(query)
+        return self._similarity_search_with_score_by_vector(embedding, *args, **kwargs)
+
+    def _descriptor_to_document(self, d: dict) -> Document:
+        metadata = {}
+        for k, v in d.items():
+            if k.startswith(PROPERTY_PREFIX):
+                metadata[k[len(PROPERTY_PREFIX) :]] = v
+        text = d[TEXT_PROPERTY]
+        uniqueid = d[UNIQUEID_PROPERTY]
+        doc = Document(page_content=text, metadata=metadata, id=uniqueid)
+        return doc
+
+    def _similarity_search_with_score_by_vector(
+        self, embedding: List[float], k: int = 4, vectors: bool = False
+    ) -> List[Tuple[Document, float]]:
+        from aperturedb.Descriptors import Descriptors
+
+        descriptors = Descriptors(self.connection)
+        start_time = time.time()
+        descriptors.find_similar(
+            set=self.descriptor_set, vector=embedding, k_neighbors=k, distances=True
+        )
+        self.logger.info(
+            f"ApertureDB similarity search took {time.time() - start_time} seconds"
+        )
+        return [(self._descriptor_to_document(d), d["_distance"]) for d in descriptors]
+
+    @override
+    def similarity_search_by_vector(
+        self, embedding: List[float], k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Returns the k most similar documents to the given embedding vector
+
+        Args:
+            embedding: The embedding vector to search for
+            k: The number of similar documents to return
+
+        Returns:
+            List of Document objects ordered by decreasing similarity to the query.
+        """
+        from aperturedb.Descriptors import Descriptors
+
+        descriptors = Descriptors(self.connection)
+        start_time = time.time()
+        descriptors.find_similar(
+            set=self.descriptor_set, vector=embedding, k_neighbors=k
+        )
+        self.logger.info(
+            f"ApertureDB similarity search took {time.time() - start_time} seconds"
+        )
+        return [self._descriptor_to_document(d) for d in descriptors]
+
+    @override
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Returns similar documents to the query that also have diversity
+
+        This algorithm balances relevance and diversity in the search results.
+
+        Args:
+            query: Query string to search for.
+            k: Number of results to return.
+            fetch_k: Number of results to fetch.
+            lambda_mult: Lambda multiplier for MMR.
+
+        Returns:
+            List of Document objects ordered by decreasing similarity/diversty.
+        """
+        self.logger.info(f"Max Marginal Relevance search for query: {query}")
+        embedding = self.embedding_function.embed_query(query)
+        return self.max_marginal_relevance_search_by_vector(
+            embedding, k, fetch_k, lambda_mult, **kwargs
+        )
+
+    @override
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Returns similar documents to the vector that also have diversity
+
+        This algorithm balances relevance and diversity in the search results.
+
+        Args:
+            embedding: Embedding vector to search for.
+            k: Number of results to return.
+            fetch_k: Number of results to fetch.
+            lambda_mult: Lambda multiplier for MMR.
+
+        Returns:
+            List of Document objects ordered by decreasing similarity/diversty.
+        """
+        from aperturedb.Descriptors import Descriptors
+
+        descriptors = Descriptors(self.connection)
+        start_time = time.time()
+        descriptors.find_similar_mmr(
+            set=self.descriptor_set,
+            vector=embedding,
+            k_neighbors=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+        )
+        self.logger.info(
+            f"ApertureDB similarity search mmr took {time.time() - start_time} seconds"
+        )
+        return [self._descriptor_to_document(d) for d in descriptors]
+
+    @classmethod
+    @override
+    def from_texts(
+        cls: Type[ApertureDB],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> ApertureDB:
+        """Creates a new vectorstore from a list of texts
+
+        Args:
+            texts: List of text strings
+            embedding: Embeddings object as for constructing the vectorstore
+            metadatas: Optional list of metadatas associated with the texts.
+            **kwargs: Additional arguments to pass to the constructor
+        """
+        store = cls(embeddings=embedding, **kwargs)
+        store.add_texts(texts, metadatas)
+        return store
+
+    @classmethod
+    @override
+    def from_documents(
+        cls: Type[ApertureDB],
+        documents: List[Document],
+        embedding: Embeddings,
+        **kwargs: Any,
+    ) -> ApertureDB:
+        """Creates a new vectorstore from a list of documents
+
+        Args:
+            documents: List of Document objects
+            embedding: Embeddings object as for constructing the vectorstore
+            metadatas: Optional list of metadatas associated with the texts.
+            **kwargs: Additional arguments to pass to the constructor
+        """
+        store = cls(embeddings=embedding, **kwargs)
+        store.add_documents(documents)
+        return store
+
+    @classmethod
+    def delete_vectorstore(class_, descriptor_set: str) -> None:
+        """Deletes a vectorstore and all its data from the database
+
+        Args:
+            descriptor_set: The name of the descriptor set to delete
+        """
+        from aperturedb.Utils import Utils, create_connector
+
+        db = create_connector()
+        utils = Utils(db)
+        utils.remove_descriptorset(descriptor_set)
+
+    @classmethod
+    def list_vectorstores(class_) -> None:
+        """Returns a list of all vectorstores in the database
+
+        Returns:
+            List of descriptor sets with properties
+        """
+        from aperturedb.Utils import create_connector
+
+        db = create_connector()
+        query = [
+            {
+                "FindDescriptorSet": {
+                    # Return all properties
+                    "results": {"all_properties": True},
+                    "engines": True,
+                    "metrics": True,
+                    "dimensions": True,
+                }
+            }
+        ]
+        response, _ = db.query(query)
+        assert db.last_query_ok(), response
+        return response[0]["FindDescriptorSet"]["entities"]
+
+    @override
+    def upsert(self, items: Sequence[Document], /, **kwargs: Any) -> UpsertResponse:
+        """Insert or update items
+
+        Updating documents is dependent on the documents' `id` attribute.
+
+        Args:
+            items: List of Document objects to upsert
+
+        Returns:
+            UpsertResponse object with succeeded and failed
+        """
+        # For now, simply delete and add
+        # We could do something more efficient to update metadata,
+        # but we don't support changing the embedding of a descriptor.
+
+        from aperturedb.ParallelLoader import ParallelLoader
+
+        ids_to_delete: List[str] = [
+            item.id for item in items if hasattr(item, "id") and item.id is not None
+        ]
+        if ids_to_delete:
+            self.delete(ids_to_delete)
+
+        texts = [doc.page_content for doc in items]
+        metadatas = [
+            doc.metadata if getattr(doc, "metadata", None) is not None else {}
+            for doc in items
+        ]
+        embeddings = self.embedding_function.embed_documents(texts)
+        ids: List[str] = [
+            doc.id if hasattr(doc, "id") and doc.id is not None else str(uuid.uuid4())
+            for doc in items
+        ]
+
+        data = []
+        for text, embedding, metadata, unique_id in zip(
+            texts, embeddings, metadatas, ids
+        ):
+            properties = {PROPERTY_PREFIX + k: v for k, v in metadata.items()}
+            properties[TEXT_PROPERTY] = text
+            properties[UNIQUEID_PROPERTY] = unique_id
+            command = {
+                "AddDescriptor": {
+                    "set": self.descriptor_set,
+                    "properties": properties,
+                }
+            }
+            query = [command]
+            blobs = [np.array(embedding, dtype=np.float32).tobytes()]
+            data.append((query, blobs))
+        loader = ParallelLoader(self.connection)
+        loader.ingest(data, batchsize=BATCHSIZE)
+        return UpsertResponse(succeeded=ids, failed=[])

libs/community/langchain_community/vectorstores/azuresearch.py

@@ -86,6 +86,7 @@ def _get_search_client(
     user_agent: Optional[str] = "langchain",
     cors_options: Optional[CorsOptions] = None,
     async_: bool = False,
+    additional_search_client_options: Optional[Dict[str, Any]] = None,
 ) -> Union[SearchClient, AsyncSearchClient]:
     from azure.core.credentials import AzureKeyCredential
     from azure.core.exceptions import ResourceNotFoundError
@@ -109,6 +110,7 @@ def _get_search_client(
         VectorSearchProfile,
     )
 
+    additional_search_client_options = additional_search_client_options or {}
     default_fields = default_fields or []
     if key is None:
         credential = DefaultAzureCredential()
@@ -225,6 +227,7 @@ def _get_search_client(
             index_name=index_name,
             credential=credential,
             user_agent=user_agent,
+            **additional_search_client_options,
         )
     else:
         return AsyncSearchClient(
@@ -232,6 +235,7 @@ def _get_search_client(
             index_name=index_name,
             credential=credential,
             user_agent=user_agent,
+            **additional_search_client_options,
         )
 
 
@@ -256,6 +260,7 @@ class AzureSearch(VectorStore):
         cors_options: Optional[CorsOptions] = None,
         *,
         vector_search_dimensions: Optional[int] = None,
+        additional_search_client_options: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ):
         try:
@@ -320,6 +325,22 @@ class AzureSearch(VectorStore):
             default_fields=default_fields,
             user_agent=user_agent,
             cors_options=cors_options,
+            additional_search_client_options=additional_search_client_options,
+        )
+        self.async_client = _get_search_client(
+            azure_search_endpoint,
+            azure_search_key,
+            index_name,
+            semantic_configuration_name=semantic_configuration_name,
+            fields=fields,
+            vector_search=vector_search,
+            semantic_configurations=semantic_configurations,
+            scoring_profiles=scoring_profiles,
+            default_scoring_profile=default_scoring_profile,
+            default_fields=default_fields,
+            user_agent=user_agent,
+            cors_options=cors_options,
+            async_=True,
         )
         self.search_type = search_type
         self.semantic_configuration_name = semantic_configuration_name
@@ -338,23 +359,6 @@ class AzureSearch(VectorStore):
         self._user_agent = user_agent
         self._cors_options = cors_options
 
-    def _async_client(self) -> AsyncSearchClient:
-        return _get_search_client(
-            self._azure_search_endpoint,
-            self._azure_search_key,
-            self._index_name,
-            semantic_configuration_name=self._semantic_configuration_name,
-            fields=self._fields,
-            vector_search=self._vector_search,
-            semantic_configurations=self._semantic_configurations,
-            scoring_profiles=self._scoring_profiles,
-            default_scoring_profile=self._default_scoring_profile,
-            default_fields=self._default_fields,
-            user_agent=self._user_agent,
-            cors_options=self._cors_options,
-            async_=True,
-        )
-
     @property
     def embeddings(self) -> Optional[Embeddings]:
         # TODO: Support embedding object directly
@@ -513,7 +517,7 @@ class AzureSearch(VectorStore):
             ids.append(key)
             # Upload data in batches
             if len(data) == MAX_UPLOAD_BATCH_SIZE:
-                async with self._async_client() as async_client:
+                async with self.async_client as async_client:
                     response = await async_client.upload_documents(documents=data)
                     # Check if all documents were successfully uploaded
                     if not all(r.succeeded for r in response):
@@ -526,7 +530,7 @@ class AzureSearch(VectorStore):
             return ids
 
         # Upload data to index
-        async with self._async_client() as async_client:
+        async with self.async_client as async_client:
             response = await async_client.upload_documents(documents=data)
         # Check if all documents were successfully uploaded
         if all(r.succeeded for r in response):
@@ -561,7 +565,7 @@ class AzureSearch(VectorStore):
             False otherwise.
         """
         if ids:
-            async with self._async_client() as async_client:
+            async with self.async_client as async_client:
                 res = await async_client.delete_documents([{"id": i} for i in ids])
                 return len(res) > 0
         else:
@@ -739,11 +743,11 @@ class AzureSearch(VectorStore):
                 to the query and score for each
         """
         embedding = await self._aembed_query(query)
-        docs, scores, _ = await self._asimple_search(
+        results = await self._asimple_search(
             embedding, "", k, filters=filters, **kwargs
         )
 
-        return list(zip(docs, scores))
+        return _results_to_documents(results)
 
     def max_marginal_relevance_search_with_score(
         self,
@@ -807,14 +811,12 @@ class AzureSearch(VectorStore):
                 to the query and score for each
         """
         embedding = await self._aembed_query(query)
-        docs, scores, vectors = await self._asimple_search(
+        results = await self._asimple_search(
             embedding, "", fetch_k, filters=filters, **kwargs
         )
 
-        return await self._areorder_results_with_maximal_marginal_relevance(
-            docs,
-            scores,
-            vectors,
+        return await _areorder_results_with_maximal_marginal_relevance(
+            results,
             query_embedding=np.array(embedding),
             lambda_mult=lambda_mult,
             k=k,
@@ -890,11 +892,11 @@ class AzureSearch(VectorStore):
         """
 
         embedding = await self._aembed_query(query)
-        docs, scores, _ = await self._asimple_search(
+        results = await self._asimple_search(
             embedding, query, k, filters=filters, **kwargs
         )
 
-        return list(zip(docs, scores))
+        return _results_to_documents(results)
 
     def hybrid_search_with_relevance_scores(
         self,
@@ -992,14 +994,12 @@ class AzureSearch(VectorStore):
         """
 
         embedding = await self._aembed_query(query)
-        docs, scores, vectors = await self._asimple_search(
+        results = await self._asimple_search(
             embedding, query, fetch_k, filters=filters, **kwargs
         )
 
-        return await self._areorder_results_with_maximal_marginal_relevance(
-            docs,
-            scores,
-            vectors,
+        return await _areorder_results_with_maximal_marginal_relevance(
+            results,
             query_embedding=np.array(embedding),
             lambda_mult=lambda_mult,
             k=k,
@@ -1049,7 +1049,7 @@ class AzureSearch(VectorStore):
         *,
         filters: Optional[str] = None,
         **kwargs: Any,
-    ) -> Tuple[List[Document], List[float], List[List[float]]]:
+    ) -> SearchItemPaged[dict]:
         """Perform vector or hybrid search in the Azure search index.
 
         Args:
@@ -1063,8 +1063,8 @@ class AzureSearch(VectorStore):
         """
         from azure.search.documents.models import VectorizedQuery
 
-        async with self._async_client() as async_client:
-            results = await async_client.search(
+        async with self.async_client as async_client:
+            return await async_client.search(
                 search_text=text_query,
                 vector_queries=[
                     VectorizedQuery(
@@ -1077,18 +1077,6 @@ class AzureSearch(VectorStore):
                 top=k,
                 **kwargs,
             )
-            docs = [
-                (
-                    _result_to_document(result),
-                    float(result["@search.score"]),
-                    result[FIELDS_CONTENT_VECTOR],
-                )
-                async for result in results
-            ]
-            if not docs:
-                raise ValueError(f"No {docs=}")
-        documents, scores, vectors = map(list, zip(*docs))
-        return documents, scores, vectors
 
     def semantic_hybrid_search(
         self, query: str, k: int = 4, **kwargs: Any
@@ -1300,7 +1288,7 @@ class AzureSearch(VectorStore):
         from azure.search.documents.models import VectorizedQuery
 
         vector = await self._aembed_query(query)
-        async with self._async_client() as async_client:
+        async with self.async_client as async_client:
             results = await async_client.search(
                 search_text=query,
                 vector_queries=[
@@ -1475,30 +1463,6 @@ class AzureSearch(VectorStore):
         azure_search.add_embeddings(text_embeddings, metadatas, **kwargs)
         return azure_search
 
-    async def _areorder_results_with_maximal_marginal_relevance(
-        self,
-        documents: List[Document],
-        scores: List[float],
-        vectors: List[List[float]],
-        query_embedding: np.ndarray,
-        lambda_mult: float = 0.5,
-        k: int = 4,
-    ) -> List[Tuple[Document, float]]:
-        # Get the new order of results.
-        new_ordering = maximal_marginal_relevance(
-            query_embedding, vectors, k=k, lambda_mult=lambda_mult
-        )
-
-        # Reorder the values and return.
-        ret: List[Tuple[Document, float]] = []
-        for x in new_ordering:
-            # Function can return -1 index
-            if x == -1:
-                break
-            ret.append((documents[x], scores[x]))  # type: ignore
-
-        return ret
-
     def as_retriever(self, **kwargs: Any) -> AzureSearchVectorStoreRetriever:  # type: ignore
         """Return AzureSearchVectorStoreRetriever initialized from this VectorStore.
 
@@ -1666,6 +1630,39 @@ def _results_to_documents(
     return docs
 
 
+async def _areorder_results_with_maximal_marginal_relevance(
+    results: SearchItemPaged[Dict],
+    query_embedding: np.ndarray,
+    lambda_mult: float = 0.5,
+    k: int = 4,
+) -> List[Tuple[Document, float]]:
+    # Convert results to Document objects
+    docs = [
+        (
+            _result_to_document(result),
+            float(result["@search.score"]),
+            result[FIELDS_CONTENT_VECTOR],
+        )
+        for result in results
+    ]
+    documents, scores, vectors = map(list, zip(*docs))
+
+    # Get the new order of results.
+    new_ordering = maximal_marginal_relevance(
+        query_embedding, vectors, k=k, lambda_mult=lambda_mult
+    )
+
+    # Reorder the values and return.
+    ret: List[Tuple[Document, float]] = []
+    for x in new_ordering:
+        # Function can return -1 index
+        if x == -1:
+            break
+        ret.append((documents[x], scores[x]))  # type: ignore
+
+    return ret
+
+
 def _reorder_results_with_maximal_marginal_relevance(
     results: SearchItemPaged[Dict],
     query_embedding: np.ndarray,

libs/community/langchain_community/vectorstores/chroma.py

@@ -478,6 +478,93 @@ class Chroma(VectorStore):
                 "Consider providing relevance_score_fn to Chroma constructor."
             )
 
+    def similarity_search_by_image(
+        self,
+        uri: str,
+        k: int = DEFAULT_K,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Search for similar images based on the given image URI.
+
+        Args:
+            uri (str): URI of the image to search for.
+            k (int, optional): Number of results to return. Defaults to DEFAULT_K.
+            filter (Optional[Dict[str, str]], optional): Filter by metadata.
+            **kwargs (Any): Additional arguments to pass to function.
+
+        Returns:
+            List of Images most similar to the provided image.
+            Each element in list is a Langchain Document Object.
+            The page content is b64 encoded image, metadata is default or
+            as defined by user.
+
+        Raises:
+            ValueError: If the embedding function does not support image embeddings.
+        """
+        if self._embedding_function is None or not hasattr(
+            self._embedding_function, "embed_image"
+        ):
+            raise ValueError("The embedding function must support image embedding.")
+
+        # Obtain image embedding
+        # Assuming embed_image returns a single embedding
+        image_embedding = self._embedding_function.embed_image(uris=[uri])
+
+        # Perform similarity search based on the obtained embedding
+        results = self.similarity_search_by_vector(
+            embedding=image_embedding,
+            k=k,
+            filter=filter,
+            **kwargs,
+        )
+
+        return results
+
+    def similarity_search_by_image_with_relevance_score(
+        self,
+        uri: str,
+        k: int = DEFAULT_K,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Search for similar images based on the given image URI.
+
+        Args:
+            uri (str): URI of the image to search for.
+            k (int, optional): Number of results to return.
+            Defaults to DEFAULT_K.
+            filter (Optional[Dict[str, str]], optional): Filter by metadata.
+            **kwargs (Any): Additional arguments to pass to function.
+
+        Returns:
+            List[Tuple[Document, float]]: List of tuples containing documents similar
+            to the query image and their similarity scores.
+            0th element in each tuple is a Langchain Document Object.
+            The page content is b64 encoded img, metadata is default or defined by user.
+
+        Raises:
+            ValueError: If the embedding function does not support image embeddings.
+        """
+        if self._embedding_function is None or not hasattr(
+            self._embedding_function, "embed_image"
+        ):
+            raise ValueError("The embedding function must support image embedding.")
+
+        # Obtain image embedding
+        # Assuming embed_image returns a single embedding
+        image_embedding = self._embedding_function.embed_image(uris=[uri])
+
+        # Perform similarity search based on the obtained embedding
+        results = self.similarity_search_by_vector_with_relevance_scores(
+            embedding=image_embedding,
+            k=k,
+            filter=filter,
+            **kwargs,
+        )
+
+        return results
+
     def max_marginal_relevance_search_by_vector(
         self,
         embedding: List[float],

libs/community/langchain_community/vectorstores/databricks_vector_search.py

@@ -277,7 +277,13 @@ class DatabricksVectorSearch(VectorStore):
         return True
 
     def similarity_search(
-        self, query: str, k: int = 4, filters: Optional[Any] = None, **kwargs: Any
+        self,
+        query: str,
+        k: int = 4,
+        filters: Optional[Any] = None,
+        *,
+        query_type: Optional[str] = None,
+        **kwargs: Any,
     ) -> List[Document]:
         """Return docs most similar to query.
 
@@ -285,17 +291,24 @@ class DatabricksVectorSearch(VectorStore):
             query: Text to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
             filters: Filters to apply to the query. Defaults to None.
+            query_type: The type of this query. Supported values are "ANN" and "HYBRID".
 
         Returns:
             List of Documents most similar to the embedding.
         """
         docs_with_score = self.similarity_search_with_score(
-            query=query, k=k, filters=filters, **kwargs
+            query=query, k=k, filters=filters, query_type=query_type, **kwargs
         )
         return [doc for doc, _ in docs_with_score]
 
     def similarity_search_with_score(
-        self, query: str, k: int = 4, filters: Optional[Any] = None, **kwargs: Any
+        self,
+        query: str,
+        k: int = 4,
+        filters: Optional[Any] = None,
+        *,
+        query_type: Optional[str] = None,
+        **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return docs most similar to query, along with scores.
 
@@ -303,6 +316,7 @@ class DatabricksVectorSearch(VectorStore):
             query: Text to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
             filters: Filters to apply to the query. Defaults to None.
+            query_type: The type of this query. Supported values are "ANN" and "HYBRID".
 
         Returns:
             List of Documents most similar to the embedding and score for each.
@@ -321,6 +335,7 @@ class DatabricksVectorSearch(VectorStore):
             query_vector=query_vector,
             filters=filters,
             num_results=k,
+            query_type=query_type,
         )
         return self._parse_search_response(search_resp)
 
@@ -343,6 +358,8 @@ class DatabricksVectorSearch(VectorStore):
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
         filters: Optional[Any] = None,
+        *,
+        query_type: Optional[str] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -359,6 +376,7 @@ class DatabricksVectorSearch(VectorStore):
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
             filters: Filters to apply to the query. Defaults to None.
+            query_type: The type of this query. Supported values are "ANN" and "HYBRID".
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
@@ -377,6 +395,7 @@ class DatabricksVectorSearch(VectorStore):
             fetch_k,
             lambda_mult=lambda_mult,
             filters=filters,
+            query_type=query_type,
         )
         return docs
 
@@ -387,6 +406,8 @@ class DatabricksVectorSearch(VectorStore):
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
         filters: Optional[Any] = None,
+        *,
+        query_type: Optional[str] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -403,6 +424,7 @@ class DatabricksVectorSearch(VectorStore):
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
             filters: Filters to apply to the query. Defaults to None.
+            query_type: The type of this query. Supported values are "ANN" and "HYBRID".
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
@@ -420,6 +442,7 @@ class DatabricksVectorSearch(VectorStore):
             query_vector=embedding,
             filters=filters,
             num_results=fetch_k,
+            query_type=query_type,
         )
 
         embeddings_result_index = (
@@ -449,6 +472,8 @@ class DatabricksVectorSearch(VectorStore):
         embedding: List[float],
         k: int = 4,
         filters: Optional[Any] = None,
+        *,
+        query_type: Optional[str] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs most similar to embedding vector.
@@ -457,12 +482,13 @@ class DatabricksVectorSearch(VectorStore):
             embedding: Embedding to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
             filters: Filters to apply to the query. Defaults to None.
+            query_type: The type of this query. Supported values are "ANN" and "HYBRID".
 
         Returns:
             List of Documents most similar to the embedding.
         """
         docs_with_score = self.similarity_search_by_vector_with_score(
-            embedding=embedding, k=k, filters=filters, **kwargs
+            embedding=embedding, k=k, filters=filters, query_type=query_type, **kwargs
         )
         return [doc for doc, _ in docs_with_score]
 
@@ -471,6 +497,8 @@ class DatabricksVectorSearch(VectorStore):
         embedding: List[float],
         k: int = 4,
         filters: Optional[Any] = None,
+        *,
+        query_type: Optional[str] = None,
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return docs most similar to embedding vector, along with scores.
@@ -479,6 +507,7 @@ class DatabricksVectorSearch(VectorStore):
             embedding: Embedding to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
             filters: Filters to apply to the query. Defaults to None.
+            query_type: The type of this query. Supported values are "ANN" and "HYBRID".
 
         Returns:
             List of Documents most similar to the embedding and score for each.
@@ -493,6 +522,7 @@ class DatabricksVectorSearch(VectorStore):
             query_vector=embedding,
             filters=filters,
             num_results=k,
+            query_type=query_type,
         )
         return self._parse_search_response(search_resp)
 

libs/community/langchain_community/vectorstores/neo4j_vector.py

@@ -854,11 +854,11 @@ class Neo4jVector(VectorStore):
             "CALL { WITH row "
             f"MERGE (c:`{self.node_label}` {{id: row.id}}) "
             "WITH c, row "
-            f"CALL db.create.setVectorProperty(c, "
+            f"CALL db.create.setNodeVectorProperty(c, "
             f"'{self.embedding_node_property}', row.embedding) "
-            "YIELD node "
             f"SET c.`{self.text_node_property}` = row.text "
-            "SET c += row.metadata } IN TRANSACTIONS OF 1000 ROWS"
+            "SET c += row.metadata "
+            "} IN TRANSACTIONS OF 1000 ROWS "
         )
 
         parameters = {
@@ -909,6 +909,11 @@ class Neo4jVector(VectorStore):
         Args:
             query (str): Query text to search for.
             k (int): Number of results to return. Defaults to 4.
+            params (Dict[str, Any]): The search params for the index type.
+                Defaults to empty dict.
+            filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
+                    filter on metadata.
+                Defaults to None.
 
         Returns:
             List of Documents most similar to the query.
@@ -936,6 +941,11 @@ class Neo4jVector(VectorStore):
         Args:
             query: Text to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
+            params (Dict[str, Any]): The search params for the index type.
+                Defaults to empty dict.
+            filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
+                    filter on metadata.
+                Defaults to None.
 
         Returns:
             List of Documents most similar to the query and score for each
@@ -972,6 +982,11 @@ class Neo4jVector(VectorStore):
         Args:
             embedding (List[float]): The embedding vector to compare against.
             k (int, optional): The number of top similar documents to retrieve.
+            filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
+                    filter on metadata.
+                Defaults to None.
+            params (Dict[str, Any]): The search params for the index type.
+                Defaults to empty dict.
 
         Returns:
             List[Tuple[Document, float]]: A list of tuples, each containing
@@ -1077,6 +1092,7 @@ class Neo4jVector(VectorStore):
         embedding: List[float],
         k: int = 4,
         filter: Optional[Dict[str, Any]] = None,
+        params: Dict[str, Any] = {},
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs most similar to embedding vector.
@@ -1084,12 +1100,17 @@ class Neo4jVector(VectorStore):
         Args:
             embedding: Embedding to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
+            filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
+                    filter on metadata.
+                Defaults to None.
+            params (Dict[str, Any]): The search params for the index type.
+                Defaults to empty dict.
 
         Returns:
             List of Documents most similar to the query vector.
         """
         docs_and_scores = self.similarity_search_with_score_by_vector(
-            embedding=embedding, k=k, filter=filter, **kwargs
+            embedding=embedding, k=k, filter=filter, params=params, **kwargs
         )
         return [doc for doc, _ in docs_and_scores]
 
@@ -1441,9 +1462,9 @@ class Neo4jVector(VectorStore):
                 "UNWIND $data AS row "
                 f"MATCH (n:`{node_label}`) "
                 "WHERE elementId(n) = row.id "
-                f"CALL db.create.setVectorProperty(n, "
+                f"CALL db.create.setNodeVectorProperty(n, "
                 f"'{embedding_node_property}', row.embedding) "
-                "YIELD node RETURN count(*)",
+                "RETURN count(*)",
                 params=params,
             )
             # If embedding calculation should be stopped

libs/community/scripts/lint_imports.sh

@@ -1,24 +1,46 @@
 #!/bin/bash
-
+# This script searches for invalid imports in tracked files within a Git repository.
+#
+# Usage: ./scripts/lint_imports.sh /path/to/repository
 set -eu
 
-# Initialize a variable to keep track of errors
-errors=0
+# Check if a path argument is provided
+if [ $# -ne 1 ]; then
+  echo "Usage: $0 /path/to/repository"
+  exit 1
+fi
 
-# make sure not importing from langchain or langchain_experimental
-git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
+repository_path="$1"
+
+# make sure not importing from langchain_experimental
+result=$(git -C "$repository_path" grep -En '^import langchain_experimental|^from langchain_experimental' -- '*.py' || true)
+
+# Check if any matching lines were found
+if [ -n "$result" ]; then
+  echo "ERROR: The following lines need to be updated:"
+  echo "$result"
+  echo "langchain_community should import from langchain_experimental."
+  exit 1
+fi
 
 # make sure no one is importing from the built-in xml library
 # instead defusedxml should be used to avoid getting CVEs.
-# Whether the standary library actually poses a risk to users
-# is very nuanced and dependns on user's environment.
+# Whether the standard library actually poses a risk to users
+# is very nuanced and depends on the user's environment.
 # https://docs.python.org/3/library/xml.etree.elementtree.html
-git --no-pager grep '^from xml\.' . | grep -vE "# OK: user-must-opt-in" && errors=$((errors+1))
-git --no-pager grep '^import xml\.' . | grep -vE "# OK: user-must-opt-in" && errors=$((errors+1))
 
-# Decide on an exit status based on the errors
-if [ "$errors" -gt 0 ]; then
-    exit 1
-else
-    exit 0
+result=$(git -C "$repository_path" grep -En '^from xml.|^import xml$|^import xml.' | grep -vE "# OK: user-must-opt-in" || true)
+
+if [ -n "$result" ]; then
+  echo "ERROR: The following lines need to be updated:"
+  echo "$result"
+  echo "Triggering an error due to usage of the built-in xml library. "
+  echo "Please see https://docs.python.org/3/library/xml.html#xml-vulnerabilities."
+  echo "If this happens, there's likely code that's relying on the standard library "
+  echo "to parse xml somewhere in the code path. "
+  echo "Please update the code to force the user to explicitly opt-in to using the standard library or running the code. "
+  echo "It should be **obvious** without reading the documentation that they are being forced to use the standard library. "
+  echo "After this is done feel free to add a comment to the line with '# OK: user-must-opt-in', after the import. "
+  echo "Lacking a clear opt-in mechanism is likely a security risk, and will result in rejection of the PR."
+  exit 1
 fi

libs/community/tests/integration_tests/chat_models/test_baichuan.py

@@ -76,3 +76,14 @@ async def test_chat_baichuan_astream() -> None:
     async for chunk in chat.astream("今天天气如何？"):
         assert isinstance(chunk, AIMessage)
         assert isinstance(chunk.content, str)
+
+
+def test_chat_baichuan_with_system_role() -> None:
+    chat = ChatBaichuan()  # type: ignore[call-arg]
+    messages = [
+        ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
+        ("human", "我喜欢编程。"),
+    ]
+    response = chat.invoke(messages)
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)

libs/community/tests/integration_tests/chat_models/test_tongyi.py

@@ -235,3 +235,34 @@ def test_manual_tool_call_msg() -> None:
     assert output.content
     # Should not have called the tool again.
     assert not output.tool_calls and not output.invalid_tool_calls
+
+
+class AnswerWithJustification(BaseModel):
+    """An answer to the user question along with justification for the answer."""
+
+    answer: str
+    justification: str
+
+
+def test_chat_tongyi_with_structured_output() -> None:
+    """Test ChatTongyi with structured output."""
+    llm = ChatTongyi()  # type: ignore
+    structured_llm = llm.with_structured_output(AnswerWithJustification)
+    response = structured_llm.invoke(
+        "What weighs more a pound of bricks or a pound of feathers"
+    )
+    assert isinstance(response, AnswerWithJustification)
+
+
+def test_chat_tongyi_with_structured_output_include_raw() -> None:
+    """Test ChatTongyi with structured output."""
+    llm = ChatTongyi()  # type: ignore
+    structured_llm = llm.with_structured_output(
+        AnswerWithJustification, include_raw=True
+    )
+    response = structured_llm.invoke(
+        "What weighs more a pound of bricks or a pound of feathers"
+    )
+    assert isinstance(response, dict)
+    assert isinstance(response.get("raw"), AIMessage)
+    assert isinstance(response.get("parsed"), AnswerWithJustification)

libs/community/tests/integration_tests/tools/zenguard/test_zenguard.py

@@ -9,7 +9,7 @@ from langchain_community.tools.zenguard.tool import Detector, ZenGuardTool
 @pytest.fixture()
 def zenguard_tool() -> ZenGuardTool:
     if os.getenv("ZENGUARD_API_KEY") is None:
-        raise ValueError("ZENGUARD_API_KEY is not set in environment varibale")
+        raise ValueError("ZENGUARD_API_KEY is not set in environment variable")
     return ZenGuardTool()
 
 

libs/community/tests/integration_tests/vectorstores/docker-compose/aperturedb.yml

@@ -0,0 +1,7 @@
+services:
+  aperturedb:
+    image: aperturedata/aperturedb-standalone:latest
+    restart: on-failure:0
+    container_name: aperturedb
+    ports:
+      - 55555:55555

libs/community/tests/integration_tests/vectorstores/test_aperturedb.py

@@ -0,0 +1,29 @@
+"""Test ApertureDB functionality."""
+
+import uuid
+
+import pytest
+from langchain_standard_tests.integration_tests.vectorstores import (
+    AsyncReadWriteTestSuite,
+    ReadWriteTestSuite,
+)
+
+from langchain_community.vectorstores import ApertureDB
+
+
+class TestApertureDBReadWriteTestSuite(ReadWriteTestSuite):
+    @pytest.fixture
+    def vectorstore(self) -> ApertureDB:
+        descriptor_set = uuid.uuid4().hex  # Fresh descriptor set for each test
+        return ApertureDB(
+            embeddings=self.get_embeddings(), descriptor_set=descriptor_set
+        )
+
+
+class TestAsyncApertureDBReadWriteTestSuite(AsyncReadWriteTestSuite):
+    @pytest.fixture
+    async def vectorstore(self) -> ApertureDB:
+        descriptor_set = uuid.uuid4().hex  # Fresh descriptor set for each test
+        return ApertureDB(
+            embeddings=self.get_embeddings(), descriptor_set=descriptor_set
+        )

libs/community/tests/integration_tests/vectorstores/test_neo4jvector.py

@@ -1,9 +1,11 @@
 """Test Neo4jVector functionality."""
 
 import os
+from math import isclose
 from typing import Any, Dict, List, cast
 
 from langchain_core.documents import Document
+from yaml import safe_load
 
 from langchain_community.graphs import Neo4jGraph
 from langchain_community.vectorstores.neo4j_vector import (
@@ -197,7 +199,10 @@ def test_neo4jvector_with_metadatas_with_scores() -> None:
         password=password,
         pre_delete_collection=True,
     )
-    output = docsearch.similarity_search_with_score("foo", k=1)
+    output = [
+        (doc, round(score, 1))
+        for doc, score in docsearch.similarity_search_with_score("foo", k=1)
+    ]
     assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
 
     drop_vector_indexes(docsearch)
@@ -217,12 +222,21 @@ def test_neo4jvector_relevance_score() -> None:
     )
 
     output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
-    assert output == [
+    expected_output = [
         (Document(page_content="foo", metadata={"page": "0"}), 1.0),
         (Document(page_content="bar", metadata={"page": "1"}), 0.9998376369476318),
         (Document(page_content="baz", metadata={"page": "2"}), 0.9993523359298706),
     ]
 
+    # Check if the length of the outputs matches
+    assert len(output) == len(expected_output)
+
+    # Check if each document and its relevance score is close to the expected value
+    for (doc, score), (expected_doc, expected_score) in zip(output, expected_output):
+        assert doc.page_content == expected_doc.page_content
+        assert doc.metadata == expected_doc.metadata
+        assert isclose(score, expected_score, rel_tol=1e-5)
+
     drop_vector_indexes(docsearch)
 
 
@@ -779,8 +793,16 @@ def test_retrieval_dictionary() -> None:
             )
         )
     ]
+
     output = docsearch.similarity_search("Foo", k=1)
-    assert output == expected_output
+
+    def parse_document(doc: Document) -> Any:
+        return safe_load(doc.page_content)
+
+    parsed_expected = [parse_document(doc) for doc in expected_output]
+    parsed_output = [parse_document(doc) for doc in output]
+
+    assert parsed_output == parsed_expected
     drop_vector_indexes(docsearch)
 
 

libs/community/tests/unit_tests/chat_models/test_baichuan.py

@@ -4,7 +4,6 @@ import pytest
 from langchain_core.messages import (
     AIMessage,
     AIMessageChunk,
-    ChatMessage,
     FunctionMessage,
     HumanMessage,
     HumanMessageChunk,
@@ -54,9 +53,9 @@ def test__convert_message_to_dict_ai() -> None:
 
 def test__convert_message_to_dict_system() -> None:
     message = SystemMessage(content="foo")
-    with pytest.raises(TypeError) as e:
-        _convert_message_to_dict(message)
-    assert "Got unknown type" in str(e)
+    result = _convert_message_to_dict(message)
+    expected_output = {"role": "system", "content": "foo"}
+    assert result == expected_output
 
 
 def test__convert_message_to_dict_function() -> None:
@@ -83,7 +82,7 @@ def test__convert_dict_to_message_ai() -> None:
 def test__convert_dict_to_message_other_role() -> None:
     message_dict = {"role": "system", "content": "foo"}
     result = _convert_dict_to_message(message_dict)
-    expected_output = ChatMessage(role="system", content="foo")
+    expected_output = SystemMessage(content="foo")
     assert result == expected_output
 
 
@@ -134,3 +133,11 @@ def test_uses_actual_secret_value_from_secret_str() -> None:
         cast(SecretStr, chat.baichuan_secret_key).get_secret_value()
         == "test-secret-key"
     )
+
+
+def test_chat_baichuan_with_base_url() -> None:
+    chat = ChatBaichuan(  # type: ignore[call-arg]
+        api_key="your-api-key",  # type: ignore[arg-type]
+        base_url="https://exmaple.com",  # type: ignore[arg-type]
+    )
+    assert chat.baichuan_api_base == "https://exmaple.com"