ci: use partial runs w/ codspeed (#32920)

Taking advantage of [partial
runs](https://codspeed.io/docs/features/partial-runs)!

This should save us minutes on every CI job, we only run codspeed for
libs w/ changes and this doesn't affect benchmarking drops

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,6 +1,7 @@
 name: "\U0001F41B Bug Report"
 description: Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the LangChain forum.
 labels: ["bug"]
+type: bug
 body:
   - type: markdown
     attributes:
@@ -13,9 +14,7 @@ body:
         if there's another way to solve your problem:
 
         * [LangChain Forum](https://forum.langchain.com/),
-        * [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
-        * [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        * [LangChain how-to guides](https://python.langchain.com/docs/how_to/),
+        * [LangChain documentation with the integrated search](https://docs.langchain.com/oss/python/langchain/overview),
         * [API Reference](https://python.langchain.com/api_reference/),
         * [LangChain ChatBot](https://chat.langchain.com/)
         * [GitHub search](https://github.com/langchain-ai/langchain),
@@ -25,7 +24,7 @@ body:
       label: Checked other resources
       description: Please confirm and check all the following options.
       options:
-        - label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
+        - label: This is a bug, not a usage question.
           required: true
         - label: I added a clear and descriptive title that summarizes this issue.
           required: true
@@ -35,6 +34,8 @@ body:
           required: true
         - label: The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).
           required: true
+        - label: This is not related to the langchain-community package.
+          required: true
         - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
           required: true
         - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
@@ -118,3 +119,7 @@ body:
         python -m langchain_core.sys_info
     validations:
       required: true
+
+
+
+

.github/ISSUE_TEMPLATE/config.yml

@@ -1,6 +1,9 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: LangChain Forum
-    url: https://forum.langchain.com/
-    about: General community discussions, support, and feature requests
+  - name: 📚 Documentation
+    url: https://github.com/langchain-ai/docs/issues/new?template=langchain.yml
+    about: Report an issue related to the LangChain documentation
+  - name: 💬 LangChain Forum
+    url:  https://forum.langchain.com/
+    about: General community discussions and support

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,59 +0,0 @@
-name: Documentation
-description: Report an issue related to the LangChain documentation.
-title: "docs: <Please write a comprehensive title after the 'docs: ' prefix>"
-labels: [documentation]
-
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thank you for taking the time to report an issue in the documentation.
-
-        Only report issues with documentation here, explain if there are
-        any missing topics or if you found a mistake in the documentation.
-
-        Do **NOT** use this to ask usage questions or reporting issues with your code.
-
-        If you have usage questions or need help solving some problem,
-        please use the [LangChain Forum](https://forum.langchain.com/).
-
-        If you're in the wrong place, here are some helpful links to find a better
-        place to ask your question:
-
-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
-        * [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        * [LangChain how-to guides](https://python.langchain.com/docs/how_to/),
-        * [API Reference](https://python.langchain.com/api_reference/),
-        * [LangChain ChatBot](https://chat.langchain.com/)
-        * [GitHub search](https://github.com/langchain-ai/langchain),
-  - type: input
-    id: url
-    attributes:
-      label: URL
-      description: URL to documentation
-    validations:
-      required: false
-  - type: checkboxes
-    id: checks
-    attributes:
-      label: Checklist
-      description: Please confirm and check all the following options.
-      options:
-        - label: I added a very descriptive title to this issue.
-          required: true
-        - label: I included a link to the documentation page I am referring to (if applicable).
-          required: true
-  - type: textarea
-    attributes:
-      label: "Issue with current documentation:"
-      description: >
-        Please make sure to leave a reference to the document/code you're
-        referring to. Feel free to include names of classes, functions, methods
-        or concepts you'd like to see documented more.
-  - type: textarea
-    attributes:
-      label: "Idea or request for content:"
-      description: >
-        Please describe as clearly as possible what topics you think are missing
-        from the current documentation.

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -0,0 +1,118 @@
+name: "✨ Feature Request"
+description: Request a new feature or enhancement for LangChain. For questions, please use the LangChain forum.
+labels: ["feature request"]
+type: feature
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for taking the time to request a new feature.
+
+        Use this to request NEW FEATURES or ENHANCEMENTS in LangChain. For bug reports, please use the bug report template. For usage questions and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+
+        Relevant links to check before filing a feature request to see if your request has already been made or
+        if there's another way to achieve what you want:
+
+        * [LangChain Forum](https://forum.langchain.com/),
+        * [LangChain documentation with the integrated search](https://docs.langchain.com/oss/python/langchain/overview),
+        * [API Reference](https://python.langchain.com/api_reference/),
+        * [LangChain ChatBot](https://chat.langchain.com/)
+        * [GitHub search](https://github.com/langchain-ai/langchain),
+  - type: checkboxes
+    id: checks
+    attributes:
+      label: Checked other resources
+      description: Please confirm and check all the following options.
+      options:
+        - label: This is a feature request, not a bug report or usage question.
+          required: true
+        - label: I added a clear and descriptive title that summarizes the feature request.
+          required: true
+        - label: I used the GitHub search to find a similar feature request and didn't find it.
+          required: true
+        - label: I checked the LangChain documentation and API reference to see if this feature already exists.
+          required: true
+        - label: This is not related to the langchain-community package.
+          required: true
+  - type: textarea
+    id: feature-description
+    validations:
+      required: true
+    attributes:
+      label: Feature Description
+      description: |
+        Please provide a clear and concise description of the feature you would like to see added to LangChain.
+        
+        What specific functionality are you requesting? Be as detailed as possible.
+      placeholder: |
+        I would like LangChain to support...
+        
+        This feature would allow users to...
+  - type: textarea
+    id: use-case
+    validations:
+      required: true
+    attributes:
+      label: Use Case
+      description: |
+        Describe the specific use case or problem this feature would solve.
+        
+        Why do you need this feature? What problem does it solve for you or other users?
+      placeholder: |
+        I'm trying to build an application that...
+        
+        Currently, I have to work around this by...
+        
+        This feature would help me/users to...
+  - type: textarea
+    id: proposed-solution
+    validations:
+      required: false
+    attributes:
+      label: Proposed Solution
+      description: |
+        If you have ideas about how this feature could be implemented, please describe them here.
+        
+        This is optional but can be helpful for maintainers to understand your vision.
+      placeholder: |
+        I think this could be implemented by...
+        
+        The API could look like...
+        
+        ```python
+        # Example of how the feature might work
+        ```
+  - type: textarea
+    id: alternatives
+    validations:
+      required: false
+    attributes:
+      label: Alternatives Considered
+      description: |
+        Have you considered any alternative solutions or workarounds?
+        
+        What other approaches have you tried or considered?
+      placeholder: |
+        I've tried using...
+        
+        Alternative approaches I considered:
+        1. ...
+        2. ...
+        
+        But these don't work because...
+  - type: textarea
+    id: additional-context
+    validations:
+      required: false
+    attributes:
+      label: Additional Context
+      description: |
+        Add any other context, screenshots, examples, or references that would help explain your feature request.
+      placeholder: |
+        Related issues: #...
+        
+        Similar features in other libraries:
+        - ...
+        
+        Additional context or examples:
+        - ...

.github/ISSUE_TEMPLATE/privileged.yml

@@ -4,12 +4,7 @@ body:
   - type: markdown
     attributes:
       value: |
-        Thanks for your interest in LangChain! 🚀
-
-        If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
-
-        You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository
-        or are a regular contributor to LangChain with previous merged pull requests.
+        If you are not a LangChain maintainer, employee, or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
   - type: checkboxes
     id: privileged
     attributes:

.github/ISSUE_TEMPLATE/task.yml

@@ -0,0 +1,91 @@
+name: "📋 Task"
+description: Create a task for project management and tracking by LangChain maintainers. If you are not a maintainer, please use other templates or the forum.
+labels: ["task"]
+type: task
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for creating a task to help organize LangChain development.
+
+        This template is for **maintainer tasks** such as project management, development planning, refactoring, documentation updates, and other organizational work.
+
+        If you are not a LangChain maintainer or were not asked directly by a maintainer to create a task, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead or use the appropriate bug report or feature request templates on the previous page.
+  - type: checkboxes
+    id: maintainer
+    attributes:
+      label: Maintainer task
+      description: Confirm that you are allowed to create a task here.
+      options:
+        - label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create a task here.
+          required: true
+  - type: textarea
+    id: task-description
+    attributes:
+      label: Task Description
+      description: |
+        Provide a clear and detailed description of the task.
+        
+        What needs to be done? Be specific about the scope and requirements.
+      placeholder: |
+        This task involves...
+        
+        The goal is to...
+        
+        Specific requirements:
+        - ...
+        - ...
+    validations:
+      required: true
+  - type: textarea
+    id: acceptance-criteria
+    attributes:
+      label: Acceptance Criteria
+      description: |
+        Define the criteria that must be met for this task to be considered complete.
+        
+        What are the specific deliverables or outcomes expected?
+      placeholder: |
+        This task will be complete when:
+        - [ ] ...
+        - [ ] ...
+        - [ ] ...
+    validations:
+      required: true
+  - type: textarea
+    id: context
+    attributes:
+      label: Context and Background
+      description: |
+        Provide any relevant context, background information, or links to related issues/PRs.
+        
+        Why is this task needed? What problem does it solve?
+      placeholder: |
+        Background:
+        - ...
+        
+        Related issues/PRs:
+        - #...
+        
+        Additional context:
+        - ...
+    validations:
+      required: false
+  - type: textarea
+    id: dependencies
+    attributes:
+      label: Dependencies
+      description: |
+        List any dependencies or blockers for this task.
+        
+        Are there other tasks, issues, or external factors that need to be completed first?
+      placeholder: |
+        This task depends on:
+        - [ ] Issue #...
+        - [ ] PR #...
+        - [ ] External dependency: ...
+        
+        Blocked by:
+        - ...
+    validations:
+      required: false

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,3 +1,5 @@
+(Replace this entire block of text)
+
 Thank you for contributing to LangChain! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
 
 - [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
@@ -9,14 +11,13 @@ Thank you for contributing to LangChain! Follow these steps to mark your pull re
     - feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
   - Allowed `{SCOPE}` values (optional):
     - core, cli, langchain, standard-tests, docs, anthropic, chroma, deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant, xai
-  - Note: the `{DESCRIPTION}` must not start with an uppercase letter.
+  - *Note:* the `{DESCRIPTION}` must not start with an uppercase letter.
   - Once you've written the title, please delete this checklist item; do not include it in the PR.
 
 - [ ] **PR message**: ***Delete this entire checklist*** and replace with
   - **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable to a relevant issue.
   - **Issue:** the issue # it fixes, if applicable (e.g. Fixes #123)
   - **Dependencies:** any dependencies required for this change
-  - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
 
 - [ ] **Add tests and docs**: If you're adding a new integration, you must include:
   1. A test for the integration, preferably unit tests that do not rely on network access,
@@ -26,7 +27,7 @@ Thank you for contributing to LangChain! Follow these steps to mark your pull re
 
 Additional guidelines:
 
-- Make sure optional dependencies are imported within a function.
-- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
 - Most PRs should not touch more than one package.
+- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
 - Changes should be backwards compatible.
+- Make sure optional dependencies are imported within a function.

.github/scripts/check_diff.py

@@ -121,25 +121,25 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     if job == "codspeed":
         py_versions = ["3.12"]  # 3.13 is not yet supported
     elif dir_ == "libs/core":
-        py_versions = ["3.10", "3.11", "3.12", "3.13"]
+        py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13"]
     # custom logic for specific directories
     elif dir_ == "libs/partners/milvus":
         # milvus doesn't allow 3.12 because they declare deps in funny way
-        py_versions = ["3.10", "3.11"]
+        py_versions = ["3.9", "3.11"]
 
     elif dir_ in PY_312_MAX_PACKAGES:
-        py_versions = ["3.10", "3.12"]
+        py_versions = ["3.9", "3.12"]
 
     elif dir_ == "libs/langchain" and job == "extended-tests":
-        py_versions = ["3.10", "3.13"]
+        py_versions = ["3.9", "3.13"]
     elif dir_ == "libs/langchain_v1":
         py_versions = ["3.10", "3.13"]
 
     elif dir_ == ".":
         # unable to install with 3.13 because tokenizers doesn't support 3.13 yet
-        py_versions = ["3.10", "3.12"]
+        py_versions = ["3.9", "3.12"]
     else:
-        py_versions = ["3.10", "3.13"]
+        py_versions = ["3.9", "3.13"]
 
     return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
 

.github/workflows/_compile_integration_test.yml

@@ -27,7 +27,7 @@ jobs:
     timeout-minutes: 20
     name: 'Python ${{ inputs.python-version }}'
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: '🐍 Set up Python ${{ inputs.python-version }} + UV'
         uses: "./.github/actions/uv_setup"

.github/workflows/_integration_test.yml

@@ -28,7 +28,7 @@ jobs:
     runs-on: ubuntu-latest
     name: 'Python ${{ inputs.python-version }}'
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: '🐍 Set up Python ${{ inputs.python-version }} + UV'
         uses: "./.github/actions/uv_setup"

.github/workflows/_lint.yml

@@ -33,7 +33,7 @@ jobs:
     timeout-minutes: 20
     steps:
       - name: '📋 Checkout Code'
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: '🐍 Set up Python ${{ inputs.python-version }} + UV'
         uses: "./.github/actions/uv_setup"

.github/workflows/_release.yml

@@ -43,7 +43,7 @@ jobs:
       version: ${{ steps.check-version.outputs.version }}
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"
@@ -92,7 +92,7 @@ jobs:
     outputs:
       release-body: ${{ steps.generate-release-body.outputs.release-body }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain
           path: langchain
@@ -183,13 +183,36 @@ jobs:
     needs:
       - build
       - release-notes
-    uses:
-      ./.github/workflows/_test_release.yml
-    permissions: write-all
-    with:
-      working-directory: ${{ inputs.working-directory }}
-      dangerous-nonmaster-release: ${{ inputs.dangerous-nonmaster-release }}
-    secrets: inherit
+    runs-on: ubuntu-latest
+    permissions:
+      # This permission is used for trusted publishing:
+      # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
+      #
+      # Trusted publishing has to also be configured on PyPI for each package:
+      # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
+      id-token: write
+
+    steps:
+      - uses: actions/checkout@v5
+
+      - uses: actions/download-artifact@v5
+        with:
+          name: dist
+          path: ${{ inputs.working-directory }}/dist/
+
+      - name: Publish to test PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: ${{ inputs.working-directory }}/dist/
+          verbose: true
+          print-hash: true
+          repository-url: https://test.pypi.org/legacy/
+          # We overwrite any existing distributions with the same name and version.
+          # This is *only for CI use* and is *extremely dangerous* otherwise!
+          # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
+          skip-existing: true
+          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
+          attestations: false
 
   pre-release-checks:
     needs:
@@ -199,7 +222,7 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 20
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       # We explicitly *don't* set up caching here. This ensures our tests are
       # maximally sensitive to catching breakage.
@@ -289,7 +312,8 @@ jobs:
         env:
           MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
         run: |
-          VIRTUAL_ENV=.venv uv pip install --force-reinstall $MIN_VERSIONS --editable .
+          VIRTUAL_ENV=.venv uv pip install --force-reinstall --editable .
+          VIRTUAL_ENV=.venv uv pip install --force-reinstall $MIN_VERSIONS
           make tests
         working-directory: ${{ inputs.working-directory }}
 
@@ -347,7 +371,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        partner: [openai]
+        partner: [openai, anthropic]
       fail-fast: false  # Continue testing other partners if one fails
     env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
@@ -362,7 +386,7 @@ jobs:
       AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
       AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       # We implement this conditional as Github Actions does not have good support
       # for conditionally needing steps. https://github.com/actions/runner/issues/491
@@ -393,7 +417,7 @@ jobs:
             git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
             | awk '{print $2}' \
             | sed 's|refs/tags/||' \
-            | grep -Ev '==[^=]*(\.?dev[0-9]*|\.?rc[0-9]*)$' \
+            | grep -E '[0-9]+\.[0-9]+\.[0-9]+$' \
             | sort -Vr \
             | head -n 1
           )"
@@ -440,7 +464,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"
@@ -479,7 +503,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"

.github/workflows/_test.yml

@@ -32,7 +32,7 @@ jobs:
     name: 'Python ${{ inputs.python-version }}'
     steps:
       - name: '📋 Checkout Code'
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: '🐍 Set up Python ${{ inputs.python-version }} + UV'
         uses: "./.github/actions/uv_setup"

.github/workflows/_test_doc_imports.yml

@@ -21,7 +21,7 @@ jobs:
     name: '🔍 Check Doc Imports (Python ${{ inputs.python-version }})'
     steps:
       - name: '📋 Checkout Code'
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: '🐍 Set up Python ${{ inputs.python-version }} + UV'
         uses: "./.github/actions/uv_setup"

.github/workflows/_test_pydantic.yml

@@ -34,7 +34,7 @@ jobs:
     name: 'Pydantic ~=${{ inputs.pydantic-version }}'
     steps:
       - name: '📋 Checkout Code'
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: '🐍 Set up Python ${{ inputs.python-version }} + UV'
         uses: "./.github/actions/uv_setup"

.github/workflows/_test_release.yml

@@ -1,106 +0,0 @@
-name: '🧪 Test Release Package'
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      dangerous-nonmaster-release:
-        required: false
-        type: boolean
-        default: false
-        description: "Release from a non-master branch (danger!)"
-
-env:
-  PYTHON_VERSION: "3.11"
-  UV_FROZEN: "true"
-
-jobs:
-  build:
-    if: github.ref == 'refs/heads/master' || inputs.dangerous-nonmaster-release
-    runs-on: ubuntu-latest
-
-    outputs:
-      pkg-name: ${{ steps.check-version.outputs.pkg-name }}
-      version: ${{ steps.check-version.outputs.version }}
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: '🐍 Set up Python + UV'
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      # We want to keep this build stage *separate* from the release stage,
-      # so that there's no sharing of permissions between them.
-      # The release stage has trusted publishing and GitHub repo contents write access,
-      # and we want to keep the scope of that access limited just to the release job.
-      # Otherwise, a malicious `build` step (e.g. via a compromised dependency)
-      # could get access to our GitHub or PyPI credentials.
-      #
-      # Per the trusted publishing GitHub Action:
-      # > It is strongly advised to separate jobs for building [...]
-      # > from the publish job.
-      # https://github.com/pypa/gh-action-pypi-publish#non-goals
-      - name: '📦 Build Project for Distribution'
-        run: uv build
-        working-directory: ${{ inputs.working-directory }}
-
-      - name: '⬆️ Upload Build Artifacts'
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: '🔍 Extract Version Information'
-        id: check-version
-        shell: python
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          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:
-      - build
-    runs-on: ubuntu-latest
-    permissions:
-      # This permission is used for trusted publishing:
-      # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
-      #
-      # Trusted publishing has to also be configured on PyPI for each package:
-      # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
-      id-token: write
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/download-artifact@v5
-        with:
-          name: test-dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Publish to test PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          packages-dir: ${{ inputs.working-directory }}/dist/
-          verbose: true
-          print-hash: true
-          repository-url: https://test.pypi.org/legacy/
-
-          # We overwrite any existing distributions with the same name and version.
-          # This is *only for CI use* and is *extremely dangerous* otherwise!
-          # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
-          skip-existing: true
-          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
-          attestations: false

.github/workflows/api_doc_build.yml

@@ -17,10 +17,10 @@ jobs:
     permissions:
       contents: read
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           path: langchain
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain-api-docs-html
           path: langchain-api-docs-html
@@ -72,7 +72,7 @@ jobs:
           done
 
       - name: '🐍 Setup Python ${{ env.PYTHON_VERSION }}'
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         id: setup-python
         with:
           python-version: ${{ env.PYTHON_VERSION }}

.github/workflows/check-broken-links.yml

@@ -13,7 +13,7 @@ jobs:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - name: '🟢 Setup Node.js 18.x'
         uses: actions/setup-node@v4
         with:

.github/workflows/check_core_versions.yml

@@ -16,19 +16,34 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: '✅ Verify pyproject.toml & version.py Match'
         run: |
-          PYPROJECT_VERSION=$(grep -Po '(?<=^version = ")[^"]*' libs/core/pyproject.toml)
-          VERSION_PY_VERSION=$(grep -Po '(?<=^VERSION = ")[^"]*' libs/core/langchain_core/version.py)
+          # Check core versions
+          CORE_PYPROJECT_VERSION=$(grep -Po '(?<=^version = ")[^"]*' libs/core/pyproject.toml)
+          CORE_VERSION_PY_VERSION=$(grep -Po '(?<=^VERSION = ")[^"]*' libs/core/langchain_core/version.py)
 
-          # Compare the two versions
-          if [ "$PYPROJECT_VERSION" != "$VERSION_PY_VERSION" ]; then
+          # Compare core versions
+          if [ "$CORE_PYPROJECT_VERSION" != "$CORE_VERSION_PY_VERSION" ]; then
             echo "langchain-core versions in pyproject.toml and version.py do not match!"
-            echo "pyproject.toml version: $PYPROJECT_VERSION"
-            echo "version.py version: $VERSION_PY_VERSION"
+            echo "pyproject.toml version: $CORE_PYPROJECT_VERSION"
+            echo "version.py version: $CORE_VERSION_PY_VERSION"
             exit 1
           else
-            echo "Versions match: $PYPROJECT_VERSION"
+            echo "Core versions match: $CORE_PYPROJECT_VERSION"
+          fi
+
+          # Check langchain_v1 versions
+          LANGCHAIN_PYPROJECT_VERSION=$(grep -Po '(?<=^version = ")[^"]*' libs/langchain_v1/pyproject.toml)
+          LANGCHAIN_INIT_PY_VERSION=$(grep -Po '(?<=^__version__ = ")[^"]*' libs/langchain_v1/langchain/__init__.py)
+
+          # Compare langchain_v1 versions
+          if [ "$LANGCHAIN_PYPROJECT_VERSION" != "$LANGCHAIN_INIT_PY_VERSION" ]; then
+            echo "langchain_v1 versions in pyproject.toml and __init__.py do not match!"
+            echo "pyproject.toml version: $LANGCHAIN_PYPROJECT_VERSION"
+            echo "version.py version: $LANGCHAIN_INIT_PY_VERSION"
+            exit 1
+          else
+            echo "Langchain v1 versions match: $LANGCHAIN_PYPROJECT_VERSION"
           fi

.github/workflows/check_diffs.yml

@@ -33,9 +33,9 @@ jobs:
     if: ${{ !contains(github.event.pull_request.labels.*.name, 'ci-ignore') }}
     steps:
       - name: '📋 Checkout Code'
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
       - name: '🐍 Setup Python 3.11'
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: '3.11'
       - name: '📂 Get Changed Files'
@@ -54,6 +54,7 @@ jobs:
       dependencies: ${{ steps.set-matrix.outputs.dependencies }}
       test-doc-imports: ${{ steps.set-matrix.outputs.test-doc-imports }}
       test-pydantic: ${{ steps.set-matrix.outputs.test-pydantic }}
+      codspeed: ${{ steps.set-matrix.outputs.codspeed }}
   # Run linting only on packages that have changed files
   lint:
     needs: [ build ]
@@ -138,7 +139,7 @@ jobs:
       run:
         working-directory: ${{ matrix.job-configs.working-directory }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: '🐍 Set up Python ${{ matrix.job-configs.python-version }} + UV'
         uses: "./.github/actions/uv_setup"
@@ -166,10 +167,50 @@ jobs:
           # and `set -e` above will cause the step to fail.
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
 
+  # Run codspeed benchmarks only on packages that have changed files
+  codspeed:
+    name: '⚡ CodSpeed Benchmarks'
+    needs: [ build ]
+    if: ${{ needs.build.outputs.codspeed != '[]' && !contains(github.event.pull_request.labels.*.name, 'codspeed-ignore') }}
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        job-configs: ${{ fromJson(needs.build.outputs.codspeed) }}
+      fail-fast: false
+    steps:
+      - uses: actions/checkout@v5
+
+      # We have to use 3.12 as 3.13 is not yet supported
+      - name: '📦 Install UV Package Manager'
+        uses: astral-sh/setup-uv@v6
+        with:
+          python-version: "3.12"
+
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+
+      - name: '📦 Install Test Dependencies'
+        run: uv sync --group test
+        working-directory: ${{ matrix.job-configs.working-directory }}
+
+      - name: '⚡ Run Benchmarks: ${{ matrix.job-configs.working-directory }}'
+        uses: CodSpeedHQ/action@v3
+        with:
+          token: ${{ secrets.CODSPEED_TOKEN }}
+          run: |
+            cd ${{ matrix.job-configs.working-directory }}
+            if [ "${{ matrix.job-configs.working-directory }}" = "libs/core" ]; then
+              uv run --no-sync pytest ./tests/benchmarks --codspeed
+            else
+              uv run --no-sync pytest ./tests/ --codspeed
+            fi
+          mode: ${{ matrix.job-configs.working-directory == 'libs/core' && 'walltime' || 'instrumentation' }}
+
   # Final status check - ensures all required jobs passed before allowing merge
   ci_success:
     name: '✅ CI Success'
-    needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports, test-pydantic]
+    needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports, test-pydantic, codspeed]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/check_new_docs.yml

@@ -22,8 +22,8 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: '3.10'
       - id: files

.github/workflows/codspeed.yml

@@ -1,66 +0,0 @@
-name: '⚡ CodSpeed'
-
-on:
-  push:
-    branches:
-        - master
-  pull_request:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-env:
-  AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: foo
-  AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: foo
-  DEEPSEEK_API_KEY: foo
-  FIREWORKS_API_KEY: foo
-
-jobs:
-  codspeed:
-    name: 'Benchmark'
-    runs-on: ubuntu-latest
-    if: ${{ !contains(github.event.pull_request.labels.*.name, 'codspeed-ignore') }}
-    strategy:
-      matrix:
-        include:
-          - working-directory: libs/core
-            mode: walltime
-          - working-directory: libs/partners/openai
-          - working-directory: libs/partners/anthropic
-          - working-directory: libs/partners/deepseek
-          - working-directory: libs/partners/fireworks
-          - working-directory: libs/partners/xai
-          - working-directory: libs/partners/mistralai
-          - working-directory: libs/partners/groq
-      fail-fast: false
-
-    steps:
-      - uses: actions/checkout@v4
-
-      # We have to use 3.12 as 3.13 is not yet supported
-      - name: '📦 Install UV Package Manager'
-        uses: astral-sh/setup-uv@v6
-        with:
-          python-version: "3.12"
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: '📦 Install Test Dependencies'
-        run: uv sync --group test
-        working-directory: ${{ matrix.working-directory }}
-
-      - name: '⚡ Run Benchmarks: ${{ matrix.working-directory }}'
-        uses: CodSpeedHQ/action@v3
-        with:
-          token: ${{ secrets.CODSPEED_TOKEN }}
-          run: |
-            cd ${{ matrix.working-directory }}
-            if [ "${{ matrix.working-directory }}" = "libs/core" ]; then
-              uv run --no-sync pytest ./tests/benchmarks --codspeed
-            else
-              uv run --no-sync pytest ./tests/ --codspeed
-            fi
-          mode: ${{ matrix.mode || 'instrumentation' }}

.github/workflows/people.yml

@@ -19,7 +19,7 @@ jobs:
         env:
           GITHUB_CONTEXT: ${{ toJson(github) }}
         run: echo "$GITHUB_CONTEXT"
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       # Ref: https://github.com/actions/runner/issues/2033
       - name: '🔧 Fix Git Safe Directory in Container'
         run: mkdir -p /home/runner/work/_temp/_github_home && printf "[safe]\n\tdirectory = /github/workspace" > /home/runner/work/_temp/_github_home/.gitconfig

.github/workflows/pr_lint.yml

@@ -62,7 +62,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: '✅ Validate Conventional Commits Format'
-        uses: amannn/action-semantic-pull-request@v5
+        uses: amannn/action-semantic-pull-request@v6
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:

.github/workflows/run_notebooks.yml

@@ -26,7 +26,7 @@ jobs:
     if: github.repository == 'langchain-ai/langchain' || github.event_name != 'schedule'
     name: '📑 Test Documentation Notebooks'
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: '🐍 Set up Python + UV'
         uses: "./.github/actions/uv_setup"
@@ -35,12 +35,12 @@ jobs:
 
       - name: '🔐 Authenticate to Google Cloud'
         id: 'auth'
-        uses: google-github-actions/auth@v2
+        uses: google-github-actions/auth@v3
         with:
           credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
 
       - name: '🔐 Configure AWS Credentials'
-        uses: aws-actions/configure-aws-credentials@v4
+        uses: aws-actions/configure-aws-credentials@v5
         with:
           aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
           aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

.github/workflows/scheduled_test.yml

@@ -1,5 +1,5 @@
 name: '⏰ Scheduled Integration Tests'
-run-name: "Run Integration Tests - ${{ inputs.working-directory-force || 'all libs' }} (Python ${{ inputs.python-version-force || '3.10, 3.13' }})"
+run-name: "Run Integration Tests - ${{ inputs.working-directory-force || 'all libs' }} (Python ${{ inputs.python-version-force || '3.9, 3.11' }})"
 
 on:
   workflow_dispatch:  # Allows maintainers to trigger the workflow manually in GitHub UI
@@ -9,7 +9,7 @@ on:
         description: "From which folder this pipeline executes - defaults to all in matrix - example value: libs/partners/anthropic"
       python-version-force:
         type: string
-        description: "Python version to use - defaults to 3.10 and 3.13 in matrix - example value: 3.11"
+        description: "Python version to use - defaults to 3.9 and 3.11 in matrix - example value: 3.9"
   schedule:
     - cron:  '0 13 * * *'  # Runs daily at 1PM UTC (9AM EDT/6AM PDT)
 
@@ -20,7 +20,7 @@ env:
   POETRY_VERSION: "1.8.4"
   UV_FROZEN: "true"
   DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/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")
+  POETRY_LIBS: ("libs/partners/aws")
 
 jobs:
   # Generate dynamic test matrix based on input parameters or defaults
@@ -40,9 +40,9 @@ jobs:
           PYTHON_VERSION_FORCE: ${{ github.event.inputs.python-version-force || '' }}
         run: |
           # echo "matrix=..." where matrix is a json formatted str with keys python-version and working-directory
-          # python-version should default to 3.10 and 3.13, but is overridden to [PYTHON_VERSION_FORCE] if set
+          # python-version should default to 3.9 and 3.11, but is overridden to [PYTHON_VERSION_FORCE] if set
           # working-directory should default to DEFAULT_LIBS, but is overridden to [WORKING_DIRECTORY_FORCE] if set
-          python_version='["3.10", "3.13"]'
+          python_version='["3.9", "3.11"]'
           working_directory="$DEFAULT_LIBS"
           if [ -n "$PYTHON_VERSION_FORCE" ]; then
             python_version="[\"$PYTHON_VERSION_FORCE\"]"
@@ -68,14 +68,14 @@ jobs:
         working-directory: ${{ fromJSON(needs.compute-matrix.outputs.matrix).working-directory }}
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           path: langchain
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain-google
           path: langchain-google
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain-aws
           path: langchain-aws
@@ -106,12 +106,12 @@ jobs:
 
       - name: '🔐 Authenticate to Google Cloud'
         id: 'auth'
-        uses: google-github-actions/auth@v2
+        uses: google-github-actions/auth@v3
         with:
           credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
 
       - name: '🔐 Configure AWS Credentials'
-        uses: aws-actions/configure-aws-credentials@v4
+        uses: aws-actions/configure-aws-credentials@v5
         with:
           aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
           aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

README.md

@@ -8,10 +8,8 @@
 <br>
 </div>
 
-[![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/releases)
 [![PyPI - License](https://img.shields.io/pypi/l/langchain-core?style=flat-square)](https://opensource.org/licenses/MIT)
 [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain)](https://pypistats.org/packages/langchain-core)
-[![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=flat-square)](https://star-history.com/#langchain-ai/langchain)
 [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
 [<img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">](https://codespaces.new/langchain-ai/langchain)
 [![CodSpeed Badge](https://img.shields.io/endpoint?url=https://codspeed.io/badge.json)](https://codspeed.io/langchain-ai/langchain)

cookbook/README.md

@@ -64,3 +64,4 @@ Notebook | Description
 [visual_RAG_vdms.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/visual_RAG_vdms.ipynb) | Performs Visual Retrieval-Augmented-Generation (RAG) using videos and scene descriptions generated by open source models.
 [contextual_rag.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/contextual_rag.ipynb) | Performs contextual retrieval-augmented generation (RAG) prepending chunk-specific explanatory context to each chunk before embedding.
 [rag-agents-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/local_rag_agents_intel_cpu.ipynb) | Build a RAG agent locally with open source models that routes questions through one of two paths to find answers. The agent generates answers based on documents retrieved from either the vector database or retrieved from web search. If the vector database lacks relevant information, the agent opts for web search. Open-source models for LLM and embeddings are used locally on an Intel Xeon CPU to execute this pipeline.
+[rag_mlflow_tracking_evaluation.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_mlflow_tracking_evaluation.ipynb) | Guide on how to create a RAG pipeline and track + evaluate it with MLflow.

cookbook/rag_mlflow_tracking_evaluation.ipynb

@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3716230e",
+   "metadata": {},
+   "source": [
+    "# RAG Pipeline with MLflow Tracking, Tracing & Evaluation\n",
+    "\n",
+    "This notebook demonstrates how to build a complete Retrieval-Augmented Generation (RAG) pipeline using LangChain and integrate it with MLflow for experiment tracking, tracing, and evaluation.\n",
+    "\n",
+    "\n",
+    "- **RAG Pipeline Construction**: Build a complete RAG system using LangChain components\n",
+    "- **MLflow Integration**: Track experiments, parameters, and artifacts\n",
+    "- **Tracing**: Monitor inputs, outputs, retrieved documents, scores, prompts, and timings\n",
+    "- **Evaluation**: Use MLflow's built-in scorers to assess RAG performance\n",
+    "- **Best Practices**: Implement proper configuration management and reproducible experiments\n",
+    "\n",
+    "We'll build a RAG system that can answer questions about academic papers by:\n",
+    "1. Loading and chunking documents from ArXiv\n",
+    "2. Creating embeddings and a vector store\n",
+    "3. Setting up a retrieval-augmented generation chain\n",
+    "4. Tracking all experiments with MLflow\n",
+    "5. Evaluating the system's performance\n",
+    "\n",
+    "![System Diagram](https://miro.medium.com/v2/resize:fit:720/format:webp/1*eiw86PP4hrBBxhjTjP0JUQ.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f7561c4",
+   "metadata": {},
+   "source": [
+    "#### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0814ebe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -U langchain mlflow langchain-community arxiv pymupdf langchain-text-splitters langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "747399b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import mlflow\n",
+    "from mlflow.genai.scorers import RelevanceToQuery, Correctness, ExpectationsGuidelines\n",
+    "from langchain_community.document_loaders import ArxivLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.output_parsers import StrOutputParser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4141ee05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"OPENAI_API_KEY\"] = \"<YOUR OPENAI API KEY>\"\n",
+    "\n",
+    "mlflow.set_experiment(\"LangChain-RAG-MLflow\")\n",
+    "mlflow.langchain.autolog()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd5eb41b",
+   "metadata": {},
+   "source": [
+    "Define all hyperparameters and configuration in a centralized dictionary. This makes it easy to:\n",
+    "- Track different experiment configurations\n",
+    "- Reproduce results\n",
+    "- Perform hyperparameter tuning\n",
+    "\n",
+    "**Key Parameters**:\n",
+    "- `chunk_size`: Size of text chunks for document splitting\n",
+    "- `chunk_overlap`: Overlap between consecutive chunks\n",
+    "- `retriever_k`: Number of documents to retrieve\n",
+    "- `embeddings_model`: OpenAI embedding model\n",
+    "- `llm`: Language model for generation\n",
+    "- `temperature`: Sampling temperature for the LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6dcdc5d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CONFIG = {\n",
+    "    \"chunk_size\": 400,\n",
+    "    \"chunk_overlap\": 80,\n",
+    "    \"retriever_k\": 3,\n",
+    "    \"embeddings_model\": \"text-embedding-3-small\",\n",
+    "    \"system_prompt\": \"You are a helpful assistant. Use the following context to answer the question. Use three sentences maximum and keep the answer concise.\",\n",
+    "    \"llm\": \"gpt-5-nano\",\n",
+    "    \"temperature\": 0,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a2985f1",
+   "metadata": {},
+   "source": [
+    "#### ArXiv Dcoument Loading and Processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f32aa36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Published': '2023-08-02', 'Title': 'Attention Is All You Need', 'Authors': 'Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin', 'Summary': 'The dominant sequence transduction models are based on complex recurrent or\\nconvolutional neural networks in an encoder-decoder configuration. The best\\nperforming models also connect the encoder and decoder through an attention\\nmechanism. We propose a new simple network architecture, the Transformer, based\\nsolely on attention mechanisms, dispensing with recurrence and convolutions\\nentirely. Experiments on two machine translation tasks show these models to be\\nsuperior in quality while being more parallelizable and requiring significantly\\nless time to train. Our model achieves 28.4 BLEU on the WMT 2014\\nEnglish-to-German translation task, improving over the existing best results,\\nincluding ensembles by over 2 BLEU. On the WMT 2014 English-to-French\\ntranslation task, our model establishes a new single-model state-of-the-art\\nBLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction\\nof the training costs of the best models from the literature. We show that the\\nTransformer generalizes well to other tasks by applying it successfully to\\nEnglish constituency parsing both with large and limited training data.'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load documents from ArXiv\n",
+    "loader = ArxivLoader(\n",
+    "    query=\"1706.03762\",\n",
+    "    load_max_docs=1,\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "print(docs[0].metadata)\n",
+    "\n",
+    "# Split documents into chunks\n",
+    "splitter = RecursiveCharacterTextSplitter(\n",
+    "    chunk_size=CONFIG[\"chunk_size\"],\n",
+    "    chunk_overlap=CONFIG[\"chunk_overlap\"],\n",
+    ")\n",
+    "chunks = splitter.split_documents(docs)\n",
+    "\n",
+    "\n",
+    "# Join chunks into a single string\n",
+    "def join_chunks(chunks):\n",
+    "    return \"\\n\\n\".join([chunk.page_content for chunk in chunks])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e194ab4",
+   "metadata": {},
+   "source": [
+    "#### Vector Store and Retriever Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "26dfbeaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create embeddings\n",
+    "embeddings = OpenAIEmbeddings(model=CONFIG[\"embeddings_model\"])\n",
+    "\n",
+    "# Create vector store from documents\n",
+    "vectorstore = InMemoryVectorStore.from_documents(\n",
+    "    chunks,\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Create retriever\n",
+    "retriever = vectorstore.as_retriever(search_kwargs={\"k\": CONFIG[\"retriever_k\"]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc1f181b",
+   "metadata": {},
+   "source": [
+    "#### RAG Chain Construction using [LCEL](https://python.langchain.com/docs/concepts/lcel/)\n",
+    "\n",
+    "Flow:\n",
+    "1. Query → Retriever (finds relevant chunks)\n",
+    "2. Chunks → join_chunks (creates context)\n",
+    "3. Context + Query → Prompt Template\n",
+    "4. Prompt → Language Model → Response\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a810dc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the language model\n",
+    "llm = ChatOpenAI(model=CONFIG[\"llm\"], temperature=CONFIG[\"temperature\"])\n",
+    "\n",
+    "# Create the prompt template\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", CONFIG[\"system_prompt\"] + \"\\n\\nContext:\\n{context}\\n\\n\"),\n",
+    "        (\"human\", \"\\n{question}\\n\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Construct the RAG chain\n",
+    "rag_chain = (\n",
+    "    {\n",
+    "        \"context\": retriever | RunnableLambda(join_chunks),\n",
+    "        \"question\": RunnablePassthrough(),\n",
+    "    }\n",
+    "    | prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c04bd019",
+   "metadata": {},
+   "source": [
+    "#### Prediction Function with MLflow Tracing\n",
+    "\n",
+    "Create a prediction function decorated with `@mlflow.trace` to automatically log:\n",
+    "- Input queries\n",
+    "- Retrieved documents\n",
+    "- Generated responses\n",
+    "- Execution time\n",
+    "- Chain intermediate steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7b45fc04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: What is the main idea of the paper?\n",
+      "Response: The main idea is to replace recurrent/convolutional sequence models with a pure attention-based architecture called the Transformer. It uses self-attention to model dependencies between all positions in the input and output, enabling full parallelization and better handling of long-range relations. This approach achieves strong results on translation and can extend to other modalities.\n"
+     ]
+    }
+   ],
+   "source": [
+    "@mlflow.trace\n",
+    "def predict_fn(question: str) -> str:\n",
+    "    return rag_chain.invoke(question)\n",
+    "\n",
+    "\n",
+    "# Test the prediction function\n",
+    "sample_question = \"What is the main idea of the paper?\"\n",
+    "response = predict_fn(sample_question)\n",
+    "print(f\"Question: {sample_question}\")\n",
+    "print(f\"Response: {response}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "421469de",
+   "metadata": {},
+   "source": [
+    "#### Evaluation Dataset and Scoring\n",
+    "\n",
+    "Define an evaluation dataset and run systematic evaluation using [MLflow's built-in scorers](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/predefined/#available-scorers):\n",
+    "\n",
+    "<u>Evaluation Components:</u>\n",
+    "- **Dataset**: Questions with expected concepts and facts\n",
+    "- **Scorers**: \n",
+    "  - `RelevanceToQuery`: Measures how relevant the response is to the question\n",
+    "  - `Correctness`: Evaluates factual accuracy of the response\n",
+    "  - `ExpectationsGuidelines`: Checks that output matches expectation guidelines\n",
+    "\n",
+    "<u>Best Practices:</u>\n",
+    "- Create diverse test cases covering different query types\n",
+    "- Include expected concepts to guide evaluation\n",
+    "- Use multiple scoring metrics for comprehensive assessment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5c1dc4f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025/08/23 20:14:39 INFO mlflow.models.evaluation.utils.trace: Auto tracing is temporarily enabled during the model evaluation for computing some metrics and debugging. To disable tracing, call `mlflow.autolog(disable=True)`.\n",
+      "2025/08/23 20:14:39 INFO mlflow.genai.utils.data_validation: Testing model prediction with the first sample in the dataset.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b6c6687efa24796b39c7951d589d481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Evaluating:   0%|          | 0/3 [Elapsed: 00:00, Remaining: ?] "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✨ Evaluation completed.\n",
+      "\n",
+      "Metrics and evaluation results are logged to the MLflow run:\n",
+      "  Run name: \u001b[94mbaseline_eval\u001b[0m\n",
+      "  Run ID: \u001b[94ma2218d9f24c9415f8040d3b77af103a9\u001b[0m\n",
+      "\n",
+      "To view the detailed evaluation results with sample-wise scores,\n",
+      "open the \u001b[93m\u001b[1mTraces\u001b[0m tab in the Run page in the MLflow UI.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define evaluation dataset\n",
+    "eval_dataset = [\n",
+    "    {\n",
+    "        \"inputs\": {\"question\": \"What is the main idea of the paper?\"},\n",
+    "        \"expectations\": {\n",
+    "            \"key_concepts\": [\"attention mechanism\", \"transformer\", \"neural network\"],\n",
+    "            \"expected_facts\": [\n",
+    "                \"attention mechanism is a key component of the transformer model\"\n",
+    "            ],\n",
+    "            \"guidelines\": [\"The response must be factual and concise\"],\n",
+    "        },\n",
+    "    },\n",
+    "    {\n",
+    "        \"inputs\": {\n",
+    "            \"question\": \"What's the difference between a transformer and a recurrent neural network?\"\n",
+    "        },\n",
+    "        \"expectations\": {\n",
+    "            \"key_concepts\": [\"sequential\", \"attention mechanism\", \"hidden state\"],\n",
+    "            \"expected_facts\": [\n",
+    "                \"transformer processes data in parallel while RNN processes data sequentially\"\n",
+    "            ],\n",
+    "            \"guidelines\": [\n",
+    "                \"The response must be factual and focus on the difference between the two models\"\n",
+    "            ],\n",
+    "        },\n",
+    "    },\n",
+    "    {\n",
+    "        \"inputs\": {\"question\": \"What does the attention mechanism do?\"},\n",
+    "        \"expectations\": {\n",
+    "            \"key_concepts\": [\"query\", \"key\", \"value\", \"relationship\", \"similarity\"],\n",
+    "            \"expected_facts\": [\n",
+    "                \"attention allows the model to weigh the importance of different parts of the input sequence when processing it\"\n",
+    "            ],\n",
+    "            \"guidelines\": [\n",
+    "                \"The response must be factual and explain the concept of attention\"\n",
+    "            ],\n",
+    "        },\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Run evaluation with MLflow\n",
+    "with mlflow.start_run(run_name=\"baseline_eval\") as run:\n",
+    "    # Log configuration parameters\n",
+    "    mlflow.log_params(CONFIG)\n",
+    "\n",
+    "    # Run evaluation\n",
+    "    results = mlflow.genai.evaluate(\n",
+    "        data=eval_dataset,\n",
+    "        predict_fn=predict_fn,\n",
+    "        scorers=[RelevanceToQuery(), Correctness(), ExpectationsGuidelines()],\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52b137c7",
+   "metadata": {},
+   "source": [
+    "#### Launch MLflow UI to check out the results\n",
+    "\n",
+    "<u>What you'll see in the UI:</u>\n",
+    "- **Experiments**: Compare different RAG configurations\n",
+    "- **Runs**: Individual experiment runs with metrics and parameters\n",
+    "- **Traces**: Detailed execution traces showing retrieval and generation steps\n",
+    "- **Evaluation Results**: Scoring metrics and detailed comparisons\n",
+    "- **Artifacts**: Saved models, datasets, and other files\n",
+    "\n",
+    "Navigate to `http://localhost:5000` after running the command below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "817c3799",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mlflow ui"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c75861e3",
+   "metadata": {},
+   "source": [
+    "You should see something like this\n",
+    "\n",
+    "![MLflow UI image](https://miro.medium.com/v2/resize:fit:720/format:webp/1*Cx7MMy53pAP7150x_hvztA.png)"
+   ]
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/api_reference/create_api_rst.py

@@ -468,7 +468,7 @@ def _build_rst_file(package_name: str = "langchain") -> None:
     """Create a rst file for building of documentation.
 
     Args:
-        package_name: Can be either "langchain" or "core" or "experimental".
+        package_name: Can be either "langchain" or "core"
     """
     package_dir = _package_dir(package_name)
     package_members = _load_package_modules(package_dir)
@@ -487,7 +487,7 @@ def _package_namespace(package_name: str) -> str:
     """Returns the package name used.
 
     Args:
-        package_name: Can be either "langchain" or "core" or "experimental".
+        package_name: Can be either "langchain" or "core"
 
     Returns:
         modified package_name: Can be either "langchain" or "langchain_{package_name}"
@@ -550,7 +550,6 @@ def _build_index(dirs: List[str]) -> None:
         "langchain",
         "text-splitters",
         "community",
-        "experimental",
         "standard-tests",
     ]
     main_ = [dir_ for dir_ in ordered if dir_ in dirs]

docs/docs/concepts/index.mdx

@@ -31,7 +31,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[Vector stores](/docs/concepts/vectorstores)**: Storage of and efficient search over vectors and associated metadata.
 - **[Retriever](/docs/concepts/retrievers)**: A component that returns relevant documents from a knowledge base in response to a query.
 - **[Retrieval Augmented Generation (RAG)](/docs/concepts/rag)**: A technique that enhances language models by combining them with external knowledge bases.
-- **[Agents](/docs/concepts/agents)**: Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tool](/docs/concepts/tools).
+- **[Agents](/docs/concepts/agents)**: Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tools](/docs/concepts/tools).
 - **[Prompt templates](/docs/concepts/prompt_templates)**: Component for factoring out the static parts of a model "prompt" (usually a sequence of messages). Useful for serializing, versioning, and reusing these static parts.
 - **[Output parsers](/docs/concepts/output_parsers)**: Responsible for taking the output of a model and transforming it into a more suitable format for downstream tasks. Output parsers were primarily useful prior to the general availability of [tool calling](/docs/concepts/tool_calling) and [structured outputs](/docs/concepts/structured_outputs).
 - **[Few-shot prompting](/docs/concepts/few_shot_prompting)**: A technique for improving model performance by providing a few examples of the task to perform in the prompt.
@@ -48,7 +48,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[AIMessage](/docs/concepts/messages#aimessage)**: Represents a complete response from an AI model.
 - **[astream_events](/docs/concepts/chat_models#key-methods)**: Stream granular information from [LCEL](/docs/concepts/lcel) chains.
 - **[BaseTool](/docs/concepts/tools/#tool-interface)**: The base class for all tools in LangChain.
-- **[batch](/docs/concepts/runnables)**: Use to execute a runnable with batch inputs.
+- **[batch](/docs/concepts/runnables)**: Used to execute a runnable with batch inputs.
 - **[bind_tools](/docs/concepts/tool_calling/#tool-binding)**: Allows models to interact with tools.
 - **[Caching](/docs/concepts/chat_models#caching)**: Storing results to avoid redundant calls to a chat model.
 - **[Chat models](/docs/concepts/multimodality/#multimodality-in-chat-models)**: Chat models that handle multiple data modalities.

docs/docs/concepts/tracing.mdx

@@ -7,4 +7,4 @@ Traces contain individual steps called `runs`. These can be individual calls fro
 tool, or sub-chains.
 Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues.
 
-For a deeper dive, check out [this LangSmith conceptual guide](https://docs.smith.langchain.com/concepts/tracing).
+For a deeper dive, check out [this LangSmith conceptual guide](https://docs.langchain.com/langsmith/observability-quickstart).

docs/docs/contributing/how_to/code/guidelines.mdx

@@ -3,9 +3,9 @@
 Here are some things to keep in mind for all types of contributions:
 
 - Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
-- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
+- Fill out the checked-in pull request template when opening pull requests. Note related issues.
 - Ensure your PR passes formatting, linting, and testing checks before requesting a review.
-  - If you would like comments or feedback on your current progress, please open an issue or discussion and tag a maintainer.
+  - If you would like comments or feedback on your current progress, please open an issue or discussion.
   - See the sections on [Testing](setup.mdx#testing) and [Formatting and Linting](setup.mdx#formatting-and-linting) for how to run these checks locally.
 - Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
 - Look for duplicate PRs or issues that have already been opened before opening a new one.

docs/docs/contributing/how_to/documentation/style_guide.mdx

@@ -79,7 +79,7 @@ Here are some high-level tips on writing a good how-to guide:
 
 ### Conceptual guide
 
-LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. These guides should cover LangChain terms and concepts
+LangChain's conceptual guides fall under the **Explanation** quadrant of Diataxis. These guides should cover LangChain terms and concepts
 in a more abstract way than how-to guides or tutorials, targeting curious users interested in
 gaining a deeper understanding and insights of the framework. Try to avoid excessively large code examples as the primary goal is to
 provide perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
@@ -105,7 +105,7 @@ Here are some high-level tips on writing a good conceptual guide:
 ### References
 
 References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
-In LangChain, this is mainly our API reference pages, which are populated from docstrings within code.
+In LangChain, these are mainly our API reference pages, which are populated from docstrings within code.
 References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
 how to use something specific.
 
@@ -119,7 +119,7 @@ but here are some high-level tips on writing a good docstring:
 - Be concise
 - Discuss special cases and deviations from a user's expectations
 - Go into detail on required inputs and outputs
-- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
+- Light details on when one might use the feature are fine, but in-depth details belong in other sections
 
 Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
 
@@ -127,17 +127,17 @@ Each category serves a distinct purpose and requires a specific approach to writ
 
 Here are some other guidelines you should think about when writing and organizing documentation.
 
-We generally do not merge new tutorials from outside contributors without an actue need.
+We generally do not merge new tutorials from outside contributors without an acute need.
 We welcome updates as well as new integration docs, how-tos, and references.
 
 ### Avoid duplication
 
 Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
-be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
+be only one (very rarely two) canonical pages for a given concept or feature. Instead, you should link to other guides.
 
 ### Link to other sections
 
-Because sections of the docs do not exist in a vacuum, it is important to link to other sections frequently,
+Because sections of the docs do not exist in a vacuum, it is important to link to other sections frequently
 to allow a developer to learn more about an unfamiliar topic within the flow of reading.
 
 This includes linking to the API references and conceptual sections!

docs/docs/contributing/tutorials/docs.mdx

@@ -33,7 +33,7 @@ Sometimes you want to make a small change, like fixing a typo, and the easiest w
    - Click the "Commit changes..." button at the top-right corner of the page.
    - Give your commit a title like "Fix typo in X section."
    - Optionally, write an extended commit description.
-   - Click "Propose changes"
+   - Click "Propose changes".
 
 5. **Submit a pull request (PR):**
    - GitHub will redirect you to a page where you can create a pull request.

docs/docs/how_to/index.mdx

@@ -5,7 +5,7 @@ sidebar_class_name: hidden
 
 # How-to guides
 
-Here you’ll find answers to “How do I….?” types of questions.
+Here you’ll find answers to "How do I….?" types of questions.
 These guides are *goal-oriented* and *concrete*; they're meant to help you complete a specific task.
 For conceptual explanations see the [Conceptual guide](/docs/concepts/).
 For end-to-end walkthroughs see [Tutorials](/docs/tutorials).

docs/docs/how_to/indexing.ipynb

@@ -61,7 +61,7 @@
     "   * document addition by id (`add_documents` method with `ids` argument)\n",
     "   * delete by id (`delete` method with `ids` argument)\n",
     "\n",
-    "Compatible Vectorstores: `Aerospike`, `AnalyticDB`, `AstraDB`, `AwaDB`, `AzureCosmosDBNoSqlVectorSearch`, `AzureCosmosDBVectorSearch`, `AzureSearch`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MongoDBAtlasVectorSearch`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SingleStoreDB`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `Yellowbrick`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
+    "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `AzureCosmosDBNoSqlVectorSearch`, `AzureCosmosDBVectorSearch`, `AzureSearch`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MongoDBAtlasVectorSearch`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SingleStoreDB`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `Yellowbrick`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
     "  \n",
     "## Caution\n",
     "\n",

docs/docs/how_to/multimodal_inputs.ipynb

@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "1fcf7b27-1cc3-420a-b920-0420b5892e20",
    "metadata": {},
    "outputs": [
@@ -102,7 +102,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "99d27f8f-ae78-48bc-9bf2-3cef35213ec7",
    "metadata": {},
    "outputs": [
@@ -163,7 +163,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "325fb4ca",
    "metadata": {},
    "outputs": [
@@ -198,7 +198,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "6c1455a9-699a-4702-a7e0-7f6eaec76a21",
    "metadata": {},
    "outputs": [
@@ -284,7 +284,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "55e1d937-3b22-4deb-b9f0-9e688f0609dc",
    "metadata": {},
    "outputs": [
@@ -342,7 +342,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -417,7 +417,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "83593b9d-a8d3-4c99-9dac-64e0a9d397cb",
    "metadata": {},
    "outputs": [
@@ -488,13 +488,13 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)\n",
+    "print(response.text())\n",
     "response.usage_metadata"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "9bbf578e-794a-4dc0-a469-78c876ccd4a3",
    "metadata": {},
    "outputs": [
@@ -530,7 +530,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message, response, next_message])\n",
-    "print(response.text)\n",
+    "print(response.text())\n",
     "response.usage_metadata"
    ]
   },
@@ -600,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "ae076c9b-ff8f-461d-9349-250f396c9a25",
    "metadata": {},
    "outputs": [
@@ -641,7 +641,7 @@
     "    ],\n",
     "}\n",
     "response = llm.invoke([message])\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {

docs/docs/how_to/multimodal_prompts.ipynb

@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "5df2e558-321d-4cf7-994e-2815ac37e704",
    "metadata": {},
    "outputs": [
@@ -75,7 +75,7 @@
     "\n",
     "chain = prompt | llm\n",
     "response = chain.invoke({\"image_url\": url})\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "25e4829e-0073-49a8-9669-9f43e5778383",
    "metadata": {},
    "outputs": [
@@ -144,7 +144,7 @@
     "        \"cache_type\": \"ephemeral\",\n",
     "    }\n",
     ")\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {

docs/docs/how_to/tool_calling.ipynb

@@ -55,7 +55,7 @@
    "source": [
     "## Defining tool schemas\n",
     "\n",
-    "For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what it's arguments are. Chat models that support tool calling features implement a `.bind_tools()` method for passing tool schemas to the model. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain [Tool objects](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#basetool). Subsequent invocations of the model will pass in these tool schemas along with the prompt.\n",
+    "For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what its arguments are. Chat models that support tool calling features implement a `.bind_tools()` method for passing tool schemas to the model. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain [Tool objects](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#basetool). Subsequent invocations of the model will pass in these tool schemas along with the prompt.\n",
     "\n",
     "### Python functions\n",
     "Our tool schemas can be Python functions:"

docs/docs/integrations/callbacks/confident.ipynb

@@ -7,10 +7,7 @@
    "source": [
     "# Confident\n",
     "\n",
-    ">[DeepEval](https://confident-ai.com) package for unit testing LLMs.\n",
-    "> Using Confident, everyone can build robust language models through faster iterations\n",
-    "> using both unit testing and integration testing. We provide support for each step in the iteration\n",
-    "> from synthetic data creation to testing.\n"
+    ">[DeepEval](https://confident-ai.com) package for unit testing LLMs."
    ]
   },
   {
@@ -42,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain langchain-openai langchain-community deepeval langchain-chroma"
+    "!pip install deepeval langchain langchain-openai"
    ]
   },
   {
@@ -64,11 +61,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">🎉🥳 Congratulations! You've successfully logged in! 🙌 \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "🎉🥳 Congratulations! You've successfully logged in! 🙌 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "!deepeval login"
+    "import os\n",
+    "import deepeval\n",
+    "\n",
+    "api_key = os.getenv(\"DEEPEVAL_API_KEY\")\n",
+    "deepeval.login(api_key)"
    ]
   },
   {
@@ -76,12 +91,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setup DeepEval\n",
+    "### Setup Confident AI Callback (Modern)\n",
     "\n",
-    "You can, by default, use the `DeepEvalCallbackHandler` to set up the metrics you want to track. However, this has limited support for metrics at the moment (more to be added soon). It currently supports:\n",
-    "- [Answer Relevancy](https://docs.confident-ai.com/docs/measuring_llm_performance/answer_relevancy)\n",
-    "- [Bias](https://docs.confident-ai.com/docs/measuring_llm_performance/debias)\n",
-    "- [Toxicness](https://docs.confident-ai.com/docs/measuring_llm_performance/non_toxic)"
+    "The previous DeepEvalCallbackHandler and metric tracking are deprecated. Please use the new integration below."
    ]
   },
   {
@@ -90,10 +102,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from deepeval.metrics.answer_relevancy import AnswerRelevancy\n",
+    "from deepeval.integrations.langchain import CallbackHandler\n",
     "\n",
-    "# Here we want to make sure the answer is minimally relevant\n",
-    "answer_relevancy_metric = AnswerRelevancy(minimum_score=0.5)"
+    "handler = CallbackHandler(\n",
+    "    name=\"My Trace\",\n",
+    "    tags=[\"production\", \"v1\"],\n",
+    "    metadata={\"experiment\": \"A/B\"},\n",
+    "    thread_id=\"thread-123\",\n",
+    "    user_id=\"user-456\",\n",
+    ")"
    ]
   },
   {
@@ -103,186 +120,11 @@
    "source": [
     "## Get Started"
    ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To use the `DeepEvalCallbackHandler`, we need the `implementation_name`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.callbacks.confident_callback import DeepEvalCallbackHandler\n",
-    "\n",
-    "deepeval_callback = DeepEvalCallbackHandler(\n",
-    "    implementation_name=\"langchainQuickstart\", metrics=[answer_relevancy_metric]\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 1: Feeding into LLM\n",
-    "\n",
-    "You can then feed it into your LLM with OpenAI."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[Generation(text='\\n\\nQ: What did the fish say when he hit the wall? \\nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nThe Moon \\n\\nThe moon is high in the midnight sky,\\nSparkling like a star above.\\nThe night so peaceful, so serene,\\nFilling up the air with love.\\n\\nEver changing and renewing,\\nA never-ending light of grace.\\nThe moon remains a constant view,\\nA reminder of life’s gentle pace.\\n\\nThrough time and space it guides us on,\\nA never-fading beacon of hope.\\nThe moon shines down on us all,\\nAs it continues to rise and elope.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nQ. What did one magnet say to the other magnet?\\nA. \"I find you very attractive!\"', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text=\"\\n\\nThe world is charged with the grandeur of God.\\nIt will flame out, like shining from shook foil;\\nIt gathers to a greatness, like the ooze of oil\\nCrushed. Why do men then now not reck his rod?\\n\\nGenerations have trod, have trod, have trod;\\nAnd all is seared with trade; bleared, smeared with toil;\\nAnd wears man's smudge and shares man's smell: the soil\\nIs bare now, nor can foot feel, being shod.\\n\\nAnd for all this, nature is never spent;\\nThere lives the dearest freshness deep down things;\\nAnd though the last lights off the black West went\\nOh, morning, at the brown brink eastward, springs —\\n\\nBecause the Holy Ghost over the bent\\nWorld broods with warm breast and with ah! bright wings.\\n\\n~Gerard Manley Hopkins\", generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nQ: What did one ocean say to the other ocean?\\nA: Nothing, they just waved.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text=\"\\n\\nA poem for you\\n\\nOn a field of green\\n\\nThe sky so blue\\n\\nA gentle breeze, the sun above\\n\\nA beautiful world, for us to love\\n\\nLife is a journey, full of surprise\\n\\nFull of joy and full of surprise\\n\\nBe brave and take small steps\\n\\nThe future will be revealed with depth\\n\\nIn the morning, when dawn arrives\\n\\nA fresh start, no reason to hide\\n\\nSomewhere down the road, there's a heart that beats\\n\\nBelieve in yourself, you'll always succeed.\", generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 504, 'total_tokens': 528, 'prompt_tokens': 24}, 'model_name': 'text-davinci-003'})"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_openai import OpenAI\n",
-    "\n",
-    "llm = OpenAI(\n",
-    "    temperature=0,\n",
-    "    callbacks=[deepeval_callback],\n",
-    "    verbose=True,\n",
-    "    openai_api_key=\"<YOUR_API_KEY>\",\n",
-    ")\n",
-    "output = llm.generate(\n",
-    "    [\n",
-    "        \"What is the best evaluation tool out there? (no bias at all)\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can then check the metric if it was successful by calling the `is_successful()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "answer_relevancy_metric.is_successful()\n",
-    "# returns True/False"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once you have ran that, you should be able to see our dashboard below. \n",
-    "\n",
-    "![Dashboard](https://docs.confident-ai.com/assets/images/dashboard-screenshot-b02db73008213a211b1158ff052d969e.png)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 2: Tracking an LLM in a chain without callbacks\n",
-    "\n",
-    "To track an LLM in a chain without callbacks, you can plug into it at the end.\n",
-    "\n",
-    "We can start by defining a simple chain as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from langchain.chains import RetrievalQA\n",
-    "from langchain_chroma import Chroma\n",
-    "from langchain_community.document_loaders import TextLoader\n",
-    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
-    "from langchain_text_splitters import CharacterTextSplitter\n",
-    "\n",
-    "text_file_url = \"https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt\"\n",
-    "\n",
-    "openai_api_key = \"sk-XXX\"\n",
-    "\n",
-    "with open(\"state_of_the_union.txt\", \"w\") as f:\n",
-    "    response = requests.get(text_file_url)\n",
-    "    f.write(response.text)\n",
-    "\n",
-    "loader = TextLoader(\"state_of_the_union.txt\")\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)\n",
-    "docsearch = Chroma.from_documents(texts, embeddings)\n",
-    "\n",
-    "qa = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAI(openai_api_key=openai_api_key),\n",
-    "    chain_type=\"stuff\",\n",
-    "    retriever=docsearch.as_retriever(),\n",
-    ")\n",
-    "\n",
-    "# Providing a new question-answering pipeline\n",
-    "query = \"Who is the president?\"\n",
-    "result = qa.run(query)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After defining a chain, you can then manually check for answer similarity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "answer_relevancy_metric.measure(result, query)\n",
-    "answer_relevancy_metric.is_successful()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What's next?\n",
-    "\n",
-    "You can create your own custom metrics [here](https://docs.confident-ai.com/docs/quickstart/custom-metrics). \n",
-    "\n",
-    "DeepEval also offers other features such as being able to [automatically create unit tests](https://docs.confident-ai.com/docs/quickstart/synthetic-data-creation), [tests for hallucination](https://docs.confident-ai.com/docs/measuring_llm_performance/factual_consistency).\n",
-    "\n",
-    "If you are interested, check out our Github repository here [https://github.com/confident-ai/deepeval](https://github.com/confident-ai/deepeval). We welcome any PRs and discussions on how to improve LLM performance."
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain",
    "language": "python",
    "name": "python3"
   },
@@ -296,12 +138,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a53ebf4a859167383b364e7e7521d0add3c2dbbdecce4edf676e8c4634ff3fbb"
-   }
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/anthropic.ipynb

@@ -970,8 +970,8 @@
    "source": [
     "### In tool results (agentic RAG)\n",
     "\n",
-    ":::info Requires ``langchain-anthropic>=0.3.17``\n",
-    "\n",
+    ":::info\n",
+    "Requires ``langchain-anthropic>=0.3.17``\n",
     ":::\n",
     "\n",
     "Claude supports a [search_result](https://docs.anthropic.com/en/docs/build-with-claude/search-results) content block representing citable results from queries against a knowledge base or other custom source. These content blocks can be passed to claude both top-line (as in the above example) and within a tool result. This allows Claude to cite elements of its response using the result of a tool call.\n",
@@ -998,8 +998,6 @@
     "    ]\n",
     "```\n",
     "\n",
-    "We also need to specify the `search-results-2025-06-09` beta when instantiating ChatAnthropic. You can see an end-to-end example below.\n",
-    "\n",
     "<details>\n",
     "<summary>End to end example with LangGraph</summary>\n",
     "\n",
@@ -1292,6 +1290,58 @@
     "print(f\"Key Points: {result.key_points}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c580c20a",
+   "metadata": {},
+   "source": [
+    "### Web fetching\n",
+    "\n",
+    "Claude can use a [web fetching tool](https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/web-fetch-tool) to run searches and ground its responses with citations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cf6ad08",
+   "metadata": {},
+   "source": [
+    ":::info\n",
+    "Web search tool is supported since ``langchain-anthropic>=0.3.20``\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4804be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(\n",
+    "    model=\"claude-3-5-haiku-latest\",\n",
+    "    betas=[\"web-fetch-2025-09-10\"],  # Enable web fetch beta\n",
+    ")\n",
+    "\n",
+    "tool = {\"type\": \"web_fetch_20250910\", \"name\": \"web_fetch\", \"max_uses\": 3}\n",
+    "llm_with_tools = llm.bind_tools([tool])\n",
+    "\n",
+    "response = llm_with_tools.invoke(\n",
+    "    \"Please analyze the content at https://example.com/article\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "088c41d0",
+   "metadata": {},
+   "source": [
+    ":::warning\n",
+    "Note: you must add the `'web-fetch-2025-09-10'` beta header to use this tool.\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1478cdc6-2e52-4870-80f9-b4ddf88f2db2",
@@ -1301,14 +1351,14 @@
     "\n",
     "Claude can use a [code execution tool](https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/code-execution-tool) to execute Python code in a sandboxed environment.\n",
     "\n",
-    ":::info Code execution is supported since ``langchain-anthropic>=0.3.14``\n",
-    "\n",
+    ":::info\n",
+    "Code execution is supported since ``langchain-anthropic>=0.3.14``\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "2ce13632-a2da-439f-a429-f66481501630",
    "metadata": {},
    "outputs": [],
@@ -1317,7 +1367,7 @@
     "\n",
     "llm = ChatAnthropic(\n",
     "    model=\"claude-sonnet-4-20250514\",\n",
-    "    betas=[\"code-execution-2025-05-22\"],\n",
+    "    betas=[\"code-execution-2025-05-22\"],  # Enable code execution beta\n",
     ")\n",
     "\n",
     "tool = {\"type\": \"code_execution_20250522\", \"name\": \"code_execution\"}\n",
@@ -1328,6 +1378,16 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a6b5e15a",
+   "metadata": {},
+   "source": [
+    ":::warning\n",
+    "Note: you must add the `'code_execution_20250522'` beta header to use this tool.\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "24076f91-3a3d-4e53-9618-429888197061",
@@ -1406,14 +1466,14 @@
     "\n",
     "Claude can use a [MCP connector tool](https://docs.anthropic.com/en/docs/agents-and-tools/mcp-connector) for model-generated calls to remote MCP servers.\n",
     "\n",
-    ":::info Remote MCP is supported since ``langchain-anthropic>=0.3.14``\n",
-    "\n",
+    ":::info\n",
+    "Remote MCP is supported since ``langchain-anthropic>=0.3.14``\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "22fc4a89-e6d8-4615-96cb-2e117349aebf",
    "metadata": {},
    "outputs": [],
@@ -1425,17 +1485,17 @@
     "        \"type\": \"url\",\n",
     "        \"url\": \"https://mcp.deepwiki.com/mcp\",\n",
     "        \"name\": \"deepwiki\",\n",
-    "        \"tool_configuration\": {  # optional configuration\n",
+    "        \"tool_configuration\": {  # Optional configuration\n",
     "            \"enabled\": True,\n",
     "            \"allowed_tools\": [\"ask_question\"],\n",
     "        },\n",
-    "        \"authorization_token\": \"PLACEHOLDER\",  # optional authorization\n",
+    "        \"authorization_token\": \"PLACEHOLDER\",  # Optional authorization\n",
     "    }\n",
     "]\n",
     "\n",
     "llm = ChatAnthropic(\n",
     "    model=\"claude-sonnet-4-20250514\",\n",
-    "    betas=[\"mcp-client-2025-04-04\"],\n",
+    "    betas=[\"mcp-client-2025-04-04\"],  # Enable MCP beta\n",
     "    mcp_servers=mcp_servers,\n",
     ")\n",
     "\n",
@@ -1445,6 +1505,16 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0d6d7197",
+   "metadata": {},
+   "source": [
+    ":::warning\n",
+    "Note: you must add the `'mcp-client-2025-04-04'` beta header to use this tool.\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "2fd5d545-a40d-42b1-ad0c-0a79e2536c9b",
@@ -1457,7 +1527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "30a0af36-2327-4b1d-9ba5-e47cb72db0be",
    "metadata": {},
    "outputs": [
@@ -1493,7 +1563,7 @@
     "response = llm_with_tools.invoke(\n",
     "    \"There's a syntax error in my primes.py file. Can you help me fix it?\"\n",
     ")\n",
-    "print(response.text)\n",
+    "print(response.text())\n",
     "response.tool_calls"
    ]
   },

docs/docs/integrations/chat/bedrock.ipynb

@@ -243,12 +243,12 @@
    "id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
    "metadata": {},
    "source": [
-    "You can filter to text using the [.text](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.text) property on the output:"
+    "You can filter to text using the [.text()](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.text) method on the output:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
    "metadata": {},
    "outputs": [
@@ -262,7 +262,7 @@
    ],
    "source": [
     "for chunk in llm.stream(messages):\n",
-    "    print(chunk.text, end=\"|\")"
+    "    print(chunk.text(), end=\"|\")"
    ]
   },
   {

docs/docs/integrations/chat/litellm.ipynb

@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
    "metadata": {
     "colab": {
@@ -286,7 +286,7 @@
    ],
    "source": [
     "async for token in llm.astream(\"Hello, please explain how antibiotics work\"):\n",
-    "    print(token.text, end=\"\")"
+    "    print(token.text(), end=\"\")"
    ]
   },
   {

docs/docs/integrations/chat/openai.ipynb

@@ -814,7 +814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "1f758726-33ef-4c04-8a54-49adb783bbb3",
    "metadata": {},
    "outputs": [
@@ -860,7 +860,7 @@
     "llm_with_tools = llm.bind_tools([tool])\n",
     "\n",
     "response = llm_with_tools.invoke(\"What is deep research by OpenAI?\")\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
@@ -1151,7 +1151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "073f6010-6b0e-4db6-b2d3-7427c8dec95b",
    "metadata": {},
    "outputs": [
@@ -1167,7 +1167,7 @@
     }
    ],
    "source": [
-    "response_2.text"
+    "response_2.text()"
    ]
   },
   {
@@ -1198,7 +1198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "b6da5bd6-a44a-4c64-970b-30da26b003d6",
    "metadata": {},
    "outputs": [
@@ -1214,7 +1214,7 @@
     }
    ],
    "source": [
-    "response_2.text"
+    "response_2.text()"
    ]
   },
   {
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "51d3e4d3-ea78-426c-9205-aecb0937fca7",
    "metadata": {},
    "outputs": [
@@ -1428,13 +1428,13 @@
     "messages = [{\"role\": \"user\", \"content\": first_query}]\n",
     "\n",
     "response = llm_with_tools.invoke(messages)\n",
-    "response_text = response.text\n",
+    "response_text = response.text()\n",
     "print(f\"{response_text[:100]}... {response_text[-100:]}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b248bedf-2050-4c17-a90e-3a26eeb1b055",
    "metadata": {},
    "outputs": [
@@ -1460,7 +1460,7 @@
     "    ]\n",
     ")\n",
     "second_response = llm_with_tools.invoke(messages)\n",
-    "print(second_response.text)"
+    "print(second_response.text())"
    ]
   },
   {
@@ -1482,7 +1482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "009e541a-b372-410e-b9dd-608a8052ce09",
    "metadata": {},
    "outputs": [
@@ -1502,12 +1502,12 @@
     "    output_version=\"responses/v1\",\n",
     ")\n",
     "response = llm.invoke(\"Hi, I'm Bob.\")\n",
-    "print(response.text)"
+    "print(response.text())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "393a443a-4c5f-4a07-bc0e-c76e529b35e3",
    "metadata": {},
    "outputs": [
@@ -1524,7 +1524,7 @@
     "    \"What is my name?\",\n",
     "    previous_response_id=response.response_metadata[\"id\"],\n",
     ")\n",
-    "print(second_response.text)"
+    "print(second_response.text())"
    ]
   },
   {
@@ -1589,7 +1589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "8d322f3a-0732-45ab-ac95-dfd4596e0d85",
    "metadata": {},
    "outputs": [
@@ -1616,7 +1616,7 @@
     "response = llm.invoke(\"What is 3^3?\")\n",
     "\n",
     "# Output\n",
-    "response.text"
+    "response.text()"
    ]
   },
   {

docs/docs/integrations/document_loaders/oracleadb_loader.ipynb

@@ -2,67 +2,91 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "# Oracle Autonomous Database\n",
     "\n",
-    "Oracle autonomous database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs.\n",
+    "Oracle Autonomous Database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs.\n",
     "\n",
-    "This notebook covers how to load documents from oracle autonomous database, the loader supports connection with connection string or tns configuration.\n",
+    "This notebook covers how to load documents from Oracle Autonomous Database.\n",
     "\n",
     "## Prerequisites\n",
-    "1. Database runs in a 'Thin' mode:\n",
-    "   https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_b.html\n",
-    "2. `pip install oracledb`:\n",
-    "   https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+    "1. Install python-oracledb:\n",
+    "\n",
+    "   `pip install oracledb`\n",
+    "  \n",
+    "   See [Installing python-oracledb](https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html).\n",
+    "\n",
+    "2. A database that python-oracledb's default 'Thin' mode can connected to. This is true of Oracle Autonomous Database, see [python-oracledb Architecture](https://python-oracledb.readthedocs.io/en/latest/user_guide/introduction.html#architecture).\n"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Instructions"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "pip install oracledb"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_community.document_loaders import OracleAutonomousDatabaseLoader\n",
     "from settings import s"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "With mutual TLS authentication (mTLS), wallet_location and wallet_password are required to create the connection, user can create connection by providing either connection string or tns configuration details."
-   ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "With mutual TLS authentication (mTLS), wallet_location and wallet_password parameters are required to create the connection. See python-oracledb documentation [Connecting to Oracle Cloud Autonomous Databases](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html#connecting-to-oracle-cloud-autonomous-databases)."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "SQL_QUERY = \"select prod_id, time_id from sh.costs fetch first 5 rows only\"\n",
@@ -89,24 +113,30 @@
     "    wallet_password=s.PASSWORD,\n",
     ")\n",
     "doc_2 = doc_loader_2.load()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "With TLS authentication, wallet_location and wallet_password are not required.\n",
-    "Bind variable option is provided by argument \"parameters\"."
-   ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "With 1-way TLS authentication, only the database credentials and connection string are required to establish a connection.\n",
+    "The example below also shows passing bind variable values with the argument \"parameters\"."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "SQL_QUERY = \"select channel_id, channel_desc from sh.channels where channel_desc = :1 fetch first 5 rows only\"\n",
@@ -131,31 +161,28 @@
     "    parameters=[\"Direct Sales\"],\n",
     ")\n",
     "doc_4 = doc_loader_4.load()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }

docs/docs/integrations/providers/aerospike.mdx

@@ -1,26 +0,0 @@
-# Aerospike
-
->[Aerospike](https://aerospike.com/docs/vector) is a high-performance, distributed database known for its speed and scalability, now with support for vector storage and search, enabling retrieval and search of embedding vectors for machine learning and AI applications.
-> See the documentation for Aerospike Vector Search (AVS) [here](https://aerospike.com/docs/vector).
-
-## Installation and Setup
-
-Install the AVS Python SDK and AVS langchain vector store:
-
-```bash
-pip install aerospike-vector-search langchain-aerospike
-```
-
-See the documentation for the Python SDK [here](https://aerospike-vector-search-python-client.readthedocs.io/en/latest/index.html).
-The documentation for the AVS langchain vector store is [here](https://langchain-aerospike.readthedocs.io/en/latest/).
-
-## Vector Store
-
-To import this vectorstore:
-
-```python
-from langchain_aerospike.vectorstores import Aerospike
-```
-
-See a usage example [here](https://python.langchain.com/docs/integrations/vectorstores/aerospike/).
-

docs/docs/integrations/providers/confident.mdx

@@ -21,6 +21,38 @@ pip install deepeval
 
 See an [example](/docs/integrations/callbacks/confident).
 
-```python
-from langchain.callbacks.confident_callback import DeepEvalCallbackHandler
+
+## Modern Integration Example
+
+Install the required packages:
+
+```bash
+pip install deepeval langchain langchain-openai
 ```
+
+Authenticate with your API key:
+
+```python
+import os
+import deepeval
+
+# Load API key from environment variable for security
+api_key = os.environ.get("DEEPEVAL_API_KEY")
+deepeval.login(api_key)
+```
+
+Use the new callback handler:
+
+```python
+from deepeval.integrations.langchain import CallbackHandler
+
+handler = CallbackHandler(
+    name="My Trace",
+    tags=["production", "v1"],
+    metadata={"experiment": "A/B"},
+    thread_id="thread-123",
+    user_id="user-456"
+)
+```
+
+See the [full example](/docs/integrations/callbacks/confident).

docs/docs/integrations/providers/ibm.mdx

@@ -77,7 +77,7 @@ from langchain_ibm import WatsonxRerank
 See a [usage example](/docs/integrations/tools/ibm_watsonx).
 
 ```python
-from langchain_ibm import WatsonxToolkit
+from langchain_ibm.agent_toolkits.utility import WatsonxToolkit
 ```
 
 ## DB2

docs/docs/integrations/providers/modelscope.mdx

@@ -40,11 +40,11 @@ embeddings.embed_query("What is the meaning of life?")
 ```
 
 ## LLMs
-`ModelScopeLLM` class exposes LLMs from ModelScope.
+`ModelScopeEndpoint` class exposes LLMs from ModelScope.
 
 ```python
-from langchain_modelscope import ModelScopeLLM
+from langchain_modelscope import ModelScopeEndpoint
 
-llm = ModelScopeLLM(model="Qwen/Qwen2.5-Coder-32B-Instruct")
+llm = ModelScopeEndpoint(model="Qwen/Qwen2.5-Coder-32B-Instruct")
 llm.invoke("The meaning of life is")
 ```

docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb

@@ -103,7 +103,9 @@
       "cell_type": "markdown",
       "id": "c84fb993",
       "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
     },
     {
       "cell_type": "code",
@@ -157,7 +159,7 @@
         "from langchain_google_vertexai import VertexAIEmbeddings\n",
         "\n",
         "# Initialize the a specific Embeddings Model version\n",
-        "embeddings = VertexAIEmbeddings(model_name=\"text-embedding-004\")"
+        "embeddings = VertexAIEmbeddings(model_name=\"gemini-embedding-001\")"
       ]
     },
     {

docs/docs/integrations/tools/anchor_browser.ipynb

@@ -1,19 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "2ce4bdbc",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "---\n",
-    "sidebar_label: anchor_browser\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "a6f91f20",
@@ -63,7 +49,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --quiet -U langchain-anchorbrowser"
+    "%pip install --quiet -U langchain-anchorbrowser pydantic"
    ]
   },
   {
@@ -147,16 +133,27 @@
     "    {\"url\": \"https://docs.anchorbrowser.io\", \"width\": 1280, \"height\": 720}\n",
     ")\n",
     "\n",
-    "# Get a Screenshot for https://docs.anchorbrowser.io\n",
+    "# Define a Pydantic model for the web task output schema\n",
+    "from pydantic import BaseModel\n",
+    "from typing import List\n",
+    "\n",
+    "\n",
+    "class NodeCpuUsage(BaseModel):\n",
+    "    node: str\n",
+    "    cluster: str\n",
+    "    cpu_avg_percentage: float\n",
+    "\n",
+    "\n",
+    "class OutputSchema(BaseModel):\n",
+    "    nodes_cpu_usage: List[NodeCpuUsage]\n",
+    "\n",
+    "\n",
+    "# Run a web task to collect data from a web page\n",
     "anchor_advanced_web_task_tool.invoke(\n",
     "    {\n",
     "        \"prompt\": \"Collect the node names and their CPU average %\",\n",
     "        \"url\": \"https://play.grafana.org/a/grafana-k8s-app/navigation/nodes?from=now-1h&to=now&refresh=1m\",\n",
-    "        \"output_schema\": {\n",
-    "            \"nodes_cpu_usage\": [\n",
-    "                {\"node\": \"string\", \"cluster\": \"string\", \"cpu_avg_percentage\": \"number\"}\n",
-    "            ]\n",
-    "        },\n",
+    "        \"output_schema\": OutputSchema.model_json_schema(),\n",
     "    }\n",
     ")"
    ]

docs/docs/integrations/tools/ibm_watsonx.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/toolkits/ibm/) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: | :---: | :---: |\n",
-    "| [WatsonxToolkit](https://python.langchain.com/api_reference/ibm/toolkit/langchain_ibm.toolkit.WatsonxToolkit.html) | [langchain-ibm](https://python.langchain.com/api_reference/ibm/index.html) | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |"
+    "| [WatsonxToolkit](https://python.langchain.com/api_reference/ibm/agent_toolkits/langchain_ibm.agent_toolkits.utility.toolkit.WatsonxToolkit.html) | [langchain-ibm](https://python.langchain.com/api_reference/ibm/index.html) | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |"
    ]
   },
   {
@@ -78,7 +78,9 @@
     "import os\n",
     "\n",
     "os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n",
-    "os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the service instance\""
+    "os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the CLOUD or CPD cluster\"\n",
+    "os.environ[\"WATSONX_PASSWORD\"] = \"your password for accessing the CPD cluster\"\n",
+    "os.environ[\"WATSONX_USERNAME\"] = \"your username for accessing the CPD cluster\""
    ]
   },
   {
@@ -116,17 +118,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_ibm import WatsonxToolkit\n",
+    "from langchain_ibm.agent_toolkits.utility import WatsonxToolkit\n",
     "\n",
     "watsonx_toolkit = WatsonxToolkit(\n",
     "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can use Cloud Pak for Data credentials. For details, see [watsonx.ai software setup](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "watsonx_toolkit = WatsonxToolkit(\n",
+    "    url=\"PASTE YOUR URL HERE\",\n",
+    "    username=\"PASTE YOUR USERNAME HERE\",\n",
+    "    password=\"PASTE YOUR PASSWORD HERE\",\n",
+    "    version=\"5.2\",\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -153,7 +176,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Tools\n"
+    "## Tools"
    ]
   },
   {
@@ -187,6 +210,14 @@
     "watsonx_toolkit.get_tools()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **NOTE**  \n",
+    "> The list of available tools may vary depending on whether it is IBM watsonx.ai for IBM Cloud or IBM watsonx.ai software."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -220,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +266,7 @@
     }
    ],
    "source": [
-    "search_result = google_search.invoke(input=\"IBM\")\n",
+    "search_result = google_search.invoke({\"q\": \"IBM\"})\n",
     "search_result"
    ]
   },
@@ -308,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +348,7 @@
     "config = {\"maxResults\": 3}\n",
     "google_search.set_tool_config(config)\n",
     "\n",
-    "search_result = google_search.invoke(input=\"IBM\")\n",
+    "search_result = google_search.invoke({\"q\": \"IBM\"})\n",
     "output = json.loads(search_result.get(\"output\"))"
    ]
   },
@@ -578,13 +609,13 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `WatsonxToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/ibm/toolkit/langchain_ibm.toolkit.WatsonxToolkit.html)."
+    "For detailed documentation of all `WatsonxToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/ibm/agent_toolkits/langchain_ibm.agent_toolkits.utility.toolkit.WatsonxToolkit.html)."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain_env",
+   "display_name": "langchain_ibm_repo_env",
    "language": "python",
    "name": "python3"
   },

docs/docs/integrations/tools/scrapegraph.ipynb

@@ -32,6 +32,7 @@
     "| [SmartScraperTool](https://python.langchain.com/docs/integrations/tools/scrapegraph) | langchain-scrapegraph | ✅ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-scrapegraph?style=flat-square&label=%20) |\n",
     "| [SmartCrawlerTool](https://python.langchain.com/docs/integrations/tools/scrapegraph) | langchain-scrapegraph | ✅ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-scrapegraph?style=flat-square&label=%20) |\n",
     "| [MarkdownifyTool](https://python.langchain.com/docs/integrations/tools/scrapegraph) | langchain-scrapegraph | ✅ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-scrapegraph?style=flat-square&label=%20) |\n",
+    "| [AgenticScraperTool](https://python.langchain.com/docs/integrations/tools/scrapegraph) | langchain-scrapegraph | ✅ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-scrapegraph?style=flat-square&label=%20) |\n",
     "| [GetCreditsTool](https://python.langchain.com/docs/integrations/tools/scrapegraph) | langchain-scrapegraph | ✅ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-scrapegraph?style=flat-square&label=%20) |\n",
     "\n",
     "### Tool features\n",
@@ -41,6 +42,7 @@
     "| SmartScraperTool | Extract structured data from websites | URL + prompt | JSON |\n",
     "| SmartCrawlerTool | Extract data from multiple pages with crawling | URL + prompt + crawl options | JSON |\n",
     "| MarkdownifyTool | Convert webpages to markdown | URL | Markdown text |\n",
+    "| AgenticScraperTool | Extract specifying steps | URL | Markdown text |\n",
     "| GetCreditsTool | Check API credits | None | Credit info |\n",
     "\n",
     "\n",
@@ -51,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "f85b4089",
    "metadata": {},
    "outputs": [
@@ -79,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "e0b178a2",
    "metadata": {},
    "outputs": [],
@@ -285,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "f90e33a7",
    "metadata": {},
    "outputs": [
@@ -329,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "af3123ad",
    "metadata": {},
    "outputs": [
@@ -353,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "fdbf35b5",
    "metadata": {},
    "outputs": [

docs/docs/integrations/vectorstores/aerospike.ipynb

@@ -1,555 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Aerospike\n",
-    "\n",
-    "[Aerospike Vector Search](https://aerospike.com/docs/vector) (AVS) is an\n",
-    "extension to the Aerospike Database that enables searches across very large\n",
-    "datasets stored in Aerospike. This new service lives outside of Aerospike and\n",
-    "builds an index to perform those searches.\n",
-    "\n",
-    "This notebook showcases the functionality of the [LangChain Aerospike VectorStore\n",
-    "integration](https://github.com/aerospike/langchain-aerospike).\n",
-    "\n",
-    "## Install AVS\n",
-    "\n",
-    "Before using this notebook, we need to have a running AVS instance. Use one of\n",
-    "the [available installation methods](https://aerospike.com/docs/vector/install). \n",
-    "\n",
-    "When finished, store your AVS instance's IP address and port to use later\n",
-    "in this demo:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "AVS_HOST = \"<avs_ip>\"\n",
-    "AVS_PORT = 5000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Install Dependencies \n",
-    "The `sentence-transformers` dependency is large. This step could take several minutes to complete."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\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;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\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\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install --upgrade --quiet aerospike-vector-search==4.2.0 langchain-aerospike langchain-community sentence-transformers langchain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download Quotes Dataset\n",
-    "\n",
-    "We will download a dataset of approximately 100,000 quotes and use a subset of those quotes for semantic search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2025-05-07 21:06:30--  https://github.com/aerospike/aerospike-vector-search-examples/raw/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz\n",
-      "Resolving github.com (github.com)... 140.82.116.3\n",
-      "Connecting to github.com (github.com)|140.82.116.3|:443... connected.\n",
-      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
-      "Location: https://github.com/aerospike/aerospike-vector/raw/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz [following]\n",
-      "--2025-05-07 21:06:30--  https://github.com/aerospike/aerospike-vector/raw/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz\n",
-      "Reusing existing connection to github.com:443.\n",
-      "HTTP request sent, awaiting response... 302 Found\n",
-      "Location: https://raw.githubusercontent.com/aerospike/aerospike-vector/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz [following]\n",
-      "--2025-05-07 21:06:30--  https://raw.githubusercontent.com/aerospike/aerospike-vector/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz\n",
-      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
-      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 11597643 (11M) [application/octet-stream]\n",
-      "Saving to: ‘quotes.csv.tgz’\n",
-      "\n",
-      "quotes.csv.tgz      100%[===================>]  11.06M  12.7MB/s    in 0.9s    \n",
-      "\n",
-      "2025-05-07 21:06:32 (12.7 MB/s) - ‘quotes.csv.tgz’ saved [11597643/11597643]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget https://github.com/aerospike/aerospike-vector-search-examples/raw/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the Quotes Into Documents\n",
-    "\n",
-    "We will load our quotes dataset using the `CSVLoader` document loader. In this case, `lazy_load` returns an iterator to ingest our quotes more efficiently. In this example, we only load 5,000 quotes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import itertools\n",
-    "import os\n",
-    "import tarfile\n",
-    "\n",
-    "from langchain_community.document_loaders.csv_loader import CSVLoader\n",
-    "\n",
-    "filename = \"./quotes.csv\"\n",
-    "\n",
-    "if not os.path.exists(filename) and os.path.exists(filename + \".tgz\"):\n",
-    "    # Untar the file\n",
-    "    with tarfile.open(filename + \".tgz\", \"r:gz\") as tar:\n",
-    "        tar.extractall(path=os.path.dirname(filename))\n",
-    "\n",
-    "NUM_QUOTES = 5000\n",
-    "documents = CSVLoader(filename, metadata_columns=[\"author\", \"category\"]).lazy_load()\n",
-    "documents = list(\n",
-    "    itertools.islice(documents, NUM_QUOTES)\n",
-    ")  # Allows us to slice an iterator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='quote: I'm selfish, impatient and a little insecure. I make mistakes, I am out of control and at times hard to handle. But if you can't handle me at my worst, then you sure as hell don't deserve me at my best.' metadata={'source': './quotes.csv', 'row': 0, 'author': 'Marilyn Monroe', 'category': 'attributed-no-source, best, life, love, mistakes, out-of-control, truth, worst'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(documents[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create your Embedder\n",
-    "\n",
-    "In this step, we use HuggingFaceEmbeddings and the \"all-MiniLM-L6-v2\" sentence transformer model to embed our documents so we can perform a vector search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/h5/lm2_c1xs3s32kwp11prnpftw0000gp/T/ipykernel_84638/3255399720.py:6: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFaceEmbeddings``.\n",
-      "  embedder = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
-      "/Users/dwelch/Desktop/everything/projects/langchain/myfork/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": [
-    "from aerospike_vector_search.types import VectorDistanceMetric\n",
-    "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
-    "\n",
-    "MODEL_DIM = 384\n",
-    "MODEL_DISTANCE_CALC = VectorDistanceMetric.COSINE\n",
-    "embedder = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create an Aerospike Index and Embed Documents\n",
-    "\n",
-    "Before we add documents, we need to create an index in the Aerospike Database. In the example below, we use some convenience code that checks to see if the expected index already exists."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "quote-miniLM-L6-v2 does not exist. Creating index\n"
-     ]
-    }
-   ],
-   "source": [
-    "from aerospike_vector_search import Client, HostPort\n",
-    "from aerospike_vector_search.types import VectorDistanceMetric\n",
-    "from langchain_aerospike.vectorstores import Aerospike\n",
-    "\n",
-    "# Here we are using the AVS host and port you configured earlier\n",
-    "seed = HostPort(host=AVS_HOST, port=AVS_PORT)\n",
-    "\n",
-    "# The namespace of where to place our vectors. This should match the vector configured in your docstore.conf file.\n",
-    "NAMESPACE = \"test\"\n",
-    "\n",
-    "# The name of our new index.\n",
-    "INDEX_NAME = \"quote-miniLM-L6-v2\"\n",
-    "\n",
-    "# AVS needs to know which metadata key contains our vector when creating the index and inserting documents.\n",
-    "VECTOR_KEY = \"vector\"\n",
-    "\n",
-    "client = Client(seeds=seed)\n",
-    "index_exists = False\n",
-    "\n",
-    "# Check if the index already exists. If not, create it\n",
-    "for index in client.index_list():\n",
-    "    if index[\"id\"][\"namespace\"] == NAMESPACE and index[\"id\"][\"name\"] == INDEX_NAME:\n",
-    "        index_exists = True\n",
-    "        print(f\"{INDEX_NAME} already exists. Skipping creation\")\n",
-    "        break\n",
-    "\n",
-    "if not index_exists:\n",
-    "    print(f\"{INDEX_NAME} does not exist. Creating index\")\n",
-    "    client.index_create(\n",
-    "        namespace=NAMESPACE,\n",
-    "        name=INDEX_NAME,\n",
-    "        vector_field=VECTOR_KEY,\n",
-    "        vector_distance_metric=MODEL_DISTANCE_CALC,\n",
-    "        dimensions=MODEL_DIM,\n",
-    "        index_labels={\n",
-    "            \"model\": \"miniLM-L6-v2\",\n",
-    "            \"date\": \"05/04/2024\",\n",
-    "            \"dim\": str(MODEL_DIM),\n",
-    "            \"distance\": \"cosine\",\n",
-    "        },\n",
-    "    )\n",
-    "\n",
-    "docstore = Aerospike.from_documents(\n",
-    "    documents,\n",
-    "    embedder,\n",
-    "    client=client,\n",
-    "    namespace=NAMESPACE,\n",
-    "    vector_key=VECTOR_KEY,\n",
-    "    index_name=INDEX_NAME,\n",
-    "    distance_strategy=MODEL_DISTANCE_CALC,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Search the Documents\n",
-    "Now that we have embedded our vectors, we can use vector search on our quotes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "~~~~ Document 0 ~~~~\n",
-      "auto-generated id: 4984b472-8a32-4552-b3eb-f03b31b68031\n",
-      "author:  Carl Sagan, Cosmos\n",
-      "quote: The Cosmos is all that is or was or ever will be. Our feeblest contemplations of the Cosmos stir us -- there is a tingling in the spine, a catch in the voice, a faint sensation, as if a distant memory, of falling from a height. We know we are approaching the greatest of mysteries.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 1 ~~~~\n",
-      "auto-generated id: 486c8d87-8dd7-450d-9008-d7549e680ffb\n",
-      "author:  Renee Ahdieh, The Rose & the Dagger\n",
-      "quote: From the stars, to the stars.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 2 ~~~~\n",
-      "auto-generated id: 4b43b309-ce51-498c-b225-5254383b5b4a\n",
-      "author:  Elizabeth Gilbert\n",
-      "quote: The love that moves the sun and the other stars.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 3 ~~~~\n",
-      "auto-generated id: af784a10-f498-4570-bf81-2ffdca35440e\n",
-      "author:  Dante Alighieri, Paradiso\n",
-      "quote: Love, that moves the sun and the other stars\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 4 ~~~~\n",
-      "auto-generated id: b45d5d5e-d818-4206-ae6b-b1d166ea3d43\n",
-      "author:  Thich Nhat Hanh, Teachings on Love\n",
-      "quote: Through my love for you, I want to express my love for the whole cosmos, the whole of humanity, and all beings. By living with you, I want to learn to love everyone and all species. If I succeed in loving you, I will be able to love everyone and all species on Earth... This is the real message of love.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"A quote about the beauty of the cosmos\"\n",
-    "docs = docstore.similarity_search(\n",
-    "    query, k=5, index_name=INDEX_NAME, metadata_keys=[\"_id\", \"author\"]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def print_documents(docs):\n",
-    "    for i, doc in enumerate(docs):\n",
-    "        print(\"~~~~ Document\", i, \"~~~~\")\n",
-    "        print(\"auto-generated id:\", doc.metadata[\"_id\"])\n",
-    "        print(\"author: \", doc.metadata[\"author\"])\n",
-    "        print(doc.page_content)\n",
-    "        print(\"~~~~~~~~~~~~~~~~~~~~\\n\")\n",
-    "\n",
-    "\n",
-    "print_documents(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Embedding Additional Quotes as Text\n",
-    "\n",
-    "We can use `add_texts` to add additional quotes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "New IDs\n",
-      "['adf8064e-9c0e-46e2-b193-169c36432f4c', 'cf65b5ed-a0f4-491a-86ad-dcacc23c2815', '2ef52efd-d9b7-4077-bc14-defdf0b7dd2f']\n"
-     ]
-    }
-   ],
-   "source": [
-    "docstore = Aerospike(\n",
-    "    client,\n",
-    "    embedder,\n",
-    "    NAMESPACE,\n",
-    "    index_name=INDEX_NAME,\n",
-    "    vector_key=VECTOR_KEY,\n",
-    "    distance_strategy=MODEL_DISTANCE_CALC,\n",
-    ")\n",
-    "\n",
-    "ids = docstore.add_texts(\n",
-    "    [\n",
-    "        \"quote: Rebellions are built on hope.\",\n",
-    "        \"quote: Logic is the beginning of wisdom, not the end.\",\n",
-    "        \"quote: If wishes were fishes, we’d all cast nets.\",\n",
-    "    ],\n",
-    "    metadatas=[\n",
-    "        {\"author\": \"Jyn Erso, Rogue One\"},\n",
-    "        {\"author\": \"Spock, Star Trek\"},\n",
-    "        {\"author\": \"Frank Herbert, Dune\"},\n",
-    "    ],\n",
-    ")\n",
-    "\n",
-    "print(\"New IDs\")\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Search Documents Using Max Marginal Relevance Search\n",
-    "\n",
-    "We can use max marginal relevance search to find vectors that are similar to our query but dissimilar to each other. In this example, we create a retriever object using `as_retriever`, but this could be done just as easily by calling `docstore.max_marginal_relevance_search` directly. The `lambda_mult` search argument determines the diversity of our query response. 0 corresponds to maximum diversity and 1 to minimum diversity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "~~~~ Document 0 ~~~~\n",
-      "auto-generated id: 91e77b39-a528-40c6-a58a-486ae85f991a\n",
-      "author:  John Grogan, Marley and Me: Life and Love With the World's Worst Dog\n",
-      "quote: Such short little lives our pets have to spend with us, and they spend most of it waiting for us to come home each day. It is amazing how much love and laughter they bring into our lives and even how much closer we become with each other because of them.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 1 ~~~~\n",
-      "auto-generated id: c585b4ec-92b5-4579-948c-0529373abc2a\n",
-      "author:  John Grogan, Marley and Me: Life and Love With the World's Worst Dog\n",
-      "quote: Dogs are great. Bad dogs, if you can really call them that, are perhaps the greatest of them all.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 2 ~~~~\n",
-      "auto-generated id: 5768b31c-fac4-4af7-84b4-fb11bbfcb590\n",
-      "author:  Colleen Houck, Tiger's Curse\n",
-      "quote: He then put both hands on the door on either side of my head and leaned in close, pinning me against it. I trembled like a downy rabbit caught in the clutches of a wolf. The wolf came closer. He bent his head and began nuzzling my cheek. The problem was…I wanted the wolf to devour me.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 3 ~~~~\n",
-      "auto-generated id: 94f1b9fb-ad57-4f65-b470-7f49dd6c274c\n",
-      "author:  Ray Bradbury\n",
-      "quote: Stuff your eyes with wonder,\" he said, \"live as if you'd drop dead in ten seconds. See the world. It's more fantastic than any dream made or paid for in factories. Ask no guarantees, ask for no security, there never was such an animal. And if there were, it would be related to the great sloth which hangs upside down in a tree all day every day, sleeping its life away. To hell with that,\" he said, \"shake the tree and knock the great sloth down on his ass.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"A quote about our favorite four-legged pets\"\n",
-    "retriever = docstore.as_retriever(\n",
-    "    search_type=\"mmr\", search_kwargs={\"fetch_k\": 20, \"lambda_mult\": 0.7}\n",
-    ")\n",
-    "matched_docs = retriever.invoke(query)\n",
-    "\n",
-    "print_documents(matched_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Search Documents with a Relevance Threshold\n",
-    "\n",
-    "Another useful feature is a similarity search with a relevance threshold. Generally, we only want results that are most similar to our query but also within some range of proximity. A relevance of 1 is most similar and a relevance of 0 is most dissimilar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "~~~~ Document 0 ~~~~\n",
-      "auto-generated id: 6d9e67a6-0427-41e6-9e24-050518120d74\n",
-      "author:  Roy T. Bennett, The Light in the Heart\n",
-      "quote: Never lose hope. Storms make people stronger and never last forever.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 1 ~~~~\n",
-      "auto-generated id: 7d426e59-7935-4bcf-a676-cbe8dd4860e7\n",
-      "author:  Roy T. Bennett, The Light in the Heart\n",
-      "quote: Difficulties and adversities viciously force all their might on us and cause us to fall apart, but they are necessary elements of individual growth and reveal our true potential. We have got to endure and overcome them, and move forward. Never lose hope. Storms make people stronger and never last forever.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 2 ~~~~\n",
-      "auto-generated id: 6ec05e48-d162-440d-8819-001d2f3712f9\n",
-      "author:  Vincent van Gogh, The Letters of Vincent van Gogh\n",
-      "quote: There is peace even in the storm\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n",
-      "~~~~ Document 3 ~~~~\n",
-      "auto-generated id: d3c3de59-4da4-4ae6-8f6d-83ed905dd320\n",
-      "author:  Edwin Morgan, A Book of Lives\n",
-      "quote: Valentine WeatherKiss me with rain on your eyelashes,come on, let us sway together,under the trees, and to hell with thunder.\n",
-      "~~~~~~~~~~~~~~~~~~~~\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"A quote about stormy weather\"\n",
-    "retriever = docstore.as_retriever(\n",
-    "    search_type=\"similarity_score_threshold\",\n",
-    "    search_kwargs={\n",
-    "        \"score_threshold\": 0.4\n",
-    "    },  # A greater value returns items with more relevance\n",
-    ")\n",
-    "matched_docs = retriever.invoke(query)\n",
-    "\n",
-    "print_documents(matched_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clean up\n",
-    "\n",
-    "We need to make sure we close our client to release resources and clean up threads."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ready. Set. Search!\n",
-    "\n",
-    "Now that you are up to speed with Aerospike Vector Search's LangChain integration, you have the power of the Aerospike Database and the LangChain ecosystem at your finger tips. Happy building!"
-   ]
-  }
- ],
- "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": "3.11.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/vectorstores/jaguar.ipynb

@@ -28,10 +28,25 @@
     "1. You must install and set up the JaguarDB server and its HTTP gateway server.\n",
     "   Please refer to the instructions in:\n",
     "   [www.jaguardb.com](http://www.jaguardb.com)\n",
+    "\n",
+    "   **Method One: Docker**\n",
+    "\n",
     "   For quick setup in docker environment:\n",
     "   docker pull jaguardb/jaguardb\n",
     "   docker run -d -p 8888:8888 -p 8080:8080 --name jaguardb  jaguardb/jaguardb\n",
     "\n",
+    "   **Method Two: Quick Setup(Linux)**\n",
+    "\n",
+    "   Without Docker, run:\n",
+    "   ```\n",
+    "      curl -fsSL http://jaguardb.com/install.sh | sh\n",
+    "   ```\n",
+    "   This installs both the Jaguar vector database and HTTP gateway.\n",
+    "   The servers will start automatically after installation.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
     "2. You must install the http client package for JaguarDB:\n",
     "   ```\n",
     "       pip install -U jaguardb-http-client\n",

docs/docs/integrations/vectorstores/pgvectorstore.ipynb

@@ -496,7 +496,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create a custom Vector Store\n",
+    "## Create a custom Vector Store\n",
     "\n",
     "Customize the vectorstore with special column names or with custom metadata columns.\n",
     "\n",
@@ -617,7 +617,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create a Vector Store using existing table\n",
+    "## Create a Vector Store using existing table\n",
     "\n",
     "A Vector Store can be built up on an existing table.\n",
     "\n",
@@ -713,6 +713,260 @@
     "1. For new records, added via `VectorStore` embeddings are automatically generated."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hybrid Search with PGVectorStore\n",
+    "\n",
+    "A Hybrid Search combines multiple lookup strategies to provide more comprehensive and relevant search results. Specifically, it leverages both dense embedding vector search (for semantic similarity) and TSV (Text Search Vector) based keyword search (for lexical matching). This approach is particularly powerful for applications requiring efficient searching through customized text and metadata, especially when a specialized embedding model isn't feasible or necessary.\n",
+    "\n",
+    "By integrating both semantic and lexical capabilities, hybrid search helps overcome the limitations of each individual method:\n",
+    "* **Semantic Search**: Excellent for understanding the meaning of a query, even if the exact keywords aren't present. However, it can sometimes miss highly relevant documents that contain the precise keywords but have a slightly different semantic context.\n",
+    "* **Keyword Search**: Highly effective for finding documents with exact keyword matches and is generally fast. Its weakness lies in its inability to understand synonyms, misspellings, or conceptual relationships."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hybrid Search Config\n",
+    "\n",
+    "You can take advantage of hybrid search with PGVectorStore using the `HybridSearchConfig`.\n",
+    "\n",
+    "With a `HybridSearchConfig` provided, the `PGVectorStore` class can efficiently manage a hybrid search vector store using PostgreSQL as the backend, automatically handling the creation and population of the necessary TSV columns when possible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building the config\n",
+    "\n",
+    "Here are the parameters to the hybrid search config:\n",
+    "* **tsv_column:** The column name for TSV column. Default: `<content_column>_tsv`\n",
+    "* **tsv_lang:** Value representing a supported language. Default: `pg_catalog.english`\n",
+    "* **fts_query:** If provided, this would be used for secondary retrieval instead of user provided query.\n",
+    "* **fusion_function:** Determines how the results are to be merged, default is equal weighted sum ranking.\n",
+    "* **fusion_function_parameters:** Parameters for the fusion function\n",
+    "* **primary_top_k:** Max results fetched for primary retrieval. Default: `4`\n",
+    "* **secondary_top_k:** Max results fetched for secondary retrieval. Default: `4`\n",
+    "* **index_name:** Name of the index built on the `tsv_column`\n",
+    "* **index_type:** GIN or GIST. Default: `GIN`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is an example `HybridSearchConfig`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_postgres.v2.hybrid_search_config import (\n",
+    "    HybridSearchConfig,\n",
+    "    reciprocal_rank_fusion,\n",
+    ")\n",
+    "\n",
+    "hybrid_search_config = HybridSearchConfig(\n",
+    "    tsv_column=\"hybrid_description\",\n",
+    "    tsv_lang=\"pg_catalog.english\",\n",
+    "    fusion_function=reciprocal_rank_fusion,\n",
+    "    fusion_function_parameters={\n",
+    "        \"rrf_k\": 60,\n",
+    "        \"fetch_top_k\": 10,\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** In this case, we have mentioned the fusion function to be a `reciprocal rank fusion` but you can also use the `weighted_sum_ranking`.\n",
+    "\n",
+    "Make sure to use the right fusion function parameters\n",
+    "\n",
+    "`reciprocal_rank_fusion`:\n",
+    "* rrf_k: The RRF parameter k. Defaults to 60\n",
+    "* fetch_top_k: The number of documents to fetch after merging the results. Defaults to 4\n",
+    "\n",
+    "`weighted_sum_ranking`:\n",
+    "* primary_results_weight: The weight for the primary source's scores. Defaults to 0.5\n",
+    "* secondary_results_weight: The weight for the secondary source's scores. Defaults to 0.5\n",
+    "* fetch_top_k: The number of documents to fetch after merging the results. Defaults to 4\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Usage\n",
+    "\n",
+    "Let's assume we are using the previously mentioned table [`products`](#create-a-vector-store-using-existing-table), which stores product details for an eComm venture.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### With a new hybrid search table\n",
+    "To create a new postgres table with the tsv column, specify the hybrid search config during the initialization of the vector store.\n",
+    "\n",
+    "In this case, all the similarity searches will make use of hybrid search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_postgres import PGVectorStore\n",
+    "\n",
+    "TABLE_NAME = \"hybrid_search_products\"\n",
+    "\n",
+    "await pg_engine.ainit_vectorstore_table(\n",
+    "    table_name=TABLE_NAME,\n",
+    "    # schema_name=SCHEMA_NAME,\n",
+    "    vector_size=VECTOR_SIZE,\n",
+    "    id_column=\"product_id\",\n",
+    "    content_column=\"description\",\n",
+    "    embedding_column=\"embed\",\n",
+    "    metadata_columns=[\"name\", \"category\", \"price_usd\", \"quantity\", \"sku\", \"image_url\"],\n",
+    "    metadata_json_column=\"metadata\",\n",
+    "    hybrid_search_config=hybrid_search_config,\n",
+    "    store_metadata=True,\n",
+    ")\n",
+    "\n",
+    "vs_hybrid = await PGVectorStore.create(\n",
+    "    pg_engine,\n",
+    "    table_name=TABLE_NAME,\n",
+    "    # schema_name=SCHEMA_NAME,\n",
+    "    embedding_service=embedding,\n",
+    "    # Connect to existing VectorStore by customizing below column names\n",
+    "    id_column=\"product_id\",\n",
+    "    content_column=\"description\",\n",
+    "    embedding_column=\"embed\",\n",
+    "    metadata_columns=[\"name\", \"category\", \"price_usd\", \"quantity\", \"sku\", \"image_url\"],\n",
+    "    metadata_json_column=\"metadata\",\n",
+    "    hybrid_search_config=hybrid_search_config,\n",
+    ")\n",
+    "\n",
+    "# Fetch documents from the previously created store to fetch product documents\n",
+    "docs = await custom_store.asimilarity_search(\"products\", k=5)\n",
+    "# Add data normally to the hybrid search vector store, which will also add the tsv values in tsv_column\n",
+    "await vs_hybrid.aadd_documents(docs)\n",
+    "\n",
+    "# Use hybrid search\n",
+    "hybrid_docs = await vs_hybrid.asimilarity_search(\"products\", k=5)\n",
+    "print(hybrid_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### With a pre-existing table\n",
+    "\n",
+    "If a hybrid search config is **NOT** provided during `init_vectorstore_table` while creating a table, the table will not contain a tsv_column. In this case you can still take advantage of hybrid search using the `HybridSearchConfig`.\n",
+    "\n",
+    "The specified TSV column is not present but the TSV vectors are created dynamically on-the-go for hybrid search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_postgres import PGVectorStore\n",
+    "\n",
+    "# Set the existing table name\n",
+    "TABLE_NAME = \"products\"\n",
+    "# SCHEMA_NAME = \"my_schema\"\n",
+    "\n",
+    "hybrid_search_config = HybridSearchConfig(\n",
+    "    tsv_lang=\"pg_catalog.english\",\n",
+    "    fusion_function=reciprocal_rank_fusion,\n",
+    "    fusion_function_parameters={\n",
+    "        \"rrf_k\": 60,\n",
+    "        \"fetch_top_k\": 10,\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Initialize PGVectorStore with the hybrid search config\n",
+    "custom_hybrid_store = await PGVectorStore.create(\n",
+    "    pg_engine,\n",
+    "    table_name=TABLE_NAME,\n",
+    "    # schema_name=SCHEMA_NAME,\n",
+    "    embedding_service=embedding,\n",
+    "    # Connect to existing VectorStore by customizing below column names\n",
+    "    id_column=\"product_id\",\n",
+    "    content_column=\"description\",\n",
+    "    embedding_column=\"embed\",\n",
+    "    metadata_columns=[\"name\", \"category\", \"price_usd\", \"quantity\", \"sku\", \"image_url\"],\n",
+    "    metadata_json_column=\"metadata\",\n",
+    "    hybrid_search_config=hybrid_search_config,\n",
+    ")\n",
+    "\n",
+    "# Use hybrid search\n",
+    "hybrid_docs = await custom_hybrid_store.asimilarity_search(\"products\", k=5)\n",
+    "print(hybrid_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, all the similarity searches will make use of hybrid search."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Applying Hybrid Search to Specific Queries\n",
+    "\n",
+    "To use hybrid search only for certain queries, omit the configuration during initialization and pass it directly to the search method when needed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use hybrid search\n",
+    "hybrid_docs = await custom_store.asimilarity_search(\n",
+    "    \"products\", k=5, hybrid_search_config=hybrid_search_config\n",
+    ")\n",
+    "print(hybrid_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hybrid Search Index\n",
+    "\n",
+    "Optionally, if you have created a Postgres table with a tsv_column, you can create an index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await vs_hybrid.aapply_hybrid_search_index()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/docs/integrations/vectorstores/weaviate.ipynb

@@ -47,7 +47,20 @@
     "\n",
     "Some Weaviate instances, such as those running on WCS, have authentication enabled, such as API key and/or username+password authentication.\n",
     "\n",
-    "Read the [client authentication guide](https://weaviate.io/developers/weaviate/client-libraries/python#authentication) for more information, as well as the [in-depth authentication configuration page](https://weaviate.io/developers/weaviate/configuration/authentication)."
+    "Read the [client authentication guide](https://weaviate.io/developers/weaviate/client-libraries/python#authentication) for more information, as well as the [in-depth authentication configuration page](https://weaviate.io/developers/weaviate/configuration/authentication).\n",
+     "\n",
+    "### Connect to an existing collection (reuse an index)\n",
+    "If you already created a collection in your local Weaviate instance, you can connect to it directly:",
+    "\n",
+    "```python\n",
+    "from langchain_weaviate import WeaviateVectorStore\n",
+    "\n",
+    "store = WeaviateVectorStore(\n",
+    "    client=weaviate_client,\n",
+    "    index_name=\"Test\",\n",
+    "    text_key=\"text\",\n",
+    ")\n",
+    "```\n"
    ]
   },
   {

docs/docs/introduction.mdx

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

docs/docs/tutorials/agents.ipynb

@@ -302,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "c96c960b",
    "metadata": {},
    "outputs": [
@@ -320,7 +320,7 @@
    "source": [
     "query = \"Hi!\"\n",
     "response = model.invoke([{\"role\": \"user\", \"content\": query}])\n",
-    "response.text"
+    "response.text()"
    ]
   },
   {
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "b6a7e925",
    "metadata": {},
    "outputs": [
@@ -371,7 +371,7 @@
     "query = \"Hi!\"\n",
     "response = model_with_tools.invoke([{\"role\": \"user\", \"content\": query}])\n",
     "\n",
-    "print(f\"Message content: {response.text}\\n\")\n",
+    "print(f\"Message content: {response.text()}\\n\")\n",
     "print(f\"Tool calls: {response.tool_calls}\")"
    ]
   },
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "688b465d",
    "metadata": {},
    "outputs": [
@@ -403,7 +403,7 @@
     "query = \"Search for the weather in SF\"\n",
     "response = model_with_tools.invoke([{\"role\": \"user\", \"content\": query}])\n",
     "\n",
-    "print(f\"Message content: {response.text}\\n\")\n",
+    "print(f\"Message content: {response.text()}\\n\")\n",
     "print(f\"Tool calls: {response.tool_calls}\")"
    ]
   },
@@ -615,12 +615,19 @@
     "## Streaming tokens\n",
     "\n",
     "In addition to streaming back messages, it is also useful to stream back tokens.\n",
-    "We can do this by specifying `stream_mode=\"messages\"`."
+    "We can do this by specifying `stream_mode=\"messages\"`.\n",
+    "\n",
+    "\n",
+    "::: note\n",
+    "\n",
+    "Below we use `message.text()`, which requires `langchain-core>=0.3.37`.\n",
+    "\n",
+    ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "63198158-380e-43a3-a2ad-d4288949c1d4",
    "metadata": {},
    "outputs": [
@@ -642,9 +649,9 @@
    ],
    "source": [
     "for step, metadata in agent_executor.stream(\n",
-    "    {\"messages\": [input_message]}, stream_mode=\"messages\"\n",
+    "    {\"messages\": [input_message]}, config, stream_mode=\"messages\"\n",
     "):\n",
-    "    if metadata[\"langgraph_node\"] == \"agent\" and (text := step.text):\n",
+    "    if metadata[\"langgraph_node\"] == \"agent\" and (text := step.text()):\n",
     "        print(text, end=\"|\")"
    ]
   },

docs/docs/tutorials/classification.ipynb

@@ -95,7 +95,6 @@
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
     "from pydantic import BaseModel, Field\n",
     "\n",
     "tagging_prompt = ChatPromptTemplate.from_template(\n",
@@ -253,9 +252,7 @@
     "\"\"\"\n",
     ")\n",
     "\n",
-    "llm = ChatOpenAI(temperature=0, model=\"gpt-4o-mini\").with_structured_output(\n",
-    "    Classification\n",
-    ")"
+    "structured_llm = llm.with_structured_output(Classification)"
    ]
   },
   {
@@ -286,7 +283,7 @@
    "source": [
     "inp = \"Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!\"\n",
     "prompt = tagging_prompt.invoke({\"input\": inp})\n",
-    "llm.invoke(prompt)"
+    "structured_llm.invoke(prompt)"
    ]
   },
   {
@@ -309,7 +306,7 @@
    "source": [
     "inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"\n",
     "prompt = tagging_prompt.invoke({\"input\": inp})\n",
-    "llm.invoke(prompt)"
+    "structured_llm.invoke(prompt)"
    ]
   },
   {
@@ -332,7 +329,7 @@
    "source": [
     "inp = \"Weather is ok here, I can go outside without much more than a coat\"\n",
     "prompt = tagging_prompt.invoke({\"input\": inp})\n",
-    "llm.invoke(prompt)"
+    "structured_llm.invoke(prompt)"
    ]
   },
   {

docs/docs/tutorials/index.mdx

@@ -44,4 +44,4 @@ You can peruse [LangSmith tutorials here](https://docs.smith.langchain.com/).
 
 LangSmith helps you evaluate the performance of your LLM applications. The tutorial below is a great way to get started:
 
-- [Evaluate your LLM application](https://docs.smith.langchain.com/tutorials/Developers/evaluation)
+- [Evaluate your LLM application](https://docs.langchain.com/langsmith/evaluate-llm-application)

docs/docs/tutorials/llm_chain.ipynb

@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "1b2481f0",
    "metadata": {},
    "outputs": [
@@ -178,8 +178,8 @@
     "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
-    "    SystemMessage(\"Translate the following from English into Italian\"),\n",
-    "    HumanMessage(\"hi!\"),\n",
+    "    SystemMessage(content=\"Translate the following from English into Italian\"),\n",
+    "    HumanMessage(content=\"hi!\"),\n",
     "]\n",
     "\n",
     "model.invoke(messages)"

docs/docusaurus.config.js

@@ -142,8 +142,8 @@ const config = {
         respectPrefersColorScheme: true,
       },
       announcementBar: {
-        content: "Our new LangChain Academy Course Deep Research with LangGraph is now live! <a href='https://academy.langchain.com/courses/deep-research-with-langgraph/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2025_deep-research-course_co' target='_blank'>Enroll for free</a>.",
-        backgroundColor: "#d0c9fe",
+        content: "These docs will be deprecated and no longer maintained with the release of LangChain v1.0 in October 2025. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank'>Visit the v1.0 alpha docs</a>",
+        backgroundColor: "#FFAE42",
       },
       prism: {
         theme: {

docs/scripts/check_imports.py

@@ -86,7 +86,7 @@ def _is_relevant_import(module: str) -> bool:
         "langchain",
         "langchain_core",
         "langchain_community",
-        # "langchain_experimental",
+        "langchain_experimental",
         "langchain_text_splitters",
     ]
     return module.split(".")[0] in recognized_packages

docs/scripts/tool_feat_table.py

@@ -167,6 +167,11 @@ WEBBROWSING_TOOL_FEAT_TABLE = {
         "interactions": False,
         "pricing": "Free trial, with flat rate plans and pre-paid credits after",
     },
+    "Anchor Browser": {
+        "link": "/docs/integrations/tools/anchor_browser",
+        "interactions": True,
+        "pricing": "Free trial, with flat rate plans and pre-paid credits after",
+    },
 }
 
 DATABASE_TOOL_FEAT_TABLE = {

docs/src/theme/ChatModelTabs.js

@@ -118,7 +118,8 @@ export default function ChatModelTabs(props) {
     {
       value: "anthropic",
       label: "Anthropic",
-      model: "claude-3-5-sonnet-latest",
+      model: "claude-3-7-sonnet-20250219",
+      comment: "# Note: Model versions may become outdated. Check https://docs.anthropic.com/en/docs/models-overview for latest versions",
       apiKeyName: "ANTHROPIC_API_KEY",
       packageName: "langchain[anthropic]",
     },
@@ -269,6 +270,9 @@ if not os.environ.get("${selectedTabItem.apiKeyName}"):
 
 ${llmVarName} = init_chat_model("${selectedTabItem.model}", model_provider="${selectedTabItem.value}"${selectedTabItem?.kwargs ? `, ${selectedTabItem.kwargs}` : ""})`;
 
+  // Add comment if available
+  const commentText = selectedTabItem?.comment ? selectedTabItem.comment + "\n\n" : "";
+
   return (
     <div>
       <CustomDropdown
@@ -282,7 +286,7 @@ ${llmVarName} = init_chat_model("${selectedTabItem.model}", model_provider="${se
         {`pip install -qU "${selectedTabItem.packageName}"`}
       </CodeBlock>
       <CodeBlock language="python">
-        {apiKeyText ? apiKeyText + "\n\n" + initModelText : initModelText}
+        {apiKeyText ? apiKeyText + "\n\n" + commentText + initModelText : commentText + initModelText}
       </CodeBlock>
     </div>
   );

libs/cli/langchain_cli/dev_scripts.py

@@ -14,7 +14,19 @@ def create_demo_server(
     config_keys: Sequence[str] = (),
     playground_type: Literal["default", "chat"] = "default",
 ) -> FastAPI:
-    """Create a demo server for the current template."""
+    """Create a demo server for the current template.
+
+    Args:
+        config_keys: Optional sequence of config keys to expose in the playground.
+        playground_type: The type of playground to use. Can be `'default'` or `'chat'`.
+
+    Returns:
+        The demo server.
+
+    Raises:
+        KeyError: If the `pyproject.toml` file is missing required fields.
+        ImportError: If the module defined in `pyproject.toml` cannot be imported.
+    """
     app = FastAPI()
     package_root = get_package_root()
     pyproject = package_root / "pyproject.toml"
@@ -41,10 +53,18 @@ def create_demo_server(
 
 
 def create_demo_server_configurable() -> FastAPI:
-    """Create a configurable demo server."""
+    """Create a configurable demo server.
+
+    Returns:
+        The configurable demo server.
+    """
     return create_demo_server(config_keys=["configurable"])
 
 
 def create_demo_server_chat() -> FastAPI:
-    """Create a chat demo server."""
+    """Create a chat demo server.
+
+    Returns:
+        The chat demo server.
+    """
     return create_demo_server(playground_type="chat")

libs/cli/langchain_cli/integration_template/docs/chat.ipynb

@@ -1,260 +1,262 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "afaf8039",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: __ModuleName__\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e49f1e0d",
-      "metadata": {},
-      "source": [
-        "# Chat__ModuleName__\n",
-        "\n",
-        "- TODO: Make sure API reference link is correct.\n",
-        "\n",
-        "This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
-        "\n",
-        "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "- TODO: Fill in table features.\n",
-        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-        "- TODO: Make sure API reference links are correct.\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?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",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___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(\"__MODULE_NAME___API_KEY\"):\n",
-        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "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 __ModuleName__ integration lives in the `__package_name__` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU __package_name__"
-      ]
-    },
-    {
-      "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:\n",
-        "\n",
-        "- TODO: Update model instantiation with relevant params."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from __module_name__ import Chat__ModuleName__\n",
-        "\n",
-        "llm = Chat__ModuleName__(\n",
-        "    model=\"model-name\",\n",
-        "    temperature=0,\n",
-        "    max_tokens=None,\n",
-        "    timeout=None,\n",
-        "    max_retries=2,\n",
-        "    # other params...\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2b4f3e15",
-      "metadata": {},
-      "source": [
-        "## Invocation\n",
-        "\n",
-        "- TODO: Run cells so output can be seen."
-      ]
-    },
-    {
-      "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",
-        "\n",
-        "- TODO: Run cells so output can be seen."
-      ]
-    },
-    {
-      "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": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-      "metadata": {},
-      "source": [
-        "## TODO: Any functionality specific to this model provider\n",
-        "\n",
-        "E.g. creating/using finetuned models via this provider. Delete if not relevant."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.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"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# Chat__ModuleName__\n",
+    "\n",
+    "- TODO: Make sure API reference link is correct.\n",
+    "\n",
+    "This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
+    "\n",
+    "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "- TODO: Fill in table features.\n",
+    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+    "- TODO: Make sure API reference links are correct.\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?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",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___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(\"__MODULE_NAME___API_KEY\"):\n",
+    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your __ModuleName__ API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "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 __ModuleName__ integration lives in the `__package_name__` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU __package_name__"
+   ]
+  },
+  {
+   "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:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __module_name__ import Chat__ModuleName__\n",
+    "\n",
+    "llm = Chat__ModuleName__(\n",
+    "    model=\"model-name\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "- TODO: Run cells so output can be seen."
+   ]
+  },
+  {
+   "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",
+    "\n",
+    "- TODO: Run cells so output can be seen."
+   ]
+  },
+  {
+   "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": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": [
+    "## TODO: Any functionality specific to this model provider\n",
+    "\n",
+    "E.g. creating/using finetuned models via this provider. Delete if not relevant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.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
 }

libs/cli/langchain_cli/integration_template/docs/document_loaders.ipynb

@@ -1,217 +1,219 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: __ModuleName__\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# __ModuleName__Loader\n",
-        "\n",
-        "- TODO: Make sure API reference link is correct.\n",
-        "\n",
-        "This notebook provides a quick overview for getting started with __ModuleName__ [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html).\n",
-        "\n",
-        "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "- TODO: Fill in table features.\n",
-        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-        "- TODO: Make sure API reference links are correct.\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/__module_name___loader)|\n",
-        "| :--- | :--- | :---: | :---: |  :---: |\n",
-        "| [__ModuleName__Loader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name__loader.__ModuleName__Loader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | \n",
-        "### Loader features\n",
-        "| Source | Document Lazy Loading | Native Async Support\n",
-        "| :---: | :---: | :---: | \n",
-        "| __ModuleName__Loader | ✅/❌ | ✅/❌ | \n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "To access __ModuleName__ document loader you'll need to install the `__package_name__` integration package, and create a **ModuleName** account and get an API key.\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import getpass\n",
-        "import os\n",
-        "\n",
-        "os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "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**.\n",
-        "\n",
-        "- TODO: Add any other required packages"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU langchain_community"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialization\n",
-        "\n",
-        "Now we can instantiate our model object and load documents:\n",
-        "\n",
-        "- TODO: Update model instantiation with relevant params."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_community.document_loaders import __ModuleName__Loader\n",
-        "\n",
-        "loader = __ModuleName__Loader(\n",
-        "    # required params = ...\n",
-        "    # optional params = ...\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load\n",
-        "\n",
-        "- TODO: Run cells to show loading capabilities"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "docs = loader.load()\n",
-        "docs[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(docs[0].metadata)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Lazy Load\n",
-        "\n",
-        "- TODO: Run cells to show lazy loading capabilities. Delete if lazy loading is not implemented."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "page = []\n",
-        "for doc in loader.lazy_load():\n",
-        "    page.append(doc)\n",
-        "    if len(page) >= 10:\n",
-        "        # do some paged operation, e.g.\n",
-        "        # index.upsert(page)\n",
-        "\n",
-        "        page = []"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## TODO: Any functionality specific to this document loader\n",
-        "\n",
-        "E.g. using specific configs for different loading behavior. Delete if not relevant."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.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"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__Loader\n",
+    "\n",
+    "- TODO: Make sure API reference link is correct.\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with __ModuleName__ [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html).\n",
+    "\n",
+    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "- TODO: Fill in table features.\n",
+    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+    "- TODO: Make sure API reference links are correct.\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/__module_name___loader)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [__ModuleName__Loader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name__loader.__ModuleName__Loader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| __ModuleName__Loader | ✅/❌ | ✅/❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "To access __ModuleName__ document loader you'll need to install the `__package_name__` integration package, and create a **ModuleName** account and get an API key.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "    \"Enter your __ModuleName__ API key: \"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "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**.\n",
+    "\n",
+    "- TODO: Add any other required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import __ModuleName__Loader\n",
+    "\n",
+    "loader = __ModuleName__Loader(\n",
+    "    # required params = ...\n",
+    "    # optional params = ...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load\n",
+    "\n",
+    "- TODO: Run cells to show loading capabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load\n",
+    "\n",
+    "- TODO: Run cells to show lazy loading capabilities. Delete if lazy loading is not implemented."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TODO: Any functionality specific to this document loader\n",
+    "\n",
+    "E.g. using specific configs for different loading behavior. Delete if not relevant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.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": 4
 }

libs/cli/langchain_cli/integration_template/docs/llms.ipynb

@@ -1,236 +1,236 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "67db2992",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: __ModuleName__\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "9597802c",
-      "metadata": {},
-      "source": [
-        "# __ModuleName__LLM\n",
-        "\n",
-        "- [ ] TODO: Make sure API reference link is correct\n",
-        "\n",
-        "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "- TODO: Fill in table features.\n",
-        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-        "- TODO: Make sure API reference links are correct.\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "bc51e756",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import getpass\n",
-        "import os\n",
-        "\n",
-        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4b6e1ca6",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "196c2b41",
-      "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": "809c6577",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "59c710c4",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU __package_name__"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0a760037",
-      "metadata": {},
-      "source": [
-        "## Instantiation\n",
-        "\n",
-        "Now we can instantiate our model object and generate chat completions:\n",
-        "\n",
-        "- TODO: Update model instantiation with relevant params."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a0562a13",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from __module_name__ import __ModuleName__LLM\n",
-        "\n",
-        "llm = __ModuleName__LLM(\n",
-        "    model=\"model-name\",\n",
-        "    temperature=0,\n",
-        "    max_tokens=None,\n",
-        "    timeout=None,\n",
-        "    max_retries=2,\n",
-        "    # other params...\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0ee90032",
-      "metadata": {},
-      "source": [
-        "## Invocation\n",
-        "\n",
-        "- [ ] TODO: Run cells so output can be seen."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "035dea0f",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "input_text = \"__ModuleName__ is an AI company that \"\n",
-        "\n",
-        "completion = llm.invoke(input_text)\n",
-        "completion"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "add38532",
-      "metadata": {},
-      "source": [
-        "## Chaining\n",
-        "\n",
-        "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
-        "\n",
-        "- TODO: Run cells so output can be seen."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "078e9db2",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_core.prompts import PromptTemplate\n",
-        "\n",
-        "prompt = PromptTemplate(\n",
-        "    \"How to say {input} in {output_language}:\\n\"\n",
-        ")\n",
-        "\n",
-        "chain = prompt | llm\n",
-        "chain.invoke(\n",
-        "    {\n",
-        "        \"output_language\": \"German\",\n",
-        "        \"input\": \"I love programming.\",\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e99eef30",
-      "metadata": {},
-      "source": [
-        "## TODO: Any functionality specific to this model provider\n",
-        "\n",
-        "E.g. creating/using finetuned models via this provider. Delete if not relevant"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e9bdfcef",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.11.1 64-bit",
-      "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": "3.9.7"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-      }
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "67db2992",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__LLM\n",
+    "\n",
+    "- [ ] TODO: Make sure API reference link is correct\n",
+    "\n",
+    "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "- TODO: Fill in table features.\n",
+    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+    "- TODO: Make sure API reference links are correct.\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc51e756",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your __ModuleName__ API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b6e1ca6",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "196c2b41",
+   "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": "809c6577",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59c710c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU __package_name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a760037",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0562a13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __module_name__ import __ModuleName__LLM\n",
+    "\n",
+    "llm = __ModuleName__LLM(\n",
+    "    model=\"model-name\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ee90032",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "- [ ] TODO: Run cells so output can be seen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "035dea0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "input_text = \"__ModuleName__ is an AI company that \"\n",
+    "\n",
+    "completion = llm.invoke(input_text)\n",
+    "completion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "add38532",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
+    "\n",
+    "- TODO: Run cells so output can be seen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "078e9db2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import PromptTemplate\n",
+    "\n",
+    "prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e99eef30",
+   "metadata": {},
+   "source": [
+    "## TODO: Any functionality specific to this model provider\n",
+    "\n",
+    "E.g. creating/using finetuned models via this provider. Delete if not relevant"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9bdfcef",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.1 64-bit",
+   "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": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

libs/cli/langchain_cli/integration_template/docs/stores.ipynb

@@ -62,7 +62,9 @@
     "import os\n",
     "\n",
     "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
+    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your __ModuleName__ API key: \"\n",
+    "    )"
    ]
   },
   {

libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb

@@ -1,244 +1,246 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "afaf8039",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: __ModuleName__\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "9a3d6f34",
-      "metadata": {},
-      "source": [
-        "# __ModuleName__Embeddings\n",
-        "\n",
-        "- [ ] TODO: Make sure API reference link is correct\n",
-        "\n",
-        "This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/__package_name_short__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html).\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "| Provider | Package |\n",
-        "|:--------:|:-------:|\n",
-        "| [__ModuleName__](/docs/integrations/providers/__package_name_short__/) | [__package_name__](https://python.langchain.com/v0.2/api_reference/__module_name__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html) |\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "- [ ] TODO: Update with relevant info.\n",
-        "\n",
-        "To access __ModuleName__ embedding models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "36521c2a",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import getpass\n",
-        "import os\n",
-        "\n",
-        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c84fb993",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "39a4953b",
-      "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": "d9664366",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "64853226",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU __package_name__"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "45dd1724",
-      "metadata": {},
-      "source": [
-        "## Instantiation\n",
-        "\n",
-        "Now we can instantiate our model object and generate chat completions:\n",
-        "\n",
-        "- TODO: Update model instantiation with relevant params."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "9ea7a09b",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from __module_name__ import __ModuleName__Embeddings\n",
-        "\n",
-        "embeddings = __ModuleName__Embeddings(\n",
-        "    model=\"model-name\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "77d271b6",
-      "metadata": {},
-      "source": [
-        "## Indexing and Retrieval\n",
-        "\n",
-        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-        "\n",
-        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "d817716b",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Create a vector store with a sample text\n",
-        "from langchain_core.vectorstores import InMemoryVectorStore\n",
-        "\n",
-        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-        "\n",
-        "vectorstore = InMemoryVectorStore.from_texts(\n",
-        "    [text],\n",
-        "    embedding=embeddings,\n",
-        ")\n",
-        "\n",
-        "# Use the vectorstore as a retriever\n",
-        "retriever = vectorstore.as_retriever()\n",
-        "\n",
-        "# Retrieve the most similar text\n",
-        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-        "\n",
-        "# show the retrieved document's content\n",
-        "retrieved_documents[0].page_content"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e02b9855",
-      "metadata": {},
-      "source": [
-        "## Direct Usage\n",
-        "\n",
-        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-        "\n",
-        "You can directly call these methods to get embeddings for your own use cases.\n",
-        "\n",
-        "### Embed single texts\n",
-        "\n",
-        "You can embed single texts or documents with `embed_query`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "0d2befcd",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "single_vector = embeddings.embed_query(text)\n",
-        "print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "1b5a7d03",
-      "metadata": {},
-      "source": [
-        "### Embed multiple texts\n",
-        "\n",
-        "You can embed multiple texts with `embed_documents`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "2f4d6e97",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "text2 = (\n",
-        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-        ")\n",
-        "two_vectors = embeddings.embed_documents([text, text2])\n",
-        "for vector in two_vectors:\n",
-        "    print(str(vector)[:100]) # Show the first 100 characters of the vector"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "98785c12",
-      "metadata": {},
-      "source": [
-        "## API Reference\n",
-        "\n",
-        "For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.5"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "9a3d6f34",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__Embeddings\n",
+    "\n",
+    "- [ ] TODO: Make sure API reference link is correct\n",
+    "\n",
+    "This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/__package_name_short__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Provider | Package |\n",
+    "|:--------:|:-------:|\n",
+    "| [__ModuleName__](/docs/integrations/providers/__package_name_short__/) | [__package_name__](https://python.langchain.com/v0.2/api_reference/__module_name__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "- [ ] TODO: Update with relevant info.\n",
+    "\n",
+    "To access __ModuleName__ embedding models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36521c2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your __ModuleName__ API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84fb993",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39a4953b",
+   "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": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64853226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU __package_name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ea7a09b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __module_name__ import __ModuleName__Embeddings\n",
+    "\n",
+    "embeddings = __ModuleName__Embeddings(\n",
+    "    model=\"model-name\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d271b6",
+   "metadata": {},
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d817716b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

libs/cli/langchain_cli/integration_template/docs/tools.ipynb

@@ -120,9 +120,7 @@
     "from langchain_community.tools import __ModuleName__\n",
     "\n",
     "\n",
-    "tool = __ModuleName__(\n",
-    "    ...\n",
-    ")"
+    "tool = __ModuleName__(...)"
    ]
   },
   {

libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb

@@ -1,340 +1,333 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "1957f5cb",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: __ModuleName__\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "ef1f0986",
-      "metadata": {},
-      "source": [
-        "# __ModuleName__VectorStore\n",
-        "\n",
-        "This notebook covers how to get started with the __ModuleName__ vector store."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "36fdc060",
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "- TODO: Update minimum version to be correct.\n",
-        "\n",
-        "To access __ModuleName__ vector stores you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package."
-      ]
-    },
-    {
-      "cell_type": "raw",
-      "id": "64e28aa6",
-      "metadata": {
-        "vscode": {
-          "languageId": "raw"
-        }
-      },
-      "source": [
-        "%pip install -qU \"__package_name__>=MINIMUM_VERSION\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "9695dee7",
-      "metadata": {},
-      "source": [
-        "### Credentials\n",
-        "\n",
-        "- TODO: Update with relevant info.\n",
-        "\n",
-        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "894c30e4",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import getpass\n",
-        "import os\n",
-        "\n",
-        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7f98392b",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "e7b6a6e0",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "93df377e",
-      "metadata": {},
-      "source": [
-        "## Initialization\n",
-        "\n",
-        "- TODO: Fill out with relevant init params\n",
-        "\n",
-        "\n",
-        "```{=mdx}\n",
-        "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
-        "\n",
-        "<EmbeddingTabs/>\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "from __module_name__.vectorstores import __ModuleName__VectorStore\n",
-        "\n",
-        "vector_store = __ModuleName__VectorStore(embeddings=embeddings)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "ac6071d4",
-      "metadata": {},
-      "source": [
-        "## Manage vector store\n",
-        "\n",
-        "### Add items to vector store\n",
-        "\n",
-        "- TODO: Edit and then run code cell to generate output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "17f5efc0",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_core.documents import Document\n",
-        "\n",
-        "document_1 = Document(\n",
-        "    page_content=\"foo\",\n",
-        "    metadata={\"source\": \"https://example.com\"}\n",
-        ")\n",
-        "\n",
-        "document_2 = Document(\n",
-        "    page_content=\"bar\",\n",
-        "    metadata={\"source\": \"https://example.com\"}\n",
-        ")\n",
-        "\n",
-        "document_3 = Document(\n",
-        "    page_content=\"baz\",\n",
-        "    metadata={\"source\": \"https://example.com\"}\n",
-        ")\n",
-        "\n",
-        "documents = [document_1, document_2, document_3]\n",
-        "\n",
-        "vector_store.add_documents(documents=documents,ids=[\"1\",\"2\",\"3\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c738c3e0",
-      "metadata": {},
-      "source": [
-        "### Update items in vector store\n",
-        "\n",
-        "- TODO: Edit and then run code cell to generate output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "f0aa8b71",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "updated_document = Document(\n",
-        "    page_content=\"qux\",\n",
-        "    metadata={\"source\": \"https://another-example.com\"}\n",
-        ")\n",
-        "\n",
-        "vector_store.update_documents(document_id=\"1\",document=updated_document)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "dcf1b905",
-      "metadata": {},
-      "source": [
-        "### Delete items from vector store\n",
-        "\n",
-        "- TODO: Edit and then run code cell to generate output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ef61e188",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "vector_store.delete(ids=[\"3\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c3620501",
-      "metadata": {},
-      "source": [
-        "## Query vector store\n",
-        "\n",
-        "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.\n",
-        "\n",
-        "### Query directly\n",
-        "\n",
-        "Performing a simple similarity search can be done as follows:\n",
-        "\n",
-        "- TODO: Edit and then run code cell to generate output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "aa0a16fa",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "results = vector_store.similarity_search(query=\"thud\",k=1,filter={\"source\":\"https://another-example.com\"})\n",
-        "for doc in results:\n",
-        "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "3ed9d733",
-      "metadata": {},
-      "source": [
-        "If you want to execute a similarity search and receive the corresponding scores you can run:\n",
-        "\n",
-        "- TODO: Edit and then run code cell to generate output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "5efd2eaa",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "results = vector_store.similarity_search_with_score(query=\"thud\",k=1,filter={\"source\":\"https://example.com\"})\n",
-        "for doc, score in results:\n",
-        "    print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0c235cdc",
-      "metadata": {},
-      "source": [
-        "### Query by turning into retriever\n",
-        "\n",
-        "You can also transform the vector store into a retriever for easier usage in your chains.\n",
-        "\n",
-        "- TODO: Edit and then run code cell to generate output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "f3460093",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "retriever = vector_store.as_retriever(\n",
-        "    search_type=\"mmr\",\n",
-        "    search_kwargs={\"k\": 1}\n",
-        ")\n",
-        "retriever.invoke(\"thud\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "901c75dc",
-      "metadata": {},
-      "source": [
-        "## Usage for retrieval-augmented generation\n",
-        "\n",
-        "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
-        "\n",
-        "- [Tutorials](/docs/tutorials/)\n",
-        "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-        "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "069f1b5f",
-      "metadata": {},
-      "source": [
-        "## TODO: Any functionality specific to this vector store\n",
-        "\n",
-        "E.g. creating a persisten database to save to your disk, etc."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "8a27244f",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/__module_name__.vectorstores.__ModuleName__VectorStore.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": "3.10.12"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "1957f5cb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "ef1f0986",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__VectorStore\n",
+    "\n",
+    "This notebook covers how to get started with the __ModuleName__ vector store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36fdc060",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "- TODO: Update minimum version to be correct.\n",
+    "\n",
+    "To access __ModuleName__ vector stores you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "64e28aa6",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "%pip install -qU \"__package_name__>=MINIMUM_VERSION\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9695dee7",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "894c30e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your __ModuleName__ API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f98392b",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7b6a6e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93df377e",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "- TODO: Fill out with relevant init params\n",
+    "\n",
+    "\n",
+    "```{=mdx}\n",
+    "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
+    "\n",
+    "<EmbeddingTabs/>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from __module_name__.vectorstores import __ModuleName__VectorStore\n",
+    "\n",
+    "vector_store = __ModuleName__VectorStore(embeddings=embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac6071d4",
+   "metadata": {},
+   "source": [
+    "## Manage vector store\n",
+    "\n",
+    "### Add items to vector store\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17f5efc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "document_1 = Document(page_content=\"foo\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_2 = Document(page_content=\"bar\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_3 = Document(page_content=\"baz\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "documents = [document_1, document_2, document_3]\n",
+    "\n",
+    "vector_store.add_documents(documents=documents, ids=[\"1\", \"2\", \"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c738c3e0",
+   "metadata": {},
+   "source": [
+    "### Update items in vector store\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0aa8b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "updated_document = Document(\n",
+    "    page_content=\"qux\", metadata={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "\n",
+    "vector_store.update_documents(document_id=\"1\", document=updated_document)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcf1b905",
+   "metadata": {},
+   "source": [
+    "### Delete items from vector store\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef61e188",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_store.delete(ids=[\"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3620501",
+   "metadata": {},
+   "source": [
+    "## Query vector store\n",
+    "\n",
+    "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.\n",
+    "\n",
+    "### Query directly\n",
+    "\n",
+    "Performing a simple similarity search can be done as follows:\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0a16fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "for doc in results:\n",
+    "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ed9d733",
+   "metadata": {},
+   "source": [
+    "If you want to execute a similarity search and receive the corresponding scores you can run:\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5efd2eaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search_with_score(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://example.com\"}\n",
+    ")\n",
+    "for doc, score in results:\n",
+    "    print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c235cdc",
+   "metadata": {},
+   "source": [
+    "### Query by turning into retriever\n",
+    "\n",
+    "You can also transform the vector store into a retriever for easier usage in your chains.\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3460093",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = vector_store.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 1})\n",
+    "retriever.invoke(\"thud\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "901c75dc",
+   "metadata": {},
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
+    "\n",
+    "- [Tutorials](/docs/tutorials/)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "069f1b5f",
+   "metadata": {},
+   "source": [
+    "## TODO: Any functionality specific to this vector store\n",
+    "\n",
+    "E.g. creating a persisten database to save to your disk, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a27244f",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/__module_name__.vectorstores.__ModuleName__VectorStore.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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

libs/cli/langchain_cli/integration_template/integration_template/chat_models.py

@@ -21,11 +21,13 @@ class Chat__ModuleName__(BaseChatModel):
     # https://github.com/langchain-ai/langchain/blob/7ff05357bac6eaedf5058a2af88f23a1817d40fe/libs/partners/openai/langchain_openai/chat_models/base.py#L1120
     """__ModuleName__ chat model integration.
 
-    The default implementation echoes the first `parrot_buffer_length` characters of the input.
+    The default implementation echoes the first `parrot_buffer_length` characters of
+    the input.
 
     # TODO: Replace with relevant packages, env vars.
     Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
+        Install ``__package_name__`` and set environment variable
+        ``__MODULE_NAME___API_KEY``.
 
         .. code-block:: bash
 
@@ -48,7 +50,8 @@ class Chat__ModuleName__(BaseChatModel):
         max_retries: int
             Max number of retries.
         api_key: Optional[str]
-            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.
+            __ModuleName__ API key. If not passed in will be read from env var
+            __MODULE_NAME___API_KEY.
 
     See full list of supported init args and their descriptions in the params section.
 
@@ -86,7 +89,7 @@ class Chat__ModuleName__(BaseChatModel):
         .. code-block:: python
 
             for chunk in llm.stream(messages):
-                print(chunk.text, end="")
+                print(chunk.text(), end="")
 
         .. code-block:: python
 

libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py

@@ -14,7 +14,8 @@ class __ModuleName__Loader(BaseLoader):
 
     # TODO: Replace with relevant packages, env vars.
     Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
+        Install ``__package_name__`` and set environment variable
+        ``__MODULE_NAME___API_KEY``.
 
         .. code-block:: bash
 
@@ -62,7 +63,7 @@ class __ModuleName__Loader(BaseLoader):
 
             TODO: Example output
 
-    """  # noqa: E501
+    """
 
     # TODO: This method must be implemented to load documents.
     # Do not implement load(), a default implementation is already available.

libs/cli/langchain_cli/integration_template/integration_template/toolkits.py

@@ -12,7 +12,8 @@ class __ModuleName__Toolkit(BaseToolkit):
 
     # TODO: Replace with relevant packages, env vars, etc.
     Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
+        Install ``__package_name__`` and set environment variable
+        ``__MODULE_NAME___API_KEY``.
 
         .. code-block:: bash
 
@@ -65,7 +66,7 @@ class __ModuleName__Toolkit(BaseToolkit):
 
              # TODO: Example output.
 
-    """  # noqa: E501
+    """
 
     # TODO: This method must be implemented to list tools.
     def get_tools(self) -> List[BaseTool]:

libs/cli/langchain_cli/integration_template/integration_template/tools.py

@@ -27,7 +27,8 @@ class __ModuleName__Tool(BaseTool):  # type: ignore[override]
 
     Setup:
         # TODO: Replace with relevant packages, env vars.
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
+        Install ``__package_name__`` and set environment variable
+        ``__MODULE_NAME___API_KEY``.
 
         .. code-block:: bash
 

libs/cli/langchain_cli/integration_template/pyproject.toml

@@ -25,6 +25,9 @@ langchain-core = "^0.3.15"
 [tool.ruff.lint]
 select = ["E", "F", "I", "T201"]
 
+[tool.ruff.lint.per-file-ignores]
+"docs/**" = [ "ALL",]
+
 [tool.coverage.run]
 omit = ["tests/*"]
 

libs/cli/langchain_cli/namespaces/app.py

@@ -72,9 +72,7 @@ def new(
         name_str = name
         pip_bool = bool(pip)  # None should be false
     else:
-        name_str = (
-            name if name else typer.prompt("What folder would you like to create?")
-        )
+        name_str = name or typer.prompt("What folder would you like to create?")
         if not has_packages:
             package = []
             package_prompt = "What package would you like to add? (leave blank to skip)"

libs/cli/langchain_cli/namespaces/migrate/generate/generic.py

@@ -11,7 +11,16 @@ def generate_raw_migrations(
     to_package: str,
     filter_by_all: bool = False,  # noqa: FBT001, FBT002
 ) -> list[tuple[str, str]]:
-    """Scan the `langchain` package and generate migrations for all modules."""
+    """Scan the `langchain` package and generate migrations for all modules.
+
+    Args:
+        from_package: The package to migrate from.
+        to_package: The package to migrate to.
+        filter_by_all: Whether to only consider items in `__all__`.
+
+    Returns:
+        A list of tuples containing the original import path and the new import path.
+    """
     package = importlib.import_module(from_package)
 
     items = []
@@ -84,6 +93,13 @@ def generate_top_level_imports(pkg: str) -> list[tuple[str, str]]:
     and the second tuple will contain the path
     to importing it from the top level namespaces
     (e.g., ``langchain_community.chat_models.XYZ``)
+
+    Args:
+        pkg: The package to scan.
+
+    Returns:
+        A list of tuples containing the fully qualified path and the top-level
+        import path.
     """
     package = importlib.import_module(pkg)
 
@@ -130,7 +146,17 @@ def generate_simplified_migrations(
     to_package: str,
     filter_by_all: bool = True,  # noqa: FBT001, FBT002
 ) -> list[tuple[str, str]]:
-    """Get all the raw migrations, then simplify them if possible."""
+    """Get all the raw migrations, then simplify them if possible.
+
+    Args:
+        from_package: The package to migrate from.
+        to_package: The package to migrate to.
+        filter_by_all: Whether to only consider items in `__all__`.
+
+    Returns:
+        A list of tuples containing the original import path and the simplified
+        import path.
+    """
     raw_migrations = generate_raw_migrations(
         from_package,
         to_package,

libs/cli/langchain_cli/namespaces/migrate/generate/grit.py

@@ -2,13 +2,28 @@
 
 
 def split_package(package: str) -> tuple[str, str]:
-    """Split a package name into the containing package and the final name."""
+    """Split a package name into the containing package and the final name.
+
+    Args:
+        package: The full package name.
+
+    Returns:
+        A tuple of `(containing_package, final_name)`.
+    """
     parts = package.split(".")
     return ".".join(parts[:-1]), parts[-1]
 
 
 def dump_migrations_as_grit(name: str, migration_pairs: list[tuple[str, str]]) -> str:
-    """Dump the migration pairs as a Grit file."""
+    """Dump the migration pairs as a Grit file.
+
+    Args:
+        name: The name of the migration.
+        migration_pairs: A list of tuples `(from_module, to_module)`.
+
+    Returns:
+        The Grit file as a string.
+    """
     remapped = ",\n".join(
         [
             f"""

libs/cli/langchain_cli/namespaces/migrate/generate/utils.py

@@ -6,7 +6,9 @@ import os
 import pathlib
 from pathlib import Path
 from types import ModuleType
-from typing import Any, Optional
+from typing import Optional
+
+from typing_extensions import override
 
 HERE = Path(__file__).parent
 # Should bring us to [root]/src
@@ -22,10 +24,11 @@ class ImportExtractor(ast.NodeVisitor):
 
     def __init__(self, *, from_package: Optional[str] = None) -> None:
         """Extract all imports from the given code, optionally filtering by package."""
-        self.imports: list = []
+        self.imports: list[tuple[str, str]] = []
         self.package = from_package
 
-    def visit_ImportFrom(self, node: ast.ImportFrom) -> None:  # noqa: N802
+    @override
+    def visit_ImportFrom(self, node: ast.ImportFrom) -> None:
         if node.module and (
             self.package is None or str(node.module).startswith(self.package)
         ):
@@ -44,7 +47,8 @@ def _get_class_names(code: str) -> list[str]:
 
     # Define a node visitor class to collect class names
     class ClassVisitor(ast.NodeVisitor):
-        def visit_ClassDef(self, node: ast.ClassDef) -> None:  # noqa: N802
+        @override
+        def visit_ClassDef(self, node: ast.ClassDef) -> None:
             class_names.append(node.name)
             self.generic_visit(node)
 
@@ -54,8 +58,16 @@ def _get_class_names(code: str) -> list[str]:
     return class_names
 
 
-def is_subclass(class_obj: Any, classes_: list[type]) -> bool:
-    """Check if the given class object is a subclass of any class in list classes."""
+def is_subclass(class_obj: type, classes_: list[type]) -> bool:
+    """Check if the given class object is a subclass of any class in list classes.
+
+    Args:
+        class_obj: The class to check.
+        classes_: A list of classes to check against.
+
+    Returns:
+        True if `class_obj` is a subclass of any class in `classes_`, False otherwise.
+    """
     return any(
         issubclass(class_obj, kls)
         for kls in classes_
@@ -64,7 +76,15 @@ def is_subclass(class_obj: Any, classes_: list[type]) -> bool:
 
 
 def find_subclasses_in_module(module: ModuleType, classes_: list[type]) -> list[str]:
-    """Find all classes in the module that inherit from one of the classes."""
+    """Find all classes in the module that inherit from one of the classes.
+
+    Args:
+        module: The module to inspect.
+        classes_: A list of classes to check against.
+
+    Returns:
+        A list of class names that are subclasses of any class in `classes_`.
+    """
     subclasses = []
     # Iterate over all attributes of the module that are classes
     for _name, obj in inspect.getmembers(module, inspect.isclass):
@@ -87,7 +107,15 @@ def identify_all_imports_in_file(
     *,
     from_package: Optional[str] = None,
 ) -> list[tuple[str, str]]:
-    """Let's also identify all the imports in the given file."""
+    """Identify all the imports in the given file.
+
+    Args:
+        file: The file to analyze.
+        from_package: If provided, only return imports from this package.
+
+    Returns:
+        A list of tuples `(module, name)` representing the imports found in the file.
+    """
     code = Path(file).read_text(encoding="utf-8")
     return find_imports_from_package(code, from_package=from_package)
 
@@ -102,6 +130,9 @@ def identify_pkg_source(pkg_root: str) -> pathlib.Path:
     Returns:
         Returns the path to the source code for the package.
 
+    Raises:
+        ValueError: If there is not exactly one directory starting with `'langchain_'`
+            in the package root.
     """
     dirs = [d for d in Path(pkg_root).iterdir() if d.is_dir()]
     matching_dirs = [d for d in dirs if d.name.startswith("langchain_")]
@@ -112,7 +143,15 @@ def identify_pkg_source(pkg_root: str) -> pathlib.Path:
 
 
 def list_classes_by_package(pkg_root: str) -> list[tuple[str, str]]:
-    """List all classes in a package."""
+    """List all classes in a package.
+
+    Args:
+        pkg_root: the root of the package.
+
+    Returns:
+        A list of tuples `(module, class_name)` representing all classes found in the
+        package, excluding test files.
+    """
     module_classes = []
     pkg_source = identify_pkg_source(pkg_root)
     files = list(pkg_source.rglob("*.py"))
@@ -126,7 +165,15 @@ def list_classes_by_package(pkg_root: str) -> list[tuple[str, str]]:
 
 
 def list_init_imports_by_package(pkg_root: str) -> list[tuple[str, str]]:
-    """List all the things that are being imported in a package by module."""
+    """List all the things that are being imported in a package by module.
+
+    Args:
+        pkg_root: the root of the package.
+
+    Returns:
+        A list of tuples `(module, name)` representing the imports found in
+        `__init__.py` files.
+    """
     imports = []
     pkg_source = identify_pkg_source(pkg_root)
     # Scan all the files in the package
@@ -146,7 +193,15 @@ def find_imports_from_package(
     *,
     from_package: Optional[str] = None,
 ) -> list[tuple[str, str]]:
-    """Find imports in code."""
+    """Find imports in code.
+
+    Args:
+        code: The code to analyze.
+        from_package: If provided, only return imports from this package.
+
+    Returns:
+        A list of tuples `(module, name)` representing the imports found.
+    """
     # Parse the code into an AST
     tree = ast.parse(code)
     # Create an instance of the visitor

libs/cli/langchain_cli/namespaces/migrate/main.py

@@ -4,7 +4,7 @@ from pathlib import Path
 
 import rich
 import typer
-from gritql import run  # type: ignore[import]
+from gritql import run  # type: ignore[import-untyped]
 from typer import Option
 
 
@@ -68,7 +68,7 @@ def migrate(
     final_code = run.apply_pattern(
         "langchain_all_migrations()",
         args,
-        grit_dir=get_gritdir_path(),
+        grit_dir=str(get_gritdir_path()),
     )
 
     raise typer.Exit(code=final_code)

libs/cli/langchain_cli/namespaces/template.py

@@ -34,7 +34,7 @@ def new(
     package_name_split = computed_name.split("/")
     package_name = (
         package_name_split[-2]
-        if len(package_name_split) > 1 and package_name_split[-1] == ""
+        if len(package_name_split) > 1 and not package_name_split[-1]
         else package_name_split[-1]
     )
     module_name = re.sub(

libs/cli/langchain_cli/package_template/package_template/__init__.py

@@ -1,3 +1,5 @@
+"""__module_name__ module."""
+
 from __module_name__.chain import chain
 
 __all__ = ["chain"]

libs/cli/langchain_cli/package_template/package_template/chain.py

@@ -1,3 +1,5 @@
+"""Chain definition."""
+
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 

libs/cli/langchain_cli/package_template/tests/__init__.py

@@ -0,0 +1 @@
+"""Tests."""

libs/cli/langchain_cli/project_template/app/__init__.py

@@ -0,0 +1 @@
+"""Server application."""

libs/cli/langchain_cli/project_template/app/server.py

@@ -1,3 +1,5 @@
+"""Chain server."""
+
 from fastapi import FastAPI
 from fastapi.responses import RedirectResponse
 from langserve import add_routes
@@ -6,7 +8,7 @@ app = FastAPI()
 
 
 @app.get("/")
-async def redirect_root_to_docs():
+async def _redirect_root_to_docs() -> RedirectResponse:
     return RedirectResponse("/docs")
 
 
@@ -16,4 +18,4 @@ add_routes(app, NotImplemented)
 if __name__ == "__main__":
     import uvicorn
 
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)  # noqa: S104

libs/cli/langchain_cli/utils/events.py

@@ -21,8 +21,15 @@ class EventDict(TypedDict):
     properties: Optional[dict[str, Any]]
 
 
-def create_events(events: list[EventDict]) -> Optional[Any]:
-    """Create events."""
+def create_events(events: list[EventDict]) -> Optional[dict[str, Any]]:
+    """Create events.
+
+    Args:
+        events: A list of event dictionaries.
+
+    Returns:
+        The response from the event tracking service, or None if there was an error.
+    """
     try:
         data = {
             "events": [
@@ -48,7 +55,8 @@ def create_events(events: list[EventDict]) -> Optional[Any]:
 
         res = conn.getresponse()
 
-        return json.loads(res.read())
+        response_data = json.loads(res.read())
+        return response_data if isinstance(response_data, dict) else None
     except (http.client.HTTPException, OSError, json.JSONDecodeError) as exc:
         typer.echo(f"Error sending events: {exc}")
         return None

libs/cli/langchain_cli/utils/find_replace.py

@@ -4,7 +4,15 @@ from pathlib import Path
 
 
 def find_and_replace(source: str, replacements: dict[str, str]) -> str:
-    """Find and replace text in a string."""
+    """Find and replace text in a string.
+
+    Args:
+        source: The source string.
+        replacements: A dictionary of `{find: replace}` pairs.
+
+    Returns:
+        The modified string.
+    """
     rtn = source
 
     # replace keys in deterministic alphabetical order

libs/cli/langchain_cli/utils/git.py

@@ -6,7 +6,7 @@ import re
 import shutil
 from collections.abc import Sequence
 from pathlib import Path
-from typing import Optional, TypedDict
+from typing import Any, Optional, TypedDict
 
 from git import Repo
 
@@ -26,7 +26,7 @@ class DependencySource(TypedDict):
     ref: Optional[str]
     subdirectory: Optional[str]
     api_path: Optional[str]
-    event_metadata: dict
+    event_metadata: dict[str, Any]
 
 
 # use poetry dependency string format
@@ -36,7 +36,20 @@ def parse_dependency_string(
     branch: Optional[str],
     api_path: Optional[str],
 ) -> DependencySource:
-    """Parse a dependency string into a DependencySource."""
+    """Parse a dependency string into a DependencySource.
+
+    Args:
+        dep: the dependency string.
+        repo: optional repository.
+        branch: optional branch.
+        api_path: optional API path.
+
+    Returns:
+        The parsed dependency source information.
+
+    Raises:
+        ValueError: if the dependency string is invalid.
+    """
     if dep is not None and dep.startswith("git+"):
         if repo is not None or branch is not None:
             msg = (
@@ -129,7 +142,22 @@ def parse_dependencies(
     branch: list[str],
     api_path: list[str],
 ) -> list[DependencySource]:
-    """Parse dependencies."""
+    """Parse dependencies.
+
+    Args:
+        dependencies: the dependencies to parse
+        repo: the repositories to use
+        branch: the branches to use
+        api_path: the api paths to use
+
+    Returns:
+        A list of DependencySource objects.
+
+    Raises:
+        ValueError: if the number of `dependencies`, `repos`, `branches`, or `api_paths`
+            do not match.
+
+    """
     num_deps = max(
         len(dependencies) if dependencies is not None else 0,
         len(repo),
@@ -138,8 +166,8 @@ def parse_dependencies(
     if (
         (dependencies and len(dependencies) != num_deps)
         or (api_path and len(api_path) != num_deps)
-        or (repo and len(repo) not in [1, num_deps])
-        or (branch and len(branch) not in [1, num_deps])
+        or (repo and len(repo) not in {1, num_deps})
+        or (branch and len(branch) not in {1, num_deps})
     ):
         msg = (
             "Number of defined repos/branches/api_paths did not match the "
@@ -151,15 +179,15 @@ def parse_dependencies(
     inner_repos = _list_arg_to_length(repo, num_deps)
     inner_branches = _list_arg_to_length(branch, num_deps)
 
-    return [
-        parse_dependency_string(iter_dep, iter_repo, iter_branch, iter_api_path)
-        for iter_dep, iter_repo, iter_branch, iter_api_path in zip(
+    return list(
+        map(
+            parse_dependency_string,
             inner_deps,
             inner_repos,
             inner_branches,
             inner_api_paths,
         )
-    ]
+    )
 
 
 def _get_repo_path(gitstring: str, ref: Optional[str], repo_dir: Path) -> Path:
@@ -167,7 +195,7 @@ def _get_repo_path(gitstring: str, ref: Optional[str], repo_dir: Path) -> Path:
     ref_str = ref if ref is not None else ""
     hashed = hashlib.sha256((f"{gitstring}:{ref_str}").encode()).hexdigest()[:8]
 
-    removed_protocol = gitstring.split("://")[-1]
+    removed_protocol = gitstring.split("://", maxsplit=1)[-1]
     removed_basename = re.split(r"[/:]", removed_protocol, maxsplit=1)[-1]
     removed_extras = removed_basename.split("#")[0]
     foldername = re.sub(r"\W", "_", removed_extras)
@@ -177,7 +205,18 @@ def _get_repo_path(gitstring: str, ref: Optional[str], repo_dir: Path) -> Path:
 
 
 def update_repo(gitstring: str, ref: Optional[str], repo_dir: Path) -> Path:
-    """Update a git repository to the specified ref."""
+    """Update a git repository to the specified ref.
+
+    Tries to pull if the repo already exists, otherwise clones it.
+
+    Args:
+        gitstring: The git repository URL.
+        ref: The git reference.
+        repo_dir: The directory to clone the repository into.
+
+    Returns:
+        The path to the cloned repository.
+    """
     # see if path already saved
     repo_path = _get_repo_path(gitstring, ref, repo_dir)
     if repo_path.exists():

libs/cli/langchain_cli/utils/github.py

@@ -6,7 +6,14 @@ from typing import Optional
 
 
 def list_packages(*, contains: Optional[str] = None) -> list[str]:
-    """List all packages in the langchain repository templates directory."""
+    """List all packages in the langchain repository templates directory.
+
+    Args:
+        contains: Optional substring that the package name must contain.
+
+    Returns:
+        A list of package names.
+    """
     conn = http.client.HTTPSConnection("api.github.com")
     try:
         headers = {

libs/cli/langchain_cli/utils/packages.py

@@ -7,7 +7,19 @@ from tomlkit import load
 
 
 def get_package_root(cwd: Optional[Path] = None) -> Path:
-    """Get package root directory."""
+    """Get package root directory.
+
+    Args:
+        cwd: The current working directory to start the search from.
+            If None, uses the current working directory of the process.
+
+    Returns:
+        The path to the package root directory.
+
+    Raises:
+        FileNotFoundError: If no `pyproject.toml` file is found in the directory
+            hierarchy.
+    """
     # traverse path for routes to host (any directory holding a pyproject.toml file)
     package_root = Path.cwd() if cwd is None else cwd
     visited: set[Path] = set()
@@ -38,7 +50,17 @@ class LangServeExport(TypedDict):
 
 
 def get_langserve_export(filepath: Path) -> LangServeExport:
-    """Get LangServe export information from a pyproject.toml file."""
+    """Get LangServe export information from a `pyproject.toml` file.
+
+    Args:
+        filepath: Path to the `pyproject.toml` file.
+
+    Returns:
+        The LangServeExport information.
+
+    Raises:
+        KeyError: If the `pyproject.toml` file is missing required fields.
+    """
     with filepath.open() as f:
         data: dict[str, Any] = load(f)
     try:

libs/cli/pyproject.toml

@@ -15,7 +15,7 @@ dependencies = [
     "gritql<1.0.0,>=0.2.0",
 ]
 name = "langchain-cli"
-version = "0.0.36"
+version = "0.0.37"
 description = "CLI for interacting with LangChain"
 readme = "README.md"
 
@@ -29,8 +29,8 @@ langchain = "langchain_cli.cli:app"
 langchain-cli = "langchain_cli.cli:app"
 
 [dependency-groups]
-dev = ["pytest<8.0.0,>=7.4.2", "pytest-watcher<1.0.0,>=0.3.4"]
-lint = ["ruff<0.13,>=0.12.2", "mypy<2.0.0,>=1.13.0"]
+dev = ["pytest<9.0.0,>=7.4.2", "pytest-watcher<1.0.0,>=0.3.4"]
+lint = ["ruff<0.13,>=0.12.2", "mypy<1.19,>=1.18.1"]
 test = ["langchain-core", "langchain"]
 typing = ["langchain"]
 test_integration = []
@@ -41,17 +41,16 @@ langchain = { path = "../langchain", editable = true }
 
 [tool.ruff]
 target-version = "py39"
-exclude = [
-    "langchain_cli/integration_template",
-    "langchain_cli/package_template",
-    "langchain_cli/project_template",
-]
+
+[tool.ruff.format]
+docstring-code-format = true
 
 [tool.ruff.lint]
 select = [ "ALL",]
 ignore = [
     "C90",     # McCabe complexity
     "COM812",  # Messes with the formatter
+    "CPY",     # No copyright
     "FIX002",  # Line contains TODO
     "PERF203", # Rarely useful
     "PLR09",   # Too many something (arg, statements, etc)
@@ -63,9 +62,7 @@ ignore = [
     "TD003",   # Missing issue link in TODO
 
     # TODO rules
-    "ANN401",
     "BLE",
-    "D1",
 ]
 unfixable = [
     "B028",    # People should intentionally tune the stacklevel
@@ -80,10 +77,15 @@ pydocstyle.convention = "google"
 pyupgrade.keep-runtime-typing = true
 
 [tool.ruff.lint.per-file-ignores]
-"tests/**" = [ "D1", "S", "SLF",]
+"tests/**" = [ "D1", "DOC", "S", "SLF",]
 "scripts/**" = [ "INP", "S",]
 
 [tool.mypy]
+plugins = ["pydantic.mypy"]
+strict = true
+enable_error_code = "deprecated"
+warn_unreachable = true
+
 exclude = [
     "langchain_cli/integration_template",
     "langchain_cli/package_template",

libs/cli/scripts/generate_migrations.py

@@ -52,7 +52,7 @@ def cli() -> None:
 def generic(
     pkg1: str,
     pkg2: str,
-    output: str,
+    output: Optional[str],
     filter_by_all: bool,  # noqa: FBT001
     format_: str,
 ) -> None:
@@ -73,7 +73,7 @@ def generic(
     else:
         dumped = dump_migrations_as_grit(name, migrations)
 
-    Path(output).write_text(dumped)
+    Path(output).write_text(dumped, encoding="utf-8")
 
 
 def handle_partner(pkg: str, output: Optional[str] = None) -> None:
@@ -84,7 +84,7 @@ def handle_partner(pkg: str, output: Optional[str] = None) -> None:
     data = dump_migrations_as_grit(name, migrations)
     output_name = f"{name}.grit" if output is None else output
     if migrations:
-        Path(output_name).write_text(data)
+        Path(output_name).write_text(data, encoding="utf-8")
         click.secho(f"LangChain migration script saved to {output_name}")
     else:
         click.secho(f"No migrations found for {pkg}", fg="yellow")
@@ -109,7 +109,7 @@ def json_to_grit(json_file: str) -> None:
     name = file.stem
     data = dump_migrations_as_grit(name, migrations)
     output_name = f"{name}.grit"
-    Path(output_name).write_text(data)
+    Path(output_name).write_text(data, encoding="utf-8")
     click.secho(f"GritQL migration script saved to {output_name}")
 
 

libs/cli/tests/unit_tests/test_utils.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Any, Optional
 
 import pytest
 
@@ -16,7 +16,7 @@ def _assert_dependency_equals(
     git: Optional[str] = None,
     ref: Optional[str] = None,
     subdirectory: Optional[str] = None,
-    event_metadata: Optional[dict] = None,
+    event_metadata: Optional[dict[str, Any]] = None,
 ) -> None:
     if dep["git"] != git:
         msg = f"Expected git to be {git} but got {dep['git']}"