anthropic[patch]: release 0.3.9 (#30103)

Document refinement: optimize milvus server description. The description
of "milvus standalone", and "milvus server" is confusing, so I clarify
it with a detailed description.

Signed-off-by: ChengZi <chen.zhang@zilliz.com>

.github/CODEOWNERS

@@ -1,2 +1,2 @@
-/.github/   @efriis @baskaryan @ccurme
-/libs/packages.yml   @efriis
+/.github/   @baskaryan @ccurme
+/libs/packages.yml   @ccurme

.github/PULL_REQUEST_TEMPLATE.md

@@ -26,4 +26,4 @@ Additional guidelines:
 - 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.
+If no one reviews your PR within a few days, please @-mention one of baskaryan, 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 +39,7 @@ IGNORED_PARTNERS = [
 
 PY_312_MAX_PACKAGES = [
     "libs/partners/huggingface",  # https://github.com/pytorch/pytorch/issues/130249
+    "libs/partners/voyageai",
 ]
 
 
@@ -61,15 +64,17 @@ 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 +125,7 @@ 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 +152,17 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
 def _get_pydantic_test_configs(
     dir_: str, *, python_version: str = "3.11"
 ) -> List[Dict[str, str]]:
-    with open("./libs/core/poetry.lock", "rb") as f:
-        core_poetry_lock_data = tomllib.load(f)
-    for package in core_poetry_lock_data["package"]:
+    with open("./libs/core/uv.lock", "rb") as f:
+        core_uv_lock_data = tomllib.load(f)
+    for package in core_uv_lock_data["package"]:
         if package["name"] == "pydantic":
             core_max_pydantic_minor = package["version"].split(".")[1]
             break
 
-    with open(f"./{dir_}/poetry.lock", "rb") as f:
-        dir_poetry_lock_data = tomllib.load(f)
+    with open(f"./{dir_}/uv.lock", "rb") as f:
+        dir_uv_lock_data = tomllib.load(f)
 
-    for package in dir_poetry_lock_data["package"]:
+    for package in dir_uv_lock_data["package"]:
         if package["name"] == "pydantic":
             dir_max_pydantic_minor = package["version"].split(".")[1]
             break
@@ -304,7 +308,7 @@ if __name__ == "__main__":
                 f"Unknown lib: {file}. check_diff.py likely needs "
                 "an update for this new library!"
             )
-        elif file.startswith("docs/") or file in ["pyproject.toml", "poetry.lock"]: # docs or root poetry files
+        elif file.startswith("docs/") or file in ["pyproject.toml", "uv.lock"]: # docs or root uv files
             docs_edited = True
             dirs_to_run["lint"].add(".")
 

.github/scripts/check_prerelease_dependencies.py

@@ -10,26 +10,25 @@ if __name__ == "__main__":
         toml_data = tomllib.load(file)
 
     # see if we're releasing an rc
-    version = toml_data["tool"]["poetry"]["version"]
+    version = toml_data["project"]["version"]
     releasing_rc = "rc" in version or "dev" in version
 
     # if not, iterate through dependencies and make sure none allow prereleases
     if not releasing_rc:
-        dependencies = toml_data["tool"]["poetry"]["dependencies"]
-        for lib in dependencies:
-            dep_version = dependencies[lib]
+        dependencies = toml_data["project"]["dependencies"]
+        for dep_version in dependencies:
             dep_version_string = (
                 dep_version["version"] if isinstance(dep_version, dict) else dep_version
             )
 
             if "rc" in dep_version_string:
                 raise ValueError(
-                    f"Dependency {lib} has a prerelease version. Please remove this."
+                    f"Dependency {dep_version} has a prerelease version. Please remove this."
                 )
 
             if isinstance(dep_version, dict) and dep_version.get(
                 "allow-prereleases", False
             ):
                 raise ValueError(
-                    f"Dependency {lib} has allow-prereleases set to true. Please remove this."
+                    f"Dependency {dep_version} has allow-prereleases set to true. Please remove this."
                 )

.github/scripts/get_min_versions.py

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

.github/workflows/_compile_integration_test.yml

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

.github/workflows/_integration_test.yml

@@ -12,7 +12,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
 
 jobs:
   build:
@@ -24,22 +24,19 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ inputs.python-version }} + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: core
 
       - name: Install dependencies
         shell: bash
-        run: poetry install --with test,test_integration
+        run: uv sync --group test --group test_integration
 
       - name: Install deps outside pyproject
         if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
         shell: bash
-        run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2"
+        run: VIRTUAL_ENV=.venv uv pip install "boto3<2" "google-cloud-aiplatform<2"
 
       - name: Run integration tests
         shell: bash
@@ -67,8 +64,6 @@ jobs:
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
           WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
-          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
           ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
           ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
           ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}

.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 --inexact --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 --inexact --group test --group test_integration
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_release.yml

@@ -21,7 +21,8 @@ on:
 
 env:
   PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
+  UV_NO_SYNC: "true"
 
 jobs:
   build:
@@ -36,13 +37,10 @@ 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 +54,7 @@ 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 +65,18 @@ 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 +189,11 @@ jobs:
       # - The package is published, and it breaks on the missing dependency when
       #   used in the real world.
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         id: setup-python
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
 
       - uses: actions/download-artifact@v4
         with:
@@ -213,17 +216,18 @@ jobs:
         # - attempt install again after 5 seconds if it fails because there is
         #   sometimes a delay in availability on test pypi
         run: |
-          poetry run pip install dist/*.whl
+          uv venv
+          VIRTUAL_ENV=.venv uv pip install dist/*.whl
 
           # Replace all dashes in the package name with underscores,
           # since that's how Python imports packages with dashes in the name.
           # also remove _official suffix
           IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g | sed s/_official//g)"
 
-          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+          uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: poetry install --with test --no-root
+        run: uv sync --group test
         working-directory: ${{ inputs.working-directory }}
 
       # Overwrite the local version of the package with the built version
@@ -234,7 +238,7 @@ jobs:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
         run: |
-          poetry run pip install dist/*.whl
+          VIRTUAL_ENV=.venv uv pip install dist/*.whl
 
       - name: Run unit tests
         run: make tests
@@ -243,15 +247,15 @@ jobs:
       - name: Check for prerelease versions
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
+          uv run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
 
       - name: Get minimum versions
         working-directory: ${{ inputs.working-directory }}
         id: min-version
         run: |
-          poetry run pip install packaging requests
-          python_version="$(poetry run python --version | awk '{print $2}')"
-          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release $python_version)"
+          VIRTUAL_ENV=.venv uv pip install packaging requests
+          python_version="$(uv run python --version | awk '{print $2}')"
+          min_versions="$(uv run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release $python_version)"
           echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
           echo "min-versions=$min_versions"
 
@@ -260,12 +264,12 @@ 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
@@ -293,8 +297,6 @@ jobs:
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
           WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
-          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
           ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
           ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
           ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
@@ -306,6 +308,7 @@ jobs:
           UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
+          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
@@ -331,13 +334,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
 
       - uses: actions/download-artifact@v4
         with:
@@ -373,13 +373,10 @@ 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 +13,8 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
+  UV_NO_SYNC: "true"
 
 jobs:
   build:
@@ -26,17 +27,14 @@ 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 +46,9 @@ 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 +57,7 @@ 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 +18,8 @@ on:
         description: "Pydantic version to test."
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
+  UV_NO_SYNC: "true"
 
 jobs:
   build:
@@ -31,21 +32,18 @@ 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 +18,8 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  POETRY_VERSION: "1.8.4"
+  UV_FROZEN: "true"
+  UV_NO_SYNC: "true"
 
 jobs:
   build:
@@ -127,24 +128,19 @@ 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"
-  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"]'
+  UV_FROZEN: "true"
+  DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/xai", "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 }}
@@ -117,10 +134,12 @@ 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 }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}

.pre-commit-config.yaml

@@ -97,12 +97,6 @@ repos:
       entry: make -C libs/partners/openai format
       files: ^libs/partners/openai/
       pass_filenames: false
-    - id: pinecone
-      name: format partners/pinecone
-      language: system
-      entry: make -C libs/partners/pinecone format
-      files: ^libs/partners/pinecone/
-      pass_filenames: false
     - id: prompty
       name: format partners/prompty
       language: system

Makefile

@@ -1,5 +1,9 @@
 .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
+UV_NO_SYNC = 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 +29,20 @@ 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 +54,15 @@ 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 +70,9 @@ 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 +81,8 @@ 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
+
+update-package-downloads:
+	uv run python docs/scripts/packages_yml_get_downloads.py

cookbook/README.md

@@ -21,7 +21,6 @@ Notebook | Description
 [code-analysis-deeplake.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/code-analysis-deeplake.ipynb) | Analyze its own code base with the help of gpt and activeloop's deep lake.
 [custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval.ipynb) | Build a custom agent that can interact with ai plugins by retrieving tools and creating natural language wrappers around openapi endpoints.
 [custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb) | Build a custom agent with plugin retrieval functionality, utilizing ai plugins from the `plugnplai` directory.
-[databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
 [deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
 [elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
 [extraction_openai_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/extraction_openai_tools.ipynb) | Structured Data Extraction with OpenAI Tools

cookbook/code-analysis-deeplake.ipynb

@@ -66,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "#!python3 -m pip install --upgrade langchain deeplake openai"
+    "#!python3 -m pip install --upgrade langchain langchain-deeplake openai"
    ]
   },
   {
@@ -666,89 +666,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Your Deep Lake dataset has been successfully created!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://adilkhan/langchain-code', tensors=['embedding', 'id', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor      htype       shape       dtype  compression\n",
-      "  -------    -------     -------     -------  ------- \n",
-      " embedding  embedding  (8244, 1536)  float32   None   \n",
-      "    id        text      (8244, 1)      str     None   \n",
-      " metadata     json      (8244, 1)      str     None   \n",
-      "   text       text      (8244, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<langchain_community.vectorstores.deeplake.DeepLake at 0x7fe1b67d7a30>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain_community.vectorstores import DeepLake\n",
+    "from langchain_deeplake.vectorstores import DeeplakeVectorStore\n",
     "\n",
     "username = \"<USERNAME_OR_ORG>\"\n",
     "\n",
     "\n",
-    "db = DeepLake.from_documents(\n",
-    "    texts, embeddings, dataset_path=f\"hub://{username}/langchain-code\", overwrite=True\n",
+    "db = DeeplakeVectorStore.from_documents(\n",
+    "    documents=texts,\n",
+    "    embedding=embeddings,\n",
+    "    dataset_path=f\"hub://{username}/langchain-code\",\n",
+    "    overwrite=True,\n",
     ")\n",
     "db"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from langchain_community.vectorstores import DeepLake\n",
-    "\n",
-    "# db = DeepLake.from_documents(\n",
-    "#     texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
-    "# )\n",
-    "# db"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -760,24 +697,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in hub://adilkhan/langchain-code already exists, loading from the storage\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "db = DeepLake(\n",
+    "db = DeeplakeVectorStore(\n",
     "    dataset_path=f\"hub://{username}/langchain-code\",\n",
     "    read_only=True,\n",
-    "    embedding=embeddings,\n",
+    "    embedding_function=embeddings,\n",
     ")"
    ]
   },
@@ -796,36 +725,6 @@
     "retriever.search_kwargs[\"k\"] = 20"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def filter(x):\n",
-    "    # filter based on source code\n",
-    "    if \"something\" in x[\"text\"].data()[\"value\"]:\n",
-    "        return False\n",
-    "\n",
-    "    # filter based on path e.g. extension\n",
-    "    metadata = x[\"metadata\"].data()[\"value\"]\n",
-    "    return \"only_this\" in metadata[\"source\"] or \"also_that\" in metadata[\"source\"]\n",
-    "\n",
-    "\n",
-    "### turn on below for custom filtering\n",
-    "# retriever.search_kwargs['filter'] = filter"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -837,10 +736,8 @@
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(\n",
-    "    model_name=\"gpt-3.5-turbo-0613\"\n",
-    ")  # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
-    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0613\")  # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
+    "qa = RetrievalQA.from_llm(model, retriever=retriever)"
    ]
   },
   {

cookbook/databricks_sql_db.ipynb

@@ -1,273 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "707d13a7",
-   "metadata": {},
-   "source": [
-    "# Databricks\n",
-    "\n",
-    "This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
-    "It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0076d072",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "739b489b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install databricks-sql-connector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73113163",
-   "metadata": {},
-   "source": [
-    "## Connecting to Databricks\n",
-    "\n",
-    "You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
-    "\n",
-    "### Syntax\n",
-    "```python\n",
-    "SQLDatabase.from_databricks(\n",
-    "    catalog: str,\n",
-    "    schema: str,\n",
-    "    host: Optional[str] = None,\n",
-    "    api_token: Optional[str] = None,\n",
-    "    warehouse_id: Optional[str] = None,\n",
-    "    cluster_id: Optional[str] = None,\n",
-    "    engine_args: Optional[dict] = None,\n",
-    "    **kwargs: Any)\n",
-    "```\n",
-    "### Required Parameters\n",
-    "* `catalog`: The catalog name in the Databricks database.\n",
-    "* `schema`: The schema name in the catalog.\n",
-    "\n",
-    "### Optional Parameters\n",
-    "There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
-    "* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
-    "* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
-    "* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
-    "* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
-    "* `engine_args`: The arguments to be used when connecting Databricks.\n",
-    "* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b11c7e48",
-   "metadata": {},
-   "source": [
-    "## Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8102bca0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Connecting to Databricks with SQLDatabase wrapper\n",
-    "from langchain_community.utilities import SQLDatabase\n",
-    "\n",
-    "db = SQLDatabase.from_databricks(catalog=\"samples\", schema=\"nyctaxi\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9dd36f58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creating a OpenAI Chat LLM wrapper\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b5c5f1a",
-   "metadata": {},
-   "source": [
-    "### SQL Chain example\n",
-    "\n",
-    "This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "36f2270b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.utilities import SQLDatabaseChain\n",
-    "\n",
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4e2b5f25",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What is the average duration of taxi rides that start between midnight and 6am?\n",
-      "SQLQuery:\u001b[32;1m\u001b[1;3mSELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration\n",
-      "FROM trips\n",
-      "WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6\u001b[0m\n",
-      "SQLResult: \u001b[33;1m\u001b[1;3m[(987.8122786304605,)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db_chain.run(\n",
-    "    \"What is the average duration of taxi rides that start between midnight and 6am?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e496d5e5",
-   "metadata": {},
-   "source": [
-    "### SQL Database Agent example\n",
-    "\n",
-    "This example demonstrates the use of the [SQL Database Agent](/docs/integrations/tools/sql_database) for answering questions over a Databricks database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9918e86a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import create_sql_agent\n",
-    "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
-    "\n",
-    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
-    "agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c484a76e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
-      "Action Input: \u001b[0m\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3mtrips\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
-      "Action: schema_sql_db\n",
-      "Action Input: trips\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m\n",
-      "CREATE TABLE trips (\n",
-      "\ttpep_pickup_datetime TIMESTAMP, \n",
-      "\ttpep_dropoff_datetime TIMESTAMP, \n",
-      "\ttrip_distance FLOAT, \n",
-      "\tfare_amount FLOAT, \n",
-      "\tpickup_zip INT, \n",
-      "\tdropoff_zip INT\n",
-      ") USING DELTA\n",
-      "\n",
-      "/*\n",
-      "3 rows from trips table:\n",
-      "tpep_pickup_datetime\ttpep_dropoff_datetime\ttrip_distance\tfare_amount\tpickup_zip\tdropoff_zip\n",
-      "2016-02-14 16:52:13+00:00\t2016-02-14 17:16:04+00:00\t4.94\t19.0\t10282\t10171\n",
-      "2016-02-04 18:44:19+00:00\t2016-02-04 18:46:00+00:00\t0.28\t3.5\t10110\t10110\n",
-      "2016-02-17 17:13:57+00:00\t2016-02-17 17:17:55+00:00\t0.7\t5.0\t10103\t10023\n",
-      "*/\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
-      "Action: query_checker_sql_db\n",
-      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
-      "Observation: \u001b[31;1m\u001b[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
-      "Action: query_sql_db\n",
-      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m[(30.6, '0 00:43:31.000000000')]\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"What is the longest trip distance and how long did it take?\")"
-   ]
-  }
- ],
- "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/elasticsearch_db_qa.ipynb

@@ -115,7 +115,7 @@
     "\n",
     "PROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n",
     "\n",
-    "Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.\n",
+    "Unless told to do not query for all the columns from a specific index, only ask for a few relevant columns given the question.\n",
     "\n",
     "Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.\n",
     "\n",

cookbook/multi_modal_RAG_vdms.ipynb

@@ -21,40 +21,6 @@
     "* Passing raw images and text chunks to a multimodal LLM for answer synthesis "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6a6b6e73",
-   "metadata": {},
-   "source": [
-    "## Start VDMS Server\n",
-    "\n",
-    "Let's start a VDMS docker using port 55559 instead of default 55555. \n",
-    "Keep note of the port and hostname as this is needed for the vector store as it uses the VDMS Python client to connect to the server."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5f483872",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a1b9206b08ef626e15b356bf9e031171f7c7eb8f956a2733f196f0109246fe2b\n"
-     ]
-    }
-   ],
-   "source": [
-    "! docker run --rm -d -p 55559:55555 --name vdms_rag_nb intellabs/vdms:latest\n",
-    "\n",
-    "# Connect to VDMS Vector Store\n",
-    "from langchain_community.vectorstores.vdms import VDMS_Client\n",
-    "\n",
-    "vdms_client = VDMS_Client(port=55559)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "2498a0a1",
@@ -67,20 +33,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "febbc459-ebba-4c1a-a52b-fed7731593f8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install --quiet -U vdms langchain-experimental\n",
+    "! pip install --quiet -U langchain-vdms langchain-experimental langchain-ollama\n",
     "\n",
     "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
-    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml open_clip_torch"
+    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" \"onnxruntime==1.17.0\" pillow pydantic lxml open_clip_torch"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "78ac6543",
    "metadata": {},
    "outputs": [],
@@ -89,6 +55,40 @@
     "# load_dotenv(find_dotenv(), override=True);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e5c8916e",
+   "metadata": {},
+   "source": [
+    "## Start VDMS Server\n",
+    "\n",
+    "Let's start a VDMS docker using port 55559 instead of default 55555. \n",
+    "Keep note of the port and hostname as this is needed for the vector store as it uses the VDMS Python client to connect to the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e6e2c15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a701e5ac3523006e9540b5355e2d872d5d78383eab61562a675d5b9ac21fde65\n"
+     ]
+    }
+   ],
+   "source": [
+    "! docker run --rm -d -p 55559:55555 --name vdms_rag_nb intellabs/vdms:latest\n",
+    "\n",
+    "# Connect to VDMS Vector Store\n",
+    "from langchain_vdms.vectorstores import VDMS_Client\n",
+    "\n",
+    "vdms_client = VDMS_Client(port=55559)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1e94b3fb-8e3e-4736-be0a-ad881626c7bd",
@@ -115,11 +115,12 @@
     "import requests\n",
     "\n",
     "# Folder to store pdf and extracted images\n",
-    "datapath = Path(\"./data/multimodal_files\").resolve()\n",
+    "base_datapath = Path(\"./data/multimodal_files\").resolve()\n",
+    "datapath = base_datapath / \"images\"\n",
     "datapath.mkdir(parents=True, exist_ok=True)\n",
     "\n",
     "pdf_url = \"https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\"\n",
-    "pdf_path = str(datapath / pdf_url.split(\"/\")[-1])\n",
+    "pdf_path = str(base_datapath / pdf_url.split(\"/\")[-1])\n",
     "with open(pdf_path, \"wb\") as f:\n",
     "    f.write(requests.get(pdf_url).content)"
    ]
@@ -185,8 +186,8 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain_community.vectorstores import VDMS\n",
     "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
+    "from langchain_vdms import VDMS\n",
     "\n",
     "# Create VDMS\n",
     "vectorstore = VDMS(\n",
@@ -312,10 +313,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.llms.ollama import Ollama\n",
-    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_ollama.llms import OllamaLLM\n",
     "\n",
     "\n",
     "def prompt_func(data_dict):\n",
@@ -340,8 +341,8 @@
     "            \"As an expert art critic and historian, your task is to analyze and interpret images, \"\n",
     "            \"considering their historical and cultural significance. Alongside the images, you will be \"\n",
     "            \"provided with related text to offer context. Both will be retrieved from a vectorstore based \"\n",
-    "            \"on user-input keywords. Please convert answers to english and use your extensive knowledge \"\n",
-    "            \"and analytical skills to provide a comprehensive summary that includes:\\n\"\n",
+    "            \"on user-input keywords. Please use your extensive knowledge and analytical skills to provide a \"\n",
+    "            \"comprehensive summary that includes:\\n\"\n",
     "            \"- A detailed description of the visual elements in the image.\\n\"\n",
     "            \"- The historical and cultural context of the image.\\n\"\n",
     "            \"- An interpretation of the image's symbolism and meaning.\\n\"\n",
@@ -359,7 +360,7 @@
     "    \"\"\"Multi-modal RAG chain\"\"\"\n",
     "\n",
     "    # Multi-modal LLM\n",
-    "    llm_model = Ollama(\n",
+    "    llm_model = OllamaLLM(\n",
     "        verbose=True, temperature=0.5, model=\"llava\", base_url=\"http://localhost:11434\"\n",
     "    )\n",
     "\n",
@@ -419,6 +420,121 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "© 2017 LARRY D. MOORE\n",
+      "\n",
+      "contemporary criticism of the less-than- thoughtful circumstances under which Lange photographed Thomson, the picture’s power to engage has not diminished. Artists in other countries have appropriated the image, changing the mother’s features into those of other ethnicities, but keeping her expression and the positions of her clinging children. Long after anyone could help the Thompson family, this picture has resonance in another time of national crisis, unemployment and food shortages.\n",
+      "\n",
+      "A striking, but very different picture is a 1900 portrait of the legendary Hin-mah-too-yah- lat-kekt (Chief Joseph) of the Nez Percé people. The Bureau of American Ethnology in Washington, D.C., regularly arranged for its photographer, De Lancey Gill, to photograph Native American delegations that came to the capital to confer with officials about tribal needs and concerns. Although Gill described Chief Joseph as having “an air of gentleness and quiet reserve,” the delegate skeptically appraises the photographer, which is not surprising given that the United States broke five treaties with Chief Joseph and his father between 1855 and 1885.\n",
+      "\n",
+      "More than a glance, second looks may reveal new knowledge into complex histories.\n",
+      "\n",
+      "Anne Wilkes Tucker is the photography curator emeritus of the Museum of Fine Arts, Houston and curator of the “Not an Ostrich” exhibition.\n",
+      "\n",
+      "28\n",
+      "\n",
+      "28 LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "THEYRE WILLING TO HAVE MEENTERTAIN THEM DURING THE DAY,BUT AS SOON AS IT STARTSGETTING DARK, THEY ALLGO OFF, AND LEAVE ME!   \n",
+      "ROSA PARKS: IN HER OWN WORDS\n",
+      "\n",
+      "COMIC ART: 120 YEARS OF PANELS AND PAGES\n",
+      "\n",
+      "SHALL NOT BE DENIED: WOMEN FIGHT FOR THE VOTE\n",
+      "\n",
+      "More information loc.gov/exhibits\n",
+      "Nuestra Sefiora de las Iguanas\n",
+      "\n",
+      "Graciela Iturbide’s 1979 portrait of Zobeida Díaz in the town of Juchitán in southeastern Mexico conveys the strength of women and reflects their important contributions to the economy. Díaz, a merchant, was selling iguanas to cook and eat, carrying them on her head, as is customary.\n",
+      "\n",
+      "GRACIELA ITURBIDE. “NUESTRA SEÑORA DE LAS IGUANAS.” 1979. GELATIN SILVER PRINT. © GRACIELA ITURBIDE, USED BY PERMISSION. PRINTS AND PHOTOGRAPHS DIVISION.\n",
+      "\n",
+      "Iturbide requested permission to take a photograph, but this proved challenging because the iguanas were constantly moving, causing Díaz to laugh. The result, however, was a brilliant portrait that the inhabitants of Juchitán claimed with pride. They have reproduced it on posters and erected a statue honoring Díaz and her iguanas. The photo now appears throughout the world, inspiring supporters of feminism, women’s rights and gender equality.\n",
+      "\n",
+      "—Adam Silvia is a curator in the Prints and Photographs Division.\n",
+      "\n",
+      "6\n",
+      "\n",
+      "6 LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "‘Migrant Mother’ is Florence Owens Thompson\n",
+      "\n",
+      "The iconic portrait that became the face of the Great Depression is also the most famous photograph in the collections of the Library of Congress.\n",
+      "\n",
+      "The Library holds the original source of the photo — a nitrate negative measuring 4 by 5 inches. Do you see a faint thumb in the bottom right? The photographer, Dorothea Lange, found the thumb distracting and after a few years had the negative altered to make the thumb almost invisible. Lange’s boss at the Farm Security Administration, Roy Stryker, criticized her action because altering a negative undermines the credibility of a documentary photo.\n",
+      "Shrimp Picker\n",
+      "\n",
+      "The photos and evocative captions of Lewis Hine served as source material for National Child Labor Committee reports and exhibits exposing abusive child labor practices in the United States in the first decades of the 20th century.\n",
+      "\n",
+      "LEWIS WICKES HINE. “MANUEL, THE YOUNG SHRIMP-PICKER, FIVE YEARS OLD, AND A MOUNTAIN OF CHILD-LABOR OYSTER SHELLS BEHIND HIM. HE WORKED LAST YEAR. UNDERSTANDS NOT A WORD OF ENGLISH. DUNBAR, LOPEZ, DUKATE COMPANY. LOCATION: BILOXI, MISSISSIPPI.” FEBRUARY 1911. NATIONAL CHILD LABOR COMMITTEE COLLECTION. PRINTS AND PHOTOGRAPHS DIVISION.\n",
+      "\n",
+      "For 15 years, Hine\n",
+      "\n",
+      "crisscrossed the country, documenting the practices of the worst offenders. His effective use of photography made him one of the committee's greatest publicists in the campaign for legislation to ban child labor.\n",
+      "\n",
+      "Hine was a master at taking photos that catch attention and convey a message and, in this photo, he framed Manuel in a setting that drove home the boy’s small size and unsafe environment.\n",
+      "\n",
+      "Captions on photos of other shrimp pickers emphasized their long working hours as well as one hazard of the job: The acid from the shrimp made pickers’ hands sore and “eats the shoes off your feet.”\n",
+      "\n",
+      "Such images alerted viewers to all that workers, their families and the nation sacrificed when children were part of the labor force. The Library holds paper records of the National Child Labor Committee as well as over 5,000 photographs.\n",
+      "\n",
+      "—Barbara Natanson is head of the Reference Section in the Prints and Photographs Division.\n",
+      "\n",
+      "8\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "Intergenerational Portrait\n",
+      "\n",
+      "Raised on the Apsáalooke (Crow) reservation in Montana, photographer Wendy Red Star created her “Apsáalooke Feminist” self-portrait series with her daughter Beatrice. With a dash of wry humor, mother and daughter are their own first-person narrators.\n",
+      "\n",
+      "Red Star explains the significance of their appearance: “The dress has power: You feel strong and regal wearing it. In my art, the elk tooth dress specifically symbolizes Crow womanhood and the matrilineal line connecting me to my ancestors. As a mother, I spend hours searching for the perfect elk tooth dress materials to make a prized dress for my daughter.”\n",
+      "\n",
+      "In a world that struggles with cultural identities, this photograph shows us the power and beauty of blending traditional and contemporary styles.\n",
+      "‘American Gothic’ Product #216040262 Price: $24\n",
+      "\n",
+      "U.S. Capitol at Night Product #216040052 Price: $24\n",
+      "\n",
+      "Good Reading Ahead Product #21606142 Price: $24\n",
+      "\n",
+      "Gordon Parks created an iconic image with this 1942 photograph of cleaning woman Ella Watson.\n",
+      "\n",
+      "Snow blankets the U.S. Capitol in this classic image by Ernest L. Crandall.\n",
+      "\n",
+      "Start your new year out right with a poster promising good reading for months to come.\n",
+      "\n",
+      "▪ Order online: loc.gov/shop ▪ Order by phone: 888.682.3557\n",
+      "\n",
+      "26\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "SUPPORT\n",
+      "\n",
+      "A PICTURE OF PHILANTHROPY Annenberg Foundation Gives $1 Million and a Photographic Collection to the Library.\n",
+      "\n",
+      "A major gift by Wallis Annenberg and the Annenberg Foundation in Los Angeles will support the effort to reimagine the visitor experience at the Library of Congress. The foundation also is donating 1,000 photographic prints from its Annenberg Space for Photography exhibitions to the Library.\n",
+      "\n",
+      "The Library is pursuing a multiyear plan to transform the experience of its nearly 2 million annual visitors, share more of its treasures with the public and show how Library collections connect with visitors’ own creativity and research. The project is part of a strategic plan established by Librarian of Congress Carla Hayden to make the Library more user-centered for Congress, creators and learners of all ages.\n",
+      "\n",
+      "A 2018 exhibition at the Annenberg Space for Photography in Los Angeles featured over 400 photographs from the Library. The Library is planning a future photography exhibition, based on the Annenberg-curated show, along with a documentary film on the Library and its history, produced by the Annenberg Space for Photography.\n",
+      "\n",
+      "“The nation’s library is honored to have the strong support of Wallis Annenberg and the Annenberg Foundation as we enhance the experience for our visitors,” Hayden said. “We know that visitors will find new connections to the Library through the incredible photography collections and countless other treasures held here to document our nation’s history and creativity.”\n",
+      "\n",
+      "To enhance the Library’s holdings, the foundation is giving the Library photographic prints for long-term preservation from 10 other exhibitions hosted at the Annenberg Space for Photography. The Library holds one of the world’s largest photography collections, with about 14 million photos and over 1 million images digitized and available online.\n",
+      "18  LIBRARY OF CONGRESS MAGAZINE\n"
+     ]
     }
    ],
    "source": [
@@ -461,10 +577,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " The image depicts a woman with several children. The woman appears to be of Cherokee heritage, as suggested by the text provided. The image is described as having been initially regretted by the subject, Florence Owens Thompson, due to her feeling that it did not accurately represent her leadership qualities.\n",
-      "The historical and cultural context of the image is tied to the Great Depression and the Dust Bowl, both of which affected the Cherokee people in Oklahoma. The photograph was taken during this period, and its subject, Florence Owens Thompson, was a leader within her community who worked tirelessly to help those affected by these crises.\n",
-      "The image's symbolism and meaning can be interpreted as a representation of resilience and strength in the face of adversity. The woman is depicted with multiple children, which could signify her role as a caregiver and protector during difficult times.\n",
-      "Connections between the image and the related text include Florence Owens Thompson's leadership qualities and her regretted feelings about the photograph. Additionally, the mention of Dorothea Lange, the photographer who took this photo, ties the image to its historical context and the broader narrative of the Great Depression and Dust Bowl in Oklahoma. \n"
+      " The image is a black and white photograph by Dorothea Lange titled \"Destitute Pea Pickers in California. Mother of Seven Children. Age Thirty-Two. Nipomo, California.\" It was taken in March 1936 as part of the Farm Security Administration-Office of War Information Collection.\n",
+      "\n",
+      "The photograph features a woman with seven children, who appear to be in a state of poverty and hardship. The woman is seated, looking directly at the camera, while three of her children are standing behind her. They all seem to be dressed in ragged clothing, indicative of their impoverished condition.\n",
+      "\n",
+      "The historical context of this image is related to the Great Depression, which was a period of economic hardship in the United States that lasted from 1929 to 1939. During this time, many people struggled to make ends meet, and poverty was widespread. This photograph captures the plight of one such family during this difficult period.\n",
+      "\n",
+      "The symbolism of the image is multifaceted. The woman's direct gaze at the camera can be seen as a plea for help or an expression of desperation. The ragged clothing of the children serves as a stark reminder of the poverty and hardship experienced by many during this time.\n",
+      "\n",
+      "In terms of connections to the related text, it is mentioned that Florence Owens Thompson, the woman in the photograph, initially regretted having her picture taken. However, she later came to appreciate the importance of the image as a representation of the struggles faced by many during the Great Depression. The mention of Helena Zinkham suggests that she may have played a role in the creation or distribution of this photograph.\n",
+      "\n",
+      "Overall, this image is a powerful depiction of poverty and hardship during the Great Depression, capturing the resilience and struggles of one family amidst difficult times. \n"
      ]
     }
    ],
@@ -491,11 +614,17 @@
    "source": [
     "! docker kill vdms_rag_nb"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe4a98ee",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".langchain-venv",
+   "display_name": ".test-venv",
    "language": "python",
    "name": "python3"
   },
@@ -509,7 +638,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

cookbook/sql_db_qa.mdx

@@ -233,7 +233,7 @@ Question: {input}"""
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
 
-Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
+Never query for all the columns from a specific table, only ask for a few relevant columns given the question.
 
 Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
 

cookbook/visual_RAG_vdms.ipynb

@@ -26,7 +26,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2e44b44201c8778b462342ac97f5ccf05a4e02aa8a04505ecde97bf20dcc4cbb\n"
+      "76e78b89cee4d6d31154823f93592315df79c28410dfbfc87c9f70cbfdfa648b\n"
      ]
     }
    ],
@@ -49,7 +49,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install --quiet -U vdms langchain-experimental sentence-transformers opencv-python open_clip_torch torch accelerate"
+    "! pip install --quiet -U langchain-vdms langchain-experimental sentence-transformers opencv-python open_clip_torch torch accelerate"
    ]
   },
   {
@@ -63,7 +63,16 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data1/cwlacewe/apps/cwlacewe_langchain/.langchain-venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import json\n",
     "import os\n",
@@ -80,10 +89,10 @@
     "from langchain_community.embeddings.sentence_transformer import (\n",
     "    SentenceTransformerEmbeddings,\n",
     ")\n",
-    "from langchain_community.vectorstores.vdms import VDMS, VDMS_Client\n",
     "from langchain_core.callbacks.manager import CallbackManagerForLLMRun\n",
     "from langchain_core.runnables import ConfigurableField\n",
     "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
+    "from langchain_vdms.vectorstores import VDMS, VDMS_Client\n",
     "from transformers import (\n",
     "    AutoModelForCausalLM,\n",
     "    AutoTokenizer,\n",
@@ -363,7 +372,7 @@
       "\t\tThere are 2 shoppers in this video. Shopper 1 is wearing a plaid shirt and a spectacle. Shopper 2 who is not completely captured in the frame seems to wear a black shirt and is moving away with his back turned towards the camera. There is a shelf towards the right of the camera frame. Shopper 2 is hanging an item back to a hanger and then quickly walks away in a similar fashion as shopper 2. Contents of the nearer side of the shelf with respect to camera seems to be camping lanterns and cleansing agents, arranged at the top. In the middle part of the shelf, various tools including grommets, a pocket saw, candles, and other helpful camping items can be observed. Midway through the shelf contains items which appear to be steel containers and items made up of plastic with red, green, orange, and yellow colors, while those at the bottom are packed in cardboard boxes. Contents at the farther part of the shelf are well stocked and organized but are not glaringly visible.\n",
       "\n",
       "\tMetadata:\n",
-      "\t\t{'fps': 24.0, 'id': 'c6e5f894-b905-46f5-ac9e-4487a9235561', 'total_frames': 120.0, 'video': 'clip16.mp4'}\n",
+      "\t\t{'fps': 24.0, 'total_frames': 120.0, 'video': 'clip16.mp4'}\n",
       "Retrieved Top matching video!\n",
       "\n",
       "\n"
@@ -392,18 +401,12 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3edf8783e114487ca490d8dec5c46884",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading checkpoint shards: 100%|██████████| 2/2 [00:18<00:00,  9.01s/it]\n",
+      "WARNING:accelerate.big_modeling:Some parameters are on the meta device because they were offloaded to the cpu.\n"
+     ]
     }
    ],
    "source": [
@@ -555,7 +558,7 @@
       "\t\tA single shopper is seen in this video standing facing the shelf and in the bottom part of the frame. He's wearing a light-colored shirt and a spectacle. The shopper is carrying a red colored basket in his left hand. The entire basket is not clearly visible, but it does seem to contain something in a blue colored package which the shopper has just placed in the basket given his right hand was seen inside the basket. Then the shopper leans towards the shelf and checks out an item in orange package. He picks this single item with his right hand and proceeds to place the item in the basket. The entire shelf looks well stocked except for the top part of the shelf which is empty. The shopper has not picked any item from this part of the shelf. The rest of the shelf looks well stocked and does not need any restocking. The contents on the farther part of the shelf consists of items, majority of which are packed in black, yellow, and green packages. No other details are visible of these items.\n",
       "\n",
       "\tMetadata:\n",
-      "\t\t{'fps': 24.0, 'id': '37ddc212-994e-4db0-877f-5ed09965ab90', 'total_frames': 162.0, 'video': 'clip10.mp4'}\n",
+      "\t\t{'fps': 24.0, 'total_frames': 162.0, 'video': 'clip10.mp4'}\n",
       "Retrieved Top matching video!\n",
       "\n",
       "\n"
@@ -585,7 +588,7 @@
       "User :  Find a man holding a red shopping basket\n",
       "Assistant :  Most relevant retrieved video is **clip9.mp4** \n",
       "\n",
-      "I see a person standing in front of a well-stocked shelf, they are wearing a light-colored shirt and glasses, and they have a red shopping basket in their left hand. They are leaning forward and picking up an item from the shelf with their right hand. The item is packaged in a blue-green box. Based on the scene description, I can confirm that the person is indeed holding a red shopping basket.</s>\n"
+      "I see a person standing in front of a well-stocked shelf, they are wearing a light-colored shirt and glasses, and they have a red shopping basket in their left hand. They are leaning forward and picking up an item from the shelf with their right hand. The item is packaged in a blue-green box. Based on the available information, I cannot confirm whether the basket is empty or contains items. However, the rest of the\n"
      ]
     }
    ],
@@ -655,7 +658,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": ".langchain-venv",
    "language": "python",
    "name": "python3"
   },
@@ -669,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

docs/api_reference/_static/css/custom.css

@@ -328,7 +328,7 @@ html[data-theme=dark] .MathJax_SVG * {
 }
 
 .bd-sidebar-primary {
-    width: 22%;  /* Adjust this value to your preference */
+    width: max-content;  /* Adjust this value to your preference */
     line-height: 1.4;
 }
 

docs/api_reference/create_api_rst.py

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

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

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

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

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

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

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

@@ -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/retrieval.mdx

@@ -74,6 +74,8 @@ As an example, query decomposition can simply be accomplished using prompting an
 These can then be run sequentially or in parallel on a downstream retrieval system.
 
 ```python
+from typing import List
+
 from pydantic import BaseModel, Field
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import SystemMessage, HumanMessage

docs/docs/concepts/streaming.mdx

@@ -125,7 +125,7 @@ prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
 parser = StrOutputParser()
 chain = prompt | model | parser
 
-async for event in chain.astream_events({"topic": "parrot"}, version="v2"):
+async for event in chain.astream_events({"topic": "parrot"}):
     kind = event["event"]
     if kind == "on_chat_model_stream":
         print(event, end="|", flush=True)

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/documentation/setup.mdx

@@ -50,11 +50,6 @@ locally to ensure that it looks good and is free of errors.
 If you're unable to build it locally that's okay as well, as you will be able to
 see a preview of the documentation on the pull request page.
 
-From the **monorepo root**, run the following command to install the dependencies:
-
-```bash
-poetry install --with lint,docs --no-root
-````
 
 ### Building
 
@@ -158,14 +153,6 @@ the working directory to the `langchain-community` directory:
 cd [root]/libs/langchain-community
 ```
 
-Set up a virtual environment for the package if you haven't done so already.
-
-Install the dependencies for the package.
-
-```bash
-poetry install --with lint
-```
-
 Then you can run the following commands to lint and format the in-code documentation:
 
 ```bash

docs/docs/contributing/reference/faq.mdx

@@ -35,5 +35,5 @@ Please reference our [Review Process](review_process.mdx).
 
 ### I think my PR was closed in a way that didn't follow the review process. What should I do?
 
-Tag `@efriis` in the PR comments referencing the portion of the review
+Tag `@ccurme` in the PR comments referencing the portion of the review
 process that you believe was not followed. We'll take a look!

docs/docs/how_to/agent_executor.ipynb

@@ -270,7 +270,7 @@
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n"
+    "<ChatModelTabs overrideParams={{openai: {model: \"gpt-4\"}}} />\n"
    ]
   },
   {

docs/docs/how_to/chat_streaming.ipynb

@@ -127,20 +127,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "27bd1dfd-8ae2-49d6-b526-97180c81b5f4",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 3,
+   "id": "5a03086e-2813-4cb1-b12b-d00e7eeba122",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_start', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {}, 'data': {'input': 'Write me a 1 verse song about goldfish on the moon'}}\n",
-      "{'event': 'on_chat_model_stream', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'tags': [], 'metadata': {}, 'name': 'ChatAnthropic', 'data': {'chunk': AIMessageChunk(content='Here', id='run-08da631a-12a0-4f07-baee-fc9a175ad4ba')}}\n",
-      "{'event': 'on_chat_model_stream', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'tags': [], 'metadata': {}, 'name': 'ChatAnthropic', 'data': {'chunk': AIMessageChunk(content=\"'s\", id='run-08da631a-12a0-4f07-baee-fc9a175ad4ba')}}\n",
-      "{'event': 'on_chat_model_stream', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'tags': [], 'metadata': {}, 'name': 'ChatAnthropic', 'data': {'chunk': AIMessageChunk(content=' a', id='run-08da631a-12a0-4f07-baee-fc9a175ad4ba')}}\n",
+      "{'event': 'on_chat_model_start', 'data': {'input': 'Write me a 1 verse song about goldfish on the moon'}, 'name': 'ChatAnthropic', 'tags': [], 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-1d430164-52b1-4d00-8c00-b16460f7737e', usage_metadata={'input_tokens': 21, 'output_tokens': 2, 'total_tokens': 23, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'data': {'chunk': AIMessageChunk(content=\"Here's\", additional_kwargs={}, response_metadata={}, id='run-1d430164-52b1-4d00-8c00-b16460f7737e')}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'data': {'chunk': AIMessageChunk(content=' a short one-verse song', additional_kwargs={}, response_metadata={}, id='run-1d430164-52b1-4d00-8c00-b16460f7737e')}, 'parent_ids': []}\n",
       "...Truncated\n"
      ]
     }
@@ -152,7 +150,7 @@
     "idx = 0\n",
     "\n",
     "async for event in chat.astream_events(\n",
-    "    \"Write me a 1 verse song about goldfish on the moon\", version=\"v1\"\n",
+    "    \"Write me a 1 verse song about goldfish on the moon\"\n",
     "):\n",
     "    idx += 1\n",
     "    if idx >= 5:  # Truncate the output\n",
@@ -178,7 +176,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/custom_tools.ipynb

@@ -22,7 +22,7 @@
     "2. LangChain [Runnables](/docs/concepts/runnables);\n",
     "3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
     "\n",
-    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
+    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
     "\n",
     "In this guide we provide an overview of these methods.\n",
     "\n",

docs/docs/how_to/document_loader_custom.ipynb

@@ -551,7 +551,7 @@
     "\n",
     "While a parser encapsulates the logic needed to parse binary data into documents, *blob loaders* encapsulate the logic that's necessary to load blobs from a given storage location.\n",
     "\n",
-    "A the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
+    "At the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
     "\n",
     "You can use the `FileSystemBlobLoader` to load blobs and then use the parser to parse them."
    ]

docs/docs/how_to/extraction_examples.ipynb

@@ -354,7 +354,7 @@
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "  openaiParams={`model=\"gpt-4-0125-preview\", temperature=0`}\n",
+    "  overrideParams={{openai: {model: \"gpt-4-0125-preview\", kwargs: \"temperature=0\"}}}\n",
     "/>\n"
    ]
   },

docs/docs/how_to/extraction_long_text.ipynb

@@ -179,7 +179,7 @@
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "  openaiParams={`model=\"gpt-4o\", temperature=0`}\n",
+    "  overrideParams={{openai: {model: \"gpt-4o\", kwargs: \"temperature=0\"}}}\n",
     "/>\n"
    ]
   },

docs/docs/how_to/function_calling.ipynb

@@ -167,7 +167,7 @@
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
+    "  overrideParams={{fireworks: {model: \"accounts/fireworks/models/firefunction-v1\", kwargs: \"temperature=0\"}}}\n",
     "/>\n",
     "\n",
     "We can use the `bind_tools()` method to handle converting\n",

docs/docs/how_to/functions.ipynb

@@ -99,8 +99,6 @@
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"what is {a} + {b}\")\n",
     "\n",
-    "chain1 = prompt | model\n",
-    "\n",
     "chain = (\n",
     "    {\n",
     "        \"a\": itemgetter(\"foo\") | RunnableLambda(length_function),\n",

docs/docs/how_to/local_llms.ipynb

@@ -68,7 +68,7 @@
     "\n",
     "### Formatting prompts\n",
     "\n",
-    "Some providers have [chat model](/docs/concepts/chat_models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/text_llms) wrapper, you may need to use a prompt tailed for your specific model.\n",
+    "Some providers have [chat model](/docs/concepts/chat_models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/text_llms) wrapper, you may need to use a prompt tailored for your specific model.\n",
     "\n",
     "This can [require the inclusion of special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). [Here's an example for LLaMA 2](https://smith.langchain.com/hub/rlm/rag-prompt-llama).\n",
     "\n",

docs/docs/how_to/migrate_agent.ipynb

@@ -329,7 +329,7 @@
    "id": "fc6059fd-0df7-4b6f-a84c-b5874e983638",
    "metadata": {},
    "source": [
-    "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 also pass in an arbitrary function or a runnable. This function/runnable should take in a 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."
    ]
   },

docs/docs/how_to/sql_large_db.ipynb

@@ -512,44 +512,6 @@
     "db.run(query)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "fcdd8432-07a4-4609-8214-b1591dd94950",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SELECT DISTINCT Genre.Name\n",
-      "FROM Genre\n",
-      "JOIN Track ON Genre.GenreId = Track.GenreId\n",
-      "JOIN Album ON Track.AlbumId = Album.AlbumId\n",
-      "JOIN Artist ON Album.ArtistId = Artist.ArtistId\n",
-      "WHERE Artist.Name = 'Elenis Moriset'\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Without retrieval\n",
-    "query = query_chain.invoke(\n",
-    "    {\"question\": \"What are all the genres of elenis moriset songs\", \"proper_nouns\": \"\"}\n",
-    ")\n",
-    "print(query)\n",
-    "db.run(query)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 14,

docs/docs/how_to/streaming.ipynb

@@ -720,22 +720,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "c00df46e-7f6b-4e06-8abf-801898c8d57f",
+   "execution_count": 13,
+   "id": "bab5f910-fee0-4a94-9f05-b469006333b8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.\n",
-      "  warn_beta(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "events = []\n",
-    "async for event in model.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in model.astream_events(\"hello\"):\n",
     "    events.append(event)"
    ]
   },
@@ -746,15 +737,7 @@
    "source": [
     ":::note\n",
     "\n",
-    "Hey what's that funny version=\"v2\" parameter in the API?! 😾\n",
-    "\n",
-    "This is a **beta API**, and we're almost certainly going to make some changes to it (in fact, we already have!)\n",
-    "\n",
-    "This version parameter will allow us to minimize such breaking changes to your code. \n",
-    "\n",
-    "In short, we are annoying you now, so we don't have to annoy you later.\n",
-    "\n",
-    "`v2` is only available for langchain-core>=0.2.0.\n",
+    "For `langchain-core<0.3.37`, set the `version` kwarg explicitly (e.g., `model.astream_events(\"hello\", version=\"v2\")`).\n",
     "\n",
     ":::"
    ]
@@ -769,8 +752,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "ce31b525-f47d-4828-85a7-912ce9f2e79b",
+   "execution_count": 14,
+   "id": "c4a2f5dc-2c75-4be4-a8ca-b5b84a3cdbef",
    "metadata": {},
    "outputs": [
     {
@@ -780,23 +763,38 @@
        "  'data': {'input': 'hello'},\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
-       "  'metadata': {}},\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'parent_ids': []},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='Hello', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 8, 'output_tokens': 4, 'total_tokens': 12, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n",
+       "  'parent_ids': []},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='!', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}}]"
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'data': {'chunk': AIMessageChunk(content='Hello! How can', additional_kwargs={}, response_metadata={}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66')},\n",
+       "  'parent_ids': []}]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -807,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "76cfe826-ee63-4310-ad48-55a95eb3b9d6",
    "metadata": {},
    "outputs": [
@@ -815,20 +813,30 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 0, 'output_tokens': 12, 'total_tokens': 12, 'input_token_details': {}})},\n",
+       "  'parent_ids': []},\n",
        " {'event': 'on_chat_model_end',\n",
-       "  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 8, 'output_tokens': 16, 'total_tokens': 24, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}}]"
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'parent_ids': []}]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -849,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "4328c56c-a303-427b-b1f2-f354e9af555c",
    "metadata": {},
    "outputs": [],
@@ -864,7 +872,6 @@
     "        \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "        'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "        \"Each country should have the key `name` and `population`\",\n",
-    "        version=\"v2\",\n",
     "    )\n",
     "]"
    ]
@@ -947,29 +954,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Chat model chunk: ''\n",
       "Chat model chunk: '{'\n",
       "Parser chunk: {}\n",
-      "Chat model chunk: '\\n  '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'countries'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' ['\n",
+      "Chat model chunk: '\\n  \"countries'\n",
+      "Chat model chunk: '\": [\\n    '\n",
       "Parser chunk: {'countries': []}\n",
-      "Chat model chunk: '\\n    '\n",
-      "Chat model chunk: '{'\n",
+      "Chat model chunk: '{\\n      \"'\n",
       "Parser chunk: {'countries': [{}]}\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'name'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' \"'\n",
-      "Parser chunk: {'countries': [{'name': ''}]}\n",
-      "Chat model chunk: 'France'\n",
+      "Chat model chunk: 'name\": \"France'\n",
       "Parser chunk: {'countries': [{'name': 'France'}]}\n",
-      "Chat model chunk: '\",'\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'population'\n",
+      "Chat model chunk: '\",\\n      \"'\n",
+      "Chat model chunk: 'population\": 67'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}\n",
+      "Chat model chunk: '413'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413}]}\n",
+      "Chat model chunk: '000\\n    },'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}]}\n",
+      "Chat model chunk: '\\n    {'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {}]}\n",
+      "Chat model chunk: '\\n      \"name\":'\n",
       "...\n"
      ]
     }
@@ -981,7 +985,6 @@
     "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
     "):\n",
     "    kind = event[\"event\"]\n",
     "    if kind == \"on_chat_model_stream\":\n",
@@ -1023,24 +1026,24 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "4f0b581b-be63-4663-baba-c6d2b625cdf9",
+   "id": "42145735-25e8-4e67-b081-b0c15ea45dd1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': []}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
+      "{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'metadata': {}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
       "...\n"
      ]
     }
@@ -1055,7 +1058,6 @@
     "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
     "    include_names=[\"my_parser\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1077,24 +1079,24 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "096cd904-72f0-4ebe-a8b7-d0e730faea7f",
+   "id": "2a7d8fe0-47ca-4ab4-9c10-b34e3f6106ee",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n    ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n      ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
+      "{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c', usage_metadata={'input_tokens': 56, 'output_tokens': 1, 'total_tokens': 57, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  \"countries', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\": [\\n    ', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{\\n      \"', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='name\": \"France', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\",\\n      \"', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='population\": 67', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='413', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='000\\n    },', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
       "...\n"
      ]
     }
@@ -1107,7 +1109,6 @@
     "max_events = 0\n",
     "async for event in chain.astream_events(\n",
     "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
-    "    version=\"v2\",\n",
     "    include_types=[\"chat_model\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1136,24 +1137,24 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "26bac0d2-76d9-446e-b346-82790236b88d",
+   "id": "c237c218-5fd6-4146-ac68-020a038cf582",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chain_stream', 'data': {'chunk': {}}, 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
+      "{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'metadata': {}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`', additional_kwargs={}, response_metadata={})]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b', usage_metadata={'input_tokens': 56, 'output_tokens': 1, 'total_tokens': 57, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'metadata': {}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chain_stream', 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  \"countries', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\": [\\n    ', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chain_stream', 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': []}\n",
       "...\n"
      ]
     }
@@ -1164,7 +1165,6 @@
     "max_events = 0\n",
     "async for event in chain.astream_events(\n",
     "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
-    "    version=\"v2\",\n",
     "    include_tags=[\"my_chain\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1263,40 +1263,40 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "b08215cd-bffa-4e76-aaf3-c52ee34f152c",
+   "id": "2c83701e-b801-429f-b2ac-47ed44d2d11a",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Chat model chunk: ''\n",
       "Chat model chunk: '{'\n",
       "Parser chunk: {}\n",
-      "Chat model chunk: '\\n  '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'countries'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' ['\n",
+      "Chat model chunk: '\\n  \"countries'\n",
+      "Chat model chunk: '\": [\\n    '\n",
       "Parser chunk: {'countries': []}\n",
-      "Chat model chunk: '\\n    '\n",
-      "Chat model chunk: '{'\n",
+      "Chat model chunk: '{\\n      \"'\n",
       "Parser chunk: {'countries': [{}]}\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'name'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' \"'\n",
-      "Parser chunk: {'countries': [{'name': ''}]}\n",
-      "Chat model chunk: 'France'\n",
+      "Chat model chunk: 'name\": \"France'\n",
       "Parser chunk: {'countries': [{'name': 'France'}]}\n",
-      "Chat model chunk: '\",'\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'population'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' '\n",
-      "Chat model chunk: '67'\n",
+      "Chat model chunk: '\",\\n      \"'\n",
+      "Chat model chunk: 'population\": 67'\n",
       "Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}\n",
+      "Chat model chunk: '413'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413}]}\n",
+      "Chat model chunk: '000\\n    },'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}]}\n",
+      "Chat model chunk: '\\n    {'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {}]}\n",
+      "Chat model chunk: '\\n      \"name\":'\n",
+      "Chat model chunk: ' \"Spain\",'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}\n",
+      "Chat model chunk: '\\n      \"population\":'\n",
+      "Chat model chunk: ' 47'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}\n",
+      "Chat model chunk: '351'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351}]}\n",
       "...\n"
      ]
     }
@@ -1308,7 +1308,6 @@
     "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
     "):\n",
     "    kind = event[\"event\"]\n",
     "    if kind == \"on_chat_model_stream\":\n",
@@ -1376,7 +1375,7 @@
     "    return reverse_word.invoke(word)\n",
     "\n",
     "\n",
-    "async for event in bad_tool.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in bad_tool.astream_events(\"hello\"):\n",
     "    print(event)"
    ]
   },
@@ -1412,7 +1411,7 @@
     "    return reverse_word.invoke(word, {\"callbacks\": callbacks})\n",
     "\n",
     "\n",
-    "async for event in correct_tool.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in correct_tool.astream_events(\"hello\"):\n",
     "    print(event)"
    ]
   },
@@ -1454,7 +1453,7 @@
     "\n",
     "await reverse_and_double.ainvoke(\"1234\")\n",
     "\n",
-    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
+    "async for event in reverse_and_double.astream_events(\"1234\"):\n",
     "    print(event)"
    ]
   },
@@ -1495,7 +1494,7 @@
     "\n",
     "await reverse_and_double.ainvoke(\"1234\")\n",
     "\n",
-    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
+    "async for event in reverse_and_double.astream_events(\"1234\"):\n",
     "    print(event)"
    ]
   },
@@ -1528,7 +1527,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_artifacts.ipynb

@@ -87,13 +87,6 @@
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Failed to batch ingest runs: LangSmithRateLimitError('Rate limit exceeded for https://api.smith.langchain.com/runs/batch. HTTPError(\\'429 Client Error: Too Many Requests for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Monthly unique traces usage limit exceeded\"}\\')')\n"
-     ]
     }
    ],
    "source": [

docs/docs/how_to/tool_calling.ipynb

@@ -200,7 +200,12 @@
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
+    "  overrideParams={{\n",
+    "    fireworks: {\n",
+    "      model: \"accounts/fireworks/models/firefunction-v1\",\n",
+    "      kwargs: \"temperature=0\",\n",
+    "    }\n",
+    "  }}\n",
     "/>\n"
    ]
   },

docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
+    "  overrideParams={{fireworks: {model: \"accounts/fireworks/models/firefunction-v1\", kwargs: \"temperature=0\"}}}\n",
     "/>\n"
    ]
   },

docs/docs/how_to/tool_runtime.ipynb

@@ -46,7 +46,7 @@
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
+    "  overrideParams={{fireworks: {model: \"accounts/fireworks/models/firefunction-v1\", kwargs: \"temperature=0\"}}}\n",
     "/>\n"
    ]
   },

docs/docs/how_to/tool_stream_events.ipynb

@@ -131,13 +131,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "stream = special_summarization_tool.astream_events(\n",
-    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
-    ")\n",
+    "stream = special_summarization_tool.astream_events({\"long_text\": LONG_TEXT})\n",
     "\n",
     "async for event in stream:\n",
     "    if event[\"event\"] == \"on_chat_model_end\":\n",
@@ -156,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,21 +188,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_end', 'data': {'output': AIMessage(content='Bee defies physics; Barry chooses outfit for graduation day.', response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-d23abc80-0dce-4f74-9d7b-fb98ca4f2a9e', usage_metadata={'input_tokens': 182, 'output_tokens': 16, 'total_tokens': 198}), 'input': {'messages': [[HumanMessage(content=\"You are an expert writer. Summarize the following text in 10 words or less:\\n\\n\\nNARRATOR:\\n(Black screen with text; The sound of buzzing bees can be heard)\\nAccording to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.\\nBARRY BENSON:\\n(Barry is picking out a shirt)\\nYellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.\\nJANET BENSON:\\nBarry! Breakfast is ready!\\nBARRY:\\nComing! Hang on a second.\\n\")]]}}, 'run_id': 'd23abc80-0dce-4f74-9d7b-fb98ca4f2a9e', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['f25c41fe-8972-4893-bc40-cecf3922c1fa']}\n"
+      "{'event': 'on_chat_model_end', 'data': {'output': AIMessage(content='Bee defies physics; Barry chooses outfit for graduation day.', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-337ac14e-8da8-4c6d-a69f-1573f93b651e', usage_metadata={'input_tokens': 182, 'output_tokens': 19, 'total_tokens': 201, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}), 'input': {'messages': [[HumanMessage(content=\"You are an expert writer. Summarize the following text in 10 words or less:\\n\\n\\nNARRATOR:\\n(Black screen with text; The sound of buzzing bees can be heard)\\nAccording to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.\\nBARRY BENSON:\\n(Barry is picking out a shirt)\\nYellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.\\nJANET BENSON:\\nBarry! Breakfast is ready!\\nBARRY:\\nComing! Hang on a second.\\n\", additional_kwargs={}, response_metadata={})]]}}, 'run_id': '337ac14e-8da8-4c6d-a69f-1573f93b651e', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['225beaa6-af73-4c91-b2d3-1afbbb88d53e']}\n"
      ]
     }
    ],
    "source": [
-    "stream = special_summarization_tool_with_config.astream_events(\n",
-    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
-    ")\n",
+    "stream = special_summarization_tool_with_config.astream_events({\"long_text\": LONG_TEXT})\n",
     "\n",
     "async for event in stream:\n",
     "    if event[\"event\"] == \"on_chat_model_end\":\n",
@@ -222,33 +218,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42', usage_metadata={'input_tokens': 182, 'output_tokens': 0, 'total_tokens': 182})}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='Bee', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' def', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='ies physics', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=';', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Barry', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' cho', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='oses outfit', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' for', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' graduation', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' day', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='.', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42', usage_metadata={'input_tokens': 0, 'output_tokens': 16, 'total_tokens': 16})}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n"
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', usage_metadata={'input_tokens': 182, 'output_tokens': 2, 'total_tokens': 184, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='Bee', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' defies physics;', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Barry chooses outfit for', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' graduation day.', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', usage_metadata={'input_tokens': 0, 'output_tokens': 17, 'total_tokens': 17, 'input_token_details': {}})}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n"
      ]
     }
    ],
    "source": [
-    "stream = special_summarization_tool_with_config.astream_events(\n",
-    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
-    ")\n",
+    "stream = special_summarization_tool_with_config.astream_events({\"long_text\": LONG_TEXT})\n",
     "\n",
     "async for event in stream:\n",
     "    if event[\"event\"] == \"on_chat_model_stream\":\n",
@@ -290,7 +277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_prompting.ipynb

@@ -91,7 +91,7 @@
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
+    "<ChatModelTabs overrideParams={{openai: {model: \"gpt-4\"}}} />\n",
     "\n",
     "To illustrate the idea, we'll use `phi3` via Ollama, which does **NOT** have native support for tool calling. If you'd like to use `Ollama` as well follow [these instructions](/docs/integrations/chat/ollama/)."
    ]

docs/docs/integrations/chat/abso.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Abso\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatAbso\n",
+    "\n",
+    "This will help you getting started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
+    "\n",
+    "- You can find the full documentation for the Abso router [here] (https://abso.ai)\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/abso) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatAbso](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html) | [langchain-abso](https://python.langchain.com/api_reference/en/latest/abso_api_reference.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-abso?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-abso?style=flat-square&label=%20) |\n",
+    "\n",
+    "## Setup\n",
+    "To access ChatAbso models you'll need to create an OpenAI account, get an API key, and install the `langchain-abso` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to ChatAbso and generate an API key. Once you've done this set the ABSO_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(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain ChatAbso integration lives in the `langchain-abso` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-abso"
+   ]
+  },
+  {
+   "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_abso import ChatAbso\n",
+    "\n",
+    "llm = ChatAbso(fast_model=\"gpt-4o\", slow_model=\"o3-mini\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n"
+   ]
+  },
+  {
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [],
+   "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:\n"
+   ]
+  },
+  {
+   "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 ChatAbso features and configurations head to the API reference: https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.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/anthropic.ipynb

@@ -315,6 +315,59 @@
     "ai_msg.tool_calls"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6e36d25c-f358-49e5-aefa-b99fbd3fec6b",
+   "metadata": {},
+   "source": [
+    "## Extended thinking\n",
+    "\n",
+    "Claude 3.7 Sonnet supports an [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) feature, which will output the step-by-step reasoning process that led to its final answer.\n",
+    "\n",
+    "To use it, specify the `thinking` parameter when initializing `ChatAnthropic`. It can also be passed in as a kwarg during invocation.\n",
+    "\n",
+    "You will need to specify a token budget to use this feature. See usage example below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a34cf93b-8522-43a6-a3f3-8a189ddf54a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "  {\n",
+      "    \"signature\": \"ErUBCkYIARgCIkCx7bIPj35jGPHpoVOB2y5hvPF8MN4lVK75CYGftmVNlI4axz2+bBbSexofWsN1O/prwNv8yPXnIXQmwT6zrJsKEgwJzvks0yVRZtaGBScaDOm9xcpOxbuhku1zViIw9WDgil/KZL8DsqWrhVpC6TzM0RQNCcsHcmgmyxbgG9g8PR0eJGLxCcGoEw8zMQu1Kh1hQ1/03hZ2JCOgigpByR9aNPTwwpl64fQUe6WwIw==\",\n",
+      "    \"thinking\": \"To find the cube root of 50.653, I need to find the value of $x$ such that $x^3 = 50.653$.\\n\\nI can try to estimate this first. \\n$3^3 = 27$\\n$4^3 = 64$\\n\\nSo the cube root of 50.653 will be somewhere between 3 and 4, but closer to 4.\\n\\nLet me try to compute this more precisely. I can use the cube root function:\\n\\ncube root of 50.653 = 50.653^(1/3)\\n\\nLet me calculate this:\\n50.653^(1/3) \\u2248 3.6998\\n\\nLet me verify:\\n3.6998^3 \\u2248 50.6533\\n\\nThat's very close to 50.653, so I'm confident that the cube root of 50.653 is approximately 3.6998.\\n\\nActually, let me compute this more precisely:\\n50.653^(1/3) \\u2248 3.69981\\n\\nLet me verify once more:\\n3.69981^3 \\u2248 50.652998\\n\\nThat's extremely close to 50.653, so I'll say that the cube root of 50.653 is approximately 3.69981.\",\n",
+      "    \"type\": \"thinking\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"text\": \"The cube root of 50.653 is approximately 3.6998.\\n\\nTo verify: 3.6998\\u00b3 = 50.6530, which is very close to our original number.\",\n",
+      "    \"type\": \"text\"\n",
+      "  }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(\n",
+    "    model=\"claude-3-7-sonnet-latest\",\n",
+    "    max_tokens=5000,\n",
+    "    thinking={\"type\": \"enabled\", \"budget_tokens\": 2000},\n",
+    ")\n",
+    "\n",
+    "response = llm.invoke(\"What is the cube root of 50.653?\")\n",
+    "print(json.dumps(response.content, indent=2))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "301d372f-4dec-43e6-b58c-eee25633e1a6",

docs/docs/integrations/chat/azure_ai.ipynb

@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AzureAIChatCompletionsModel\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# AzureAIChatCompletionsModel\n",
+    "\n",
+    "This will help you getting started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n",
+    "\n",
+    "The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1 to name a few. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://v03.api.js.langchain.com/classes/_langchain_openai.AzureChatOpenAI.html) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [AzureAIChatCompletionsModel](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html) | [langchain-azure-ai](https://python.langchain.com/api_reference/langchain_azure_ai/index.html) | ❌ | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-azure-ai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-azure-ai?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 AzureAIChatCompletionsModel models you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:"
+   ]
+  },
+  {
+   "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(\"AZURE_INFERENCE_CREDENTIAL\"):\n",
+    "    os.environ[\"AZURE_INFERENCE_CREDENTIAL\"] = getpass.getpass(\n",
+    "        \"Enter your AzureAIChatCompletionsModel API key: \"\n",
+    "    )\n",
+    "\n",
+    "if not os.getenv(\"AZURE_INFERENCE_ENDPOINT\"):\n",
+    "    os.environ[\"AZURE_INFERENCE_ENDPOINT\"] = getpass.getpass(\n",
+    "        \"Enter your model endpoint: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "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[\"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 AzureAIChatCompletionsModel integration lives in the `langchain-azure-ai` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-azure-ai"
+   ]
+  },
+  {
+   "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": 2,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel\n",
+    "\n",
+    "llm = AzureAIChatCompletionsModel(\n",
+    "    model_name=\"gpt-4\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={}, response_metadata={'model': 'gpt-4o-2024-05-13', 'token_usage': {'input_tokens': 31, 'output_tokens': 4, 'total_tokens': 35}, 'finish_reason': 'stop'}, id='run-c082dffd-b1de-4b3f-943f-863836663ddb-0', usage_metadata={'input_tokens': 31, 'output_tokens': 4, 'total_tokens': 35})"
+      ]
+     },
+     "execution_count": 3,
+     "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": 4,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore programmer.\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": 5,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'model': 'gpt-4o-2024-05-13', 'token_usage': {'input_tokens': 26, 'output_tokens': 5, 'total_tokens': 31}, 'finish_reason': 'stop'}, id='run-01ba6587-6ff4-4554-8039-13204a7d95db-0', usage_metadata={'input_tokens': 26, 'output_tokens': 5, 'total_tokens': 31})"
+      ]
+     },
+     "execution_count": 5,
+     "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 AzureAIChatCompletionsModel features and configurations head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-3-9",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

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

docs/docs/integrations/chat/goodfire.ipynb

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

docs/docs/integrations/chat/ibm_watsonx.ipynb

@@ -210,7 +210,7 @@
    "id": "96ed13d4",
    "metadata": {},
    "source": [
-    "Instead of `model_id`, you can also pass the `deployment_id` of the previously tuned model. The entire model tuning workflow is described in [Working with TuneExperiment and PromptTuner](https://ibm.github.io/watsonx-ai-python-sdk/pt_working_with_class_and_prompt_tuner.html)."
+    "Instead of `model_id`, you can also pass the `deployment_id` of the previously [deployed model with reference to a Prompt Template](https://cloud.ibm.com/apidocs/watsonx-ai#deployments-text-chat)."
    ]
   },
   {
@@ -228,6 +228,31 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3d29767c",
+   "metadata": {},
+   "source": [
+    "For certain requirements, there is an option to pass the IBM's [`APIClient`](https://ibm.github.io/watsonx-ai-python-sdk/base.html#apiclient) object into the `ChatWatsonx` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ae9531e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ibm_watsonx_ai import APIClient\n",
+    "\n",
+    "api_client = APIClient(...)\n",
+    "\n",
+    "chat = ChatWatsonx(\n",
+    "    model_id=\"ibm/granite-34b-code-instruct\",\n",
+    "    watsonx_client=api_client,\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f571001d",
@@ -448,9 +473,7 @@
    "source": [
     "## Tool calling\n",
     "\n",
-    "### ChatWatsonx.bind_tools()\n",
-    "\n",
-    "Please note that `ChatWatsonx.bind_tools` is on beta state, so we recommend using `mistralai/mistral-large` model."
+    "### ChatWatsonx.bind_tools()"
    ]
   },
   {
@@ -563,7 +586,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain_ibm",
    "language": "python",
    "name": "python3"
   },

docs/docs/integrations/chat/index.mdx

@@ -17,7 +17,7 @@ If you'd like to contribute an integration, see [Contributing integrations](/doc
 
 import ChatModelTabs from "@theme/ChatModelTabs";
 
-<ChatModelTabs openaiParams={`model="gpt-4o-mini"`} />
+<ChatModelTabs overrideParams={{openai: {model: "gpt-4o-mini"}}} />
 
 ```python
 model.invoke("Hello, world!")

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/sambanova/chat_models/langchain_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://docs.sambanova.ai/cloud/docs/get-started/overview).\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",
@@ -28,7 +28,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\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",
+    "| [ChatSambaNovaCloud](https://docs.sambanova.ai/cloud/docs/get-started/overview) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) | ❌ | ❌ | ❌ | ![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",
@@ -545,7 +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/sambanova/chat_models/langchain_sambanova.ChatSambaNovaCloud.html"
+    "For detailed documentation of all SambaNovaCloud features and configurations head to the API reference: https://docs.sambanova.ai/cloud/docs/get-started/overview"
    ]
   }
  ],

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/sambanova/chat_models/langchain_sambanova.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://docs.sambanova.ai/sambastudio/latest/index.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",
@@ -28,7 +28,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\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",
+    "| [ChatSambaStudio](https://docs.sambanova.ai/sambastudio/latest/index.html) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) | ❌ | ❌ | ❌ | ![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",
@@ -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/sambanova/chat_models/langchain_sambanova.sambanova.chat_models.ChatSambaStudio.html"
+    "For detailed documentation of all SambaStudio features and configurations head to the API reference: https://docs.sambanova.ai/sambastudio/latest/api-ref-landing.html"
    ]
   }
  ],

docs/docs/integrations/chat/writer.ipynb

@@ -1,362 +1,231 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "85e07aae70a15572",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Writer\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "cb4dd00a-8893-4a45-96f7-9a9fc341cd61",
+   "id": "e815de6298bf07ca",
    "metadata": {},
    "source": [
-    "# ChatWriter\n",
+    "# Chat Writer\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Writer [chat models](/docs/concepts/chat_models).\n",
+    "This notebook provides a quick overview for getting started with Writer [chat](/docs/concepts/chat_models/).\n",
+    "\n",
+    "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home).\n",
     "\n",
-    "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home/models).\n",
     "\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "617a6e98205ab7c8",
-   "metadata": {},
-   "source": [
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |:----------:| :---: | :---: |\n",
-    "| ChatWriter | langchain-community | ❌ | ❌ |     ❌      | ❌ | ❌ |\n",
-    "\n",
+    "| Class                                                                                                                    | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-------------------------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [ChatWriter](https://github.com/writer/langchain-writer/blob/main/langchain_writer/chat_models.py#L308) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | 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 |\n",
     "| :---: |:-----------------:| :---: | :---: |  :---: | :---: | :---: | :---: |:--------------------------------:|:--------:|\n",
-    "| ✅ |         ❌         | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ |                ✅                 |    ❌     |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Writer models you'll need to create a Writer account, get an API key, and install the `writer-sdk` and `langchain-community` packages.\n",
-    "\n",
+    "| ✅ |         ❌         | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ |                ✅                 |    ❌     |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fd9903e685808d9",
+   "metadata": {},
+   "source": [
     "### Credentials\n",
     "\n",
-    "Head to [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to sign up to OpenAI and generate an API key. Once you've done this set the WRITER_API_KEY environment variable:"
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) and follow this [Quickstart](https://dev.writer.com/api-guides/quickstart) to obtain an API key. Then, set the WRITER_API_KEY environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "e817fe2e-4f1d-4533-b19e-2400b1cf6ce8",
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:26.800627Z",
-     "start_time": "2024-11-14T09:27:59.652281Z"
+    "jupyter": {
+     "is_executing": true
     }
    },
-   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if not os.environ.get(\"WRITER_API_KEY\"):\n",
-    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key:\")"
-   ]
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "c59722a9-6dbb-45f7-ae59-5be50ca5733d",
+   "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",
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
    "metadata": {},
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain Writer integration lives in the `langchain-community` package:"
+    "`ChatWriter` is available from the `langchain-writer` package. Install it with:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "2113471c-75d7-45df-b784-d78da4ef7aba",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:32.415354Z",
-     "start_time": "2024-11-14T09:46:26.826112Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
    "source": [
-    "%pip install -qU langchain-community writer-sdk"
-   ]
+    "%pip install -qU langchain-writer"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "1098bc9d-ce83-462b-8c19-f85bf3a159dc",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
    "metadata": {},
    "source": [
-    "## Instantiation\n",
+    "### Instantiation\n",
     "\n",
-    "Now we can instantiate our model object and generate chat completions:"
+    "Now we can instantiate our model object in order to generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "522686de",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:33.504711Z",
-     "start_time": "2024-11-14T09:46:32.574505Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
    "source": [
-    "from langchain_community.chat_models.writer import ChatWriter\n",
+    "from langchain_writer import ChatWriter\n",
     "\n",
     "llm = ChatWriter(\n",
     "    model=\"palmyra-x-004\",\n",
-    "    temperature=0.7,\n",
-    "    max_tokens=1000,\n",
-    "    # other params...\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "6511982a-734a-4193-a47d-254f8dcaff5e",
+   "id": "2b4f3e15",
    "metadata": {},
    "source": [
-    "## Invocation"
+    "## Usage\n",
+    "\n",
+    "To use the model, you pass in a list of messages and call the `invoke` method:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
+   "id": "62e0dbc3",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.856174Z",
-     "start_time": "2024-11-14T09:46:33.520062Z"
-    },
     "tags": []
    },
-   "outputs": [],
    "source": [
     "messages = [\n",
     "    (\n",
     "        \"system\",\n",
-    "        \"You are a helpful assistant that writes poems about the Python programming language.\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
     "    ),\n",
-    "    (\"human\", \"Write a poem about Python.\"),\n",
+    "    (\"human\", \"I love programming.\"),\n",
     "]\n",
-    "ai_msg = llm.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2cd224b8-4499-41fb-a604-d53a7ff17b2e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.866651Z",
-     "start_time": "2024-11-14T09:46:38.863817Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In realms of code, where logic weaves and flows,\n",
-      "A language rises, Python by its name,\n",
-      "With syntax clear, where elegance it shows,\n",
-      "A serpent, wise, that time and space can tame.\n",
-      "\n",
-      "Born from the mind of Guido, pure and bright,\n",
-      "Its beauty lies in simplicity and grace,\n",
-      "A tool of power, yet gentle in its might,\n",
-      "In every programmer's heart, a cherished place.\n",
-      "\n",
-      "It dances through the data, vast and deep,\n",
-      "With libraries that span the digital realm,\n",
-      "From machine learning's secrets to keep,\n",
-      "To web development, it wields the helm.\n",
-      "\n",
-      "In the hands of the novice and the sage,\n",
-      "Python spins the threads of digital dreams,\n",
-      "A language that can turn the age,\n",
-      "With a gentle learning curve, its appeal gleams.\n",
-      "\n",
-      "It's more than code, a community it builds,\n",
-      "Where knowledge freely flows, and all are heard,\n",
-      "In Python's world, the future unfolds,\n",
-      "A language of the people, for the world.\n",
-      "\n",
-      "So here's to Python, in its gentle might,\n",
-      "A master of the modern coding art,\n",
-      "May it continue to light our path each night,\n",
-      "In the vast, evolving world of code, its heart.\n"
-     ]
-    }
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "35b3a5b3dabef65",
+   "id": "5cf7293d",
    "metadata": {},
    "source": [
-    "## Streaming"
+    "Then, you can access the content of the message:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "2725770182bf96dc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.914883Z",
-     "start_time": "2024-11-14T09:46:38.912564Z"
-    }
-   },
-   "outputs": [],
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
    "source": [
-    "ai_stream = llm.stream(messages)"
+    "print(ai_msg.content)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4391289ce0a80e19",
+   "metadata": {},
+   "source": [
+    "## Streaming\n",
+    "\n",
+    "You can also stream the response. First, create a stream:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "a48410d9488162e3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:43.226449Z",
-     "start_time": "2024-11-14T09:46:38.955512Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In realms of code where logic weaves,\n",
-      "A language rises, Python, it breezes,\n",
-      "With syntax clear and simple to read,\n",
-      "Through its elegance, our spirits are fed.\n",
-      "\n",
-      "Like rivers flowing, smooth and serene,\n",
-      "Its structure harmonious, a coder's dream,\n",
-      "Indentations guide the flow of control,\n",
-      "In Python's world, confusion takes no toll.\n",
-      "\n",
-      "A vast library, a treasure trove so bright,\n",
-      "For web and data, it offers its might,\n",
-      "With modules and packages, a rich array,\n",
-      "Python empowers us to code in play.\n",
-      "\n",
-      "From AI to scripts, in flexibility it thrives,\n",
-      "A language of the future, as many now derive,\n",
-      "Its community, a beacon of support and cheer,\n",
-      "With Python, the possibilities are vast, far and near.\n",
-      "\n",
-      "So here's to Python, in its gentle grace,\n",
-      "A tool that enhances, a language that embraces,\n",
-      "The art of coding, with a fluent, flowing pen,\n",
-      "In the Python world, we code, and we begin."
-     ]
-    }
+   "id": "4a0f2112b3a4c79e",
+   "metadata": {},
+   "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. Sing a song about it\"),\n",
+    "]\n",
+    "ai_stream = llm.stream(messages)\n",
+    "ai_stream"
    ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23cc74b6",
+   "metadata": {},
+   "source": [
+    "Then, iterate over the stream to get the chunks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8c4b7b9b9308c757",
+   "metadata": {},
    "source": [
     "for chunk in ai_stream:\n",
     "    print(chunk.content, end=\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
-   "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": 8,
-   "id": "fbb043e6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:50.721645Z",
-     "start_time": "2024-11-14T09:46:43.234590Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessageChunk(content='In the realm of code, where logic weaves and flows,  \\nA language rises, like a phoenix from the code\\'s throes.  \\nJava, the name, a cup of coffee\\'s steam,  \\nBrewed in the minds, where digital dreams gleam.\\n\\nWith syntax clear, like morning\\'s misty hue,  \\nIn classes and objects, it spins a tale so true.  \\nA platform agnostic, with a byte to spare,  \\nAcross the devices, it journeys everywhere.\\n\\nInheritance and polymorphism, its power\\'s core,  \\nLike ancient runes, in every line they bore.  \\nEncapsulation, a shield, with data it does hide,  \\nIn the vast jungle of code, it stands as a guide.\\n\\nFrom applets small, to vast, server-side apps,  \\nIts threads run swift, through the computing traps.  \\nA language of the people, by the people, for the people’s use,  \\nBuilt on the principle, \"write once, run anywhere, with no excuse.\"\\n\\nIn the heart of Android, it beats, a steady drum,  \\nCrafting experiences, in every smartphone\\'s hum.  \\nIn the cloud, in the enterprise, its presence is vast,  \\nA cornerstone of computing, built to last.\\n\\nOh Java, thy elegance, thy robust design,  \\nA language that stands, in any computing line.  \\nWith every update, with every new release,  \\nThy community grows, with a vibrant, diverse peace.\\n\\nSo here\\'s to Java, the versatile, the grand,  \\nA language that shapes the digital land.  \\nMay it continue to evolve, to grow, to inspire,  \\nIn the endless quest of turning thoughts into digital fire.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 345, 'prompt_tokens': 33, 'total_tokens': 378, 'completion_tokens_details': None, 'prompt_token_details': None}, 'model_name': 'palmyra-x-004', 'system_fingerprint': 'v1', 'finish_reason': 'stop'}, id='run-a5b4be59-0eb0-41bd-80f7-72477861b0bd-0')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that writes poems about the {input_language} programming language.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"Java\",\n",
-    "        \"input\": \"Write a poem about Java.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "0b1b52a5-b58d-40c9-bcdd-88eb8fb351e2",
+   "id": "e632bf7d0873f933",
    "metadata": {},
    "source": [
     "## Tool calling\n",
     "\n",
-    "Writer supports [tool calling](https://dev.writer.com/api-guides/tool-calling), which lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool.\n",
+    "Writer models like Palmyra X 004 support [tool calling](https://dev.writer.com/api-guides/tool-calling), which lets you describe tools and their arguments. The model will return a JSON object with a tool to invoke and the inputs to that tool.\n",
     "\n",
-    "### ChatWriter.bind_tools()\n",
+    "### Binding tools\n",
     "\n",
-    "With `ChatWriter.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to tool schemas, which looks like:\n",
+    "With `ChatWriter.bind_tools`, you can easily pass in Pydantic classes, dictionary schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to tool schemas, which look like this:\n",
     "```\n",
     "{\n",
     "    \"name\": \"...\",\n",
@@ -364,20 +233,15 @@
     "    \"parameters\": {...}  # JSONSchema\n",
     "}\n",
     "```\n",
-    "and passed in every model invocation."
+    "These are passed in every model invocation.\n",
+    "\n",
+    "For example, to use a tool that gets the weather in a given location, you can define a Pydantic class and pass it to `ChatWriter.bind_tools`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "b7ea7690-ec7a-4337-b392-e87d1f39a6ec",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:50.891937Z",
-     "start_time": "2024-11-14T09:46:50.733463Z"
-    }
-   },
-   "outputs": [],
+   "id": "47e2f0faceca533",
+   "metadata": {},
    "source": [
     "from pydantic import BaseModel, Field\n",
     "\n",
@@ -388,86 +252,175 @@
     "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
     "\n",
     "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1d1ab955-6a68-42f8-bb5d-86eb1111478a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:51.725422Z",
-     "start_time": "2024-11-14T09:46:50.904699Z"
-    }
-   },
+    "llm.bind_tools([GetWeather])"
+   ],
    "outputs": [],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"what is the weather like in New York City\",\n",
-    ")"
-   ]
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "768d1ae4-4b1a-48eb-a329-c8d5051067a3",
+   "id": "68e22d3b",
    "metadata": {},
    "source": [
-    "### AIMessage.tool_calls\n",
-    "Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized ToolCall format that is model-provider agnostic."
+    "Then, you can invoke the model with the tool:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "166cb7ce-831d-4a7c-9721-abc107f11084",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:51.744202Z",
-     "start_time": "2024-11-14T09:46:51.738431Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'GetWeather',\n",
-       "  'args': {'location': 'New York City, NY'},\n",
-       "  'id': 'chatcmpl-tool-fe70912c800d40fc8700d604d4823001',\n",
-       "  'type': 'tool_call'}]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "id": "765527dd533ec967",
+   "metadata": {},
+   "source": [
+    "ai_msg = llm.invoke(\n",
+    "    \"what is the weather like in New York City\",\n",
+    ")\n",
+    "ai_msg"
    ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57544bdf",
+   "metadata": {},
+   "source": [
+    "Finally, you can access the tool calls and proceed to execute your functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f361c4769e772fe",
+   "metadata": {},
    "source": [
     "print(ai_msg.tool_calls)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "e082c9ac-c7c7-4aff-a8ec-8e220262a59c",
+   "id": "3baf53021834d2ff",
    "metadata": {},
    "source": [
-    "For more on binding tools and tool call outputs, head to the [tool calling](/docs/how_to/function_calling) docs."
+    "### A note on tool binding\n",
+    "\n",
+    "The `ChatWriter.bind_tools()` method does not create new instance with bound tools, but stores the received `tools` and `tool_choice` in the initial class instance attributes to pass them as parameters during the Palmyra LLM call while using `ChatWriter` invocation. This approach allows the support of different tool types, e.g. `function` and `graph`. `Graph` is one of the remotely called Writer Palmyra tools. For further information visit our [docs](https://dev.writer.com/api-guides/knowledge-graph#knowledge-graph). \n",
+    "\n",
+    "For more information about tool usage in LangChain, visit the [LangChain tool calling documentation](https://python.langchain.com/docs/concepts/tool_calling/)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a796d728-971b-408b-88d5-440015bbb941",
+   "id": "a4674b1b82ce9d1f",
+   "metadata": {},
+   "source": [
+    "## Batching\n",
+    "\n",
+    "You can also batch requests and set the `max_concurrency`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c8a217f6190747fe",
+   "metadata": {},
+   "source": [
+    "ai_batch = llm.batch(\n",
+    "    [\n",
+    "        \"How to cook pancakes?\",\n",
+    "        \"How to compose poem?\",\n",
+    "        \"How to run faster?\",\n",
+    "    ],\n",
+    "    config={\"max_concurrency\": 3},\n",
+    ")\n",
+    "ai_batch"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eb81e1d",
+   "metadata": {},
+   "source": [
+    "Then, iterate over the batch to get the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b6a228d448f3df23",
+   "metadata": {},
+   "source": [
+    "for batch in ai_batch:\n",
+    "    print(batch.content)\n",
+    "    print(\"-\" * 100)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a9ab241fe09a71",
+   "metadata": {},
+   "source": [
+    "## Asynchronous usage\n",
+    "\n",
+    "All features above (invocation, streaming, batching, tools calling) also support asynchronous usage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Prompt templates\n",
+    "\n",
+    "[Prompt templates](https://python.langchain.com/docs/concepts/prompt_templates/) help to translate user input and parameters into instructions for a language model. You can use `ChatWriter` with a prompt templates like so:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "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",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
    "metadata": {},
    "source": [
     "## API reference\n",
+    "For detailed documentation of all ChatWriter features and configurations head to the [API reference](https://python.langchain.com/api_reference/writer/chat_models/langchain_writer.chat_models.ChatWriter.html#langchain_writer.chat_models.ChatWriter).\n",
     "\n",
-    "For detailed documentation of all Writer features, head to our [API reference](https://dev.writer.com/api-guides/api-reference/completion-api/chat-completion)."
+    "## Additional resources\n",
+    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home)."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -481,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/apify_dataset.ipynb

@@ -2,7 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "xwiDq5fOuoRn"
+   },
    "source": [
     "# Apify Dataset\n",
     "\n",
@@ -20,33 +22,63 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "id": "qRW2-mokuoRp",
     "tags": []
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  apify-client"
+    "%pip install --upgrade --quiet langchain langchain-apify langchain-openai"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "8jRVq16LuoRq"
+   },
    "source": [
     "First, import `ApifyDatasetLoader` into your source code:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "id": "umXQHqIJuoRq"
+   },
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import ApifyDatasetLoader\n",
+    "from langchain_apify import ApifyDatasetLoader\n",
     "from langchain_core.documents import Document"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "NjGwKy59vz1X"
+   },
+   "source": [
+    "Find your [Apify API token](https://console.apify.com/account/integrations) and [OpenAI API key](https://platform.openai.com/account/api-keys) and initialize these into environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "AvzNtyCxwDdr"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"APIFY_API_TOKEN\"] = \"your-apify-api-token\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d1O-KL48uoRr"
+   },
    "source": [
     "Then provide a function that maps Apify dataset record fields to LangChain `Document` format.\n",
     "\n",
@@ -64,8 +96,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "id": "m1SpA7XZuoRr"
+   },
    "outputs": [],
    "source": [
     "loader = ApifyDatasetLoader(\n",
@@ -78,8 +112,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "id": "0hWX7ABsuoRs"
+   },
    "outputs": [],
    "source": [
     "data = loader.load()"
@@ -87,7 +123,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "EJCVFVKNuoRs"
+   },
    "source": [
     "## An example with question answering\n",
     "\n",
@@ -96,21 +134,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "id": "sNisJKzZuoRt"
+   },
    "outputs": [],
    "source": [
     "from langchain.indexes import VectorstoreIndexCreator\n",
-    "from langchain_community.utilities import ApifyWrapper\n",
+    "from langchain_apify import ApifyWrapper\n",
     "from langchain_core.documents import Document\n",
-    "from langchain_openai import OpenAI\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "from langchain_openai import ChatOpenAI\n",
     "from langchain_openai.embeddings import OpenAIEmbeddings"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "id": "qcfmnbdDuoRu"
+   },
    "outputs": [],
    "source": [
     "loader = ApifyDatasetLoader(\n",
@@ -123,27 +166,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "id": "8b0xzKJxuoRv"
+   },
    "outputs": [],
    "source": [
-    "index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])"
+    "index = VectorstoreIndexCreator(\n",
+    "    vectorstore_cls=InMemoryVectorStore, embedding=OpenAIEmbeddings()\n",
+    ").from_loaders([loader])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "id": "7zPXGsVFwUGA"
+   },
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "ecWrdM4guoRv"
+   },
    "outputs": [],
    "source": [
     "query = \"What is Apify?\"\n",
-    "result = index.query_with_sources(query, llm=OpenAI())"
+    "result = index.query_with_sources(query, llm=llm)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "id": "QH8r44e9uoRv",
+    "outputId": "361fe050-f75d-4d5a-c327-5e7bd190fba5"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -162,6 +225,9 @@
   }
  ],
  "metadata": {
+  "colab": {
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -181,5 +247,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat_minor": 0
+}

docs/docs/integrations/document_loaders/docling.ipynb

@@ -443,6 +443,7 @@
     "llm = HuggingFaceEndpoint(\n",
     "    repo_id=GEN_MODEL_ID,\n",
     "    huggingfacehub_api_token=HF_TOKEN,\n",
+    "    task=\"text-generation\",\n",
     ")"
    ]
   },

docs/docs/integrations/document_loaders/parsers/writer_pdf_parser.ipynb

@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "db23d51760310705",
+   "metadata": {},
+   "source": [
+    "# Writer PDF Parser\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with the Writer `PDFParser` [document loader](/docs/concepts/document_loaders/).\n",
+    "\n",
+    "Writer's [PDF Parser](https://dev.writer.com/api-guides/api-reference/tool-api/pdf-parser#parse-pdf) converts PDF documents into other formats like text or Markdown. This is particularly useful when you need to extract and process text content from PDF files for further analysis or integration into your workflow. In `langchain-writer`, we provide usage of Writer's PDF Parser as a LangChain document parser.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "| Class                                                                                                                              | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [PDFParser](https://github.com/writer/langchain-writer/blob/main/langchain_writer/pdf_parser.py#L55) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f08d23df5dc127",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The `PDFParser` is available in the `langchain-writer` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a8d653f15b7ee32d",
+   "metadata": {},
+   "source": [
+    "%pip install --quiet -U langchain-writer"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b9709c26797edf",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to generate an API key (you can follow this [Quickstart](https://dev.writer.com/api-guides/quickstart)). Then, set the WRITER_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2983e19c9d555e58",
+   "metadata": {},
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92a22c77f03d43dc",
+   "metadata": {},
+   "source": [
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability. If you wish to do so, you can set the `LANGCHAIN_TRACING_V2` and `LANGCHAIN_API_KEY` environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "98d8422ecee77403",
+   "metadata": {},
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ab78950a3da8ba",
+   "metadata": {},
+   "source": [
+    "### Instantiation\n",
+    "\n",
+    "Next, instantiate an instance of the Writer PDF Parser with the desired output format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "787b3ba8af32533f",
+   "metadata": {},
+   "source": [
+    "from langchain_writer.pdf_parser import PDFParser\n",
+    "\n",
+    "parser = PDFParser(format=\"markdown\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d91c6f752fd31cee",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "There are two ways to use the PDF Parser, either synchronously or asynchronously. In either case, the PDF Parser will return a list of `Document` objects, each containing the parsed content of a page from the PDF file.\n",
+    "\n",
+    "### Synchronous usage\n",
+    "\n",
+    "To invoke the PDF Parser synchronously, pass a `Blob` object to the `parse` method referencing the PDF file you want to parse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d1a24b81a8a96f09",
+   "metadata": {},
+   "source": [
+    "from langchain_core.documents.base import Blob\n",
+    "\n",
+    "file = Blob.from_path(\"../example_data/layout-parser-paper.pdf\")\n",
+    "\n",
+    "parsed_pages = parser.parse(blob=file)\n",
+    "parsed_pages"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f89c048c7d23807a",
+   "metadata": {},
+   "source": [
+    "### Asynchronous usage\n",
+    "\n",
+    "To invoke the PDF Parser asynchronously, pass a `Blob` object to the `aparse` method referencing the PDF file you want to parse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e2f7fd52b7188c6c",
+   "metadata": {},
+   "source": [
+    "parsed_pages_async = await parser.aparse(blob=file)\n",
+    "parsed_pages_async"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab25a3bed8437a05",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `PDFParser` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/writer/pdf_parser/langchain_writer.pdf_parser.PDFParser.html#langchain_writer.pdf_parser.PDFParser).\n",
+    "\n",
+    "## Additional resources\n",
+    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/document_loaders/pymupdf4llm.ipynb

@@ -0,0 +1,721 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: PyMuPDF4LLM\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyMuPDF4LLMLoader\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with PyMuPDF4LLM [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all PyMuPDF4LLMLoader features and configurations head to the [GitHub repository](https://github.com/lakinduboteju/langchain-pymupdf4llm).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support |\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PyMuPDF4LLMLoader](https://github.com/lakinduboteju/langchain-pymupdf4llm) | [langchain_pymupdf4llm](https://pypi.org/project/langchain-pymupdf4llm) | ✅ | ❌ | ❌ |\n",
+    "\n",
+    "### Loader features\n",
+    "\n",
+    "| Source | Document Lazy Loading | Native Async Support | Extract Images | Extract Tables |\n",
+    "| :---: | :---: | :---: | :---: | :---: |\n",
+    "| PyMuPDF4LLMLoader | ✅ | ❌ | ✅ | ✅ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access PyMuPDF4LLM document loader you'll need to install the `langchain-pymupdf4llm` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are required to use PyMuPDF4LLMLoader."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to get automated best in-class 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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community** and **langchain-pymupdf4llm**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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_community langchain-pymupdf4llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_pymupdf4llm import PyMuPDF4LLMLoader\n",
+    "\n",
+    "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
+    "loader = PyMuPDF4LLMLoader(file_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'trapped': '', 'modDate': 'D:20210622012710Z', 'creationDate': 'D:20210622012710Z', 'page': 0}, page_content='```\\nLayoutParser: A Unified Toolkit for Deep\\n\\n## Learning Based Document Image Analysis\\n\\n```\\n\\nZejiang Shen[1] (<28>), Ruochen Zhang[2], Melissa Dell[3], Benjamin Charles Germain\\nLee[4], Jacob Carlson[3], and Weining Li[5]\\n\\n1 Allen Institute for AI\\n```\\n              shannons@allenai.org\\n\\n```\\n2 Brown University\\n```\\n             ruochen zhang@brown.edu\\n\\n```\\n3 Harvard University\\n_{melissadell,jacob carlson}@fas.harvard.edu_\\n4 University of Washington\\n```\\n              bcgl@cs.washington.edu\\n\\n```\\n5 University of Waterloo\\n```\\n              w422li@uwaterloo.ca\\n\\n```\\n\\n**Abstract. 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 important 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 applications. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout detection, 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 digitization pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\n[The library is publicly available at https://layout-parser.github.io.](https://layout-parser.github.io)\\n\\n**Keywords: Document Image Analysis · Deep Learning · Layout Analysis**\\n\\n    - Character Recognition · Open Source library · Toolkit.\\n\\n### 1 Introduction\\n\\n\\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,\\n\\n')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pages = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        pages = []\n",
+    "len(pages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Markdown, display\n",
+    "\n",
+    "part = pages[0].page_content[778:1189]\n",
+    "print(part)\n",
+    "# Markdown rendering\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint.pp(pages[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata attribute contains at least the following keys:\n",
+    "- source\n",
+    "- page (if in mode *page*)\n",
+    "- total_page\n",
+    "- creationdate\n",
+    "- creator\n",
+    "- producer\n",
+    "\n",
+    "Additional metadata are specific to each parser.\n",
+    "These pieces of information can be helpful (to categorize your PDFs for example)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Splitting mode & custom pages delimiter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When loading the PDF file you can split it in two different ways:\n",
+    "- By page\n",
+    "- As a single text flow\n",
+    "\n",
+    "By default PyMuPDF4LLMLoader will split the PDF by page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the PDF by page. Each page is extracted as a langchain Document object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16\n",
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(len(docs))\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this mode the pdf is split by pages and the resulting Documents metadata contains the `page` (page number). But in some cases we could want to process the pdf as a single text flow (so we don't cut some paragraphs in half). In this case you can use the *single* mode :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the whole PDF as a single langchain Document object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"single\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(len(docs))\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Logically, in this mode, the `page` (page_number) metadata disappears. Here's how to clearly identify where pages end in the text flow :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add a custom *pages_delimiter* to identify where are ends of pages in *single* mode:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"single\",\n",
+    "    pages_delimiter=\"\\n-------THIS IS A CUSTOM END OF PAGE-------\\n\\n\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[0].page_content[10663:11317]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default `pages_delimiter` is \\n-----\\n\\n.\n",
+    "But this could simply be \\n, or \\f to clearly indicate a page change, or \\<!-- PAGE BREAK --> for seamless injection in a Markdown viewer without a visual effect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract images from the PDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can extract images from your PDFs (in text form) with a choice of three different solutions:\n",
+    "- rapidOCR (lightweight Optical Character Recognition tool)\n",
+    "- Tesseract (OCR tool with high precision)\n",
+    "- Multimodal language model\n",
+    "\n",
+    "The result is inserted at the end of text of the page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with rapidOCR:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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 rapidocr-onnxruntime pillow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import RapidOCRBlobParser\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=RapidOCRBlobParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[5].page_content[1863:]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Be careful, RapidOCR is designed to work with Chinese and English, not other languages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with Tesseract:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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 pytesseract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import TesseractBlobParser\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=TesseractBlobParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(docs[5].page_content[1863:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with multimodal model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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_openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "\n",
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API key =\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import LLMImageBlobParser\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=LLMImageBlobParser(\n",
+    "        model=ChatOpenAI(model=\"gpt-4o-mini\", max_tokens=1024)\n",
+    "    ),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(docs[5].page_content[1863:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract tables from the PDF\n",
+    "\n",
+    "With PyMUPDF4LLM you can extract tables from your PDFs in *markdown* format :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    # \"lines_strict\" is the default strategy and\n",
+    "    # is the most accurate for tables with column and row lines,\n",
+    "    # but may not work well with all documents.\n",
+    "    # \"lines\" is a less strict strategy that may work better with\n",
+    "    # some documents.\n",
+    "    # \"text\" is the least strict strategy and may work better\n",
+    "    # with documents that do not have tables with lines.\n",
+    "    table_strategy=\"lines\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[4].page_content[3210:]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Files\n",
+    "\n",
+    "Many document loaders involve parsing files. The difference between such loaders usually stems from how the file is parsed, rather than how the file is loaded. For example, you can use `open` to read the binary content of either a PDF or a markdown file, but you need different parsing logic to convert that binary data into text.\n",
+    "\n",
+    "As a result, it can be helpful to decouple the parsing logic from the loading logic, which makes it easier to re-use a given parser regardless of how the data was loaded.\n",
+    "You can use this strategy to analyze different files, with the same parsing parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import FileSystemBlobLoader\n",
+    "from langchain_community.document_loaders.generic import GenericLoader\n",
+    "from langchain_pymupdf4llm import PyMuPDF4LLMParser\n",
+    "\n",
+    "loader = GenericLoader(\n",
+    "    blob_loader=FileSystemBlobLoader(\n",
+    "        path=\"./example_data/\",\n",
+    "        glob=\"*.pdf\",\n",
+    "    ),\n",
+    "    blob_parser=PyMuPDF4LLMParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[0].page_content[:562]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PyMuPDF4LLMLoader features and configurations head to the GitHub repository: https://github.com/lakinduboteju/langchain-pymupdf4llm"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.21"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}