core[patch]: Release 0.3.0dev4 (#26178)

For now we'll only support pydantic ^ 2.9. We'll relax the constraint
next week once we work around some issues with pydantic 2.7 / 2.8.

.github/scripts/check_diff.py

@@ -16,6 +16,10 @@ LANGCHAIN_DIRS = [
     "libs/experimental",
 ]
 
+# for 0.3rc, we are ignoring core dependents
+# in order to be able to get CI to pass for individual PRs.
+IGNORE_CORE_DEPENDENTS = True
+
 # ignored partners are removed from dependents
 # but still run if directly edited
 IGNORED_PARTNERS = [
@@ -102,9 +106,9 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     if dir_ == "libs/core":
         return [
             {"working-directory": dir_, "python-version": f"3.{v}"}
-            for v in range(8, 13)
+            for v in range(9, 13)
         ]
-    min_python = "3.8"
+    min_python = "3.9"
     max_python = "3.12"
 
     # custom logic for specific directories
@@ -184,6 +188,9 @@ if __name__ == "__main__":
             # for extended testing
             found = False
             for dir_ in LANGCHAIN_DIRS:
+                if dir_ == "libs/core" and IGNORE_CORE_DEPENDENTS:
+                    dirs_to_run["extended-test"].add(dir_)
+                    continue
                 if file.startswith(dir_):
                     found = True
                 if found:

.github/scripts/check_prerelease_dependencies.py

@@ -11,7 +11,7 @@ if __name__ == "__main__":
 
     # see if we're releasing an rc
     version = toml_data["tool"]["poetry"]["version"]
-    releasing_rc = "rc" in version
+    releasing_rc = "rc" in version or "dev" in version
 
     # if not, iterate through dependencies and make sure none allow prereleases
     if not releasing_rc:

.github/scripts/get_min_versions.py

@@ -15,6 +15,7 @@ MIN_VERSION_LIBS = [
     "langchain",
     "langchain-text-splitters",
     "SQLAlchemy",
+    "pydantic",
 ]
 
 SKIP_IF_PULL_REQUEST = ["langchain-core"]

.github/workflows/_dependencies.yml

@@ -1,114 +0,0 @@
-name: dependencies
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      langchain-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain library folder"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
-
-env:
-  POETRY_VERSION: "1.7.1"
-
-jobs:
-  build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    name: dependency checks ${{ inputs.python-version }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: pydantic-cross-compat
-
-      - name: Install dependencies
-        shell: bash
-        run: poetry install
-
-      - name: Check imports with base dependencies
-        shell: bash
-        run: poetry run make check_imports
-
-      - name: Install test dependencies
-        shell: bash
-        run: poetry install --with test
-
-      - name: Install langchain editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-location }}
-        env:
-          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_LOCATION"
-
-      - name: Install the opposite major version of pydantic
-        # If normal tests use pydantic v1, here we'll use v2, and vice versa.
-        shell: bash
-        # airbyte currently doesn't support pydantic v2
-        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
-        run: |
-          # Determine the major part of pydantic version
-          REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
-
-          if [[ "$REGULAR_VERSION" == "1" ]]; then
-            PYDANTIC_DEP=">=2.1,<3"
-            TEST_WITH_VERSION="2"
-          elif [[ "$REGULAR_VERSION" == "2" ]]; then
-            PYDANTIC_DEP="<2"
-            TEST_WITH_VERSION="1"
-          else
-            echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test."
-            exit 1
-          fi
-
-          # Install via `pip` instead of `poetry add` to avoid changing lockfile,
-          # which would prevent caching from working: the cache would get saved
-          # to a different key than where it gets loaded from.
-          poetry run pip install "pydantic${PYDANTIC_DEP}"
-
-          # Ensure that the correct pydantic is installed now.
-          echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}"
-
-          # Determine the major part of pydantic version
-          CURRENT_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
-
-          # Check that the major part of pydantic version is as expected, if not
-          # raise an error
-          if [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then
-            echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}"
-            exit 1
-          fi
-          echo "Found pydantic version ${CURRENT_VERSION}, as expected"
-      - name: Run pydantic compatibility tests
-        # airbyte currently doesn't support pydantic v2
-        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
-        shell: bash
-        run: make test
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_integration_test.yml

@@ -67,7 +67,6 @@ jobs:
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}

.github/workflows/_release.yml

@@ -85,7 +85,7 @@ jobs:
           path: langchain
           sparse-checkout: | # this only grabs files for relevant dir
             ${{ inputs.working-directory }}
-          ref: ${{ github.ref }} # this scopes to just ref'd branch
+          ref: master # this scopes to just master branch
           fetch-depth: 0 # this fetches entire commit history
       - name: Check Tags
         id: check-tags
@@ -273,7 +273,6 @@ jobs:
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
@@ -336,8 +335,6 @@ jobs:
           packages-dir: ${{ inputs.working-directory }}/dist/
           verbose: true
           print-hash: true
-          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
-          attestations: false
 
   mark-release:
     needs:

.github/workflows/_test_release.yml

@@ -98,5 +98,3 @@ jobs:
           # This is *only for CI use* and is *extremely dangerous* otherwise!
           # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
           skip-existing: true
-          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
-          attestations: false

.github/workflows/check_diffs.yml

@@ -46,6 +46,7 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.lint) }}
+      fail-fast: false
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
@@ -59,6 +60,7 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.test) }}
+      fail-fast: false
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
@@ -71,6 +73,7 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.test-doc-imports) }}
+      fail-fast: false
     uses: ./.github/workflows/_test_doc_imports.yml
     secrets: inherit
     with:
@@ -83,25 +86,13 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.compile-integration-tests) }}
+      fail-fast: false
     uses: ./.github/workflows/_compile_integration_test.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
       python-version: ${{ matrix.job-configs.python-version }}
     secrets: inherit
 
-  dependencies:
-    name: cd ${{ matrix.job-configs.working-directory }}
-    needs: [ build ]
-    if: ${{ needs.build.outputs.dependencies != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.dependencies) }}
-    uses: ./.github/workflows/_dependencies.yml
-    with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
-    secrets: inherit
-
   extended-tests:
     name: "cd ${{ matrix.job-configs.working-directory }} / make extended_tests #${{ matrix.job-configs.python-version }}"
     needs: [ build ]
@@ -110,6 +101,7 @@ jobs:
       matrix:
         # note different variable for extended test dirs
         job-configs: ${{ fromJson(needs.build.outputs.extended-tests) }}
+      fail-fast: false
     runs-on: ubuntu-latest
     defaults:
       run:
@@ -149,7 +141,7 @@ jobs:
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
   ci_success:
     name: "CI Success"
-    needs: [build, lint, test, compile-integration-tests, dependencies, extended-tests, test-doc-imports]
+    needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/codespell.yml

@@ -3,8 +3,9 @@ name: CI / cd . / make spell_check
 
 on:
   push:
-    branches: [master, v0.1, v0.2]
+    branches: [master, v0.1]
   pull_request:
+    branches: [master, v0.1]
 
 permissions:
   contents: read

.github/workflows/scheduled_test.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         python-version:
-          - "3.8"
+          - "3.9"
           - "3.11"
         working-directory:
           - "libs/partners/openai"
@@ -90,7 +90,6 @@ jobs:
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}

README.md

@@ -49,7 +49,7 @@ For these applications, LangChain simplifies the entire application lifecycle:
 - **`langchain-community`**: Third party integrations.
   - Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
+- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
 
 ### Productionization:
 

docs/Makefile

@@ -82,7 +82,7 @@ vercel-build: install-vercel-deps build generate-references
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
 	rm -rf build
 	mkdir static/api_reference
-	git clone --depth=1 -b v0.2 https://github.com/baskaryan/langchain-api-docs-build.git
+	git clone --depth=1 https://github.com/baskaryan/langchain-api-docs-build.git
 	mv langchain-api-docs-build/api_reference_build/html/* static/api_reference/
 	rm -rf langchain-api-docs-build
 	NODE_OPTIONS="--max-old-space-size=5000" yarn run docusaurus build

docs/docs/how_to/agent_executor.ipynb

@@ -461,7 +461,7 @@
    "id": "f8014c9d",
    "metadata": {},
    "source": [
-    "Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
+    "Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
     "\n",
     "Note that we are passing in the `model`, not `model_with_tools`. That is because `create_tool_calling_agent` will call `.bind_tools` for us under the hood."
    ]

docs/docs/how_to/graph_constructing.ipynb

@@ -24,7 +24,7 @@
     "\n",
     "## Architecture\n",
     "\n",
-    "At a high-level, the steps of constructing a knowledge graph from text are:\n",
+    "At a high-level, the steps of constructing a knowledge are from text are:\n",
     "\n",
     "1. **Extracting structured information from text**: Model is used to extract structured graph information from text.\n",
     "2. **Storing into graph database**: Storing the extracted structured graph information into a graph database enables downstream RAG applications\n",

docs/docs/how_to/tools_human.ipynb

@@ -129,13 +129,13 @@
     "\n",
     "@tool\n",
     "def count_emails(last_n_days: int) -> int:\n",
-    "    \"\"\"Dummy function to count number of e-mails. Returns 2 * last_n_days.\"\"\"\n",
+    "    \"\"\"Multiply two integers together.\"\"\"\n",
     "    return last_n_days * 2\n",
     "\n",
     "\n",
     "@tool\n",
     "def send_email(message: str, recipient: str) -> str:\n",
-    "    \"\"\"Dummy function for sending an e-mail.\"\"\"\n",
+    "    \"Add two integers.\"\n",
     "    return f\"Successfully sent email to {recipient}.\"\n",
     "\n",
     "\n",

docs/docs/integrations/chat/ai21.ipynb

@@ -50,18 +50,18 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
    },
+   "outputs": [],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
     "\n",
     "os.environ[\"AI21_API_KEY\"] = getpass()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -73,14 +73,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
    "metadata": {},
+   "outputs": [],
    "source": [
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -115,15 +115,15 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
    "metadata": {},
+   "outputs": [],
    "source": [
     "from langchain_ai21 import ChatAI21\n",
     "\n",
     "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -135,8 +135,21 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer.\", id='run-2e8d16d6-a06e-45cb-8d0c-1c8208645033-0')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "messages = [\n",
     "    (\n",
@@ -147,9 +160,7 @@
     "]\n",
     "ai_msg = llm.invoke(messages)\n",
     "ai_msg"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -163,6 +174,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "id": "39353473fce5dd2e",
    "metadata": {
     "collapsed": false,
@@ -170,6 +182,18 @@
      "outputs_hidden": false
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren.', id='run-e1bd82dc-1a7e-4b2e-bde9-ac995929ac0f-0')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
@@ -191,95 +215,7 @@
     "        \"input\": \"I love programming.\",\n",
     "    }\n",
     ")"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "# Tool Calls / Function Calling",
-   "id": "39c0ccd229927eab"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "This example shows how to use tool calling with AI21 models:",
-   "id": "2bf6b40be07fe2d4"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_ai21.chat_models import ChatAI21\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
-    "from langchain_core.tools import tool\n",
-    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
-    "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(location: str, date: str) -> str:\n",
-    "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
-    "    if location == \"New York\" and date == \"2024-12-05\":\n",
-    "        return \"25 celsius\"\n",
-    "    elif location == \"New York\" and date == \"2024-12-06\":\n",
-    "        return \"27 celsius\"\n",
-    "    elif location == \"London\" and date == \"2024-12-05\":\n",
-    "        return \"22 celsius\"\n",
-    "    return \"32 celsius\"\n",
-    "\n",
-    "\n",
-    "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
-    "\n",
-    "chat_messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
-    "        \"to assist with various tasks and provide accurate information\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "human_messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
-    "    HumanMessage(content=\"OK, thank you.\"),\n",
-    "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
-    "]\n",
-    "\n",
-    "\n",
-    "for human_message in human_messages:\n",
-    "    print(f\"User: {human_message.content}\")\n",
-    "    chat_messages.append(human_message)\n",
-    "    response = llm_with_tools.invoke(chat_messages)\n",
-    "    chat_messages.append(response)\n",
-    "    if response.tool_calls:\n",
-    "        tool_call = response.tool_calls[0]\n",
-    "        if tool_call[\"name\"] == \"get_weather\":\n",
-    "            weather = get_weather.invoke(\n",
-    "                {\n",
-    "                    \"location\": tool_call[\"args\"][\"location\"],\n",
-    "                    \"date\": tool_call[\"args\"][\"date\"],\n",
-    "                }\n",
-    "            )\n",
-    "            chat_messages.append(\n",
-    "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
-    "            )\n",
-    "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
-    "            print(f\"Assistant: {llm_answer.content}\")\n",
-    "    else:\n",
-    "        print(f\"Assistant: {response.content}\")"
-   ],
-   "id": "a181a28df77120fb",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "::: {.callout-warning}\n",
     "\n",
-    "The Anthropic API officially supports tool-calling so this workaround is no longer needed. Please use [ChatAnthropic](/docs/integrations/chat/anthropic) with `langchain-anthropic>=0.1.15`.\n",
+    "The Anthropic API officially supports tool-calling so this workaround is no longer needed. Please use [ChatAnthropic](/docs/integrations/chat/anthropic) with `langchain-anthropic>=0.1.5`.\n",
     "\n",
     ":::\n",
     "\n",

docs/docs/integrations/document_loaders/scrapfly.ipynb

@@ -49,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ScrapflyLoader also allows passing ScrapeConfig object for customizing the scrape request. See the documentation for the full feature details and their API params: https://scrapfly.io/docs/scrape-api/getting-started"
+    "The ScrapflyLoader also allows passigng ScrapeConfig object for customizing the scrape request. See the documentation for the full feature details and their API params: https://scrapfly.io/docs/scrape-api/getting-started"
    ]
   },
   {

docs/docs/integrations/platforms/huggingface.mdx

@@ -12,7 +12,7 @@ pip install langchain-huggingface
 
 ## Chat models
 
-### ChatHuggingFace
+### Models from Hugging Face
 
 We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.
 
@@ -24,16 +24,7 @@ from langchain_huggingface import ChatHuggingFace
 
 ## LLMs
 
-### HuggingFaceEndpoint
-
-
-See a [usage example](/docs/integrations/llms/huggingface_endpoint).
-
-```python
-from langchain_huggingface import HuggingFaceEndpoint
-```
-
-### HuggingFacePipeline
+### Hugging Face Local Pipelines
 
 Hugging Face models can be run locally through the `HuggingFacePipeline` class.
 
@@ -53,22 +44,6 @@ See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
 from langchain_huggingface import HuggingFaceEmbeddings
 ```
 
-### HuggingFaceEndpointEmbeddings
-
-See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
-
-```python
-from langchain_huggingface import HuggingFaceEndpointEmbeddings
-```
-
-### HuggingFaceInferenceAPIEmbeddings
-
-See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
-
-```python
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-```
-
 ### HuggingFaceInstructEmbeddings
 
 See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
@@ -88,6 +63,25 @@ See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 ```
 
+### Hugging Face Text Embeddings Inference (TEI)
+
+>[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source
+> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,
+>including `FlagEmbedding`, `Ember`, `GTE` and `E5`.
+
+We need to install `huggingface-hub` python package.
+
+```bash
+pip install huggingface-hub
+```
+
+See a [usage example](/docs/integrations/text_embedding/text_embeddings_inference).
+
+```python
+from langchain_community.embeddings import HuggingFaceHubEmbeddings
+```
+
+
 ## Document Loaders
 
 ### Hugging Face dataset
@@ -110,34 +104,7 @@ See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
 from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
 ```
 
-### Hugging Face model loader
 
->Load model information from `Hugging Face Hub`, including README content.
->
->This loader interfaces with the `Hugging Face Models API` to fetch 
-> and load model metadata and README files. 
-> The API allows you to search and filter models based on 
-> specific criteria such as model tags, authors, and more.
-
-```python
-from langchain_community.document_loaders import HuggingFaceModelLoader
-```
-
-### Image captions
-
-It uses the Hugging Face models to generate image captions.
-
-We need to install several python packages.
-
-```bash
-pip install transformers pillow
-```
-
-See a [usage example](/docs/integrations/document_loaders/image_captions).
-
-```python
-from langchain_community.document_loaders import ImageCaptionLoader
-```
 
 ## Tools
 
@@ -157,12 +124,3 @@ See a [usage example](/docs/integrations/tools/huggingface_tools).
 ```python
 from langchain_community.agent_toolkits.load_tools import load_huggingface_tool
 ```
-
-### Hugging Face Text-to-Speech Model Inference.
-
-> It is a wrapper around `OpenAI Text-to-Speech API`.
-
-```python
-from langchain_community.tools.audio import HuggingFaceTextToSpeechModelInference
-```
-

docs/docs/integrations/platforms/microsoft.mdx

@@ -436,8 +436,6 @@ See a [usage example](/docs/integrations/tools/azure_ai_services).
 from langchain_community.agent_toolkits import azure_ai_services
 ```
 
-#### Azure AI Services individual tools
-
 The `azure_ai_services` toolkit includes the following tools:
 
 - Image Analysis: [AzureAiServicesImageAnalysisTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.image_analysis.AzureAiServicesImageAnalysisTool.html)
@@ -462,23 +460,6 @@ See a [usage example](/docs/integrations/tools/office365).
 from langchain_community.agent_toolkits import O365Toolkit
 ```
 
-#### Office 365 individual tools
-
-You can use individual tools from the Office 365 Toolkit:
-- `O365CreateDraftMessage`: tool for creating a draft email in Office 365
-- `O365SearchEmails`: tool for searching email messages in Office 365
-- `O365SearchEvents`: tool for searching calendar events in Office 365
-- `O365SendEvent`: tool for sending calendar events in Office 365
-- `O365SendMessage`: tool for sending an email in Office 365
-
-```python
-from langchain_community.tools.office365 import O365CreateDraftMessage
-from langchain_community.tools.office365 import O365SearchEmails
-from langchain_community.tools.office365 import O365SearchEvents
-from langchain_community.tools.office365 import O365SendEvent
-from langchain_community.tools.office365 import O365SendMessage
-```
-
 ### Microsoft Azure PowerBI
 
 We need to install `azure-identity` python package.
@@ -494,20 +475,6 @@ from langchain_community.agent_toolkits import PowerBIToolkit
 from langchain_community.utilities.powerbi import PowerBIDataset
 ```
 
-#### PowerBI individual tools
-
-You can use individual tools from the Azure PowerBI Toolkit:
-- `InfoPowerBITool`: tool for getting metadata about a PowerBI Dataset
-- `ListPowerBITool`: tool for getting tables names
-- `QueryPowerBITool`: tool for querying a PowerBI Dataset
-
-```python
-from langchain_community.tools.powerbi.tool import InfoPowerBITool
-from langchain_community.tools.powerbi.tool import ListPowerBITool
-from langchain_community.tools.powerbi.tool import QueryPowerBITool
-```
-
-
 ### PlayWright Browser Toolkit
 
 >[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool 

docs/docs/integrations/providers/apache.mdx

@@ -1,63 +0,0 @@
-# Apache Software Foundation
-
->[The Apache Software Foundation (Wikipedia)](https://en.wikipedia.org/wiki/The_Apache_Software_Foundation) 
-> is a decentralized open source community of developers. The software they 
-> produce is distributed under the terms of the Apache License, a permissive 
-> open-source license for free and open-source software (FOSS). The Apache projects 
-> are characterized by a collaborative, consensus-based development process 
-> and an open and pragmatic software license, which is to say that it 
-> allows developers, who receive the software freely, to redistribute 
-> it under non-free terms. Each project is managed by a self-selected 
-> team of technical experts who are active contributors to the project.
-
-## Apache AGE
-
->[Apache AGE](https://age.apache.org/) is a `PostgreSQL` extension that provides 
-> graph database functionality. `AGE` is an acronym for `A Graph Extension`, and 
-> is inspired by Bitnine’s fork of `PostgreSQL 10`, `AgensGraph`, which is 
-> a multimodal database. The goal of the project is to create single 
-> storage that can handle both relational and graph model data so that users 
-> can use standard ANSI SQL along with `openCypher`, the Graph query language. 
-> The data elements `Apache AGE` stores are nodes, edges connecting them, and 
-> attributes of nodes and edges.
- 
-See more about [integrating with Apache AGE](/docs/integrations/graphs/apache_age).
-
-## Apache Cassandra
-
->[Apache Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, 
-> highly scalable and highly available database. Starting with version 5.0, 
-> the database ships with vector search capabilities.
- 
-See more about [integrating with Apache Cassandra](/docs/integrations/providers/cassandra/).
-
-## Apache Doris
-
->[Apache Doris](https://doris.apache.org/) is a modern data warehouse for 
-> real-time analytics. It delivers lightning-fast analytics on real-time data at scale.
->
->Usually `Apache Doris` is categorized into OLAP, and it has showed excellent 
-> performance in ClickBench — a Benchmark For Analytical DBMS. Since it has 
-> a super-fast vectorized execution engine, it could also be used as a fast vectordb.
- 
-See more about [integrating with Apache Doris](/docs/integrations/providers/apache_doris/).
-
-## Apache Kafka
-
->[Apache Kafka](https://github.com/apache/kafka) is a distributed messaging system 
-> that is used to publish and subscribe to streams of records.
- 
-See more about [integrating with Apache Kafka](/docs/integrations/memory/kafka_chat_message_history).
-
-
-## Apache Spark
-
->[Apache Spark](https://spark.apache.org/) is a unified analytics engine for 
-> large-scale data processing. It provides high-level APIs in Scala, Java, 
-> Python, and R, and an optimized engine that supports general computation 
-> graphs for data analysis. It also supports a rich set of higher-level 
-> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` 
-> for pandas workloads, `MLlib` for machine learning, 
-> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
-
-See more about [integrating with Apache Spark](/docs/integrations/providers/spark).

docs/docs/integrations/providers/apple.mdx

@@ -1,22 +0,0 @@
-# Apple
-
->[Apple Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Apple_Inc.) is an American 
-> multinational corporation and technology company.
->
-> [iMessage (Wikipedia)](https://en.wikipedia.org/wiki/IMessage) is an instant 
-> messaging service developed by Apple Inc. and launched in 2011. 
-> `iMessage` functions exclusively on Apple platforms.
-
-## Installation and Setup
-
-See [setup instructions](/docs/integrations/chat_loaders/imessage).
-
-## Chat loader
-
-It loads chat sessions from the `iMessage` `chat.db` `SQLite` file.
-
-See a [usage example](/docs/integrations/chat_loaders/imessage).
-
-```python
-from langchain_community.chat_loaders.imessage import IMessageChatLoader
-```

docs/docs/integrations/providers/nomic.ipynb

@@ -0,0 +1,69 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nomic\n",
+    "\n",
+    "Nomic currently offers two products:\n",
+    "\n",
+    "- Atlas: their Visual Data Engine\n",
+    "- GPT4All: their Open Source Edge Language Model Ecosystem\n",
+    "\n",
+    "The Nomic integration exists in its own [partner package](https://pypi.org/project/langchain-nomic/). You can install it with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-nomic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Currently, you can import their hosted [embedding model](/docs/integrations/text_embedding/nomic) as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_nomic import NomicEmbeddings"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/nomic.mdx

@@ -1,58 +0,0 @@
-# Nomic
-
->[Nomic](https://www.nomic.ai/) builds tools that enable everyone to interact with AI scale datasets and run AI models on consumer computers.
->
->`Nomic` currently offers two products:
->
->- `Atlas`: the Visual Data Engine
->- `GPT4All`: the Open Source Edge Language Model Ecosystem
-
-The Nomic integration exists in two partner packages: [langchain-nomic](https://pypi.org/project/langchain-nomic/)
-and in [langchain-community](https://pypi.org/project/langchain-community/). 
-
-## Installation
-
-You can install them with:
-
-```bash
-pip install -U langchain-nomic
-pip install -U langchain-community
-```
-
-## LLMs
-
-### GPT4All
-
-See [a usage example](/docs/integrations/llms/gpt4all).
-
-```python
-from langchain_community.llms import GPT4All
-```
-
-## Embedding models
-
-### NomicEmbeddings
-
-See [a usage example](/docs/integrations/text_embedding/nomic).
-
-```python
-from langchain_nomic import NomicEmbeddings
-```
-
-### GPT4All
-
-See [a usage example](/docs/integrations/text_embedding/gpt4all).
-
-```python
-from langchain_community.embeddings import GPT4AllEmbeddings
-```
-
-## Vector store
-
-### Atlas
-
-See [a usage example and installation instructions](/docs/integrations/vectorstores/atlas).
-
-```python
-from langchain_community.vectorstores import AtlasDB
-```

docs/docs/integrations/providers/spark.mdx

@@ -1,49 +0,0 @@
-# Spark
-
->[Apache Spark](https://spark.apache.org/) is a unified analytics engine for 
-> large-scale data processing. It provides high-level APIs in Scala, Java, 
-> Python, and R, and an optimized engine that supports general computation 
-> graphs for data analysis. It also supports a rich set of higher-level 
-> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` 
-> for pandas workloads, `MLlib` for machine learning, 
-> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
-
-## Document loaders
-
-### PySpark
-
-It loads data from a `PySpark` DataFrame.
-
-See a [usage example](/docs/integrations/document_loaders/pyspark_dataframe).
-
-```python
-from langchain_community.document_loaders import PySparkDataFrameLoader
-```
-
-## Tools/Toolkits
-
-### Spark SQL toolkit
-
-Toolkit for interacting with `Spark SQL`.
-
-See a [usage example](/docs/integrations/tools/spark_sql).
-
-```python
-from langchain_community.agent_toolkits import SparkSQLToolkit, create_spark_sql_agent
-from langchain_community.utilities.spark_sql import SparkSQL
-```
-
-#### Spark SQL individual tools
-
-You can use individual tools from the Spark SQL Toolkit:
-- `InfoSparkSQLTool`: tool for getting metadata about a Spark SQL
-- `ListSparkSQLTool`: tool for getting tables names
-- `QueryCheckerTool`: tool uses an LLM to check if a query is correct
-- `QuerySparkSQLTool`: tool for querying a Spark SQL
-
-```python
-from langchain_community.tools.spark_sql.tool import InfoSparkSQLTool
-from langchain_community.tools.spark_sql.tool import ListSparkSQLTool
-from langchain_community.tools.spark_sql.tool import QueryCheckerTool
-from langchain_community.tools.spark_sql.tool import QuerySparkSQLTool
-```

docs/docs/integrations/providers/sparkllm.mdx

@@ -4,26 +4,11 @@
 It has cross-domain knowledge and language understanding ability by learning a large amount of texts, codes and images.
 It can understand and perform tasks based on natural dialogue.
 
-## Chat models
+## SparkLLM LLM Model
+An example is available at [example](/docs/integrations/llms/sparkllm).
 
-See a [usage example](/docs/integrations/chat/sparkllm).
+## SparkLLM Chat Model
+An example is available at [example](/docs/integrations/chat/sparkllm).
 
-```python
-from langchain_community.chat_models import ChatSparkLLM
-```
-
-## LLMs
-
-See a [usage example](/docs/integrations/llms/sparkllm).
-
-```python
-from langchain_community.llms import SparkLLM
-```
-
-## Embedding models
-
-See a [usage example](/docs/integrations/text_embedding/sparkllm)
-
-```python
-from langchain_community.embeddings import SparkLLMTextEmbeddings
-```
+## SparkLLM Text Embedding Model
+An example is available at [example](/docs/integrations/text_embedding/sparkllm)

docs/docs/integrations/providers/transwarp.mdx

@@ -1,34 +0,0 @@
-# Transwarp
-
->[Transwarp](https://www.transwarp.cn/en/introduction) aims to build 
-> enterprise-level big data and AI infrastructure software, 
-> to shape the future of data world. It provides enterprises with 
-> infrastructure software and services around the whole data lifecycle, 
-> including integration, storage, governance, modeling, analysis, 
-> mining and circulation. 
-> 
-> `Transwarp` focuses on technology research and 
-> development and has accumulated core technologies in these aspects: 
-> distributed computing, SQL compilations, database technology, 
-> unification for multi-model data management, container-based cloud computing, 
-> and big data analytics and intelligence.
-
-## Installation
-
-You have to install several python packages:
-
-```bash
-pip install -U tiktoken hippo-api
-```
-
-and get the connection configuration.
-
-## Vector stores
-
-### Hippo
-
-See [a usage example and installation instructions](/docs/integrations/vectorstores/hippo).
-
-```python
-from langchain_community.vectorstores.hippo import Hippo
-```

docs/docs/integrations/providers/upstage.ipynb

@@ -6,18 +6,45 @@
    "source": [
     "# Upstage\n",
     "\n",
-    ">[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components.\n",
-    ">\n",
-    ">**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
-    "\n",
-    ">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. \n"
+    "[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components. \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Upstage LangChain integrations\n",
+    "## Solar LLM\n",
+    "\n",
+    "**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
+    "\n",
+    "Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup\n",
+    "\n",
+    "Install `langchain-upstage` package:\n",
+    "\n",
+    "```bash\n",
+    "pip install -qU langchain-core langchain-upstage\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upstage LangChain integrations\n",
     "\n",
     "| API | Description | Import | Example usage |\n",
     "| --- | --- | --- | --- |\n",
@@ -33,20 +60,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Installation and Setup\n",
+    "## Quick Examples\n",
     "\n",
-    "Install `langchain-upstage` package:\n",
-    "\n",
-    "```bash\n",
-    "pip install -qU langchain-core langchain-upstage\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
+    "### Environment Setup"
    ]
   },
   {
@@ -64,11 +80,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Chat models\n",
     "\n",
-    "### Solar LLM\n",
-    "\n",
-    "See [a usage example](/docs/integrations/chat/upstage)."
+    "### Chat\n"
    ]
   },
   {
@@ -88,9 +101,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Embedding models\n",
     "\n",
-    "See [a usage example](/docs/integrations/text_embedding/upstage)."
+    "\n",
+    "### Text embedding\n",
+    "\n"
    ]
   },
   {
@@ -120,45 +134,7 @@
     }
    },
    "source": [
-    "## Document loader\n",
-    "\n",
-    "### Layout Analysis\n",
-    "\n",
-    "See [a usage example](/docs/integrations/document_loaders/upstage)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
-    "\n",
-    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
-    "\n",
-    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
-    "docs = layzer.load()  # or layzer.lazy_load()\n",
-    "\n",
-    "for doc in docs[:3]:\n",
-    "    print(doc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "## Tools\n",
-    "\n",
-    "### Groundedness Check\n",
-    "\n",
-    "See [a usage example](/docs/integrations/tools/upstage_groundedness_check)."
+    "### Groundedness Check"
    ]
   },
   {
@@ -183,6 +159,36 @@
     "response = groundedness_check.invoke(request_input)\n",
     "print(response)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### Layout Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
+    "\n",
+    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
+    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
+    "\n",
+    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
+    "docs = layzer.load()  # or layzer.lazy_load()\n",
+    "\n",
+    "for doc in docs[:3]:\n",
+    "    print(doc)"
+   ]
   }
  ],
  "metadata": {
@@ -204,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb

@@ -325,13 +325,7 @@
     "id": "20cf6074081b"
    },
    "source": [
-    "### Searching Documents with Metadata Filters\n",
-    "The vectorstore supports two methods for applying filters to metadata fields when performing document searches:\n",
-    "\n",
-    "-   Dictionary-based Filters\n",
-    "    -   You can pass a dictionary (dict) where the keys represent metadata fields and the values specify the filter condition. This method applies an equality filter between the key and the corresponding value. When multiple key-value pairs are provided, they are combined using a logical AND operation.\n",
-    "-   SQL-based Filters\n",
-    "    -   Alternatively, you can provide a string representing an SQL WHERE clause to define more complex filtering conditions. This allows for greater flexibility, supporting SQL expressions such as comparison operators and logical operators."
+    "### Search for documents with metadata filter"
    ]
   },
   {
@@ -342,24 +336,11 @@
    },
    "outputs": [],
    "source": [
-    "# Dictionary-based Filters\n",
     "# This should only return \"Banana\" document.\n",
     "docs = store.similarity_search_by_vector(query_vector, filter={\"len\": 6})\n",
     "print(docs)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SQL-based Filters\n",
-    "# This should return \"Banana\", \"Apples and oranges\" and \"Cars and airplanes\" documents.\n",
-    "docs = store.similarity_search_by_vector(query_vector, filter={\"len = 6 AND len > 17\"})\n",
-    "print(docs)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {

docs/docs/introduction.mdx

@@ -55,7 +55,7 @@ These are the best ones to get started with:
 - [Build an Agent](/docs/tutorials/agents)
 - [Introduction to LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/)
 
-Explore the full list of LangChain tutorials [here](/docs/tutorials), and check out other [LangGraph tutorials here](https://langchain-ai.github.io/langgraph/tutorials/). To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
+Explore the full list of LangChain tutorials [here](/docs/tutorials), and check out other [LangGraph tutorials here](https://langchain-ai.github.io/langgraph/tutorials/).
 
 
 ## [How-to guides](/docs/how_to)

docs/docs/tutorials/chatbot.ipynb

@@ -105,7 +105,7 @@
     "\n",
     "## Quickstart\n",
     "\n",
-    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably - select the one you want to use below!\n",
+    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -254,7 +254,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install langchain_community"
+    "# ! pip install langchain_community"
    ]
   },
   {
@@ -952,7 +952,7 @@
    "source": [
     "## Streaming\n",
     "\n",
-    "Now we've got a functioning chatbot. However, one *really* important UX consideration for chatbot applications is streaming. LLMs can sometimes take a while to respond, and so in order to improve the user experience one thing that most applications do is stream back each token as it is generated. This allows the user to see progress.\n",
+    "Now we've got a function chatbot. However, one *really* important UX consideration for chatbot application is streaming. LLMs can sometimes take a while to respond, and so in order to improve the user experience one thing that most application do is stream back each token as it is generated. This allows the user to see progress.\n",
     "\n",
     "It's actually super easy to do this!\n",
     "\n",

docs/docs/tutorials/llm_chain.ipynb

@@ -95,7 +95,7 @@
    "source": [
     "## Using Language Models\n",
     "\n",
-    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably - select the one you want to use below!\n",
+    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -159,7 +159,9 @@
    "cell_type": "markdown",
    "id": "f83373db",
    "metadata": {},
-   "source": "If we've enabled LangSmith, we can see that this run is logged to LangSmith, and can see the [LangSmith trace](https://smith.langchain.com/public/88baa0b2-7c1a-4d09-ba30-a47985dde2ea/r)"
+   "source": [
+    "If we've enable LangSmith, we can see that this run is logged to LangSmith, and can see the [LangSmith trace](https://smith.langchain.com/public/88baa0b2-7c1a-4d09-ba30-a47985dde2ea/r)"
+   ]
   },
   {
    "cell_type": "markdown",

docs/docusaurus.config.js

@@ -125,9 +125,8 @@ const config = {
     /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
     ({
       announcementBar: {
-        content:
-          'A newer LangChain version is out! Check out the <a href="https://python.langchain.com/docs/introduction">latest version</a>.',
-        isCloseable: false,
+        content: 'LangChain 0.2 is out! Leave feedback on the v0.2 docs <a href="https://github.com/langchain-ai/langchain/discussions/21716">here</a>. You can view the v0.1 docs <a href="/v0.1/docs/get_started/introduction/">here</a>.',
+        isCloseable: true,
       },
       docs: {
         sidebar: {
@@ -220,10 +219,6 @@ const config = {
             label: "v0.2",
             position: "right",
             items: [
-              {
-                label: "Latest",
-                href: "https://python.langchain.com/docs/introduction/"
-              },
               {
                 label: "v0.2",
                 href: "/docs/introduction"

docs/ignore-step.sh

@@ -4,15 +4,17 @@ echo "VERCEL_ENV: $VERCEL_ENV"
 echo "VERCEL_GIT_COMMIT_REF: $VERCEL_GIT_COMMIT_REF"
 
 
-if [ "$VERCEL_ENV" == "production" ] || \
-    [ "$VERCEL_GIT_COMMIT_REF" == "master" ] || \
-    [ "$VERCEL_GIT_COMMIT_REF" == "v0.1" ] || \
-    [ "$VERCEL_GIT_COMMIT_REF" == "v0.2" ]
-then 
-     echo "✅ Production build - proceeding with build"
-     exit 1
+if [ "$VERCEL_ENV" == "production" ] || [ "$VERCEL_GIT_COMMIT_REF" == "master" ] || [ "$VERCEL_GIT_COMMIT_REF" == "v0.1" ]; then 
+    echo "✅ Production build - proceeding with build"
+    exit 1; 
 fi 
 
+# TODO: remove this for v0.3 launch
+# exit 0 if git commit ref is v0.3rc
+echo "🛑 v0.3rc build is ignored currently"
+exit 0; 
+# end TODO
+
 
 echo "Checking for changes in docs/"
 echo "---"

docs/src/css/custom.css

@@ -248,26 +248,16 @@ nav, h1, h2, h3, h4 {
     no-repeat;
 }
 
-div[class^='announcementBar_'] {
-  font-size: 20px;
-
-  /*
-  --site-announcement-bar-stripe-color1: hsl(
-    var(--site-primary-hue-saturation) 85%
-  );
-  --site-announcement-bar-stripe-color2: hsl(
-    var(--site-primary-hue-saturation) 95%
-  );
-   */
-  --site-announcement-bar-stripe-color1: rgb(197,186,254);
-  --site-announcement-bar-stripe-color2: rgb(255,246,224);
-
-  background: repeating-linear-gradient(
-    -35deg,
-    var(--site-announcement-bar-stripe-color1),
-    var(--site-announcement-bar-stripe-color1) 20px,
-    var(--site-announcement-bar-stripe-color2) 10px,
-    var(--site-announcement-bar-stripe-color2) 40px
-  );
-  font-weight: bold;
+div[class^=announcementBar_] {
+  height:40px !important;
+  font-size: 20px !important;
+}
+
+[data-theme='dark'] div[class^=announcementBar_] {
+  background-color: #1b1b1b;
+  color: #fff;
+}
+
+[data-theme='dark'] div[class^=announcementBar_] button {
+  color: #fff;
 }

docs/src/theme/DocVersionBanner/index.js

@@ -3,10 +3,18 @@
 
 import React from 'react';
 import clsx from 'clsx';
+import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
 import Link from '@docusaurus/Link';
 import Translate from '@docusaurus/Translate';
+import {
+  useActivePlugin,
+  useDocVersionSuggestions,
+} from '@docusaurus/plugin-content-docs/client';
 import {ThemeClassNames} from '@docusaurus/theme-common';
-import { useLocalPathname } from '@docusaurus/theme-common/internal';
+import {
+  useDocsPreferredVersion,
+  useDocsVersion,
+} from '@docusaurus/theme-common/internal';
 function UnreleasedVersionLabel({siteTitle, versionMetadata}) {
   return (
     <Translate
@@ -71,40 +79,123 @@ function LatestVersionSuggestionLabel({versionLabel, to, onClick}) {
     </Translate>
   );
 }
+function DocVersionBannerEnabled({className, versionMetadata}) {
+  const {
+    siteConfig: {title: siteTitle},
+  } = useDocusaurusContext();
+  const {pluginId} = useActivePlugin({failfast: true});
+  const getVersionMainDoc = (version) =>
+    version.docs.find((doc) => doc.id === version.mainDocId);
+  const {savePreferredVersionName} = useDocsPreferredVersion(pluginId);
+  const {latestDocSuggestion, latestVersionSuggestion} =
+    useDocVersionSuggestions(pluginId);
+  // Try to link to same doc in latest version (not always possible), falling
+  // back to main doc of latest version
+  const latestVersionSuggestedDoc =
+    latestDocSuggestion ?? getVersionMainDoc(latestVersionSuggestion);
+  return (
+    <div
+      className={clsx(
+        className,
+        ThemeClassNames.docs.docVersionBanner,
+        'alert alert--warning margin-bottom--md',
+      )}
+      role="alert">
+      <div>
+        <BannerLabel siteTitle={siteTitle} versionMetadata={versionMetadata} />
+      </div>
+      <div className="margin-top--md">
+        <LatestVersionSuggestionLabel
+          versionLabel={latestVersionSuggestion.label}
+          to={latestVersionSuggestedDoc.path}
+          onClick={() => savePreferredVersionName(latestVersionSuggestion.name)}
+        />
+      </div>
+    </div>
+  );
+}
+
+function LatestDocVersionBanner({className, versionMetadata}) {
+  const {
+    siteConfig: {title: siteTitle},
+  } = useDocusaurusContext();
+  const {pluginId} = useActivePlugin({failfast: true});
+  const getVersionMainDoc = (version) =>
+    version.docs.find((doc) => doc.id === version.mainDocId);
+  const {savePreferredVersionName} = useDocsPreferredVersion(pluginId);
+  const {latestDocSuggestion, latestVersionSuggestion} =
+    useDocVersionSuggestions(pluginId);
+  // Try to link to same doc in latest version (not always possible), falling
+  // back to main doc of latest version
+  const latestVersionSuggestedDoc =
+    latestDocSuggestion ?? getVersionMainDoc(latestVersionSuggestion);
+  const canaryPath = `/docs/0.2.x/${latestVersionSuggestedDoc.path.slice("/docs/".length)}`;
+  return (
+    <div
+      className={clsx(
+        className,
+        ThemeClassNames.docs.docVersionBanner,
+        'alert alert--info margin-bottom--md',
+      )}
+      role="alert">
+      <div>
+        <Translate
+          id="theme.docs.versions.unmaintainedVersionLabel"
+          description="The label used to encourage the user to view the experimental 0.2.x version"
+          values={{
+            siteTitle,
+            versionLabel: <b>{versionMetadata.label}</b>,
+          }}>
+          {
+            'This is a stable version of documentation for {siteTitle}\'s version {versionLabel}.'
+          }
+        </Translate>
+      </div>
+      <div className="margin-top--md">
+        <Translate
+          id="theme.docs.versions.latestVersionSuggestionLabel"
+          description="The label used to tell the user to check the experimental version"
+          values={{
+            versionLabel: <b>{versionMetadata.label}</b>,
+            latestVersionLink: (
+              <b>
+                <Link to={canaryPath} onClick={() => savePreferredVersionName("0.2.x")}>
+                  <Translate
+                    id="theme.docs.versions.latestVersionLinkLabel"
+                    description="The label used for the latest version suggestion link label">
+                    this experimental version
+                  </Translate>
+                </Link>
+              </b>
+            ),
+          }}>
+          {
+            'You can also check out {latestVersionLink} for an updated experience.'
+          }
+        </Translate>
+      </div>
+    </div>
+  );
+}
 
 export default function DocVersionBanner({className}) {
-  const versionMetadata = {
-    badge: false,
-    banner: 'unmaintained',
-    isLast: false,
-    label: 'v0.2',
-    noIndex: false,
-    pluginId: 'default',
-    version: 'Latest',
-  }
-  console.log({versionMetadata});
-  const localPathname = useLocalPathname();
+  const versionMetadata = useDocsVersion();
   if (versionMetadata.banner) {
     return (
-      <div
-        className={clsx(
-          className,
-          ThemeClassNames.docs.docVersionBanner,
-          'alert alert--warning margin-bottom--md',
-        )}
-        role="alert">
-        <div>
-          <BannerLabel siteTitle={"LangChain"} versionMetadata={versionMetadata} />
-        </div>
-        <div className="margin-top--md">
-          <LatestVersionSuggestionLabel
-            versionLabel={"Latest"}
-            to={`https://python.langchain.com${localPathname}`}
-            onClick={() => {}}
-          />
-        </div>
-      </div>
+      <DocVersionBannerEnabled
+        className={className}
+        versionMetadata={versionMetadata}
+      />
     );
+  } else if (versionMetadata.isLast) {
+    // Uncomment when we are ready to direct people to new build
+    // return (
+    //   <LatestDocVersionBanner
+    //     className={className}
+    //     versionMetadata={versionMetadata}
+    //   />
+    // );
+    return null;
   }
   return null;
 }

docs/src/theme/SiteMetadata/index.js

@@ -1,120 +0,0 @@
-import React from 'react';
-import Head from '@docusaurus/Head';
-import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
-import useBaseUrl from '@docusaurus/useBaseUrl';
-import {PageMetadata, useThemeConfig} from '@docusaurus/theme-common';
-import {
-  DEFAULT_SEARCH_TAG,
-  useAlternatePageUtils,
-  keyboardFocusedClassName,
-} from '@docusaurus/theme-common/internal';
-import {useLocation} from '@docusaurus/router';
-import {applyTrailingSlash} from '@docusaurus/utils-common';
-import SearchMetadata from '@theme/SearchMetadata';
-// TODO move to SiteMetadataDefaults or theme-common ?
-// Useful for i18n/SEO
-// See https://developers.google.com/search/docs/advanced/crawling/localized-versions
-// See https://github.com/facebook/docusaurus/issues/3317
-function AlternateLangHeaders() {
-  const {
-    i18n: {defaultLocale, localeConfigs},
-  } = useDocusaurusContext();
-  const alternatePageUtils = useAlternatePageUtils();
-  // Note: it is fine to use both "x-default" and "en" to target the same url
-  // See https://www.searchviu.com/en/multiple-hreflang-tags-one-url/
-  return (
-    <Head>
-      {Object.entries(localeConfigs).map(([locale, {htmlLang}]) => (
-        <link
-          key={locale}
-          rel="alternate"
-          href={alternatePageUtils.createUrl({
-            locale,
-            fullyQualified: true,
-          })}
-          hrefLang={htmlLang}
-        />
-      ))}
-      <link
-        rel="alternate"
-        href={alternatePageUtils.createUrl({
-          locale: defaultLocale,
-          fullyQualified: true,
-        })}
-        hrefLang="x-default"
-      />
-    </Head>
-  );
-}
-// Default canonical url inferred from current page location pathname
-function useDefaultCanonicalUrl() {
-  const {
-    siteConfig: {url: siteUrl, baseUrl, trailingSlash},
-  } = useDocusaurusContext();
-  // TODO using useLocation().pathname is not a super idea
-  // See https://github.com/facebook/docusaurus/issues/9170
-  const {pathname} = useLocation();
-  const baseUrlPathname = useBaseUrl(pathname);
-  const canonicalPathname = applyTrailingSlash(baseUrlPathname, {
-    trailingSlash,
-    baseUrl,
-  });
-  const canonicalPathnameNoVersion = canonicalPathname.startsWith('/v0.') ? "/"+canonicalPathname.split('/').slice(2).join('/') : canonicalPathname;
-  return siteUrl + canonicalPathnameNoVersion;
-}
-
-// TODO move to SiteMetadataDefaults or theme-common ?
-function CanonicalUrlHeaders({permalink}) {
-  const {
-    siteConfig: {url: siteUrl},
-  } = useDocusaurusContext();
-  const defaultCanonicalUrl = useDefaultCanonicalUrl();
-  const canonicalUrl = permalink
-    ? `${siteUrl}${permalink}`
-    : defaultCanonicalUrl;
-  return (
-    <Head>
-      <meta property="og:url" content={canonicalUrl} />
-      <link rel="canonical" href={canonicalUrl} />
-    </Head>
-  );
-}
-export default function SiteMetadata() {
-  const {
-    i18n: {currentLocale},
-  } = useDocusaurusContext();
-  // TODO maybe move these 2 themeConfig to siteConfig?
-  // These seems useful for other themes as well
-  const {metadata, image: defaultImage} = useThemeConfig();
-  return (
-    <>
-      <Head>
-        <meta name="twitter:card" content="summary_large_image" />
-        {/* The keyboard focus class name need to be applied when SSR so links
-        are outlined when JS is disabled */}
-        <body className={keyboardFocusedClassName} />
-      </Head>
-
-      {defaultImage && <PageMetadata image={defaultImage} />}
-
-      <CanonicalUrlHeaders />
-
-      <AlternateLangHeaders />
-
-      <SearchMetadata tag={DEFAULT_SEARCH_TAG} locale={currentLocale} />
-
-      {/*
-          It's important to have an additional <Head> element here, as it allows
-          react-helmet to override default metadata values set in previous <Head>
-          like "twitter:card". In same Head, the same meta would appear twice
-          instead of overriding.
-        */}
-      <Head>
-        {/* Yes, "metadatum" is the grammatically correct term */}
-        {metadata.map((metadatum, i) => (
-          <meta key={i} {...metadatum} />
-        ))}
-      </Head>
-    </>
-  );
-}

libs/cli/langchain_cli/integration_template/pyproject.toml

@@ -12,8 +12,8 @@ license = "MIT"
 "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22__package_name_short__%3D%3D0%22&expanded=true"
 
 [tool.poetry.dependencies]
-python = ">=3.8.1,<4.0"
-langchain-core = "^0.2.0"
+python = ">=3.9,<4.0"
+langchain-core = "^0.3.0.dev"
 
 [tool.poetry.group.test]
 optional = true

libs/cli/langchain_cli/package_template/pyproject.toml

@@ -6,8 +6,8 @@ authors = []
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.8.1,<4.0"
-langchain-core = ">=0.1.5,<0.3"
+python = ">=3.9,<4.0"
+langchain-core = "^0.3.0.dev"
 langchain-openai = ">=0.0.1"
 
 

libs/cli/poetry.lock

@@ -11,9 +11,6 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
-
 [[package]]
 name = "anyio"
 version = "4.4.0"
@@ -57,13 +54,13 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "certifi"
-version = "2024.7.4"
+version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
-    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
 ]
 
 [[package]]
@@ -216,13 +213,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.112.1"
+version = "0.114.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.112.1-py3-none-any.whl", hash = "sha256:bcbd45817fc2a1cd5da09af66815b84ec0d3d634eb173d1ab468ae3103e183e4"},
-    {file = "fastapi-0.112.1.tar.gz", hash = "sha256:b2537146f8c23389a7faa8b03d0bd38d4986e6983874557d95eed2acc46448ef"},
+    {file = "fastapi-0.114.0-py3-none-any.whl", hash = "sha256:fee75aa1b1d3d73f79851c432497e4394e413e1dece6234f68d3ce250d12760a"},
+    {file = "fastapi-0.114.0.tar.gz", hash = "sha256:9908f2a5cc733004de6ca5e1412698f35085cefcbfd41d539245b9edf87b73c1"},
 ]
 
 [package.dependencies]
@@ -231,8 +228,8 @@ starlette = ">=0.37.2,<0.39.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
-all = ["email_validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
-standard = ["email_validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
+all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
 
 [[package]]
 name = "gitdb"
@@ -300,13 +297,13 @@ trio = ["trio (>=0.22.0,<0.26.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.27.0"
+version = "0.27.2"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"},
 ]
 
 [package.dependencies]
@@ -321,40 +318,19 @@ brotli = ["brotli", "brotlicffi"]
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.8"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
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-name = "importlib-resources"
-version = "6.4.4"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
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-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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-test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
-type = ["pytest-mypy"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -404,9 +380,7 @@ files = [
 
 [package.dependencies]
 attrs = ">=22.2.0"
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
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 referencing = ">=0.28.4"
 rpds-py = ">=0.7.1"
 
@@ -426,18 +400,17 @@ files = [
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.34"
+version = "0.2.38"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -454,13 +427,13 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langserve"
-version = "0.2.2"
+version = "0.2.3"
 description = ""
 optional = false
 python-versions = "<4.0.0,>=3.8.1"
 files = [
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 [package.dependencies]
@@ -479,13 +452,13 @@ server = ["fastapi (>=0.90.1,<1)", "sse-starlette (>=1.3.0,<2.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.101"
+version = "0.1.115"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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@@ -660,17 +633,6 @@ files = [
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-
 [[package]]
 name = "pluggy"
 version = "1.5.0"
@@ -706,122 +668,123 @@ poetry-plugin = ["poetry (>=1.0,<2.0)"]
 
 [[package]]
 name = "pydantic"
-version = "2.8.2"
+version = "2.9.0"
 description = "Data validation using Python type hints"
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 python-versions = ">=3.8"
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 [package.dependencies]
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     {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
 ]
+tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
 
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@@ -996,19 +959,18 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
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 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
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 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
@@ -1154,19 +1116,23 @@ files = [
 
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-version = "73.0.1"
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 [package.extras]
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 [[package]]
 name = "shellingham"
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 [[package]]
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+optional = false
+python-versions = ">=2"
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 [[package]]
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+    {file = "watchdog-5.0.2.tar.gz", hash = "sha256:dcebf7e475001d2cdeb020be630dc5b687e9acdd60d16fea6bb4508e7b94cf76"},
 ]
 
 [package.extras]
@@ -1416,25 +1388,10 @@ files = [
 [package.extras]
 test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 
-[[package]]
-name = "zipp"
-version = "3.20.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.20.0-py3-none-any.whl", hash = "sha256:58da6168be89f0be59beb194da1250516fdaa062ccebd30127ac65d30045e10d"},
-    {file = "zipp-3.20.0.tar.gz", hash = "sha256:0145e43d89664cfe1a2e533adc75adafed82fe2da404b4bbb6b026c0157bdb31"},
-]
-
-[package.extras]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
-
 [extras]
 serve = []
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8.1,<4.0"
-content-hash = "f549b3468a0b27c75b171c3a4efd8df9c3b3ae737c7e097ffc3fb6fb0fe5f2ef"
+python-versions = ">=3.9,<4.0"
+content-hash = "d7ef8a78c84458975d2ff479af00f4bde06e77f25f8306c64aef5bdb34f34798"

libs/cli/pyproject.toml

@@ -12,7 +12,7 @@ license = "MIT"
 "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-cli%3D%3D0%22&expanded=true"
 
 [tool.poetry.dependencies]
-python = ">=3.8.1,<4.0"
+python = ">=3.9,<4.0"
 typer = { extras = ["all"], version = "^0.9.0" }
 gitpython = "^3.1.40"
 langserve = { extras = ["all"], version = ">=0.0.51" }

libs/community/Makefile

@@ -22,7 +22,7 @@ integration_tests:
 	poetry run pytest $(TEST_FILE)
 
 test_watch:
-	poetry run ptw --disable-socket --allow-unix-socket --snapshot-update --now . -- -vv -x tests/unit_tests
+	poetry run ptw --disable-socket --allow-unix-socket --snapshot-update --now . -- -vv tests/unit_tests
 
 check_imports: $(shell find langchain_community -name '*.py')
 	poetry run python ./scripts/check_imports.py $^
@@ -45,7 +45,6 @@ lint_tests: PYTHON_FILES=tests
 lint_tests: MYPY_CACHE=.mypy_cache_test
 
 lint lint_diff lint_package lint_tests:
-	./scripts/check_pydantic.sh .
 	./scripts/lint_imports.sh .
 	./scripts/check_pickle.sh .
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)

libs/community/langchain_community/adapters/openai.py

@@ -25,7 +25,7 @@ from langchain_core.messages import (
     SystemMessage,
     ToolMessage,
 )
-from langchain_core.pydantic_v1 import BaseModel
+from pydantic import BaseModel
 from typing_extensions import Literal
 
 

libs/community/langchain_community/agent_toolkits/ainetwork/toolkit.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, List, Literal, Optional
+from typing import TYPE_CHECKING, Any, List, Literal, Optional
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, model_validator
 
 from langchain_community.tools.ainetwork.app import AINAppOps
 from langchain_community.tools.ainetwork.owner import AINOwnerOps
@@ -36,8 +36,9 @@ class AINetworkToolkit(BaseToolkit):
     network: Optional[Literal["mainnet", "testnet"]] = "testnet"
     interface: Optional[Ain] = None
 
-    @root_validator(pre=True)
-    def set_interface(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def set_interface(cls, values: dict) -> Any:
         """Set the interface if not provided.
 
         If the interface is not provided, attempt to authenticate with the
@@ -53,9 +54,10 @@ class AINetworkToolkit(BaseToolkit):
             values["interface"] = authenticate(network=values.get("network", "testnet"))
         return values
 
-    class Config:
-        arbitrary_types_allowed = True
-        validate_all = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        validate_default=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/amadeus/toolkit.py

@@ -3,9 +3,9 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, List, Optional
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.amadeus.closest_airport import AmadeusClosestAirport
 from langchain_community.tools.amadeus.flight_search import AmadeusFlightSearch
@@ -26,8 +26,9 @@ class AmadeusToolkit(BaseToolkit):
     client: Client = Field(default_factory=authenticate)
     llm: Optional[BaseLanguageModel] = Field(default=None)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/cassandra_database/toolkit.py

@@ -2,9 +2,9 @@
 
 from typing import List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.cassandra_database.tool import (
     GetSchemaCassandraDatabaseTool,
@@ -24,8 +24,9 @@ class CassandraDatabaseToolkit(BaseToolkit):
 
     db: CassandraDatabase = Field(exclude=True)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/connery/toolkit.py

@@ -1,8 +1,8 @@
-from typing import List
+from typing import Any, List
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import model_validator
 
 from langchain_community.tools.connery import ConneryService
 
@@ -23,8 +23,9 @@ class ConneryToolkit(BaseToolkit):
         """
         return self.tools
 
-    @root_validator(pre=True)
-    def validate_attributes(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_attributes(cls, values: dict) -> Any:
         """
         Validate the attributes of the ConneryToolkit class.
 

libs/community/langchain_community/agent_toolkits/file_management/toolkit.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from typing import Dict, List, Optional, Type
+from typing import Any, Dict, List, Optional, Type
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool, BaseToolkit
 from langchain_core.utils.pydantic import get_fields
+from pydantic import model_validator
 
 from langchain_community.tools.file_management.copy import CopyFileTool
 from langchain_community.tools.file_management.delete import DeleteFileTool
@@ -63,8 +63,9 @@ class FileManagementToolkit(BaseToolkit):
     selected_tools: Optional[List[str]] = None
     """If provided, only provide the selected tools. Defaults to all."""
 
-    @root_validator(pre=True)
-    def validate_tools(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_tools(cls, values: dict) -> Any:
         selected_tools = values.get("selected_tools") or []
         for tool_name in selected_tools:
             if tool_name not in _FILE_TOOLS_MAP:

libs/community/langchain_community/agent_toolkits/financial_datasets/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.financial_datasets.balance_sheets import BalanceSheets
 from langchain_community.tools.financial_datasets.cash_flow_statements import (
@@ -31,8 +31,9 @@ class FinancialDatasetsToolkit(BaseToolkit):
         super().__init__()
         self.api_wrapper = api_wrapper
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/github/toolkit.py

@@ -2,9 +2,9 @@
 
 from typing import Dict, List
 
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import BaseModel, Field
 
 from langchain_community.tools.github.prompt import (
     COMMENT_ON_ISSUE_PROMPT,

libs/community/langchain_community/agent_toolkits/gmail/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.gmail.create_draft import GmailCreateDraft
 from langchain_community.tools.gmail.get_message import GmailGetMessage
@@ -117,8 +117,9 @@ class GmailToolkit(BaseToolkit):
 
     api_resource: Resource = Field(default_factory=build_resource_service)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/multion/toolkit.py

@@ -6,6 +6,7 @@ from typing import List
 
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict
 
 from langchain_community.tools.multion.close_session import MultionCloseSession
 from langchain_community.tools.multion.create_session import MultionCreateSession
@@ -25,8 +26,9 @@ class MultionToolkit(BaseToolkit):
         See https://python.langchain.com/docs/security for more information.
     """
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/nla/toolkit.py

@@ -3,9 +3,9 @@ from __future__ import annotations
 from typing import Any, List, Optional, Sequence
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import Field
 
 from langchain_community.agent_toolkits.nla.tool import NLATool
 from langchain_community.tools.openapi.utils.openapi_utils import OpenAPISpec

libs/community/langchain_community/agent_toolkits/office365/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.office365.create_draft_message import (
     O365CreateDraftMessage,
@@ -40,8 +40,9 @@ class O365Toolkit(BaseToolkit):
 
     account: Account = Field(default_factory=authenticate)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/openapi/planner.py

@@ -9,8 +9,8 @@ import yaml
 from langchain_core.callbacks import BaseCallbackManager
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate, PromptTemplate
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool, Tool
+from pydantic import Field
 
 from langchain_community.agent_toolkits.openapi.planner_prompt import (
     API_CONTROLLER_PROMPT,
@@ -69,7 +69,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_get"
     """Tool name."""
-    description = REQUESTS_GET_TOOL_DESCRIPTION
+    description: str = REQUESTS_GET_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -103,7 +103,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_post"
     """Tool name."""
-    description = REQUESTS_POST_TOOL_DESCRIPTION
+    description: str = REQUESTS_POST_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -134,7 +134,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_patch"
     """Tool name."""
-    description = REQUESTS_PATCH_TOOL_DESCRIPTION
+    description: str = REQUESTS_PATCH_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -167,7 +167,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_put"
     """Tool name."""
-    description = REQUESTS_PUT_TOOL_DESCRIPTION
+    description: str = REQUESTS_PUT_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -198,7 +198,7 @@ class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_delete"
     """The name of the tool."""
-    description = REQUESTS_DELETE_TOOL_DESCRIPTION
+    description: str = REQUESTS_DELETE_TOOL_DESCRIPTION
     """The description of the tool."""
 
     response_length: Optional[int] = MAX_RESPONSE_LENGTH

libs/community/langchain_community/agent_toolkits/playwright/toolkit.py

@@ -2,10 +2,10 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, List, Optional, Type, cast
+from typing import TYPE_CHECKING, Any, List, Optional, Type, cast
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool, BaseToolkit
+from pydantic import ConfigDict, model_validator
 
 from langchain_community.tools.playwright.base import (
     BaseBrowserTool,
@@ -68,12 +68,14 @@ class PlayWrightBrowserToolkit(BaseToolkit):
     sync_browser: Optional["SyncBrowser"] = None
     async_browser: Optional["AsyncBrowser"] = None
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_imports_and_browser_provided(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_imports_and_browser_provided(cls, values: dict) -> Any:
         """Check that the arguments are valid."""
         lazy_import_playwright_browsers()
         if values.get("async_browser") is None and values.get("sync_browser") is None:

libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py

@@ -13,9 +13,9 @@ from langchain_core.prompts.chat import (
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.powerbi.prompt import (
     QUESTION_TO_QUERY_BASE,
@@ -63,8 +63,9 @@ class PowerBIToolkit(BaseToolkit):
     output_token_limit: Optional[int] = None
     tiktoken_model_name: Optional[str] = None
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/slack/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.slack.get_channel import SlackGetChannel
 from langchain_community.tools.slack.get_message import SlackGetMessage
@@ -91,8 +91,9 @@ class SlackToolkit(BaseToolkit):
 
     client: WebClient = Field(default_factory=login)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/spark_sql/toolkit.py

@@ -3,9 +3,9 @@
 from typing import List
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.spark_sql.tool import (
     InfoSparkSQLTool,
@@ -27,8 +27,9 @@ class SparkSQLToolkit(BaseToolkit):
     db: SparkSQL = Field(exclude=True)
     llm: BaseLanguageModel = Field(exclude=True)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/sql/toolkit.py

@@ -3,9 +3,9 @@
 from typing import List
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.sql_database.tool import (
     InfoSQLDatabaseTool,
@@ -83,8 +83,9 @@ class SQLDatabaseToolkit(BaseToolkit):
         """Return string representation of SQL dialect to use."""
         return self.db.dialect
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agents/openai_assistant/base.py

@@ -15,10 +15,11 @@ from langchain.agents.openai_assistant.base import OpenAIAssistantRunnable, Outp
 from langchain_core._api import beta
 from langchain_core.callbacks import CallbackManager
 from langchain_core.load import dumpd
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain_core.runnables import RunnableConfig, ensure_config
 from langchain_core.tools import BaseTool
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel, Field, model_validator
+from typing_extensions import Self
 
 if TYPE_CHECKING:
     import openai
@@ -209,14 +210,14 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
     as_agent: bool = False
     """Use as a LangChain agent, compatible with the AgentExecutor."""
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_async_client(cls, values: dict) -> dict:
-        if values["async_client"] is None:
+    @model_validator(mode="after")
+    def validate_async_client(self) -> Self:
+        if self.async_client is None:
             import openai
 
-            api_key = values["client"].api_key
-            values["async_client"] = openai.AsyncOpenAI(api_key=api_key)
-        return values
+            api_key = self.client.api_key
+            self.async_client = openai.AsyncOpenAI(api_key=api_key)
+        return self
 
     @classmethod
     def create_assistant(

libs/community/langchain_community/chains/ernie_functions/base.py

@@ -22,9 +22,9 @@ from langchain_core.output_parsers import (
     BaseOutputParser,
 )
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import Runnable
 from langchain_core.utils.pydantic import is_basemodel_subclass
+from pydantic import BaseModel
 
 from langchain_community.output_parsers.ernie_functions import (
     JsonOutputFunctionsParser,

libs/community/langchain_community/chains/graph_qa/arangodb.py

@@ -10,7 +10,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     AQL_FIX_PROMPT,

libs/community/langchain_community/chains/graph_qa/base.py

@@ -9,7 +9,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     ENTITY_EXTRACTION_PROMPT,

libs/community/langchain_community/chains/graph_qa/cypher.py

@@ -22,8 +22,8 @@ from langchain_core.prompts import (
     HumanMessagePromptTemplate,
     MessagesPlaceholder,
 )
-from langchain_core.pydantic_v1 import Field
 from langchain_core.runnables import Runnable
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.cypher_utils import (
     CypherQueryCorrector,

libs/community/langchain_community/chains/graph_qa/falkordb.py

@@ -10,7 +10,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_GENERATION_PROMPT,

libs/community/langchain_community/chains/graph_qa/gremlin.py

@@ -10,7 +10,7 @@ from langchain_core.callbacks.manager import CallbackManager, CallbackManagerFor
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
 from langchain_core.prompts.prompt import PromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/hugegraph.py

@@ -9,7 +9,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/kuzu.py

@@ -10,7 +10,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/nebulagraph.py

@@ -9,7 +9,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/neptune_cypher.py

@@ -9,7 +9,7 @@ from langchain.chains.prompt_selector import ConditionalPromptSelector
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/neptune_sparql.py

@@ -12,7 +12,7 @@ from langchain_core.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
 from langchain_core.prompts.prompt import PromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import SPARQL_QA_PROMPT
 from langchain_community.graphs import NeptuneRdfGraph

libs/community/langchain_community/chains/graph_qa/ontotext_graphdb.py

@@ -12,7 +12,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks.manager import CallbackManager, CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     GRAPHDB_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/sparql.py

@@ -11,7 +11,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     SPARQL_GENERATION_SELECT_PROMPT,

libs/community/langchain_community/chains/llm_requests.py

@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional
 from langchain.chains import LLMChain
 from langchain.chains.base import Chain
 from langchain_core.callbacks import CallbackManagerForChainRun
-from langchain_core.pydantic_v1 import Field, root_validator
+from pydantic import ConfigDict, Field, model_validator
 
 from langchain_community.utilities.requests import TextRequestsWrapper
 
@@ -38,9 +38,10 @@ class LLMRequestsChain(Chain):
     input_key: str = "url"  #: :meta private:
     output_key: str = "output"  #: :meta private:
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
     @property
     def input_keys(self) -> List[str]:
@@ -58,8 +59,9 @@ class LLMRequestsChain(Chain):
         """
         return [self.output_key]
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key and python package exists in environment."""
         try:
             from bs4 import BeautifulSoup  # noqa: F401

libs/community/langchain_community/chains/openapi/chain.py

@@ -11,7 +11,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun, Callbacks
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, Field
 from requests import Response
 
 from langchain_community.tools.openapi.utils.api_models import APIOperation

libs/community/langchain_community/chains/pebblo_retrieval/base.py

@@ -17,8 +17,8 @@ from langchain_core.callbacks import (
 )
 from langchain_core.documents import Document
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field, validator
 from langchain_core.vectorstores import VectorStoreRetriever
+from pydantic import ConfigDict, Field, validator
 
 from langchain_community.chains.pebblo_retrieval.enforcement_filters import (
     SUPPORTED_VECTORSTORES,
@@ -189,10 +189,11 @@ class PebbloRetrievalQA(Chain):
         else:
             return {self.output_key: answer}
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
     @property
     def input_keys(self) -> List[str]:

libs/community/langchain_community/chains/pebblo_retrieval/models.py

@@ -2,7 +2,7 @@
 
 from typing import Any, List, Optional, Union
 
-from langchain_core.pydantic_v1 import BaseModel
+from pydantic import BaseModel
 
 
 class AuthContext(BaseModel):

libs/community/langchain_community/chains/pebblo_retrieval/utilities.py

@@ -10,9 +10,9 @@ import aiohttp
 from aiohttp import ClientTimeout
 from langchain_core.documents import Document
 from langchain_core.env import get_runtime_environment
-from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.utils import get_from_dict_or_env
 from langchain_core.vectorstores import VectorStoreRetriever
+from pydantic import BaseModel
 from requests import Response, request
 from requests.exceptions import RequestException
 

libs/community/langchain_community/chat_models/anthropic.py

@@ -20,6 +20,7 @@ from langchain_core.messages import (
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.prompt_values import PromptValue
+from pydantic import ConfigDict
 
 from langchain_community.llms.anthropic import _AnthropicCommon
 
@@ -91,9 +92,10 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon):
             model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
     """
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+    )
 
     @property
     def lc_secrets(self) -> Dict[str, str]:

libs/community/langchain_community/chat_models/anyscale.py

@@ -5,12 +5,12 @@ from __future__ import annotations
 import logging
 import os
 import sys
-from typing import TYPE_CHECKING, Dict, Optional, Set
+from typing import TYPE_CHECKING, Any, Dict, Optional, Set
 
 import requests
 from langchain_core.messages import BaseMessage
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import Field, SecretStr, model_validator
 
 from langchain_community.adapters.openai import convert_message_to_dict
 from langchain_community.chat_models.openai import (
@@ -102,8 +102,9 @@ class ChatAnyscale(ChatOpenAI):
 
         return {model["id"] for model in models_response.json()["data"]}
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: dict) -> Any:
         """Validate that api key and python package exists in environment."""
         values["anyscale_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(

libs/community/langchain_community/chat_models/azure_openai.py

@@ -9,8 +9,8 @@ from typing import Any, Callable, Dict, List, Union
 
 from langchain_core._api.deprecation import deprecated
 from langchain_core.outputs import ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.utils import get_from_dict_or_env, pre_init
+from pydantic import BaseModel, Field
 
 from langchain_community.chat_models.openai import ChatOpenAI
 from langchain_community.utils.openai import is_openai_v1

libs/community/langchain_community/chat_models/baichuan.py

@@ -44,7 +44,6 @@ from langchain_core.output_parsers.openai_tools import (
     parse_tool_call,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langchain_core.utils import (
@@ -53,6 +52,13 @@ from langchain_core.utils import (
     get_pydantic_field_names,
 )
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+    model_validator,
+)
 
 from langchain_community.chat_models.llamacpp import (
     _lc_invalid_tool_call_to_openai_tool_call,
@@ -375,11 +381,13 @@ class ChatBaichuan(BaseChatModel):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for API call not explicitly specified."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})
@@ -404,8 +412,9 @@ class ChatBaichuan(BaseChatModel):
         values["model_kwargs"] = extra
         return values
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["baichuan_api_base"] = get_from_dict_or_env(
             values,
             "baichuan_api_base",

libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py

@@ -41,17 +41,18 @@ from langchain_core.output_parsers.openai_tools import (
     PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-    Field,
-    SecretStr,
-    root_validator,
-)
 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
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import get_fields, is_basemodel_subclass
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+    model_validator,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -248,7 +249,7 @@ class QianfanChatEndpoint(BaseChatModel):
     Tool calling:
         .. code-block:: python
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class GetWeather(BaseModel):
@@ -287,7 +288,7 @@ class QianfanChatEndpoint(BaseChatModel):
 
             from typing import Optional
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class Joke(BaseModel):
@@ -380,11 +381,13 @@ class QianfanChatEndpoint(BaseChatModel):
     endpoint: Optional[str] = None
     """Endpoint of the Qianfan LLM, required if custom model used."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["qianfan_ak"] = convert_to_secret_str(
             get_from_dict_or_env(
                 values, ["qianfan_ak", "api_key"], "QIANFAN_AK", default=""
@@ -747,7 +750,7 @@ class QianfanChatEndpoint(BaseChatModel):
             .. code-block:: python
 
                 from langchain_mistralai import QianfanChatEndpoint
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
 
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''
@@ -768,7 +771,7 @@ class QianfanChatEndpoint(BaseChatModel):
             .. code-block:: python
 
                 from langchain_mistralai import QianfanChatEndpoint
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
 
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''
@@ -789,7 +792,7 @@ class QianfanChatEndpoint(BaseChatModel):
             .. code-block:: python
 
                 from langchain_mistralai import QianfanChatEndpoint
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_core.utils.function_calling import convert_to_openai_tool
 
                 class AnswerWithJustification(BaseModel):

libs/community/langchain_community/chat_models/bedrock.py

@@ -16,6 +16,7 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from pydantic import ConfigDict
 
 from langchain_community.chat_models.anthropic import (
     convert_messages_to_prompt_anthropic,
@@ -231,8 +232,9 @@ class BedrockChat(BaseChatModel, BedrockBase):
 
         return attributes
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
     def _stream(
         self,

libs/community/langchain_community/chat_models/cohere.py

@@ -19,6 +19,7 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from pydantic import ConfigDict
 
 from langchain_community.llms.cohere import BaseCohere
 
@@ -117,9 +118,10 @@ class ChatCohere(BaseChatModel, BaseCohere):
             chat.invoke(messages)
     """
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+    )
 
     @property
     def _llm_type(self) -> str:

libs/community/langchain_community/chat_models/coze.py

@@ -19,11 +19,11 @@ from langchain_core.messages import (
     HumanMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
 )
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -111,11 +111,13 @@ class ChatCoze(BaseChatModel):
     "Streaming response" will provide real-time response of the model to the client, and
     the client needs to assemble the final reply based on the type of message. """
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["coze_api_base"] = get_from_dict_or_env(
             values,
             "coze_api_base",

libs/community/langchain_community/chat_models/dappier.py

@@ -13,8 +13,8 @@ from langchain_core.messages import (
     BaseMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 
 from langchain_community.utilities.requests import Requests
 
@@ -70,11 +70,13 @@ class ChatDappierAI(BaseChatModel):
 
     dappier_api_key: Optional[SecretStr] = Field(None, description="Dappier API Token")
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key exists in environment."""
         values["dappier_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(values, "dappier_api_key", "DAPPIER_API_KEY")

libs/community/langchain_community/chat_models/deepinfra.py

@@ -54,11 +54,12 @@ from langchain_core.outputs import (
     ChatGenerationChunk,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langchain_core.utils import get_from_dict_or_env
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from typing_extensions import Self
 
 from langchain_community.utilities.requests import Requests
 
@@ -222,10 +223,9 @@ class ChatDeepInfra(BaseChatModel):
     streaming: bool = False
     max_retries: int = 1
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     @property
     def _default_params(self) -> Dict[str, Any]:
@@ -291,8 +291,9 @@ class ChatDeepInfra(BaseChatModel):
 
         return await _completion_with_retry(**kwargs)
 
-    @root_validator(pre=True)
-    def init_defaults(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def init_defaults(cls, values: Dict) -> Any:
         """Validate api key, python package exists, temperature, top_p, and top_k."""
         # For compatibility with LiteLLM
         api_key = get_from_dict_or_env(
@@ -309,18 +310,18 @@ class ChatDeepInfra(BaseChatModel):
         )
         return values
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_environment(cls, values: Dict) -> Dict:
-        if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
+    @model_validator(mode="after")
+    def validate_environment(self) -> Self:
+        if self.temperature is not None and not 0 <= self.temperature <= 1:
             raise ValueError("temperature must be in the range [0.0, 1.0]")
 
-        if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
+        if self.top_p is not None and not 0 <= self.top_p <= 1:
             raise ValueError("top_p must be in the range [0.0, 1.0]")
 
-        if values["top_k"] is not None and values["top_k"] <= 0:
+        if self.top_k is not None and self.top_k <= 0:
             raise ValueError("top_k must be positive")
 
-        return values
+        return self
 
     def _generate(
         self,

libs/community/langchain_community/chat_models/edenai.py

@@ -47,16 +47,17 @@ from langchain_core.output_parsers.openai_tools import (
     PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-    Field,
-    SecretStr,
-)
 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
 from langchain_core.utils.pydantic import is_basemodel_subclass
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+)
 
 from langchain_community.utilities.requests import Requests
 
@@ -296,8 +297,9 @@ class ChatEdenAI(BaseChatModel):
 
     edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token")
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:

libs/community/langchain_community/chat_models/ernie.py

@@ -13,8 +13,8 @@ from langchain_core.messages import (
     HumanMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -108,8 +108,9 @@ class ErnieBotChat(BaseChatModel):
 
     _lock = threading.Lock()
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["ernie_api_base"] = get_from_dict_or_env(
             values, "ernie_api_base", "ERNIE_API_BASE", "https://aip.baidubce.com"
         )

libs/community/langchain_community/chat_models/everlyai.py

@@ -4,11 +4,11 @@ from __future__ import annotations
 
 import logging
 import sys
-from typing import TYPE_CHECKING, Dict, Optional, Set
+from typing import TYPE_CHECKING, Any, Dict, Optional, Set
 
 from langchain_core.messages import BaseMessage
-from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import Field, model_validator
 
 from langchain_community.adapters.openai import convert_message_to_dict
 from langchain_community.chat_models.openai import (
@@ -76,8 +76,9 @@ class ChatEverlyAI(ChatOpenAI):
             ]
         )
 
-    @root_validator(pre=True)
-    def validate_environment_override(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment_override(cls, values: dict) -> Any:
         """Validate that api key and python package exists in environment."""
         values["openai_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(

libs/community/langchain_community/chat_models/fireworks.py

@@ -32,9 +32,9 @@ from langchain_core.messages import (
     SystemMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str
 from langchain_core.utils.env import get_from_dict_or_env
+from pydantic import Field, SecretStr, model_validator
 
 from langchain_community.adapters.openai import convert_message_to_dict
 
@@ -112,8 +112,9 @@ class ChatFireworks(BaseChatModel):
         """Get the namespace of the langchain object."""
         return ["langchain", "chat_models", "fireworks"]
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key in environment."""
         try:
             import fireworks.client

libs/community/langchain_community/chat_models/google_palm.py

@@ -21,8 +21,8 @@ from langchain_core.outputs import (
     ChatGeneration,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel, SecretStr
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from pydantic import BaseModel, SecretStr
 from tenacity import (
     before_sleep_log,
     retry,

libs/community/langchain_community/chat_models/gpt_router.py

@@ -30,8 +30,9 @@ from langchain_core.language_models.chat_models import (
 from langchain_core.language_models.llms import create_base_retry_decorator
 from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import BaseModel, Field, SecretStr, model_validator
+from typing_extensions import Self
 
 from langchain_community.adapters.openai import (
     convert_dict_to_message,
@@ -150,7 +151,7 @@ class GPTRouter(BaseChatModel):
     """
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
-    models_priority_list: List[GPTRouterModel] = Field(min_items=1)
+    models_priority_list: List[GPTRouterModel] = Field(min_length=1)
     gpt_router_api_base: str = Field(default=None)
     """WriteSonic GPTRouter custom endpoint"""
     gpt_router_api_key: Optional[SecretStr] = None
@@ -167,8 +168,9 @@ class GPTRouter(BaseChatModel):
     """Number of chat completions to generate for each prompt."""
     max_tokens: int = 256
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["gpt_router_api_base"] = get_from_dict_or_env(
             values,
             "gpt_router_api_base",
@@ -185,8 +187,8 @@ class GPTRouter(BaseChatModel):
         )
         return values
 
-    @root_validator(pre=True, skip_on_failure=True)
-    def post_init(cls, values: Dict) -> Dict:
+    @model_validator(mode="after")
+    def post_init(self) -> Self:
         try:
             from gpt_router.client import GPTRouterClient
 
@@ -197,12 +199,14 @@ class GPTRouter(BaseChatModel):
             )
 
         gpt_router_client = GPTRouterClient(
-            values["gpt_router_api_base"],
-            values["gpt_router_api_key"].get_secret_value(),
+            self.gpt_router_api_base,
+            self.gpt_router_api_key.get_secret_value()
+            if self.gpt_router_api_key
+            else None,
         )
-        values["client"] = gpt_router_client
+        self.client = gpt_router_client
 
-        return values
+        return self
 
     @property
     def lc_secrets(self) -> Dict[str, str]:

libs/community/langchain_community/chat_models/huggingface.py

@@ -25,7 +25,8 @@ from langchain_core.outputs import (
     ChatResult,
     LLMResult,
 )
-from langchain_core.pydantic_v1 import root_validator
+from pydantic import model_validator
+from typing_extensions import Self
 
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 from langchain_community.llms.huggingface_hub import HuggingFaceHub
@@ -76,17 +77,17 @@ class ChatHuggingFace(BaseChatModel):
             else self.tokenizer
         )
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_llm(cls, values: dict) -> dict:
+    @model_validator(mode="after")
+    def validate_llm(self) -> Self:
         if not isinstance(
-            values["llm"],
+            self.llm,
             (HuggingFaceTextGenInference, HuggingFaceEndpoint, HuggingFaceHub),
         ):
             raise TypeError(
                 "Expected llm to be one of HuggingFaceTextGenInference, "
-                f"HuggingFaceEndpoint, HuggingFaceHub, received {type(values['llm'])}"
+                f"HuggingFaceEndpoint, HuggingFaceHub, received {type(self.llm)}"
             )
-        return values
+        return self
 
     def _stream(
         self,

libs/community/langchain_community/chat_models/human.py

@@ -15,7 +15,7 @@ from langchain_core.messages import (
     messages_to_dict,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.llms.utils import enforce_stop_tokens
 

libs/community/langchain_community/chat_models/hunyuan.py

@@ -19,13 +19,13 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
     get_pydantic_field_names,
     pre_init,
 )
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -138,11 +138,13 @@ class ChatHunyuan(BaseChatModel):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for API call not explicitly specified."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})

libs/community/langchain_community/chat_models/javelin_ai_gateway.py

@@ -18,7 +18,7 @@ from langchain_core.outputs import (
     ChatGeneration,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
+from pydantic import BaseModel, ConfigDict, Field, SecretStr
 
 logger = logging.getLogger(__name__)
 
@@ -62,14 +62,15 @@ class ChatJavelinAIGateway(BaseChatModel):
     params: Optional[ChatParams] = None
     """Parameters for the Javelin AI Gateway LLM."""
 
-    client: Any
+    client: Any = None
     """javelin client."""
 
     javelin_api_key: Optional[SecretStr] = Field(None, alias="api_key")
     """The API key for the Javelin AI Gateway."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     def __init__(self, **kwargs: Any):
         try:

libs/community/langchain_community/chat_models/jinachat.py

@@ -40,13 +40,13 @@ from langchain_core.messages import (
     SystemMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
     get_pydantic_field_names,
     pre_init,
 )
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -188,11 +188,13 @@ class JinaChat(BaseChatModel):
     max_tokens: Optional[int] = None
     """Maximum number of tokens to generate."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})