check passing message names

Thank you for contributing to LangChain!

- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
  - Example: "community: add foobar LLM"


- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!


- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

.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
@@ -304,9 +309,8 @@ if __name__ == "__main__":
                 f"Unknown lib: {file}. check_diff.py likely needs "
                 "an update for this new library!"
             )
-        elif any(file.startswith(p) for p in ["docs/", "cookbook/"]):
-            if file.startswith("docs/"):
-                docs_edited = True
+        elif file.startswith("docs/") or file in ["pyproject.toml", "uv.lock"]: # docs or root uv files
+            docs_edited = True
             dirs_to_run["lint"].add(".")
 
     dependents = dependents_graph()

.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:
@@ -213,16 +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.
-          IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
+          # 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
@@ -233,7 +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
@@ -242,15 +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"
 
@@ -259,12 +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
@@ -305,6 +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 }}
 
@@ -330,13 +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:
@@ -372,13 +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

@@ -1 +1 @@
-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

docs/cassettes/chat_token_usage_tracking_0b1523d8-127e-4314-82fa-bd97aca37f9a.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/chat_token_usage_tracking_1837c807-136a-49d8-9c33-060e58dc16d2.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/chat_token_usage_tracking_67117f2b-ce68-4c1e-9556-2d3849f90e1b.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/chat_token_usage_tracking_9c82ff80-ec4e-4049-b019-5f0bbd7df82a.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/chat_token_usage_tracking_b04a4486-72fd-48ce-8f9e-5d281b441195.msgpack.zlib

@@ -1 +1 @@
-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

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

@@ -1 +1 @@
-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

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

@@ -1 +1 @@
-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
+eNrtWctu20YUbbdZdVOgS5boqtDIpEQ9DaOwLTsxEkeOH0icthBGM0NxbJJDzwwtyYEXTfsDXHXd1JEKw01bJOg7XXfRH3AX/Yh+QS8lObKR9KF1qYUgzty5j3MfhyIfDg+ZVFyEr5/xUDOJiYYLlTwcSnYQM6U/GQRMe4KebDS3tj+PJT9/19M6UvW5ORzxvIhYiHmeiGDu0J4jHtZz8Dvy2UjNSVvQ/vmnD8yAKYU7TJn19x+YRIClUJt1c5v5vhEwAxt7Yp+ZOVMKn8F6rJg0jz/MmYGgzIeFTqSRI1DAQw5SSkuGA7OuZcwurloiGpk06w9MHhI/pqwVpzbHYsc5U7MgggB1LGHNylvHQ49hCtH/8dobJ55QOnlyNaKvMCEMDLOQCMrDTvJl54hHOYMy18eanUIcIRvhlZzuMxYh7PNDNhifSr7GUeRzgtP9uT0lwrNJ3Ej3I/by9mkaNAKHQ508a4ITi2tzG32APjTsfNnO21/3kNKYhz5giXwM/gyi0f5PlzciTPZBCZqkNRmMDz+5LCNU8ngdk+bWFZVYEi95jGVQdp5eXpdxqHnAkuHyxsvmJptTc8W8XchXv7miWPVDkjx2sa/Yd1cOMy37iAjQkXxmDYgQ+5wl53+2WsRttYOFWyvW9SZv7hxsWiv3bzv7ivQr7dZmU8Xxmhs1mmt78Z2jUN2QXCC7UqxWqjXLqiI7b+UhZFSq7Vc92ryxHK/1Vq3GzaXm+kF1Ua5Xt9rhvYbtdqs7tUiuNMqhtR1ul+93JA706qFDaZnuVNfvx/n8dV+t+mJH7O3mC7sbrVsFqztvgHfxIacL1mJt96DX2L5Z2/TKmPb2G5ux192xSH59qVfxFuXG6uL9zuZeIepccq9sV5A18bBsOVUr/Ty5qA2fhR3tJZ/bJfsLyVQEFc0+HgBkOlYPT6AO2W+/Difd9Kh5c1rCb540oCaT53cZzRlWyVhlbaNgFUrwVS+V6yXbuL6+fbY8MbOdluC5oVlPz7HDdGXcRfMGtLBUTC/E2kXVb7YlDpULdbly0QND4sXhPqOny6+s/udp9UNq03igmRHrRUIxNHEzObuHNsdzBa01no5bDQnZwSE/GrVC8nzUBt2jXpeSmFLvsBtYtSOnyNssJu6zyZFIitQMOIQClZzYVs1+Mtm6qMRTiN5CtoUs+8cegr5nPg84IDz6nkw3OFsC+L9/WUDDQII5OHRG+bF+uSwhWQAlnBqfqnFqtdrPrxa6UFUEkVql9uNVKQD7khq7EKjvXxaYqHhkqbPehTTiNDl/By5aNsYVWnZsWrHLAAUp1+y2XXAYcZyS7VScHyC5nICWNJuRkJBtRmCU635yngtwL506C0W7VCxDpPPGZHxuxe2GSGNQ80YkmS8w/Yq4iGDiMTSuyGTY2L29uL62/O09dLm0UHM8jJNhKFTIXXewxSQkJjklvogpjE/JBsuraHNxN3lWs2iJuEWnVGtXqy520VJza4h9cPKQJE+94oJZd5yiOW8EeKFahnyMWOWjQRpU2Pn9rVOKNa4bMPYpMEVKQQQICC32Onv9u/3Npe7OjTtyxbWPogOvqA5uNSv9AEhEtPegfCcn8lPSyo8KHAQINIRmoPOid23rlYSEoNAcZFWQXU25CQLlhLU0Bwqrm8AVOPZ1utFXwEAtF3xmMgLXU9tu1KoUGK3gdskhqU1PwOExTfKQsh4wVQ6U+BqnxDahRwyVDwkIU7VTMk3pk7nAeeBfGPs+UJ4vOtApbTVeyJlgnCuvBYEB4UykgGUnPDm6vHbt/wPnFLu7Xt/M8PrveBkUos0AmwEw7bEMsFkAUwRIIoNsBsiIFN0MsFlqrMvDDLBZAMMZXrPh1cUyI8pZIHvvg/CDrMhmQWyJEQx/mjPMZmnM7G5sRqbEKgNsFsBErNO/6Okzswy4WYDLbslmnGQ868yZAHM587N7slkgeztD69/Q+neATKVFZP5PIJpG+cAEUIJIt8avMMAfO31ofeHvdLmaM7XQ2H+xUqjlrp5tUaYx90eveEdvIegLWYgNx5SL6cLxK6xcVjBOC4TzDzpgYfQuFwxFklFOrnhspc/b0yz8zfbx8Yvsvt9o3l758Nq1vwCNNtHn

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

@@ -1 +1 @@
-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

docs/cassettes/sql_qa_11.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/sql_qa_39.msgpack.zlib

@@ -0,0 +1 @@
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

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

@@ -1 +1 @@
-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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/concepts/why_langchain.mdx

@@ -31,7 +31,7 @@ This provides a standard way to interact with chat models, supporting important
 
 ### Example: chat models 
 
-Many [model providers](/docs/concepts/chat_models/) support [tool calling](/docs/concepts/tool_calling/), a critical features for many applications (e.g., [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/)), that allows a developer to request model responses that match a particular schema.
+Many [model providers](/docs/concepts/chat_models/) support [tool calling](/docs/concepts/tool_calling/), a critical feature for many applications (e.g., [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/)), that allows a developer to request model responses that match a particular schema.
 The APIs for each provider differ. 
 LangChain's [chat model](/docs/concepts/chat_models/) interface provides a common way to bind [tools](/docs/concepts/tools) to a model in order to support [tool calling](/docs/concepts/tool_calling/):
 

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/contributing/how_to/integrations/publish.mdx

@@ -118,8 +118,8 @@ langchain-cli integration create-doc \
 These commands will create the following 3 files, which you should fill out with information about your package:
 
 - `docs/docs/integrations/providers/parrot_link.ipynb`
-- `docs/docs/integrations/chat/parrot_-_link.ipynb`
-- `docs/docs/integrations/vectorstores/parrot_-_link.ipynb`
+- `docs/docs/integrations/chat/parrot_link.ipynb`
+- `docs/docs/integrations/vectorstores/parrot_link.ipynb`
 
 ### Manually create your documentation pages (if you prefer)
 

docs/docs/how_to/chatbots_tools.ipynb

@@ -120,7 +120,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Great! Now let's assemble our agent using LangGraph's prebuilt [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent), which allows you to create a [tool-calling agent](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling-agent):"
+    "Great! Now let's assemble our agent using LangGraph's prebuilt [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent), which allows you to create a [tool-calling agent](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling-agent):"
    ]
   },
   {
@@ -131,10 +131,10 @@
    "source": [
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "# state_modifier allows you to preprocess the inputs to the model inside ReAct agent\n",
+    "# prompt allows you to preprocess the inputs to the model inside ReAct agent\n",
     "# in this case, since we're passing a prompt string, we'll just always add a SystemMessage\n",
     "# with this prompt string before any other messages sent to the model\n",
-    "agent = create_react_agent(model, tools, state_modifier=prompt)"
+    "agent = create_react_agent(model, tools, prompt=prompt)"
    ]
   },
   {
@@ -266,7 +266,7 @@
     "\n",
     "# highlight-start\n",
     "memory = MemorySaver()\n",
-    "agent = create_react_agent(model, tools, state_modifier=prompt, checkpointer=memory)\n",
+    "agent = create_react_agent(model, tools, prompt=prompt, checkpointer=memory)\n",
     "# highlight-end"
    ]
   },

docs/docs/how_to/index.mdx

@@ -25,52 +25,10 @@ This highlights functionality that is core to using LangChain.
 - [How to: stream runnables](/docs/how_to/streaming)
 - [How to: debug your LLM apps](/docs/how_to/debugging/)
 
-## LangChain Expression Language (LCEL)
-
-[LangChain Expression Language](/docs/concepts/lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol.
-
-[**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
-
-[**Migration guide**](/docs/versions/migrating_chains): For migrating legacy chain abstractions to LCEL.
-
-- [How to: chain runnables](/docs/how_to/sequence)
-- [How to: stream runnables](/docs/how_to/streaming)
-- [How to: invoke runnables in parallel](/docs/how_to/parallel/)
-- [How to: add default invocation args to runnables](/docs/how_to/binding/)
-- [How to: turn any function into a runnable](/docs/how_to/functions)
-- [How to: pass through inputs from one chain step to the next](/docs/how_to/passthrough)
-- [How to: configure runnable behavior at runtime](/docs/how_to/configure)
-- [How to: add message history (memory) to a chain](/docs/how_to/message_history)
-- [How to: route between sub-chains](/docs/how_to/routing)
-- [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/)
-- [How to: inspect runnables](/docs/how_to/inspect)
-- [How to: add fallbacks to a runnable](/docs/how_to/fallbacks)
-- [How to: pass runtime secrets to a runnable](/docs/how_to/runnable_runtime_secrets)
-
 ## Components
 
 These are the core building blocks you can use when building applications.
 
-### Prompt templates
-
-[Prompt Templates](/docs/concepts/prompt_templates) are responsible for formatting user input into a format that can be passed to a language model.
-
-- [How to: use few shot examples](/docs/how_to/few_shot_examples)
-- [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
-- [How to: partially format prompt templates](/docs/how_to/prompts_partial)
-- [How to: compose prompts together](/docs/how_to/prompts_composition)
-
-### Example selectors
-
-[Example Selectors](/docs/concepts/example_selectors) are responsible for selecting the correct few shot examples to pass to the prompt.
-
-- [How to: use example selectors](/docs/how_to/example_selectors)
-- [How to: select examples by length](/docs/how_to/example_selectors_length_based)
-- [How to: select examples by semantic similarity](/docs/how_to/example_selectors_similarity)
-- [How to: select examples by semantic ngram overlap](/docs/how_to/example_selectors_ngram)
-- [How to: select examples by maximal marginal relevance](/docs/how_to/example_selectors_mmr)
-- [How to: select examples from LangSmith few-shot datasets](/docs/how_to/example_selectors_langsmith/)
-
 ### Chat models
 
 [Chat Models](/docs/concepts/chat_models) are newer forms of language models that take messages in and output a message.
@@ -101,6 +59,26 @@ See [supported integrations](/docs/integrations/chat/) for details on getting st
 - [How to: filter messages](/docs/how_to/filter_messages/)
 - [How to: merge consecutive messages of the same type](/docs/how_to/merge_message_runs/)
 
+### Prompt templates
+
+[Prompt Templates](/docs/concepts/prompt_templates) are responsible for formatting user input into a format that can be passed to a language model.
+
+- [How to: use few shot examples](/docs/how_to/few_shot_examples)
+- [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
+- [How to: partially format prompt templates](/docs/how_to/prompts_partial)
+- [How to: compose prompts together](/docs/how_to/prompts_composition)
+
+### Example selectors
+
+[Example Selectors](/docs/concepts/example_selectors) are responsible for selecting the correct few shot examples to pass to the prompt.
+
+- [How to: use example selectors](/docs/how_to/example_selectors)
+- [How to: select examples by length](/docs/how_to/example_selectors_length_based)
+- [How to: select examples by semantic similarity](/docs/how_to/example_selectors_similarity)
+- [How to: select examples by semantic ngram overlap](/docs/how_to/example_selectors_ngram)
+- [How to: select examples by maximal marginal relevance](/docs/how_to/example_selectors_mmr)
+- [How to: select examples from LangSmith few-shot datasets](/docs/how_to/example_selectors_langsmith/)
+
 ### LLMs
 
 What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of language models that take a string in and output a string.
@@ -329,6 +307,36 @@ large volumes of text. For a high-level tutorial, check out [this guide](/docs/t
 - [How to: summarize text through parallelization](/docs/how_to/summarize_map_reduce)
 - [How to: summarize text through iterative refinement](/docs/how_to/summarize_refine)
 
+## LangChain Expression Language (LCEL)
+
+:::note Should I use LCEL?
+
+LCEL is an orchestration solution. See our
+[concepts page](/docs/concepts/lcel/#should-i-use-lcel) for recommendations on when to
+use LCEL.
+
+:::
+
+[LangChain Expression Language](/docs/concepts/lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol.
+
+[**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
+
+[**Migration guide**](/docs/versions/migrating_chains): For migrating legacy chain abstractions to LCEL.
+
+- [How to: chain runnables](/docs/how_to/sequence)
+- [How to: stream runnables](/docs/how_to/streaming)
+- [How to: invoke runnables in parallel](/docs/how_to/parallel/)
+- [How to: add default invocation args to runnables](/docs/how_to/binding/)
+- [How to: turn any function into a runnable](/docs/how_to/functions)
+- [How to: pass through inputs from one chain step to the next](/docs/how_to/passthrough)
+- [How to: configure runnable behavior at runtime](/docs/how_to/configure)
+- [How to: add message history (memory) to a chain](/docs/how_to/message_history)
+- [How to: route between sub-chains](/docs/how_to/routing)
+- [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/)
+- [How to: inspect runnables](/docs/how_to/inspect)
+- [How to: add fallbacks to a runnable](/docs/how_to/fallbacks)
+- [How to: pass runtime secrets to a runnable](/docs/how_to/runnable_runtime_secrets)
+
 ## [LangGraph](https://langchain-ai.github.io/langgraph)
 
 LangGraph is an extension of LangChain aimed at

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/how_to/migrate_agent.ipynb

@@ -32,11 +32,11 @@
     "\n",
     "Here we focus on how to move from legacy LangChain agents to more flexible [LangGraph](https://langchain-ai.github.io/langgraph/) agents.\n",
     "LangChain agents (the [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
-    "In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) prebuilt helper method.\n",
+    "In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) prebuilt helper method.\n",
     "\n",
     "\n",
     ":::note\n",
-    "In LangGraph, the graph replaces LangChain's agent executor. It manages the agent's cycles and tracks the scratchpad as messages within its state. The LangChain \"agent\" corresponds to the state_modifier and LLM you've provided.\n",
+    "In LangGraph, the graph replaces LangChain's agent executor. It manages the agent's cycles and tracks the scratchpad as messages within its state. The LangChain \"agent\" corresponds to the prompt and LLM you've provided.\n",
     ":::\n",
     "\n",
     "\n",
@@ -164,7 +164,7 @@
    "id": "94205f3b-fd2b-4fd7-af69-0a3fc313dc88",
    "metadata": {},
    "source": [
-    "LangGraph's [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) manages a state that is defined by a list of messages. It will continue to process the list until there are no tool calls in the agent's output. To kick it off, we input a list of messages. The output will contain the entire state of the graph-- in this case, the conversation history.\n",
+    "LangGraph's [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) manages a state that is defined by a list of messages. It will continue to process the list until there are no tool calls in the agent's output. To kick it off, we input a list of messages. The output will contain the entire state of the graph-- in this case, the conversation history.\n",
     "\n"
    ]
   },
@@ -240,11 +240,12 @@
     "\n",
     "With legacy LangChain agents you have to pass in a prompt template. You can use this to control the agent.\n",
     "\n",
-    "With LangGraph [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent), by default there is no prompt. You can achieve similar control over the agent in a few ways:\n",
+    "With LangGraph [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent), by default there is no prompt. You can achieve similar control over the agent in a few ways:\n",
     "\n",
     "1. Pass in a system message as input\n",
     "2. Initialize the agent with a system message\n",
-    "3. Initialize the agent with a function to transform messages before passing to the model.\n",
+    "3. Initialize the agent with a function to transform messages in the graph state before passing to the model.\n",
+    "4. Initialize the agent with a [Runnable](/docs/concepts/lcel) to transform messages in the graph state before passing to the model. This includes passing prompt templates as well.\n",
     "\n",
     "Let's take a look at all of these below. We will pass in custom instructions to get the agent to respond in Spanish.\n",
     "\n",
@@ -291,9 +292,9 @@
    "id": "bd5f5500-5ae4-4000-a9fd-8c5a2cc6404d",
    "metadata": {},
    "source": [
-    "Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent).\n",
+    "Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent).\n",
     "\n",
-    "LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`state_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) parameter. This modifies the graph state before the llm is called, and can be one of four values:\n",
+    "LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`prompt`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) parameter. This modifies the graph state before the llm is called, and can be one of four values:\n",
     "\n",
     "- A `SystemMessage`, which is added to the beginning of the list of messages.\n",
     "- A `string`, which is converted to a `SystemMessage` and added to the beginning of the list of messages.\n",
@@ -317,9 +318,7 @@
     "# This could also be a SystemMessage object\n",
     "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
     "\n",
-    "langgraph_agent_executor = create_react_agent(\n",
-    "    model, tools, state_modifier=system_message\n",
-    ")\n",
+    "langgraph_agent_executor = create_react_agent(model, tools, prompt=system_message)\n",
     "\n",
     "\n",
     "messages = langgraph_agent_executor.invoke({\"messages\": [(\"user\", query)]})"
@@ -330,8 +329,8 @@
    "id": "fc6059fd-0df7-4b6f-a84c-b5874e983638",
    "metadata": {},
    "source": [
-    "We can also pass in an arbitrary function. This function should take in a list of messages and output a list of messages.\n",
-    "We can do all types of arbitrary formatting of messages here. In this case, let's just add a SystemMessage to the start of the list of messages."
+    "We can also pass in an arbitrary function or a runnable. This function/runnable should take in a the graph state and output a list of messages.\n",
+    "We can do all types of arbitrary formatting of messages here. In this case, let's add a SystemMessage to the start of the list of messages and append another user message at the end."
    ]
   },
   {
@@ -349,6 +348,7 @@
     }
    ],
    "source": [
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "from langgraph.prebuilt.chat_agent_executor import AgentState\n",
     "\n",
@@ -356,19 +356,20 @@
     "    [\n",
     "        (\"system\", \"You are a helpful assistant. Respond only in Spanish.\"),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
+    "        (\"user\", \"Also say 'Pandamonium!' after the answer.\"),\n",
     "    ]\n",
     ")\n",
     "\n",
-    "\n",
-    "def _modify_state_messages(state: AgentState):\n",
-    "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages() + [\n",
-    "        (\"user\", \"Also say 'Pandamonium!' after the answer.\")\n",
-    "    ]\n",
+    "# alternatively, this can be passed as a function, e.g.\n",
+    "# def prompt(state: AgentState):\n",
+    "#     return (\n",
+    "#         [SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")] +\n",
+    "#         state[\"messages\"] +\n",
+    "#         [HumanMessage(content=\"Also say 'Pandamonium!' after the answer.\")]\n",
+    "#     )\n",
     "\n",
     "\n",
-    "langgraph_agent_executor = create_react_agent(\n",
-    "    model, tools, state_modifier=_modify_state_messages\n",
-    ")\n",
+    "langgraph_agent_executor = create_react_agent(model, tools, prompt=prompt)\n",
     "\n",
     "\n",
     "messages = langgraph_agent_executor.invoke({\"messages\": [(\"human\", query)]})\n",
@@ -516,7 +517,7 @@
     "\n",
     "memory = MemorySaver()\n",
     "langgraph_agent_executor = create_react_agent(\n",
-    "    model, tools, state_modifier=system_message, checkpointer=memory\n",
+    "    model, tools, prompt=system_message, checkpointer=memory\n",
     ")\n",
     "\n",
     "config = {\"configurable\": {\"thread_id\": \"test-thread\"}}\n",
@@ -643,14 +644,7 @@
     "    ]\n",
     ")\n",
     "\n",
-    "\n",
-    "def _modify_state_messages(state: AgentState):\n",
-    "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages()\n",
-    "\n",
-    "\n",
-    "langgraph_agent_executor = create_react_agent(\n",
-    "    model, tools, state_modifier=_modify_state_messages\n",
-    ")\n",
+    "langgraph_agent_executor = create_react_agent(model, tools, prompt=prompt)\n",
     "\n",
     "for step in langgraph_agent_executor.stream(\n",
     "    {\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"\n",
@@ -697,7 +691,7 @@
    "source": [
     "### In LangGraph\n",
     "\n",
-    "By default the [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) in LangGraph appends all messages to the central state. Therefore, it is easy to see any intermediate steps by just looking at the full state."
+    "By default the [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) in LangGraph appends all messages to the central state. Therefore, it is easy to see any intermediate steps by just looking at the full state."
    ]
   },
   {
@@ -1244,7 +1238,7 @@
    "source": [
     "### In LangGraph\n",
     "\n",
-    "We can use the [`state_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
+    "We can use the [`prompt`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
    ]
   },
   {
@@ -1299,7 +1293,7 @@
     "\n",
     "\n",
     "langgraph_agent_executor = create_react_agent(\n",
-    "    model, tools, state_modifier=_modify_state_messages\n",
+    "    model, tools, prompt=_modify_state_messages\n",
     ")\n",
     "\n",
     "try:\n",

docs/docs/integrations/chat/anthropic.ipynb

@@ -315,6 +315,163 @@
     "ai_msg.tool_calls"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "301d372f-4dec-43e6-b58c-eee25633e1a6",
+   "metadata": {},
+   "source": [
+    "## Citations\n",
+    "\n",
+    "Anthropic supports a [citations](https://docs.anthropic.com/en/docs/build-with-claude/citations) feature that lets Claude attach context to its answers based on source documents supplied by the user. When [document content blocks](https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types) with `\"citations\": {\"enabled\": True}` are included in a query, Claude may generate citations in its response.\n",
+    "\n",
+    "### Simple example\n",
+    "\n",
+    "In this example we pass a [plain text document](https://docs.anthropic.com/en/docs/build-with-claude/citations#plain-text-documents). In the background, Claude [automatically chunks](https://docs.anthropic.com/en/docs/build-with-claude/citations#plain-text-documents) the input text into sentences, which are used when generating citations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e5370e6e-5a9a-4546-848b-5f5bf313c3e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'text': 'Based on the document, ', 'type': 'text'},\n",
+       " {'text': 'the grass is green',\n",
+       "  'type': 'text',\n",
+       "  'citations': [{'type': 'char_location',\n",
+       "    'cited_text': 'The grass is green. ',\n",
+       "    'document_index': 0,\n",
+       "    'document_title': 'My Document',\n",
+       "    'start_char_index': 0,\n",
+       "    'end_char_index': 20}]},\n",
+       " {'text': ', and ', 'type': 'text'},\n",
+       " {'text': 'the sky is blue',\n",
+       "  'type': 'text',\n",
+       "  'citations': [{'type': 'char_location',\n",
+       "    'cited_text': 'The sky is blue.',\n",
+       "    'document_index': 0,\n",
+       "    'document_title': 'My Document',\n",
+       "    'start_char_index': 20,\n",
+       "    'end_char_index': 36}]},\n",
+       " {'text': '.', 'type': 'text'}]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-5-haiku-latest\")\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\n",
+    "                \"type\": \"document\",\n",
+    "                \"source\": {\n",
+    "                    \"type\": \"text\",\n",
+    "                    \"media_type\": \"text/plain\",\n",
+    "                    \"data\": \"The grass is green. The sky is blue.\",\n",
+    "                },\n",
+    "                \"title\": \"My Document\",\n",
+    "                \"context\": \"This is a trustworthy document.\",\n",
+    "                \"citations\": {\"enabled\": True},\n",
+    "            },\n",
+    "            {\"type\": \"text\", \"text\": \"What color is the grass and sky?\"},\n",
+    "        ],\n",
+    "    }\n",
+    "]\n",
+    "response = llm.invoke(messages)\n",
+    "response.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69956596-0e6c-492b-934d-c08ed3c9de9a",
+   "metadata": {},
+   "source": [
+    "### Using with text splitters\n",
+    "\n",
+    "Anthropic also lets you specify your own splits using [custom document](https://docs.anthropic.com/en/docs/build-with-claude/citations#custom-content-documents) types. LangChain [text splitters](/docs/concepts/text_splitters/) can be used to generate meaningful splits for this purpose. See the below example, where we split the LangChain README (a markdown document) and pass it to Claude as context:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "04cc2841-7987-47a5-906c-09ea7fa28323",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'text': \"You can find LangChain's tutorials at https://python.langchain.com/docs/tutorials/\\n\\nThe tutorials section is recommended for those looking to build something specific or who prefer a hands-on learning approach. It's considered the best place to get started with LangChain.\",\n",
+       "  'type': 'text',\n",
+       "  'citations': [{'type': 'content_block_location',\n",
+       "    'cited_text': \"[Tutorials](https://python.langchain.com/docs/tutorials/):If you're looking to build something specific orare more of a hands-on learner, check out ourtutorials. This is the best place to get started.\",\n",
+       "    'document_index': 0,\n",
+       "    'document_title': None,\n",
+       "    'start_block_index': 243,\n",
+       "    'end_block_index': 248}]}]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "from langchain_text_splitters import MarkdownTextSplitter\n",
+    "\n",
+    "\n",
+    "def format_to_anthropic_documents(documents: list[str]):\n",
+    "    return {\n",
+    "        \"type\": \"document\",\n",
+    "        \"source\": {\n",
+    "            \"type\": \"content\",\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": document} for document in documents],\n",
+    "        },\n",
+    "        \"citations\": {\"enabled\": True},\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# Pull readme\n",
+    "get_response = requests.get(\n",
+    "    \"https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\"\n",
+    ")\n",
+    "readme = get_response.text\n",
+    "\n",
+    "# Split into chunks\n",
+    "splitter = MarkdownTextSplitter(\n",
+    "    chunk_overlap=0,\n",
+    "    chunk_size=50,\n",
+    ")\n",
+    "documents = splitter.split_text(readme)\n",
+    "\n",
+    "# Construct message\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        format_to_anthropic_documents(documents),\n",
+    "        {\"type\": \"text\", \"text\": \"Give me a link to LangChain's tutorials.\"},\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "# Query LLM\n",
+    "llm = ChatAnthropic(model=\"claude-3-5-haiku-latest\")\n",
+    "response = llm.invoke([message])\n",
+    "\n",
+    "response.content"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
@@ -342,7 +499,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/databricks.ipynb

@@ -509,7 +509,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatDatabricks features and configurations head to the API reference: https://python.langchain.com/api_reference/databricks/chat_models/langchain_databricks.chat_models.ChatDatabricks.html"
+    "For detailed documentation of all ChatDatabricks features and configurations head to the API reference: https://api-docs.databricks.com/python/databricks-ai-bridge/latest/databricks_langchain.html#databricks_langchain.ChatDatabricks"
    ]
   }
  ],

docs/docs/integrations/chat/deepseek.ipynb

@@ -0,0 +1,231 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: DeepSeek\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatDeepSeek\n",
+    "\n",
+    "\n",
+    "This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
+    "\n",
+    ":::tip\n",
+    "\n",
+    "DeepSeek's models are open source and can be run locally (e.g. in [Ollama](./ollama.ipynb)) or on other inference providers (e.g. [Fireworks](./fireworks.ipynb), [Together](./together.ipynb)) as well.\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\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](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",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
+    "\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` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [DeepSeek's API Key page](https://platform.deepseek.com/api_keys) to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"DEEPSEEK_API_KEY\"):\n",
+    "    os.environ[\"DEEPSEEK_API_KEY\"] = getpass.getpass(\"Enter your DeepSeek 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 DeepSeek integration lives in the `langchain-deepseek` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-deepseek"
+   ]
+  },
+  {
+   "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": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_deepseek import ChatDeepSeek\n",
+    "\n",
+    "llm = ChatDeepSeek(\n",
+    "    model=\"deepseek-chat\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "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 = llm.invoke(messages)\n",
+    "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": null,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatDeepSeek features and configurations head to the [API Reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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/chat/groq.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# ChatGroq\n",
     "\n",
-    "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models).\n",
+    "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -37,7 +37,7 @@
     "\n",
     "### Credentials\n",
     "\n",
-    "Head to the [Groq console](https://console.groq.com/keys) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:"
+    "Head to the [Groq console](https://console.groq.com/login?utm_source=langchain&utm_content=chat_page) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:"
    ]
   },
   {

docs/docs/integrations/chat/openai.ipynb

@@ -43,7 +43,7 @@
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | 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",
     "\n",
     "## Setup\n",
     "\n",

docs/docs/integrations/chat/pipeshift.ipynb

@@ -0,0 +1,278 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Pipeshift\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatPipeshift\n",
+    "\n",
+    "This will help you getting started with Pipeshift [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatPipeshift features and configurations head to the [API reference](https://dashboard.pipeshift.com/docs).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatPipeshift](https://dashboard.pipeshift.com/docs) | [langchain-pipeshift](https://pypi.org/project/langchain-pipeshift/) | ❌ | -| ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-pipeshift?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-pipeshift?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 Pipeshift models you'll need to create an account on Pipeshift, get an API key, and install the `langchain-pipeshift` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [Pipeshift](https://dashboard.pipeshift.com) to sign up to Pipeshift and generate an API key. Once you've done this set the PIPESHIFT_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(\"PIPESHIFT_API_KEY\"):\n",
+    "    os.environ[\"PIPESHIFT_API_KEY\"] = getpass.getpass(\"Enter your Pipeshift 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": 2,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_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 Pipeshift integration lives in the `langchain-pipeshift` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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-pipeshift"
+   ]
+  },
+  {
+   "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": 4,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_pipeshift import ChatPipeshift\n",
+    "\n",
+    "llm = ChatPipeshift(\n",
+    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=512,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Here is the translation:\\n\\nJe suis amoureux du programme. \\n\\nHowever, a more common translation would be:\\n\\nJ\\'aime programmer.\\n\\nNote that \"Je suis amoureux\" typically implies romantic love, whereas \"J\\'aime\" is a more casual way to express affection or enjoyment for an activity, in this case, programming.', additional_kwargs={}, response_metadata={}, id='run-5cad8e5c-d089-44a8-8dcd-22736cde7d7b-0')"
+      ]
+     },
+     "execution_count": 5,
+     "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 = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here is the translation:\n",
+      "\n",
+      "Je suis amoureux du programme. \n",
+      "\n",
+      "However, a more common translation would be:\n",
+      "\n",
+      "J'aime programmer.\n",
+      "\n",
+      "Note that \"Je suis amoureux\" typically implies romantic love, whereas \"J'aime\" is a more casual way to express affection or enjoyment for an activity, in this case, programming.\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": 7,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Das ist schön! Du liebst Programmieren! (That's great! You love programming!)\\n\\nWould you like to know the German translation of a specific programming-related term or phrase, or would you like me to help you with something else?\", additional_kwargs={}, response_metadata={}, id='run-8a4b7d56-23d9-43a7-8fb2-e05f556d94bd-0')"
+      ]
+     },
+     "execution_count": 7,
+     "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",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatPipeshift features and configurations head to the API reference: https://dashboard.pipeshift.com/docs"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/sambanova.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaNovaCloud\n",
     "\n",
-    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html).\n",
+    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.ChatSambaNovaCloud.html).\n",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
     "\n",
@@ -28,21 +28,20 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatSambaNovaCloud](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "| [ChatSambaNovaCloud](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.ChatSambaNovaCloud.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "\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",
+    "| [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",
     "\n",
     "## Setup\n",
     "\n",
-    "To access ChatSambaNovaCloud models you will need to create a [SambaNovaCloud](https://cloud.sambanova.ai/) account, get an API key, install the `langchain_community` integration package, and install the `SSEClient` Package.\n",
+    "To access ChatSambaNovaCloud models you will need to create a [SambaNovaCloud](https://cloud.sambanova.ai/) account, get an API key, install the `langchain_sambanova` integration package.\n",
     "\n",
     "```bash\n",
-    "pip install langchain-community\n",
-    "pip install sseclient-py\n",
+    "pip install langchain-sambanova\n",
     "```\n",
     "\n",
     "### Credentials\n",
@@ -82,8 +81,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
@@ -92,7 +91,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain __SambaNovaCloud__ integration lives in the `langchain_community` package:"
+    "The LangChain __SambaNovaCloud__ integration lives in the `langchain_sambanova` package:"
    ]
   },
   {
@@ -101,8 +100,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-community\n",
-    "%pip install -qu sseclient-py"
+    "%pip install -qU langchain-sambanova"
    ]
   },
   {
@@ -120,13 +118,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models.sambanova import ChatSambaNovaCloud\n",
+    "from langchain_sambanova import ChatSambaNovaCloud\n",
     "\n",
     "llm = ChatSambaNovaCloud(\n",
-    "    model=\"Meta-Llama-3.1-70B-Instruct\",\n",
+    "    model=\"Meta-Llama-3.3-70B-Instruct\",\n",
     "    max_tokens=1024,\n",
     "    temperature=0.7,\n",
-    "    top_k=1,\n",
     "    top_p=0.01,\n",
     ")"
    ]
@@ -158,7 +155,8 @@
     "messages = [\n",
     "    (\n",
     "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "        \"You are a helpful assistant that translates English to French. \"\n",
+    "        \"Translate the user sentence.\",\n",
     "    ),\n",
     "    (\"human\", \"I love programming.\"),\n",
     "]\n",
@@ -215,7 +213,8 @@
     "    [\n",
     "        (\n",
     "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "            \"You are a helpful assistant that translates {input_language} \"\n",
+    "            \"to {output_language}.\",\n",
     "        ),\n",
     "        (\"human\", \"{input}\"),\n",
     "    ]\n",
@@ -412,7 +411,8 @@
     "llm_with_tools = llm.bind_tools(tools=tools)\n",
     "messages = [\n",
     "    HumanMessage(\n",
-    "        content=\"I need to schedule a meeting for two weeks from today. Can you tell me the exact date of the meeting?\"\n",
+    "        content=\"I need to schedule a meeting for two weeks from today. \"\n",
+    "        \"Can you tell me the exact date of the meeting?\"\n",
     "    )\n",
     "]"
    ]
@@ -499,7 +499,6 @@
     "    model=\"Llama-3.2-11B-Vision-Instruct\",\n",
     "    max_tokens=1024,\n",
     "    temperature=0.7,\n",
-    "    top_k=1,\n",
     "    top_p=0.01,\n",
     ")"
    ]
@@ -546,7 +545,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatSambaNovaCloud features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html"
+    "For detailed documentation of all ChatSambaNovaCloud features and configurations head to the API reference: https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.ChatSambaNovaCloud.html"
    ]
   }
  ],

docs/docs/integrations/chat/sambastudio.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaStudio\n",
     "\n",
-    "This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
+    "This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.chat_models.sambanova.ChatSambaStudio.html).\n",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
     "\n",
@@ -28,21 +28,20 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatSambaStudio](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "| [ChatSambaStudio](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.chat_models.sambanova.ChatSambaStudio.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "\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",
     "\n",
     "## Setup\n",
     "\n",
-    "To access ChatSambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_community` integration package, and install the `SSEClient` Package.\n",
+    "To access ChatSambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_sambanova` integration package.\n",
     "\n",
     "```bash\n",
-    "pip install langchain-community\n",
-    "pip install sseclient-py\n",
+    "pip install langchain-sambanova\n",
     "```\n",
     "\n",
     "### Credentials\n",
@@ -50,7 +49,7 @@
     "Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
     "\n",
     "``` bash\n",
-    "export SAMBASTUDIO_URL=\"your-api-key-here\"\n",
+    "export SAMBASTUDIO_URL=\"sambastudio-url-key-here\"\n",
     "export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\n",
     "```"
    ]
@@ -85,8 +84,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
@@ -95,7 +94,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain __SambaStudio__ integration lives in the `langchain_community` package:"
+    "The LangChain __SambaStudio__ integration lives in the `langchain_sambanova` package:"
    ]
   },
   {
@@ -104,8 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-community\n",
-    "%pip install -qu sseclient-py"
+    "%pip install -qU langchain-sambanova"
    ]
   },
   {
@@ -123,13 +121,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models.sambanova import ChatSambaStudio\n",
+    "from langchain_sambanova import ChatSambaStudio\n",
     "\n",
     "llm = ChatSambaStudio(\n",
     "    model=\"Meta-Llama-3-70B-Instruct-4096\",  # set if using a Bundle endpoint\n",
     "    max_tokens=1024,\n",
     "    temperature=0.7,\n",
-    "    top_k=1,\n",
     "    top_p=0.01,\n",
     "    do_sample=True,\n",
     "    process_prompt=\"True\",  # set if using a Bundle endpoint\n",
@@ -163,7 +160,8 @@
     "messages = [\n",
     "    (\n",
     "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "        \"You are a helpful assistant that translates English to French.\"\n",
+    "        \"Translate the user sentence.\",\n",
     "    ),\n",
     "    (\"human\", \"I love programming.\"),\n",
     "]\n",
@@ -220,7 +218,8 @@
     "    [\n",
     "        (\n",
     "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "            \"You are a helpful assistant that translates {input_language} \"\n",
+    "            \"to {output_language}.\",\n",
     "        ),\n",
     "        (\"human\", \"{input}\"),\n",
     "    ]\n",
@@ -364,7 +363,7 @@
    "source": [
     "from datetime import datetime\n",
     "\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
+    "from langchain_core.messages import HumanMessage, ToolMessage\n",
     "from langchain_core.tools import tool\n",
     "\n",
     "\n",
@@ -408,7 +407,8 @@
     "llm_with_tools = llm.bind_tools(tools=tools)\n",
     "messages = [\n",
     "    HumanMessage(\n",
-    "        content=\"I need to schedule a meeting for two weeks from today. Can you tell me the exact date of the meeting?\"\n",
+    "        content=\"I need to schedule a meeting for two weeks from today. \"\n",
+    "        \"Can you tell me the exact date of the meeting?\"\n",
     "    )\n",
     "]"
    ]
@@ -483,7 +483,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatSambaStudio features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html"
+    "For detailed documentation of all ChatSambaStudio features and configurations head to the API reference: https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.sambanova.chat_models.ChatSambaStudio.html"
    ]
   }
  ],

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/llms/pipeshift.ipynb

@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "67db2992",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Pipeshift\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# Pipeshift\n",
+    "\n",
+    "This will help you get started with Pipeshift completion models (LLMs) using LangChain. For detailed documentation on `Pipeshift` features and configuration options, please refer to the [API reference](https://dashboard.pipeshift.com/docs).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/pipeshift) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [Pipeshift](https://dashboard.pipeshift.com/docs) | [langchain-pipeshift](https://pypi.org/project/langchain-pipeshift/) | ❌ | - | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-pipeshift?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-pipeshift?style=flat-square&label=%20) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Pipeshift models you'll need to create a Pipeshift account, get an API key, and install the `langchain-pipeshift` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [Pipeshift](https://dashboard.pipeshift.com) to sign up to Pipeshift and generate an API key. Once you've done this set the PIPESHIFT_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bc51e756",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"PIPESHIFT_API_KEY\"):\n",
+    "    os.environ[\"PIPESHIFT_API_KEY\"] = getpass.getpass(\"Enter your Pipeshift API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b6e1ca6",
+   "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": 2,
+   "id": "196c2b41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "809c6577",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Pipeshift integration lives in the `langchain-pipeshift` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "59c710c4",
+   "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-pipeshift"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a760037",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a0562a13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_pipeshift import Pipeshift\n",
+    "\n",
+    "llm = Pipeshift(\n",
+    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=512,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ee90032",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "035dea0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\xa0specializes in digital transformation and innovation for industry leaders. We leverage AI and IoT technology to provide data-driven insights, predictive analysis and decision support systems to our clients.\\nAbout our company:\\nOur company is built around the passion of creating positive impact through AI innovation. Pipeshift brings together top talent in AI, data science, software engineering, and business consultancy to deliver tailored solutions that drive growth and improvement for our clients across various industries.\\nOur Mission and Values:\\nOur mission is to revolutionize industries by harnessing the power of emerging technologies, like AI and IoT, to unlock new potential and drive progress. Our values are built around collaboration, innovation, integrity, customer value, and continuous learning.\\nJob Title: Software Engineer (Mobile App Development)\\nAbout the role:\\nAs a Software Engineer (Mobile App Development) at Pipeshift, you will be at the forefront of cutting-edge mobile app development. Your responsibilities will include designing, developing, and implementing scalable and reliable mobile apps for various platforms, such as Android and iOS.\\nResponsibilities\\nDesign and develop mobile apps for various platforms using Java, Kotlin, Swift, or React Native\\nWork collaboratively with cross-functional teams to gather requirements, plan and prioritize projects\\nImplement UI/UX design principles to deliver user-friendly and visually appealing apps\\nWrite clean, maintainable, and efficient code, adhering to best coding practices and coding standards\\nTest and debug mobile apps to ensure high-quality, reliable, and stable performance\\nCollaborate with the QA team to ensure all mobile apps meet or exceed quality and performance expectations\\nStay up-to-date with industry trends, new technologies, and platform updates to enhance app development skills\\nParticipate in code reviews to ensure code quality and adherence to coding standards\\nSupport the DevOps team in continuous integration and delivery of mobile apps\\nParticipate in the design and maintenance of technical documentation, coding standards and guidelines\\nConduct peer mentoring and training to promote knowledge sharing and growth\\nExperience and requirements\\n2+ years of experience in mobile app development, or relevant technology experience with strong passion for mobile development.\\nProficient in one or more mobile app development frameworks: React Native, iOS (Swift), Android (Kotlin/Java), Flutter, etc.\\nStrong understanding of OOP (object-oriented programming) principles, design patterns, and data structures.\\nExperience with Agile methodologies, version control systems (e.g., Git), and CI/CD pipelines (e.g., Jenkins, Travis CI, CircleCI).\\nGood coding skills in languages such as Java, Kotlin, Swift,'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_text = \"Pipeshift is an AI company that \"\n",
+    "\n",
+    "completion = llm.invoke(input_text)\n",
+    "completion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "add38532",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can also [chain](/docs/how_to/sequence/) our llm with a prompt template"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9bdfcef",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `Pipeshift` features and configurations head to the API reference: https://dashboard.pipeshift.com/docs "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/providers/aws.mdx

@@ -92,7 +92,7 @@ from langchain_aws import SagemakerEndpoint
 
 See a [usage example](/docs/integrations/text_embedding/bedrock).
 ```python
-from langchain_community.embeddings import BedrockEmbeddings
+from langchain_aws import BedrockEmbeddings
 ```
 
 ### SageMaker Endpoint

docs/docs/integrations/providers/deepseek.ipynb

@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DeepSeek\n",
+    "\n",
+    "[DeepSeek](https://www.deepseek.com/) is a Chinese artificial intelligence company that develops LLMs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_deepseek import ChatDeepSeek"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/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/groq.mdx

@@ -21,7 +21,7 @@ Install the integration package:
 pip install langchain-groq
 ```
 
-Request an [API key](https://wow.groq.com) and set it as an environment variable:
+Request an [API key](https://console.groq.com/login?utm_source=langchain&utm_content=provider_page) and set it as an environment variable:
 
 ```bash
 export GROQ_API_KEY=gsk_...

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

@@ -0,0 +1,114 @@
+# PaymanAI
+
+PaymanAI provides functionality to send and receive payments (fiat and crypto) on behalf of an AI Agent. To get started:
+
+1. **Sign up** at app.paymanai.com to create an AI Agent and obtain your **API Key**.
+2. **Set** environment variables (`PAYMAN_API_SECRET` for your API Key, `PAYMAN_ENVIRONMENT` for sandbox or production).
+
+This notebook gives a quick overview of integrating PaymanAI into LangChain as a tool. For complete reference, see the API documentation.
+
+## Overview
+
+The PaymanAI integration is part of the `langchain-community` (or your custom) package. It allows you to:
+
+- Send payments (`send_payment`) to crypto addresses or bank accounts.
+- Search for payees (`search_payees`).
+- Add new payees (`add_payee`).
+- Request money from customers with a hosted checkout link (`ask_for_money`).
+- Check agent or customer balances (`get_balance`).
+
+These can be wrapped as **LangChain Tools** for an LLM-based agent to call them automatically.
+
+### Integration details
+
+| Class | Package | Serializable | JS support | Package latest |
+| :--- | :--- | :---: | :---: | :--- |
+| 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 PaymanAI tool package:
+
+```bash
+pip install langchain-payman-tool
+```
+
+2. **Install** the PaymanAI SDK:
+```bash
+pip install paymanai
+```
+
+3. **Set** environment variables:
+```bash
+export PAYMAN_API_SECRET="YOUR_SECRET_KEY"
+export PAYMAN_ENVIRONMENT="sandbox"
+```
+
+Your `PAYMAN_API_SECRET` should be the secret key from app.paymanai.com. The `PAYMAN_ENVIRONMENT` can be `sandbox` or `production` depending on your usage.
+
+## Instantiation
+
+Here is an example of instantiating a PaymanAI tool. If you have multiple Payman methods, you can create multiple tools.
+
+```python
+from langchain_payman_tool.tool import PaymanAI
+
+# Instantiate the PaymanAI tool (example)
+tool = PaymanAI(
+    name="send_payment",
+    description="Send a payment to a specified payee.",
+)
+```
+
+## Invocation
+
+### Invoke directly with args
+
+You can call `tool.invoke(...)` and pass a dictionary matching the tool's expected fields. For example:
+
+```python
+response = tool.invoke({
+    "amount_decimal": 10.00,
+    "payment_destination_id": "abc123",
+    "customer_id": "cust_001",
+    "memo": "Payment for invoice #XYZ"
+})
+```
+
+### Invoke with ToolCall
+
+When used inside an AI workflow, the LLM might produce a `ToolCall` dict. You can simulate it as follows:
+
+```python
+model_generated_tool_call = {
+    "args": {
+        "amount_decimal": 10.00,
+        "payment_destination_id": "abc123"
+    },
+    "id": "1",
+    "name": tool.name,
+    "type": "tool_call",
+}
+tool.invoke(model_generated_tool_call)
+```
+
+## Using the Tool in a Chain or Agent
+
+You can bind a PaymanAI tool to a LangChain agent or chain that supports tool-calling.
+
+## Quick Start Summary
+
+1. **Sign up** at app.paymanai.com to get your **API Key**.
+2. **Install** dependencies:
+   ```bash
+   pip install paymanai langchain-payman-tool
+   ```
+3. **Export** environment variables:
+   ```bash
+   export PAYMAN_API_SECRET="YOUR_SECRET_KEY"
+   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.

docs/docs/integrations/providers/pipeshift.md

@@ -0,0 +1,51 @@
+# Pipeshift
+
+> [Pipeshift](https://pipeshift.com) is a fine-tuning and inference platform for open-source LLMs
+
+- You bring your datasets. Fine-tune multiple LLMs. Start inferencing in one-click and watch them scale to millions.
+
+## Installation and Setup
+
+- Install the Pipeshift integration package.
+
+  ```
+  pip install langchain-pipeshift
+  ```
+
+- Get your Pipeshift API key by signing up at [Pipeshift](https://pipeshift.com).
+
+### Authentication
+
+You can perform authentication using your Pipeshift API key in any of the following ways:
+
+1.  Adding API key to the environment variable as `PIPESHIFT_API_KEY`.
+
+    ```python
+    os.environ["PIPESHIFT_API_KEY"] = "<your_api_key>"
+    ```
+
+2.  By passing `api_key` to the pipeshift LLM module or chat module
+
+    ```python
+    llm = Pipeshift(api_key="<your_api_key>", model="meta-llama/Meta-Llama-3.1-8B-Instruct", max_tokens=512)
+
+                        OR
+
+    chat = ChatPipeshift(api_key="<your_api_key>", model="meta-llama/Meta-Llama-3.1-8B-Instruct", max_tokens=512)
+    ```
+
+## Chat models
+
+See an [example](/docs/integrations/chat/pipeshift).
+
+```python
+from langchain_pipeshift import ChatPipeshift
+```
+
+## LLMs
+
+See an [example](/docs/integrations/llms/pipeshift).
+
+```python
+from langchain_pipeshift import Pipeshift
+```

docs/docs/integrations/providers/sambanova.ipynb

@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SambaNova\n",
+    "\n",
+    "Customers are turning to [SambaNova](https://sambanova.ai/) to quickly deploy state-of-the-art AI capabilities to gain competitive advantage. Our purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing. We power the foundation models that unlock the valuable business insights trapped in data.\n",
+    "\n",
+    "Designed for AI, the SambaNova RDU was built with a revolutionary dataflow architecture. This design makes the RDU significantly more efficient for these workloads than GPUs as it eliminates redundant calls to memory, which are an inherent limitation of how GPUs function. This built-in efficiency is one of the features that makes the RDU capable of much higher performance than GPUs in a fraction of the footprint.\n",
+    "\n",
+    "On top of our architecture We have developed some platforms that allow companies and developers to get full advantage of the RDU processors and open source models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SambaNovaCloud\n",
+    "\n",
+    "SambaNova's [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
+    "\n",
+    "You can obtain a free SambaNovaCloud API key [here](https://cloud.sambanova.ai/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SambaStudio\n",
+    "\n",
+    "SambaNova's [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova DataScale systems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup\n",
+    "\n",
+    "Install the integration package:\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-sambanova\n",
+    "```\n",
+    "\n",
+    "set your API key it as an environment variable:\n",
+    "\n",
+    "If you are a SambaNovaCloud user:\n",
+    "\n",
+    "```bash\n",
+    "export SAMBANOVA_API_KEY=\"your-sambanova-cloud-api-key-here\"\n",
+    "```\n",
+    "\n",
+    "or if you are SambaStudio User\n",
+    "\n",
+    "```bash\n",
+    "export SAMBASTUDIO_API_KEY=\"your-sambastudio-api-key-here\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chat models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_sambanova import ChatSambaNovaCloud\n",
+    "\n",
+    "llm = ChatSambaNovaCloud(model=\"Meta-Llama-3.3-70B-Instruct\", temperature=0.7)\n",
+    "llm.invoke(\"Tell me a joke about artificial intelligence.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_sambanova import ChatSambaStudio\n",
+    "\n",
+    "llm = ChatSambaStudio(model=\"Meta-Llama-3.3-70B-Instruct\", temperature=0.7)\n",
+    "llm.invoke(\"Tell me a joke about artificial intelligence.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Embedding Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_sambanova import SambaStudioEmbeddings\n",
+    "\n",
+    "embeddings = SambaStudioEmbeddings(model=\"e5-mistral-7b-instruct\")\n",
+    "embeddings.embed_query(\"What is the meaning of life?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "API Reference [langchain-sambanova](https://python.langchain.com/api_reference/sambanova/index.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/tilores.ipynb

@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tilores\n",
+    "\n",
+    "[Tilores](https://tilores.io) is a platform that provides advanced entity resolution solutions for data integration and management. Using cutting-edge algorithms, machine learning, and a user-friendly interfaces, Tilores helps organizations match, resolve, and consolidate data from disparate sources, ensuring high-quality, consistent information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade tilores-langchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To access Tilores, you need to [create and configure an instance](https://app.tilores.io). If you prefer to test out Tilores first, you can use the [read-only demo credentials](https://github.com/tilotech/identity-rag-customer-insights-chatbot?tab=readme-ov-file#1-configure-customer-data-access)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from tilores import TiloresAPI\n",
+    "\n",
+    "os.environ[\"TILORES_API_URL\"] = \"<api-url>\"\n",
+    "os.environ[\"TILORES_TOKEN_URL\"] = \"<token-url>\"\n",
+    "os.environ[\"TILORES_CLIENT_ID\"] = \"<client-id>\"\n",
+    "os.environ[\"TILORES_CLIENT_SECRET\"] = \"<client-secret>\"\n",
+    "\n",
+    "tilores = TiloresAPI.from_environ()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please refer to the [Tilores documentation](https://docs.tilotech.io/tilores/publicsaaswalkthrough/) on how to create your own instance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Toolkits\n",
+    "\n",
+    "You can use the [`TiloresTools`](/docs/integrations/tools/tilores) to query data from Tilores:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tilores_langchain import TiloresTools"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "langchain",
+   "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": 1
+}

docs/docs/integrations/text_embedding/huggingfacehub.ipynb

@@ -16,7 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain sentence_transformers"
+    "%pip install --upgrade --quiet  langchain langchain-huggingface sentence_transformers"
    ]
   },
   {

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/text_embedding/sambanova.ipynb

@@ -1,137 +1,255 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: SambaStudio\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
+   "id": "9a3d6f34",
    "metadata": {},
    "source": [
-    "# SambaNova\n",
+    "# SambaStudioEmbeddings\n",
+    "\n",
+    "This will help you get started with SambaNova's SambaStudio embedding models using LangChain. For detailed documentation on `SambaStudioEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/sambanova/embeddings/langchain_sambanova.embeddingsSambaStudioEmbeddings.html).\n",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [Sambastudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform for running your own open-source models\n",
     "\n",
-    "This example goes over how to use LangChain to interact with SambaNova embedding models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SambaStudio\n",
+    "## Overview\n",
+    "### Integration details\n",
     "\n",
-    "**SambaStudio** allows you to train, run batch inference jobs, and deploy online inference endpoints to run open source models that you fine tuned yourself."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A SambaStudio environment is required to deploy a model. Get more information at [sambanova.ai/products/enterprise-ai-platform-sambanova-suite](https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Register your environment variables:"
+    "| Provider | Package |\n",
+    "|:--------:|:-------:|\n",
+    "| [SambaNova](/docs/integrations/providers/sambanova/) | [langchain-sambanova](https://python.langchain.com/api_reference/langchain_sambanova/embeddings/langchain_sambanova.embeddings.SambaStudioEmbeddings.html) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access ChatSambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_sambanova` integration package.\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-sambanova\n",
+    "```\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
+    "\n",
+    "``` bash\n",
+    "export SAMBASTUDIO_URL=\"sambastudio-url-key-here\"\n",
+    "export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "36521c2a",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import getpass\n",
     "import os\n",
     "\n",
-    "sambastudio_base_url = \"<Your SambaStudio environment URL>\"\n",
-    "sambastudio_base_uri = \"<Your SambaStudio environment URI>\"\n",
-    "sambastudio_project_id = \"<Your SambaStudio project id>\"\n",
-    "sambastudio_endpoint_id = \"<Your SambaStudio endpoint id>\"\n",
-    "sambastudio_api_key = \"<Your SambaStudio endpoint API key>\"\n",
-    "\n",
-    "# Set the environment variables\n",
-    "os.environ[\"SAMBASTUDIO_EMBEDDINGS_BASE_URL\"] = sambastudio_base_url\n",
-    "os.environ[\"SAMBASTUDIO_EMBEDDINGS_BASE_URI\"] = sambastudio_base_uri\n",
-    "os.environ[\"SAMBASTUDIO_EMBEDDINGS_PROJECT_ID\"] = sambastudio_project_id\n",
-    "os.environ[\"SAMBASTUDIO_EMBEDDINGS_ENDPOINT_ID\"] = sambastudio_endpoint_id\n",
-    "os.environ[\"SAMBASTUDIO_EMBEDDINGS_API_KEY\"] = sambastudio_api_key"
+    "if not os.getenv(\"SAMBASTUDIO_API_KEY\"):\n",
+    "    os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your SambaNova API key: \"\n",
+    "    )"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c84fb993",
    "metadata": {},
    "source": [
-    "Call SambaStudio hosted embeddings directly from LangChain!"
+    "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": "39a4953b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.embeddings.sambanova import SambaStudioEmbeddings\n",
-    "\n",
-    "embeddings = SambaStudioEmbeddings()\n",
-    "\n",
-    "text = \"Hello, this is a test\"\n",
-    "result = embeddings.embed_query(text)\n",
-    "print(result)\n",
-    "\n",
-    "texts = [\"Hello, this is a test\", \"Hello, this is another test\"]\n",
-    "results = embeddings.embed_documents(texts)\n",
-    "print(results)"
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d9664366",
    "metadata": {},
    "source": [
-    "You can manually pass the endpoint parameters and manually set the batch size you have in your SambaStudio embeddings endpoint"
+    "### Installation\n",
+    "\n",
+    "The LangChain SambaNova integration lives in the `langchain-sambanova` package:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "64853226",
    "metadata": {},
    "outputs": [],
    "source": [
+    "%pip install -qU langchain-sambanova"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ea7a09b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_sambanova import SambaStudioEmbeddings\n",
+    "\n",
     "embeddings = SambaStudioEmbeddings(\n",
-    "    sambastudio_embeddings_base_url=sambastudio_base_url,\n",
-    "    sambastudio_embeddings_base_uri=sambastudio_base_uri,\n",
-    "    sambastudio_embeddings_project_id=sambastudio_project_id,\n",
-    "    sambastudio_embeddings_endpoint_id=sambastudio_endpoint_id,\n",
-    "    sambastudio_embeddings_api_key=sambastudio_api_key,\n",
-    "    batch_size=32,  # set depending on the deployed endpoint configuration\n",
+    "    model=\"e5-mistral-7b-instruct\",\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "77d271b6",
    "metadata": {},
    "source": [
-    "Or You can use an embedding model expert included in your deployed CoE"
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "d817716b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = SambaStudioEmbeddings(\n",
-    "    batch_size=1,\n",
-    "    model_kwargs={\n",
-    "        \"select_expert\": \"e5-mistral-7b-instruct\",\n",
-    "    },\n",
-    ")"
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `SambaNovaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/langchain_sambanova/embeddings/langchain_sambanova.embeddings.SambaStudioEmbeddings.html).\n"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 5
 }

docs/docs/integrations/tools/databricks.ipynb

@@ -149,7 +149,7 @@
     "agent = create_react_agent(\n",
     "    llm,\n",
     "    tools,\n",
-    "    state_modifier=\"You are a helpful assistant. Make sure to use tool for information.\",\n",
+    "    prompt=\"You are a helpful assistant. Make sure to use tool for information.\",\n",
     ")\n",
     "agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"36939 * 8922.4\"}]})"
    ]