core: release 0.3.34 (#29644)

I made a change to how was implemented the support for GPU in
`FastEmbedEmbeddings` to be more consistent with the existing
implementation `langchain-qdrant` sparse embeddings implementation

It is directly enabling to provide the list of ONNX execution providers:
https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py#L15

It is a bit less clear to a user that just wants to enable GPU, but
gives more capabilities to work with other execution providers that are
not the `CUDAExecutionProvider`, and is more future proof

Sorry for the disturbance @ccurme

> Nice to see you just moved to `uv`! It is so much nicer to run
format/lint/test! No need to manually rerun the `poetry install` with
all required extras now

.github/actions/uv_setup/action.yml

@@ -0,0 +1,21 @@
+# TODO: https://docs.astral.sh/uv/guides/integration/github/#caching
+
+name: uv-install
+description: Set up Python and uv
+
+inputs:
+  python-version:
+    description: Python version, supporting MAJOR.MINOR only
+    required: true
+
+env:
+  UV_VERSION: "0.5.25"
+
+runs:
+  using: composite
+  steps:
+    - name: Install uv and set the python version
+      uses: astral-sh/setup-uv@v5
+      with:
+        version: ${{ env.UV_VERSION }}
+        python-version: ${{ inputs.python-version }}

.github/scripts/check_diff.py

@@ -7,6 +7,8 @@ from typing import Dict, List, Set
 from pathlib import Path
 import tomllib
 
+from packaging.requirements import Requirement
+
 from get_min_versions import get_min_version_from_toml
 
 
@@ -37,6 +39,8 @@ IGNORED_PARTNERS = [
 
 PY_312_MAX_PACKAGES = [
     "libs/partners/huggingface",  # https://github.com/pytorch/pytorch/issues/130249
+    "libs/partners/pinecone",
+    "libs/partners/voyageai",
 ]
 
 
@@ -61,15 +65,17 @@ def dependents_graph() -> dict:
 
         # load regular and test deps from pyproject.toml
         with open(path, "rb") as f:
-            pyproject = tomllib.load(f)["tool"]["poetry"]
+            pyproject = tomllib.load(f)
 
         pkg_dir = "libs" + "/".join(path.split("libs")[1].split("/")[:-1])
         for dep in [
-            *pyproject["dependencies"].keys(),
-            *pyproject["group"]["test"]["dependencies"].keys(),
+            *pyproject["project"]["dependencies"],
+            *pyproject["dependency-groups"]["test"],
         ]:
+            requirement = Requirement(dep)
+            package_name = requirement.name
             if "langchain" in dep:
-                dependents[dep].add(pkg_dir)
+                dependents[package_name].add(pkg_dir)
                 continue
 
         # load extended deps from extended_testing_deps.txt
@@ -120,8 +126,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13"]
     # custom logic for specific directories
     elif dir_ == "libs/partners/milvus":
-        # milvus poetry doesn't allow 3.12 because they
-        # declare deps in funny way
+        # milvus doesn't allow 3.12 because they declare deps in funny way
         py_versions = ["3.9", "3.11"]
 
     elif dir_ in PY_312_MAX_PACKAGES:
@@ -148,17 +153,17 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
 def _get_pydantic_test_configs(
     dir_: str, *, python_version: str = "3.11"
 ) -> List[Dict[str, str]]:
-    with open("./libs/core/poetry.lock", "rb") as f:
-        core_poetry_lock_data = tomllib.load(f)
-    for package in core_poetry_lock_data["package"]:
+    with open("./libs/core/uv.lock", "rb") as f:
+        core_uv_lock_data = tomllib.load(f)
+    for package in core_uv_lock_data["package"]:
         if package["name"] == "pydantic":
             core_max_pydantic_minor = package["version"].split(".")[1]
             break
 
-    with open(f"./{dir_}/poetry.lock", "rb") as f:
-        dir_poetry_lock_data = tomllib.load(f)
+    with open(f"./{dir_}/uv.lock", "rb") as f:
+        dir_uv_lock_data = tomllib.load(f)
 
-    for package in dir_poetry_lock_data["package"]:
+    for package in dir_uv_lock_data["package"]:
         if package["name"] == "pydantic":
             dir_max_pydantic_minor = package["version"].split(".")[1]
             break
@@ -304,7 +309,7 @@ if __name__ == "__main__":
                 f"Unknown lib: {file}. check_diff.py likely needs "
                 "an update for this new library!"
             )
-        elif file.startswith("docs/") or file in ["pyproject.toml", "poetry.lock"]: # docs or root poetry files
+        elif file.startswith("docs/") or file in ["pyproject.toml", "uv.lock"]: # docs or root uv files
             docs_edited = True
             dirs_to_run["lint"].add(".")
 

.github/scripts/check_prerelease_dependencies.py

@@ -10,26 +10,25 @@ if __name__ == "__main__":
         toml_data = tomllib.load(file)
 
     # see if we're releasing an rc
-    version = toml_data["tool"]["poetry"]["version"]
+    version = toml_data["project"]["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:
-        dependencies = toml_data["tool"]["poetry"]["dependencies"]
-        for lib in dependencies:
-            dep_version = dependencies[lib]
+        dependencies = toml_data["project"]["dependencies"]
+        for dep_version in dependencies:
             dep_version_string = (
                 dep_version["version"] if isinstance(dep_version, dict) else dep_version
             )
 
             if "rc" in dep_version_string:
                 raise ValueError(
-                    f"Dependency {lib} has a prerelease version. Please remove this."
+                    f"Dependency {dep_version} has a prerelease version. Please remove this."
                 )
 
             if isinstance(dep_version, dict) and dep_version.get(
                 "allow-prereleases", False
             ):
                 raise ValueError(
-                    f"Dependency {lib} has allow-prereleases set to true. Please remove this."
+                    f"Dependency {dep_version} has allow-prereleases set to true. Please remove this."
                 )

.github/scripts/get_min_versions.py

@@ -1,3 +1,4 @@
+from collections import defaultdict
 import sys
 from typing import Optional
 
@@ -7,6 +8,7 @@ else:
     # for python 3.10 and below, which doesnt have stdlib tomllib
     import tomli as tomllib
 
+from packaging.requirements import Requirement
 from packaging.specifiers import SpecifierSet
 from packaging.version import Version
 
@@ -94,6 +96,23 @@ def get_minimum_version(package_name: str, spec_string: str) -> Optional[str]:
     return str(min(valid_versions)) if valid_versions else None
 
 
+def _check_python_version_from_requirement(
+    requirement: Requirement, python_version: str
+) -> bool:
+    if not requirement.marker:
+        return True
+    else:
+        marker_str = str(requirement.marker)
+        if "python_version" or "python_full_version" in marker_str:
+            python_version_str = "".join(
+                char
+                for char in marker_str
+                if char.isdigit() or char in (".", "<", ">", "=", ",")
+            )
+            return check_python_version(python_version, python_version_str)
+        return True
+
+
 def get_min_version_from_toml(
     toml_path: str,
     versions_for: str,
@@ -105,8 +124,10 @@ def get_min_version_from_toml(
     with open(toml_path, "rb") as file:
         toml_data = tomllib.load(file)
 
-    # Get the dependencies from tool.poetry.dependencies
-    dependencies = toml_data["tool"]["poetry"]["dependencies"]
+    dependencies = defaultdict(list)
+    for dep in toml_data["project"]["dependencies"]:
+        requirement = Requirement(dep)
+        dependencies[requirement.name].append(requirement)
 
     # Initialize a dictionary to store the minimum versions
     min_versions = {}
@@ -121,17 +142,11 @@ def get_min_version_from_toml(
         if lib in dependencies:
             if include and lib not in include:
                 continue
-            # Get the version string
-            version_string = dependencies[lib]
-
-            if isinstance(version_string, dict):
-                version_string = version_string["version"]
-            if isinstance(version_string, list):
-                version_string = [
-                    vs
-                    for vs in version_string
-                    if check_python_version(python_version, vs["python"])
-                ][0]["version"]
+            requirements = dependencies[lib]
+            for requirement in requirements:
+                if _check_python_version_from_requirement(requirement, python_version):
+                    version_string = str(requirement.specifier)
+                    break
 
             # Use parse_version to get the minimum supported version from version_string
             min_version = get_minimum_version(lib, version_string)

.github/workflows/_compile_integration_test.yml

@@ -13,7 +13,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -22,25 +22,22 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
     timeout-minutes: 20
-    name: "poetry run pytest -m compile tests/integration_tests #${{ inputs.python-version }}"
+    name: "uv run pytest -m compile tests/integration_tests #${{ inputs.python-version }}"
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ inputs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: compile-integration
 
       - name: Install integration dependencies
         shell: bash
-        run: poetry install --with=test_integration,test
+        run: uv sync --group test --group test_integration
 
       - name: Check integration tests compile
         shell: bash
-        run: poetry run pytest -m compile tests/integration_tests
+        run: uv run pytest -m compile tests/integration_tests
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/_integration_test.yml

@@ -12,7 +12,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -24,22 +24,19 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ inputs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: core
 
       - name: Install dependencies
         shell: bash
-        run: poetry install --with test,test_integration
+        run: uv sync --group test --group test_integration
 
       - name: Install deps outside pyproject
         if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
         shell: bash
-        run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2"
+        run: VIRTUAL_ENV=.venv uv pip install "boto3<2" "google-cloud-aiplatform<2"
 
       - name: Run integration tests
         shell: bash

.github/workflows/_lint.yml

@@ -13,12 +13,13 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.8.4"
   WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
 
   # This env var allows us to get inline annotations when ruff has complaints.
   RUFF_OUTPUT_FORMAT: github
 
+  UV_FROZEN: "true"
+
 jobs:
   build:
     name: "make lint #${{ inputs.python-version }}"
@@ -27,25 +28,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ inputs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: lint-with-extras
-
-      - name: Check Poetry File
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry check
-
-      - name: Check lock file
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry lock --check
 
       - name: Install dependencies
         # Also installs dev/lint/test/typing dependencies, to ensure we have
@@ -58,17 +44,7 @@ jobs:
         # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry install --with lint,typing
-
-      - name: Get .mypy_cache to speed up mypy
-        uses: actions/cache@v4
-        env:
-          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
-        with:
-          path: |
-            ${{ env.WORKDIR }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ inputs.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
-
+          uv sync --group lint --group typing
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}
@@ -87,21 +63,12 @@ jobs:
         if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }}
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry install --with test
+          uv sync --group test
       - name: Install unit+integration test dependencies
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry install --with test,test_integration
-
-      - name: Get .mypy_cache_test to speed up mypy
-        uses: actions/cache@v4
-        env:
-          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
-        with:
-          path: |
-            ${{ env.WORKDIR }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ inputs.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
+          uv sync --group test --group test_integration
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_release.yml

@@ -21,7 +21,7 @@ on:
 
 env:
   PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -36,13 +36,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
@@ -56,7 +53,7 @@ jobs:
       # > from the publish job.
       # https://github.com/pypa/gh-action-pypi-publish#non-goals
       - name: Build project for distribution
-        run: poetry build
+        run: uv build
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
@@ -67,11 +64,18 @@ jobs:
 
       - name: Check Version
         id: check-version
-        shell: bash
+        shell: python
         working-directory: ${{ inputs.working-directory }}
         run: |
-          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
-          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
+          import os
+          import tomllib
+          with open("pyproject.toml", "rb") as f:
+              data = tomllib.load(f)
+          pkg_name = data["project"]["name"]
+          version = data["project"]["version"]
+          with open(os.environ["GITHUB_OUTPUT"], "a") as f:
+              f.write(f"pkg-name={pkg_name}\n")
+              f.write(f"version={version}\n")
   release-notes:
     needs:
       - build
@@ -184,13 +188,11 @@ jobs:
       # - The package is published, and it breaks on the missing dependency when
       #   used in the real world.
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         id: setup-python
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
 
       - uses: actions/download-artifact@v4
         with:
@@ -213,17 +215,18 @@ jobs:
         # - attempt install again after 5 seconds if it fails because there is
         #   sometimes a delay in availability on test pypi
         run: |
-          poetry run pip install dist/*.whl
+          uv venv
+          VIRTUAL_ENV=.venv uv pip install dist/*.whl
 
           # Replace all dashes in the package name with underscores,
           # since that's how Python imports packages with dashes in the name.
           # also remove _official suffix
           IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g | sed s/_official//g)"
 
-          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+          uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: poetry install --with test --no-root
+        run: uv sync --group test
         working-directory: ${{ inputs.working-directory }}
 
       # Overwrite the local version of the package with the built version
@@ -234,7 +237,7 @@ jobs:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
         run: |
-          poetry run pip install dist/*.whl
+          VIRTUAL_ENV=.venv uv pip install dist/*.whl
 
       - name: Run unit tests
         run: make tests
@@ -243,15 +246,15 @@ jobs:
       - name: Check for prerelease versions
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
+          uv run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
 
       - name: Get minimum versions
         working-directory: ${{ inputs.working-directory }}
         id: min-version
         run: |
-          poetry run pip install packaging requests
-          python_version="$(poetry run python --version | awk '{print $2}')"
-          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release $python_version)"
+          VIRTUAL_ENV=.venv uv pip install packaging requests
+          python_version="$(uv run python --version | awk '{print $2}')"
+          min_versions="$(uv run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release $python_version)"
           echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
           echo "min-versions=$min_versions"
 
@@ -260,12 +263,12 @@ jobs:
         env:
           MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
         run: |
-          poetry run pip install --force-reinstall $MIN_VERSIONS --editable .
+          VIRTUAL_ENV=.venv uv pip install --force-reinstall $MIN_VERSIONS --editable .
           make tests
         working-directory: ${{ inputs.working-directory }}
 
       - name: Import integration test dependencies
-        run: poetry install --with test,test_integration
+        run: uv sync --group test --group test_integration
         working-directory: ${{ inputs.working-directory }}
 
       - name: Run integration tests
@@ -306,6 +309,7 @@ jobs:
           UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
+          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
@@ -331,13 +335,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
 
       - uses: actions/download-artifact@v4
         with:
@@ -373,13 +374,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
 
       - uses: actions/download-artifact@v4
         with:

.github/workflows/_test.yml

@@ -13,7 +13,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -26,17 +26,14 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ inputs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         id: setup-python
         with:
           python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: core
       - name: Install dependencies
         shell: bash
-        run: poetry install --with test
+        run: uv sync --group test --dev
 
       - name: Run core tests
         shell: bash
@@ -48,9 +45,9 @@ jobs:
         id: min-version
         shell: bash
         run: |
-          poetry run pip install packaging tomli requests
-          python_version="$(poetry run python --version | awk '{print $2}')"
-          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml pull_request $python_version)"
+          VIRTUAL_ENV=.venv uv pip install packaging tomli requests
+          python_version="$(uv run python --version | awk '{print $2}')"
+          min_versions="$(uv run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml pull_request $python_version)"
           echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
           echo "min-versions=$min_versions"
 
@@ -59,8 +56,7 @@ jobs:
         env:
           MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
         run: |
-          poetry run pip install uv
-          poetry run uv pip install $MIN_VERSIONS
+          VIRTUAL_ENV=.venv uv pip install $MIN_VERSIONS
           make tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/_test_doc_imports.yml

@@ -9,7 +9,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -19,25 +19,23 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ inputs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: core
 
       - name: Install dependencies
         shell: bash
-        run: poetry install --with test
+        run: uv sync --group test
 
       - name: Install langchain editable
         run: |
-          poetry run pip install langchain-experimental -e libs/core libs/langchain libs/community
+          VIRTUAL_ENV=.venv uv pip install langchain-experimental -e libs/core libs/langchain libs/community
 
       - name: Check doc imports
         shell: bash
         run: |
-          poetry run python docs/scripts/check_imports.py
+          uv run python docs/scripts/check_imports.py
 
       - name: Ensure the test did not create any additional files
         shell: bash

.github/workflows/_test_pydantic.yml

@@ -18,7 +18,7 @@ on:
         description: "Pydantic version to test."
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -31,21 +31,18 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ inputs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: core
 
       - name: Install dependencies
         shell: bash
-        run: poetry install --with test
+        run: uv sync --group test
 
       - name: Overwrite pydantic version
         shell: bash
-        run: poetry run pip install pydantic~=${{ inputs.pydantic-version }}
+        run: VIRTUAL_ENV=.venv uv pip install pydantic~=${{ inputs.pydantic-version }}
 
       - name: Run core tests
         shell: bash

.github/workflows/_test_release.yml

@@ -14,8 +14,8 @@ on:
         description: "Release from a non-master branch (danger!)"
 
 env:
-  POETRY_VERSION: "1.8.4"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.11"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -29,13 +29,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
@@ -49,7 +46,7 @@ jobs:
       # > from the publish job.
       # https://github.com/pypa/gh-action-pypi-publish#non-goals
       - name: Build project for distribution
-        run: poetry build
+        run: uv build
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
@@ -60,11 +57,18 @@ jobs:
 
       - name: Check Version
         id: check-version
-        shell: bash
+        shell: python
         working-directory: ${{ inputs.working-directory }}
         run: |
-          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
-          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
+          import os
+          import tomllib
+          with open("pyproject.toml", "rb") as f:
+              data = tomllib.load(f)
+          pkg_name = data["project"]["name"]
+          version = data["project"]["version"]
+          with open(os.environ["GITHUB_OUTPUT"], "a") as f:
+              f.write(f"pkg-name={pkg_name}\n")
+              f.write(f"version={version}\n")
 
   publish:
     needs:

.github/workflows/api_doc_build.yml

@@ -5,7 +5,6 @@ on:
   schedule:
     - cron:  '0 13 * * *'
 env:
-  POETRY_VERSION: "1.8.4"
   PYTHON_VERSION: "3.11"
 
 jobs:
@@ -46,20 +45,18 @@ jobs:
             fi
           done
 
-      - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./langchain/.github/actions/poetry_setup"
+      - name: Setup python ${{ env.PYTHON_VERSION }}
+        uses: actions/setup-python@v5
+        id: setup-python
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: api-docs
-          working-directory: langchain
 
       - name: Install initial py deps
         working-directory: langchain
         run: |
           python -m pip install -U uv
           python -m uv pip install --upgrade --no-cache-dir pip setuptools pyyaml
-          
+
       - name: Move libs with script
         run: python langchain/.github/scripts/prep_api_docs_build.py
         env:

.github/workflows/check_diffs.yml

@@ -18,7 +18,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -127,24 +127,19 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ matrix.job-configs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ matrix.job-configs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ matrix.job-configs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ matrix.job-configs.working-directory }}
-          cache-key: extended
 
-      - name: Install dependencies
+      - name: Install dependencies and run extended tests
         shell: bash
         run: |
-          echo "Running extended tests, installing dependencies with poetry..."
-          poetry install --with test
-          poetry run pip install uv
-          poetry run uv pip install -r extended_testing_deps.txt
-
-      - name: Run extended tests
-        run: make extended_tests
+          echo "Running extended tests, installing dependencies with uv..."
+          uv venv
+          uv sync --group test
+          VIRTUAL_ENV=.venv uv pip install -r extended_testing_deps.txt
+          VIRTUAL_ENV=.venv make extended_tests
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/run_notebooks.yml

@@ -15,7 +15,7 @@ on:
     - cron: '0 13 * * *'
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -25,13 +25,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ github.event.inputs.python_version || '3.11' }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: run-notebooks
 
       - name: 'Authenticate to Google Cloud'
         id: 'auth'
@@ -48,17 +45,17 @@ jobs:
 
       - name: Install dependencies
         run: |
-          poetry install --with dev,test
+          uv sync --group dev --group test
 
       - name: Pre-download files
         run: |
-          poetry run python docs/scripts/cache_data.py
+          uv run python docs/scripts/cache_data.py
           curl -s https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql | sqlite3 docs/docs/how_to/Chinook.db
           cp docs/docs/how_to/Chinook.db docs/docs/tutorials/Chinook.db
 
       - name: Prepare notebooks
         run: |
-          poetry run python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells --working-directory ${{ github.event.inputs.working-directory || 'all' }}
+          uv run python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells --working-directory ${{ github.event.inputs.working-directory || 'all' }}
 
       - name: Run notebooks
         env:

.github/workflows/scheduled_test.yml

@@ -14,7 +14,9 @@ on:
 
 env:
   POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
   DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/google-vertexai", "libs/partners/google-genai", "libs/partners/aws"]'
+  POETRY_LIBS: ("libs/partners/google-vertexai" "libs/partners/google-genai" "libs/partners/aws")
 
 jobs:
   compute-matrix:
@@ -79,7 +81,8 @@ jobs:
           mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
           mv langchain-aws/libs/aws langchain/libs/partners/aws
 
-      - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }} with poetry
+        if: contains(env.POETRY_LIBS, matrix.working-directory)
         uses: "./langchain/.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
@@ -87,6 +90,12 @@ jobs:
           working-directory: langchain/${{ matrix.working-directory }}
           cache-key: scheduled
 
+      - name: Set up Python ${{ matrix.python-version }} + uv
+        if: "!contains(env.POETRY_LIBS, matrix.working-directory)"
+        uses: "./langchain/.github/actions/uv_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+
       - name: 'Authenticate to Google Cloud'
         id: 'auth'
         uses: google-github-actions/auth@v2
@@ -100,12 +109,20 @@ jobs:
           aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
           aws-region: ${{ secrets.AWS_REGION }}
 
-      - name: Install dependencies
+      - name: Install dependencies (poetry)
+        if: contains(env.POETRY_LIBS, matrix.working-directory)
         run: |
           echo "Running scheduled tests, installing dependencies with poetry..."
           cd langchain/${{ matrix.working-directory }}
           poetry install --with=test_integration,test
 
+      - name: Install dependencies (uv)
+        if: "!contains(env.POETRY_LIBS, matrix.working-directory)"
+        run: |
+          echo "Running scheduled tests, installing dependencies with uv..."
+          cd langchain/${{ matrix.working-directory }}
+          uv sync --group test --group test_integration
+
       - name: Run integration tests
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
@@ -117,6 +134,7 @@ jobs:
           AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
           AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
+          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
           HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}

Makefile

@@ -1,5 +1,8 @@
 .PHONY: all clean help docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck spell_check spell_fix lint lint_package lint_tests format format_diff
 
+.EXPORT_ALL_VARIABLES:
+UV_FROZEN = true
+
 ## help: Show this help info.
 help: Makefile
 	@printf "\n\033[1mUsage: make <TARGETS> ...\033[0m\n\n\033[1mTargets:\033[0m\n\n"
@@ -25,20 +28,20 @@ docs_clean:
 
 ## docs_linkcheck: Run linkchecker on the documentation.
 docs_linkcheck:
-	poetry run linkchecker _dist/docs/ --ignore-url node_modules
+	uv run --no-group test linkchecker _dist/docs/ --ignore-url node_modules
 
 ## api_docs_build: Build the API Reference documentation.
 api_docs_build:
-	poetry run python docs/api_reference/create_api_rst.py
-	cd docs/api_reference && poetry run make html
-	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/
+	uv run --no-group test python docs/api_reference/create_api_rst.py
+	cd docs/api_reference && uv run --no-group test make html
+	uv run --no-group test python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/
 
 API_PKG ?= text-splitters
 
 api_docs_quick_preview:
-	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/
+	uv run --no-group test python docs/api_reference/create_api_rst.py $(API_PKG)
+	cd docs/api_reference && uv run make html
+	uv run --no-group test python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/
 	open docs/api_reference/_build/html/reference.html
 
 ## api_docs_clean: Clean the API Reference documentation build artifacts.
@@ -50,15 +53,15 @@ api_docs_clean:
 
 ## api_docs_linkcheck: Run linkchecker on the API Reference documentation.
 api_docs_linkcheck:
-	poetry run linkchecker docs/api_reference/_build/html/index.html
+	uv run --no-group test linkchecker docs/api_reference/_build/html/index.html
 
 ## spell_check: Run codespell on the project.
 spell_check:
-	poetry run codespell --toml pyproject.toml
+	uv run --no-group test codespell --toml pyproject.toml
 
 ## spell_fix: Run codespell on the project and fix the errors.
 spell_fix:
-	poetry run codespell --toml pyproject.toml -w
+	uv run --no-group test codespell --toml pyproject.toml -w
 
 ######################
 # LINTING AND FORMATTING
@@ -66,9 +69,9 @@ spell_fix:
 
 ## lint: Run linting on the project.
 lint lint_package lint_tests:
-	poetry run ruff check docs cookbook
-	poetry run ruff format docs cookbook cookbook --diff
-	poetry run ruff check --select I docs cookbook
+	uv run --group lint ruff check docs cookbook
+	uv run --group lint ruff format docs cookbook cookbook --diff
+	uv run --group lint ruff check --select I docs cookbook
 	git --no-pager grep 'from langchain import' docs cookbook | grep -vE 'from langchain import (hub)' && echo "Error: no importing langchain from root in docs, except for hub" && exit 1 || exit 0
 	
 	git --no-pager grep 'api.python.langchain.com' -- docs/docs ':!docs/docs/additional_resources/arxiv_references.mdx' ':!docs/docs/integrations/document_loaders/sitemap.ipynb' || exit 0 && \
@@ -77,5 +80,5 @@ lint lint_package lint_tests:
 
 ## format: Format the project files.
 format format_diff:
-	poetry run ruff format docs cookbook
-	poetry run ruff check --select I --fix docs cookbook
+	uv run --group lint ruff format docs cookbook
+	uv run --group lint ruff check --select I --fix docs cookbook

README.md

@@ -52,7 +52,7 @@ For these applications, LangChain simplifies the entire application lifecycle:
 - **Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
 - **`langchain-community`**: Third-party integrations that are community maintained.
-- **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
+- **[LangGraph](https://langchain-ai.github.io/langgraph)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### Productionization:
 

docs/api_reference/create_api_rst.py

@@ -528,7 +528,12 @@ def _get_package_version(package_dir: Path) -> str:
             "Aborting the build."
         )
         exit(1)
-    return pyproject["tool"]["poetry"]["version"]
+    try:
+        # uses uv
+        return pyproject["project"]["version"]
+    except KeyError:
+        # uses poetry
+        return pyproject["tool"]["poetry"]["version"]
 
 
 def _out_file_path(package_name: str) -> Path:

docs/cassettes/chat_token_usage_tracking_05f22a1d-b021-490f-8840-f628a07459f2.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/chat_token_usage_tracking_07f0c872-6b6c-4fed-a129-9b5a858505be.msgpack.zlib

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docs/cassettes/chat_token_usage_tracking_b39bf807-4125-4db4-bbf7-28a46afff6b4.msgpack.zlib

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docs/cassettes/chat_token_usage_tracking_c00c9158-7bb4-4279-88e6-ea70f46e6ac2.msgpack.zlib

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docs/cassettes/qa_citations_daad793f-f08d-4b56-90e8-3f015a79a88e.msgpack.zlib

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docs/cassettes/tools_builtin_1365784c-e666-41c8-a1bb-e50f822b5936.msgpack.zlib

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docs/cassettes/tools_builtin_b4eaed85-c5a6-4ba9-b401-40258b0131c2.msgpack.zlib

@@ -1 +1 @@
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docs/docs/concepts/vectorstores.mdx

@@ -39,7 +39,7 @@ The interface consists of basic methods for writing, deleting and searching for
 The key methods are:
 
 - `add_documents`: Add a list of texts to the vector store.
-- `delete_documents`: Delete a list of documents from the vector store.
+- `delete`: Delete a list of documents from the vector store.
 - `similarity_search`: Search for similar documents to a given query.
 
 
@@ -89,10 +89,10 @@ vector_store.add_documents(documents=documents, ids=["doc1", "doc2"])
 
 ## Delete
 
-To delete documents, use the `delete_documents` method which takes a list of document IDs to delete.
+To delete documents, use the `delete` method which takes a list of document IDs to delete.
 
 ```python
-vector_store.delete_documents(ids=["doc1"])
+vector_store.delete(ids=["doc1"])
 ```
 
 ## Search

docs/docs/contributing/how_to/code/setup.mdx

@@ -3,16 +3,11 @@
 This guide walks through how to run the repository locally and check in your first code.
 For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchain/tree/master/.devcontainer).
 
-## Dependency Management: Poetry and other env/dependency managers
+## Dependency Management: `uv` and other env/dependency managers
 
-This project utilizes [Poetry](https://python-poetry.org/) v1.7.1+ as a dependency manager.
+This project utilizes [uv](https://docs.astral.sh/uv/) v0.5+ as a dependency manager.
 
-❗Note: *Before installing Poetry*, if you use `Conda`, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`)
-
-Install Poetry: **[documentation on how to install it](https://python-poetry.org/docs/#installation)**.
-
-❗Note: If you use `Conda` or `Pyenv` as your environment/package manager, after installing Poetry,
-tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
+Install `uv`: **[documentation on how to install it](https://docs.astral.sh/uv/getting-started/installation/)**.
 
 ## Different packages
 
@@ -37,7 +32,7 @@ cd libs/community
 Install langchain-community development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
 
 ```bash
-poetry install --with lint,typing,test,test_integration
+uv sync
 ```
 
 Then verify dependency installation:
@@ -46,12 +41,6 @@ Then verify dependency installation:
 make test
 ```
 
-If during installation you receive a `WheelFileValidationError` for `debugpy`, please make sure you are running
-Poetry v1.6.1+. This bug was present in older versions of Poetry (e.g. 1.4.1) and has been resolved in newer releases.
-If you are still seeing this bug on v1.6.1+, you may also try disabling "modern installation"
-(`poetry config installer.modern-installation false`) and re-installing requirements.
-See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
-
 ## Testing
 
 **Note:** In `langchain`, `langchain-community`, and `langchain-experimental`, some test dependencies are optional. See the following section about optional dependencies.
@@ -79,7 +68,6 @@ If you are only developing `langchain_core` or `langchain_community`, you can si
 
 ```bash
 cd libs/core
-poetry install --with test
 make test
 ```
 
@@ -87,7 +75,6 @@ Or:
 
 ```bash
 cd libs/community
-poetry install --with test
 make test
 ```
 
@@ -179,7 +166,7 @@ ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy
 
 `langchain-core` and partner packages **do not use** optional dependencies in this way.
 
-You'll notice that `pyproject.toml` and `poetry.lock` are **not** touched when you add optional dependencies below.
+You'll notice that `pyproject.toml` and `uv.lock` are **not** touched when you add optional dependencies below.
 
 If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and
 that most users won't have it installed.
@@ -196,18 +183,10 @@ test makes use of lightweight fixtures to test the logic of the code.
 
 ## Adding a Jupyter Notebook
 
-If you are adding a Jupyter Notebook example, you'll want to install the optional `dev` dependencies.
-
-To install dev dependencies:
+If you are adding a Jupyter Notebook example, you'll want to run with `test` dependencies:
 
 ```bash
-poetry install --with dev
+uv run --group test jupyter notebook
 ```
 
-Launch a notebook:
-
-```bash
-poetry run jupyter notebook
-```
-
-When you run `poetry install`, the `langchain` package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.
+When you run `uv sync`, the `langchain` package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.

docs/docs/how_to/local_llms.ipynb

@@ -477,7 +477,7 @@
     "2) Make the file executable\n",
     "3) Run the file\n",
     "\n",
-    "llamafiles bundle model weights and a [specially-compiled](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#technical-details) version of [`llama.cpp`](https://github.com/ggerganov/llama.cpp) into a single file that can run on most computers any additional dependencies. They also come with an embedded inference server that provides an [API](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md#api-endpoints) for interacting with your model. \n",
+    "llamafiles bundle model weights and a [specially-compiled](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#technical-details) version of [`llama.cpp`](https://github.com/ggerganov/llama.cpp) into a single file that can run on most computers without any additional dependencies. They also come with an embedded inference server that provides an [API](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md#api-endpoints) for interacting with your model. \n",
     "\n",
     "Here's a simple bash script that shows all 3 setup steps:\n",
     "\n",

docs/docs/integrations/chat/deepseek.ipynb

@@ -31,7 +31,7 @@
     "\n",
     "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/deepseek) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatDeepSeek](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html) | [langchain-deepseek-official](https://python.langchain.com/api_reference/deepseek/) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-deepseek-official?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek-official?style=flat-square&label=%20) |\n",
+    "| [ChatDeepSeek](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html) | [langchain-deepseek](https://python.langchain.com/api_reference/deepseek/) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-deepseek?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -40,7 +40,7 @@
     "\n",
     "## Setup\n",
     "\n",
-    "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek-official` integration package.\n",
+    "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.\n",
     "\n",
     "### Credentials\n",
     "\n",
@@ -87,7 +87,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain DeepSeek integration lives in the `langchain-deepseek-official` package:"
+    "The LangChain DeepSeek integration lives in the `langchain-deepseek` package:"
    ]
   },
   {
@@ -97,7 +97,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-deepseek-official"
+    "%pip install -qU langchain-deepseek"
    ]
   },
   {

docs/docs/integrations/chat/goodfire.ipynb

@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Goodfire\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatGoodfire\n",
+    "\n",
+    "This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatGoodfire](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html) | [langchain-goodfire](https://python.langchain.com/api_reference/goodfire/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-goodfire?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-goodfire?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Goodfire models you'll need to create a/an Goodfire account, get an API key, and install the `langchain-goodfire` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [Goodfire Settings](https://platform.goodfire.ai/organization/settings/api-keys) to sign up to Goodfire and generate an API key. Once you've done this set the GOODFIRE_API_KEY environment variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"GOODFIRE_API_KEY\"):\n",
+    "    os.environ[\"GOODFIRE_API_KEY\"] = getpass.getpass(\"Enter your Goodfire API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Goodfire integration lives in the `langchain-goodfire` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU langchain-goodfire"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import goodfire\n",
+    "from langchain_goodfire import ChatGoodfire\n",
+    "\n",
+    "base_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
+    "\n",
+    "llm = ChatGoodfire(\n",
+    "    model=base_variant,\n",
+    "    temperature=0,\n",
+    "    max_completion_tokens=1000,\n",
+    "    seed=42,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={}, id='run-8d43cf35-bce8-4827-8935-c64f8fb78cd0-0', usage_metadata={'input_tokens': 51, 'output_tokens': 39, 'total_tokens': 90})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = await llm.ainvoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore la programmation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren. How can I help you with programming today?', additional_kwargs={}, response_metadata={}, id='run-03d1a585-8234-46f1-a8df-bf9143fe3309-0', usage_metadata={'input_tokens': 46, 'output_tokens': 46, 'total_tokens': 92})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "await chain.ainvoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": [
+    "## Goodfire-specific functionality\n",
+    "\n",
+    "To use Goodfire-specific functionality such as SAE features and variants, you can use the `goodfire` package directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3aef9e0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FeatureGroup([\n",
+       "   0: \"The assistant should adopt the persona of a pirate\",\n",
+       "   1: \"The assistant should roleplay as a pirate\",\n",
+       "   2: \"The assistant should engage with pirate-themed content or roleplay as a pirate\",\n",
+       "   3: \"The assistant should roleplay as a character\",\n",
+       "   4: \"The assistant should roleplay as a specific character\",\n",
+       "   5: \"The assistant should roleplay as a game character or NPC\",\n",
+       "   6: \"The assistant should roleplay as a human character\",\n",
+       "   7: \"Requests for the assistant to roleplay or pretend to be something else\",\n",
+       "   8: \"Requests for the assistant to roleplay or pretend to be something\",\n",
+       "   9: \"The assistant is being assigned a role or persona to roleplay\"\n",
+       "])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = goodfire.Client(api_key=os.environ[\"GOODFIRE_API_KEY\"])\n",
+    "\n",
+    "pirate_features = client.features.search(\n",
+    "    \"assistant should roleplay as a pirate\", base_variant\n",
+    ")\n",
+    "pirate_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "52f03a00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field! Arrr! Hope that made ye laugh, matey!', additional_kwargs={}, response_metadata={}, id='run-7d8bd30f-7f80-41cb-bdb6-25c29c22a7ce-0', usage_metadata={'input_tokens': 35, 'output_tokens': 60, 'total_tokens': 95})"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pirate_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
+    "\n",
+    "pirate_variant.set(pirate_features[0], 0.4)\n",
+    "pirate_variant.set(pirate_features[1], 0.3)\n",
+    "\n",
+    "await llm.ainvoke(\"Tell me a joke\", model=pirate_variant)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatGoodfire features and configurations head to the [API reference](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/document_loaders/pypdfdirectory.ipynb

@@ -36,13 +36,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-21T08:00:08.878423Z",
+     "start_time": "2025-01-21T08:00:08.876042Z"
+    }
+   },
    "source": [
     "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
     "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
@@ -54,17 +59,28 @@
    ]
   },
   {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-21T08:00:12.003718Z",
+     "start_time": "2025-01-21T08:00:10.291617Z"
+    }
+   },
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
+   "source": "%pip install -qU langchain_community pypdf pillow",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "execution_count": 2
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## Initialization\n",
     "\n",
@@ -72,10 +88,13 @@
    ]
   },
   {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-21T08:00:18.512061Z",
+     "start_time": "2025-01-21T08:00:17.313969Z"
+    }
+   },
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "from langchain_community.document_loaders import PyPDFDirectoryLoader\n",
     "\n",
@@ -83,7 +102,9 @@
     "    \"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf\"\n",
     ")\n",
     "loader = PyPDFDirectoryLoader(\"example_data/\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -94,41 +115,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-21T08:00:23.549752Z",
+     "start_time": "2025-01-21T08:00:23.129010Z"
+    }
+   },
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ],
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Document(metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0}, page_content='LayoutParser : A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1( \\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1Allen Institute for AI\\nshannons@allenai.org\\n2Brown University\\nruochen zhang@brown.edu\\n3Harvard University\\n{melissadell,jacob carlson }@fas.harvard.edu\\n4University of Washington\\nbcgl@cs.washington.edu\\n5University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser , an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io .\\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\\n·Character Recognition ·Open Source library ·Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [ 11,arXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
+       "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\\x00 ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-21T08:00:26.612346Z",
+     "start_time": "2025-01-21T08:00:26.609051Z"
+    }
+   },
+   "source": [
+    "print(docs[0].metadata)"
+   ],
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'source': 'example_data/layout-parser-paper.pdf', 'page': 0}\n"
+      "{'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}\n"
      ]
     }
    ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -139,9 +170,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-21T08:00:30.251598Z",
+     "start_time": "2025-01-21T08:00:29.972141Z"
+    }
+   },
    "source": [
     "page = []\n",
     "for doc in loader.lazy_load():\n",
@@ -151,7 +185,9 @@
     "        # index.upsert(page)\n",
     "\n",
     "        page = []"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -161,6 +197,13 @@
     "\n",
     "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
    ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
   }
  ],
  "metadata": {

docs/docs/integrations/document_loaders/readthedocs_documentation.ipynb

@@ -55,7 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = ReadTheDocsLoader(\"rtdocs\", features=\"html.parser\")"
+    "loader = ReadTheDocsLoader(\"rtdocs\")"
    ]
   },
   {

docs/docs/integrations/llms/oci_generative_ai.ipynb

@@ -135,6 +135,7 @@
     "    compartment_id=\"MY_OCID\",\n",
     "    auth_type=\"SECURITY_TOKEN\",\n",
     "    auth_profile=\"MY_PROFILE\",  # replace with your profile name\n",
+    "    auth_file_location=\"MY_CONFIG_FILE_LOCATION\",  # replace with file location where profile name configs present\n",
     ")"
    ]
   },
@@ -159,6 +160,7 @@
     "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
     "    compartment_id=\"DEDICATED_COMPARTMENT_OCID\",\n",
     "    auth_profile=\"MY_PROFILE\",  # replace with your profile name,\n",
+    "    auth_file_location=\"MY_CONFIG_FILE_LOCATION\",  # replace with file location where profile name configs present\n",
     "    provider=\"MODEL_PROVIDER\",  # e.g., \"cohere\" or \"meta\"\n",
     "    context_size=\"MODEL_CONTEXT_SIZE\",  # e.g., 128000\n",
     ")"

docs/docs/integrations/providers/goodfire.mdx

@@ -0,0 +1,14 @@
+# Goodfire
+
+[Goodfire](https://www.goodfire.ai/) is a research lab focused on AI safety and
+interpretability.
+
+## Installation and Setup
+
+```bash
+pip install langchain-goodfire
+```
+
+## Chat models
+
+See detail on available chat models [here](/docs/integrations/chat/goodfire).

docs/docs/integrations/providers/jenkins.mdx

@@ -0,0 +1,17 @@
+# Jenkins
+
+[Jenkins](https://www.jenkins.io/) is an open-source automation platform that enables
+software teams to streamline their development workflows. It's widely adopted in the
+DevOps community as a tool for automating the building, testing, and deployment of
+applications through CI/CD pipelines.
+
+
+## Installation and Setup
+
+```bash
+pip install langchain-jenkins
+```
+
+## Tools
+
+See detail on available tools [here](/docs/integrations/tools/jenkins).

docs/docs/integrations/providers/nimble.ipynb

@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "id": "afaf8039"
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: Nimble\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {
+    "id": "72ee0c4b-9764-423a-9dbf-95129e185210"
+   },
+   "source": [
+    "# Nimble\n",
+    "\n",
+    "  [Nimble](https://www.linkedin.com/company/nimbledata) is the first business external data platform, making data decision-making easier than ever, with our award-winning AI-powered data structuring technology Nimble connects business users with the public web knowledge.\n",
+    "We empower businesses with mission-critical real-time external data to unlock advanced business intelligence, price comparison, and other public data for sales and marketing. We translate data into immediate business value.\n",
+    "\n",
+    "If you'd like to learn more about Nimble, visit us at [nimbleway.com](https://www.nimbleway.com/).\n",
+    "\n",
+    "\n",
+    "## Currently we expose the following components\n",
+    "\n",
+    "*   **Retriever** - Allow us to query the internet and get parsed textual results utilizing several search engines.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Usage"
+   ],
+   "metadata": {
+    "id": "AuMFgVFrKbNH"
+   },
+   "id": "AuMFgVFrKbNH"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In order to use our provider you have to provide an API key like so"
+   ],
+   "metadata": {
+    "id": "sFlPjZX9KdK6"
+   },
+   "id": "sFlPjZX9KdK6"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"NIMBLE_API_KEY\"] = getpass.getpass()"
+   ],
+   "metadata": {
+    "id": "eAqSHZ-Z8R3F"
+   },
+   "id": "eAqSHZ-Z8R3F",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For more information about the Authentication process, see [Nimble APIs Authentication Documentation](https://docs.nimbleway.com/nimble-sdk/web-api/nimble-web-api-quick-start-guide/nimble-apis-authentication)."
+   ],
+   "metadata": {
+    "id": "WfwnI_RS8PO5"
+   },
+   "id": "WfwnI_RS8PO5"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "colab": {
+   "provenance": []
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/providers/payman-tool.mdx

@@ -1,4 +1,3 @@
-
 # PaymanAI
 
 PaymanAI provides functionality to send and receive payments (fiat and crypto) on behalf of an AI Agent. To get started:
@@ -24,16 +23,16 @@ These can be wrapped as **LangChain Tools** for an LLM-based agent to call them
 
 | Class | Package | Serializable | JS support | Package latest |
 | :--- | :--- | :---: | :---: | :--- |
-| PaymanAI | `langchain_community` | ❌ | ❌ | [PyPI Version] |
+| PaymanAI | `langchain-payman-tool` | ❌ | ❌ | [PyPI Version] |
 
 If you're simply calling the PaymanAI SDK, you can do it directly or via the **Tool** interface in LangChain.
 
 ## Setup
 
-1. **Install** the `langchain-community` (or equivalent) package:
+1. **Install** the PaymanAI tool package:
 
 ```bash
-pip install --quiet -U langchain-community
+pip install langchain-payman-tool
 ```
 
 2. **Install** the PaymanAI SDK:
@@ -54,7 +53,7 @@ Your `PAYMAN_API_SECRET` should be the secret key from app.paymanai.com. The `PA
 Here is an example of instantiating a PaymanAI tool. If you have multiple Payman methods, you can create multiple tools.
 
 ```python
-from langchain_community.tools.langchain_payman_tool.tool import PaymanAI
+from langchain_payman_tool.tool import PaymanAI
 
 # Instantiate the PaymanAI tool (example)
 tool = PaymanAI(
@@ -104,7 +103,7 @@ You can bind a PaymanAI tool to a LangChain agent or chain that supports tool-ca
 1. **Sign up** at app.paymanai.com to get your **API Key**.
 2. **Install** dependencies:
    ```bash
-   pip install paymanai langchain-community
+   pip install paymanai langchain-payman-tool
    ```
 3. **Export** environment variables:
    ```bash
@@ -112,4 +111,4 @@ You can bind a PaymanAI tool to a LangChain agent or chain that supports tool-ca
    export PAYMAN_ENVIRONMENT="sandbox"
    ```
 4. **Instantiate** a PaymanAI tool, passing your desired name/description.
-5. **Call** the tool with `.invoke(...)` or integrate it into a chain or agent.
+5. **Call** the tool with `.invoke(...)` or integrate it into a chain or agent.

docs/docs/integrations/text_embedding/oci_generative_ai.ipynb

@@ -103,6 +103,7 @@
     "    compartment_id=\"MY_OCID\",\n",
     "    auth_type=\"SECURITY_TOKEN\",\n",
     "    auth_profile=\"MY_PROFILE\",  # replace with your profile name\n",
+    "    auth_file_location=\"MY_CONFIG_FILE_LOCATION\",  # replace with file location where profile name configs present\n",
     ")\n",
     "\n",
     "\n",

docs/docs/integrations/tools/jenkins.ipynb

@@ -0,0 +1,225 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Jenkins\n",
+    "\n",
+    "Tools for interacting with [Jenkins](https://www.jenkins.io/).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "The `langchain-jenkins` package allows you to execute and control CI/CD pipelines with\n",
+    "Jenkins."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "Install `langchain-jenkins`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langchain-jenkins"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "You'll need to setup or obtain authorization to access Jenkins server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"PASSWORD\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "To disable the SSL Verify, set `os.environ[\"PYTHONHTTPSVERIFY\"] = \"0\"`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_jenkins import JenkinsAPIWrapper, JenkinsJobRun\n",
+    "\n",
+    "tools = [\n",
+    "    JenkinsJobRun(\n",
+    "        api_wrapper=JenkinsAPIWrapper(\n",
+    "            jenkins_server=\"https://example.com\",\n",
+    "            username=\"admin\",\n",
+    "            password=os.environ[\"PASSWORD\"],\n",
+    "        )\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "You can now call invoke and pass arguments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Create the Jenkins job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jenkins_job_content = \"\"\n",
+    "src_file = \"job1.xml\"\n",
+    "with open(src_file) as fread:\n",
+    "    jenkins_job_content = fread.read()\n",
+    "tools[0].invoke({\"job\": \"job01\", \"config_xml\": jenkins_job_content, \"action\": \"create\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Run the Jenkins Job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools[0].invoke({\"job\": \"job01\", \"parameters\": {}, \"action\": \"run\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Get job info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resp = tools[0].invoke({\"job\": \"job01\", \"number\": 1, \"action\": \"status\"})\n",
+    "if not resp[\"inProgress\"]:\n",
+    "    print(resp[\"result\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. Delete the jenkins job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools[0].invoke({\"job\": \"job01\", \"action\": \"delete\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "TODO.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation [API reference](https://python.langchain.com/docs/integrations/tools/jenkins/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3929050b09828356c9f5ebaf862d05c053d8228eddbc70f990c168e54dd824ba"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/introduction.mdx

@@ -104,7 +104,7 @@ Head to the reference section for full documentation of all classes and methods
 Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
 
 ### [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph)
-Build stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
+Build stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it. LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more.
 
 ## Additional resources
 

docs/docusaurus.config.js

@@ -137,6 +137,11 @@ const config = {
         disableSwitch: false,
         respectPrefersColorScheme: true,
       },
+      announcementBar: {
+        content:
+          '<strong>Join us at <a href="https://interrupt.langchain.com/" target="_blank" rel="noopener noreferrer"> Interrupt: The Agent AI Conference by LangChain</a> on May 13 & 14 in San Francisco!</strong>',
+          backgroundColor: '#d0c9fe'
+      },
       prism: {
         theme: {
           ...baseLightCodeBlockTheme,

docs/scripts/execute_notebooks.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Read the list of notebooks to skip from the JSON file
-SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
+SKIP_NOTEBOOKS=$(uv run python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
 
 # Get the working directory or specific notebook file from the input parameter
 WORKING_DIRECTORY=$1
@@ -13,7 +13,7 @@ execute_notebook() {
     total="$3"
     echo "Starting execution of $file ($index/$total)"
     start_time=$(date +%s)
-    if ! output=$(time poetry run jupyter nbconvert --to notebook --execute --ExecutePreprocessor.kernel_name=python3 $file 2>&1); then
+    if ! output=$(time uv run --group dev --group test jupyter nbconvert --to notebook --execute --ExecutePreprocessor.kernel_name=python3 $file 2>&1); then
         end_time=$(date +%s)
         execution_time=$((end_time - start_time))
         echo "Error in $file. Execution time: $execution_time seconds"

docs/scripts/update_cassettes.sh

@@ -12,7 +12,7 @@
 # modifications should be discarded after the cassettes are generated.
 #
 # Usage:
-# In monorepo env, `poetry install --with dev,test`
+# In monorepo env, `uv sync --group dev --group test`
 # `./docs/scripts/update_cassettes.sh path/to/notebook`
 # e.g., `./docs/scripts/update_cassettes.sh docs/docs/how_to/tool_choice.ipynb`
 #
@@ -41,11 +41,11 @@ delete_cassettes "$WORKING_DIRECTORY"
 
 # Pre-download tiktoken files
 echo "Pre-downloading nltk and tiktoken files..."
-poetry run python docs/scripts/cache_data.py
+uv run python docs/scripts/cache_data.py
 
 # Prepare notebooks
 echo "Preparing notebooks for CI..."
-poetry run python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells --working-directory "$WORKING_DIRECTORY"
+uv run python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells --working-directory "$WORKING_DIRECTORY"
 
 # Run notebooks
 echo "Running notebooks..."

docs/src/theme/ChatModelTabs.js

@@ -121,7 +121,7 @@ export const CustomDropdown = ({ selectedOption, options, onSelect, modelType })
  * @param {ChatModelTabsProps} props - Component props.
  */
 export default function ChatModelTabs(props) {
-  const [selectedModel, setSelectedModel] = useState("OpenAI");
+  const [selectedModel, setSelectedModel] = useState("Groq");
   const {
     openaiParams,
     anthropicParams,
@@ -174,13 +174,20 @@ export default function ChatModelTabs(props) {
   const llmVarName = customVarName ?? "model";
 
   const tabItems = [
+    {
+      value: "Groq",
+      label: "Groq",
+      text: `from langchain_groq import ChatGroq\n\n${llmVarName} = ChatGroq(${groqParamsOrDefault})`,
+      apiKeyName: "GROQ_API_KEY",
+      packageName: "langchain-groq",
+      shouldHide: hideGroq,
+    },
     {
       value: "OpenAI",
       label: "OpenAI",
       text: `from langchain_openai import ChatOpenAI\n\n${llmVarName} = ChatOpenAI(${openAIParamsOrDefault})`,
       apiKeyName: "OPENAI_API_KEY",
       packageName: "langchain-openai",
-      default: true,
       shouldHide: hideOpenai,
     },
     {
@@ -189,7 +196,6 @@ export default function ChatModelTabs(props) {
       text: `from langchain_anthropic import ChatAnthropic\n\n${llmVarName} = ChatAnthropic(${anthropicParamsOrDefault})`,
       apiKeyName: "ANTHROPIC_API_KEY",
       packageName: "langchain-anthropic",
-      default: false,
       shouldHide: hideAnthropic,
     },
     {
@@ -198,7 +204,6 @@ export default function ChatModelTabs(props) {
       text: `from langchain_openai import AzureChatOpenAI\n\n${llmVarName} = AzureChatOpenAI(${azureParamsOrDefault})`,
       apiKeyName: "AZURE_OPENAI_API_KEY",
       packageName: "langchain-openai",
-      default: false,
       shouldHide: hideAzure,
     },
     {
@@ -207,7 +212,6 @@ export default function ChatModelTabs(props) {
       text: `from langchain_google_vertexai import ChatVertexAI\n\n${llmVarName} = ChatVertexAI(${googleParamsOrDefault})`,
       apiKeyText: "# Ensure your VertexAI credentials are configured",
       packageName: "langchain-google-vertexai",
-      default: false,
       shouldHide: hideGoogle,
     },
     {
@@ -216,7 +220,6 @@ export default function ChatModelTabs(props) {
       text: `from langchain_aws import ChatBedrock\n\n${llmVarName} = ChatBedrock(${awsBedrockParamsOrDefault})`,
       apiKeyText: "# Ensure your AWS credentials are configured",
       packageName: "langchain-aws",
-      default: false,
       shouldHide: hideAWS,
     },
     {
@@ -225,7 +228,6 @@ export default function ChatModelTabs(props) {
       text: `from langchain_cohere import ChatCohere\n\n${llmVarName} = ChatCohere(${cohereParamsOrDefault})`,
       apiKeyName: "COHERE_API_KEY",
       packageName: "langchain-cohere",
-      default: false,
       shouldHide: hideCohere,
     },
     {
@@ -234,7 +236,6 @@ export default function ChatModelTabs(props) {
       text: `from langchain_nvidia_ai_endpoints import ChatNVIDIA\n\n${llmVarName} = ChatNVIDIA(${nvidiaParamsOrDefault})`,
       apiKeyName: "NVIDIA_API_KEY",
       packageName: "langchain-nvidia-ai-endpoints",
-      default: false,
       shouldHide: hideNvidia,
     },
     {
@@ -243,25 +244,14 @@ export default function ChatModelTabs(props) {
       text: `from langchain_fireworks import ChatFireworks\n\n${llmVarName} = ChatFireworks(${fireworksParamsOrDefault})`,
       apiKeyName: "FIREWORKS_API_KEY",
       packageName: "langchain-fireworks",
-      default: false,
       shouldHide: hideFireworks,
     },
-    {
-      value: "Groq",
-      label: "Groq",
-      text: `from langchain_groq import ChatGroq\n\n${llmVarName} = ChatGroq(${groqParamsOrDefault})`,
-      apiKeyName: "GROQ_API_KEY",
-      packageName: "langchain-groq",
-      default: false,
-      shouldHide: hideGroq,
-    },
     {
       value: "MistralAI",
       label: "Mistral AI",
       text: `from langchain_mistralai import ChatMistralAI\n\n${llmVarName} = ChatMistralAI(${mistralParamsOrDefault})`,
       apiKeyName: "MISTRAL_API_KEY",
       packageName: "langchain-mistralai",
-      default: false,
       shouldHide: hideMistral,
     },
     {
@@ -270,7 +260,6 @@ export default function ChatModelTabs(props) {
       text: `from langchain_openai import ChatOpenAI\n\n${llmVarName} = ChatOpenAI(${togetherParamsOrDefault})`,
       apiKeyName: "TOGETHER_API_KEY",
       packageName: "langchain-openai",
-      default: false,
       shouldHide: hideTogether,
     },
     {
@@ -279,7 +268,6 @@ export default function ChatModelTabs(props) {
       text: `from databricks_langchain import ChatDatabricks\n\nos.environ["DATABRICKS_HOST"] = "https://example.staging.cloud.databricks.com/serving-endpoints"\n\n${llmVarName} = ChatDatabricks(${databricksParamsOrDefault})`,
       apiKeyName: "DATABRICKS_TOKEN",
       packageName: "databricks-langchain",
-      default: false,
       shouldHide: hideDatabricks,
     },
   ];

libs/cli/Makefile

@@ -3,6 +3,9 @@
 # LINTING AND FORMATTING
 ######################
 
+.EXPORT_ALL_VARIABLES:
+UV_FROZEN = true
+
 # Define a variable for Python and notebook files.
 PYTHON_FILES=.
 MYPY_CACHE=.mypy_cache
@@ -13,20 +16,20 @@ lint_tests: PYTHON_FILES=tests
 lint_tests: MYPY_CACHE=.mypy_cache_test
 
 lint lint_diff lint_package lint_tests:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff check $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && uv run --group typing --group lint mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
 
 format format_diff:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff format $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff check --select I --fix $(PYTHON_FILES)
 
 test tests: _test _e2e_test
 
 PYTHON = .venv/bin/python
 
 _test:
-	poetry run pytest tests
+	uv run --group test pytest tests
 
 # custom integration testing for cli integration flow
 # currently ignores vectorstores test because lacks implementation
@@ -35,7 +38,7 @@ _e2e_test:
 	mkdir .integration_test
 	cd .integration_test && \
 		python3 -m venv .venv && \
-		$(PYTHON) -m pip install --upgrade poetry && \
+		pip install --upgrade poetry && \
 		$(PYTHON) -m pip install -e .. && \
 		$(PYTHON) -m langchain_cli.cli integration new --name parrot-link --name-class ParrotLink && \
 		$(PYTHON) -m langchain_cli.cli integration new --name parrot-link --name-class ParrotLinkB --src=integration_template/chat_models.py --dst=langchain-parrot-link/langchain_parrot_link/chat_models_b.py && \

libs/cli/pyproject.toml

@@ -1,48 +1,52 @@
-[tool.poetry]
+[build-system]
+requires = ["pdm-backend"]
+build-backend = "pdm.backend"
+
+[project]
+authors = [
+    {name = "Erick Friis", email = "erick@langchain.dev"},
+]
+license = {text = "MIT"}
+requires-python = "<4.0,>=3.9"
+dependencies = [
+    "typer[all]<1.0.0,>=0.9.0",
+    "gitpython<4,>=3",
+    "langserve[all]>=0.0.51",
+    "uvicorn<1.0,>=0.23",
+    "tomlkit>=0.12",
+    "gritql<1.0.0,>=0.2.0",
+]
 name = "langchain-cli"
 version = "0.0.35"
 description = "CLI for interacting with LangChain"
-authors = ["Erick Friis <erick@langchain.dev>"]
 readme = "README.md"
-repository = "https://github.com/langchain-ai/langchain"
-license = "MIT"
 
-[tool.poetry.urls]
+[project.urls]
 "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
 "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-cli%3D%3D0%22&expanded=true"
+repository = "https://github.com/langchain-ai/langchain"
 
-[tool.poetry.dependencies]
-python = ">=3.9,<4.0"
-typer = { extras = ["all"], version = "^0.9.0" }
-gitpython = "^3"
-langserve = { extras = ["all"], version = ">=0.0.51" }
-uvicorn = ">=0.23,<1.0"
-tomlkit = ">=0.12"
-gritql = "^0.2.0"
-
-[tool.poetry.scripts]
+[project.scripts]
 langchain = "langchain_cli.cli:app"
 langchain-cli = "langchain_cli.cli:app"
 
-[tool.poetry.group.dev.dependencies]
-pytest = "^7.4.2"
-pytest-watch = "^4.2.0"
+[dependency-groups]
+dev = [
+    "pytest<8.0.0,>=7.4.2",
+    "pytest-watch<5.0.0,>=4.2.0",
+]
+lint = [
+    "ruff<1.0,>=0.5",
+    "mypy<2.0.0,>=1.13.0",
+]
+test = [
+    "langchain @ file:///${PROJECT_ROOT}/../langchain",
+]
+typing = [
+    "langchain @ file:///${PROJECT_ROOT}/../langchain",
+]
+test_integration = []
 
-[tool.poetry.group.lint.dependencies]
-ruff = "^0.5"
-mypy = "^1.13.0"
-
-[tool.poetry.group.test.dependencies]
-langchain = {path = "../langchain", develop = true}
-
-[tool.poetry.group.typing.dependencies]
-langchain = {path = "../langchain", develop = true}
-
-[tool.poetry.group.test_integration.dependencies]
-
-[tool.poetry.extras]
-# For langserve
-serve = []
 
 [tool.ruff.lint]
 select = [
@@ -57,7 +61,3 @@ exclude = [
   "langchain_cli/integration_template",
   "langchain_cli/package_template",
 ]
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

libs/community/Makefile

@@ -7,28 +7,31 @@ all: help
 TEST_FILE ?= tests/unit_tests/
 integration_tests: TEST_FILE = tests/integration_tests/
 
+.EXPORT_ALL_VARIABLES:
+UV_FROZEN = true
+
 # Run unit tests and generate a coverage report.
 coverage:
-	poetry run pytest --cov \
+	uv run --group test pytest --cov \
 		--cov-config=.coveragerc \
 		--cov-report xml \
 		--cov-report term-missing:skip-covered \
 		$(TEST_FILE)
 
 test tests:
-	poetry run pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE)
+	uv run --group test pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE)
 
 integration_tests:
-	poetry run pytest $(TEST_FILE)
+	uv run --group test --group test_integration pytest $(TEST_FILE)
 
 test_watch:
-	poetry run ptw --disable-socket --allow-unix-socket --snapshot-update --now . -- -vv tests/unit_tests
+	uv run --group test 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 $^
+	uv run --group test python ./scripts/check_imports.py $^
 
 extended_tests:
-	poetry run pytest --disable-socket --allow-unix-socket --only-extended tests/unit_tests
+	uv run --no-sync --group test pytest --disable-socket --allow-unix-socket --only-extended tests/unit_tests
 
 
 ######################
@@ -48,19 +51,19 @@ 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 $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff check $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && uv run --group typing --group lint mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
 
 format format_diff:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff format $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff check --select I --fix $(PYTHON_FILES)
 
 spell_check:
-	poetry run codespell --toml pyproject.toml
+	uv run --group typing --group lint codespell --toml pyproject.toml
 
 spell_fix:
-	poetry run codespell --toml pyproject.toml -w
+	uv run --group typing --group lint codespell --toml pyproject.toml -w
 
 ######################
 # HELP

libs/community/extended_testing_deps.txt

@@ -59,7 +59,7 @@ openapi-pydantic>=0.3.2,<0.4
 oracle-ads>=2.9.1,<3
 oracledb>=2.2.0,<3
 pandas>=2.0.1,<3
-pdfminer-six>=20221105,<20240706
+pdfminer-six==20231228
 pdfplumber>=0.11
 pgvector>=0.1.6,<0.2
 playwright>=1.48.0,<2
@@ -104,3 +104,5 @@ mlflow[genai]>=2.14.0
 databricks-sdk>=0.30.0
 websocket>=0.2.1,<1
 writer-sdk>=1.2.0
+yandexcloud==0.144.0
+unstructured[pdf]>=0.15

libs/community/langchain_community/callbacks/openai_info.py

@@ -10,6 +10,22 @@ from langchain_core.messages import AIMessage
 from langchain_core.outputs import ChatGeneration, LLMResult
 
 MODEL_COST_PER_1K_TOKENS = {
+    # OpenAI o1 input
+    "o1": 0.015,
+    "o1-2024-12-17": 0.015,
+    "o1-cached": 0.0075,
+    "o1-2024-12-17-cached": 0.0075,
+    # OpenAI o1 output
+    "o1-completion": 0.06,
+    "o1-2024-12-17-completion": 0.06,
+    # OpenAI o3-mini input
+    "o3-mini": 0.0011,
+    "o3-mini-2025-01-31": 0.0011,
+    "o3-mini-cached": 0.00055,
+    "o3-mini-2025-01-31-cached": 0.00055,
+    # OpenAI o3-mini output
+    "o3-mini-completion": 0.0044,
+    "o3-mini-2025-01-31-completion": 0.0044,
     # OpenAI o1-preview input
     "o1-preview": 0.015,
     "o1-preview-cached": 0.0075,

libs/community/langchain_community/chat_models/oci_generative_ai.py

@@ -539,6 +539,8 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
             The authentication type to use, e.g., API_KEY (default), SECURITY_TOKEN, INSTANCE_PRINCIPAL, RESOURCE_PRINCIPAL.
         auth_profile: Optional[str]
             The name of the profile in ~/.oci/config, if not specified , DEFAULT will be used.
+        auth_file_location: Optional[str]
+            Path to the config file, If not specified, ~/.oci/config will be used.
         provider: str
             Provider name of the model. Default to None, will try to be derived from the model_id otherwise, requires user input.
     See full list of supported init args and their descriptions in the params section.

libs/community/langchain_community/document_loaders/parsers/pdf.py

@@ -8,9 +8,12 @@ import logging
 import threading
 import warnings
 from datetime import datetime
+from pathlib import Path
+from tempfile import TemporaryDirectory
 from typing import (
     TYPE_CHECKING,
     Any,
+    BinaryIO,
     Iterable,
     Iterator,
     Literal,
@@ -18,6 +21,7 @@ from typing import (
     Optional,
     Sequence,
     Union,
+    cast,
 )
 from urllib.parse import urlparse
 
@@ -33,7 +37,6 @@ from langchain_community.document_loaders.parsers.images import (
 )
 
 if TYPE_CHECKING:
-    import pdfminer
     import pdfplumber
     import pymupdf
     import pypdf
@@ -240,203 +243,557 @@ def _merge_text_and_extras(extras: list[str], text_from_page: str) -> str:
 
 
 class PyPDFParser(BaseBlobParser):
-    """Load `PDF` using `pypdf`"""
+    """Parse a blob from a PDF using `pypdf` library.
+
+    This class provides methods to parse a blob from a PDF document, supporting various
+    configurations such as handling password-protected PDFs, extracting images.
+    It integrates the 'pypdf' library for PDF processing and offers synchronous blob
+    parsing.
+
+    Examples:
+        Setup:
+
+        .. code-block:: bash
+
+            pip install -U langchain-community pypdf
+
+        Load a blob from a PDF file:
+
+        .. code-block:: python
+
+            from langchain_core.documents.base import Blob
+
+            blob = Blob.from_path("./example_data/layout-parser-paper.pdf")
+
+        Instantiate the parser:
+
+        .. code-block:: python
+
+            from langchain_community.document_loaders.parsers import PyPDFParser
+
+            parser = PyPDFParser(
+                # password = None,
+                mode = "single",
+                pages_delimiter = "\n\f",
+                # images_parser = TesseractBlobParser(),
+            )
+
+        Lazily parse the blob:
+
+        .. code-block:: python
+
+            docs = []
+            docs_lazy = parser.lazy_parse(blob)
+
+            for doc in docs_lazy:
+                docs.append(doc)
+            print(docs[0].page_content[:100])
+            print(docs[0].metadata)
+    """
 
     def __init__(
         self,
         password: Optional[Union[str, bytes]] = None,
         extract_images: bool = False,
         *,
-        extraction_mode: str = "plain",
+        mode: Literal["single", "page"] = "page",
+        pages_delimiter: str = _DEFAULT_PAGES_DELIMITER,
+        images_parser: Optional[BaseImageBlobParser] = None,
+        images_inner_format: Literal["text", "markdown-img", "html-img"] = "text",
+        extraction_mode: Literal["plain", "layout"] = "plain",
         extraction_kwargs: Optional[dict[str, Any]] = None,
     ):
-        self.password = password
+        """Initialize a parser based on PyPDF.
+
+        Args:
+            password: Optional password for opening encrypted PDFs.
+            extract_images: Whether to extract images from the PDF.
+            mode: The extraction mode, either "single" for the entire document or "page"
+                for page-wise extraction.
+            pages_delimiter: A string delimiter to separate pages in single-mode
+                extraction.
+            images_parser: Optional image blob parser.
+            images_inner_format: The format for the parsed output.
+                - "text" = return the content as is
+                - "markdown-img" = wrap the content into an image markdown link, w/ link
+                pointing to (`![body)(#)`]
+                - "html-img" = wrap the content as the `alt` text of an tag and link to
+                (`<img alt="{body}" src="#"/>`)
+            extraction_mode: “plain” for legacy functionality, “layout” extract text
+                in a fixed width format that closely adheres to the rendered layout in
+                the source pdf.
+            extraction_kwargs: Optional additional parameters for the extraction
+                process.
+
+        Raises:
+            ValueError: If the `mode` is not "single" or "page".
+        """
+        super().__init__()
+        if mode not in ["single", "page"]:
+            raise ValueError("mode must be single or page")
         self.extract_images = extract_images
+        if extract_images and not images_parser:
+            images_parser = RapidOCRBlobParser()
+        self.images_parser = images_parser
+        self.images_inner_format = images_inner_format
+        self.password = password
+        self.mode = mode
+        self.pages_delimiter = pages_delimiter
         self.extraction_mode = extraction_mode
         self.extraction_kwargs = extraction_kwargs or {}
 
     def lazy_parse(self, blob: Blob) -> Iterator[Document]:  # type: ignore[valid-type]
-        """Lazily parse the blob."""
+        """
+        Lazily parse the blob.
+        Insert image, if possible, between two paragraphs.
+        In this way, a paragraph can be continued on the next page.
+
+        Args:
+            blob: The blob to parse.
+
+        Raises:
+            ImportError: If the `pypdf` package is not found.
+
+        Yield:
+            An iterator over the parsed documents.
+        """
         try:
             import pypdf
         except ImportError:
             raise ImportError(
-                "`pypdf` package not found, please install it with "
-                "`pip install pypdf`"
+                "pypdf package not found, please install it with `pip install pypdf`"
             )
 
         def _extract_text_from_page(page: pypdf.PageObject) -> str:
-            """Extract text from image given the version of pypdf."""
+            """
+            Extract text from image given the version of pypdf.
+
+            Args:
+                page: The page object to extract text from.
+
+            Returns:
+                str: The extracted text.
+            """
             if pypdf.__version__.startswith("3"):
                 return page.extract_text()
             else:
                 return page.extract_text(
-                    extraction_mode=self.extraction_mode,  # type: ignore[arg-type]
-                    **self.extraction_kwargs,  # type: ignore[arg-type]
+                    extraction_mode=self.extraction_mode,
+                    **self.extraction_kwargs,
                 )
 
         with blob.as_bytes_io() as pdf_file_obj:  # type: ignore[attr-defined]
             pdf_reader = pypdf.PdfReader(pdf_file_obj, password=self.password)
 
-            yield from [
-                Document(
-                    page_content=_extract_text_from_page(page=page)
-                    + self._extract_images_from_page(page),
-                    metadata={
-                        "source": blob.source,
-                        "page": page_number,
-                        "page_label": pdf_reader.page_labels[page_number],
-                    },
-                    # type: ignore[attr-defined]
+            doc_metadata = _purge_metadata(
+                {"producer": "PyPDF", "creator": "PyPDF", "creationdate": ""}
+                | cast(dict, pdf_reader.metadata or {})
+                | {
+                    "source": blob.source,
+                    "total_pages": len(pdf_reader.pages),
+                }
+            )
+            single_texts = []
+            for page_number, page in enumerate(pdf_reader.pages):
+                text_from_page = _extract_text_from_page(page=page)
+                images_from_page = self.extract_images_from_page(page)
+                all_text = _merge_text_and_extras(
+                    [images_from_page], text_from_page
+                ).strip()
+                if self.mode == "page":
+                    yield Document(
+                        page_content=all_text,
+                        metadata=_validate_metadata(
+                            doc_metadata
+                            | {
+                                "page": page_number,
+                                "page_label": pdf_reader.page_labels[page_number],
+                            }
+                        ),
+                    )
+                else:
+                    single_texts.append(all_text)
+            if self.mode == "single":
+                yield Document(
+                    page_content=self.pages_delimiter.join(single_texts),
+                    metadata=_validate_metadata(doc_metadata),
                 )
-                for page_number, page in enumerate(pdf_reader.pages)
-            ]
 
-    def _extract_images_from_page(self, page: pypdf.PageObject) -> str:
-        """Extract images from page and get the text with RapidOCR."""
-        if not self.extract_images or "/XObject" not in page["/Resources"].keys():  # type: ignore[attr-defined]
+    def extract_images_from_page(self, page: pypdf._page.PageObject) -> str:
+        """Extract images from a PDF page and get the text using images_to_text.
+
+        Args:
+            page: The page object from which to extract images.
+
+        Returns:
+            str: The extracted text from the images on the page.
+        """
+        if not self.images_parser:
+            return ""
+        from PIL import Image
+
+        if "/XObject" not in cast(dict, page["/Resources"]).keys():
             return ""
 
-        xObject = page["/Resources"]["/XObject"].get_object()  # type: ignore
+        xObject = page["/Resources"]["/XObject"].get_object()  # type: ignore[index]
         images = []
         for obj in xObject:
+            np_image: Any = None
             if xObject[obj]["/Subtype"] == "/Image":
                 if xObject[obj]["/Filter"][1:] in _PDF_FILTER_WITHOUT_LOSS:
                     height, width = xObject[obj]["/Height"], xObject[obj]["/Width"]
 
-                    images.append(
-                        np.frombuffer(xObject[obj].get_data(), dtype=np.uint8).reshape(
-                            height, width, -1
-                        )
-                    )
+                    np_image = np.frombuffer(
+                        xObject[obj].get_data(), dtype=np.uint8
+                    ).reshape(height, width, -1)
                 elif xObject[obj]["/Filter"][1:] in _PDF_FILTER_WITH_LOSS:
-                    images.append(xObject[obj].get_data())
-                elif (
-                    isinstance(xObject[obj]["/Filter"], list)
-                    and xObject[obj]["/Filter"]
-                    and xObject[obj]["/Filter"][0][1:] in _PDF_FILTER_WITH_LOSS
-                ):
-                    images.append(xObject[obj].get_data())
+                    np_image = np.array(Image.open(io.BytesIO(xObject[obj].get_data())))
+
                 else:
-                    warnings.warn("Unknown PDF Filter!")
-        return extract_from_images_with_rapidocr(images)
+                    logger.warning("Unknown PDF Filter!")
+                if np_image is not None:
+                    image_bytes = io.BytesIO()
+                    Image.fromarray(np_image).save(image_bytes, format="PNG")
+                    blob = Blob.from_data(image_bytes.getvalue(), mime_type="image/png")
+                    image_text = next(self.images_parser.lazy_parse(blob)).page_content
+                    images.append(
+                        _format_inner_image(blob, image_text, self.images_inner_format)
+                    )
+        return _FORMAT_IMAGE_STR.format(
+            image_text=_JOIN_IMAGES.join(filter(None, images))
+        )
 
 
 class PDFMinerParser(BaseBlobParser):
-    """Parse `PDF` using `PDFMiner`."""
+    """Parse a blob from a PDF using `pdfminer.six` library.
 
-    def __init__(self, extract_images: bool = False, *, concatenate_pages: bool = True):
+    This class provides methods to parse a blob from a PDF document, supporting various
+    configurations such as handling password-protected PDFs, extracting images, and
+    defining extraction mode.
+    It integrates the 'pdfminer.six' library for PDF processing and offers synchronous
+    blob parsing.
+
+    Examples:
+        Setup:
+
+        .. code-block:: bash
+
+            pip install -U langchain-community pdfminer.six pillow
+
+        Load a blob from a PDF file:
+
+        .. code-block:: python
+
+            from langchain_core.documents.base import Blob
+
+            blob = Blob.from_path("./example_data/layout-parser-paper.pdf")
+
+        Instantiate the parser:
+
+        .. code-block:: python
+
+            from langchain_community.document_loaders.parsers import PDFMinerParser
+
+            parser = PDFMinerParser(
+                # password = None,
+                mode = "single",
+                pages_delimiter = "\n\f",
+                # extract_images = True,
+                # images_to_text = convert_images_to_text_with_tesseract(),
+            )
+
+        Lazily parse the blob:
+
+        .. code-block:: python
+
+            docs = []
+            docs_lazy = parser.lazy_parse(blob)
+
+            for doc in docs_lazy:
+                docs.append(doc)
+            print(docs[0].page_content[:100])
+            print(docs[0].metadata)
+    """
+
+    _warn_concatenate_pages = False
+
+    def __init__(
+        self,
+        extract_images: bool = False,
+        *,
+        password: Optional[str] = None,
+        mode: Literal["single", "page"] = "single",
+        pages_delimiter: str = _DEFAULT_PAGES_DELIMITER,
+        images_parser: Optional[BaseImageBlobParser] = None,
+        images_inner_format: Literal["text", "markdown-img", "html-img"] = "text",
+        concatenate_pages: Optional[bool] = None,
+    ):
         """Initialize a parser based on PDFMiner.
 
         Args:
+            password: Optional password for opening encrypted PDFs.
+            mode: Extraction mode to use. Either "single" or "page" for page-wise
+                extraction.
+            pages_delimiter: A string delimiter to separate pages in single-mode
+                extraction.
             extract_images: Whether to extract images from PDF.
-            concatenate_pages: If True, concatenate all PDF pages into one a single
-                               document. Otherwise, return one document per page.
+            images_inner_format: The format for the parsed output.
+                - "text" = return the content as is
+                - "markdown-img" = wrap the content into an image markdown link, w/ link
+                pointing to (`![body)(#)`]
+                - "html-img" = wrap the content as the `alt` text of an tag and link to
+                (`<img alt="{body}" src="#"/>`)
+            concatenate_pages: Deprecated. If True, concatenate all PDF pages
+                into one a single document. Otherwise, return one document per page.
+
+        Returns:
+            This method does not directly return data. Use the `parse` or `lazy_parse`
+            methods to retrieve parsed documents with content and metadata.
+
+        Raises:
+            ValueError: If the `mode` is not "single" or "page".
+
+        Warnings:
+            `concatenate_pages` parameter is deprecated. Use `mode='single' or 'page'
+            instead.
         """
+        super().__init__()
+        if mode not in ["single", "page"]:
+            raise ValueError("mode must be single or page")
+        if extract_images and not images_parser:
+            images_parser = RapidOCRBlobParser()
         self.extract_images = extract_images
-        self.concatenate_pages = concatenate_pages
+        self.images_parser = images_parser
+        self.images_inner_format = images_inner_format
+        self.password = password
+        self.mode = mode
+        self.pages_delimiter = pages_delimiter
+        if concatenate_pages is not None:
+            if not PDFMinerParser._warn_concatenate_pages:
+                PDFMinerParser._warn_concatenate_pages = True
+                logger.warning(
+                    "`concatenate_pages` parameter is deprecated. "
+                    "Use `mode='single' or 'page'` instead."
+                )
+            self.mode = "single" if concatenate_pages else "page"
 
-    def lazy_parse(self, blob: Blob) -> Iterator[Document]:  # type: ignore[valid-type]
-        """Lazily parse the blob."""
+    @staticmethod
+    def decode_text(s: Union[bytes, str]) -> str:
+        """
+        Decodes a PDFDocEncoding string to Unicode.
+        Adds py3 compatibility to pdfminer's version.
 
-        if not self.extract_images:
+        Args:
+            s: The string to decode.
+
+        Returns:
+            str: The decoded Unicode string.
+        """
+        from pdfminer.utils import PDFDocEncoding
+
+        if isinstance(s, bytes) and s.startswith(b"\xfe\xff"):
+            return str(s[2:], "utf-16be", "ignore")
+        try:
+            ords = (ord(c) if isinstance(c, str) else c for c in s)
+            return "".join(PDFDocEncoding[o] for o in ords)
+        except IndexError:
+            return str(s)
+
+    @staticmethod
+    def resolve_and_decode(obj: Any) -> Any:
+        """
+        Recursively resolve the metadata values.
+
+        Args:
+            obj: The object to resolve and decode. It can be of any type.
+
+        Returns:
+            The resolved and decoded object.
+        """
+        from pdfminer.psparser import PSLiteral
+
+        if hasattr(obj, "resolve"):
+            obj = obj.resolve()
+        if isinstance(obj, list):
+            return list(map(PDFMinerParser.resolve_and_decode, obj))
+        elif isinstance(obj, PSLiteral):
+            return PDFMinerParser.decode_text(obj.name)
+        elif isinstance(obj, (str, bytes)):
+            return PDFMinerParser.decode_text(obj)
+        elif isinstance(obj, dict):
+            for k, v in obj.items():
+                obj[k] = PDFMinerParser.resolve_and_decode(v)
+            return obj
+
+        return obj
+
+    def _get_metadata(
+        self,
+        fp: BinaryIO,
+        password: str = "",
+        caching: bool = True,
+    ) -> dict[str, Any]:
+        """
+        Extract metadata from a PDF file.
+
+        Args:
+            fp: The file pointer to the PDF file.
+            password: The password for the PDF file, if encrypted. Defaults to an empty
+                string.
+            caching: Whether to cache the PDF structure. Defaults to True.
+
+        Returns:
+            Metadata of the PDF file.
+        """
+        from pdfminer.pdfpage import PDFDocument, PDFPage, PDFParser
+
+        # Create a PDF parser object associated with the file object.
+        parser = PDFParser(fp)
+        # Create a PDF document object that stores the document structure.
+        doc = PDFDocument(parser, password=password, caching=caching)
+        metadata = {}
+
+        for info in doc.info:
+            metadata.update(info)
+        for k, v in metadata.items():
             try:
-                from pdfminer.high_level import extract_text
-            except ImportError:
-                raise ImportError(
-                    "`pdfminer` package not found, please install it with "
-                    "`pip install pdfminer.six`"
+                metadata[k] = PDFMinerParser.resolve_and_decode(v)
+            except Exception as e:  # pragma: nocover
+                # This metadata value could not be parsed. Instead of failing the PDF
+                # read, treat it as a warning only if `strict_metadata=False`.
+                logger.warning(
+                    '[WARNING] Metadata key "%s" could not be parsed due to '
+                    "exception: %s",
+                    k,
+                    str(e),
                 )
 
-            with blob.as_bytes_io() as pdf_file_obj:  # type: ignore[attr-defined]
-                if self.concatenate_pages:
-                    text = extract_text(pdf_file_obj)
-                    metadata = {"source": blob.source}  # type: ignore[attr-defined]
-                    yield Document(page_content=text, metadata=metadata)
-                else:
-                    from pdfminer.pdfpage import PDFPage
+        # Count number of pages.
+        metadata["total_pages"] = len(list(PDFPage.create_pages(doc)))
 
-                    pages = PDFPage.get_pages(pdf_file_obj)
-                    for i, _ in enumerate(pages):
-                        text = extract_text(pdf_file_obj, page_numbers=[i])
-                        metadata = {"source": blob.source, "page": str(i)}  # type: ignore[attr-defined]
-                        yield Document(page_content=text, metadata=metadata)
-        else:
-            import io
+        return metadata
 
-            from pdfminer.converter import PDFPageAggregator, TextConverter
-            from pdfminer.layout import LAParams
+    def lazy_parse(self, blob: Blob) -> Iterator[Document]:  # type: ignore[valid-type]
+        """
+        Lazily parse the blob.
+        Insert image, if possible, between two paragraphs.
+        In this way, a paragraph can be continued on the next page.
+
+        Args:
+            blob: The blob to parse.
+
+        Raises:
+            ImportError: If the `pdfminer.six` or `pillow` package is not found.
+
+        Yield:
+            An iterator over the parsed documents.
+        """
+        try:
+            import pdfminer
+            from pdfminer.converter import PDFLayoutAnalyzer
+            from pdfminer.layout import (
+                LAParams,
+                LTContainer,
+                LTImage,
+                LTItem,
+                LTPage,
+                LTText,
+                LTTextBox,
+            )
             from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager
             from pdfminer.pdfpage import PDFPage
 
+            if int(pdfminer.__version__) < 20201018:
+                raise ImportError(
+                    "This parser is tested with pdfminer.six version 20201018 or "
+                    "later. Remove pdfminer, and install pdfminer.six with "
+                    "`pip uninstall pdfminer && pip install pdfminer.six`."
+                )
+        except ImportError:
+            raise ImportError(
+                "pdfminer package not found, please install it "
+                "with `pip install pdfminer.six`"
+            )
+
+        with blob.as_bytes_io() as pdf_file_obj, TemporaryDirectory() as tempdir:
+            pages = PDFPage.get_pages(pdf_file_obj, password=self.password or "")
+            rsrcmgr = PDFResourceManager()
+            doc_metadata = _purge_metadata(
+                self._get_metadata(pdf_file_obj, password=self.password or "")
+            )
+            doc_metadata["source"] = blob.source
+
+            class Visitor(PDFLayoutAnalyzer):
+                def __init__(
+                    self,
+                    rsrcmgr: PDFResourceManager,
+                    pageno: int = 1,
+                    laparams: Optional[LAParams] = None,
+                ) -> None:
+                    super().__init__(rsrcmgr, pageno=pageno, laparams=laparams)
+
+                def receive_layout(me, ltpage: LTPage) -> None:
+                    def render(item: LTItem) -> None:
+                        if isinstance(item, LTContainer):
+                            for child in item:
+                                render(child)
+                        elif isinstance(item, LTText):
+                            text_io.write(item.get_text())
+                        if isinstance(item, LTTextBox):
+                            text_io.write("\n")
+                        elif isinstance(item, LTImage):
+                            if self.images_parser:
+                                from pdfminer.image import ImageWriter
+
+                                image_writer = ImageWriter(tempdir)
+                                filename = image_writer.export_image(item)
+                                blob = Blob.from_path(Path(tempdir) / filename)
+                                blob.metadata["source"] = "#"
+                                image_text = next(
+                                    self.images_parser.lazy_parse(blob)
+                                ).page_content
+
+                                text_io.write(
+                                    _format_inner_image(
+                                        blob, image_text, self.images_inner_format
+                                    )
+                                )
+                        else:
+                            pass
+
+                    render(ltpage)
+
             text_io = io.StringIO()
-            with blob.as_bytes_io() as pdf_file_obj:  # type: ignore[attr-defined]
-                pages = PDFPage.get_pages(pdf_file_obj)
-                rsrcmgr = PDFResourceManager()
-                device_for_text = TextConverter(rsrcmgr, text_io, laparams=LAParams())
-                device_for_image = PDFPageAggregator(rsrcmgr, laparams=LAParams())
-                interpreter_for_text = PDFPageInterpreter(rsrcmgr, device_for_text)
-                interpreter_for_image = PDFPageInterpreter(rsrcmgr, device_for_image)
-                for i, page in enumerate(pages):
-                    interpreter_for_text.process_page(page)
-                    interpreter_for_image.process_page(page)
-                    content = text_io.getvalue() + self._extract_images_from_page(
-                        device_for_image.get_result()
-                    )
+            visitor_for_all = PDFPageInterpreter(
+                rsrcmgr, Visitor(rsrcmgr, laparams=LAParams())
+            )
+            all_content = []
+            for i, page in enumerate(pages):
+                text_io.truncate(0)
+                text_io.seek(0)
+                visitor_for_all.process_page(page)
+
+                all_text = text_io.getvalue()
+                # For legacy compatibility, net strip()
+                all_text = all_text.strip()
+                if self.mode == "page":
                     text_io.truncate(0)
                     text_io.seek(0)
-                    metadata = {"source": blob.source, "page": str(i)}  # type: ignore[attr-defined]
-                    yield Document(page_content=content, metadata=metadata)
-
-    def _extract_images_from_page(self, page: pdfminer.layout.LTPage) -> str:
-        """Extract images from page and get the text with RapidOCR."""
-        import pdfminer
-
-        def get_image(layout_object: Any) -> Any:
-            if isinstance(layout_object, pdfminer.layout.LTImage):
-                return layout_object
-            if isinstance(layout_object, pdfminer.layout.LTContainer):
-                for child in layout_object:
-                    return get_image(child)
-            else:
-                return None
-
-        images = []
-
-        for img in filter(bool, map(get_image, page)):
-            img_filter = img.stream["Filter"]
-            if isinstance(img_filter, list):
-                filter_names = [f.name for f in img_filter]
-            else:
-                filter_names = [img_filter.name]
-
-            without_loss = any(
-                name in _PDF_FILTER_WITHOUT_LOSS for name in filter_names
-            )
-            with_loss = any(name in _PDF_FILTER_WITH_LOSS for name in filter_names)
-            non_matching = {name for name in filter_names} - {
-                *_PDF_FILTER_WITHOUT_LOSS,
-                *_PDF_FILTER_WITH_LOSS,
-            }
-
-            if without_loss and with_loss:
-                warnings.warn(
-                    "Image has both lossy and lossless filters. Defaulting to lossless"
-                )
-
-            if non_matching:
-                warnings.warn(f"Unknown PDF Filter(s): {non_matching}")
-
-            if without_loss:
-                images.append(
-                    np.frombuffer(img.stream.get_data(), dtype=np.uint8).reshape(
-                        img.stream["Height"], img.stream["Width"], -1
+                    yield Document(
+                        page_content=all_text,
+                        metadata=_validate_metadata(doc_metadata | {"page": i}),
                     )
+                else:
+                    if all_text.endswith("\f"):
+                        all_text = all_text[:-1]
+                    all_content.append(all_text)
+            if self.mode == "single":
+                # Add pages_delimiter between pages
+                document_content = self.pages_delimiter.join(all_content)
+                yield Document(
+                    page_content=document_content,
+                    metadata=_validate_metadata(doc_metadata),
                 )
-            elif with_loss:
-                images.append(img.stream.get_data())
-
-        return extract_from_images_with_rapidocr(images)
 
 
 class PyMuPDFParser(BaseBlobParser):
@@ -473,7 +830,6 @@ class PyMuPDFParser(BaseBlobParser):
                 # password = None,
                 mode = "single",
                 pages_delimiter = "\n\f",
-                # extract_images = True,
                 # images_parser = TesseractBlobParser(),
                 # extract_tables="markdown",
                 # extract_tables_settings=None,
@@ -695,9 +1051,9 @@ class PyMuPDFParser(BaseBlobParser):
         Returns:
             dict: The extracted metadata.
         """
-        return _purge_metadata(
-            dict(
-                {
+        metadata = _purge_metadata(
+            {
+                **{
                     "producer": "PyMuPDF",
                     "creator": "PyMuPDF",
                     "creationdate": "",
@@ -710,8 +1066,12 @@ class PyMuPDFParser(BaseBlobParser):
                     for k in doc.metadata
                     if isinstance(doc.metadata[k], (str, int))
                 },
-            )
+            }
         )
+        for k in ("modDate", "creationDate"):
+            if k in doc.metadata:
+                metadata[k] = doc.metadata[k]
+        return metadata
 
     def _extract_images_from_page(
         self, doc: pymupdf.Document, page: pymupdf.Page

libs/community/langchain_community/document_loaders/pdf.py

@@ -184,64 +184,56 @@ class OnlinePDFLoader(BasePDFLoader):
 
 
 class PyPDFLoader(BasePDFLoader):
-    """PyPDFLoader document loader integration
+    """Load and parse a PDF file using 'pypdf' library.
 
-    Setup:
-        Install ``langchain-community``.
+    This class provides methods to load and parse PDF documents, supporting various
+    configurations such as handling password-protected files, extracting images, and
+    defining extraction mode. It integrates the `pypdf` library for PDF processing and
+    offers both synchronous and asynchronous document loading.
+
+    Examples:
+        Setup:
 
         .. code-block:: bash
 
-            pip install -U langchain-community
+            pip install -U langchain-community pypdf
+
+        Instantiate the loader:
 
-    Instantiate:
         .. code-block:: python
 
             from langchain_community.document_loaders import PyPDFLoader
 
             loader = PyPDFLoader(
                 file_path = "./example_data/layout-parser-paper.pdf",
-                password = "my-password",
-                extract_images = True,
                 # headers = None
-                # extraction_mode = "plain",
-                # extraction_kwargs = None,
+                # password = None,
+                mode = "single",
+                pages_delimiter = "\n\f",
+                # extract_images = True,
+                # images_parser = RapidOCRBlobParser(),
             )
 
-    Lazy load:
+        Lazy load documents:
+
         .. code-block:: python
 
             docs = []
             docs_lazy = loader.lazy_load()
 
-            # async variant:
-            # docs_lazy = await loader.alazy_load()
-
             for doc in docs_lazy:
                 docs.append(doc)
             print(docs[0].page_content[:100])
             print(docs[0].metadata)
 
-        .. code-block:: python
+        Load documents asynchronously:
 
-            LayoutParser : A Unified Toolkit for Deep
-            Learning Based Document Image Analysis
-            Zejiang Shen1( ), R
-            {'source': './example_data/layout-parser-paper.pdf', 'page': 0}
-
-    Async load:
         .. code-block:: python
 
             docs = await loader.aload()
             print(docs[0].page_content[:100])
             print(docs[0].metadata)
-
-        .. code-block:: python
-
-            LayoutParser : A Unified Toolkit for Deep
-            Learning Based Document Image Analysis
-            Zejiang Shen1( ), R
-            {'source': './example_data/layout-parser-paper.pdf', 'page': 0}
-    """  # noqa: E501
+    """
 
     def __init__(
         self,
@@ -250,20 +242,50 @@ class PyPDFLoader(BasePDFLoader):
         headers: Optional[dict] = None,
         extract_images: bool = False,
         *,
-        extraction_mode: str = "plain",
+        mode: Literal["single", "page"] = "page",
+        images_parser: Optional[BaseImageBlobParser] = None,
+        images_inner_format: Literal["text", "markdown-img", "html-img"] = "text",
+        pages_delimiter: str = _DEFAULT_PAGES_DELIMITER,
+        extraction_mode: Literal["plain", "layout"] = "plain",
         extraction_kwargs: Optional[dict] = None,
     ) -> None:
-        """Initialize with a file path."""
-        try:
-            import pypdf  # noqa:F401
-        except ImportError:
-            raise ImportError(
-                "pypdf package not found, please install it with `pip install pypdf`"
-            )
+        """Initialize with a file path.
+
+        Args:
+            file_path: The path to the PDF file to be loaded.
+            headers: Optional headers to use for GET request to download a file from a
+              web path.
+            password: Optional password for opening encrypted PDFs.
+            mode: The extraction mode, either "single" for the entire document or "page"
+                for page-wise extraction.
+            pages_delimiter: A string delimiter to separate pages in single-mode
+                extraction.
+            extract_images: Whether to extract images from the PDF.
+            images_parser: Optional image blob parser.
+            images_inner_format: The format for the parsed output.
+                - "text" = return the content as is
+                - "markdown-img" = wrap the content into an image markdown link, w/ link
+                pointing to (`![body)(#)`]
+                - "html-img" = wrap the content as the `alt` text of an tag and link to
+                (`<img alt="{body}" src="#"/>`)
+            extraction_mode: “plain” for legacy functionality, “layout” extract text
+                in a fixed width format that closely adheres to the rendered layout in
+                the source pdf
+            extraction_kwargs: Optional additional parameters for the extraction
+                process.
+
+        Returns:
+            This method does not directly return data. Use the `load`, `lazy_load` or
+            `aload` methods to retrieve parsed documents with content and metadata.
+        """
         super().__init__(file_path, headers=headers)
         self.parser = PyPDFParser(
             password=password,
+            mode=mode,
             extract_images=extract_images,
+            images_parser=images_parser,
+            images_inner_format=images_inner_format,
+            pages_delimiter=pages_delimiter,
             extraction_mode=extraction_mode,
             extraction_kwargs=extraction_kwargs,
         )
@@ -271,12 +293,18 @@ class PyPDFLoader(BasePDFLoader):
     def lazy_load(
         self,
     ) -> Iterator[Document]:
-        """Lazy load given path as pages."""
+        """
+        Lazy load given path as pages.
+        Insert image, if possible, between two paragraphs.
+        In this way, a paragraph can be continued on the next page.
+        """
         if self.web_path:
-            blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)  # type: ignore[attr-defined]
+            blob = Blob.from_data(  # type: ignore[attr-defined]
+                open(self.file_path, "rb").read(), path=self.web_path
+            )
         else:
             blob = Blob.from_path(self.file_path)  # type: ignore[attr-defined]
-        yield from self.parser.parse(blob)
+        yield from self.parser.lazy_parse(blob)
 
 
 class PyPDFium2Loader(BasePDFLoader):
@@ -305,9 +333,56 @@ class PyPDFium2Loader(BasePDFLoader):
 
 
 class PyPDFDirectoryLoader(BaseLoader):
-    """Load a directory with `PDF` files using `pypdf` and chunks at character level.
+    """Load and parse a directory of PDF files using 'pypdf' library.
 
-    Loader also stores page numbers in metadata.
+    This class provides methods to load and parse multiple PDF documents in a directory,
+    supporting options for recursive search, handling password-protected files,
+    extracting images, and defining extraction modes. It integrates the `pypdf` library
+    for PDF processing and offers synchronous document loading.
+
+    Examples:
+        Setup:
+
+        .. code-block:: bash
+
+            pip install -U langchain-community pypdf
+
+        Instantiate the loader:
+
+        .. code-block:: python
+
+            from langchain_community.document_loaders import PyPDFDirectoryLoader
+
+            loader = PyPDFDirectoryLoader(
+                path = "./example_data/",
+                glob = "**/[!.]*.pdf",
+                silent_errors = False,
+                load_hidden = False,
+                recursive = False,
+                extract_images = False,
+                password = None,
+                mode = "page",
+                images_to_text = None,
+                headers = None,
+                extraction_mode = "plain",
+                # extraction_kwargs = None,
+            )
+
+        Load documents:
+
+        .. code-block:: python
+
+            docs = loader.load()
+            print(docs[0].page_content[:100])
+            print(docs[0].metadata)
+
+        Load documents asynchronously:
+
+        .. code-block:: python
+
+            docs = await loader.aload()
+            print(docs[0].page_content[:100])
+            print(docs[0].metadata)
     """
 
     def __init__(
@@ -318,16 +393,53 @@ class PyPDFDirectoryLoader(BaseLoader):
         load_hidden: bool = False,
         recursive: bool = False,
         extract_images: bool = False,
+        *,
+        password: Optional[str] = None,
+        mode: Literal["single", "page"] = "page",
+        images_parser: Optional[BaseImageBlobParser] = None,
+        headers: Optional[dict] = None,
+        extraction_mode: Literal["plain", "layout"] = "plain",
+        extraction_kwargs: Optional[dict] = None,
     ):
+        """Initialize with a directory path.
+
+        Args:
+            path: The path to the directory containing PDF files to be loaded.
+            glob: The glob pattern to match files in the directory.
+            silent_errors: Whether to log errors instead of raising them.
+            load_hidden: Whether to include hidden files in the search.
+            recursive: Whether to search subdirectories recursively.
+            extract_images: Whether to extract images from PDFs.
+            password: Optional password for opening encrypted PDFs.
+            mode: The extraction mode, either "single" for extracting the entire
+                document or "page" for page-wise extraction.
+            images_parser: Optional image blob parser..
+            headers: Optional headers to use for GET request to download a file from a
+              web path.
+            extraction_mode: “plain” for legacy functionality, “layout” for
+              experimental layout mode functionality
+            extraction_kwargs: Optional additional parameters for the extraction
+              process.
+
+        Returns:
+            This method does not directly return data. Use the `load` method to
+            retrieve parsed documents with content and metadata.
+        """
+        self.password = password
+        self.mode = mode
         self.path = path
         self.glob = glob
         self.load_hidden = load_hidden
         self.recursive = recursive
         self.silent_errors = silent_errors
         self.extract_images = extract_images
+        self.images_parser = images_parser
+        self.headers = headers
+        self.extraction_mode = extraction_mode
+        self.extraction_kwargs = extraction_kwargs
 
     @staticmethod
-    def _is_visible(path: Path) -> bool:
+    def _is_visible(path: PurePath) -> bool:
         return not any(part.startswith(".") for part in path.parts)
 
     def load(self) -> list[Document]:
@@ -338,7 +450,16 @@ class PyPDFDirectoryLoader(BaseLoader):
             if i.is_file():
                 if self._is_visible(i.relative_to(p)) or self.load_hidden:
                     try:
-                        loader = PyPDFLoader(str(i), extract_images=self.extract_images)
+                        loader = PyPDFLoader(
+                            str(i),
+                            password=self.password,
+                            mode=self.mode,
+                            extract_images=self.extract_images,
+                            images_parser=self.images_parser,
+                            headers=self.headers,
+                            extraction_mode=self.extraction_mode,
+                            extraction_kwargs=self.extraction_kwargs,
+                        )
                         sub_docs = loader.load()
                         for doc in sub_docs:
                             doc.metadata["source"] = str(i)
@@ -352,45 +473,122 @@ class PyPDFDirectoryLoader(BaseLoader):
 
 
 class PDFMinerLoader(BasePDFLoader):
-    """Load `PDF` files using `PDFMiner`."""
+    """Load and parse a PDF file using 'pdfminer.six' library.
+
+    This class provides methods to load and parse PDF documents, supporting various
+    configurations such as handling password-protected files, extracting images, and
+    defining extraction mode. It integrates the `pdfminer.six` library for PDF
+    processing and offers both synchronous and asynchronous document loading.
+
+    Examples:
+        Setup:
+
+        .. code-block:: bash
+
+            pip install -U langchain-community pdfminer.six
+
+        Instantiate the loader:
+
+        .. code-block:: python
+
+            from langchain_community.document_loaders import PDFMinerLoader
+
+            loader = PDFMinerLoader(
+                file_path = "./example_data/layout-parser-paper.pdf",
+                # headers = None
+                # password = None,
+                mode = "single",
+                pages_delimiter = "\n\f",
+                # extract_images = True,
+                # images_to_text = convert_images_to_text_with_tesseract(),
+            )
+
+        Lazy load documents:
+
+        .. code-block:: python
+
+            docs = []
+            docs_lazy = loader.lazy_load()
+
+            for doc in docs_lazy:
+                docs.append(doc)
+            print(docs[0].page_content[:100])
+            print(docs[0].metadata)
+
+        Load documents asynchronously:
+
+        .. code-block:: python
+
+            docs = await loader.aload()
+            print(docs[0].page_content[:100])
+            print(docs[0].metadata)
+    """
 
     def __init__(
         self,
         file_path: Union[str, PurePath],
         *,
-        headers: Optional[dict] = None,
+        password: Optional[str] = None,
+        mode: Literal["single", "page"] = "single",
+        pages_delimiter: str = _DEFAULT_PAGES_DELIMITER,
         extract_images: bool = False,
-        concatenate_pages: bool = True,
+        images_parser: Optional[BaseImageBlobParser] = None,
+        images_inner_format: Literal["text", "markdown-img", "html-img"] = "text",
+        headers: Optional[dict] = None,
+        concatenate_pages: Optional[bool] = None,
     ) -> None:
-        """Initialize with file path.
+        """Initialize with a file path.
 
         Args:
-            extract_images: Whether to extract images from PDF.
-            concatenate_pages: If True, concatenate all PDF pages into one a single
-                               document. Otherwise, return one document per page.
-        """
-        try:
-            from pdfminer.high_level import extract_text  # noqa:F401
-        except ImportError:
-            raise ImportError(
-                "`pdfminer` package not found, please install it with "
-                "`pip install pdfminer.six`"
-            )
+            file_path: The path to the PDF file to be loaded.
+            headers: Optional headers to use for GET request to download a file from a
+              web path.
+            password: Optional password for opening encrypted PDFs.
+            mode: The extraction mode, either "single" for the entire document or "page"
+                for page-wise extraction.
+            pages_delimiter: A string delimiter to separate pages in single-mode
+                extraction.
+            extract_images: Whether to extract images from the PDF.
+            images_parser: Optional image blob parser.
+            images_inner_format: The format for the parsed output.
+                - "text" = return the content as is
+                - "markdown-img" = wrap the content into an image markdown link, w/ link
+                pointing to (`![body)(#)`]
+                - "html-img" = wrap the content as the `alt` text of an tag and link to
+                (`<img alt="{body}" src="#"/>`)
+            concatenate_pages: Deprecated. If True, concatenate all PDF pages into one
+                a single document. Otherwise, return one document per page.
 
+        Returns:
+            This method does not directly return data. Use the `load`, `lazy_load` or
+            `aload` methods to retrieve parsed documents with content and metadata.
+        """
         super().__init__(file_path, headers=headers)
         self.parser = PDFMinerParser(
-            extract_images=extract_images, concatenate_pages=concatenate_pages
+            password=password,
+            extract_images=extract_images,
+            images_parser=images_parser,
+            concatenate_pages=concatenate_pages,
+            mode=mode,
+            pages_delimiter=pages_delimiter,
+            images_inner_format=images_inner_format,
         )
 
     def lazy_load(
         self,
     ) -> Iterator[Document]:
-        """Lazily load documents."""
+        """
+        Lazy load given path as pages.
+        Insert image, if possible, between two paragraphs.
+        In this way, a paragraph can be continued on the next page.
+        """
         if self.web_path:
-            blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)  # type: ignore[attr-defined]
+            blob = Blob.from_data(  # type: ignore[attr-defined]
+                open(self.file_path, "rb").read(), path=self.web_path
+            )
         else:
             blob = Blob.from_path(self.file_path)  # type: ignore[attr-defined]
-        yield from self.parser.parse(blob)
+        yield from self.parser.lazy_parse(blob)
 
 
 class PDFMinerPDFasHTMLLoader(BasePDFLoader):

libs/community/langchain_community/embeddings/fastembed.py

@@ -1,6 +1,6 @@
 import importlib
 import importlib.metadata
-from typing import Any, Dict, List, Literal, Optional, cast
+from typing import Any, Dict, List, Literal, Optional, Sequence, cast
 
 import numpy as np
 from langchain_core.embeddings import Embeddings
@@ -65,6 +65,14 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
     Defaults to `None`.
     """
 
+    providers: Optional[Sequence[Any]] = None
+    """List of ONNX execution providers. Use `["CUDAExecutionProvider"]` to enable the
+    use of GPU when generating embeddings. This requires to install `fastembed-gpu`
+    instead of `fastembed`. See https://qdrant.github.io/fastembed/examples/FastEmbed_GPU
+    for more details.
+    Defaults to `None`.
+    """
+
     model: Any = None  # : :meta private:
 
     model_config = ConfigDict(extra="allow", protected_namespaces=())
@@ -76,6 +84,12 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
         max_length = values.get("max_length")
         cache_dir = values.get("cache_dir")
         threads = values.get("threads")
+        providers = values.get("providers")
+        pkg_to_install = (
+            "fastembed-gpu"
+            if providers and "CUDAExecutionProvider" in providers
+            else "fastembed"
+        )
 
         try:
             fastembed = importlib.import_module("fastembed")
@@ -83,12 +97,13 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
         except ModuleNotFoundError:
             raise ImportError(
                 "Could not import 'fastembed' Python package. "
-                "Please install it with `pip install fastembed`."
+                f"Please install it with `pip install {pkg_to_install}`."
             )
 
-        if importlib.metadata.version("fastembed") < MIN_VERSION:
+        if importlib.metadata.version(pkg_to_install) < MIN_VERSION:
             raise ImportError(
-                'FastEmbedEmbeddings requires `pip install -U "fastembed>=0.2.0"`.'
+                f"FastEmbedEmbeddings requires "
+                f'`pip install -U "{pkg_to_install}>={MIN_VERSION}"`.'
             )
 
         values["model"] = fastembed.TextEmbedding(
@@ -96,6 +111,7 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
             max_length=max_length,
             cache_dir=cache_dir,
             threads=threads,
+            providers=providers,
         )
         return values
 

libs/community/langchain_community/embeddings/oci_generative_ai.py

@@ -29,6 +29,9 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
     Make sure you have the required policies (profile/roles) to
     access the OCI Generative AI service. If a specific config profile is used,
     you must pass the name of the profile (~/.oci/config) through auth_profile.
+    If a specific config file location is used, you must pass
+    the file location where profile name configs present
+    through auth_file_location
 
     To use, you must provide the compartment id
     along with the endpoint url, and model id
@@ -66,6 +69,11 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
     If not specified , DEFAULT will be used 
     """
 
+    auth_file_location: Optional[str] = "~/.oci/config"
+    """Path to the config file.
+    If not specified, ~/.oci/config will be used
+    """
+
     model_id: Optional[str] = None
     """Id of the model to call, e.g., cohere.embed-english-light-v2.0"""
 
@@ -108,7 +116,8 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
 
             if values["auth_type"] == OCIAuthType(1).name:
                 client_kwargs["config"] = oci.config.from_file(
-                    profile_name=values["auth_profile"]
+                    file_location=values["auth_file_location"],
+                    profile_name=values["auth_profile"],
                 )
                 client_kwargs.pop("signer", None)
             elif values["auth_type"] == OCIAuthType(2).name:
@@ -124,7 +133,8 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
                     return oci.auth.signers.SecurityTokenSigner(st_string, pk)
 
                 client_kwargs["config"] = oci.config.from_file(
-                    profile_name=values["auth_profile"]
+                    file_location=values["auth_file_location"],
+                    profile_name=values["auth_profile"],
                 )
                 client_kwargs["signer"] = make_security_token_signer(
                     oci_config=client_kwargs["config"]
@@ -151,11 +161,11 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
             ) from ex
         except Exception as e:
             raise ValueError(
-                "Could not authenticate with OCI client. "
-                "Please check if ~/.oci/config exists. "
-                "If INSTANCE_PRINCIPLE or RESOURCE_PRINCIPLE is used, "
-                "Please check the specified "
-                "auth_profile and auth_type are valid."
+                """Could not authenticate with OCI client.
+                If INSTANCE_PRINCIPAL or RESOURCE_PRINCIPAL is used,
+                please check the specified
+                auth_profile, auth_file_location and auth_type are valid.""",
+                e,
             ) from e
 
         return values

libs/community/langchain_community/graphs/age_graph.py

@@ -473,71 +473,78 @@ class AGEGraph(GraphStore):
     @staticmethod
     def _wrap_query(query: str, graph_name: str) -> str:
         """
-        Convert a cypher query to an Apache Age compatible
-        sql query by wrapping the cypher query in ag_catalog.cypher,
-        casting results to agtype and building a select statement
+        Convert a Cyper query to an Apache Age compatible Sql Query.
+        Handles combined queries with UNION/EXCEPT operators
 
         Args:
-            query (str): a valid cypher query
-            graph_name (str): the name of the graph to query
+            query (str) : A valid cypher query, can include UNION/EXCEPT operators
+            graph_name (str) : The name of the graph to query
 
-        Returns:
-            str: an equivalent pgsql query
+        Returns :
+            str : An equivalent pgSql query wrapped with ag_catalog.cypher
+
+        Raises:
+            ValueError : If query is empty, contain RETURN *, or has invalid field names
         """
 
+        if not query.strip():
+            raise ValueError("Empty query provided")
+
         # pgsql template
         template = """SELECT {projection} FROM ag_catalog.cypher('{graph_name}', $$
             {query}
         $$) AS ({fields});"""
 
-        # if there are any returned fields they must be added to the pgsql query
-        return_match = re.search(r'\breturn\b(?![^"]*")', query, re.IGNORECASE)
-        if return_match:
-            # Extract the part of the query after the RETURN keyword
-            return_clause = query[return_match.end() :]
+        # split the query into parts based on UNION and EXCEPT
+        parts = re.split(r"\b(UNION\b|\bEXCEPT)\b", query, flags=re.IGNORECASE)
 
-            # parse return statement to identify returned fields
-            fields = (
-                return_clause.lower()
-                .split("distinct")[-1]
-                .split("order by")[0]
-                .split("skip")[0]
-                .split("limit")[0]
-                .split(",")
-            )
+        all_fields = []
 
-            # raise exception if RETURN * is found as we can't resolve the fields
-            if "*" in [x.strip() for x in fields]:
-                raise ValueError(
-                    "AGE graph does not support 'RETURN *'"
-                    + " statements in Cypher queries"
+        for part in parts:
+            if part.strip().upper() in ("UNION", "EXCEPT"):
+                continue
+
+            # if there are any returned fields they must be added to the pgsql query
+            return_match = re.search(r'\breturn\b(?![^"]*")', part, re.IGNORECASE)
+            if return_match:
+                # Extract the part of the query after the RETURN keyword
+                return_clause = part[return_match.end() :]
+
+                # parse return statement to identify returned fields
+                fields = (
+                    return_clause.lower()
+                    .split("distinct")[-1]
+                    .split("order by")[0]
+                    .split("skip")[0]
+                    .split("limit")[0]
+                    .split(",")
                 )
 
-            # get pgsql formatted field names
-            fields = [
-                AGEGraph._get_col_name(field, idx) for idx, field in enumerate(fields)
-            ]
+                # raise exception if RETURN * is found as we can't resolve the fields
+                clean_fileds = [f.strip() for f in fields if f.strip()]
+                if "*" in clean_fileds:
+                    raise ValueError(
+                        "Apache Age does not support RETURN * in Cypher queries"
+                    )
 
-            # build resulting pgsql relation
-            fields_str = ", ".join(
-                [
-                    field.split(".")[-1] + " agtype"
-                    for field in fields
-                    if field.split(".")[-1]
-                ]
-            )
+                # Format fields and maintain order of appearance
+                for idx, field in enumerate(clean_fileds):
+                    field_name = AGEGraph._get_col_name(field, idx)
+                    if field_name not in all_fields:
+                        all_fields.append(field_name)
 
-        # if no return statement we still need to return a single field of type agtype
-        else:
+        # if no return statements found in any part
+        if not all_fields:
             fields_str = "a agtype"
 
-        select_str = "*"
+        else:
+            fields_str = ", ".join(f"{field} agtype" for field in all_fields)
 
         return template.format(
             graph_name=graph_name,
             query=query,
             fields=fields_str,
-            projection=select_str,
+            projection="*",
         )
 
     @staticmethod

libs/community/langchain_community/llms/oci_generative_ai.py

@@ -79,6 +79,11 @@ class OCIGenAIBase(BaseModel, ABC):
     If not specified , DEFAULT will be used 
     """
 
+    auth_file_location: Optional[str] = "~/.oci/config"
+    """Path to the config file.
+    If not specified, ~/.oci/config will be used
+    """
+
     model_id: Optional[str] = None
     """Id of the model to call, e.g., cohere.command"""
 
@@ -125,7 +130,8 @@ class OCIGenAIBase(BaseModel, ABC):
 
             if values["auth_type"] == OCIAuthType(1).name:
                 client_kwargs["config"] = oci.config.from_file(
-                    profile_name=values["auth_profile"]
+                    file_location=values["auth_file_location"],
+                    profile_name=values["auth_profile"],
                 )
                 client_kwargs.pop("signer", None)
             elif values["auth_type"] == OCIAuthType(2).name:
@@ -141,7 +147,8 @@ class OCIGenAIBase(BaseModel, ABC):
                     return oci.auth.signers.SecurityTokenSigner(st_string, pk)
 
                 client_kwargs["config"] = oci.config.from_file(
-                    profile_name=values["auth_profile"]
+                    file_location=values["auth_file_location"],
+                    profile_name=values["auth_profile"],
                 )
                 client_kwargs["signer"] = make_security_token_signer(
                     oci_config=client_kwargs["config"]
@@ -171,11 +178,10 @@ class OCIGenAIBase(BaseModel, ABC):
             ) from ex
         except Exception as e:
             raise ValueError(
-                """Could not authenticate with OCI client. 
-                Please check if ~/.oci/config exists.
+                """Could not authenticate with OCI client.
                 If INSTANCE_PRINCIPAL or RESOURCE_PRINCIPAL is used, 
                 please check the specified
-                auth_profile and auth_type are valid.""",
+                auth_profile, auth_file_location and auth_type are valid.""",
                 e,
             ) from e
 
@@ -223,6 +229,9 @@ class OCIGenAI(LLM, OCIGenAIBase):
     access the OCI Generative AI service.
     If a specific config profile is used, you must pass
     the name of the profile (from ~/.oci/config) through auth_profile.
+    If a specific config file location is used, you must pass
+    the file location where profile name configs present
+    through auth_file_location
 
     To use, you must provide the compartment id
     along with the endpoint url, and model id

libs/community/langchain_community/tools/office365/send_event.py

@@ -6,10 +6,10 @@ https://learn.microsoft.com/en-us/graph/auth/
 
 from datetime import datetime as dt
 from typing import List, Optional, Type
+from zoneinfo import ZoneInfo
 
 from langchain_core.callbacks import CallbackManagerForToolRun
 from pydantic import BaseModel, Field
-from zoneinfo import ZoneInfo
 
 from langchain_community.tools.office365.base import O365BaseTool
 from langchain_community.tools.office365.utils import UTC_FORMAT

libs/community/langchain_community/tools/yahoo_finance_news.py

@@ -36,7 +36,16 @@ class YahooFinanceNewsTool(BaseTool):  # type: ignore[override, override]
         query: str,
         run_manager: Optional[CallbackManagerForToolRun] = None,
     ) -> str:
-        """Use the Yahoo Finance News tool."""
+        """
+        Use the Yahoo Finance News tool.
+
+        Args:
+            query: Company ticker symbol (e.g., 'AAPL' for Apple).
+            run_manager: Optional callback manager.
+
+        Returns:
+            str: Formatted news results or error message.
+        """
         try:
             import yfinance
         except ImportError:
@@ -53,7 +62,11 @@ class YahooFinanceNewsTool(BaseTool):  # type: ignore[override, override]
 
         links = []
         try:
-            links = [n["link"] for n in company.news if n["type"] == "STORY"]
+            links = [
+                n["content"]["canonicalUrl"]["url"]
+                for n in company.news
+                if n["content"]["contentType"] == "STORY"
+            ]
         except (HTTPError, ReadTimeout, ConnectionError):
             if not links:
                 return f"No news found for company that searched with {query} ticker."
@@ -69,8 +82,9 @@ class YahooFinanceNewsTool(BaseTool):  # type: ignore[override, override]
     @staticmethod
     def _format_results(docs: Iterable[Document], query: str) -> str:
         doc_strings = [
-            "\n".join([doc.metadata["title"], doc.metadata["description"]])
+            "\n".join([doc.metadata["title"], doc.metadata.get("description", "")])
             for doc in docs
-            if query in doc.metadata["description"] or query in doc.metadata["title"]
+            if query in doc.metadata.get("description", "")
+            or query in doc.metadata["title"]
         ]
         return "\n\n".join(doc_strings)

libs/community/langchain_community/vectorstores/azure_cosmos_db_no_sql.py

@@ -789,6 +789,13 @@ class AzureCosmosDBNoSqlVectorSearch(VectorStore):
                 elif isinstance(condition.value, list):
                     # e.g., for IN clauses
                     value = f"({', '.join(map(str, condition.value))})"
+                elif isinstance(condition.value, (int, float, bool)):
+                    value = str(condition.value)
+                elif condition.value is None:
+                    value = "NULL"
+                else:
+                    raise ValueError(f"Unsupported value type: {type(condition.value)}")
+
                 clauses.append(f"c.{condition.property} {sql_operator} {value}")
         return f""" WHERE {' {} '.format(sql_logical_operator).join(clauses)}""".strip()
 

libs/community/langchain_community/vectorstores/deeplake.py

@@ -168,7 +168,7 @@ class DeepLake(VectorStore):
         if _DEEPLAKE_INSTALLED is False:
             raise ImportError(
                 "Could not import deeplake python package. "
-                "Please install it with `pip install deeplake[enterprise]`."
+                "Please install it with `pip install deeplake[enterprise]<4.0.0`."
             )
 
         if (

libs/community/pyproject.toml

@@ -1,16 +1,92 @@
 [build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
+requires = ["pdm-backend"]
+build-backend = "pdm.backend"
 
-[tool.poetry]
-name = "langchain-community"
-version = "0.3.16"
-description = "Community contributed LangChain integrations."
+[project]
 authors = []
-license = "MIT"
+license = {text = "MIT"}
+requires-python = "<4.0,>=3.9"
+dependencies = [
+    "langchain-core<1.0.0,>=0.3.34rc1",
+    "langchain<1.0.0,>=0.3.18rc1",
+    "SQLAlchemy<3,>=1.4",
+    "requests<3,>=2",
+    "PyYAML>=5.3",
+    "aiohttp<4.0.0,>=3.8.3",
+    "tenacity!=8.4.0,<10,>=8.1.0",
+    "dataclasses-json<0.7,>=0.5.7",
+    "pydantic-settings<3.0.0,>=2.4.0",
+    "langsmith<0.4,>=0.1.125",
+    "httpx-sse<1.0.0,>=0.4.0",
+    "numpy<2,>=1.26.4; python_version < \"3.12\"",
+    "numpy<3,>=1.26.2; python_version >= \"3.12\"",
+]
+name = "langchain-community"
+version = "0.3.17rc1"
+description = "Community contributed LangChain integrations."
 readme = "README.md"
+
+[project.urls]
+"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/community"
+"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-community%3D%3D0%22&expanded=true"
 repository = "https://github.com/langchain-ai/langchain"
 
+[dependency-groups]
+test = [
+    "pytest<8.0.0,>=7.4.4",
+    "pytest-cov<5.0.0,>=4.1.0",
+    "pytest-dotenv<1.0.0,>=0.5.2",
+    "duckdb-engine<1.0.0,>=0.13.6",
+    "pytest-watcher<1.0.0,>=0.2.6",
+    "freezegun<2.0.0,>=1.2.2",
+    "responses<1.0.0,>=0.22.0",
+    "pytest-asyncio<1.0.0,>=0.20.3",
+    "lark<2.0.0,>=1.1.5",
+    "pandas<3.0.0,>=2.0.0",
+    "pytest-mock<4.0.0,>=3.10.0",
+    "pytest-socket<1.0.0,>=0.6.0",
+    "syrupy<5.0.0,>=4.0.2",
+    "requests-mock<2.0.0,>=1.11.0",
+    "pytest-xdist<4.0.0,>=3.6.1",
+    "cffi<1.17.1; python_version < \"3.10\"",
+    "cffi; python_version >= \"3.10\"",
+    "langchain-core @ file:///${PROJECT_ROOT}/../core",
+    "langchain @ file:///${PROJECT_ROOT}/../langchain",
+    "langchain-tests @ file:///${PROJECT_ROOT}/../standard-tests",
+    "toml>=0.10.2",
+]
+codespell = [
+    "codespell<3.0.0,>=2.2.0",
+]
+test_integration = [
+    "pytest-vcr<2.0.0,>=1.0.2",
+    "vcrpy<7,>=6",
+]
+lint = [
+    "ruff<0.6,>=0.5",
+    "cffi<1.17.1; python_version < \"3.10\"",
+    "cffi; python_version >= \"3.10\"",
+]
+dev = [
+    "jupyter<2.0.0,>=1.0.0",
+    "setuptools<68.0.0,>=67.6.1",
+    "langchain-core @ file:///${PROJECT_ROOT}/../core",
+]
+typing = [
+    "mypy<2.0,>=1.12",
+    "types-pyyaml<7.0.0.0,>=6.0.12.2",
+    "types-requests<3.0.0.0,>=2.28.11.5",
+    "types-toml<1.0.0.0,>=0.10.8.1",
+    "types-pytz<2024.0.0.0,>=2023.3.0.0",
+    "types-chardet<6.0.0.0,>=5.0.4.6",
+    "types-redis<5.0.0.0,>=4.3.21.6",
+    "mypy-protobuf<4.0.0,>=3.0.0",
+    "langchain-core @ file:///${PROJECT_ROOT}/../core",
+    "langchain-text-splitters @ file:///${PROJECT_ROOT}/../text-splitters",
+    "langchain @ file:///${PROJECT_ROOT}/../langchain",
+]
+
+
 [tool.ruff]
 exclude = [
     "tests/examples/non-utf8-encoding.py",
@@ -27,31 +103,6 @@ skip = ".git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package
 ignore-regex = ".*(Stati Uniti|Tense=Pres).*"
 ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon,crate,aadd,symbl,precesses,accademia,nin,cann"
 
-[tool.poetry.urls]
-"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/community"
-"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-community%3D%3D0%22&expanded=true"
-
-[tool.poetry.dependencies]
-python = ">=3.9,<4.0"
-langchain-core = "^0.3.32"
-langchain = "^0.3.16"
-SQLAlchemy = ">=1.4,<3"
-requests = "^2"
-PyYAML = ">=5.3"
-aiohttp = "^3.8.3"
-tenacity = ">=8.1.0,!=8.4.0,<10"
-dataclasses-json = ">= 0.5.7, < 0.7"
-pydantic-settings = "^2.4.0"
-langsmith = ">=0.1.125,<0.4"
-httpx-sse = "^0.4.0"
-[[tool.poetry.dependencies.numpy]]
-version = ">=1.22.4,<2"
-python = "<3.12"
-
-[[tool.poetry.dependencies.numpy]]
-version = ">=1.26.2,<3"
-python = ">=3.12"
-
 [tool.ruff.lint]
 select = ["E", "F", "I", "T201"]
 
@@ -71,101 +122,3 @@ filterwarnings = [
     "ignore::langchain_core._api.deprecation.LangChainDeprecationWarning:test",
     "ignore::langchain_core._api.deprecation.LangChainPendingDeprecationWarning:test",
 ]
-
-[tool.poetry.group.test]
-optional = true
-
-[tool.poetry.group.codespell]
-optional = true
-
-[tool.poetry.group.test_integration]
-optional = true
-
-[tool.poetry.group.lint]
-optional = true
-
-[tool.poetry.group.dev]
-optional = true
-
-[tool.poetry.group.test.dependencies]
-pytest = "^7.4.4"
-pytest-cov = "^4.1.0"
-pytest-dotenv = "^0.5.2"
-duckdb-engine = "^0.13.6"
-pytest-watcher = "^0.2.6"
-freezegun = "^1.2.2"
-responses = "^0.22.0"
-pytest-asyncio = "^0.20.3"
-lark = "^1.1.5"
-pandas = "^2.0.0"
-pytest-mock = "^3.10.0"
-pytest-socket = "^0.6.0"
-syrupy = "^4.0.2"
-requests-mock = "^1.11.0"
-pytest-xdist = "^3.6.1"
-[[tool.poetry.group.test.dependencies.cffi]]
-version = "<1.17.1"
-python = "<3.10"
-
-[[tool.poetry.group.test.dependencies.cffi]]
-version = "*"
-python = ">=3.10"
-
-[tool.poetry.group.codespell.dependencies]
-codespell = "^2.2.0"
-
-[tool.poetry.group.test_integration.dependencies]
-pytest-vcr = "^1.0.2"
-vcrpy = "^6"
-
-[tool.poetry.group.lint.dependencies]
-ruff = "^0.5"
-[[tool.poetry.group.lint.dependencies.cffi]]
-version = "<1.17.1"
-python = "<3.10"
-
-[[tool.poetry.group.lint.dependencies.cffi]]
-version = "*"
-python = ">=3.10"
-
-[tool.poetry.group.dev.dependencies]
-jupyter = "^1.0.0"
-setuptools = "^67.6.1"
-
-[tool.poetry.group.typing.dependencies]
-mypy = "^1.12"
-types-pyyaml = "^6.0.12.2"
-types-requests = "^2.28.11.5"
-types-toml = "^0.10.8.1"
-types-pytz = "^2023.3.0.0"
-types-chardet = "^5.0.4.6"
-types-redis = "^4.3.21.6"
-mypy-protobuf = "^3.0.0"
-
-[tool.poetry.group.test.dependencies.langchain-core]
-path = "../core"
-develop = true
-
-[tool.poetry.group.test.dependencies.langchain]
-path = "../langchain"
-develop = true
-
-[tool.poetry.group.test.dependencies.langchain-tests]
-path = "../standard-tests"
-develop = true
-
-[tool.poetry.group.dev.dependencies.langchain-core]
-path = "../core"
-develop = true
-
-[tool.poetry.group.typing.dependencies.langchain-core]
-path = "../core"
-develop = true
-
-[tool.poetry.group.typing.dependencies.langchain-text-splitters]
-path = "../text-splitters"
-develop = true
-
-[tool.poetry.group.typing.dependencies.langchain]
-path = "../langchain"
-develop = true

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

@@ -0,0 +1,33 @@
+"""Standard LangChain interface tests"""
+
+from typing import Type
+
+import pytest
+from langchain_core.language_models import BaseChatModel
+from langchain_tests.integration_tests import ChatModelIntegrationTests
+
+from langchain_community.chat_models import ChatPerplexity
+
+
+class TestPerplexityStandard(ChatModelIntegrationTests):
+    @property
+    def chat_model_class(self) -> Type[BaseChatModel]:
+        return ChatPerplexity
+
+    @property
+    def chat_model_params(self) -> dict:
+        return {"model": "sonar"}
+
+    @property
+    def returns_usage_metadata(self) -> bool:
+        # TODO: add usage metadata and delete this property
+        # https://docs.perplexity.ai/api-reference/chat-completions#response-usage
+        return False
+
+    @pytest.mark.xfail(reason="TODO: handle in integration.")
+    def test_double_messages_conversation(self, model: BaseChatModel) -> None:
+        super().test_double_messages_conversation(model)
+
+    @pytest.mark.xfail(reason="Raises 400: Custom stop words not supported.")
+    def test_stop_sequence(self, model: BaseChatModel) -> None:
+        super().test_stop_sequence(model)

libs/community/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py

@@ -11,10 +11,8 @@ from langchain_community.document_loaders.base import BaseBlobParser
 from langchain_community.document_loaders.blob_loaders import Blob
 from langchain_community.document_loaders.parsers import (
     BaseImageBlobParser,
-    PDFMinerParser,
     PDFPlumberParser,
     PyPDFium2Parser,
-    PyPDFParser,
 )
 
 if TYPE_CHECKING:
@@ -98,17 +96,6 @@ def _assert_with_duplicate_parser(parser: BaseBlobParser, dedupe: bool = False)
         assert "11000000 SSeerriieess" == docs[0].page_content.split("\n")[0]
 
 
-def test_pypdf_parser() -> None:
-    """Test PyPDF parser."""
-    _assert_with_parser(PyPDFParser())
-
-
-def test_pdfminer_parser() -> None:
-    """Test PDFMiner parser."""
-    # Does not follow defaults to split by page.
-    _assert_with_parser(PDFMinerParser(), splits_by_page=False)
-
-
 def test_pypdfium2_parser() -> None:
     """Test PyPDFium2 parser."""
     # Does not follow defaults to split by page.
@@ -122,16 +109,6 @@ def test_pdfplumber_parser() -> None:
     _assert_with_duplicate_parser(PDFPlumberParser(dedupe=True), dedupe=True)
 
 
-def test_extract_images_text_from_pdf_pypdfparser() -> None:
-    """Test extract image from pdf and recognize text with rapid ocr - PyPDFParser"""
-    _assert_with_parser(PyPDFParser(extract_images=True))
-
-
-def test_extract_images_text_from_pdf_pdfminerparser() -> None:
-    """Test extract image from pdf and recognize text with rapid ocr - PDFMinerParser"""
-    _assert_with_parser(PDFMinerParser(extract_images=True))
-
-
 def test_extract_images_text_from_pdf_pypdfium2parser() -> None:
     """Test extract image from pdf and recognize text with rapid ocr - PyPDFium2Parser"""  # noqa: E501
     _assert_with_parser(PyPDFium2Parser(extract_images=True))
@@ -149,7 +126,10 @@ class EmptyImageBlobParser(BaseImageBlobParser):
 @pytest.mark.parametrize(
     "parser_factory,params",
     [
+        ("PDFMinerParser", {}),
         ("PyMuPDFParser", {}),
+        ("PyPDFParser", {"extraction_mode": "plain"}),
+        ("PyPDFParser", {"extraction_mode": "layout"}),
     ],
 )
 @pytest.mark.requires("pillow")
@@ -175,7 +155,10 @@ def test_mode_and_extract_images_variations(
 @pytest.mark.parametrize(
     "parser_factory,params",
     [
+        ("PDFMinerParser", {}),
         ("PyMuPDFParser", {}),
+        ("PyPDFParser", {"extraction_mode": "plain"}),
+        ("PyPDFParser", {"extraction_mode": "layout"}),
     ],
 )
 @pytest.mark.requires("pillow")

libs/community/tests/integration_tests/document_loaders/test_pdf.py

@@ -8,7 +8,6 @@ import langchain_community.document_loaders as pdf_loaders
 from langchain_community.document_loaders import (
     AmazonTextractPDFLoader,
     MathpixPDFLoader,
-    PDFMinerLoader,
     PDFMinerPDFasHTMLLoader,
     PyPDFium2Loader,
     UnstructuredPDFLoader,
@@ -42,34 +41,6 @@ def test_unstructured_pdf_loader_default_mode() -> None:
     assert len(docs) == 1
 
 
-def test_pdfminer_loader() -> None:
-    """Test PDFMiner loader."""
-    file_path = Path(__file__).parent.parent / "examples/hello.pdf"
-    loader = PDFMinerLoader(file_path)
-    docs = loader.load()
-
-    assert len(docs) == 1
-
-    file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
-    loader = PDFMinerLoader(file_path)
-
-    docs = loader.load()
-    assert len(docs) == 1
-
-    # Verify that concatenating pages parameter works
-    file_path = Path(__file__).parent.parent / "examples/hello.pdf"
-    loader = PDFMinerLoader(file_path, concatenate_pages=True)
-    docs = loader.load()
-
-    assert len(docs) == 1
-
-    file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
-    loader = PDFMinerLoader(file_path, concatenate_pages=False)
-
-    docs = loader.load()
-    assert len(docs) == 16
-
-
 def test_pdfminer_pdf_as_html_loader() -> None:
     """Test PDFMinerPDFasHTMLLoader."""
     file_path = Path(__file__).parent.parent / "examples/hello.pdf"
@@ -211,7 +182,9 @@ def test_amazontextract_loader_failures() -> None:
 @pytest.mark.parametrize(
     "parser_factory,params",
     [
+        ("PDFMinerLoader", {}),
         ("PyMuPDFLoader", {}),
+        ("PyPDFLoader", {}),
     ],
 )
 def test_standard_parameters(
@@ -229,7 +202,7 @@ def test_standard_parameters(
     loader = loader_class(
         file_path,
         mode="page",
-        page_delimiter="---",
+        pages_delimiter="---",
         images_parser=None,
         images_inner_format="text",
         password=None,

libs/community/tests/integration_tests/tools/test_yahoo_finance_news.py

@@ -9,7 +9,7 @@ yfinance = pytest.importorskip("yfinance")
 def test_success() -> None:
     """Test that the tool runs successfully."""
     tool = YahooFinanceNewsTool()
-    query = "Microsoft"
+    query = "AAPL"
     result = tool.run(query)
     assert result is not None
     assert f"Company ticker {query} not found." not in result

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

@@ -82,7 +82,7 @@ class MockResponse:
     def raise_for_status(self) -> None:
         """Mocked raise for status."""
         if 400 <= self.status_code < 600:
-            raise HTTPError()
+            raise HTTPError()  # type: ignore[call-arg]
 
     def json(self) -> Dict:
         """Returns mocked json data."""

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

@@ -1,11 +1,13 @@
 """Test Perplexity Chat API wrapper."""
 
 import os
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple, Type
 from unittest.mock import MagicMock
 
 import pytest
+from langchain_core.language_models import BaseChatModel
 from langchain_core.messages import AIMessageChunk, BaseMessageChunk
+from langchain_tests.unit_tests import ChatModelUnitTests
 from pytest_mock import MockerFixture
 
 from langchain_community.chat_models import ChatPerplexity
@@ -13,6 +15,21 @@ from langchain_community.chat_models import ChatPerplexity
 os.environ["PPLX_API_KEY"] = "foo"
 
 
+@pytest.mark.requires("openai")
+class TestPerplexityStandard(ChatModelUnitTests):
+    @property
+    def chat_model_class(self) -> Type[BaseChatModel]:
+        return ChatPerplexity
+
+    @property
+    def init_from_env_params(self) -> Tuple[dict, dict, dict]:
+        return (
+            {"PPLX_API_KEY": "api_key"},
+            {},
+            {"pplx_api_key": "api_key"},
+        )
+
+
 @pytest.mark.requires("openai")
 def test_perplexity_model_name_param() -> None:
     llm = ChatPerplexity(model="foo")  # type: ignore[call-arg]

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

@@ -50,6 +50,7 @@ def test_yandexgpt_invalid_model_params() -> None:
     [dict(), dict(disable_request_logging=True), dict(disable_request_logging=False)],
 )
 @mock.patch.dict(os.environ, {}, clear=True)
+@pytest.mark.requires("yandexcloud")  # TODO: remove this
 def test_completion_call(api_key_or_token: dict, disable_logging: dict) -> None:
     absent_yandex_module_stub = MagicMock()
     grpc_mock = MagicMock()