remove unused type ignores

Deprecate shim namespace

.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/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/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"

Makefile

@@ -36,7 +36,6 @@ api_docs_build:
 API_PKG ?= text-splitters
 
 api_docs_quick_preview:
-	poetry run pip install "pydantic<2"
 	poetry run python docs/api_reference/create_api_rst.py $(API_PKG)
 	cd docs/api_reference && poetry run make html
 	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/

docs/api_reference/scripts/custom_formatter.py

@@ -17,7 +17,10 @@ def process_toc_h3_elements(html_content: str) -> str:
 
     # Process each element
     for element in toc_h3_elements:
-        element = element.a.code.span
+        try:
+            element = element.a.code.span
+        except Exception:
+            continue
         # Get the text content of the element
         content = element.get_text()
 

docs/api_reference/templates/pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, model_extra, model_fields_set, model_json_schema
 
 
     {% block attributes %}

docs/api_reference/templates/runnable_pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool, get_config_jsonschema, get_input_jsonschema, get_output_jsonschema, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, to_json, model_extra, model_fields_set, model_json_schema
 
     .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 

docs/docs/concepts.mdx

@@ -945,7 +945,7 @@ Here's an example:
 ```python
 from typing import Optional
 
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, Field
 
 
 class Joke(BaseModel):
@@ -1062,7 +1062,7 @@ a `tool_calls` field containing `args` that match the desired shape.
 There are several acceptable formats you can use to bind tools to a model in LangChain. Here's one example:
 
 ```python
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, Field
 from langchain_openai import ChatOpenAI
 
 class ResponseFormatter(BaseModel):

docs/docs/how_to/MultiQueryRetriever.ipynb

@@ -144,7 +144,7 @@
     "\n",
     "from langchain_core.output_parsers import BaseOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Output parser will split the LLM result into a list of queries\n",

docs/docs/how_to/chat_models_universal_init.ipynb

@@ -263,7 +263,7 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",

docs/docs/how_to/chat_token_usage_tracking.ipynb

@@ -289,7 +289,7 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Joke(BaseModel):\n",

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -190,7 +190,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GSchema(BaseModel):\n",

docs/docs/how_to/custom_tools.ipynb

@@ -13,7 +13,7 @@
     "|---------------|---------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
     "| name          | str                             | Must be unique within a set of tools provided to an LLM or agent.                                                                                                              |\n",
     "| description   | str                             | Describes what the tool does. Used as context by the LLM or agent.                                                                                                             |\n",
-    "| args_schema   | langchain.pydantic_v1.BaseModel | Optional but recommended, and required if using callback handlers. It can be used to provide more information (e.g., few-shot examples) or validation for expected parameters. |\n",
+    "| args_schema   | pydantic.BaseModel | Optional but recommended, and required if using callback handlers. It can be used to provide more information (e.g., few-shot examples) or validation for expected parameters. |\n",
     "| return_direct | boolean                         | Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user.                                             |\n",
     "\n",
     "LangChain supports the creation of tools from:\n",
@@ -177,7 +177,7 @@
     }
    ],
    "source": [
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",
@@ -435,12 +435,12 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",

docs/docs/how_to/extraction_examples.ipynb

@@ -111,8 +111,8 @@
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -177,7 +177,7 @@
     "    SystemMessage,\n",
     "    ToolMessage,\n",
     ")\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Example(TypedDict):\n",

docs/docs/how_to/extraction_long_text.ipynb

@@ -93,7 +93,7 @@
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class KeyDevelopment(BaseModel):\n",

docs/docs/how_to/extraction_parse.ipynb

@@ -71,7 +71,7 @@
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
+    "from pydantic import BaseModel, Field, validator\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -221,7 +221,7 @@
     "from langchain_anthropic.chat_models import ChatAnthropic\n",
     "from langchain_core.messages import AIMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
+    "from pydantic import BaseModel, Field, validator\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/how_to/function_calling.ipynb

@@ -136,7 +136,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Note that the docstrings here are crucial, as they will be passed along\n",

docs/docs/how_to/graph_mapping.ipynb

@@ -163,8 +163,8 @@
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",

docs/docs/how_to/graph_semantic.ipynb

@@ -177,14 +177,15 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
     "\n",
+    "# Import things that are needed generically\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
     "description_query = \"\"\"\n",
     "MATCH (m:Movie|Person)\n",
     "WHERE m.title CONTAINS $candidate OR m.name CONTAINS $candidate\n",
@@ -226,14 +227,15 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
     "\n",
+    "# Import things that are needed generically\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
     "\n",
     "class InformationInput(BaseModel):\n",
     "    entity: str = Field(description=\"movie or a person mentioned in the question\")\n",

docs/docs/how_to/multi_vector.ipynb

@@ -440,7 +440,7 @@
    "source": [
     "from typing import List\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class HypotheticalQuestions(BaseModel):\n",

docs/docs/how_to/output_parser_fixing.ipynb

@@ -24,8 +24,8 @@
     "from typing import List\n",
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field"
    ]
   },
   {

docs/docs/how_to/output_parser_json.ipynb

@@ -71,8 +71,8 @@
    "source": [
     "from langchain_core.output_parsers import JsonOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "model = ChatOpenAI(temperature=0)\n",
     "\n",

docs/docs/how_to/output_parser_retry.ipynb

@@ -20,8 +20,8 @@
     "from langchain.output_parsers import OutputFixingParser\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_openai import ChatOpenAI, OpenAI"
+    "from langchain_openai import ChatOpenAI, OpenAI\n",
+    "from pydantic import BaseModel, Field"
    ]
   },
   {

docs/docs/how_to/output_parser_structured.ipynb

@@ -35,17 +35,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "1594b2bf-2a6f-47bb-9a81-38930f8e606b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')"
+       "Joke(setup='Why did the tomato turn red?', punchline='Because it saw the salad dressing!')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -53,8 +53,8 @@
    "source": [
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
     "from langchain_openai import OpenAI\n",
+    "from pydantic import BaseModel, Field, model_validator\n",
     "\n",
     "model = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0.0)\n",
     "\n",
@@ -65,11 +65,13 @@
     "    punchline: str = Field(description=\"answer to resolve the joke\")\n",
     "\n",
     "    # You can add custom validation logic easily with Pydantic.\n",
-    "    @validator(\"setup\")\n",
-    "    def question_ends_with_question_mark(cls, field):\n",
-    "        if field[-1] != \"?\":\n",
+    "    @model_validator(mode=\"before\")\n",
+    "    @classmethod\n",
+    "    def question_ends_with_question_mark(cls, values: dict) -> dict:\n",
+    "        setup = values[\"setup\"]\n",
+    "        if setup[-1] != \"?\":\n",
     "            raise ValueError(\"Badly formed question!\")\n",
-    "        return field\n",
+    "        return values\n",
     "\n",
     "\n",
     "# Set up a parser + inject instructions into the prompt template.\n",
@@ -239,9 +241,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -253,7 +255,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/output_parser_yaml.ipynb

@@ -70,8 +70,8 @@
    "source": [
     "from langchain.output_parsers import YamlOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Define your desired data structure.\n",

docs/docs/how_to/qa_citations.ipynb

@@ -269,7 +269,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class CitedAnswer(BaseModel):\n",

docs/docs/how_to/query_constructing_filters.ipynb

@@ -40,7 +40,7 @@
     ")\n",
     "from langchain_community.query_constructors.chroma import ChromaTranslator\n",
     "from langchain_community.query_constructors.elasticsearch import ElasticsearchTranslator\n",
-    "from langchain_core.pydantic_v1 import BaseModel"
+    "from pydantic import BaseModel"
    ]
   },
   {

docs/docs/how_to/query_few_shot.ipynb

@@ -87,7 +87,7 @@
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "sub_queries_description = \"\"\"\\\n",
     "If the original question contains multiple distinct sub-questions, \\\n",

docs/docs/how_to/query_high_cardinality.ipynb

@@ -154,12 +154,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "0ae69afc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field"
+    "from pydantic import BaseModel, Field, model_validator"
    ]
   },
   {
@@ -492,21 +492,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 3,
    "id": "a2e8b434",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import validator\n",
-    "\n",
-    "\n",
     "class Search(BaseModel):\n",
     "    query: str\n",
     "    author: str\n",
     "\n",
-    "    @validator(\"author\")\n",
-    "    def double(cls, v: str) -> str:\n",
-    "        return vectorstore.similarity_search(v, k=1)[0].page_content"
+    "    @model_validator(mode=\"before\")\n",
+    "    @classmethod\n",
+    "    def double(cls, values: dict) -> dict:\n",
+    "        author = values[\"author\"]\n",
+    "        closest_valid_author = vectorstore.similarity_search(author, k=1)[\n",
+    "            0\n",
+    "        ].page_content\n",
+    "        values[\"author\"] = closest_valid_author\n",
+    "        return values"
    ]
   },
   {
@@ -563,9 +566,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -577,7 +580,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_multiple_queries.ipynb

@@ -115,7 +115,7 @@
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",

docs/docs/how_to/query_multiple_retrievers.ipynb

@@ -117,7 +117,7 @@
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",

docs/docs/how_to/query_no_queries.ipynb

@@ -117,7 +117,7 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",

docs/docs/how_to/sql_large_db.ipynb

@@ -136,7 +136,7 @@
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Table(BaseModel):\n",

docs/docs/how_to/structured_output.ipynb

@@ -101,7 +101,7 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Pydantic\n",
@@ -667,7 +667,7 @@
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -794,7 +794,7 @@
     "\n",
     "from langchain_core.messages import AIMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/how_to/tool_calling.ipynb

@@ -112,7 +112,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class add(BaseModel):\n",
@@ -321,7 +321,7 @@
    ],
    "source": [
     "from langchain_core.output_parsers import PydanticToolsParser\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class add(BaseModel):\n",

docs/docs/how_to/tool_runtime.ipynb

@@ -418,8 +418,8 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import BaseTool\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class UpdateFavoritePetsSchema(BaseModel):\n",

docs/docs/how_to/tools_builtin.ipynb

@@ -140,7 +140,7 @@
    "source": [
     "from langchain_community.tools import WikipediaQueryRun\n",
     "from langchain_community.utilities import WikipediaAPIWrapper\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class WikiInputs(BaseModel):\n",

docs/docs/integrations/chat/anthropic.ipynb

@@ -274,7 +274,7 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -69,7 +69,7 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/integrations/chat/deepinfra.ipynb

@@ -123,8 +123,8 @@
     "from dotenv import find_dotenv, load_dotenv\n",
     "from langchain_community.chat_models import ChatDeepInfra\n",
     "from langchain_core.messages import HumanMessage\n",
-    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_core.tools import tool\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "model_name = \"meta-llama/Meta-Llama-3-70B-Instruct\"\n",
     "\n",

docs/docs/integrations/chat/edenai.ipynb

@@ -264,7 +264,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "llm = ChatEdenAI(provider=\"openai\", temperature=0.2, max_tokens=500)\n",
     "\n",

docs/docs/integrations/chat/ibm_watsonx.ipynb

@@ -483,7 +483,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",

docs/docs/integrations/chat/llamacpp.ipynb

@@ -226,8 +226,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import tool\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class WeatherInput(BaseModel):\n",
@@ -343,8 +343,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "\n",
     "class Joke(BaseModel):\n",

docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb

@@ -658,8 +658,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import Field\n",
     "from langchain_core.tools import tool\n",
+    "from pydantic import Field\n",
     "\n",
     "\n",
     "@tool\n",

docs/docs/integrations/chat/premai.ipynb

@@ -426,8 +426,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import tool\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Define the schema for function arguments\n",

docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb

@@ -72,7 +72,7 @@
    "source": [
     "from enum import Enum\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Operation(Enum):\n",
@@ -135,8 +135,8 @@
    "source": [
     "from pprint import pprint\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_core.utils.function_calling import convert_pydantic_to_openai_function\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "openai_function_def = convert_pydantic_to_openai_function(Calculator)\n",
     "pprint(openai_function_def)"

docs/docs/integrations/providers/premai.md

@@ -286,7 +286,7 @@ In order to pass tools and let the LLM choose the tool it needs to call, we need
 
 ```python
 from langchain_core.tools import tool
-from langchain_core.pydantic_v1 import BaseModel, Field 
+from pydantic import BaseModel, Field 
 
 # Define the schema for function arguments
 class OperationInput(BaseModel):

docs/docs/integrations/tools/infobip.ipynb

@@ -106,9 +106,9 @@
     "from langchain import hub\n",
     "from langchain.agents import AgentExecutor, create_openai_functions_agent\n",
     "from langchain_community.utilities.infobip import InfobipAPIWrapper\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import StructuredTool\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "instructions = \"You are a coding teacher. You are teaching a student how to code. The student asks you a question. You answer the question.\"\n",
     "base_prompt = hub.pull(\"langchain-ai/openai-functions-template\")\n",

docs/docs/tutorials/classification.ipynb

@@ -72,8 +72,8 @@
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "tagging_prompt = ChatPromptTemplate.from_template(\n",
     "    \"\"\"\n",

docs/docs/tutorials/data_generation.ipynb

@@ -62,7 +62,6 @@
     "# dotenv.load_dotenv()\n",
     "\n",
     "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_experimental.tabular_synthetic_data.openai import (\n",
     "    OPENAI_TEMPLATE,\n",
     "    create_openai_data_generator,\n",
@@ -71,7 +70,8 @@
     "    SYNTHETIC_FEW_SHOT_PREFIX,\n",
     "    SYNTHETIC_FEW_SHOT_SUFFIX,\n",
     ")\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel"
    ]
   },
   {

docs/docs/tutorials/extraction.ipynb

@@ -115,7 +115,7 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -167,7 +167,7 @@
     "from typing import Optional\n",
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "# Define a custom prompt to provide instructions and any additional context.\n",
     "# 1) You can add examples into the prompt template to improve extraction quality\n",
@@ -290,7 +290,7 @@
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/tutorials/query_analysis.ipynb

@@ -376,7 +376,7 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",

docs/ignore-step.sh

@@ -7,17 +7,24 @@ echo "VERCEL_GIT_COMMIT_REF: $VERCEL_GIT_COMMIT_REF"
 if [ "$VERCEL_ENV" == "production" ] || [ "$VERCEL_GIT_COMMIT_REF" == "master" ] || [ "$VERCEL_GIT_COMMIT_REF" == "v0.1" ]; then 
     echo "✅ Production build - proceeding with build"
     exit 1; 
-else 
-    echo "Checking for changes in docs/"
+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 "---"
+git log -n 50 --pretty=format:"%s" -- . | grep -v '(#'
+if [ $? -eq 0 ]; then
     echo "---"
-    git log -n 50 --pretty=format:"%s" -- . | grep -v '(#'
-    if [ $? -eq 0 ]; then
-        echo "---"
-        echo "✅ Changes detected in docs/ - proceeding with build"
-        exit 1
-    else
-        echo "---"
-        echo "🛑 No changes detected in docs/ - ignoring build"
-        exit 0
-    fi
+    echo "✅ Changes detected in docs/ - proceeding with build"
+    exit 1
+else
+    echo "---"
+    echo "🛑 No changes detected in docs/ - ignoring build"
+    exit 0
 fi

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/integration_template/scripts/check_pydantic.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-#
-# This script searches for lines starting with "import pydantic" or "from pydantic"
-# in tracked files within a Git repository.
-#
-# Usage: ./scripts/check_pydantic.sh /path/to/repository
-
-# Check if a path argument is provided
-if [ $# -ne 1 ]; then
-  echo "Usage: $0 /path/to/repository"
-  exit 1
-fi
-
-repository_path="$1"
-
-# Search for lines matching the pattern within the specified repository
-result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
-
-# Check if any matching lines were found
-if [ -n "$result" ]; then
-  echo "ERROR: The following lines need to be updated:"
-  echo "$result"
-  echo "Please replace the code with an import from langchain_core.pydantic_v1."
-  echo "For example, replace 'from pydantic import BaseModel'"
-  echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
-  exit 1
-fi

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 = [
-    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
-    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
+    {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"},
+    {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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+    {file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
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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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-
-[package.dependencies]
-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"]
-enabler = ["pytest-enabler (>=2.2)"]
-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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-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 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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+    {file = "langchain_core-0.2.38.tar.gz", hash = "sha256:eb69dbedd344f2ee1f15bcea6c71a05884b867588fadc42d04632e727c1238f3"},
 ]
 
 [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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 ]
 
 [package.dependencies]
@@ -660,17 +633,6 @@ files = [
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-[[package]]
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-version = "1.3.10"
-description = "Resolve a name to an object."
-optional = false
-python-versions = ">=3.6"
-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"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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 typing-extensions = [
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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)"]
 
 [[package]]
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-version = "2.20.1"
+version = "2.23.2"
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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"
@@ -1220,13 +1186,13 @@ uvicorn = "*"
 
 [[package]]
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+version = "2024.1"
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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/extended_testing_deps.txt

@@ -34,7 +34,6 @@ hologres-vector==0.0.6
 html2text>=2020.1.16
 httpx>=0.24.1,<0.25
 httpx-sse>=0.4.0,<0.5
-javelin-sdk>=0.1.8,<0.2
 jinja2>=3,<4
 jq>=1.4.1,<2
 jsonschema>1
@@ -60,6 +59,7 @@ pgvector>=0.1.6,<0.2
 praw>=7.7.1,<8
 premai>=0.3.25,<0.4
 psychicapi>=0.8.0,<0.9
+pydantic>=2.7.4,<3
 py-trello>=0.19.0,<0.20
 pyjwt>=2.8.0,<3
 pymupdf>=1.22.3,<2

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,
@@ -243,7 +243,7 @@ def create_ernie_fn_runnable(
                 from langchain.chains.ernie_functions import create_ernie_fn_chain
                 from langchain_community.chat_models import ErnieBotChat
                 from langchain_core.prompts import ChatPromptTemplate
-                from langchain.pydantic_v1 import BaseModel, Field
+                from pydantic import BaseModel, Field
 
 
                 class RecordPerson(BaseModel):
@@ -317,7 +317,7 @@ def create_structured_output_runnable(
             from langchain.chains.ernie_functions import create_structured_output_chain
             from langchain_community.chat_models import ErnieBotChat
             from langchain_core.prompts import ChatPromptTemplate
-            from langchain.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
             class Dog(BaseModel):
                 \"\"\"Identifying information about a dog.\"\"\"
@@ -415,7 +415,7 @@ def create_ernie_fn_chain(
                 from langchain_community.chat_models import ErnieBotChat
                 from langchain_core.prompts import ChatPromptTemplate
 
-                from langchain.pydantic_v1 import BaseModel, Field
+                from pydantic import BaseModel, Field
 
 
                 class RecordPerson(BaseModel):
@@ -502,7 +502,7 @@ def create_structured_output_chain(
                 from langchain_community.chat_models import ErnieBotChat
                 from langchain_core.prompts import ChatPromptTemplate
 
-                from langchain.pydantic_v1 import BaseModel, Field
+                from pydantic import BaseModel, Field
 
                 class Dog(BaseModel):
                     \"\"\"Identifying information about a dog.\"\"\"

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,
@@ -57,6 +57,37 @@ class ArangoGraphQAChain(Chain):
     # Specify the maximum amount of AQL Generation attempts that should be made
     max_aql_generation_attempts: int = 3
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         return [self.input_key]

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,
@@ -180,6 +180,36 @@ class GraphCypherQAChain(Chain):
     """Optional cypher validation tool"""
     use_function_response: bool = False
     """Whether to wrap the database context as tool/function response"""
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+    
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
 
     @property
     def input_keys(self) -> List[str]:

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,
@@ -66,6 +66,37 @@ class FalkorDBQAChain(Chain):
     return_direct: bool = False
     """Whether or not to return the result of querying the graph directly."""
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         """Return the input keys.

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,
@@ -63,6 +63,37 @@ class GremlinQAChain(Chain):
     return_direct: bool = False
     return_intermediate_steps: bool = False
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         """Input keys.

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,
@@ -39,6 +39,37 @@ class HugeGraphQAChain(Chain):
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         """Input keys.

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,
@@ -73,6 +73,37 @@ class KuzuQAChain(Chain):
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         """Return the input keys.

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,
@@ -39,6 +39,37 @@ class NebulaGraphQAChain(Chain):
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         """Return the input keys.

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,
@@ -120,6 +120,37 @@ class NeptuneOpenCypherQAChain(Chain):
     extra_instructions: Optional[str] = None
     """Extra instructions by the appended to the query generation prompt."""
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         """Return the input keys.

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
@@ -113,6 +113,37 @@ class NeptuneSparqlQAChain(Chain):
     extra_instructions: Optional[str] = None
     """Extra instructions by the appended to the query generation prompt."""
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         return [self.input_key]

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,
@@ -46,6 +46,37 @@ class OntotextGraphDBQAChain(Chain):
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         return [self.input_key]

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,
@@ -47,6 +47,37 @@ class GraphSparqlQAChain(Chain):
     output_key: str = "result"  #: :meta private:
     sparql_query_key: str = "sparql_query"  #: :meta private:
 
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
     @property
     def input_keys(self) -> List[str]:
         """Return the input keys.

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