cr

.devcontainer/README.md

@@ -1,49 +0,0 @@
-# Dev container
-
-This project includes a [dev container](https://containers.dev/), which lets you use a container as a full-featured dev environment.
-
-You can use the dev container configuration in this folder to build and run the app without needing to install any of its tools locally! You can use it in [GitHub Codespaces](https://github.com/features/codespaces) or the [VS Code Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers).
-
-## GitHub Codespaces
-
-[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)
-
-You may use the button above, or follow these steps to open this repo in a Codespace:
-
-1. Click the **Code** drop-down menu at the top of <https://github.com/langchain-ai/langchain>.
-1. Click on the **Codespaces** tab.
-1. Click **Create codespace on master**.
-
-For more info, check out the [GitHub documentation](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace#creating-a-codespace).
-
-## VS Code Dev Containers
-
-[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
-
-> [!NOTE]
-> If you click the link above you will open the main repo (`langchain-ai/langchain`) and *not* your local cloned repo. This is fine if you only want to run and test the library, but if you want to contribute you can use the link below and replace with your username and cloned repo name:
-
-```txt
-https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/&lt;YOUR_USERNAME&gt;/&lt;YOUR_CLONED_REPO_NAME&gt;
-```
-
-Then you will have a local cloned repo where you can contribute and then create pull requests.
-
-If you already have VS Code and Docker installed, you can use the button above to get started. This will use VSCode to automatically install the Dev Containers extension if needed, clone the source code into a container volume, and spin up a dev container for use.
-
-Alternatively you can also follow these steps to open this repo in a container using the VS Code Dev Containers extension:
-
-1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in the [getting started steps](https://aka.ms/vscode-remote/containers/getting-started).
-
-2. Open a locally cloned copy of the code:
-
-   - Fork and Clone this repository to your local filesystem.
-   - Press <kbd>F1</kbd> and select the **Dev Containers: Open Folder in Container...** command.
-   - Select the cloned copy of this folder, wait for the container to start, and try things out!
-
-You can learn more in the [Dev Containers documentation](https://code.visualstudio.com/docs/devcontainers/containers).
-
-## Tips and tricks
-
-- If you are working with the same repository folder in a container and Windows, you'll want consistent line endings (otherwise you may see hundreds of changes in the SCM view). The `.gitattributes` file in the root of this repo will disable line ending conversion and should prevent this. See [tips and tricks](https://code.visualstudio.com/docs/devcontainers/tips-and-tricks#_resolving-git-line-ending-issues-in-containers-resulting-in-many-modified-files) for more info.
-- If you'd like to review the contents of the image used in this dev container, you can check it out in the [devcontainers/images](https://github.com/devcontainers/images/tree/main/src/python) repo.

.devcontainer/devcontainer.json

@@ -1,58 +0,0 @@
-// For format details, see https://aka.ms/devcontainer.json. For config options, see the
-// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-docker-compose
-{
-  // Name for the dev container
-  "name": "langchain",
-  // Point to a Docker Compose file
-  "dockerComposeFile": "./docker-compose.yaml",
-  // Required when using Docker Compose. The name of the service to connect to once running
-  "service": "langchain",
-  // The optional 'workspaceFolder' property is the path VS Code should open by default when
-  // connected. This is typically a file mount in .devcontainer/docker-compose.yml
-  "workspaceFolder": "/workspaces/langchain",
-  "mounts": [
-    "source=langchain-workspaces,target=/workspaces/langchain,type=volume"
-  ],
-  // Prevent the container from shutting down
-  "overrideCommand": true,
-  // Features to add to the dev container. More info: https://containers.dev/features
-  "features": {
-    "ghcr.io/devcontainers/features/git:1": {},
-    "ghcr.io/devcontainers/features/github-cli:1": {}
-  },
-  "containerEnv": {
-    "UV_LINK_MODE": "copy"
-  },
-  // Use 'forwardPorts' to make a list of ports inside the container available locally.
-  // "forwardPorts": [],
-  // Run commands after the container is created
-  "postCreateCommand": "uv sync && echo 'LangChain (Python) dev environment ready!'",
-  // Configure tool-specific properties.
-  "customizations": {
-    "vscode": {
-      "extensions": [
-        "ms-python.python",
-        "ms-python.debugpy",
-        "ms-python.mypy-type-checker",
-        "ms-python.isort",
-        "unifiedjs.vscode-mdx",
-        "davidanson.vscode-markdownlint",
-        "ms-toolsai.jupyter",
-        "GitHub.copilot",
-        "GitHub.copilot-chat"
-      ],
-      "settings": {
-        "python.defaultInterpreterPath": ".venv/bin/python",
-        "python.formatting.provider": "none",
-        "[python]": {
-          "editor.formatOnSave": true,
-          "editor.codeActionsOnSave": {
-            "source.organizeImports": true
-          }
-        }
-      }
-    }
-  }
-  // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
-  // "remoteUser": "root"
-}

.devcontainer/docker-compose.yaml

@@ -1,13 +0,0 @@
-version: '3'
-services:
-  langchain:
-    build:
-      dockerfile: libs/langchain/dev.Dockerfile
-      context: ..
-
-    networks:
-      - langchain-network
-
-networks:
-  langchain-network:
-    driver: bridge

.editorconfig

@@ -1,52 +0,0 @@
-# top-most EditorConfig file
-root = true
-
-# All files
-[*]
-charset = utf-8
-end_of_line = lf
-insert_final_newline = true
-trim_trailing_whitespace = true
-
-# Python files
-[*.py]
-indent_style = space
-indent_size = 4
-max_line_length = 88
-
-# JSON files
-[*.json]
-indent_style = space
-indent_size = 2
-
-# YAML files
-[*.{yml,yaml}]
-indent_style = space
-indent_size = 2
-
-# Markdown files
-[*.md]
-indent_style = space
-indent_size = 2
-trim_trailing_whitespace = false
-
-# Configuration files
-[*.{toml,ini,cfg}]
-indent_style = space
-indent_size = 4
-
-# Shell scripts
-[*.sh]
-indent_style = space
-indent_size = 2
-
-# Makefile
-[Makefile]
-indent_style = tab
-indent_size = 4
-
-# Jupyter notebooks
-[*.ipynb]
-# Jupyter may include trailing whitespace in cell
-# outputs that's semantically meaningful
-trim_trailing_whitespace = false

.flake8

@@ -1,6 +1,5 @@
 [flake8]
 exclude =
-    venv
     .venv
     __pycache__
     notebooks

.gitattributes

@@ -1,3 +0,0 @@
-* text=auto eol=lf
-*.{cmd,[cC][mM][dD]} text eol=crlf
-*.{bat,[bB][aA][tT]} text eol=crlf

.github/CODEOWNERS

@@ -1,3 +0,0 @@
-/.github/   @baskaryan @ccurme @eyurtsev
-/libs/core/ @eyurtsev
-/libs/partners/ @ccurme @mdrxy

.github/CODE_OF_CONDUCT.md

@@ -1,132 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual
-identity and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
-  community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or advances of
-  any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email address,
-  without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-conduct@langchain.dev.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series of
-actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or permanent
-ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the
-community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
-[https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations

.github/CONTRIBUTING.md

@@ -1,6 +0,0 @@
-# Contributing to LangChain
-
-Hi there! Thank you for even being interested in contributing to LangChain.
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
-
-To learn how to contribute to LangChain, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,121 +0,0 @@
-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:
-      value: |
-        Thank you for taking the time to file a bug report.
-
-        Use this to report BUGS in LangChain. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
-
-        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
-        if there's another way to solve your problem:
-
-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/oss/python/langchain/overview),
-        * [API Reference](https://reference.langchain.com/python/),
-        * [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 bug, not a usage question.
-          required: true
-        - label: I added a clear and descriptive title that summarizes this issue.
-          required: true
-        - label: I used the GitHub search to find a similar question and didn't find it.
-          required: true
-        - label: I am sure that this is a bug in LangChain rather than my code.
-          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.
-          required: true
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Example Code
-      description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
-
-        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
-
-        **Important!**
-
-        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
-        * Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
-        * INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
-
-      placeholder: |
-        The following code:
-
-        ```python
-        from langchain_core.runnables import RunnableLambda
-
-        def bad_code(inputs) -> int:
-          raise NotImplementedError('For demo purpose')
-
-          chain = RunnableLambda(bad_code)
-          chain.invoke('Hello!')
-        ```
-  - type: textarea
-    id: error
-    validations:
-      required: false
-    attributes:
-      label: Error Message and Stack Trace (if applicable)
-      description: |
-        If you are reporting an error, please include the full error message and stack trace.
-      placeholder: |
-        Exception + full stack trace
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: |
-        What is the problem, question, or error?
-
-        Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
-      placeholder: |
-        * I'm trying to use the `langchain` library to do X.
-        * I expect to see Y.
-        * Instead, it does Z.
-    validations:
-      required: true
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: |
-        Please share your system info with us. Do NOT skip this step and please don't trim
-        the output. Most users don't include enough information here and it makes it harder
-        for us to help you.
-
-        Run the following command in your terminal and paste the output here:
-
-        `python -m langchain_core.sys_info`
-
-        or if you have an existing python interpreter running:
-
-        ```python
-        from langchain_core import sys_info
-        sys_info.print_sys_info()
-        ```
-
-        alternatively, put the entire output of `pip freeze` here.
-      placeholder: |
-        python -m langchain_core.sys_info
-    validations:
-      required: true

.github/ISSUE_TEMPLATE/config.yml

@@ -1,9 +0,0 @@
-blank_issues_enabled: false
-version: 2.1
-contact_links:
-  - 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/feature-request.yml

@@ -1,118 +0,0 @@
-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://reference.langchain.com/python/),
-        * [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

@@ -1,20 +0,0 @@
-name: 🔒 Privileged
-description: You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
-body:
-  - type: markdown
-    attributes:
-      value: |
-        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:
-      label: Privileged issue
-      description: Confirm that you are allowed to create an issue here.
-      options:
-        - label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
-          required: true
-  - type: textarea
-    id: content
-    attributes:
-      label: Issue Content
-      description: Add the content of the issue here.

.github/ISSUE_TEMPLATE/task.yml

@@ -1,91 +0,0 @@
-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

@@ -1,28 +0,0 @@
-(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}
-  - Examples:
-    - feat(core): add multi-tenant support
-    - fix(cli): resolve flag parsing error
-    - docs(openai): update API usage examples
-  - Allowed `{TYPE}` values:
-    - feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
-  - Allowed `{SCOPE}` values (optional):
-    - core, cli, langchain, standard-tests, text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant, xai, infra
-  - 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
-
-- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. **We will not consider a PR unless these three are passing in CI.** See [contribution guidelines](https://docs.langchain.com/oss/python/contributing) for more.
-
-Additional guidelines:
-
-- 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. Likewise, please do not update the `uv.lock` files unless you are adding a required dependency.
-- Changes should be backwards compatible.
-- Make sure optional dependencies are imported within a function.

.github/actions/poetry_setup/action.yml

@@ -1,93 +0,0 @@
-# An action for setting up poetry install with caching.
-# Using a custom action since the default action does not
-# take poetry install groups into account.
-# Action code from:
-# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
-name: poetry-install-with-caching
-description: Poetry install with support for caching of dependency groups.
-
-inputs:
-  python-version:
-    description: Python version, supporting MAJOR.MINOR only
-    required: true
-
-  poetry-version:
-    description: Poetry version
-    required: true
-
-  cache-key:
-    description: Cache key to use for manual handling of caching
-    required: true
-
-  working-directory:
-    description: Directory whose poetry.lock file should be cached
-    required: true
-
-runs:
-  using: composite
-  steps:
-    - uses: actions/setup-python@v5
-      name: Setup python ${{ inputs.python-version }}
-      id: setup-python
-      with:
-        python-version: ${{ inputs.python-version }}
-
-    - uses: actions/cache@v4
-      id: cache-bin-poetry
-      name: Cache Poetry binary - Python ${{ inputs.python-version }}
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
-      with:
-        path: |
-          /opt/pipx/venvs/poetry
-        # This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
-        key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
-
-    - name: Refresh shell hashtable and fixup softlinks
-      if: steps.cache-bin-poetry.outputs.cache-hit == 'true'
-      shell: bash
-      env:
-        POETRY_VERSION: ${{ inputs.poetry-version }}
-        PYTHON_VERSION: ${{ inputs.python-version }}
-      run: |
-        set -eux
-
-        # Refresh the shell hashtable, to ensure correct `which` output.
-        hash -r
-
-        # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
-        # Delete and recreate the softlinks pipx expects to have.
-        rm /opt/pipx/venvs/poetry/bin/python
-        cd /opt/pipx/venvs/poetry/bin
-        ln -s "$(which "python$PYTHON_VERSION")" python
-        chmod +x python
-        cd /opt/pipx_bin/
-        ln -s /opt/pipx/venvs/poetry/bin/poetry poetry
-        chmod +x poetry
-
-        # Ensure everything got set up correctly.
-        /opt/pipx/venvs/poetry/bin/python --version
-        /opt/pipx_bin/poetry --version
-
-    - name: Install poetry
-      if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
-      shell: bash
-      env:
-        POETRY_VERSION: ${{ inputs.poetry-version }}
-        PYTHON_VERSION: ${{ inputs.python-version }}
-      # Install poetry using the python version installed by setup-python step.
-      run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
-
-    - name: Restore pip and poetry cached dependencies
-      uses: actions/cache@v4
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
-        WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
-      with:
-        path: |
-          ~/.cache/pip
-          ~/.cache/pypoetry/virtualenvs
-          ~/.cache/pypoetry/cache
-          ~/.cache/pypoetry/artifacts
-          ${{ env.WORKDIR }}/.venv
-        key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles(format('{0}/**/poetry.lock', env.WORKDIR)) }}

.github/actions/uv_setup/action.yml

@@ -1,39 +0,0 @@
-# Helper to set up Python and uv with caching
-
-name: uv-install
-description: Set up Python and uv with caching
-
-inputs:
-  python-version:
-    description: Python version, supporting MAJOR.MINOR only
-    required: true
-  enable-cache:
-    description: Enable caching for uv dependencies
-    required: false
-    default: "true"
-  cache-suffix:
-    description: Custom cache key suffix for cache invalidation
-    required: false
-    default: ""
-  working-directory:
-    description: Working directory for cache glob scoping
-    required: false
-    default: "**"
-
-env:
-  UV_VERSION: "0.5.25"
-
-runs:
-  using: composite
-  steps:
-    - name: Install uv and set the python version
-      uses: astral-sh/setup-uv@v6
-      with:
-        version: ${{ env.UV_VERSION }}
-        python-version: ${{ inputs.python-version }}
-        enable-cache: ${{ inputs.enable-cache }}
-        cache-dependency-glob: |
-          ${{ inputs.working-directory }}/pyproject.toml
-          ${{ inputs.working-directory }}/uv.lock
-          ${{ inputs.working-directory }}/requirements*.txt
-        cache-suffix: ${{ inputs.cache-suffix }}

.github/copilot-instructions.md

@@ -1,330 +0,0 @@
-# Global Development Guidelines for LangChain Projects
-
-## Core Development Principles
-
-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
-
-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
-
-❌ **Bad - Breaking Change:**
-
-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
-```
-
-✅ **Good - Stable Interface:**
-
-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
-
-**Before making ANY changes to public APIs:**
-
-- Check if the function/class is exported in `__init__.py`
-- Look for existing usage patterns in tests and examples
-- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
-- Mark experimental features clearly with docstring admonitions (using MkDocs Material, like `!!! warning`)
-
-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
-- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
-- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
-- Avoid unnecessary abstraction or premature optimization
-- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
-- Unit tests: `tests/unit_tests/` (no network calls allowed)
-- Integration tests: `tests/integration_tests/` (network calls permitted)
-- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
-- [ ] Tests fail when your new logic is broken
-- [ ] Happy path is covered
-- [ ] Edge cases and error conditions are tested
-- [ ] Use fixtures/mocks for external dependencies
-- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
-- [ ] Does the test suite fail if your new logic is broken?
-- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
-- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
-- No `eval()`, `exec()`, or `pickle` on user-controlled input
-- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
-- Remove unreachable/commented code before committing
-- Race conditions or resource leaks (file handles, sockets, threads).
-- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args and Returns sections for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level.
-
-    Returns:
-        True if email was sent successfully, False otherwise.
-
-    Raises:
-        InvalidEmailError: If the email address format is invalid.
-        SMTPConnectionError: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
-- Types go in function signatures, NOT in docstrings
-- Focus on "why" rather than "what" in descriptions
-- Document all parameters, return values, and exceptions
-- Keep descriptions concise but clear
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications.
-
-        Args:
-            data: List of data items to process.
-
-        Returns:
-            ProcessingResult with details of the operation.
-        """
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
-- Reduce code duplication through shared utilities
-- Make unit testing easier
-- Improve separation of concerns (single responsibility)
-- Make unit testing easier through dependency injection
-- Add clarity without adding complexity
-- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
-
-```bash
-# Run unit tests (no network)
-make test
-
-# Don't run integration tests, as API keys must be set
-
-# Run specific test file
-uv run --group test pytest tests/unit_tests/test_specific.py
-```
-
-### Code Quality
-
-```bash
-# Lint code
-make lint
-
-# Format code
-make format
-
-# Type checking
-uv run --group lint mypy .
-```
-
-### Dependency Management Patterns
-
-**Local Development Dependencies:**
-
-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
-
-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
-
-```python
-from langchain_core.tools import tool
-
-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
-
-    Args:
-        query: The search query string.
-    """
-    # Implementation here
-    return results
-```
-
-## Commit Standards
-
-**Use Conventional Commits format for PR titles:**
-
-- `feat(core): add multi-tenant support`
-- `!fix(cli): resolve flag parsing error` (breaking change uses exclamation mark)
-- `docs: update API usage examples`
-- `docs(openai): update API usage examples`
-
-## Framework-Specific Guidelines
-
-- Follow the existing patterns in `langchain_core` for base abstractions
-- Implement proper streaming support where applicable
-- Avoid deprecated components
-
-### Partner Integrations
-
-- Follow the established patterns in existing partner libraries
-- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
-- Include comprehensive integration tests
-- Document API key requirements and authentication
-
----
-
-## Quick Reference Checklist
-
-Before submitting code changes:
-
-- [ ] **Breaking Changes**: Verified no public API changes
-- [ ] **Type Hints**: All functions have complete type annotations
-- [ ] **Tests**: New functionality is fully tested
-- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
-- [ ] **Documentation**: Google-style docstrings for public functions
-- [ ] **Code Quality**: `make lint` and `make format` pass
-- [ ] **Architecture**: Suggested improvements where applicable
-- [ ] **Commit Message**: Follows Conventional Commits format

.github/dependabot.yml

@@ -1,11 +0,0 @@
-# Please see the documentation for all configuration options:
-# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
-# and
-# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
-
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"

.github/images/logo-dark.svg

@@ -1,25 +0,0 @@
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-  <defs>
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-      .cls-1 {
-        fill: #1c3c3c;
-        stroke-width: 0px;
-      }
-    </style>
-  </defs>
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.github/images/logo-light.svg

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-        fill: #fff;
-        stroke-width: 0px;
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-    <g id="LangChain-logotype">
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.github/pr-file-labeler.yml

@@ -1,84 +0,0 @@
-# Label PRs (config)
-# Automatically applies labels based on changed files and branch patterns
-
-# Core packages
-core:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/core/**/*"
-
-langchain:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/langchain/**/*"
-          - "libs/langchain_v1/**/*"
-
-v1:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/langchain_v1/**/*"
-
-cli:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/cli/**/*"
-
-standard-tests:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/standard-tests/**/*"
-
-text-splitters:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/text-splitters/**/*"
-
-# Partner integrations
-integration:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/**/*"
-
-# Infrastructure and DevOps
-infra:
-  - changed-files:
-      - any-glob-to-any-file:
-          - ".github/**/*"
-          - "Makefile"
-          - ".pre-commit-config.yaml"
-          - "scripts/**/*"
-          - "docker/**/*"
-          - "Dockerfile*"
-
-github_actions:
-  - changed-files:
-      - any-glob-to-any-file:
-          - ".github/workflows/**/*"
-          - ".github/actions/**/*"
-
-dependencies:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "**/pyproject.toml"
-          - "uv.lock"
-          - "**/requirements*.txt"
-          - "**/poetry.lock"
-
-# Documentation
-documentation:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "**/*.md"
-          - "**/*.rst"
-          - "**/README*"
-
-# Security related changes
-security:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "**/*security*"
-          - "**/*auth*"
-          - "**/*credential*"
-          - "**/*secret*"
-          - "**/*token*"
-          - ".github/workflows/security*"

.github/pr-title-labeler.yml

@@ -1,41 +0,0 @@
-# PR title labeler config
-#
-# Labels PRs based on conventional commit patterns in titles
-#
-# Format: type(scope): description or type!: description (breaking)
-
-add-missing-labels: true
-clear-prexisting: false
-include-commits: false
-include-title: true
-label-for-breaking-changes: breaking
-
-label-mapping:
-  documentation: ["docs"]
-  feature: ["feat"]
-  fix: ["fix"]
-  infra: ["build", "ci", "chore"]
-  integration:
-    [
-      "anthropic",
-      "chroma",
-      "deepseek",
-      "exa",
-      "fireworks",
-      "groq",
-      "huggingface",
-      "mistralai",
-      "nomic",
-      "ollama",
-      "openai",
-      "perplexity",
-      "prompty",
-      "qdrant",
-      "xai",
-    ]
-  linting: ["style"]
-  performance: ["perf"]
-  refactor: ["refactor"]
-  release: ["release"]
-  revert: ["revert"]
-  tests: ["test"]

.github/scripts/check_diff.py

@@ -1,339 +0,0 @@
-"""Analyze git diffs to determine which directories need to be tested.
-
-Intelligently determines which LangChain packages and directories need to be tested,
-linted, or built based on the changes. Handles dependency relationships between
-packages, maps file changes to appropriate CI job configurations, and outputs JSON
-configurations for GitHub Actions.
-
-- Maps changed files to affected package directories (libs/core, libs/partners/*, etc.)
-- Builds dependency graph to include dependent packages when core components change
-- Generates test matrix configurations with appropriate Python versions
-- Handles special cases for Pydantic version testing and performance benchmarks
-
-Used as part of the check_diffs workflow.
-"""
-
-import glob
-import json
-import os
-import sys
-from collections import defaultdict
-from pathlib import Path
-from typing import Dict, List, Set
-
-import tomllib
-from get_min_versions import get_min_version_from_toml
-from packaging.requirements import Requirement
-
-LANGCHAIN_DIRS = [
-    "libs/core",
-    "libs/text-splitters",
-    "libs/langchain",
-    "libs/langchain_v1",
-]
-
-# When set to True, we are ignoring core dependents
-# in order to be able to get CI to pass for each individual
-# package that depends on core
-# e.g. if you touch core, we don't then add textsplitters/etc to CI
-IGNORE_CORE_DEPENDENTS = False
-
-# ignored partners are removed from dependents
-# but still run if directly edited
-IGNORED_PARTNERS = [
-    # remove huggingface from dependents because of CI instability
-    # specifically in huggingface jobs
-    # https://github.com/langchain-ai/langchain/issues/25558
-    "huggingface",
-    # prompty exhibiting issues with numpy for Python 3.13
-    # https://github.com/langchain-ai/langchain/actions/runs/12651104685/job/35251034969?pr=29065
-    "prompty",
-]
-
-
-def all_package_dirs() -> Set[str]:
-    return {
-        "/".join(path.split("/")[:-1]).lstrip("./")
-        for path in glob.glob("./libs/**/pyproject.toml", recursive=True)
-        if "libs/cli" not in path and "libs/standard-tests" not in path
-    }
-
-
-def dependents_graph() -> dict:
-    """Construct a mapping of package -> dependents
-
-    Done such that we can run tests on all dependents of a package when a change is made.
-    """
-    dependents = defaultdict(set)
-
-    for path in glob.glob("./libs/**/pyproject.toml", recursive=True):
-        if "template" in path:
-            continue
-
-        # load regular and test deps from pyproject.toml
-        with open(path, "rb") as f:
-            pyproject = tomllib.load(f)
-
-        pkg_dir = "libs" + "/".join(path.split("libs")[1].split("/")[:-1])
-        for dep in [
-            *pyproject["project"]["dependencies"],
-            *pyproject["dependency-groups"]["test"],
-        ]:
-            requirement = Requirement(dep)
-            package_name = requirement.name
-            if "langchain" in dep:
-                dependents[package_name].add(pkg_dir)
-                continue
-
-        # load extended deps from extended_testing_deps.txt
-        package_path = Path(path).parent
-        extended_requirement_path = package_path / "extended_testing_deps.txt"
-        if extended_requirement_path.exists():
-            with open(extended_requirement_path, "r") as f:
-                extended_deps = f.read().splitlines()
-                for depline in extended_deps:
-                    if depline.startswith("-e "):
-                        # editable dependency
-                        assert depline.startswith("-e ../partners/"), (
-                            "Extended test deps should only editable install partner packages"
-                        )
-                        partner = depline.split("partners/")[1]
-                        dep = f"langchain-{partner}"
-                    else:
-                        dep = depline.split("==")[0]
-
-                    if "langchain" in dep:
-                        dependents[dep].add(pkg_dir)
-
-    for k in dependents:
-        for partner in IGNORED_PARTNERS:
-            if f"libs/partners/{partner}" in dependents[k]:
-                dependents[k].remove(f"libs/partners/{partner}")
-    return dependents
-
-
-def add_dependents(dirs_to_eval: Set[str], dependents: dict) -> List[str]:
-    updated = set()
-    for dir_ in dirs_to_eval:
-        # handle core manually because it has so many dependents
-        if "core" in dir_:
-            updated.add(dir_)
-            continue
-        pkg = "langchain-" + dir_.split("/")[-1]
-        updated.update(dependents[pkg])
-        updated.add(dir_)
-    return list(updated)
-
-
-def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
-    if job == "test-pydantic":
-        return _get_pydantic_test_configs(dir_)
-
-    if job == "codspeed":
-        py_versions = ["3.13"]
-    elif dir_ == "libs/core":
-        py_versions = ["3.10", "3.11", "3.12", "3.13", "3.14"]
-    # custom logic for specific directories
-    elif dir_ in {"libs/partners/chroma"}:
-        py_versions = ["3.10", "3.13"]
-    else:
-        py_versions = ["3.10", "3.14"]
-
-    return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
-
-
-def _get_pydantic_test_configs(
-    dir_: str, *, python_version: str = "3.12"
-) -> List[Dict[str, str]]:
-    with open("./libs/core/uv.lock", "rb") as f:
-        core_uv_lock_data = tomllib.load(f)
-    for package in core_uv_lock_data["package"]:
-        if package["name"] == "pydantic":
-            core_max_pydantic_minor = package["version"].split(".")[1]
-            break
-
-    with open(f"./{dir_}/uv.lock", "rb") as f:
-        dir_uv_lock_data = tomllib.load(f)
-
-    for package in dir_uv_lock_data["package"]:
-        if package["name"] == "pydantic":
-            dir_max_pydantic_minor = package["version"].split(".")[1]
-            break
-
-    core_min_pydantic_version = get_min_version_from_toml(
-        "./libs/core/pyproject.toml", "release", python_version, include=["pydantic"]
-    )["pydantic"]
-    core_min_pydantic_minor = (
-        core_min_pydantic_version.split(".")[1]
-        if "." in core_min_pydantic_version
-        else "0"
-    )
-    dir_min_pydantic_version = get_min_version_from_toml(
-        f"./{dir_}/pyproject.toml", "release", python_version, include=["pydantic"]
-    ).get("pydantic", "0.0.0")
-    dir_min_pydantic_minor = (
-        dir_min_pydantic_version.split(".")[1]
-        if "." in dir_min_pydantic_version
-        else "0"
-    )
-
-    max_pydantic_minor = min(
-        int(dir_max_pydantic_minor),
-        int(core_max_pydantic_minor),
-    )
-    min_pydantic_minor = max(
-        int(dir_min_pydantic_minor),
-        int(core_min_pydantic_minor),
-    )
-
-    configs = [
-        {
-            "working-directory": dir_,
-            "pydantic-version": f"2.{v}.0",
-            "python-version": python_version,
-        }
-        for v in range(min_pydantic_minor, max_pydantic_minor + 1)
-    ]
-    return configs
-
-
-def _get_configs_for_multi_dirs(
-    job: str, dirs_to_run: Dict[str, Set[str]], dependents: dict
-) -> List[Dict[str, str]]:
-    if job == "lint":
-        dirs = add_dependents(
-            dirs_to_run["lint"] | dirs_to_run["test"] | dirs_to_run["extended-test"],
-            dependents,
-        )
-    elif job in ["test", "compile-integration-tests", "dependencies", "test-pydantic"]:
-        dirs = add_dependents(
-            dirs_to_run["test"] | dirs_to_run["extended-test"], dependents
-        )
-    elif job == "extended-tests":
-        dirs = list(dirs_to_run["extended-test"])
-    elif job == "codspeed":
-        dirs = list(dirs_to_run["codspeed"])
-    else:
-        raise ValueError(f"Unknown job: {job}")
-
-    return [
-        config for dir_ in dirs for config in _get_configs_for_single_dir(job, dir_)
-    ]
-
-
-if __name__ == "__main__":
-    files = sys.argv[1:]
-
-    dirs_to_run: Dict[str, set] = {
-        "lint": set(),
-        "test": set(),
-        "extended-test": set(),
-        "codspeed": set(),
-    }
-    docs_edited = False
-
-    if len(files) >= 300:
-        # max diff length is 300 files - there are likely files missing
-        dirs_to_run["lint"] = all_package_dirs()
-        dirs_to_run["test"] = all_package_dirs()
-        dirs_to_run["extended-test"] = set(LANGCHAIN_DIRS)
-
-    for file in files:
-        if any(
-            file.startswith(dir_)
-            for dir_ in (
-                ".github/workflows",
-                ".github/tools",
-                ".github/actions",
-                ".github/scripts/check_diff.py",
-            )
-        ):
-            # Infrastructure changes (workflows, actions, CI scripts) trigger tests on
-            # all core packages as a safety measure. This ensures that changes to CI/CD
-            # infrastructure don't inadvertently break package testing, even if the change
-            # appears unrelated (e.g., documentation build workflows). This is intentionally
-            # conservative to catch unexpected side effects from workflow modifications.
-            #
-            # Example: A PR modifying .github/workflows/api_doc_build.yml will trigger
-            # lint/test jobs for libs/core, libs/text-splitters, libs/langchain, and
-            # libs/langchain_v1, even though the workflow may only affect documentation.
-            dirs_to_run["extended-test"].update(LANGCHAIN_DIRS)
-
-        if file.startswith("libs/core"):
-            dirs_to_run["codspeed"].add("libs/core")
-        if any(file.startswith(dir_) for dir_ in LANGCHAIN_DIRS):
-            # add that dir and all dirs after in LANGCHAIN_DIRS
-            # for extended testing
-
-            found = False
-            for dir_ in LANGCHAIN_DIRS:
-                if dir_ == "libs/core" and IGNORE_CORE_DEPENDENTS:
-                    dirs_to_run["extended-test"].add(dir_)
-                    continue
-                if file.startswith(dir_):
-                    found = True
-                if found:
-                    dirs_to_run["extended-test"].add(dir_)
-        elif file.startswith("libs/standard-tests"):
-            # TODO: update to include all packages that rely on standard-tests (all partner packages)
-            # Note: won't run on external repo partners
-            dirs_to_run["lint"].add("libs/standard-tests")
-            dirs_to_run["test"].add("libs/standard-tests")
-            dirs_to_run["test"].add("libs/partners/mistralai")
-            dirs_to_run["test"].add("libs/partners/openai")
-            dirs_to_run["test"].add("libs/partners/anthropic")
-            dirs_to_run["test"].add("libs/partners/fireworks")
-            dirs_to_run["test"].add("libs/partners/groq")
-
-        elif file.startswith("libs/cli"):
-            dirs_to_run["lint"].add("libs/cli")
-            dirs_to_run["test"].add("libs/cli")
-
-        elif file.startswith("libs/partners"):
-            partner_dir = file.split("/")[2]
-            if os.path.isdir(f"libs/partners/{partner_dir}") and [
-                filename
-                for filename in os.listdir(f"libs/partners/{partner_dir}")
-                if not filename.startswith(".")
-            ] != ["README.md"]:
-                dirs_to_run["test"].add(f"libs/partners/{partner_dir}")
-                # Skip codspeed for partners without benchmarks or in IGNORED_PARTNERS
-                if partner_dir not in IGNORED_PARTNERS:
-                    dirs_to_run["codspeed"].add(f"libs/partners/{partner_dir}")
-            # Skip if the directory was deleted or is just a tombstone readme
-        elif file.startswith("libs/"):
-            # Check if this is a root-level file in libs/ (e.g., libs/README.md)
-            file_parts = file.split("/")
-            if len(file_parts) == 2:
-                # Root-level file in libs/, skip it (no tests needed)
-                continue
-            raise ValueError(
-                f"Unknown lib: {file}. check_diff.py likely needs "
-                "an update for this new library!"
-            )
-        elif file in [
-            "pyproject.toml",
-            "uv.lock",
-        ]:  # root uv files
-            docs_edited = True
-
-    dependents = dependents_graph()
-
-    # we now have dirs_by_job
-    # todo: clean this up
-    map_job_to_configs = {
-        job: _get_configs_for_multi_dirs(job, dirs_to_run, dependents)
-        for job in [
-            "lint",
-            "test",
-            "extended-tests",
-            "compile-integration-tests",
-            "dependencies",
-            "test-pydantic",
-            "codspeed",
-        ]
-    }
-
-    for key, value in map_job_to_configs.items():
-        json_output = json.dumps(value)
-        print(f"{key}={json_output}")

.github/scripts/check_prerelease_dependencies.py

@@ -1,36 +0,0 @@
-"""Check that no dependencies allow prereleases unless we're releasing a prerelease."""
-
-import sys
-
-import tomllib
-
-if __name__ == "__main__":
-    # Get the TOML file path from the command line argument
-    toml_file = sys.argv[1]
-
-    with open(toml_file, "rb") as file:
-        toml_data = tomllib.load(file)
-
-    # See if we're releasing an rc or dev version
-    version = toml_data["project"]["version"]
-    releasing_rc = "rc" in version or "dev" in version
-
-    # If not, iterate through dependencies and make sure none allow prereleases
-    if not releasing_rc:
-        dependencies = toml_data["project"]["dependencies"]
-        for dep_version in dependencies:
-            dep_version_string = (
-                dep_version["version"] if isinstance(dep_version, dict) else dep_version
-            )
-
-            if "rc" in dep_version_string:
-                raise ValueError(
-                    f"Dependency {dep_version} has a prerelease version. Please remove this."
-                )
-
-            if isinstance(dep_version, dict) and dep_version.get(
-                "allow-prereleases", False
-            ):
-                raise ValueError(
-                    f"Dependency {dep_version} has allow-prereleases set to true. Please remove this."
-                )

.github/scripts/get_min_versions.py

@@ -1,199 +0,0 @@
-"""Get minimum versions of dependencies from a pyproject.toml file."""
-
-import sys
-from collections import defaultdict
-
-if sys.version_info >= (3, 11):
-    import tomllib
-else:
-    # For Python 3.10 and below, which doesnt have stdlib tomllib
-    import tomli as tomllib
-
-import re
-from typing import List
-
-import requests
-from packaging.requirements import Requirement
-from packaging.specifiers import SpecifierSet
-from packaging.version import Version, parse
-
-MIN_VERSION_LIBS = [
-    "langchain-core",
-    "langchain",
-    "langchain-text-splitters",
-    "numpy",
-    "SQLAlchemy",
-]
-
-# some libs only get checked on release because of simultaneous changes in
-# multiple libs
-SKIP_IF_PULL_REQUEST = [
-    "langchain-core",
-    "langchain-text-splitters",
-    "langchain",
-]
-
-
-def get_pypi_versions(package_name: str) -> List[str]:
-    """Fetch all available versions for a package from PyPI.
-
-    Args:
-        package_name: Name of the package
-
-    Returns:
-        List of all available versions
-
-    Raises:
-        requests.exceptions.RequestException: If PyPI API request fails
-        KeyError: If package not found or response format unexpected
-    """
-    pypi_url = f"https://pypi.org/pypi/{package_name}/json"
-    response = requests.get(pypi_url)
-    response.raise_for_status()
-    return list(response.json()["releases"].keys())
-
-
-def get_minimum_version(package_name: str, spec_string: str) -> str | None:
-    """Find the minimum published version that satisfies the given constraints.
-
-    Args:
-        package_name: Name of the package
-        spec_string: Version specification string (e.g., ">=0.2.43,<0.4.0,!=0.3.0")
-
-    Returns:
-        Minimum compatible version or None if no compatible version found
-    """
-    # Rewrite occurrences of ^0.0.z to 0.0.z (can be anywhere in constraint string)
-    spec_string = re.sub(r"\^0\.0\.(\d+)", r"0.0.\1", spec_string)
-    # Rewrite occurrences of ^0.y.z to >=0.y.z,<0.y+1 (can be anywhere in constraint string)
-    for y in range(1, 10):
-        spec_string = re.sub(
-            rf"\^0\.{y}\.(\d+)", rf">=0.{y}.\1,<0.{y + 1}", spec_string
-        )
-    # Rewrite occurrences of ^x.y.z to >=x.y.z,<x+1.0.0 (can be anywhere in constraint string)
-    for x in range(1, 10):
-        spec_string = re.sub(
-            rf"\^{x}\.(\d+)\.(\d+)", rf">={x}.\1.\2,<{x + 1}", spec_string
-        )
-
-    spec_set = SpecifierSet(spec_string)
-    all_versions = get_pypi_versions(package_name)
-
-    valid_versions = []
-    for version_str in all_versions:
-        try:
-            version = parse(version_str)
-            if spec_set.contains(version):
-                valid_versions.append(version)
-        except ValueError:
-            continue
-
-    return str(min(valid_versions)) if valid_versions else None
-
-
-def _check_python_version_from_requirement(
-    requirement: Requirement, python_version: str
-) -> bool:
-    if not requirement.marker:
-        return True
-    else:
-        marker_str = str(requirement.marker)
-        if "python_version" or "python_full_version" in marker_str:
-            python_version_str = "".join(
-                char
-                for char in marker_str
-                if char.isdigit() or char in (".", "<", ">", "=", ",")
-            )
-            return check_python_version(python_version, python_version_str)
-        return True
-
-
-def get_min_version_from_toml(
-    toml_path: str,
-    versions_for: str,
-    python_version: str,
-    *,
-    include: list | None = None,
-):
-    # Parse the TOML file
-    with open(toml_path, "rb") as file:
-        toml_data = tomllib.load(file)
-
-    dependencies = defaultdict(list)
-    for dep in toml_data["project"]["dependencies"]:
-        requirement = Requirement(dep)
-        dependencies[requirement.name].append(requirement)
-
-    # Initialize a dictionary to store the minimum versions
-    min_versions = {}
-
-    # Iterate over the libs in MIN_VERSION_LIBS
-    for lib in set(MIN_VERSION_LIBS + (include or [])):
-        if versions_for == "pull_request" and lib in SKIP_IF_PULL_REQUEST:
-            # some libs only get checked on release because of simultaneous
-            # changes in multiple libs
-            continue
-        # Check if the lib is present in the dependencies
-        if lib in dependencies:
-            if include and lib not in include:
-                continue
-            requirements = dependencies[lib]
-            for requirement in requirements:
-                if _check_python_version_from_requirement(requirement, python_version):
-                    version_string = str(requirement.specifier)
-                    break
-
-            # Use parse_version to get the minimum supported version from version_string
-            min_version = get_minimum_version(lib, version_string)
-
-            # Store the minimum version in the min_versions dictionary
-            min_versions[lib] = min_version
-
-    return min_versions
-
-
-def check_python_version(version_string, constraint_string):
-    """Check if the given Python version matches the given constraints.
-
-    Args:
-        version_string: A string representing the Python version (e.g. "3.8.5").
-        constraint_string: A string representing the package's Python version
-            constraints (e.g. ">=3.6, <4.0").
-
-    Returns:
-        True if the version matches the constraints
-    """
-
-    # Rewrite occurrences of ^0.0.z to 0.0.z (can be anywhere in constraint string)
-    constraint_string = re.sub(r"\^0\.0\.(\d+)", r"0.0.\1", constraint_string)
-    # Rewrite occurrences of ^0.y.z to >=0.y.z,<0.y+1.0 (can be anywhere in constraint string)
-    for y in range(1, 10):
-        constraint_string = re.sub(
-            rf"\^0\.{y}\.(\d+)", rf">=0.{y}.\1,<0.{y + 1}.0", constraint_string
-        )
-    # Rewrite occurrences of ^x.y.z to >=x.y.z,<x+1.0.0 (can be anywhere in constraint string)
-    for x in range(1, 10):
-        constraint_string = re.sub(
-            rf"\^{x}\.0\.(\d+)", rf">={x}.0.\1,<{x + 1}.0.0", constraint_string
-        )
-
-    try:
-        version = Version(version_string)
-        constraints = SpecifierSet(constraint_string)
-        return version in constraints
-    except Exception as e:
-        print(f"Error: {e}")
-        return False
-
-
-if __name__ == "__main__":
-    # Get the TOML file path from the command line argument
-    toml_file = sys.argv[1]
-    versions_for = sys.argv[2]
-    python_version = sys.argv[3]
-    assert versions_for in ["release", "pull_request"]
-
-    # Call the function to get the minimum versions
-    min_versions = get_min_version_from_toml(toml_file, versions_for, python_version)
-
-    print(" ".join([f"{lib}=={version}" for lib, version in min_versions.items()]))

.github/tools/git-restore-mtime

@@ -1,756 +0,0 @@
-#!/usr/bin/env python3
-#
-# git-restore-mtime - Change mtime of files based on commit date of last change
-#
-#    Copyright (C) 2012 Rodrigo Silva (MestreLion) <linux@rodrigosilva.com>
-#
-#    This program is free software: you can redistribute it and/or modify
-#    it under the terms of the GNU General Public License as published by
-#    the Free Software Foundation, either version 3 of the License, or
-#    (at your option) any later version.
-#
-#    This program is distributed in the hope that it will be useful,
-#    but WITHOUT ANY WARRANTY; without even the implied warranty of
-#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#    GNU General Public License for more details.
-#
-#    You should have received a copy of the GNU General Public License
-#    along with this program. See <http://www.gnu.org/licenses/gpl.html>
-#
-# Source: https://github.com/MestreLion/git-tools
-# Version: July 13, 2023 (commit hash 5f832e72453e035fccae9d63a5056918d64476a2)
-"""
-Change the modification time (mtime) of files in work tree, based on the
-date of the most recent commit that modified the file, including renames.
-
-Ignores untracked files and uncommitted deletions, additions and renames, and
-by default modifications too.
----
-Useful prior to generating release tarballs, so each file is archived with a
-date that is similar to the date when the file was actually last modified,
-assuming the actual modification date and its commit date are close.
-"""
-
-# TODO:
-# - Add -z on git whatchanged/ls-files, so we don't deal with filename decoding
-# - When Python is bumped to 3.7, use text instead of universal_newlines on subprocess
-# - Update "Statistics for some large projects" with modern hardware and repositories.
-# - Create a README.md for git-restore-mtime alone. It deserves extensive documentation
-#   - Move Statistics there
-# - See git-extras as a good example on project structure and documentation
-
-# FIXME:
-# - When current dir is outside the worktree, e.g. using --work-tree, `git ls-files`
-#   assume any relative pathspecs are to worktree root, not the current dir. As such,
-#   relative pathspecs may not work.
-# - Renames are tricky:
-#   - R100 should not change mtime, but original name is not on filelist. Should
-#     track renames until a valid (A, M) mtime found and then set on current name.
-#   - Should set mtime for both current and original directories.
-#   - Check mode changes with unchanged blobs?
-# - Check file (A, D) for the directory mtime is not sufficient:
-#   - Renames also change dir mtime, unless rename was on a parent dir
-#   - If most recent change of all files in a dir was a Modification (M),
-#     dir might not be touched at all.
-#   - Dirs containing only subdirectories but no direct files will also
-#     not be touched. They're files' [grand]parent dir, but never their dirname().
-#   - Some solutions:
-#     - After files done, perform some dir processing for missing dirs, finding latest
-#       file (A, D, R)
-#     - Simple approach: dir mtime is the most recent child (dir or file) mtime
-#     - Use a virtual concept of "created at most at" to fill missing info, bubble up
-#       to parents and grandparents
-#   - When handling [grand]parent dirs, stay inside <pathspec>
-# - Better handling of merge commits. `-m` is plain *wrong*. `-c/--cc` is perfect, but
-#   painfully slow. First pass without merge commits is not accurate. Maybe add a new
-#   `--accurate` mode for `--cc`?
-
-if __name__ != "__main__":
-    raise ImportError("{} should not be used as a module.".format(__name__))
-
-import argparse
-import datetime
-import logging
-import os.path
-import shlex
-import signal
-import subprocess
-import sys
-import time
-
-__version__ = "2022.12+dev"
-
-# Update symlinks only if the platform supports not following them
-UPDATE_SYMLINKS = bool(os.utime in getattr(os, "supports_follow_symlinks", []))
-
-# Call os.path.normpath() only if not in a POSIX platform (Windows)
-NORMALIZE_PATHS = os.path.sep != "/"
-
-# How many files to process in each batch when re-trying merge commits
-STEPMISSING = 100
-
-# (Extra) keywords for the os.utime() call performed by touch()
-UTIME_KWS = {} if not UPDATE_SYMLINKS else {"follow_symlinks": False}
-
-
-# Command-line interface ######################################################
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description=__doc__.split("\n---")[0])
-
-    group = parser.add_mutually_exclusive_group()
-    group.add_argument(
-        "--quiet",
-        "-q",
-        dest="loglevel",
-        action="store_const",
-        const=logging.WARNING,
-        default=logging.INFO,
-        help="Suppress informative messages and summary statistics.",
-    )
-    group.add_argument(
-        "--verbose",
-        "-v",
-        action="count",
-        help="""
-        Print additional information for each processed file.
-        Specify twice to further increase verbosity.
-        """,
-    )
-
-    parser.add_argument(
-        "--cwd",
-        "-C",
-        metavar="DIRECTORY",
-        help="""
-        Run as if %(prog)s was started in directory %(metavar)s.
-        This affects how --work-tree, --git-dir and PATHSPEC arguments are handled.
-        See 'man 1 git' or 'git --help' for more information.
-        """,
-    )
-
-    parser.add_argument(
-        "--git-dir",
-        dest="gitdir",
-        metavar="GITDIR",
-        help="""
-        Path to the git repository, by default auto-discovered by searching
-        the current directory and its parents for a .git/ subdirectory.
-        """,
-    )
-
-    parser.add_argument(
-        "--work-tree",
-        dest="workdir",
-        metavar="WORKTREE",
-        help="""
-        Path to the work tree root, by default the parent of GITDIR if it's
-        automatically discovered, or the current directory if GITDIR is set.
-        """,
-    )
-
-    parser.add_argument(
-        "--force",
-        "-f",
-        default=False,
-        action="store_true",
-        help="""
-        Force updating files with uncommitted modifications.
-        Untracked files and uncommitted deletions, renames and additions are
-        always ignored.
-        """,
-    )
-
-    parser.add_argument(
-        "--merge",
-        "-m",
-        default=False,
-        action="store_true",
-        help="""
-        Include merge commits.
-        Leads to more recent times and more files per commit, thus with the same
-        time, which may or may not be what you want.
-        Including merge commits may lead to fewer commits being evaluated as files
-        are found sooner, which can improve performance, sometimes substantially.
-        But as merge commits are usually huge, processing them may also take longer.
-        By default, merge commits are only used for files missing from regular commits.
-        """,
-    )
-
-    parser.add_argument(
-        "--first-parent",
-        default=False,
-        action="store_true",
-        help="""
-        Consider only the first parent, the "main branch", when evaluating merge commits.
-        Only effective when merge commits are processed, either when --merge is
-        used or when finding missing files after the first regular log search.
-        See --skip-missing.
-        """,
-    )
-
-    parser.add_argument(
-        "--skip-missing",
-        "-s",
-        dest="missing",
-        default=True,
-        action="store_false",
-        help="""
-        Do not try to find missing files.
-        If merge commits were not evaluated with --merge and some files were
-        not found in regular commits, by default %(prog)s searches for these
-        files again in the merge commits.
-        This option disables this retry, so files found only in merge commits
-        will not have their timestamp updated.
-        """,
-    )
-
-    parser.add_argument(
-        "--no-directories",
-        "-D",
-        dest="dirs",
-        default=True,
-        action="store_false",
-        help="""
-        Do not update directory timestamps.
-        By default, use the time of its most recently created, renamed or deleted file.
-        Note that just modifying a file will NOT update its directory time.
-        """,
-    )
-
-    parser.add_argument(
-        "--test",
-        "-t",
-        default=False,
-        action="store_true",
-        help="Test run: do not actually update any file timestamp.",
-    )
-
-    parser.add_argument(
-        "--commit-time",
-        "-c",
-        dest="commit_time",
-        default=False,
-        action="store_true",
-        help="Use commit time instead of author time.",
-    )
-
-    parser.add_argument(
-        "--oldest-time",
-        "-o",
-        dest="reverse_order",
-        default=False,
-        action="store_true",
-        help="""
-        Update times based on the oldest, instead of the most recent commit of a file.
-        This reverses the order in which the git log is processed to emulate a
-        file "creation" date. Note this will be inaccurate for files deleted and
-        re-created at later dates.
-        """,
-    )
-
-    parser.add_argument(
-        "--skip-older-than",
-        metavar="SECONDS",
-        type=int,
-        help="""
-        Ignore files that are currently older than %(metavar)s.
-        Useful in workflows that assume such files already have a correct timestamp,
-        as it may improve performance by processing fewer files.
-        """,
-    )
-
-    parser.add_argument(
-        "--skip-older-than-commit",
-        "-N",
-        default=False,
-        action="store_true",
-        help="""
-        Ignore files older than the timestamp it would be updated to.
-        Such files may be considered "original", likely in the author's repository.
-        """,
-    )
-
-    parser.add_argument(
-        "--unique-times",
-        default=False,
-        action="store_true",
-        help="""
-        Set the microseconds to a unique value per commit.
-        Allows telling apart changes that would otherwise have identical timestamps,
-        as git's time accuracy is in seconds.
-        """,
-    )
-
-    parser.add_argument(
-        "pathspec",
-        nargs="*",
-        metavar="PATHSPEC",
-        help="""
-        Only modify paths matching %(metavar)s, relative to current directory.
-        By default, update all but untracked files and submodules.
-        """,
-    )
-
-    parser.add_argument(
-        "--version",
-        "-V",
-        action="version",
-        version="%(prog)s version {version}".format(version=get_version()),
-    )
-
-    args_ = parser.parse_args()
-    if args_.verbose:
-        args_.loglevel = max(logging.TRACE, logging.DEBUG // args_.verbose)
-    args_.debug = args_.loglevel <= logging.DEBUG
-    return args_
-
-
-def get_version(version=__version__):
-    if not version.endswith("+dev"):
-        return version
-    try:
-        cwd = os.path.dirname(os.path.realpath(__file__))
-        return Git(cwd=cwd, errors=False).describe().lstrip("v")
-    except Git.Error:
-        return "-".join((version, "unknown"))
-
-
-# Helper functions ############################################################
-
-
-def setup_logging():
-    """Add TRACE logging level and corresponding method, return the root logger"""
-    logging.TRACE = TRACE = logging.DEBUG // 2
-    logging.Logger.trace = lambda _, m, *a, **k: _.log(TRACE, m, *a, **k)
-    return logging.getLogger()
-
-
-def normalize(path):
-    r"""Normalize paths from git, handling non-ASCII characters.
-
-    Git stores paths as UTF-8 normalization form C.
-    If path contains non-ASCII or non-printable characters, git outputs the UTF-8
-    in octal-escaped notation, escaping double-quotes and backslashes, and then
-    double-quoting the whole path.
-    https://git-scm.com/docs/git-config#Documentation/git-config.txt-corequotePath
-
-    This function reverts this encoding, so:
-    normalize(r'"Back\\slash_double\"quote_a\303\247a\303\255"') =>
-        r'Back\slash_double"quote_açaí')
-
-    Paths with invalid UTF-8 encoding, such as single 0x80-0xFF bytes (e.g, from
-    Latin1/Windows-1251 encoding) are decoded using surrogate escape, the same
-    method used by Python for filesystem paths. So 0xE6 ("æ" in Latin1, r'\\346'
-    from Git) is decoded as "\udce6". See https://peps.python.org/pep-0383/ and
-    https://vstinner.github.io/painful-history-python-filesystem-encoding.html
-
-    Also see notes on `windows/non-ascii-paths.txt` about path encodings on
-    non-UTF-8 platforms and filesystems.
-    """
-    if path and path[0] == '"':
-        # Python 2: path = path[1:-1].decode("string-escape")
-        # Python 3: https://stackoverflow.com/a/46650050/624066
-        path = (
-            path[1:-1]  # Remove enclosing double quotes
-            .encode("latin1")  # Convert to bytes, required by 'unicode-escape'
-            .decode("unicode-escape")  # Perform the actual octal-escaping decode
-            .encode("latin1")  # 1:1 mapping to bytes, UTF-8 encoded
-            .decode("utf8", "surrogateescape")
-        )  # Decode from UTF-8
-    if NORMALIZE_PATHS:
-        # Make sure the slash matches the OS; for Windows we need a backslash
-        path = os.path.normpath(path)
-    return path
-
-
-def dummy(*_args, **_kwargs):
-    """No-op function used in dry-run tests"""
-
-
-def touch(path, mtime):
-    """The actual mtime update"""
-    os.utime(path, (mtime, mtime), **UTIME_KWS)
-
-
-def touch_ns(path, mtime_ns):
-    """The actual mtime update, using nanoseconds for unique timestamps"""
-    os.utime(path, None, ns=(mtime_ns, mtime_ns), **UTIME_KWS)
-
-
-def isodate(secs: int):
-    # time.localtime() accepts floats, but discards fractional part
-    return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(secs))
-
-
-def isodate_ns(ns: int):
-    # for integers fromtimestamp() is equivalent and ~16% slower than isodate()
-    return datetime.datetime.fromtimestamp(ns / 1000000000).isoformat(sep=" ")
-
-
-def get_mtime_ns(secs: int, idx: int):
-    # Time resolution for filesystems and functions:
-    # ext-4 and other POSIX filesystems: 1 nanosecond
-    # NTFS (Windows default): 100 nanoseconds
-    # datetime.datetime() (due to 64-bit float epoch): 1 microsecond
-    us = idx % 1000000  # 10**6
-    return 1000 * (1000000 * secs + us)
-
-
-def get_mtime_path(path):
-    return os.path.getmtime(path)
-
-
-# Git class and parse_log(), the heart of the script ##########################
-
-
-class Git:
-    def __init__(self, workdir=None, gitdir=None, cwd=None, errors=True):
-        self.gitcmd = ["git"]
-        self.errors = errors
-        self._proc = None
-        if workdir:
-            self.gitcmd.extend(("--work-tree", workdir))
-        if gitdir:
-            self.gitcmd.extend(("--git-dir", gitdir))
-        if cwd:
-            self.gitcmd.extend(("-C", cwd))
-        self.workdir, self.gitdir = self._get_repo_dirs()
-
-    def ls_files(self, paths: list = None):
-        return (normalize(_) for _ in self._run("ls-files --full-name", paths))
-
-    def ls_dirty(self, force=False):
-        return (
-            normalize(_[3:].split(" -> ", 1)[-1])
-            for _ in self._run("status --porcelain")
-            if _[:2] != "??" and (not force or (_[0] in ("R", "A") or _[1] == "D"))
-        )
-
-    def log(
-        self,
-        merge=False,
-        first_parent=False,
-        commit_time=False,
-        reverse_order=False,
-        paths: list = None,
-    ):
-        cmd = "whatchanged --pretty={}".format("%ct" if commit_time else "%at")
-        if merge:
-            cmd += " -m"
-        if first_parent:
-            cmd += " --first-parent"
-        if reverse_order:
-            cmd += " --reverse"
-        return self._run(cmd, paths)
-
-    def describe(self):
-        return self._run("describe --tags", check=True)[0]
-
-    def terminate(self):
-        if self._proc is None:
-            return
-        try:
-            self._proc.terminate()
-        except OSError:
-            # Avoid errors on OpenBSD
-            pass
-
-    def _get_repo_dirs(self):
-        return (
-            os.path.normpath(_)
-            for _ in self._run(
-                "rev-parse --show-toplevel --absolute-git-dir", check=True
-            )
-        )
-
-    def _run(self, cmdstr: str, paths: list = None, output=True, check=False):
-        cmdlist = self.gitcmd + shlex.split(cmdstr)
-        if paths:
-            cmdlist.append("--")
-            cmdlist.extend(paths)
-        popen_args = dict(universal_newlines=True, encoding="utf8")
-        if not self.errors:
-            popen_args["stderr"] = subprocess.DEVNULL
-        log.trace("Executing: %s", " ".join(cmdlist))
-        if not output:
-            return subprocess.call(cmdlist, **popen_args)
-        if check:
-            try:
-                stdout: str = subprocess.check_output(cmdlist, **popen_args)
-                return stdout.splitlines()
-            except subprocess.CalledProcessError as e:
-                raise self.Error(e.returncode, e.cmd, e.output, e.stderr)
-        self._proc = subprocess.Popen(cmdlist, stdout=subprocess.PIPE, **popen_args)
-        return (_.rstrip() for _ in self._proc.stdout)
-
-    def __del__(self):
-        self.terminate()
-
-    class Error(subprocess.CalledProcessError):
-        """Error from git executable"""
-
-
-def parse_log(filelist, dirlist, stats, git, merge=False, filterlist=None):
-    mtime = 0
-    datestr = isodate(0)
-    for line in git.log(
-        merge, args.first_parent, args.commit_time, args.reverse_order, filterlist
-    ):
-        stats["loglines"] += 1
-
-        # Blank line between Date and list of files
-        if not line:
-            continue
-
-        # Date line
-        if line[0] != ":":  # Faster than `not line.startswith(':')`
-            stats["commits"] += 1
-            mtime = int(line)
-            if args.unique_times:
-                mtime = get_mtime_ns(mtime, stats["commits"])
-            if args.debug:
-                datestr = isodate(mtime)
-            continue
-
-        # File line: three tokens if it describes a renaming, otherwise two
-        tokens = line.split("\t")
-
-        # Possible statuses:
-        # M: Modified (content changed)
-        # A: Added (created)
-        # D: Deleted
-        # T: Type changed: to/from regular file, symlinks, submodules
-        # R099: Renamed (moved), with % of unchanged content. 100 = pure rename
-        # Not possible in log: C=Copied, U=Unmerged, X=Unknown, B=pairing Broken
-        status = tokens[0].split(" ")[-1]
-        file = tokens[-1]
-
-        # Handles non-ASCII chars and OS path separator
-        file = normalize(file)
-
-        def do_file():
-            if args.skip_older_than_commit and get_mtime_path(file) <= mtime:
-                stats["skip"] += 1
-                return
-            if args.debug:
-                log.debug(
-                    "%d\t%d\t%d\t%s\t%s",
-                    stats["loglines"],
-                    stats["commits"],
-                    stats["files"],
-                    datestr,
-                    file,
-                )
-            try:
-                touch(os.path.join(git.workdir, file), mtime)
-                stats["touches"] += 1
-            except Exception as e:
-                log.error("ERROR: %s: %s", e, file)
-                stats["errors"] += 1
-
-        def do_dir():
-            if args.debug:
-                log.debug(
-                    "%d\t%d\t-\t%s\t%s",
-                    stats["loglines"],
-                    stats["commits"],
-                    datestr,
-                    "{}/".format(dirname or "."),
-                )
-            try:
-                touch(os.path.join(git.workdir, dirname), mtime)
-                stats["dirtouches"] += 1
-            except Exception as e:
-                log.error("ERROR: %s: %s", e, dirname)
-                stats["direrrors"] += 1
-
-        if file in filelist:
-            stats["files"] -= 1
-            filelist.remove(file)
-            do_file()
-
-        if args.dirs and status in ("A", "D"):
-            dirname = os.path.dirname(file)
-            if dirname in dirlist:
-                dirlist.remove(dirname)
-                do_dir()
-
-        # All files done?
-        if not stats["files"]:
-            git.terminate()
-            return
-
-
-# Main Logic ##################################################################
-
-
-def main():
-    start = time.time()  # yes, Wall time. CPU time is not realistic for users.
-    stats = {
-        _: 0
-        for _ in (
-            "loglines",
-            "commits",
-            "touches",
-            "skip",
-            "errors",
-            "dirtouches",
-            "direrrors",
-        )
-    }
-
-    logging.basicConfig(level=args.loglevel, format="%(message)s")
-    log.trace("Arguments: %s", args)
-
-    # First things first: Where and Who are we?
-    if args.cwd:
-        log.debug("Changing directory: %s", args.cwd)
-        try:
-            os.chdir(args.cwd)
-        except OSError as e:
-            log.critical(e)
-            return e.errno
-    # Using both os.chdir() and `git -C` is redundant, but might prevent side effects
-    # `git -C` alone could be enough if we make sure that:
-    # - all paths, including args.pathspec, are processed by git: ls-files, rev-parse
-    # - touch() / os.utime() path argument is always prepended with git.workdir
-    try:
-        git = Git(workdir=args.workdir, gitdir=args.gitdir, cwd=args.cwd)
-    except Git.Error as e:
-        # Not in a git repository, and git already informed user on stderr. So we just...
-        return e.returncode
-
-    # Get the files managed by git and build file list to be processed
-    if UPDATE_SYMLINKS and not args.skip_older_than:
-        filelist = set(git.ls_files(args.pathspec))
-    else:
-        filelist = set()
-        for path in git.ls_files(args.pathspec):
-            fullpath = os.path.join(git.workdir, path)
-
-            # Symlink (to file, to dir or broken - git handles the same way)
-            if not UPDATE_SYMLINKS and os.path.islink(fullpath):
-                log.warning(
-                    "WARNING: Skipping symlink, no OS support for updates: %s", path
-                )
-                continue
-
-            # skip files which are older than given threshold
-            if (
-                args.skip_older_than
-                and start - get_mtime_path(fullpath) > args.skip_older_than
-            ):
-                continue
-
-            # Always add files relative to worktree root
-            filelist.add(path)
-
-    # If --force, silently ignore uncommitted deletions (not in the filesystem)
-    # and renames / additions (will not be found in log anyway)
-    if args.force:
-        filelist -= set(git.ls_dirty(force=True))
-    # Otherwise, ignore any dirty files
-    else:
-        dirty = set(git.ls_dirty())
-        if dirty:
-            log.warning(
-                "WARNING: Modified files in the working directory were ignored."
-                "\nTo include such files, commit your changes or use --force."
-            )
-            filelist -= dirty
-
-    # Build dir list to be processed
-    dirlist = set(os.path.dirname(_) for _ in filelist) if args.dirs else set()
-
-    stats["totalfiles"] = stats["files"] = len(filelist)
-    log.info("{0:,} files to be processed in work dir".format(stats["totalfiles"]))
-
-    if not filelist:
-        # Nothing to do. Exit silently and without errors, just like git does
-        return
-
-    # Process the log until all files are 'touched'
-    log.debug("Line #\tLog #\tF.Left\tModification Time\tFile Name")
-    parse_log(filelist, dirlist, stats, git, args.merge, args.pathspec)
-
-    # Missing files
-    if filelist:
-        # Try to find them in merge logs, if not done already
-        # (usually HUGE, thus MUCH slower!)
-        if args.missing and not args.merge:
-            filterlist = list(filelist)
-            missing = len(filterlist)
-            log.info(
-                "{0:,} files not found in log, trying merge commits".format(missing)
-            )
-            for i in range(0, missing, STEPMISSING):
-                parse_log(
-                    filelist,
-                    dirlist,
-                    stats,
-                    git,
-                    merge=True,
-                    filterlist=filterlist[i : i + STEPMISSING],
-                )
-
-        # Still missing some?
-        for file in filelist:
-            log.warning("WARNING: not found in the log: %s", file)
-
-    # Final statistics
-    # Suggestion: use git-log --before=mtime to brag about skipped log entries
-    def log_info(msg, *a, width=13):
-        ifmt = "{:%d,}" % (width,)  # not using 'n' for consistency with ffmt
-        ffmt = "{:%d,.2f}" % (width,)
-        # %-formatting lacks a thousand separator, must pre-render with .format()
-        log.info(msg.replace("%d", ifmt).replace("%f", ffmt).format(*a))
-
-    log_info(
-        "Statistics:\n%f seconds\n%d log lines processed\n%d commits evaluated",
-        time.time() - start,
-        stats["loglines"],
-        stats["commits"],
-    )
-
-    if args.dirs:
-        if stats["direrrors"]:
-            log_info("%d directory update errors", stats["direrrors"])
-        log_info("%d directories updated", stats["dirtouches"])
-
-    if stats["touches"] != stats["totalfiles"]:
-        log_info("%d files", stats["totalfiles"])
-    if stats["skip"]:
-        log_info("%d files skipped", stats["skip"])
-    if stats["files"]:
-        log_info("%d files missing", stats["files"])
-    if stats["errors"]:
-        log_info("%d file update errors", stats["errors"])
-
-    log_info("%d files updated", stats["touches"])
-
-    if args.test:
-        log.info("TEST RUN - No files modified!")
-
-
-# Keep only essential, global assignments here. Any other logic must be in main()
-log = setup_logging()
-args = parse_args()
-
-# Set the actual touch() and other functions based on command-line arguments
-if args.unique_times:
-    touch = touch_ns
-    isodate = isodate_ns
-
-# Make sure this is always set last to ensure --test behaves as intended
-if args.test:
-    touch = dummy
-
-# UI done, it's showtime!
-try:
-    sys.exit(main())
-except KeyboardInterrupt:
-    log.info("\nAborting")
-    signal.signal(signal.SIGINT, signal.SIG_DFL)
-    os.kill(os.getpid(), signal.SIGINT)

.github/workflows/_compile_integration_test.yml

@@ -1,65 +0,0 @@
-# Validates that a package's integration tests compile without syntax or import errors.
-#
-# (If an integration test fails to compile, it won't run.)
-#
-# Called as part of check_diffs.yml workflow
-#
-# Runs pytest with compile marker to check syntax/imports.
-
-name: "🔗 Compile Integration Tests"
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
-
-permissions:
-  contents: read
-
-env:
-  UV_FROZEN: "true"
-
-jobs:
-  build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    timeout-minutes: 20
-    name: "Python ${{ inputs.python-version }}"
-    steps:
-      - uses: actions/checkout@v5
-
-      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ inputs.python-version }}
-          cache-suffix: compile-integration-tests-${{ inputs.working-directory }}
-          working-directory: ${{ inputs.working-directory }}
-
-      - name: "📦 Install Integration Dependencies"
-        shell: bash
-        run: uv sync --group test --group test_integration
-
-      - name: "🔗 Check Integration Tests Compile"
-        shell: bash
-        run: uv run pytest -m compile tests/integration_tests
-
-      - name: "🧹 Verify Clean Working Directory"
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_lint.yml

@@ -1,75 +0,0 @@
-# Runs linting.
-#
-# Uses the package's Makefile to run the checks, specifically the
-# `lint_package` and `lint_tests` targets.
-#
-# Called as part of check_diffs.yml workflow.
-
-name: "🧹 Linting"
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
-
-permissions:
-  contents: read
-
-env:
-  WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
-
-  # This env var allows us to get inline annotations when ruff has complaints.
-  RUFF_OUTPUT_FORMAT: github
-
-  UV_FROZEN: "true"
-
-jobs:
-  # Linting job - runs quality checks on package and test code
-  build:
-    name: "Python ${{ inputs.python-version }}"
-    runs-on: ubuntu-latest
-    timeout-minutes: 20
-    steps:
-      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
-
-      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ inputs.python-version }}
-          cache-suffix: lint-${{ inputs.working-directory }}
-          working-directory: ${{ inputs.working-directory }}
-
-      - name: "📦 Install Lint & Typing Dependencies"
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          uv sync --group lint --group typing
-
-      - name: "🔍 Analyze Package Code with Linters"
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          make lint_package
-
-      - name: "📦 Install Test Dependencies (non-partners)"
-        # (For directories NOT starting with libs/partners/)
-        if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }}
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          uv sync --inexact --group test
-      - name: "📦 Install Test Dependencies"
-        if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          uv sync --inexact --group test --group test_integration
-
-      - name: "🔍 Analyze Test Code with Linters"
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          make lint_tests

.github/workflows/_release.yml

@@ -1,555 +0,0 @@
-# Builds and publishes LangChain packages to PyPI.
-#
-# Manually triggered, though can be used as a reusable workflow (workflow_call).
-#
-# Handles version bumping, building, and publishing to PyPI with authentication.
-
-name: "🚀 Package Release"
-run-name: "Release ${{ inputs.working-directory }} ${{ inputs.release-version }}"
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-  workflow_dispatch:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-        default: "libs/langchain"
-      release-version:
-        required: true
-        type: string
-        default: "0.1.0"
-        description: "New version of package being released"
-      dangerous-nonmaster-release:
-        required: false
-        type: boolean
-        default: false
-        description: "Release from a non-master branch (danger!) - Only use for hotfixes"
-
-env:
-  PYTHON_VERSION: "3.11"
-  UV_FROZEN: "true"
-  UV_NO_SYNC: "true"
-
-permissions:
-  contents: write # Required for creating GitHub releases
-
-jobs:
-  # Build the distribution package and extract version info
-  # Runs in isolated environment with minimal permissions for security
-  build:
-    if: github.ref == 'refs/heads/master' || inputs.dangerous-nonmaster-release
-    environment: Scheduled testing
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-
-    outputs:
-      pkg-name: ${{ steps.check-version.outputs.pkg-name }}
-      version: ${{ steps.check-version.outputs.version }}
-
-    steps:
-      - uses: actions/checkout@v5
-
-      - 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.
-      # (Release stage has trusted publishing and GitHub repo contents write access,
-      #
-      # 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
-        uses: actions/upload-artifact@v5
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Check version
-        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")
-  release-notes:
-    needs:
-      - build
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    outputs:
-      release-body: ${{ steps.generate-release-body.outputs.release-body }}
-    steps:
-      - uses: actions/checkout@v5
-        with:
-          repository: langchain-ai/langchain
-          path: langchain
-          sparse-checkout: | # this only grabs files for relevant dir
-            ${{ inputs.working-directory }}
-          ref: ${{ github.ref }} # this scopes to just ref'd branch
-          fetch-depth: 0 # this fetches entire commit history
-      - name: Check tags
-        id: check-tags
-        shell: bash
-        working-directory: langchain/${{ inputs.working-directory }}
-        env:
-          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
-          VERSION: ${{ needs.build.outputs.version }}
-        run: |
-          # Handle regular versions and pre-release versions differently
-          if [[ "$VERSION" == *"-"* ]]; then
-            # This is a pre-release version (contains a hyphen)
-            # Extract the base version without the pre-release suffix
-            BASE_VERSION=${VERSION%%-*}
-            # Look for the latest release of the same base version
-            REGEX="^$PKG_NAME==$BASE_VERSION\$"
-            PREV_TAG=$(git tag --sort=-creatordate | (grep -P "$REGEX" || true) | head -1)
-
-            # If no exact base version match, look for the latest release of any kind
-            if [ -z "$PREV_TAG" ]; then
-              REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
-              PREV_TAG=$(git tag --sort=-creatordate | (grep -P "$REGEX" || true) | head -1)
-            fi
-          else
-            # Regular version handling
-            PREV_TAG="$PKG_NAME==${VERSION%.*}.$(( ${VERSION##*.} - 1 ))"; [[ "${VERSION##*.}" -eq 0 ]] && PREV_TAG=""
-
-            # backup case if releasing e.g. 0.3.0, looks up last release
-            # note if last release (chronologically) was e.g. 0.1.47 it will get
-            # that instead of the last 0.2 release
-            if [ -z "$PREV_TAG" ]; then
-              REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
-              echo $REGEX
-              PREV_TAG=$(git tag --sort=-creatordate | (grep -P $REGEX || true) | head -1)
-            fi
-          fi
-
-          # if PREV_TAG is empty, let it be empty
-          if [ -z "$PREV_TAG" ]; then
-            echo "No previous tag found - first release"
-          else
-            # confirm prev-tag actually exists in git repo with git tag
-            GIT_TAG_RESULT=$(git tag -l "$PREV_TAG")
-            if [ -z "$GIT_TAG_RESULT" ]; then
-              echo "Previous tag $PREV_TAG not found in git repo"
-              exit 1
-            fi
-          fi
-
-
-          TAG="${PKG_NAME}==${VERSION}"
-          if [ "$TAG" == "$PREV_TAG" ]; then
-            echo "No new version to release"
-            exit 1
-          fi
-          echo tag="$TAG" >> $GITHUB_OUTPUT
-          echo prev-tag="$PREV_TAG" >> $GITHUB_OUTPUT
-      - name: Generate release body
-        id: generate-release-body
-        working-directory: langchain
-        env:
-          WORKING_DIR: ${{ inputs.working-directory }}
-          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
-          TAG: ${{ steps.check-tags.outputs.tag }}
-          PREV_TAG: ${{ steps.check-tags.outputs.prev-tag }}
-        run: |
-          PREAMBLE="Changes since $PREV_TAG"
-          # if PREV_TAG is empty, then we are releasing the first version
-          if [ -z "$PREV_TAG" ]; then
-            PREAMBLE="Initial release"
-            PREV_TAG=$(git rev-list --max-parents=0 HEAD)
-          fi
-          {
-            echo 'release-body<<EOF'
-            echo $PREAMBLE
-            echo
-            git log --format="%s" "$PREV_TAG"..HEAD -- $WORKING_DIR
-            echo EOF
-          } >> "$GITHUB_OUTPUT"
-
-  test-pypi-publish:
-    needs:
-      - build
-      - release-notes
-    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@v6
-        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:
-      - build
-      - release-notes
-      - test-pypi-publish
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    timeout-minutes: 20
-    steps:
-      - uses: actions/checkout@v5
-
-      # We explicitly *don't* set up caching here. This ensures our tests are
-      # maximally sensitive to catching breakage.
-      #
-      # For example, here's a way that caching can cause a falsely-passing test:
-      # - Make the langchain package manifest no longer list a dependency package
-      #   as a requirement. This means it won't be installed by `pip install`,
-      #   and attempting to use it would cause a crash.
-      # - That dependency used to be required, so it may have been cached.
-      #   When restoring the venv packages from cache, that dependency gets included.
-      # - Tests pass, because the dependency is present even though it wasn't specified.
-      # - The package is published, and it breaks on the missing dependency when
-      #   used in the real world.
-
-      - name: Set up Python + uv
-        uses: "./.github/actions/uv_setup"
-        id: setup-python
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - uses: actions/download-artifact@v6
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Import dist package
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        env:
-          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
-          VERSION: ${{ needs.build.outputs.version }}
-        # Here we use:
-        # - The default regular PyPI index as the *primary* index, meaning
-        #   that it takes priority (https://pypi.org/simple)
-        # - The test PyPI index as an extra index, so that any dependencies that
-        #   are not found on test PyPI can be resolved and installed anyway.
-        #   (https://test.pypi.org/simple). This will include the PKG_NAME==VERSION
-        #   package because VERSION will not have been uploaded to regular PyPI yet.
-        # - attempt install again after 5 seconds if it fails because there is
-        #   sometimes a delay in availability on test pypi
-        run: |
-          uv venv
-          VIRTUAL_ENV=.venv uv pip install dist/*.whl
-
-          # Replace all dashes in the package name with underscores,
-          # since that's how Python imports packages with dashes in the name.
-          # also remove _official suffix
-          IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g | sed s/_official//g)"
-
-          uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
-
-      - name: Import test dependencies
-        run: uv sync --group test
-        working-directory: ${{ inputs.working-directory }}
-
-      # Overwrite the local version of the package with the built version
-      - name: Import published package (again)
-        working-directory: ${{ inputs.working-directory }}
-        shell: bash
-        env:
-          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
-          VERSION: ${{ needs.build.outputs.version }}
-        run: |
-          VIRTUAL_ENV=.venv uv pip install dist/*.whl
-
-      - name: Check for prerelease versions
-        # Block release if any dependencies allow prerelease versions
-        # (unless this is itself a prerelease version)
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          uv run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
-
-      - name: Run unit tests
-        run: make tests
-        working-directory: ${{ inputs.working-directory }}
-
-      - name: Get minimum versions
-        # Find the minimum published versions that satisfies the given constraints
-        working-directory: ${{ inputs.working-directory }}
-        id: min-version
-        run: |
-          VIRTUAL_ENV=.venv uv pip install packaging requests
-          python_version="$(uv run python --version | awk '{print $2}')"
-          min_versions="$(uv run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release $python_version)"
-          echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
-          echo "min-versions=$min_versions"
-
-      - name: Run unit tests with minimum dependency versions
-        if: ${{ steps.min-version.outputs.min-versions != '' }}
-        env:
-          MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
-        run: |
-          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 }}
-
-      - name: Import integration test dependencies
-        run: uv sync --group test --group test_integration
-        working-directory: ${{ inputs.working-directory }}
-
-      - name: Run integration tests
-        # Uses the Makefile's `integration_tests` target for the specified package
-        if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
-        env:
-          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
-          GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
-          TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
-          NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
-          GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
-          GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
-          EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
-          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
-          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
-          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
-          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
-          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
-          ES_URL: ${{ secrets.ES_URL }}
-          ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }}
-          ES_API_KEY: ${{ secrets.ES_API_KEY }}
-          MONGODB_ATLAS_URI: ${{ secrets.MONGODB_ATLAS_URI }}
-          UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
-          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
-          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
-          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
-          PPLX_API_KEY: ${{ secrets.PPLX_API_KEY }}
-        run: make integration_tests
-        working-directory: ${{ inputs.working-directory }}
-
-  # Test select published packages against new core
-  # Done when code changes are made to langchain-core
-  test-prior-published-packages-against-new-core:
-    # Installs the new core with old partners: Installs the new unreleased core
-    # alongside the previously published partner packages and runs integration tests
-    if: false
-    needs:
-      - build
-      - release-notes
-      - test-pypi-publish
-      - pre-release-checks
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    strategy:
-      matrix:
-        partner: [anthropic]
-      fail-fast: false # Continue testing other partners if one fails
-    env:
-      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-      ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
-      ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }}
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-      AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-      AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
-      AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
-      AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
-      AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
-    steps:
-      - uses: actions/checkout@v5
-
-      # We implement this conditional as Github Actions does not have good support
-      # for conditionally needing steps. https://github.com/actions/runner/issues/491
-      # TODO: this seems to be resolved upstream, so we can probably remove this workaround
-      - name: Check if libs/core
-        run: |
-          if [ "${{ startsWith(inputs.working-directory, 'libs/core') }}" != "true" ]; then
-            echo "Not in libs/core. Exiting successfully."
-            exit 0
-          fi
-
-      - name: Set up Python + uv
-        if: startsWith(inputs.working-directory, 'libs/core')
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - uses: actions/download-artifact@v6
-        if: startsWith(inputs.working-directory, 'libs/core')
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Test against ${{ matrix.partner }}
-        if: startsWith(inputs.working-directory, 'libs/core')
-        run: |
-          # Identify latest tag, excluding pre-releases
-          LATEST_PACKAGE_TAG="$(
-            git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
-            | awk '{print $2}' \
-            | sed 's|refs/tags/||' \
-            | grep -E '[0-9]+\.[0-9]+\.[0-9]+$' \
-            | sort -Vr \
-            | head -n 1
-          )"
-          echo "Latest package tag: $LATEST_PACKAGE_TAG"
-
-          # Shallow-fetch just that single tag
-          git fetch --depth=1 origin tag "$LATEST_PACKAGE_TAG"
-
-          # Checkout the latest package files
-          rm -rf $GITHUB_WORKSPACE/libs/partners/${{ matrix.partner }}/*
-          rm -rf $GITHUB_WORKSPACE/libs/standard-tests/*
-          cd $GITHUB_WORKSPACE/libs/
-          git checkout "$LATEST_PACKAGE_TAG" -- standard-tests/
-          git checkout "$LATEST_PACKAGE_TAG" -- partners/${{ matrix.partner }}/
-          cd partners/${{ matrix.partner }}
-
-          # Print as a sanity check
-          echo "Version number from pyproject.toml: "
-          cat pyproject.toml | grep "version = "
-
-          # Run tests
-          uv sync --group test --group test_integration
-          uv pip install ../../core/dist/*.whl
-          make integration_tests
-
-  publish:
-    # Publishes the package to PyPI
-    needs:
-      - build
-      - release-notes
-      - test-pypi-publish
-      - pre-release-checks
-      # - test-prior-published-packages-against-new-core
-    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
-
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-
-    steps:
-      - uses: actions/checkout@v5
-
-      - name: Set up Python + uv
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - uses: actions/download-artifact@v6
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          packages-dir: ${{ inputs.working-directory }}/dist/
-          verbose: true
-          print-hash: true
-          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
-          attestations: false
-
-  mark-release:
-    # Marks the GitHub release with the new version tag
-    needs:
-      - build
-      - release-notes
-      - test-pypi-publish
-      - pre-release-checks
-      - publish
-    runs-on: ubuntu-latest
-    permissions:
-      # This permission is needed by `ncipollo/release-action` to
-      # create the GitHub release/tag
-      contents: write
-
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-
-    steps:
-      - uses: actions/checkout@v5
-
-      - name: Set up Python + uv
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - uses: actions/download-artifact@v6
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Create Tag
-        uses: ncipollo/release-action@v1
-        with:
-          artifacts: "dist/*"
-          token: ${{ secrets.GITHUB_TOKEN }}
-          generateReleaseNotes: false
-          tag: ${{needs.build.outputs.pkg-name}}==${{ needs.build.outputs.version }}
-          body: ${{ needs.release-notes.outputs.release-body }}
-          commit: ${{ github.sha }}
-          makeLatest: ${{ needs.build.outputs.pkg-name == 'langchain-core'}}

.github/workflows/_test.yml

@@ -1,85 +0,0 @@
-# Runs unit tests with both current and minimum supported dependency versions
-# to ensure compatibility across the supported range.
-
-name: "🧪 Unit Testing"
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
-
-permissions:
-  contents: read
-
-env:
-  UV_FROZEN: "true"
-  UV_NO_SYNC: "true"
-
-jobs:
-  # Main test job - runs unit tests with current deps, then retests with minimum versions
-  build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    timeout-minutes: 20
-    name: "Python ${{ inputs.python-version }}"
-    steps:
-      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
-
-      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
-        uses: "./.github/actions/uv_setup"
-        id: setup-python
-        with:
-          python-version: ${{ inputs.python-version }}
-          cache-suffix: test-${{ inputs.working-directory }}
-          working-directory: ${{ inputs.working-directory }}
-
-      - name: "📦 Install Test Dependencies"
-        shell: bash
-        run: uv sync --group test --dev
-
-      - name: "🧪 Run Core Unit Tests"
-        shell: bash
-        run: |
-          make test
-
-      - name: "🔍 Calculate Minimum Dependency Versions"
-        working-directory: ${{ inputs.working-directory }}
-        id: min-version
-        shell: bash
-        run: |
-          VIRTUAL_ENV=.venv uv pip install packaging tomli requests
-          python_version="$(uv run python --version | awk '{print $2}')"
-          min_versions="$(uv run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml pull_request $python_version)"
-          echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
-          echo "min-versions=$min_versions"
-
-      - name: "🧪 Run Tests with Minimum Dependencies"
-        if: ${{ steps.min-version.outputs.min-versions != '' }}
-        env:
-          MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
-        run: |
-          VIRTUAL_ENV=.venv uv pip install $MIN_VERSIONS
-          make tests
-        working-directory: ${{ inputs.working-directory }}
-
-      - name: "🧹 Verify Clean Working Directory"
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_test_pydantic.yml

@@ -1,73 +0,0 @@
-# Facilitate unit testing against different Pydantic versions for a provided package.
-
-name: "🐍 Pydantic Version Testing"
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      python-version:
-        required: false
-        type: string
-        description: "Python version to use"
-        default: "3.12"
-      pydantic-version:
-        required: true
-        type: string
-        description: "Pydantic version to test."
-
-permissions:
-  contents: read
-
-env:
-  UV_FROZEN: "true"
-  UV_NO_SYNC: "true"
-
-jobs:
-  build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    timeout-minutes: 20
-    name: "Pydantic ~=${{ inputs.pydantic-version }}"
-    steps:
-      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
-
-      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ inputs.python-version }}
-          cache-suffix: test-pydantic-${{ inputs.working-directory }}
-          working-directory: ${{ inputs.working-directory }}
-
-      - name: "📦 Install Test Dependencies"
-        shell: bash
-        run: uv sync --group test
-
-      - name: "🔄 Install Specific Pydantic Version"
-        shell: bash
-        env:
-          PYDANTIC_VERSION: ${{ inputs.pydantic-version }}
-        run: VIRTUAL_ENV=.venv uv pip install "pydantic~=$PYDANTIC_VERSION"
-
-      - name: "🧪 Run Core Tests"
-        shell: bash
-        run: |
-          make test
-
-      - name: "🧹 Verify Clean Working Directory"
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/check_core_versions.yml

@@ -1,51 +0,0 @@
-# Ensures version numbers in pyproject.toml and version.py stay in sync.
-#
-# (Prevents releases with mismatched version numbers)
-
-name: "🔍 Check Version Equality"
-
-on:
-  pull_request:
-    paths:
-      - "libs/core/pyproject.toml"
-      - "libs/core/langchain_core/version.py"
-
-permissions:
-  contents: read
-
-jobs:
-  check_version_equality:
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: actions/checkout@v5
-
-      - name: "✅ Verify pyproject.toml & version.py Match"
-        run: |
-          # 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 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: $CORE_PYPROJECT_VERSION"
-            echo "version.py version: $CORE_VERSION_PY_VERSION"
-            exit 1
-          else
-            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

@@ -1,261 +0,0 @@
-# Primary CI workflow.
-#
-# Only runs against packages that have changed files.
-#
-# Runs:
-# - Linting (_lint.yml)
-# - Unit Tests (_test.yml)
-# - Pydantic compatibility tests (_test_pydantic.yml)
-# - Integration test compilation checks (_compile_integration_test.yml)
-# - Extended test suites that require additional dependencies
-# - Codspeed benchmarks (if not labeled 'codspeed-ignore')
-#
-# Reports status to GitHub checks and PR status.
-
-name: "🔧 CI"
-
-on:
-  push:
-    branches: [master]
-  pull_request:
-  merge_group:
-
-# Optimizes CI performance by canceling redundant workflow runs
-# If another push to the same PR or branch happens while this workflow is still running,
-# cancel the earlier run in favor of the next run.
-#
-# There's no point in testing an outdated version of the code. GitHub only allows
-# a limited number of job runners to be active at the same time, so it's better to
-# cancel pointless jobs early so that more useful jobs can run sooner.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-env:
-  UV_FROZEN: "true"
-  UV_NO_SYNC: "true"
-
-jobs:
-  # This job analyzes which files changed and creates a dynamic test matrix
-  # to only run tests/lints for the affected packages, improving CI efficiency
-  build:
-    name: "Detect Changes & Set Matrix"
-    runs-on: ubuntu-latest
-    if: ${{ !contains(github.event.pull_request.labels.*.name, 'ci-ignore') }}
-    steps:
-      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
-      - name: "🐍 Setup Python 3.11"
-        uses: actions/setup-python@v6
-        with:
-          python-version: "3.11"
-      - name: "📂 Get Changed Files"
-        id: files
-        uses: Ana06/get-changed-files@v2.3.0
-      - name: "🔍 Analyze Changed Files & Generate Build Matrix"
-        id: set-matrix
-        run: |
-          python -m pip install packaging requests
-          python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
-    outputs:
-      lint: ${{ steps.set-matrix.outputs.lint }}
-      test: ${{ steps.set-matrix.outputs.test }}
-      extended-tests: ${{ steps.set-matrix.outputs.extended-tests }}
-      compile-integration-tests: ${{ steps.set-matrix.outputs.compile-integration-tests }}
-      dependencies: ${{ steps.set-matrix.outputs.dependencies }}
-      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]
-    if: ${{ needs.build.outputs.lint != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.lint) }}
-      fail-fast: false
-    uses: ./.github/workflows/_lint.yml
-    with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
-    secrets: inherit
-
-  # Run unit tests only on packages that have changed files
-  test:
-    needs: [build]
-    if: ${{ needs.build.outputs.test != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.test) }}
-      fail-fast: false
-    uses: ./.github/workflows/_test.yml
-    with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
-    secrets: inherit
-
-  # Test compatibility with different Pydantic versions for affected packages
-  test-pydantic:
-    needs: [build]
-    if: ${{ needs.build.outputs.test-pydantic != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.test-pydantic) }}
-      fail-fast: false
-    uses: ./.github/workflows/_test_pydantic.yml
-    with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      pydantic-version: ${{ matrix.job-configs.pydantic-version }}
-    secrets: inherit
-
-  # Verify integration tests compile without actually running them (faster feedback)
-  compile-integration-tests:
-    name: "Compile Integration Tests"
-    needs: [build]
-    if: ${{ needs.build.outputs.compile-integration-tests != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.compile-integration-tests) }}
-      fail-fast: false
-    uses: ./.github/workflows/_compile_integration_test.yml
-    with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
-    secrets: inherit
-
-  # Run extended test suites that require additional dependencies
-  extended-tests:
-    name: "Extended Tests"
-    needs: [build]
-    if: ${{ needs.build.outputs.extended-tests != '[]' }}
-    strategy:
-      matrix:
-        # note different variable for extended test dirs
-        job-configs: ${{ fromJson(needs.build.outputs.extended-tests) }}
-      fail-fast: false
-    runs-on: ubuntu-latest
-    timeout-minutes: 20
-    defaults:
-      run:
-        working-directory: ${{ matrix.job-configs.working-directory }}
-    steps:
-      - uses: actions/checkout@v5
-
-      - name: "🐍 Set up Python ${{ matrix.job-configs.python-version }} + UV"
-        uses: "./.github/actions/uv_setup"
-        with:
-          python-version: ${{ matrix.job-configs.python-version }}
-          cache-suffix: extended-tests-${{ matrix.job-configs.working-directory }}
-          working-directory: ${{ matrix.job-configs.working-directory }}
-
-      - name: "📦 Install Dependencies & Run Extended Tests"
-        shell: bash
-        run: |
-          echo "Running extended tests, installing dependencies with uv..."
-          uv venv
-          uv sync --group test
-          VIRTUAL_ENV=.venv uv pip install -r extended_testing_deps.txt
-          VIRTUAL_ENV=.venv make extended_tests
-
-      - name: "🧹 Verify Clean Working Directory"
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'
-
-  # 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
-
-      - name: "📦 Install UV Package Manager"
-        uses: astral-sh/setup-uv@v7
-        with:
-          python-version: "3.13"
-
-      - uses: actions/setup-python@v6
-        with:
-          python-version: "3.13"
-
-      - 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@v4
-        env:
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
-          ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }}
-          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
-          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
-          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
-          EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
-          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
-          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
-          MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
-          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          PPLX_API_KEY: ${{ secrets.PPLX_API_KEY }}
-          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
-        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-pydantic,
-        codspeed,
-      ]
-    if: |
-      always()
-    runs-on: ubuntu-latest
-    env:
-      JOBS_JSON: ${{ toJSON(needs) }}
-      RESULTS_JSON: ${{ toJSON(needs.*.result) }}
-      EXIT_CODE: ${{!contains(needs.*.result, 'failure') && !contains(needs.*.result, 'cancelled') && '0' || '1'}}
-    steps:
-      - name: "🎉 All Checks Passed"
-        run: |
-          echo $JOBS_JSON
-          echo $RESULTS_JSON
-          echo "Exiting with $EXIT_CODE"
-          exit $EXIT_CODE

.github/workflows/integration_tests.yml

@@ -1,180 +0,0 @@
-# Routine integration tests against partner libraries with live API credentials.
-#
-# Uses `make integration_tests` for each library in the matrix.
-#
-# Runs daily. Can also be triggered manually for immediate updates.
-
-name: "⏰ Integration Tests"
-run-name: "Run Integration Tests - ${{ inputs.working-directory-force || 'all libs' }} (Python ${{ inputs.python-version-force || '3.10, 3.13' }})"
-
-on:
-  workflow_dispatch:
-    inputs:
-      working-directory-force:
-        type: string
-        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"
-  schedule:
-    - cron: "0 13 * * *" # Runs daily at 1PM UTC (9AM EDT/6AM PDT)
-
-permissions:
-  contents: read
-
-env:
-  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"]'
-
-jobs:
-  # Generate dynamic test matrix based on input parameters or defaults
-  # Only runs on the main repo (for scheduled runs) or when manually triggered
-  compute-matrix:
-    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
-    runs-on: ubuntu-latest
-    name: "📋 Compute Test Matrix"
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - name: "🔢 Generate Python & Library Matrix"
-        id: set-matrix
-        env:
-          DEFAULT_LIBS: ${{ env.DEFAULT_LIBS }}
-          WORKING_DIRECTORY_FORCE: ${{ github.event.inputs.working-directory-force || '' }}
-          PYTHON_VERSION_FORCE: ${{ github.event.inputs.python-version-force || '' }}
-        run: |
-          # echo "matrix=..." where matrix is a json formatted str with keys python-version and working-directory
-          # python-version should default to 3.10 and 3.13, but is overridden to [PYTHON_VERSION_FORCE] if set
-          # working-directory should default to DEFAULT_LIBS, but is overridden to [WORKING_DIRECTORY_FORCE] if set
-          python_version='["3.10", "3.13"]'
-          working_directory="$DEFAULT_LIBS"
-          if [ -n "$PYTHON_VERSION_FORCE" ]; then
-            python_version="[\"$PYTHON_VERSION_FORCE\"]"
-          fi
-          if [ -n "$WORKING_DIRECTORY_FORCE" ]; then
-            working_directory="[\"$WORKING_DIRECTORY_FORCE\"]"
-          fi
-          matrix="{\"python-version\": $python_version, \"working-directory\": $working_directory}"
-          echo $matrix
-          echo "matrix=$matrix" >> $GITHUB_OUTPUT
-  # Run integration tests against partner libraries with live API credentials
-  build:
-    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
-    name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.working-directory }}"
-    runs-on: ubuntu-latest
-    needs: [compute-matrix]
-    timeout-minutes: 30
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ${{ fromJSON(needs.compute-matrix.outputs.matrix).python-version }}
-        working-directory: ${{ fromJSON(needs.compute-matrix.outputs.matrix).working-directory }}
-
-    steps:
-      - uses: actions/checkout@v5
-        with:
-          path: langchain
-      - uses: actions/checkout@v5
-        with:
-          repository: langchain-ai/langchain-google
-          path: langchain-google
-      - uses: actions/checkout@v5
-        with:
-          repository: langchain-ai/langchain-aws
-          path: langchain-aws
-
-      - name: "📦 Organize External Libraries"
-        run: |
-          rm -rf \
-            langchain/libs/partners/google-genai \
-            langchain/libs/partners/google-vertexai
-          mv langchain-google/libs/genai langchain/libs/partners/google-genai
-          mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
-          mv langchain-aws/libs/aws langchain/libs/partners/aws
-
-      - name: "🐍 Set up Python ${{ matrix.python-version }} + UV"
-        uses: "./langchain/.github/actions/uv_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: "🔐 Authenticate to Google Cloud"
-        id: "auth"
-        uses: google-github-actions/auth@v3
-        with:
-          credentials_json: "${{ secrets.GOOGLE_CREDENTIALS }}"
-
-      - name: "🔐 Configure AWS Credentials"
-        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 }}
-          aws-region: ${{ secrets.AWS_REGION }}
-
-      - name: "📦 Install Dependencies"
-        run: |
-          echo "Running scheduled tests, installing dependencies with uv..."
-          cd langchain/${{ matrix.working-directory }}
-          uv sync --group test --group test_integration
-
-      - name: "🚀 Run Integration Tests"
-        env:
-          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
-          ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }}
-          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
-          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
-          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
-          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
-          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
-          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
-          ES_URL: ${{ secrets.ES_URL }}
-          ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }}
-          ES_API_KEY: ${{ secrets.ES_API_KEY }}
-          EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
-          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
-          GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
-          GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
-          GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
-          MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
-          MONGODB_ATLAS_URI: ${{ secrets.MONGODB_ATLAS_URI }}
-          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
-          NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          PPLX_API_KEY: ${{ secrets.PPLX_API_KEY }}
-          TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
-          UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
-          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
-          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
-        run: |
-          cd langchain/${{ matrix.working-directory }}
-          make integration_tests
-
-      - name: "🧹 Clean up External Libraries"
-        # Clean up external libraries to avoid affecting the following git status check
-        run: |
-          rm -rf \
-            langchain/libs/partners/google-genai \
-            langchain/libs/partners/google-vertexai \
-            langchain/libs/partners/aws
-
-      - name: "🧹 Verify Clean Working Directory"
-        working-directory: langchain
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/lint.yml

@@ -0,0 +1,35 @@
+name: lint
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+env:
+  POETRY_VERSION: "1.2.0"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: 
+         - "3.8"
+         - "3.9"
+         - "3.10"
+    steps:
+    - uses: actions/checkout@v3
+    - name: Install poetry
+      run: |
+        pipx install poetry==$POETRY_VERSION
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+        cache: poetry
+    - name: Install dependencies
+      run: |
+        poetry install
+    - name: Analysing the code with our lint
+      run: |
+        make lint

.github/workflows/pr_labeler_file.yml

@@ -1,28 +0,0 @@
-# Label PRs based on changed files.
-#
-# See `.github/pr-file-labeler.yml` to see rules for each label/directory.
-
-name: "🏷️ Pull Request Labeler"
-
-on:
-  # Safe since we're not checking out or running the PR's code
-  # Never check out the PR's head in a pull_request_target job
-  pull_request_target:
-    types: [opened, synchronize, reopened, edited]
-
-jobs:
-  labeler:
-    name: "label"
-    permissions:
-      contents: read
-      pull-requests: write
-      issues: write
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Label Pull Request
-        uses: actions/labeler@v6
-        with:
-          repo-token: "${{ secrets.GITHUB_TOKEN }}"
-          configuration-path: .github/pr-file-labeler.yml
-          sync-labels: false

.github/workflows/pr_labeler_title.yml

@@ -1,44 +0,0 @@
-# Label PRs based on their titles.
-#
-# Uses conventional commit types from PR titles to apply labels.
-# Note: Scope-based labeling (e.g., integration labels) is handled by pr_labeler_file.yml
-
-name: "🏷️ PR Title Labeler"
-
-on:
-  # Safe since we're not checking out or running the PR's code
-  # Never check out the PR's head in a pull_request_target job
-  pull_request_target:
-    types: [opened, edited]
-
-jobs:
-  pr-title-labeler:
-    name: "label"
-    permissions:
-      contents: read
-      pull-requests: write
-      issues: write
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Label PR based on title
-        uses: bcoe/conventional-release-labels@v1
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          type_labels: >-
-            {
-              "feat": "feature",
-              "fix": "fix",
-              "docs": "documentation",
-              "style": "linting",
-              "refactor": "refactor",
-              "perf": "performance",
-              "test": "tests",
-              "build": "infra",
-              "ci": "infra",
-              "chore": "infra",
-              "revert": "revert",
-              "release": "release",
-              "breaking": "breaking"
-            }
-          ignored_types: '[]'

.github/workflows/pr_lint.yml

@@ -1,108 +0,0 @@
-# PR title linting.
-#
-# FORMAT (Conventional Commits 1.0.0):
-#
-#   <type>[optional scope]: <description>
-#   [optional body]
-#   [optional footer(s)]
-#
-# Examples:
-#     feat(core): add multi‐tenant support
-#     fix(cli): resolve flag parsing error
-#     docs: update API usage examples
-#     docs(openai): update API usage examples
-#
-# Allowed Types:
-#   * feat       — a new feature (MINOR)
-#   * fix        — a bug fix (PATCH)
-#   * docs       — documentation only changes
-#   * style      — formatting, linting, etc.; no code change or typing refactors
-#   * refactor   — code change that neither fixes a bug nor adds a feature
-#   * perf       — code change that improves performance
-#   * test       — adding tests or correcting existing
-#   * build      — changes that affect the build system/external dependencies
-#   * ci         — continuous integration/configuration changes
-#   * chore      — other changes that don't modify source or test files
-#   * revert     — reverts a previous commit
-#   * release    — prepare a new release
-#
-# Allowed Scopes (optional):
-#   core, cli, langchain, langchain_v1, langchain-classic, standard-tests,
-#   text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq,
-#   huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant,
-#   xai, infra, deps
-#
-# Rules:
-#   1. The 'Type' must start with a lowercase letter.
-#   2. Breaking changes: append "!" after type/scope (e.g., feat!: drop x support)
-#   3. When releasing (updating the pyproject.toml and uv.lock), the commit message
-#      should be: `release(scope): x.y.z` (e.g., `release(core): 1.2.0` with no
-#      body, footer, or preceeding/proceeding text).
-#
-# Enforces Conventional Commits format for pull request titles to maintain a clear and
-# machine-readable change history.
-
-name: "🏷️ PR Title Lint"
-
-permissions:
-  pull-requests: read
-
-on:
-  pull_request:
-    types: [opened, edited, synchronize]
-
-jobs:
-  # Validates that PR title follows Conventional Commits 1.0.0 specification
-  lint-pr-title:
-    name: "validate format"
-    runs-on: ubuntu-latest
-    steps:
-      - name: "✅ Validate Conventional Commits Format"
-        uses: amannn/action-semantic-pull-request@v6
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
-          types: |
-            feat
-            fix
-            docs
-            style
-            refactor
-            perf
-            test
-            build
-            ci
-            chore
-            revert
-            release
-          scopes: |
-            core
-            cli
-            langchain
-            langchain_v1
-            langchain-classic
-            standard-tests
-            text-splitters
-            docs
-            anthropic
-            chroma
-            deepseek
-            exa
-            fireworks
-            groq
-            huggingface
-            mistralai
-            nomic
-            ollama
-            openai
-            perplexity
-            prompty
-            qdrant
-            xai
-            infra
-          requireScope: false
-          disallowScopes: |
-            release
-            [A-Z]+
-          ignoreLabels: |
-            ignore-lint-pr-title

.github/workflows/test.yml

@@ -0,0 +1,33 @@
+name: test
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+env:
+  POETRY_VERSION: "1.2.0"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+         - "3.8"
+         - "3.9"
+         - "3.10"
+    steps:
+    - uses: actions/checkout@v3
+    - name: Install poetry
+      run: pipx install poetry==$POETRY_VERSION
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+        cache: 'poetry'
+    - name: Install dependencies
+      run: poetry install
+    - name: Run unit tests
+      run: |
+        make tests

.github/workflows/v03_api_doc_build.yml

@@ -1,164 +0,0 @@
-# Build the API reference documentation for v0.3 branch.
-#
-# Manual trigger only.
-#
-# Built HTML pushed to langchain-ai/langchain-api-docs-html.
-#
-# Looks for langchain-ai org repos in packages.yml and checks them out.
-# Calls prep_api_docs_build.py.
-
-name: "📚 API Docs (v0.3)"
-run-name: "Build & Deploy API Reference (v0.3)"
-
-on:
-  workflow_dispatch:
-
-env:
-  PYTHON_VERSION: "3.11"
-
-jobs:
-  build:
-    if: github.repository == 'langchain-ai/langchain' || github.event_name != 'schedule'
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    steps:
-      - uses: actions/checkout@v5
-        with:
-          ref: v0.3
-          path: langchain
-
-      - uses: actions/checkout@v5
-        with:
-          repository: langchain-ai/langchain-api-docs-html
-          path: langchain-api-docs-html
-          token: ${{ secrets.TOKEN_GITHUB_API_DOCS_HTML }}
-
-      - name: "📋 Extract Repository List with yq"
-        id: get-unsorted-repos
-        uses: mikefarah/yq@master
-        with:
-          cmd: |
-            # Extract repos from packages.yml that are in the langchain-ai org
-            # (excluding 'langchain' itself)
-            yq '
-              .packages[]
-              | select(
-                  (
-                    (.repo | test("^langchain-ai/"))
-                    and
-                    (.repo != "langchain-ai/langchain")
-                  )
-                  or
-                  (.include_in_api_ref // false)
-                )
-              | .repo
-            ' langchain/libs/packages.yml
-
-      - name: "📋 Parse YAML & Checkout Repositories"
-        env:
-          REPOS_UNSORTED: ${{ steps.get-unsorted-repos.outputs.result }}
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          # Get unique repositories
-          REPOS=$(echo "$REPOS_UNSORTED" | sort -u)
-          # Checkout each unique repository
-          for repo in $REPOS; do
-            # Validate repository format (allow any org with proper format)
-            if [[ ! "$repo" =~ ^[a-zA-Z0-9_.-]+/[a-zA-Z0-9_.-]+$ ]]; then
-              echo "Error: Invalid repository format: $repo"
-              exit 1
-            fi
-
-            REPO_NAME=$(echo $repo | cut -d'/' -f2)
-
-            # Additional validation for repo name
-            if [[ ! "$REPO_NAME" =~ ^[a-zA-Z0-9_.-]+$ ]]; then
-              echo "Error: Invalid repository name: $REPO_NAME"
-              exit 1
-            fi
-            echo "Checking out $repo to $REPO_NAME"
-
-            # Special handling for langchain-tavily: checkout by commit hash
-            if [[ "$REPO_NAME" == "langchain-tavily" ]]; then
-              git clone https://github.com/$repo.git $REPO_NAME
-              cd $REPO_NAME
-              git checkout f3515654724a9e87bdfe2c2f509d6cdde646e563
-              cd ..
-            else
-              git clone --depth 1 --branch v0.3 https://github.com/$repo.git $REPO_NAME
-            fi
-          done
-
-      - name: "🐍 Setup Python ${{ env.PYTHON_VERSION }}"
-        uses: actions/setup-python@v6
-        id: setup-python
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - name: "📦 Install Initial Python Dependencies using uv"
-        working-directory: langchain
-        run: |
-          python -m pip install -U uv
-          python -m uv pip install --upgrade --no-cache-dir pip setuptools pyyaml
-
-      - name: "📦 Organize Library Directories"
-        # Places cloned partner packages into libs/partners structure
-        run: python langchain/.github/scripts/prep_api_docs_build.py
-
-      - name: "🧹 Clear Prior Build"
-        run:
-          # Remove artifacts from prior docs build
-          rm -rf langchain-api-docs-html/api_reference_build/html
-
-      - name: "📦 Install Documentation Dependencies using uv"
-        working-directory: langchain
-        run: |
-          # Install all partner packages in editable mode with overrides
-          python -m uv pip install $(ls ./libs/partners | grep -v azure-ai | xargs -I {} echo "./libs/partners/{}") --overrides ./docs/vercel_overrides.txt --prerelease=allow
-
-          # Install langchain-azure-ai with tools extra
-          python -m uv pip install "./libs/partners/azure-ai[tools]" --overrides ./docs/vercel_overrides.txt --prerelease=allow
-
-          # Install core langchain and other main packages
-          python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental libs/standard-tests
-
-          # Install Sphinx and related packages for building docs
-          python -m uv pip install -r docs/api_reference/requirements.txt
-
-      - name: "🔧 Configure Git Settings"
-        working-directory: langchain
-        run: |
-          git config --local user.email "actions@github.com"
-          git config --local user.name "Github Actions"
-
-      - name: "📚 Build API Documentation"
-        working-directory: langchain
-        run: |
-          # Generate the API reference RST files
-          python docs/api_reference/create_api_rst.py
-
-          # Build the HTML documentation using Sphinx
-          # -T: show full traceback on exception
-          # -E: don't use cached environment (force rebuild, ignore cached doctrees)
-          # -b html: build HTML docs (vs PDS, etc.)
-          # -d: path for the cached environment (parsed document trees / doctrees)
-          #     - Separate from output dir for faster incremental builds
-          # -c: path to conf.py
-          # -j auto: parallel build using all available CPU cores
-          python -m sphinx -T -E -b html -d ../langchain-api-docs-html/_build/doctrees -c docs/api_reference docs/api_reference ../langchain-api-docs-html/api_reference_build/html -j auto
-
-          # Post-process the generated HTML
-          python docs/api_reference/scripts/custom_formatter.py ../langchain-api-docs-html/api_reference_build/html
-
-          # Default index page is blank so we copy in the actual home page.
-          cp ../langchain-api-docs-html/api_reference_build/html/{reference,index}.html
-
-          # Removes Sphinx's intermediate build artifacts after the build is complete.
-          rm -rf ../langchain-api-docs-html/_build/
-
-      # Commit and push changes to langchain-api-docs-html repo
-      - uses: EndBug/add-and-commit@v9
-        with:
-          cwd: langchain-api-docs-html
-          message: "Update API docs build from v0.3 branch"

.github/workflows/v1_changes.md

@@ -1,8 +0,0 @@
-With the deprecation of v0 docs, the following files will need to be migrated/supported
-in the new docs repo:
-
-- run_notebooks.yml: New repo should run Integration tests on code snippets?
-- people.yml: Need to fix and somehow display on the new docs site
-  - Subsequently, `.github/actions/people/`
-- _test_doc_imports.yml
-- check-broken-links.yml

.gitignore

@@ -1,8 +1,4 @@
-.vs/
-.claude/
-.idea/
-#Emacs backup
-*~
+.vscode/
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -32,12 +28,6 @@ share/python-wheels/
 *.egg
 MANIFEST
 
-# Google GitHub Actions credentials files created by:
-# https://github.com/google-github-actions/auth
-#
-# That action recommends adding this gitignore to prevent accidentally committing keys.
-gha-creds-*.json
-
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
@@ -61,7 +51,6 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
-.codspeed/
 
 # Translations
 *.mo
@@ -80,6 +69,9 @@ instance/
 # Scrapy stuff:
 .scrapy
 
+# Sphinx documentation
+docs/_build/
+
 # PyBuilder
 target/
 
@@ -113,12 +105,12 @@ celerybeat.pid
 
 # Environments
 .env
-.envrc
-.venv*
-venv*
+.venv
 env/
+venv/
 ENV/
 env.bak/
+venv.bak/
 
 # Spyder project settings
 .spyderproject
@@ -132,34 +124,8 @@ env.bak/
 
 # mypy
 .mypy_cache/
-.mypy_cache_test/
 .dmypy.json
 dmypy.json
 
 # Pyre type checker
 .pyre/
-
-# macOS display setting files
-.DS_Store
-
-# Wandb directory
-wandb/
-
-# asdf tool versions
-.tool-versions
-/.ruff_cache/
-
-*.pkl
-*.bin
-
-# integration test artifacts
-data_map*
-\[('_type', 'fake'), ('stop', None)]
-
-# Replit files
-*replit*
-
-node_modules
-
-prof
-virtualenv/

.markdownlint.json

@@ -1,14 +0,0 @@
-{
-  "MD013": false,
-  "MD024": {
-    "siblings_only": true
-  },
-  "MD025": false,
-  "MD033": false,
-  "MD034": false,
-  "MD036": false,
-  "MD041": false,
-  "MD046": {
-    "style": "fenced"
-  }
-}

.mcp.json

@@ -1,8 +0,0 @@
-{
-  "mcpServers": {
-    "docs-langchain": {
-      "type": "http",
-      "url": "https://docs.langchain.com/mcp"
-    }
-  }
-}

.pre-commit-config.yaml

@@ -1,99 +0,0 @@
-repos:
-  - repo: local
-    hooks:
-      - id: core
-        name: format and lint core
-        language: system
-        entry: make -C libs/core format lint
-        files: ^libs/core/
-        pass_filenames: false
-      - id: langchain
-        name: format and lint langchain
-        language: system
-        entry: make -C libs/langchain format lint
-        files: ^libs/langchain/
-        pass_filenames: false
-      - id: standard-tests
-        name: format and lint standard-tests
-        language: system
-        entry: make -C libs/standard-tests format lint
-        files: ^libs/standard-tests/
-        pass_filenames: false
-      - id: text-splitters
-        name: format and lint text-splitters
-        language: system
-        entry: make -C libs/text-splitters format lint
-        files: ^libs/text-splitters/
-        pass_filenames: false
-      - id: anthropic
-        name: format and lint partners/anthropic
-        language: system
-        entry: make -C libs/partners/anthropic format lint
-        files: ^libs/partners/anthropic/
-        pass_filenames: false
-      - id: chroma
-        name: format and lint partners/chroma
-        language: system
-        entry: make -C libs/partners/chroma format lint
-        files: ^libs/partners/chroma/
-        pass_filenames: false
-      - id: exa
-        name: format and lint partners/exa
-        language: system
-        entry: make -C libs/partners/exa format lint
-        files: ^libs/partners/exa/
-        pass_filenames: false
-      - id: fireworks
-        name: format and lint partners/fireworks
-        language: system
-        entry: make -C libs/partners/fireworks format lint
-        files: ^libs/partners/fireworks/
-        pass_filenames: false
-      - id: groq
-        name: format and lint partners/groq
-        language: system
-        entry: make -C libs/partners/groq format lint
-        files: ^libs/partners/groq/
-        pass_filenames: false
-      - id: huggingface
-        name: format and lint partners/huggingface
-        language: system
-        entry: make -C libs/partners/huggingface format lint
-        files: ^libs/partners/huggingface/
-        pass_filenames: false
-      - id: mistralai
-        name: format and lint partners/mistralai
-        language: system
-        entry: make -C libs/partners/mistralai format lint
-        files: ^libs/partners/mistralai/
-        pass_filenames: false
-      - id: nomic
-        name: format and lint partners/nomic
-        language: system
-        entry: make -C libs/partners/nomic format lint
-        files: ^libs/partners/nomic/
-        pass_filenames: false
-      - id: ollama
-        name: format and lint partners/ollama
-        language: system
-        entry: make -C libs/partners/ollama format lint
-        files: ^libs/partners/ollama/
-        pass_filenames: false
-      - id: openai
-        name: format and lint partners/openai
-        language: system
-        entry: make -C libs/partners/openai format lint
-        files: ^libs/partners/openai/
-        pass_filenames: false
-      - id: prompty
-        name: format and lint partners/prompty
-        language: system
-        entry: make -C libs/partners/prompty format lint
-        files: ^libs/partners/prompty/
-        pass_filenames: false
-      - id: qdrant
-        name: format and lint partners/qdrant
-        language: system
-        entry: make -C libs/partners/qdrant format lint
-        files: ^libs/partners/qdrant/
-        pass_filenames: false

.vscode/extensions.json

@@ -1,21 +0,0 @@
-{
-  "recommendations": [
-    "ms-python.python",
-    "charliermarsh.ruff",
-    "ms-python.mypy-type-checker",
-    "ms-toolsai.jupyter",
-    "ms-toolsai.jupyter-keymap",
-    "ms-toolsai.jupyter-renderers",
-    "ms-toolsai.vscode-jupyter-cell-tags",
-    "ms-toolsai.vscode-jupyter-slideshow",
-    "yzhang.markdown-all-in-one",
-    "davidanson.vscode-markdownlint",
-    "bierner.markdown-mermaid",
-    "bierner.markdown-preview-github-styles",
-    "eamodio.gitlens",
-    "github.vscode-pull-request-github",
-    "github.vscode-github-actions",
-    "redhat.vscode-yaml",
-    "editorconfig.editorconfig",
-  ],
-}

.vscode/settings.json

@@ -1,78 +0,0 @@
-{
-  "python.analysis.include": [
-    "libs/**",
-  ],
-  "python.analysis.exclude": [
-    "**/node_modules",
-    "**/__pycache__",
-    "**/.pytest_cache",
-    "**/.*",
-  ],
-  "python.analysis.autoImportCompletions": true,
-  "python.analysis.typeCheckingMode": "basic",
-  "python.testing.cwd": "${workspaceFolder}",
-  "python.linting.enabled": true,
-  "python.linting.ruffEnabled": true,
-  "[python]": {
-    "editor.formatOnSave": true,
-    "editor.codeActionsOnSave": {
-      "source.organizeImports.ruff": "explicit",
-      "source.fixAll": "explicit"
-    },
-    "editor.defaultFormatter": "charliermarsh.ruff"
-  },
-  "editor.rulers": [
-    88
-  ],
-  "editor.tabSize": 4,
-  "editor.insertSpaces": true,
-  "editor.trimAutoWhitespace": true,
-  "files.trimTrailingWhitespace": true,
-  "files.insertFinalNewline": true,
-  "files.exclude": {
-    "**/__pycache__": true,
-    "**/.pytest_cache": true,
-    "**/*.pyc": true,
-    "**/.mypy_cache": true,
-    "**/.ruff_cache": true,
-    "_dist/**": true,
-    "**/node_modules": true,
-    "**/.git": false
-  },
-  "search.exclude": {
-    "**/__pycache__": true,
-    "**/*.pyc": true,
-    "_dist/**": true,
-    "**/node_modules": true,
-    "**/.git": true,
-    "uv.lock": true,
-    "yarn.lock": true
-  },
-  "git.autofetch": true,
-  "git.enableSmartCommit": true,
-  "jupyter.askForKernelRestart": false,
-  "jupyter.interactiveWindow.textEditor.executeSelection": true,
-  "[markdown]": {
-    "editor.wordWrap": "on",
-    "editor.quickSuggestions": {
-      "comments": "off",
-      "strings": "off",
-      "other": "off"
-    }
-  },
-  "[yaml]": {
-    "editor.tabSize": 2,
-    "editor.insertSpaces": true
-  },
-  "[json]": {
-    "editor.tabSize": 2,
-    "editor.insertSpaces": true
-  },
-  "python.terminal.activateEnvironment": false,
-  "python.defaultInterpreterPath": "./.venv/bin/python",
-  "github.copilot.chat.commitMessageGeneration.instructions": [
-    {
-      "file": ".github/workflows/pr_lint.yml"
-    }
-  ]
-}

AGENTS.md

@@ -1,326 +0,0 @@
-# Global Development Guidelines for LangChain Projects
-
-## Core Development Principles
-
-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
-
-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
-
-❌ **Bad - Breaking Change:**
-
-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
-```
-
-✅ **Good - Stable Interface:**
-
-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
-
-**Before making ANY changes to public APIs:**
-
-- Check if the function/class is exported in `__init__.py`
-- Look for existing usage patterns in tests and examples
-- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
-- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
-
-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
-- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
-- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
-- Avoid unnecessary abstraction or premature optimization
-- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
-- Unit tests: `tests/unit_tests/` (no network calls allowed)
-- Integration tests: `tests/integration_tests/` (network calls permitted)
-- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
-- [ ] Tests fail when your new logic is broken
-- [ ] Happy path is covered
-- [ ] Edge cases and error conditions are tested
-- [ ] Use fixtures/mocks for external dependencies
-- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
-- [ ] Does the test suite fail if your new logic is broken?
-- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
-- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
-- No `eval()`, `exec()`, or `pickle` on user-controlled input
-- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
-- Remove unreachable/commented code before committing
-- Race conditions or resource leaks (file handles, sockets, threads).
-- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args section for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
-
-    Returns:
-        `True` if email was sent successfully, `False` otherwise.
-
-    Raises:
-        `InvalidEmailError`: If the email address format is invalid.
-        `SMTPConnectionError`: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
-- Types go in function signatures, NOT in docstrings
-  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
-- Focus on "why" rather than "what" in descriptions
-- Document all parameters, return values, and exceptions
-- Keep descriptions concise but clear
-- Ensure American English spelling (e.g., "behavior", not "behaviour")
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications."""
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
-- Reduce code duplication through shared utilities
-- Make unit testing easier
-- Improve separation of concerns (single responsibility)
-- Make unit testing easier through dependency injection
-- Add clarity without adding complexity
-- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
-
-```bash
-# Run unit tests (no network)
-make test
-
-# Don't run integration tests, as API keys must be set
-
-# Run specific test file
-uv run --group test pytest tests/unit_tests/test_specific.py
-```
-
-### Code Quality
-
-```bash
-# Lint code
-make lint
-
-# Format code
-make format
-
-# Type checking
-uv run --group lint mypy .
-```
-
-### Dependency Management Patterns
-
-**Local Development Dependencies:**
-
-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
-
-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
-
-```python
-from langchain_core.tools import tool
-
-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
-
-    Args:
-        query: The search query string.
-    """
-    # Implementation here
-    return results
-```
-
-## Commit Standards
-
-**Use Conventional Commits format for PR titles:**
-
-- `feat(core): add multi-tenant support`
-- `fix(cli): resolve flag parsing error`
-- `docs: update API usage examples`
-- `docs(openai): update API usage examples`
-
-## Framework-Specific Guidelines
-
-- Follow the existing patterns in `langchain-core` for base abstractions
-- Use `langchain_core.callbacks` for execution tracking
-- Implement proper streaming support where applicable
-- Avoid deprecated components like legacy `LLMChain`
-
-### Partner Integrations
-
-- Follow the established patterns in existing partner libraries
-- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
-- Include comprehensive integration tests
-- Document API key requirements and authentication
-
----
-
-## Quick Reference Checklist
-
-Before submitting code changes:
-
-- [ ] **Breaking Changes**: Verified no public API changes
-- [ ] **Type Hints**: All functions have complete type annotations
-- [ ] **Tests**: New functionality is fully tested
-- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
-- [ ] **Documentation**: Google-style docstrings for public functions
-- [ ] **Code Quality**: `make lint` and `make format` pass
-- [ ] **Architecture**: Suggested improvements where applicable
-- [ ] **Commit Message**: Follows Conventional Commits format

CITATION.cff

@@ -1,8 +0,0 @@
-cff-version: 1.2.0
-message: "If you use this software, please cite it as below."
-authors:
-- family-names: "Chase"
-  given-names: "Harrison"
-title: "LangChain"
-date-released: 2022-10-17
-url: "https://github.com/langchain-ai/langchain"

CLAUDE.md

@@ -1,326 +0,0 @@
-# Global Development Guidelines for LangChain Projects
-
-## Core Development Principles
-
-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
-
-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
-
-❌ **Bad - Breaking Change:**
-
-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
-```
-
-✅ **Good - Stable Interface:**
-
-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
-
-**Before making ANY changes to public APIs:**
-
-- Check if the function/class is exported in `__init__.py`
-- Look for existing usage patterns in tests and examples
-- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
-- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
-
-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
-- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
-- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
-- Avoid unnecessary abstraction or premature optimization
-- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
-- Unit tests: `tests/unit_tests/` (no network calls allowed)
-- Integration tests: `tests/integration_tests/` (network calls permitted)
-- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
-- [ ] Tests fail when your new logic is broken
-- [ ] Happy path is covered
-- [ ] Edge cases and error conditions are tested
-- [ ] Use fixtures/mocks for external dependencies
-- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
-- [ ] Does the test suite fail if your new logic is broken?
-- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
-- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
-- No `eval()`, `exec()`, or `pickle` on user-controlled input
-- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
-- Remove unreachable/commented code before committing
-- Race conditions or resource leaks (file handles, sockets, threads).
-- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args section for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
-
-    Returns:
-        `True` if email was sent successfully, `False` otherwise.
-
-    Raises:
-        `InvalidEmailError`: If the email address format is invalid.
-        `SMTPConnectionError`: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
-- Types go in function signatures, NOT in docstrings
-  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
-- Focus on "why" rather than "what" in descriptions
-- Document all parameters, return values, and exceptions
-- Keep descriptions concise but clear
-- Ensure American English spelling (e.g., "behavior", not "behaviour")
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications."""
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
-- Reduce code duplication through shared utilities
-- Make unit testing easier
-- Improve separation of concerns (single responsibility)
-- Make unit testing easier through dependency injection
-- Add clarity without adding complexity
-- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
-
-```bash
-# Run unit tests (no network)
-make test
-
-# Don't run integration tests, as API keys must be set
-
-# Run specific test file
-uv run --group test pytest tests/unit_tests/test_specific.py
-```
-
-### Code Quality
-
-```bash
-# Lint code
-make lint
-
-# Format code
-make format
-
-# Type checking
-uv run --group lint mypy .
-```
-
-### Dependency Management Patterns
-
-**Local Development Dependencies:**
-
-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
-
-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
-
-```python
-from langchain_core.tools import tool
-
-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
-
-    Args:
-        query: The search query string.
-    """
-    # Implementation here
-    return results
-```
-
-## Commit Standards
-
-**Use Conventional Commits format for PR titles:**
-
-- `feat(core): add multi-tenant support`
-- `fix(cli): resolve flag parsing error`
-- `docs: update API usage examples`
-- `docs(openai): update API usage examples`
-
-## Framework-Specific Guidelines
-
-- Follow the existing patterns in `langchain-core` for base abstractions
-- Use `langchain_core.callbacks` for execution tracking
-- Implement proper streaming support where applicable
-- Avoid deprecated components like legacy `LLMChain`
-
-### Partner Integrations
-
-- Follow the established patterns in existing partner libraries
-- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
-- Include comprehensive integration tests
-- Document API key requirements and authentication
-
----
-
-## Quick Reference Checklist
-
-Before submitting code changes:
-
-- [ ] **Breaking Changes**: Verified no public API changes
-- [ ] **Type Hints**: All functions have complete type annotations
-- [ ] **Tests**: New functionality is fully tested
-- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
-- [ ] **Documentation**: Google-style docstrings for public functions
-- [ ] **Code Quality**: `make lint` and `make format` pass
-- [ ] **Architecture**: Suggested improvements where applicable
-- [ ] **Commit Message**: Follows Conventional Commits format

LICENSE

@@ -1,6 +1,6 @@
-MIT License
+The MIT License
 
-Copyright (c) LangChain, Inc.
+Copyright (c) Harrison Chase
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -9,13 +9,13 @@ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

MIGRATE.md

@@ -1,9 +0,0 @@
-# Migrating
-
-Please see the following guides for migrating LangChain code:
-
-* Migrate to [LangChain v1.0](https://docs.langchain.com/oss/python/migrate/langchain-v1)
-* Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
-* Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
-* Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)
-* Upgrade to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)

Makefile

@@ -0,0 +1,17 @@
+.PHONY: format lint tests integration_tests
+
+format:
+	poetry run black .
+	poetry run isort .
+
+lint:
+	poetry run mypy .
+	poetry run black . --check
+	poetry run isort . --check
+	poetry run flake8 .
+
+tests:
+	poetry run pytest tests/unit_tests
+
+integration_tests:
+	poetry run pytest tests/integration_tests

README.md

@@ -1,77 +1,188 @@
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-dark.svg">
-    <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-light.svg">
-    <img alt="LangChain Logo" src=".github/images/logo-dark.svg" width="80%">
-  </picture>
-</p>
+# 🦜️🔗 LangChain
 
-<p align="center">
-    The platform for reliable agents.
-</p>
+⚡ Building applications with LLMs through composability ⚡
 
-<p align="center">
-  <a href="https://opensource.org/licenses/MIT" target="_blank">
-      <img src="https://img.shields.io/pypi/l/langchain" alt="PyPI - License">
-  </a>
-  <a href="https://pypistats.org/packages/langchain" target="_blank">
-      <img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads">
-  </a>
-  <a href="https://pypi.org/project/langchain/#history" target="_blank">
-      <img src="https://img.shields.io/pypi/v/langchain?label=%20" alt="Version">
-  </a>
-  <a href="https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain" target="_blank">
-      <img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode" alt="Open in Dev Containers">
-  </a>
-  <a href="https://codespaces.new/langchain-ai/langchain" target="_blank">
-      <img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">
-  </a>
-  <a href="https://codspeed.io/langchain-ai/langchain" target="_blank">
-      <img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed Badge">
-  </a>
-  <a href="https://twitter.com/langchainai" target="_blank">
-      <img src="https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI" alt="Twitter / X">
-  </a>
-</p>
+[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
 
-LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development —  all while future-proofing decisions as the underlying technology evolves.
+## Quick Install
+
+`pip install langchain`
+
+## 🤔 What is this?
+
+Large language models (LLMs) are emerging as a transformative technology, enabling
+developers to build applications that they previously could not.
+But using these LLMs in isolation is often not enough to
+create a truly powerful app - the real power comes when you are able to
+combine them with other sources of computation or knowledge.
+
+This library is aimed at assisting in the development of those types of applications.
+
+## 📖 Documentation
+
+Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documentation on:
+- Getting started (installation, setting up environment, simple examples)
+- How-To examples (demos, integrations, helper functions)
+- Reference (full API docs)
+- Resources (high level explanation of core concepts)
+
+## 🚀 What can this help with?
+
+There are three main areas (with a forth coming soon) that LangChain is designed to help with.
+These are, in increasing order of complexity:
+1. LLM and Prompts
+2. Chains
+3. Agents
+4. Memory
+
+Let's go through these categories and for each one identify key concepts (to clarify terminology) as well as the problems in this area LangChain helps solve.
+
+### LLMs and Prompts
+Calling out to an LLM once is pretty easy, with most of them being behind well documented APIs.
+However, there are still some challenges going from that to an application running in production that LangChain attempts to address.
+
+**Key Concepts**
+- LLM: A large language model, in particular a text-to-text model.
+- Prompt: The input to a language model. Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
+- Prompt Template: An object responsible for constructing the final prompt to pass to a LLM.
+- Examples: Datapoints that can be included in the prompt in order to give the model more context what to do.
+- Few Shot Prompt Template: A subclass of the PromptTemplate class that uses examples.
+- Example Selector: A class responsible to selecting examples to use dynamically (depending on user input) in a few shot prompt.
+
+**Problems Solved**
+- Switching costs: by exposing a standard interface for all the top LLM providers, LangChain makes it easy to switch from one provider to another, whether it be for production use cases or just for testing stuff out.
+- Prompt management: managing your prompts is easy when you only have one simple one, but can get tricky when you have a bunch or when they start to get more complex. LangChain provides a standard way for storing, constructing, and referencing prompts.
+- Prompt optimization: despite the underlying models getting better and better, there is still currently a need for carefully constructing prompts. 
+
+### Chains
+Using an LLM in isolation is fine for some simple applications, but many more complex ones require chaining LLMs - either with eachother or with other experts.
+LangChain provides several parts to help with that.
+
+**Key Concepts**
+- Tools: APIs designed for assisting with a particular use case (search, databases, Python REPL, etc). Prompt templates, LLMs, and chains can also be considered tools.
+- Chains: A combination of multiple tools in a deterministic manner.
+
+**Problems Solved**
+- Standard interface for working with Chains
+- Easy way to construct chains of LLMs
+- Lots of integrations with other tools that you may want to use in conjunction with LLMs 
+- End-to-end chains for common workflows (database question/answer, recursive summarization, etc)
+
+### Agents
+Some applications will require not just a predetermined chain of calls to LLMs/other tools, but potentially an unknown chain that depends on the user input.
+In these types of chains, there is a “agent” which has access to a suite of tools.
+Depending on the user input, the agent can then decide which, if any, of these tools to call.
+
+**Key Concepts**
+- Tools: same as above.
+- Agent: An LLM-powered class responsible for determining which tools to use and in what order.
+
+
+**Problems Solved**
+- Standard agent interfaces
+- A selection of powerful agents to choose from
+- Common chains that can be used as tools
+
+### Memory
+By default, Chains and Agents are stateless, meaning that they treat each incoming query independently.
+In some applications (chatbots being a GREAT example) it is highly important to remember previous interactions,
+both at a short term but also at a long term level. The concept of "Memory" exists to do exactly that.
+
+**Key Concepts**
+- Memory: A class that can be added to an Agent or Chain to (1) pull in memory variables before calling that chain/agent, and (2) create new memories after the chain/agent finishes.
+- Memory Variables: Variables returned from a Memory class, to be passed into the chain/agent along with the user input.
+
+**Problems Solved**
+- Standard memory interfaces
+- A collection of common memory implementations to choose from
+- Common chains/agents that use memory (e.g. chatbots)
+
+## 🤖 Developer Guide
+
+To begin developing on this project, first clone the repo locally.
+
+### Quick Start
+
+This project uses [Poetry](https://python-poetry.org/) as a dependency manager. Check out Poetry's own [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding.
+
+To install requirements:
 
 ```bash
-pip install langchain
+poetry install -E all
 ```
 
-If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows.
+This will install all requirements for running the package, examples, linting, formatting, and tests. Note the `-E all` flag will install all optional dependencies necessary for integration testing.
 
----
+Now, you should be able to run the common tasks in the following section.
 
-**Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/oss/python/langchain/overview).
+### Common Tasks
 
-**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
+#### Code Formatting
 
-> [!NOTE]
-> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
+Formatting for this project is a combination of [Black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/).
 
-## Why use LangChain?
+To run formatting for this project:
 
-LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
+```bash
+make format
+```
 
-Use LangChain for:
+#### Linting
 
-- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
-- **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
+Linting for this project is a combination of [Black](https://black.readthedocs.io/en/stable/), [isort](https://pycqa.github.io/isort/), [flake8](https://flake8.pycqa.org/en/latest/), and [mypy](http://mypy-lang.org/).
 
-## LangChain ecosystem
+To run linting for this project:
 
-While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
+```bash
+make lint
+```
 
-To improve your LLM application development, pair LangChain with:
+We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer and they can help you with it. We do not want this to be a blocker for good code getting contributed.
 
-- [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
-- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
+#### Testing
 
-## Additional resources
+Unit tests cover modular logic that does not require calls to outside apis.
 
-- [API Reference](https://reference.langchain.com/python): Detailed reference on navigating base packages and integrations for LangChain.
-- [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview): List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
-- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview): Learn how to contribute to LangChain and find good first issues.
+To run unit tests:
+
+```bash
+make tests
+```
+
+If you add new logic, please add a unit test.
+
+Integration tests cover logic that requires making calls to outside APIs (often integration with other services).
+
+To run integration tests:
+
+```bash
+make integration_tests
+```
+
+If you add support for a new external API, please add a new integration test.
+
+#### Adding a Jupyter Notebook
+
+If you are adding a Jupyter notebook example, you'll want to install the optional `dev` dependencies.
+
+To install dev dependencies:
+
+```bash
+poetry install --with dev
+```
+
+Launch a notebook:
+
+```bash
+poetry run jupyter notebook
+```
+
+When you run `poetry install`, the `langchain` package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.
+
+#### Contribute Documentation
+
+Docs are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
+
+For that reason, we ask that you add good documentation to all classes and methods.
+
+Similar to linting, we recognize documentation can be annoying - if you do not want to do it, please contact a project maintainer and they can help you with it. We do not want this to be a blocker for good code getting contributed.

SECURITY.md

@@ -1,80 +0,0 @@
-# Security Policy
-
-LangChain has a large ecosystem of integrations with various external resources like local and remote file systems, APIs and databases. These integrations allow developers to create versatile applications that combine the power of LLMs with the ability to access, interact with and manipulate external resources.
-
-## Best practices
-
-When building such applications, developers should remember to follow good security practices:
-
-* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc., as appropriate for your application.
-* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it's safest to assume that any LLM able to use those credentials may in fact delete data.
-* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It's best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
-
-Risks of not doing so include, but are not limited to:
-
-* Data corruption or loss.
-* Unauthorized access to confidential information.
-* Compromised performance or availability of critical resources.
-
-Example scenarios with mitigation strategies:
-
-* A user may ask an agent with access to the file system to delete files that should not be deleted or read the content of files that contain sensitive information. To mitigate, limit the agent to only use a specific directory and only allow it to read or write files that are safe to read or write. Consider further sandboxing the agent by running it in a container.
-* A user may ask an agent with write access to an external API to write malicious data to the API, or delete data from that API. To mitigate, give the agent read-only API keys, or limit it to only use endpoints that are already resistant to such misuse.
-* A user may ask an agent with access to a database to drop a table or mutate the schema. To mitigate, scope the credentials to only the tables that the agent needs to access and consider issuing READ-ONLY credentials.
-
-If you're building applications that access external resources like file systems, APIs or databases, consider speaking with your company's security team to determine how to best design and secure your applications.
-
-## Reporting OSS Vulnerabilities
-
-LangChain is partnered with [huntr by Protect AI](https://huntr.com/) to provide
-a bounty program for our open source projects.
-
-Please report security vulnerabilities associated with the LangChain
-open source projects at [huntr](https://huntr.com/bounties/disclose/?target=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain&validSearch=true).
-
-Before reporting a vulnerability, please review:
-
-1) In-Scope Targets and Out-of-Scope Targets below.
-2) The [langchain-ai/langchain](https://docs.langchain.com/oss/python/contributing/code#repository-structure) monorepo structure.
-3) The [Best Practices](#best-practices) above to understand what we consider to be a security vulnerability vs. developer responsibility.
-
-### In-Scope Targets
-
-The following packages and repositories are eligible for bug bounties:
-
-* langchain-core
-* langchain (see exceptions)
-* langchain-community (see exceptions)
-* langgraph
-* langserve
-
-### Out of Scope Targets
-
-All out of scope targets defined by huntr as well as:
-
-* **langchain-experimental**: This repository is for experimental code and is not
-  eligible for bug bounties (see [package warning](https://pypi.org/project/langchain-experimental/)), bug reports to it will be marked as interesting or waste of
-  time and published with no bounty attached.
-* **tools**: Tools in either langchain or langchain-community are not eligible for bug
-  bounties. This includes the following directories
-  * libs/langchain/langchain/tools
-  * libs/community/langchain_community/tools
-  * Please review the [Best Practices](#best-practices)
-    for more details, but generally tools interact with the real world. Developers are
-    expected to understand the security implications of their code and are responsible
-    for the security of their tools.
-* Code documented with security notices. This will be decided on a case-by-case basis, but likely will not be eligible for a bounty as the code is already
-  documented with guidelines for developers that should be followed for making their
-  application secure.
-* Any LangSmith related repositories or APIs (see [Reporting LangSmith Vulnerabilities](#reporting-langsmith-vulnerabilities)).
-
-## Reporting LangSmith Vulnerabilities
-
-Please report security vulnerabilities associated with LangSmith by email to `security@langchain.dev`.
-
-* LangSmith site: [https://smith.langchain.com](https://smith.langchain.com)
-* SDK client: [https://github.com/langchain-ai/langsmith-sdk](https://github.com/langchain-ai/langsmith-sdk)
-
-### Other Security Concerns
-
-For any other security concerns, please contact us at `security@langchain.dev`.

docs/Makefile

@@ -0,0 +1,21 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SPHINXAUTOBUILD   ?= sphinx-autobuild
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/conf.py

@@ -0,0 +1,90 @@
+"""Configuration file for the Sphinx documentation builder."""
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+import toml
+
+with open("../pyproject.toml") as f:
+    data = toml.load(f)
+
+# -- Project information -----------------------------------------------------
+
+project = "LangChain"
+copyright = "2022, Harrison Chase"
+author = "Harrison Chase"
+
+version = data["tool"]["poetry"]["version"]
+release = version
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autodoc.typehints",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinxcontrib.autodoc_pydantic",
+    "myst_parser",
+    "nbsphinx",
+    "sphinx_panels",
+]
+
+
+autodoc_pydantic_model_show_json = False
+autodoc_pydantic_field_list_validators = False
+autodoc_pydantic_config_members = False
+autodoc_pydantic_model_show_config_summary = False
+autodoc_pydantic_model_show_validator_members = False
+autodoc_pydantic_model_show_field_summary = False
+autodoc_pydantic_model_members = False
+autodoc_pydantic_model_undoc_members = False
+# autodoc_typehints = "signature"
+# autodoc_typehints = "description"
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_rtd_theme"
+# html_theme = "sphinx_typlog_theme"
+
+html_context = {
+    "display_github": True,  # Integrate GitHub
+    "github_user": "hwchase17",  # Username
+    "github_repo": "langchain",  # Repo name
+    "github_version": "master",  # Version
+    "conf_py_path": "/docs/",  # Path in the checkout to the docs root
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path: list = []

docs/examples/agents.rst

@@ -0,0 +1,47 @@
+Agents
+======
+
+The examples here are all end-to-end agents for specific applications.
+In all examples there is an Agent with a particular set of tools.
+
+- Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <chains.rst>`_ documentation.
+- Agents: An agent uses an LLMChain to determine which tools to use. For a list of all available agent types, see `here <../explanation/agents.md>`_.
+
+**MRKL**
+
+- **Tools used**: Search, SQLDatabaseChain, LLMMathChain
+- **Agent used**: `zero-shot-react-description`
+- `Paper <https://arxiv.org/pdf/2205.00445.pdf>`_
+- **Note**: This is the most general purpose example, so if you are looking to use an agent with arbitrary tools, please start here.
+- `Example Notebook <agents/mrkl.ipynb>`_
+
+**Self-Ask-With-Search**
+
+- **Tools used**: Search
+- **Agent used**: `self-ask-with-search`
+- `Paper <https://ofir.io/self-ask.pdf>`_
+- `Example Notebook <agents/self_ask_with_search.ipynb>`_
+
+**ReAct**
+
+- **Tools used**: Wikipedia Docstore
+- **Agent used**: `react-docstore`
+- `Paper <https://arxiv.org/pdf/2210.03629.pdf>`_
+- `Example Notebook <agents/react.ipynb>`_
+
+
+
+Additionally, we also provide examples for how to do more customizability:
+
+**Custom Agent**
+
+- Purpose: How to create custom agents.
+- `Example Notebook <agents/custom_agent.ipynb>`_
+
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :hidden:
+
+   agents/*

docs/examples/agents/custom_agent.ipynb

@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba5f8741",
+   "metadata": {},
+   "source": [
+    "# Custom Agent\n",
+    "\n",
+    "This notebook goes through how to create your own custom agent.\n",
+    "\n",
+    "An agent consists of three parts:\n",
+    "    \n",
+    "    - Tools: The tools the agent has available to use.\n",
+    "    - LLMChain: The LLMChain that produces the text that is parsed in a certain way to determine which action to take.\n",
+    "    - The agent class itself: this parses the output of the LLMChain to determin which action to take.\n",
+    "        \n",
+    "        \n",
+    "In this notebook we walk through two types of custom agents. The first type shows how to create a custom LLMChain, but still use an existing agent class to parse the output. The second shows how to create a custom agent class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6064f080",
+   "metadata": {},
+   "source": [
+    "### Custom LLMChain\n",
+    "\n",
+    "The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly reccomended that you work with the `ZeroShotAgent`, as at the moment that is by far the most generalizable one. \n",
+    "\n",
+    "Most of the work in creating the custom LLMChain comes down to the prompt. Because we are using an existing agent class to parse the output, it is very important that the prompt say to produce text in that format. However, besides those instructions, you can customize the prompt as you wish.\n",
+    "\n",
+    "To ensure that the prompt contains the appropriate instructions, we will utilize a helper method on that class. The helper method for the `ZeroShotAgent` takes the following arguments:\n",
+    "\n",
+    "- tools: List of tools the agent will have access to, used to format the prompt.\n",
+    "- prefix: String to put before the list of tools.\n",
+    "- suffix: String to put after the list of tools.\n",
+    "- input_variables: List of input variables the final prompt will expect.\n",
+    "\n",
+    "For this exercise, we will give our agent access to Google Search, and we will customize it in that we will have it answer as a pirate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9af9734e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import ZeroShotAgent, Tool\n",
+    "from langchain import OpenAI, SerpAPIWrapper, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "becda2a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = SerpAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "339b1bb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\"\"\"\n",
+    "suffix = \"\"\"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Args\"\n",
+    "\n",
+    "Question: {input}\"\"\"\n",
+    "\n",
+    "prompt = ZeroShotAgent.create_prompt(\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59db7b58",
+   "metadata": {},
+   "source": [
+    "In case we are curious, we can now take a look at the final prompt template to see what it looks like when its all put together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e21d2098",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
+      "\n",
+      "Search: useful for when you need to answer questions about current events\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [Search]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Args\"\n",
+      "\n",
+      "Question: {input}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(prompt.template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b1cc2a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e4f5092f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "653b1617",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many people live in canada?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look this up\n",
+      "Action: Search\n",
+      "Action Input: How many people live in canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Arrr, there be 38,533,678 people in Canada\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Arrr, there be 38,533,678 people in Canada'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"How many people live in canada?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90171b2b",
+   "metadata": {},
+   "source": [
+    "### Custom Agent Class\n",
+    "\n",
+    "Coming soon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adefb4c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/agents/mrkl.ipynb

@@ -0,0 +1,217 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f1390152",
+   "metadata": {},
+   "source": [
+    "# MRKL\n",
+    "\n",
+    "This notebook showcases using an agent to replicate the MRKL chain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39ea3638",
+   "metadata": {},
+   "source": [
+    "This uses the example Chinook database.\n",
+    "To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ac561cc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
+    "from langchain.agents import initialize_agent, Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "07e96d99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "search = SerpAPIWrapper()\n",
+    "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../notebooks/Chinook.db\")\n",
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name=\"Calculator\",\n",
+    "        func=llm_math_chain.run,\n",
+    "        description=\"useful for when you need to answer questions about math\"\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name=\"FooBar DB\",\n",
+    "        func=db_chain.run,\n",
+    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question\"\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a069c4b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e603cd7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is the age of Olivia Wilde's boyfriend raised to the 0.23 power?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find the age of Olivia Wilde's boyfriend\n",
+      "Action: Search\n",
+      "Action Input: \"Olivia Wilde's boyfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find the age of Harry Styles\n",
+      "Action: Search\n",
+      "Action Input: \"Harry Styles age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 to the 0.23 power\n",
+      "Action: Calculator\n",
+      "Action Input: 28^0.23\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "28^0.23\u001b[32;1m\u001b[1;3m\n",
+      "\n",
+      "```python\n",
+      "print(28**0.23)\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m2.1520202182226886\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 2.1520202182226886\u001b[0m"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'2.1520202182226886'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(\"What is the age of Olivia Wilde's boyfriend raised to the 0.23 power?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a5c07010",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find an album called 'The Storm Before the Calm'\n",
+      "Action: Search\n",
+      "Action Input: \"The Storm Before the Calm album\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to check if Alanis is in the FooBar database\n",
+      "Action: FooBar DB\n",
+      "Action Input: \"Does Alanis Morissette exist in the FooBar database?\"\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "Does Alanis Morissette exist in the FooBar database?\n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT * FROM Artist WHERE Name = 'Alanis Morissette'\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[(4, 'Alanis Morissette')]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Yes\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3m Yes\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out what albums of Alanis's are in the FooBar database\n",
+      "Action: FooBar DB\n",
+      "Action Input: \"What albums by Alanis Morissette are in the FooBar database?\"\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "What albums by Alanis Morissette are in the FooBar database?\n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Album.Title FROM Album JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alanis Morissette'\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Jagged Little Pill\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3m Jagged Little Pill\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The album is by Alanis Morissette and the albums in the FooBar database by her are Jagged Little Pill\u001b[0m"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The album is by Alanis Morissette and the albums in the FooBar database by her are Jagged Little Pill'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(\"Who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7c2e6ac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/agents/natbot.py

@@ -0,0 +1,88 @@
+"""Run NatBot."""
+import time
+
+from langchain.chains.natbot.base import NatBotChain
+from langchain.chains.natbot.crawler import Crawler  # type: ignore
+
+
+def run_cmd(cmd: str, _crawler: Crawler) -> None:
+    """Run command."""
+    cmd = cmd.split("\n")[0]
+
+    if cmd.startswith("SCROLL UP"):
+        _crawler.scroll("up")
+    elif cmd.startswith("SCROLL DOWN"):
+        _crawler.scroll("down")
+    elif cmd.startswith("CLICK"):
+        commasplit = cmd.split(",")
+        id = commasplit[0].split(" ")[1]
+        _crawler.click(id)
+    elif cmd.startswith("TYPE"):
+        spacesplit = cmd.split(" ")
+        id = spacesplit[1]
+        text_pieces = spacesplit[2:]
+        text = " ".join(text_pieces)
+        # Strip leading and trailing double quotes
+        text = text[1:-1]
+
+        if cmd.startswith("TYPESUBMIT"):
+            text += "\n"
+        _crawler.type(id, text)
+
+    time.sleep(2)
+
+
+if __name__ == "__main__":
+
+    objective = "Make a reservation for 2 at 7pm at bistro vida in menlo park"
+    print("\nWelcome to natbot! What is your objective?")
+    i = input()
+    if len(i) > 0:
+        objective = i
+    quiet = False
+    nat_bot_chain = NatBotChain.from_default(objective)
+    _crawler = Crawler()
+    _crawler.go_to_page("google.com")
+    try:
+        while True:
+            browser_content = "\n".join(_crawler.crawl())
+            llm_command = nat_bot_chain.execute(_crawler.page.url, browser_content)
+            if not quiet:
+                print("URL: " + _crawler.page.url)
+                print("Objective: " + objective)
+                print("----------------\n" + browser_content + "\n----------------\n")
+            if len(llm_command) > 0:
+                print("Suggested command: " + llm_command)
+
+            command = input()
+            if command == "r" or command == "":
+                run_cmd(llm_command, _crawler)
+            elif command == "g":
+                url = input("URL:")
+                _crawler.go_to_page(url)
+            elif command == "u":
+                _crawler.scroll("up")
+                time.sleep(1)
+            elif command == "d":
+                _crawler.scroll("down")
+                time.sleep(1)
+            elif command == "c":
+                id = input("id:")
+                _crawler.click(id)
+                time.sleep(1)
+            elif command == "t":
+                id = input("id:")
+                text = input("text:")
+                _crawler.type(id, text)
+                time.sleep(1)
+            elif command == "o":
+                objective = input("Objective:")
+            else:
+                print(
+                    "(g) to visit url\n(u) scroll up\n(d) scroll down\n(c) to click"
+                    "\n(t) to type\n(h) to view commands again"
+                    "\n(r/enter) to run suggested command\n(o) change objective"
+                )
+    except KeyboardInterrupt:
+        print("\n[!] Ctrl+C detected, exiting gracefully.")
+        exit(0)

docs/examples/agents/react.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "82140df0",
+   "metadata": {},
+   "source": [
+    "# ReAct\n",
+    "\n",
+    "This notebook showcases using an agent to implement the ReAct logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4e272b47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, Wikipedia\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.react.base import DocstoreExplorer\n",
+    "docstore=DocstoreExplorer(Wikipedia())\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Search\",\n",
+    "        func=docstore.search\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name=\"Lookup\",\n",
+    "        func=docstore.lookup\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
+    "react = initialize_agent(tools, llm, agent=\"react-docstore\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8078c8f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ReActDocstoreAgent chain...\u001b[0m\n",
+      "Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\n",
+      "Thought 1:\u001b[32;1m\u001b[1;3m I need to search David Chanoff and find the U.S. Navy admiral he collaborated\n",
+      "with.\n",
+      "Action 1: Search[David Chanoff]\u001b[0m\n",
+      "Observation 1: \u001b[36;1m\u001b[1;3mDavid Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.\u001b[0m\n",
+      "Thought 2:\u001b[32;1m\u001b[1;3m The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe.\n",
+      "Action 2: Search[William J. Crowe]\u001b[0m\n",
+      "Observation 2: \u001b[36;1m\u001b[1;3mWilliam James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.\u001b[0m\n",
+      "Thought 3:\u001b[32;1m\u001b[1;3m William J. Crowe served as the ambassador to the United Kingdom under President Bill Clinton.\n",
+      "Action 3: Finish[Bill Clinton]\u001b[0m\n",
+      "\u001b[1m> Finished ReActDocstoreAgent chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Bill Clinton'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\"\n",
+    "react.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ff64e81",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/agents/self_ask_with_search.ipynb

@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0c3f1df8",
+   "metadata": {},
+   "source": [
+    "# Self Ask With Search\n",
+    "\n",
+    "This notebook showcases the Self Ask With Search chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e3b513e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SelfAskWithSearchAgent chain...\u001b[0m\n",
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mCarlos Alcaraz\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz from?\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mEl Palmar, Spain\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
+      "\u001b[1m> Finished SelfAskWithSearchAgent chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'El Palmar, Spain'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import OpenAI, SerpAPIWrapper\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "search = SerpAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Intermediate Answer\",\n",
+    "        func=search.run\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "self_ask_with_search = initialize_agent(tools, llm, agent=\"self-ask-with-search\", verbose=True)\n",
+    "\n",
+    "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "683d69e7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains.rst

@@ -0,0 +1,71 @@
+Chains
+======
+
+The examples here are all end-to-end chains for specific applications.
+A chain is made up of links, which can be either primitives or other chains.
+
+The following primitives exist as options to use for links:
+
+#. `LLM: <../modules/llms.rst>`_ A language model takes text as input and outputs text.
+#. `PromptTemplate: <../modules/prompt.rst>`_ A prompt template takes arbitrary string inputs and returns a final formatted string.
+#. `TextSplitter: <../modules/text_splitter.rst>`_ A text splitter takes a longer document and splits it into smaller chunks.
+#. `Python REPL: <../modules/python.rst>`_ A Python REPL takes a string representing a Python command to run, runs that command, and then returns anything that was printed during that run.
+#. `SQL Database: <../modules/sql_database.rst>`_ A SQL database takes a string representing a SQL command as input and executes that command against the database. If any rows are returned, then those are cast to a string and returned.
+#. `Search: <../modules/serpapi.rst>`_ A search object takes a string as input and executes that against a search object, returning any results.
+#. `Docstore: <../modules/docstore.rst>`_ A docstore object can be used to lookup a document in a database by exact match.
+#. `Vectorstore: <../modules/vectorstore.rst>`_ A vectorstore object uses embeddings stored in a vector database to take in an input string and return documents similar to that string.
+
+With these primitives in mind, the following chains exist:
+
+**LLMChain**
+
+- **Links Used**: PromptTemplate, LLM
+- **Notes**: This chain is the simplest chain, and is widely used by almost every other chain. This chain takes arbitrary user input, creates a prompt with it from the PromptTemplate, passes that to the LLM, and then returns the output of the LLM as the final output.
+- `Example Notebook <chains/llm_chain.ipynb>`_
+
+**LLMMath**
+
+- **Links Used**: Python REPL, LLMChain
+- **Notes**: This chain takes user input (a math question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
+- `Example Notebook <chains/llm_math.ipynb>`_
+
+**PAL**
+
+- **Links Used**: Python REPL, LLMChain
+- **Notes**: This chain takes user input (a reasoning question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
+- `Paper <https://arxiv.org/abs/2211.10435>`_
+- `Example Notebook <chains/pal.ipynb>`_
+
+**Recursive Summarization**
+
+- **Links Used**: TextSplitter, LLMChain
+- **Notes**: This chain splits a document into chunks, runs a first LLMChain over each chunk to summarize it, and then runs a second LLMChain over those results to get a summary of the summaries.
+- `Example Notebook <chains/map_reduce.ipynb>`_
+
+**SQLDatabase Chain**
+
+- **Links Used**: SQLDatabase, LLMChain
+- **Notes**: This chain takes user input (a question), uses a first LLM chain to construct a SQL query to run against the SQL database, and then uses another LLMChain to take the results of that query and use it to answer the original question.
+- `Example Notebook <chains/sqlite.ipynb>`_
+
+
+**Vector Database Question-Answering**
+
+- **Links Used**: Vectorstore, LLMChain
+- **Notes**: This chain takes user input (a question), uses the Vectorstore and semantic search to find relevant documents, and then passes the documents plus to the original question to another LLM to generate a final answer.
+- `Example Notebook <chains/vector_db_qa.ipynb>`_
+
+**Question-Answering With Sources**
+
+- **Links Used**: LLMChain
+- **Notes**: This chain takes a question and multiple documents as input. It then runs a first LLMChain over all documents attempting to answer the provided question. It then runs a second LLMChain over the results of the first pass, combining the answers from documents into a single response that is returned.
+- `Example Notebook <chains/combine_documents.ipynb>`_
+
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :caption: Chains
+   :hidden:
+
+   chains/*

docs/examples/chains/combine_documents.ipynb

@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "efc5be67",
+   "metadata": {},
+   "source": [
+    "# Question-Answering with Sources\n",
+    "\n",
+    "This notebook goes over how to do question-answering with sources. It does this in a few different ways - first showing how you can use the `QAWithSourcesChain` to take in documents and use those, and next showing the `VectorDBQAWithSourcesChain`, which also does the lookup of the documents from a vector database. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c613960",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.embeddings.cohere import CohereEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
+    "from langchain.vectorstores.faiss import FAISS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "17d1306e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e745d99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = FAISS.from_texts(texts, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f42d79dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add in a fake source information\n",
+    "for i, d in enumerate(docsearch.docstore._dict.values()):\n",
+    "    d.metadata = {'source': f\"{i}-pl\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa1c1b60",
+   "metadata": {},
+   "source": [
+    "### QAWithSourcesChain\n",
+    "This shows how to use the `QAWithSourcesChain`, which takes in document objects and uses them directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "61bce191",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "57ddf8c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import QAWithSourcesChain\n",
+    "from langchain.llms import OpenAI, Cohere\n",
+    "from langchain.docstore.document import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f908a92a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = QAWithSourcesChain.from_llm(OpenAI(temperature=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a505ac89",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': ' The president thanked Justice Breyer for his service.',\n",
+       " 'sources': '27-pl'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain({\"docs\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6fc81de",
+   "metadata": {},
+   "source": [
+    "### VectorDBQAWithSourcesChain\n",
+    "\n",
+    "This shows how to use the `VectorDBQAWithSourcesChain`, which uses a vector database to look up relevant documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8aa571ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import VectorDBQAWithSourcesChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "aa859d4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = VectorDBQAWithSourcesChain.from_llm(OpenAI(temperature=0), vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ba36fa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "980fae3b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/llm_chain.ipynb

@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d8a5c5d4",
+   "metadata": {},
+   "source": [
+    "# LLM Chain\n",
+    "\n",
+    "This notebook showcases a simple LLM chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "51a54c4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
+      "\n",
+      "Answer: Let's think step by step.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' The year Justin Beiber was born was 1994. In 1994, the Dallas Cowboys won the Super Bowl.'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import PromptTemplate, OpenAI, LLMChain\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
+    "\n",
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03dd6918",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/llm_math.ipynb

@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e71e720f",
+   "metadata": {},
+   "source": [
+    "# LLM Math\n",
+    "\n",
+    "This notebook showcases using LLMs and Python REPLs to do complex word math problems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "44e9ba31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many of the integers between 0 and 99 inclusive are divisible by 8?\u001b[102m\n",
+      "\n",
+      "```python\n",
+      "count = 0\n",
+      "for i in range(100):\n",
+      "    if i % 8 == 0:\n",
+      "        count += 1\n",
+      "print(count)\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Answer: \u001b[103m13\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Answer: 13\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import OpenAI, LLMMathChain\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "llm_math = LLMMathChain(llm=llm, verbose=True)\n",
+    "\n",
+    "llm_math.run(\"How many of the integers between 0 and 99 inclusive are divisible by 8?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f62f0c75",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/map_reduce.ipynb

@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d9a0131f",
+   "metadata": {},
+   "source": [
+    "# Map Reduce\n",
+    "\n",
+    "This notebok showcases an example of map-reduce chains: recursive summarization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e9db25f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, PromptTemplate, LLMChain\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.chains.mapreduce import MapReduceChain\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "\n",
+    "_prompt = \"\"\"Write a concise summary of the following:\n",
+    "\n",
+    "\n",
+    "{text}\n",
+    "\n",
+    "\n",
+    "CONCISE SUMMARY:\"\"\"\n",
+    "prompt = PromptTemplate(template=_prompt, input_variables=[\"text\"])\n",
+    "\n",
+    "text_splitter = CharacterTextSplitter()\n",
+    "\n",
+    "mp_chain = MapReduceChain.from_params(llm, prompt, text_splitter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "99bbe19b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nThe President discusses the recent aggression by Russia, and the response by the United States and its allies. He announces new sanctions against Russia, and says that the free world is united in holding Putin accountable. The President also discusses the American Rescue Plan, the Bipartisan Infrastructure Law, and the Bipartisan Innovation Act. Finally, the President addresses the need for women's rights and equality for LGBTQ+ Americans.\""
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "mp_chain.run(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baa6e808",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/pal.ipynb

@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "32e022a2",
+   "metadata": {},
+   "source": [
+    "# PAL\n",
+    "\n",
+    "Implements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1370e40f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import PALChain\n",
+    "from langchain import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "beddcac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
+    "pal_chain = PALChain.from_math_prompt(llm, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e2eab9d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3ef64b27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mdef solution():\n",
+      "    \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
+      "    cindy_pets = 4\n",
+      "    marcia_pets = cindy_pets + 2\n",
+      "    jan_pets = marcia_pets * 3\n",
+      "    total_pets = cindy_pets + marcia_pets + jan_pets\n",
+      "    result = total_pets\n",
+      "    return result\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'28'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pal_chain.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e524f81f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
+    "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "03a237b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a84a4352",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "objects = []\n",
+      "objects += [('booklet', 'blue')] * 2\n",
+      "objects += [('booklet', 'purple')] * 2\n",
+      "objects += [('sunglasses', 'yellow')] * 2\n",
+      "\n",
+      "# Remove all pairs of sunglasses\n",
+      "objects = [object for object in objects if object[0] != 'sunglasses']\n",
+      "\n",
+      "# Count number of purple objects\n",
+      "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
+      "answer = num_purple\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'2'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pal_chain.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ab20fec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/sqlite.ipynb

@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0ed6aab1",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# SQLite example\n",
+    "\n",
+    "This example showcases hooking up an LLM to answer questions over a database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2f66479",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "This uses the example Chinook database.\n",
+    "To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d0e27d88",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "72ede462",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../notebooks/Chinook.db\")\n",
+    "llm = OpenAI(temperature=0)\n",
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "15ff81df",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many employees are there?\n",
+      "SQLQuery:\u001b[102m SELECT COUNT(*) FROM Employee\u001b[0m\n",
+      "SQLResult: \u001b[103m[(8,)]\u001b[0m\n",
+      "Answer:\u001b[102m 8\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' 8'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db_chain.run(\"How many employees are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61d91b85",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/vector_db_qa.ipynb

@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "07c1e3b9",
+   "metadata": {},
+   "source": [
+    "# Vector DB Question/Answering\n",
+    "\n",
+    "This example showcases question answering over a vector database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "82525493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain import OpenAI, VectorDBQA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5c7049db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "docsearch = FAISS.from_texts(texts, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3018f865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa = VectorDBQA(llm=OpenAI(), vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "032a47f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The President said that Ketanji Brown Jackson is a consensus builder and has received a broad range of support since she was nominated.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "qa.run(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0f20b92",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/integrations.rst

@@ -0,0 +1,10 @@
+Integrations
+============
+
+The examples here all highlight a specific type of integration.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   integrations/*

docs/examples/integrations/embeddings.ipynb

@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7ef4d402-6662-4a26-b612-35b542066487",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "#  Embeddings & VectorStores\n",
+    "\n",
+    "This notebook show cases how to use embeddings to create a VectorStore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "965eecee",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
+    "from langchain.vectorstores.faiss import FAISS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "68481687",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "015f4ff5",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "docsearch = FAISS.from_texts(texts, embeddings)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "67baf32e",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eea6e627",
+   "metadata": {},
+   "source": [
+    "## Requires having ElasticSearch setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4906b8a3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "docsearch = ElasticVectorSearch.from_texts(texts, embeddings, elasticsearch_url=\"http://localhost:9200\")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "95f9eee9",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/integrations/huggingface_hub.ipynb

@@ -0,0 +1,71 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "959300d4",
+   "metadata": {},
+   "source": [
+    "# HuggingFace Hub\n",
+    "\n",
+    "This example showcases how to connect to the HuggingFace Hub."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3acf0069",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Seattle Seahawks won the Super Bowl in 2010. Justin Beiber was born in 2010. The\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1e-10}))\n",
+    "\n",
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "print(llm_chain.run(question))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae4559c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/integrations/huggingface_tokenizer_text_splitter.ipynb

@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b118c9dc",
+   "metadata": {},
+   "source": [
+    "# HuggingFace Tokenizers\n",
+    "\n",
+    "This notebook show cases how to use HuggingFace tokenizers to split text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e82c4685",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import CharacterTextSplitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8ce51d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import GPT2TokenizerFast\n",
+    "\n",
+    "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ca5e72c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "37cdfbeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n",
+      "\n",
+      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
+      "\n",
+      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
+      "\n",
+      "With a duty to one another to the American people to the Constitution. \n",
+      "\n",
+      "And with an unwavering resolve that freedom will always triumph over tyranny. \n",
+      "\n",
+      "Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n",
+      "\n",
+      "He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n",
+      "\n",
+      "He met the Ukrainian people. \n",
+      "\n",
+      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n",
+      "\n",
+      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n",
+      "\n",
+      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. \n",
+      "\n",
+      "Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \n",
+      "\n",
+      "Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \n",
+      "\n",
+      "Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos.   \n",
+      "\n",
+      "They keep moving.   \n",
+      "\n",
+      "And the costs and the threats to America and the world keep rising.   \n",
+      "\n",
+      "That’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \n",
+      "\n",
+      "The United States is a member along with 29 other nations. \n",
+      "\n",
+      "It matters. American diplomacy matters. American resolve matters. \n",
+      "\n",
+      "Putin’s latest attack on Ukraine was premeditated and unprovoked. \n",
+      "\n",
+      "He rejected repeated efforts at diplomacy. \n",
+      "\n",
+      "He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready.  Here is what we did.   \n",
+      "\n",
+      "We prepared extensively and carefully. \n",
+      "\n",
+      "We spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. \n",
+      "\n",
+      "I spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression.  \n",
+      "\n",
+      "We countered Russia’s lies with truth.   \n",
+      "\n",
+      "And now that he has acted the free world is holding him accountable. \n",
+      "\n",
+      "Along with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland. \n",
+      "\n",
+      "We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n",
+      "\n",
+      "Together with our allies –we are right now enforcing powerful economic sanctions. \n",
+      "\n",
+      "We are cutting off Russia’s largest banks from the international financial system.  \n",
+      "\n",
+      "Preventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.   \n",
+      "\n",
+      "We are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.  \n",
+      "\n",
+      "Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n",
+      "\n",
+      "The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.  \n",
+      "\n",
+      "We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains. \n",
+      "\n",
+      "And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value. \n",
+      "\n",
+      "The Russian stock market has lost 40% of its value and trading remains suspended. Russia’s economy is reeling and Putin alone is to blame. \n",
+      "\n",
+      "Together with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance. \n",
+      "\n",
+      "We are giving more than $1 Billion in direct assistance to Ukraine. \n",
+      "\n",
+      "And we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.  \n",
+      "\n",
+      "Let me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.  \n",
+      "\n",
+      "Our forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west.  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(texts[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d214aec2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/integrations/manifest.ipynb

@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b4462a94",
+   "metadata": {},
+   "source": [
+    "# Manifest\n",
+    "\n",
+    "This notebook goes over how to use Manifest and LangChain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59fcaebc",
+   "metadata": {},
+   "source": [
+    "For more detailed information on `manifest`, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "04a0170a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from manifest import Manifest\n",
+    "from langchain.llms.manifest import ManifestWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "de250a6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'model_name': 'bigscience/T0_3B', 'model_path': 'bigscience/T0_3B'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "manifest = Manifest(\n",
+    "    client_name = \"huggingface\",\n",
+    "    client_connection = \"http://127.0.0.1:5000\"\n",
+    ")\n",
+    "print(manifest.client.get_model_params())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "67b719d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = ManifestWrapper(client=manifest, llm_kwargs={\"temperature\": 0.001, \"max_tokens\": 256})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5af505a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Map reduce example\n",
+    "from langchain import PromptTemplate\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.chains.mapreduce import MapReduceChain\n",
+    "\n",
+    "\n",
+    "_prompt = \"\"\"Write a concise summary of the following:\n",
+    "\n",
+    "\n",
+    "{text}\n",
+    "\n",
+    "\n",
+    "CONCISE SUMMARY:\"\"\"\n",
+    "prompt = PromptTemplate(template=_prompt, input_variables=[\"text\"])\n",
+    "\n",
+    "text_splitter = CharacterTextSplitter()\n",
+    "\n",
+    "mp_chain = MapReduceChain.from_params(llm, prompt, text_splitter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "485b3ec3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'President Obama delivered his annual State of the Union address on Tuesday night, laying out his priorities for the coming year. Obama said the government will provide free flu vaccines to all Americans, ending the government shutdown and allowing businesses to reopen. The president also said that the government will continue to send vaccines to 112 countries, more than any other nation. \"We have lost so much to COVID-19,\" Trump said. \"Time with one another. And worst of all, so much loss of life.\" He said the CDC is working on a vaccine for kids under 5, and that the government will be ready with plenty of vaccines when they are available. Obama says the new guidelines are a \"great step forward\" and that the virus is no longer a threat. He says the government is launching a \"Test to Treat\" initiative that will allow people to get tested at a pharmacy and get antiviral pills on the spot at no cost. Obama says the new guidelines are a \"great step forward\" and that the virus is no longer a threat. He says the government will continue to send vaccines to 112 countries, more than any other nation. \"We are coming for your'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "mp_chain.run(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e9d45a8",
+   "metadata": {},
+   "source": [
+    "## Compare HF Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "33407ab3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.model_laboratory import ModelLaboratory\n",
+    "\n",
+    "manifest1 = ManifestWrapper(\n",
+    "    client=Manifest(\n",
+    "        client_name=\"huggingface\",\n",
+    "        client_connection=\"http://127.0.0.1:5000\"\n",
+    "    ),\n",
+    "    llm_kwargs={\"temperature\": 0.01}\n",
+    ")\n",
+    "manifest2 = ManifestWrapper(\n",
+    "    client=Manifest(\n",
+    "        client_name=\"huggingface\",\n",
+    "        client_connection=\"http://127.0.0.1:5001\"\n",
+    "    ),\n",
+    "    llm_kwargs={\"temperature\": 0.01}\n",
+    ")\n",
+    "manifest3 = ManifestWrapper(\n",
+    "    client=Manifest(\n",
+    "        client_name=\"huggingface\",\n",
+    "        client_connection=\"http://127.0.0.1:5002\"\n",
+    "    ),\n",
+    "    llm_kwargs={\"temperature\": 0.01}\n",
+    ")\n",
+    "llms = [manifest1, manifest2, manifest3]\n",
+    "model_lab = ModelLaboratory(llms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "448935c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mInput:\u001b[0m\n",
+      "What color is a flamingo?\n",
+      "\n",
+      "\u001b[1mManifestWrapper\u001b[0m\n",
+      "Params: {'model_name': 'bigscience/T0_3B', 'model_path': 'bigscience/T0_3B', 'temperature': 0.01}\n",
+      "\u001b[104mpink\u001b[0m\n",
+      "\n",
+      "\u001b[1mManifestWrapper\u001b[0m\n",
+      "Params: {'model_name': 'EleutherAI/gpt-neo-125M', 'model_path': 'EleutherAI/gpt-neo-125M', 'temperature': 0.01}\n",
+      "\u001b[103mA flamingo is a small, round\u001b[0m\n",
+      "\n",
+      "\u001b[1mManifestWrapper\u001b[0m\n",
+      "Params: {'model_name': 'google/flan-t5-xl', 'model_path': 'google/flan-t5-xl', 'temperature': 0.01}\n",
+      "\u001b[101mpink\u001b[0m\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_lab.compare(\"What color is a flamingo?\")"
+   ]
+  }
+ ],
+ "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": "3.8.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "51b9b5b89a4976ad21c8b4273a6c78d700e2954ce7d7452948b7774eb33bbce4"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/memory.rst

@@ -0,0 +1,11 @@
+Memory
+======
+
+The examples here are all related to working with the concept of Memory in LangChain.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :caption: Memory
+
+   memory/*

docs/examples/memory/adding_memory.ipynb

@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "00695447",
+   "metadata": {},
+   "source": [
+    "# Adding Memory To an LLMChain\n",
+    "\n",
+    "This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add  the `ConversationBufferMemory` class, although this can be any memory class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9f1aaf47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
+    "from langchain import OpenAI, LLMChain, PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b066ced",
+   "metadata": {},
+   "source": [
+    "The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the PromptTemplate and the ConversationBufferMemory match up (`chat_history`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e5501eda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"You are a chatbot having a conversation with a human.\n",
+    "\n",
+    "{chat_history}\n",
+    "Human: {human_input}\n",
+    "Chatbot:\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"chat_history\", \"human_input\"], \n",
+    "    template=template\n",
+    ")\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f6566275",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(\n",
+    "    llm=OpenAI(), \n",
+    "    prompt=prompt, \n",
+    "    verbose=True, \n",
+    "    memory=memory,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e2b189dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
+      "\n",
+      "\n",
+      "Human: Hi there my friend\n",
+      "Chatbot:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Hi there!'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_chain.predict(human_input=\"Hi there my friend\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a902729f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
+      "\n",
+      "\n",
+      "Human: Hi there my friend\n",
+      "AI:  Hi there!\n",
+      "Human: Not to bad - how are you?\n",
+      "Chatbot:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nI'm doing well, thanks for asking. How about you?\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_chain.predict(human_input=\"Not to bad - how are you?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae5309bb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/memory/agent_with_memory.ipynb

@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fa6802ac",
+   "metadata": {},
+   "source": [
+    "# Adding Memory to an Agent\n",
+    "\n",
+    "This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
+    "\n",
+    "- [Adding memory to an LLM Chain](adding_memory.ipynb)\n",
+    "- [Custom Agents](../agents/custom_agent.ipynb)\n",
+    "\n",
+    "In order to add a memory to an agent we are going to the the following steps:\n",
+    "\n",
+    "1. We are going to create an LLMChain with memory.\n",
+    "2. We are going to use that LLMChain to create a custom Agent.\n",
+    "\n",
+    "For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the `ConversationBufferMemory` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8db95912",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import ZeroShotAgent, Tool\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
+    "from langchain import OpenAI, SerpAPIWrapper, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "97ad8467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = SerpAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad2e708",
+   "metadata": {},
+   "source": [
+    "Notice the usage of the `chat_history` variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e3439cd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
+    "suffix = \"\"\"Begin!\"\n",
+    "\n",
+    "{chat_history}\n",
+    "Question: {input}\"\"\"\n",
+    "\n",
+    "prompt = ZeroShotAgent.create_prompt(\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\", \"chat_history\"]\n",
+    ")\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0021675b",
+   "metadata": {},
+   "source": [
+    "We can now construct the LLMChain, with the Memory object, and then create the agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c56a0e73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt, memory=memory)\n",
+    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ca4bc1fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many people live in canada?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
+      "Action: Search\n",
+      "Action Input: \"How many people live in canada?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"How many people live in canada?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45627664",
+   "metadata": {},
+   "source": [
+    "To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "eecc0462",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "what is their national anthem called?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m\n",
+      "AI:  I should look up the name of Canada's national anthem\n",
+      "Action: Search\n",
+      "Action Input: \"What is the name of Canada's national anthem?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAfter 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m\n",
+      "AI:  I now know the final answer\n",
+      "Final Answer: After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa Lavallée.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa Lavallée.\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"what is their national anthem called?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc3d0aa4",
+   "metadata": {},
+   "source": [
+    "We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada's national anthem was.\n",
+    "\n",
+    "For fun, let's compare this to an agent that does NOT have memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3359d043",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
+    "suffix = \"\"\"Begin!\"\n",
+    "\n",
+    "Question: {input}\"\"\"\n",
+    "\n",
+    "prompt = ZeroShotAgent.create_prompt(\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\"]\n",
+    ")\n",
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
+    "agent_without_memory = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "970d23df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many people live in canada?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
+      "Action: Search\n",
+      "Action Input: \"How many people live in canada?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The current population of Canada is 38,533,678\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The current population of Canada is 38,533,678'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_without_memory.run(\"How many people live in canada?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d9ea82f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "what is their national anthem called?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should probably look this up\n",
+      "Action: Search\n",
+      "Action Input: \"What is the national anthem of [country]\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mMost nation states have an anthem, defined as \"a song, as of praise, devotion, or patriotism\"; most anthems are either marches or hymns in style.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The national anthem is called \"the national anthem.\"\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The national anthem is called \"the national anthem.\"'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_without_memory.run(\"what is their national anthem called?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b1f9223",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/memory/custom_memory.ipynb

@@ -0,0 +1,295 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "94e33ebe",
+   "metadata": {},
+   "source": [
+    "# Custom Memory\n",
+    "Although there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. This notebook covers how to do that."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdfd0305",
+   "metadata": {},
+   "source": [
+    "For this notebook, we will add a custom memory type to `ConversationChain`. In order to add a custom memory class, we need to import the base memory class and subclass it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6d787ef2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, ConversationChain\n",
+    "from langchain.chains.base import Memory\n",
+    "from pydantic import BaseModel\n",
+    "from typing import List, Dict, Any"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9489e5e1",
+   "metadata": {},
+   "source": [
+    "In this example, we will write a custom memory class that uses spacy to extract entities and save information about them in a simple hash table. Then, during the conversation, we will look at the input text, extract any entities, and put any information about them into the context.\n",
+    "\n",
+    "* Please note that this implementation is pretty simple and brittle and probably not useful in a production setting. Its purpose is to showcase that you can add custom memory implementations.\n",
+    "\n",
+    "For this, we will need spacy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "12bbed4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install spacy\n",
+    "# !python -m spacy download en_core_web_lg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ff065f58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import spacy\n",
+    "nlp = spacy.load('en_core_web_lg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1d45d429",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SpacyEntityMemory(Memory, BaseModel):\n",
+    "    \"\"\"Memory class for storing information about entities.\"\"\"\n",
+    "\n",
+    "    # Define dictionary to store information about entities.\n",
+    "    entities: dict = {}\n",
+    "    # Define key to pass information about entities into prompt.\n",
+    "    memory_key: str = \"entities\"\n",
+    "\n",
+    "    @property\n",
+    "    def memory_variables(self) -> List[str]:\n",
+    "        \"\"\"Define the variables we are providing to the prompt.\"\"\"\n",
+    "        return [self.memory_key]\n",
+    "\n",
+    "    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n",
+    "        \"\"\"Load the memory variables, in this case the entity key.\"\"\"\n",
+    "        # Get the input text and run through spacy\n",
+    "        doc = nlp(inputs[list(inputs.keys())[0]])\n",
+    "        # Extract known information about entities, if they exist.\n",
+    "        entities = [self.entities[str(ent)] for ent in doc.ents if str(ent) in self.entities]\n",
+    "        # Return combined information about entities to put into context.\n",
+    "        return {self.memory_key: \"\\n\".join(entities)}\n",
+    "\n",
+    "    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n",
+    "        \"\"\"Save context from this conversation to buffer.\"\"\"\n",
+    "        # Get the input text and run through spacy\n",
+    "        text = inputs[list(inputs.keys())[0]]\n",
+    "        doc = nlp(text)\n",
+    "        # For each entity that was mentioned, save this information to the dictionary.\n",
+    "        for ent in doc.ents:\n",
+    "            ent_str = str(ent)\n",
+    "            if ent_str in self.entities:\n",
+    "                self.entities[ent_str] += f\"\\n{text}\"\n",
+    "            else:\n",
+    "                self.entities[ent_str] = text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "429ba264",
+   "metadata": {},
+   "source": [
+    "We now define a prompt that takes in information about entities as well as user input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c05159b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "template = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.\n",
+    "\n",
+    "Relevant entity information:\n",
+    "{entities}\n",
+    "\n",
+    "Conversation:\n",
+    "Human: {input}\n",
+    "AI:\"\"\"\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"entities\", \"input\"], template=template\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db611041",
+   "metadata": {},
+   "source": [
+    "And now we put it all together!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f08dc8ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "conversation = ConversationChain(llm=llm, prompt=prompt, verbose=True, memory=SpacyEntityMemory())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92a5f685",
+   "metadata": {},
+   "source": [
+    "In the first example, with no prior knowledge about Harrison, the \"Relevant entity information\" section is empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5b96e836",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.\n",
+      "\n",
+      "Relevant entity information:\n",
+      "\n",
+      "\n",
+      "Conversation:\n",
+      "Human: Harrison likes machine learning\n",
+      "AI:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nThat's really interesting! I'm sure he has a lot of fun with it.\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"Harrison likes machine learning\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1faa743",
+   "metadata": {},
+   "source": [
+    "Now in the second example, we can see that it pulls in information about Harrison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4bca7070",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.\n",
+      "\n",
+      "Relevant entity information:\n",
+      "Harrison likes machine learning\n",
+      "\n",
+      "Conversation:\n",
+      "Human: What do you think Harrison's favorite subject in college was?\n",
+      "AI:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\" Harrison's favorite subject in college was machine learning.\""
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"What do you think Harrison's favorite subject in college was?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58b856e3",
+   "metadata": {},
+   "source": [
+    "Again, please note that this implementation is pretty simple and brittle and probably not useful in a production setting. Its purpose is to showcase that you can add custom memory implementations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1994600",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/model_laboratory.ipynb

@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "920a3c1a",
+   "metadata": {},
+   "source": [
+    "# Model Laboratory\n",
+    "\n",
+    "This example goes over basic functionality of how to use the ModelLaboratory to test out and try different models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ab9e95ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import LLMChain, OpenAI, Cohere, HuggingFaceHub, PromptTemplate\n",
+    "from langchain.model_laboratory import ModelLaboratory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "32cb94e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llms = [\n",
+    "    OpenAI(temperature=0), \n",
+    "    Cohere(model=\"command-xlarge-20221108\", max_tokens=20, temperature=0), \n",
+    "    HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1})\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "14cde09d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_lab = ModelLaboratory.from_llms(llms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f186c741",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mInput:\u001b[0m\n",
+      "What color is a flamingo?\n",
+      "\n",
+      "\u001b[1mOpenAI\u001b[0m\n",
+      "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
+      "\u001b[36;1m\u001b[1;3m\n",
+      "\n",
+      "Flamingos are pink.\u001b[0m\n",
+      "\n",
+      "\u001b[1mCohere\u001b[0m\n",
+      "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 20, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
+      "\u001b[33;1m\u001b[1;3m\n",
+      "\n",
+      "Pink\u001b[0m\n",
+      "\n",
+      "\u001b[1mHuggingFaceHub\u001b[0m\n",
+      "Params: {'repo_id': 'google/flan-t5-xl', 'temperature': 1}\n",
+      "\u001b[38;5;200m\u001b[1;3mpink\u001b[0m\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_lab.compare(\"What color is a flamingo?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "248b652a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = PromptTemplate(template=\"What is the capital of {state}?\", input_variables=[\"state\"])\n",
+    "model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f64377ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mInput:\u001b[0m\n",
+      "New York\n",
+      "\n",
+      "\u001b[1mOpenAI\u001b[0m\n",
+      "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
+      "\u001b[36;1m\u001b[1;3m\n",
+      "\n",
+      "The capital of New York is Albany.\u001b[0m\n",
+      "\n",
+      "\u001b[1mCohere\u001b[0m\n",
+      "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 20, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
+      "\u001b[33;1m\u001b[1;3m\n",
+      "\n",
+      "The capital of New York is Albany.\u001b[0m\n",
+      "\n",
+      "\u001b[1mHuggingFaceHub\u001b[0m\n",
+      "Params: {'repo_id': 'google/flan-t5-xl', 'temperature': 1}\n",
+      "\u001b[38;5;200m\u001b[1;3mst john s\u001b[0m\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_lab_with_prompt.compare(\"New York\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "54336dbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import SelfAskWithSearchChain, SerpAPIWrapper\n",
+    "\n",
+    "open_ai_llm = OpenAI(temperature=0)\n",
+    "search = SerpAPIWrapper()\n",
+    "self_ask_with_search_openai = SelfAskWithSearchChain(llm=open_ai_llm, search_chain=search, verbose=True)\n",
+    "\n",
+    "cohere_llm = Cohere(temperature=0, model=\"command-xlarge-20221108\")\n",
+    "search = SerpAPIWrapper()\n",
+    "self_ask_with_search_cohere = SelfAskWithSearchChain(llm=cohere_llm, search_chain=search, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6a50a9f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chains = [self_ask_with_search_openai, self_ask_with_search_cohere]\n",
+    "names = [str(open_ai_llm), str(cohere_llm)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d3549e99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_lab = ModelLaboratory(chains, names=names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "362f7f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mInput:\u001b[0m\n",
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "\n",
+      "\u001b[1mOpenAI\u001b[0m\n",
+      "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[33;1m\u001b[1;3mCarlos Alcaraz.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "Follow up: Where is Carlos Alcaraz from?\u001b[0m\n",
+      "Intermediate answer: \u001b[33;1m\u001b[1;3mEl Palmar, Spain.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "So the final answer is: El Palmar, Spain\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3m\n",
+      "So the final answer is: El Palmar, Spain\u001b[0m\n",
+      "\n",
+      "\u001b[1mCohere\u001b[0m\n",
+      "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 256, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[33;1m\u001b[1;3mCarlos Alcaraz.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "So the final answer is:\n",
+      "\n",
+      "Carlos Alcaraz\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m\n",
+      "So the final answer is:\n",
+      "\n",
+      "Carlos Alcaraz\u001b[0m\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_lab.compare(\"What is the hometown of the reigning men's U.S. Open champion?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94159131",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts.rst

@@ -0,0 +1,10 @@
+Prompts
+=======
+
+The examples here all highlight how to work with prompts.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   prompts/*

docs/examples/prompts/custom_example_selector.ipynb

@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f897c784",
+   "metadata": {},
+   "source": [
+    "# Custom ExampleSelector\n",
+    "\n",
+    "This notebook goes over how to implement a custom ExampleSelector. ExampleSelectors are used to select examples to use in few shot prompts.\n",
+    "\n",
+    "An ExampleSelector must implement two methods:\n",
+    "\n",
+    "1. An `add_example` method which takes in an example and adds it into the ExampleSelector\n",
+    "2. A `select_examples` method which takes in input variables (which are meant to be user input) and returns a list of examples to use in the few shot prompt.\n",
+    "\n",
+    "\n",
+    "Let's implement a custom ExampleSelector that just selects two examples at random."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1a945da1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.example_selector.base import BaseExampleSelector\n",
+    "from typing import Dict, List\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "62cf0ad7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CustomExampleSelector(BaseExampleSelector):\n",
+    "    \n",
+    "    def __init__(self, examples: List[Dict[str, str]]):\n",
+    "        self.examples = examples\n",
+    "    \n",
+    "    def add_example(self, example: Dict[str, str]) -> None:\n",
+    "        \"\"\"Add new example to store for a key.\"\"\"\n",
+    "        self.examples.append(example)\n",
+    "\n",
+    "    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:\n",
+    "        \"\"\"Select which examples to use based on the inputs.\"\"\"\n",
+    "        return np.random.choice(self.examples, size=2, replace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "242d3213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "examples = [{\"foo\": \"1\"}, {\"foo\": \"2\"}, {\"foo\": \"3\"}]\n",
+    "example_selector = CustomExampleSelector(examples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a038065",
+   "metadata": {},
+   "source": [
+    "Let's now try it out! We can select some examples and try adding examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "74fbbef5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([{'foo': '2'}, {'foo': '3'}], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_selector.select_examples({\"foo\": \"foo\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9bbb5421",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_selector.add_example({\"foo\": \"4\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c0eb9f22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_selector.examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cc39b1e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([{'foo': '1'}, {'foo': '4'}], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_selector.select_examples({\"foo\": \"foo\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1739dd96",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts/custom_llm.ipynb

@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9e9b7651",
+   "metadata": {},
+   "source": [
+    "# Custom LLM\n",
+    "\n",
+    "This notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is supported in LangChain.\n",
+    "\n",
+    "There is only one required thing that a custom LLM needs to implement:\n",
+    "\n",
+    "1. A `__call__` method that takes in a string, some optional stop words, and returns a string\n",
+    "\n",
+    "There is a second optional thing it can implement:\n",
+    "\n",
+    "1. An `_identifying_params` property that is used to help with printing of this class. Should return a dictionary.\n",
+    "\n",
+    "Let's implement a very simple custom LLM that just returns the first N characters of the input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a65696a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms.base import LLM\n",
+    "from typing import Optional, List, Mapping, Any"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d5ceff02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CustomLLM(LLM):\n",
+    "    \n",
+    "    def __init__(self, n: int):\n",
+    "        self.n = n\n",
+    "    \n",
+    "    def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:\n",
+    "        if stop is not None:\n",
+    "            raise ValueError(\"stop kwargs are not permitted.\")\n",
+    "        return prompt[:self.n]\n",
+    "    \n",
+    "    @property\n",
+    "    def _identifying_params(self) -> Mapping[str, Any]:\n",
+    "        \"\"\"Get the identifying parameters.\"\"\"\n",
+    "        return {\"n\": self.n}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "714dede0",
+   "metadata": {},
+   "source": [
+    "We can now use this as an any other LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "10e5ece6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = CustomLLM(n=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8cd49199",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This is a '"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm(\"This is a foobar thing\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbfebea1",
+   "metadata": {},
+   "source": [
+    "We can also print the LLM and see its custom print."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9c33fa19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mCustomLLM\u001b[0m\n",
+      "Params: {'n': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dac3f47",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts/custom_prompt_template.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a37d9694",
+   "metadata": {},
+   "source": [
+    "# Custom Prompt Template\n",
+    "\n",
+    "This notebook goes over how to create a custom prompt template, in case you want to create your own methodology for creating prompts.\n",
+    "\n",
+    "The only two requirements for all prompt templates are:\n",
+    "\n",
+    "1. They have a `input_variables` attribute that exposes what input variables this prompt template expects.\n",
+    "2. They expose a `format` method which takes in keyword arguments corresponding to the expected `input_variables` and returns the formatted prompt.\n",
+    "\n",
+    "Let's imagine that we want to create a prompt template that takes in input variables and formats them into the template AFTER capitalizing them. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "26f796e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import BasePromptTemplate\n",
+    "from pydantic import BaseModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "27919e96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CustomPromptTemplate(BasePromptTemplate, BaseModel):\n",
+    "    template: str\n",
+    "        \n",
+    "    def format(self, **kwargs) -> str:\n",
+    "        capitalized_kwargs = {k: v.upper() for k, v in kwargs.items()}\n",
+    "        return self.template.format(**capitalized_kwargs)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76d1d84d",
+   "metadata": {},
+   "source": [
+    "We can now see that when we use this, the input variables get formatted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eed1ff28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = CustomPromptTemplate(input_variables=[\"foo\"], template=\"Capitalized: {foo}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "94892a3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Capitalized: LOWERCASE'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt.format(foo=\"lowercase\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3d9a7c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts/example_prompt.json

@@ -1,5 +1,4 @@
 {
-    "_type": "prompt",
     "input_variables": ["input", "output"],
     "template": "Input: {input}\nOutput: {output}" 
 }

docs/examples/prompts/few shot examples.ipynb

@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f8b01b97",
+   "metadata": {},
+   "source": [
+    "# Few Shot Prompt examples\n",
+    "Notebook showing off how canonical prompts in LangChain can be recreated as FewShotPrompts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "18c67cc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.few_shot import FewShotPromptTemplate\n",
+    "from langchain.prompts.prompt import PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a729c9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Self Ask with Search\n",
+    "\n",
+    "examples = [\n",
+    "  {\n",
+    "    \"question\": \"Who lived longer, Muhammad Ali or Alan Turing?\",\n",
+    "    \"answer\": \"Are follow up questions needed here: Yes.\\nFollow up: How old was Muhammad Ali when he died?\\nIntermediate answer: Muhammad Ali was 74 years old when he died.\\nFollow up: How old was Alan Turing when he died?\\nIntermediate answer: Alan Turing was 41 years old when he died.\\nSo the final answer is: Muhammad Ali\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"When was the founder of craigslist born?\",\n",
+    "    \"answer\": \"Are follow up questions needed here: Yes.\\nFollow up: Who was the founder of craigslist?\\nIntermediate answer: Craigslist was founded by Craig Newmark.\\nFollow up: When was Craig Newmark born?\\nIntermediate answer: Craig Newmark was born on December 6, 1952.\\nSo the final answer is: December 6, 1952\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Who was the maternal grandfather of George Washington?\",\n",
+    "    \"answer\": \"Are follow up questions needed here: Yes.\\nFollow up: Who was the mother of George Washington?\\nIntermediate answer: The mother of George Washington was Mary Ball Washington.\\nFollow up: Who was the father of Mary Ball Washington?\\nIntermediate answer: The father of Mary Ball Washington was Joseph Ball.\\nSo the final answer is: Joseph Ball\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Are both the directors of Jaws and Casino Royale from the same country?\",\n",
+    "    \"answer\": \"Are follow up questions needed here: Yes.\\nFollow up: Who is the director of Jaws?\\nIntermediate Answer: The director of Jaws is Steven Spielberg.\\nFollow up: Where is Steven Spielberg from?\\nIntermediate Answer: The United States.\\nFollow up: Who is the director of Casino Royale?\\nIntermediate Answer: The director of Casino Royale is Martin Campbell.\\nFollow up: Where is Martin Campbell from?\\nIntermediate Answer: New Zealand.\\nSo the final answer is: No\"\n",
+    "  }\n",
+    "]\n",
+    "example_prompt = PromptTemplate(input_variables=[\"question\", \"answer\"], template=\"Question: {question}\\n{answer}\")\n",
+    "\n",
+    "prompt = FewShotPromptTemplate(\n",
+    "    examples=examples, \n",
+    "    example_prompt=example_prompt, \n",
+    "    suffix=\"Question: {input}\", \n",
+    "    input_variables=[\"input\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "95fc0059",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ReAct\n",
+    "\n",
+    "examples = [\n",
+    "  {\n",
+    "    \"question\": \"What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of that area.\\nAction 1: Search[Colorado orogeny]\\nObservation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.\\nThought 2: It does not mention the eastern sector. So I need to look up eastern sector.\\nAction 2: Lookup[eastern sector]\\nObservation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny.\\nThought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range.\\nAction 3: Search[High Plains]\\nObservation 3: High Plains refers to one of two distinct land regions\\nThought 4: I need to instead search High Plains (United States).\\nAction 4: Search[High Plains (United States)]\\nObservation 4: The High Plains are a subregion of the Great Plains. From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]\\nThought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.\\nAction 5: Finish[1,800 to 7,000 ft]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Musician and satirist Allie Goertz wrote a song about the \\\"The Simpsons\\\" character Milhouse, who Matt Groening named after who?\",\n",
+    "    \"answer\": \"Thought 1: The question simplifies to \\\"The Simpsons\\\" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.\\nAction 1: Search[Milhouse]\\nObservation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.\\nThought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up \\\"named after\\\".\\nAction 2: Lookup[named after]\\nObservation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.\\nThought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.\\nAction 3: Finish[Richard Nixon]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.\\nAction 1: Search[Adam Clayton Powell]\\nObservation 1 Could not find [Adam Clayton Powell]. Similar: [’Adam Clayton Powell III’, ’Seventh Avenue (Manhattan)’, ’Adam Clayton Powell Jr. State Office Building’, ’Isabel Washington Powell’, ’Adam Powell’, ’Adam Clayton Powell (film)’, ’Giancarlo Esposito’].\\nThought 2: To find the documentary, I can search Adam Clayton Powell (film).\\nAction 2: Search[Adam Clayton Powell (film)]\\nObservation 2: Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg. The film is about the rise and fall of influential African-American politician Adam Clayton Powell Jr.[3][4] It was later aired as part of the PBS series The American Experience.\\nThought 3: Adam Clayton Powell (film) is a documentary about an African-American politician, not Finnish rock groups. So the documentary about Finnish rock groups must instead be The Saimaa Gesture.\\nAction 3: Finish[The Saimaa Gesture]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"What profession does Nicholas Ray and Elia Kazan have in common?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.\\nAction 1: Search[Nicholas Ray]\\nObservation 1: Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 - June 16, 1979) was an American film director, screenwriter, and actor best known for the 1955 film Rebel Without a Cause.\\nThought 2: Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions.\\nAction 2: Search[Elia Kazan]\\nObservation 2: Elia Kazan was an American film and theatre director, producer, screenwriter and actor.\\nThought 3: Professions of Elia Kazan are director, producer, screenwriter, and actor. So profession Nicholas Ray and Elia Kazan have in common is director, screenwriter, and actor.\\nAction 3: Finish[director, screenwriter, actor]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Which magazine was started first Arthur’s Magazine or First for Women?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Arthur’s Magazine and First for Women, and find which was started first.\\nAction 1: Search[Arthur’s Magazine]\\nObservation 1: Arthur’s Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century.\\nThought 2: Arthur’s Magazine was started in 1844. I need to search First for Women next.\\nAction 2: Search[First for Women]\\nObservation 2: First for Women is a woman’s magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989.\\nThought 3: First for Women was started in 1989. 1844 (Arthur’s Magazine) < 1989 (First for Women), so Arthur’s Magazine was started first.\\nAction 3: Finish[Arthur’s Magazine]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Were Pavel Urysohn and Leonid Levin known for the same type of work?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.\\nAction 1: Search[Pavel Urysohn]\\nObservation 1: Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory.\\nThought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work.\\nAction 2: Search[Leonid Levin]\\nObservation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist.\\nThought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work.\\nAction 3: Finish[yes]\"\n",
+    "  }\n",
+    "]\n",
+    "example_prompt = PromptTemplate(input_variables=[\"question\", \"answer\"], template=\"Question: {question}\\n{answer}\")\n",
+    "\n",
+    "prompt = FewShotPromptTemplate(\n",
+    "    examples=examples, \n",
+    "    example_prompt=example_prompt, \n",
+    "    suffix=\"Question: {input}\", \n",
+    "    input_variables=[\"input\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "897d4e08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# LLM Math\n",
+    "examples = [\n",
+    "  {\n",
+    "    \"question\": \"What is 37593 * 67?\",\n",
+    "    \"answer\": \"```python\\nprint(37593 * 67)\\n```\\n```output\\n2518731\\n```\\nAnswer: 2518731\"\n",
+    "  }\n",
+    "]\n",
+    "example_prompt = PromptTemplate(input_variables=[\"question\", \"answer\"], template=\"Question: {question}\\n\\n{answer}\")\n",
+    "\n",
+    "prompt = FewShotPromptTemplate(\n",
+    "    examples=examples, \n",
+    "    example_prompt=example_prompt, \n",
+    "    suffix=\"Question: {input}\", \n",
+    "    input_variables=[\"input\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7ab7379f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NatBot\n",
+    "example_seperator = \"==================================================\"\n",
+    "content_1 = \"\"\"<link id=1>About</link>\n",
+    "<link id=2>Store</link>\n",
+    "<link id=3>Gmail</link>\n",
+    "<link id=4>Images</link>\n",
+    "<link id=5>(Google apps)</link>\n",
+    "<link id=6>Sign in</link>\n",
+    "<img id=7 alt=\"(Google)\"/>\n",
+    "<input id=8 alt=\"Search\"></input>\n",
+    "<button id=9>(Search by voice)</button>\n",
+    "<button id=10>(Google Search)</button>\n",
+    "<button id=11>(I'm Feeling Lucky)</button>\n",
+    "<link id=12>Advertising</link>\n",
+    "<link id=13>Business</link>\n",
+    "<link id=14>How Search works</link>\n",
+    "<link id=15>Carbon neutral since 2007</link>\n",
+    "<link id=16>Privacy</link>\n",
+    "<link id=17>Terms</link>\n",
+    "<text id=18>Settings</text>\"\"\"\n",
+    "content_2 = \"\"\"<link id=1>About</link>\n",
+    "<link id=2>Store</link>\n",
+    "<link id=3>Gmail</link>\n",
+    "<link id=4>Images</link>\n",
+    "<link id=5>(Google apps)</link>\n",
+    "<link id=6>Sign in</link>\n",
+    "<img id=7 alt=\"(Google)\"/>\n",
+    "<input id=8 alt=\"Search\"></input>\n",
+    "<button id=9>(Search by voice)</button>\n",
+    "<button id=10>(Google Search)</button>\n",
+    "<button id=11>(I'm Feeling Lucky)</button>\n",
+    "<link id=12>Advertising</link>\n",
+    "<link id=13>Business</link>\n",
+    "<link id=14>How Search works</link>\n",
+    "<link id=15>Carbon neutral since 2007</link>\n",
+    "<link id=16>Privacy</link>\n",
+    "<link id=17>Terms</link>\n",
+    "<text id=18>Settings</text>\"\"\"\n",
+    "content_3 = \"\"\"<button id=1>For Businesses</button>\n",
+    "<button id=2>Mobile</button>\n",
+    "<button id=3>Help</button>\n",
+    "<button id=4 alt=\"Language Picker\">EN</button>\n",
+    "<link id=5>OpenTable logo</link>\n",
+    "<button id=6 alt =\"search\">Search</button>\n",
+    "<text id=7>Find your table for any occasion</text>\n",
+    "<button id=8>(Date selector)</button>\n",
+    "<text id=9>Sep 28, 2022</text>\n",
+    "<text id=10>7:00 PM</text>\n",
+    "<text id=11>2 people</text>\n",
+    "<input id=12 alt=\"Location, Restaurant, or Cuisine\"></input>\n",
+    "<button id=13>Let’s go</button>\n",
+    "<text id=14>It looks like you're in Peninsula. Not correct?</text>\n",
+    "<button id=15>Get current location</button>\n",
+    "<button id=16>Next</button>\"\"\"\n",
+    "examples = [\n",
+    "    {\n",
+    "        \"i\": 1,\n",
+    "        \"content\": content_1,\n",
+    "        \"objective\": \"Find a 2 bedroom house for sale in Anchorage AK for under $750k\",\n",
+    "        \"current_url\": \"https://www.google.com/\",\n",
+    "        \"command\": 'TYPESUBMIT 8 \"anchorage redfin\"'\n",
+    "    },\n",
+    "    {\n",
+    "        \"i\": 2,\n",
+    "        \"content\": content_2,\n",
+    "        \"objective\": \"Make a reservation for 4 at Dorsia at 8pm\",\n",
+    "        \"current_url\": \"https://www.google.com/\",\n",
+    "        \"command\": 'TYPESUBMIT 8 \"dorsia nyc opentable\"'\n",
+    "    },\n",
+    "    {\n",
+    "        \"i\": 3,\n",
+    "        \"content\": content_3,\n",
+    "        \"objective\": \"Make a reservation for 4 for dinner at Dorsia in New York City at 8pm\",\n",
+    "        \"current_url\": \"https://www.opentable.com/\",\n",
+    "        \"command\": 'TYPESUBMIT 12 \"dorsia new york city\"'\n",
+    "    },\n",
+    "]\n",
+    "example_prompt_template=\"\"\"EXAMPLE {i}:\n",
+    "==================================================\n",
+    "CURRENT BROWSER CONTENT:\n",
+    "------------------\n",
+    "{content}\n",
+    "------------------\n",
+    "OBJECTIVE: {objective}\n",
+    "CURRENT URL: {current_url}\n",
+    "YOUR COMMAND:\n",
+    "{command}\"\"\"\n",
+    "example_prompt = PromptTemplate(input_variables=[\"i\", \"content\", \"objective\", \"current_url\", \"command\"], template=example_prompt_template)\n",
+    "\n",
+    "\n",
+    "prefix = \"\"\"\n",
+    "You are an agent controlling a browser. You are given:\n",
+    "\t(1) an objective that you are trying to achieve\n",
+    "\t(2) the URL of your current web page\n",
+    "\t(3) a simplified text description of what's visible in the browser window (more on that below)\n",
+    "You can issue these commands:\n",
+    "\tSCROLL UP - scroll up one page\n",
+    "\tSCROLL DOWN - scroll down one page\n",
+    "\tCLICK X - click on a given element. You can only click on links, buttons, and inputs!\n",
+    "\tTYPE X \"TEXT\" - type the specified text into the input with id X\n",
+    "\tTYPESUBMIT X \"TEXT\" - same as TYPE above, except then it presses ENTER to submit the form\n",
+    "The format of the browser content is highly simplified; all formatting elements are stripped.\n",
+    "Interactive elements such as links, inputs, buttons are represented like this:\n",
+    "\t\t<link id=1>text</link>\n",
+    "\t\t<button id=2>text</button>\n",
+    "\t\t<input id=3>text</input>\n",
+    "Images are rendered as their alt text like this:\n",
+    "\t\t<img id=4 alt=\"\"/>\n",
+    "Based on your given objective, issue whatever command you believe will get you closest to achieving your goal.\n",
+    "You always start on Google; you should submit a search query to Google that will take you to the best page for\n",
+    "achieving your objective. And then interact with that page to achieve your objective.\n",
+    "If you find yourself on Google and there are no search results displayed yet, you should probably issue a command\n",
+    "like \"TYPESUBMIT 7 \"search query\"\" to get to a more useful page.\n",
+    "Then, if you find yourself on a Google search results page, you might issue the command \"CLICK 24\" to click\n",
+    "on the first link in the search results. (If your previous command was a TYPESUBMIT your next command should\n",
+    "probably be a CLICK.)\n",
+    "Don't try to interact with elements that you can't see.\n",
+    "Here are some examples:\n",
+    "\"\"\"\n",
+    "suffix=\"\"\"\n",
+    "The current browser content, objective, and current URL follow. Reply with your next command to the browser.\n",
+    "CURRENT BROWSER CONTENT:\n",
+    "------------------\n",
+    "{browser_content}\n",
+    "------------------\n",
+    "OBJECTIVE: {objective}\n",
+    "CURRENT URL: {url}\n",
+    "PREVIOUS COMMAND: {previous_command}\n",
+    "YOUR COMMAND:\n",
+    "\"\"\"\n",
+    "PROMPT = FewShotPromptTemplate(\n",
+    "    examples = examples,\n",
+    "    example_prompt=example_prompt,\n",
+    "    example_separator=example_seperator,\n",
+    "    input_variables=[\"browser_content\", \"url\", \"previous_command\", \"objective\"],\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce5927c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts/few_shot_prompt.json

@@ -3,7 +3,6 @@
     "input_variables": ["adjective"],
     "prefix": "Write antonyms for the following words.",
     "example_prompt": {
-        "_type": "prompt",
         "input_variables": ["input", "output"],
         "template": "Input: {input}\nOutput: {output}"
     },

docs/examples/prompts/few_shot_prompt.yaml

@@ -4,7 +4,6 @@ input_variables:
 prefix: 
     Write antonyms for the following words.
 example_prompt:
-    _type: prompt
     input_variables:
         ["input", "output"]
     template:

docs/examples/prompts/few_shot_prompt_examples_in.json

@@ -3,7 +3,6 @@
     "input_variables": ["adjective"],
     "prefix": "Write antonyms for the following words.",
     "example_prompt": {
-        "_type": "prompt",
         "input_variables": ["input", "output"],
         "template": "Input: {input}\nOutput: {output}"
     },

docs/examples/prompts/generate_examples.ipynb

@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5d249ee",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Generate Examples\n",
+    "\n",
+    "This notebook shows how to use LangChain to generate more examples similar to the ones you already have."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1685fa2f",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.example_generator import generate_example\n",
+    "from langchain.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "334ef4f7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Use examples from ReAct\n",
+    "examples = [\n",
+    "  {\n",
+    "    \"question\": \"What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of that area.\\nAction 1: Search[Colorado orogeny]\\nObservation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.\\nThought 2: It does not mention the eastern sector. So I need to look up eastern sector.\\nAction 2: Lookup[eastern sector]\\nObservation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny.\\nThought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range.\\nAction 3: Search[High Plains]\\nObservation 3: High Plains refers to one of two distinct land regions\\nThought 4: I need to instead search High Plains (United States).\\nAction 4: Search[High Plains (United States)]\\nObservation 4: The High Plains are a subregion of the Great Plains. From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]\\nThought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.\\nAction 5: Finish[1,800 to 7,000 ft]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Musician and satirist Allie Goertz wrote a song about the \\\"The Simpsons\\\" character Milhouse, who Matt Groening named after who?\",\n",
+    "    \"answer\": \"Thought 1: The question simplifies to \\\"The Simpsons\\\" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.\\nAction 1: Search[Milhouse]\\nObservation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.\\nThought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up \\\"named after\\\".\\nAction 2: Lookup[named after]\\nObservation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.\\nThought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.\\nAction 3: Finish[Richard Nixon]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.\\nAction 1: Search[Adam Clayton Powell]\\nObservation 1 Could not find [Adam Clayton Powell]. Similar: [’Adam Clayton Powell III’, ’Seventh Avenue (Manhattan)’, ’Adam Clayton Powell Jr. State Office Building’, ’Isabel Washington Powell’, ’Adam Powell’, ’Adam Clayton Powell (film)’, ’Giancarlo Esposito’].\\nThought 2: To find the documentary, I can search Adam Clayton Powell (film).\\nAction 2: Search[Adam Clayton Powell (film)]\\nObservation 2: Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg. The film is about the rise and fall of influential African-American politician Adam Clayton Powell Jr.[3][4] It was later aired as part of the PBS series The American Experience.\\nThought 3: Adam Clayton Powell (film) is a documentary about an African-American politician, not Finnish rock groups. So the documentary about Finnish rock groups must instead be The Saimaa Gesture.\\nAction 3: Finish[The Saimaa Gesture]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"What profession does Nicholas Ray and Elia Kazan have in common?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.\\nAction 1: Search[Nicholas Ray]\\nObservation 1: Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 - June 16, 1979) was an American film director, screenwriter, and actor best known for the 1955 film Rebel Without a Cause.\\nThought 2: Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions.\\nAction 2: Search[Elia Kazan]\\nObservation 2: Elia Kazan was an American film and theatre director, producer, screenwriter and actor.\\nThought 3: Professions of Elia Kazan are director, producer, screenwriter, and actor. So profession Nicholas Ray and Elia Kazan have in common is director, screenwriter, and actor.\\nAction 3: Finish[director, screenwriter, actor]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Which magazine was started first Arthur’s Magazine or First for Women?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Arthur’s Magazine and First for Women, and find which was started first.\\nAction 1: Search[Arthur’s Magazine]\\nObservation 1: Arthur’s Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century.\\nThought 2: Arthur’s Magazine was started in 1844. I need to search First for Women next.\\nAction 2: Search[First for Women]\\nObservation 2: First for Women is a woman’s magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989.\\nThought 3: First for Women was started in 1989. 1844 (Arthur’s Magazine) < 1989 (First for Women), so Arthur’s Magazine was started first.\\nAction 3: Finish[Arthur’s Magazine]\"\n",
+    "  },\n",
+    "  {\n",
+    "    \"question\": \"Were Pavel Urysohn and Leonid Levin known for the same type of work?\",\n",
+    "    \"answer\": \"Thought 1: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.\\nAction 1: Search[Pavel Urysohn]\\nObservation 1: Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory.\\nThought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work.\\nAction 2: Search[Leonid Levin]\\nObservation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist.\\nThought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work.\\nAction 3: Finish[yes]\"\n",
+    "  }\n",
+    "]\n",
+    "example_template = PromptTemplate(template=\"Question: {question}\\n{answer}\", input_variables=[\"question\", \"answer\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a7bd36bc",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_example = generate_example(examples, OpenAI(), example_template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e1efb008",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['',\n",
+       " '',\n",
+       " 'Question: What is the highest mountain peak in North America?',\n",
+       " '',\n",
+       " 'Thought 1: I need to search North America and find the highest mountain peak.',\n",
+       " '',\n",
+       " 'Action 1: Search[North America]',\n",
+       " '',\n",
+       " 'Observation 1: North America is a continent entirely within the Northern Hemisphere and almost all within the Western Hemisphere.',\n",
+       " '',\n",
+       " 'Thought 2: I need to look up \"highest mountain peak\".',\n",
+       " '',\n",
+       " 'Action 2: Lookup[highest mountain peak]',\n",
+       " '',\n",
+       " 'Observation 2: (Result 1 / 1) Denali, formerly Mount McKinley, is the highest mountain peak in North America, with a summit elevation of 20,310 feet (6,190 m) above sea level.',\n",
+       " '',\n",
+       " 'Thought 3: Denali is the highest mountain peak in North America, with a summit elevation of 20,310 feet.',\n",
+       " '',\n",
+       " 'Action 3: Finish[20,310 feet]']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_example.split('\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ed01ba2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts/prompt_management.ipynb

@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "43fb16cb",
+   "metadata": {},
+   "source": [
+    "# Prompt Management\n",
+    "\n",
+    "Managing your prompts is annoying and tedious, with everyone writing their own slightly different variants of the same ideas. But it shouldn't be this way. \n",
+    "\n",
+    "LangChain provides a standard and flexible way for specifying and managing all your prompts, as well as clear and specific terminology around them. This notebook goes through the core components of working with prompts, showing how to use them as well as explaining what they do.\n",
+    "\n",
+    "This notebook covers how to work with prompts in Python. If you are interested in how to work with serialized versions of prompts and load them from disk, see [this notebook](prompt_serialization.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "890aad4d",
+   "metadata": {},
+   "source": [
+    "### The BasePromptTemplate Interface\n",
+    "\n",
+    "A prompt template is a mechanism for constructing a prompt to pass to the language model given some user input. Below is the interface that all different types of prompt templates should expose.\n",
+    "\n",
+    "```python\n",
+    "class BasePromptTemplate(ABC):\n",
+    "\n",
+    "    input_variables: List[str]\n",
+    "    \"\"\"A list of the names of the variables the prompt template expects.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def format(self, **kwargs: Any) -> str:\n",
+    "        \"\"\"Format the prompt with the inputs.\n",
+    "\n",
+    "        Args:\n",
+    "            kwargs: Any arguments to be passed to the prompt template.\n",
+    "\n",
+    "        Returns:\n",
+    "            A formatted string.\n",
+    "\n",
+    "        Example:\n",
+    "\n",
+    "        .. code-block:: python\n",
+    "\n",
+    "            prompt.format(variable1=\"foo\")\n",
+    "        \"\"\"\n",
+    "```\n",
+    "\n",
+    "The only two things that define a prompt are:\n",
+    "\n",
+    "1. `input_variables`: The user inputted variables that are needed to format the prompt.\n",
+    "2. `format`: A method which takes in keyword arguments are returns a formatted prompt. The keys are expected to be the input variables\n",
+    "    \n",
+    "The rest of the logic of how the prompt is constructed is left up to different implementations. Let's take a look at some below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cddb465e",
+   "metadata": {},
+   "source": [
+    "### PromptTemplate\n",
+    "\n",
+    "This is the most simple type of prompt template, consisting of a string template that takes any number of input variables. The template should be formatted as a Python f-string, although we will support other formats (Jinja, Mako, etc) in the future. \n",
+    "\n",
+    "If you just want to use a hardcoded prompt template, you should use this implementation.\n",
+    "\n",
+    "Let's walk through a few examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "094229f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ab46bd2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tell me a joke.'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# An example prompt with no input variables\n",
+    "no_input_prompt = PromptTemplate(input_variables=[], template=\"Tell me a joke.\")\n",
+    "no_input_prompt.format()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c3ad0fa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tell me a funny joke.'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# An example prompt with one input variable\n",
+    "one_input_prompt = PromptTemplate(input_variables=[\"adjective\"], template=\"Tell me a {adjective} joke.\")\n",
+    "one_input_prompt.format(adjective=\"funny\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ba577dcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tell me a funny joke about chickens.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# An example prompt with multiple input variables\n",
+    "multiple_input_prompt = PromptTemplate(\n",
+    "    input_variables=[\"adjective\", \"content\"], \n",
+    "    template=\"Tell me a {adjective} joke about {content}.\"\n",
+    ")\n",
+    "multiple_input_prompt.format(adjective=\"funny\", content=\"chickens\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1492b49d",
+   "metadata": {},
+   "source": [
+    "### Few Shot Prompts\n",
+    "\n",
+    "A FewShotPromptTemplate is a prompt template that includes some examples. If you have collected some examples of how the task should be done, you can insert them into prompt using this class.\n",
+    "\n",
+    "Examples are datapoints that can be included in the prompt in order to give the model more context what to do. Examples are represented as a dictionary of key-value pairs, with the key being the input (or label) name, and the value being the input (or label) value. \n",
+    "\n",
+    "In addition to the example, we also need to specify how the example should be formatted when it's inserted in the prompt. We can do this using the above `PromptTemplate`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3eb36972",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These are some examples of a pretend task of creating antonyms.\n",
+    "examples = [\n",
+    "    {\"input\": \"happy\", \"output\": \"sad\"},\n",
+    "    {\"input\": \"tall\", \"output\": \"short\"},\n",
+    "]\n",
+    "# This how we specify how the example should be formatted.\n",
+    "example_prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\",\"output\"],\n",
+    "    template=\"Input: {input}\\nOutput: {output}\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "80a91d96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import FewShotPromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7931e5f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: big\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt_from_string_examples = FewShotPromptTemplate(\n",
+    "    # These are the examples we want to insert into the prompt.\n",
+    "    examples=examples,\n",
+    "    # This is how we want to format the examples when we insert them into the prompt.\n",
+    "    example_prompt=example_prompt,\n",
+    "    # The prefix is some text that goes before the examples in the prompt.\n",
+    "    # Usually, this consists of intructions.\n",
+    "    prefix=\"Give the antonym of every input\",\n",
+    "    # The suffix is some text that goes after the examples in the prompt.\n",
+    "    # Usually, this is where the user input will go\n",
+    "    suffix=\"Input: {adjective}\\nOutput:\", \n",
+    "    # The input variables are the variables that the overall prompt expects.\n",
+    "    input_variables=[\"adjective\"],\n",
+    "    # The example_separator is the string we will use to join the prefix, examples, and suffix together with.\n",
+    "    example_separator=\"\\n\\n\"\n",
+    "    \n",
+    ")\n",
+    "print(prompt_from_string_examples.format(adjective=\"big\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf038596",
+   "metadata": {},
+   "source": [
+    "### ExampleSelector\n",
+    "If you have a large number of examples, you may need to select which ones to include in the prompt. The ExampleSelector is the class responsible for doing so. The base interface is defined as below.\n",
+    "\n",
+    "```python\n",
+    "class BaseExampleSelector(ABC):\n",
+    "    \"\"\"Interface for selecting examples to include in prompts.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:\n",
+    "        \"\"\"Select which examples to use based on the inputs.\"\"\"\n",
+    "\n",
+    "```\n",
+    "\n",
+    "The only method it needs to expose is a `select_examples` method. This takes in the input variables and then returns a list of examples. It is up to each specific implementation as to how those examples are selected. Let's take a look at some below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "861a4d1f",
+   "metadata": {},
+   "source": [
+    "### LengthBased ExampleSelector\n",
+    "\n",
+    "This ExampleSelector selects which examples to use based on length. This is useful when you are worried about constructing a prompt that will go over the length of the context window. For longer inputs, it will select fewer examples to include, while for shorter inputs it will select more.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7c469c95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.example_selector import LengthBasedExampleSelector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0ec6d950",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These are a lot of examples of a pretend task of creating antonyms.\n",
+    "examples = [\n",
+    "    {\"input\": \"happy\", \"output\": \"sad\"},\n",
+    "    {\"input\": \"tall\", \"output\": \"short\"},\n",
+    "    {\"input\": \"energetic\", \"output\": \"lethargic\"},\n",
+    "    {\"input\": \"sunny\", \"output\": \"gloomy\"},\n",
+    "    {\"input\": \"windy\", \"output\": \"calm\"},\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "207e55f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_selector = LengthBasedExampleSelector(\n",
+    "    # These are the examples is has available to choose from.\n",
+    "    examples=examples, \n",
+    "    # This is the PromptTemplate being used to format the examples.\n",
+    "    example_prompt=example_prompt, \n",
+    "    # This is the maximum length that the formatted examples should be.\n",
+    "    # Length is measured by the get_text_length function below.\n",
+    "    max_length=25,\n",
+    "    # This is the function used to get the length of a string, which is used\n",
+    "    # to determine which examples to include. It is commented out because\n",
+    "    # it is provided as a default value if none is specified.\n",
+    "    # get_text_length: Callable[[str], int] = lambda x: len(re.split(\"\\n| \", x))\n",
+    ")\n",
+    "dynamic_prompt = FewShotPromptTemplate(\n",
+    "    # We provide an ExampleSelector instead of examples.\n",
+    "    example_selector=example_selector,\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=\"Give the antonym of every input\",\n",
+    "    suffix=\"Input: {adjective}\\nOutput:\", \n",
+    "    input_variables=[\"adjective\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d00b4385",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: energetic\n",
+      "Output: lethargic\n",
+      "\n",
+      "Input: sunny\n",
+      "Output: gloomy\n",
+      "\n",
+      "Input: windy\n",
+      "Output: calm\n",
+      "\n",
+      "Input: big\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# An example with small input, so it selects all examples.\n",
+    "print(dynamic_prompt.format(adjective=\"big\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "878bcde9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# An example with long input, so it selects only one example.\n",
+    "long_string = \"big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else\"\n",
+    "print(dynamic_prompt.format(adjective=long_string))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e4bebcd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: energetic\n",
+      "Output: lethargic\n",
+      "\n",
+      "Input: sunny\n",
+      "Output: gloomy\n",
+      "\n",
+      "Input: windy\n",
+      "Output: calm\n",
+      "\n",
+      "Input: big\n",
+      "Output: small\n",
+      "\n",
+      "Input: enthusiastic\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can add an example to an example selector as well.\n",
+    "new_example = {\"input\": \"big\", \"output\": \"small\"}\n",
+    "dynamic_prompt.example_selector.add_example(new_example)\n",
+    "print(dynamic_prompt.format(adjective=\"enthusiastic\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d007b0a",
+   "metadata": {},
+   "source": [
+    "### Similarity ExampleSelector\n",
+    "\n",
+    "The SemanticSimilarityExampleSelector selects examples based on which examples are most similar to the inputs. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "241bfe80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.example_selector import SemanticSimilarityExampleSelector\n",
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.embeddings import OpenAIEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "50d0a701",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_selector = SemanticSimilarityExampleSelector.from_examples(\n",
+    "    # This is the list of examples available to select from.\n",
+    "    examples, \n",
+    "    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n",
+    "    OpenAIEmbeddings(), \n",
+    "    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n",
+    "    FAISS, \n",
+    "    # This is the number of examples to produce.\n",
+    "    k=1\n",
+    ")\n",
+    "similar_prompt = FewShotPromptTemplate(\n",
+    "    # We provide an ExampleSelector instead of examples.\n",
+    "    example_selector=example_selector,\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=\"Give the antonym of every input\",\n",
+    "    suffix=\"Input: {adjective}\\nOutput:\", \n",
+    "    input_variables=[\"adjective\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4c8fdf45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: worried\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Input is a feeling, so should select the happy/sad example\n",
+    "print(similar_prompt.format(adjective=\"worried\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "829af21a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: fat\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Input is a measurement, so should select the tall/short example\n",
+    "print(similar_prompt.format(adjective=\"fat\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3c16fe23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: enthusiastic\n",
+      "Output: apathetic\n",
+      "\n",
+      "Input: joyful\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can add new examples to the SemanticSimilarityExampleSelector as well\n",
+    "similar_prompt.example_selector.add_example({\"input\": \"enthusiastic\", \"output\": \"apathetic\"})\n",
+    "print(similar_prompt.format(adjective=\"joyful\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbc32551",
+   "metadata": {},
+   "source": [
+    "### Serialization\n",
+    "\n",
+    "PromptTemplates and examples can be serialized and loaded from disk, making it easy to share and store prompts. For a detailed walkthrough on how to do that, see [this notebook](prompt_serialization.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e1e13c6",
+   "metadata": {},
+   "source": [
+    "### Customizability\n",
+    "The above covers all the ways currently supported in LangChain to represent prompts and example selectors. However, due to the simple interface that the base classes (`BasePromptTemplate`, `BaseExampleSelector`) expose, it should be easy to subclass them and write your own implementation in your own codebase. And of course, if you'd like to contribute that back to LangChain, we'd love that :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c746d6f4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts/prompt_serialization.ipynb

@@ -0,0 +1,542 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "43fb16cb",
+   "metadata": {},
+   "source": [
+    "# Prompt Serialization\n",
+    "\n",
+    "It is often preferrable to store prompts not as python code but as files. This can make it easy to share, store, and version prompts. This notebook covers how to do that in LangChain, walking through all the different types of prompts and the different serialization options.\n",
+    "\n",
+    "At a high level, the following design principles are applied to serialization:\n",
+    "\n",
+    "1. Both JSON and YAML are supported. We want to support serialization methods are human readable on disk, and YAML and JSON are two of the most popular methods for that. Note that this rule applies to prompts. For other assets, like Examples, different serialization methods may be supported.\n",
+    "\n",
+    "2. We support specifying everything in one file, or storing different components (templates, examples, etc) in different files and referencing them. For some cases, storing everything in file makes the most sense, but for others it is preferrable to split up some of the assets (long templates, large examples, reusable components). LangChain supports both.\n",
+    "\n",
+    "There is also a single entry point to load prompts from disk, making it easy to load any type of prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2c8d7587",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All prompts are loaded through the `load_prompt` function.\n",
+    "from langchain.prompts import load_prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cddb465e",
+   "metadata": {},
+   "source": [
+    "## PromptTemplate\n",
+    "\n",
+    "This section covers examples for loading a PromptTemplate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d4b40f2",
+   "metadata": {},
+   "source": [
+    "### Loading from YAML\n",
+    "This shows an example of loading a PromptTemplate from YAML."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2d6e5117",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input_variables:\r\n",
+      "    [\"adjective\", \"content\"]\r\n",
+      "template: \r\n",
+      "    Tell me a {adjective} joke about {content}.\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat simple_prompt.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4f4ca686",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tell me a funny joke about chickens.\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = load_prompt(\"simple_prompt.yaml\")\n",
+    "print(prompt.format(adjective=\"funny\", content=\"chickens\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "362eadb2",
+   "metadata": {},
+   "source": [
+    "### Loading from JSON\n",
+    "This shows an example of loading a PromptTemplate from JSON."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "510def23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"input_variables\": [\"adjective\", \"content\"],\r\n",
+      "    \"template\": \"Tell me a {adjective} joke about {content}.\"\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat simple_prompt.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d788a83c",
+   "metadata": {},
+   "source": [
+    "### Loading Template from a File\n",
+    "This shows an example of storing the template in a separate file and then referencing it in the config. Notice that the key changes from `template` to `template_path`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5547760d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tell me a {adjective} joke about {content}."
+     ]
+    }
+   ],
+   "source": [
+    "!cat simple_template.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9cb13ac5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"input_variables\": [\"adjective\", \"content\"],\r\n",
+      "    \"template_path\": \"simple_template.txt\"\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat simple_prompt_with_template_file.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "762cb4bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tell me a funny joke about chickens.\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = load_prompt(\"simple_prompt_with_template_file.json\")\n",
+    "print(prompt.format(adjective=\"funny\", content=\"chickens\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ae191cc",
+   "metadata": {},
+   "source": [
+    "## FewShotPromptTemplate\n",
+    "\n",
+    "This section covers examples for loading few shot prompt templates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9828f94c",
+   "metadata": {},
+   "source": [
+    "### Examples\n",
+    "This shows an example of what examples stored as json might look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b21f5b95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\r\n",
+      "    {\"input\": \"happy\", \"output\": \"sad\"},\r\n",
+      "    {\"input\": \"tall\", \"output\": \"short\"}\r\n",
+      "]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e300335",
+   "metadata": {},
+   "source": [
+    "### Loading from YAML\n",
+    "This shows an example of loading a few shot example from YAML."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e2bec0fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_type: few_shot\r\n",
+      "input_variables:\r\n",
+      "    [\"adjective\"]\r\n",
+      "prefix: \r\n",
+      "    Write antonyms for the following words.\r\n",
+      "example_prompt:\r\n",
+      "    input_variables:\r\n",
+      "        [\"input\", \"output\"]\r\n",
+      "    template:\r\n",
+      "        \"Input: {input}\\nOutput: {output}\"\r\n",
+      "examples:\r\n",
+      "    examples.json\r\n",
+      "suffix:\r\n",
+      "    \"Input: {adjective}\\nOutput:\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat few_shot_prompt.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "98c8f356",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Write antonyms for the following words.\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: funny\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = load_prompt(\"few_shot_prompt.yaml\")\n",
+    "print(prompt.format(adjective=\"funny\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4870aa9d",
+   "metadata": {},
+   "source": [
+    "### Loading from JSON\n",
+    "This shows an example of loading a few shot example from JSON."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9d996a86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"_type\": \"few_shot\",\r\n",
+      "    \"input_variables\": [\"adjective\"],\r\n",
+      "    \"prefix\": \"Write antonyms for the following words.\",\r\n",
+      "    \"example_prompt\": {\r\n",
+      "        \"input_variables\": [\"input\", \"output\"],\r\n",
+      "        \"template\": \"Input: {input}\\nOutput: {output}\"\r\n",
+      "    },\r\n",
+      "    \"examples\": \"examples.json\",\r\n",
+      "    \"suffix\": \"Input: {adjective}\\nOutput:\"\r\n",
+      "}   \r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat few_shot_prompt.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "dd2c10bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Write antonyms for the following words.\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: funny\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = load_prompt(\"few_shot_prompt.json\")\n",
+    "print(prompt.format(adjective=\"funny\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d23faf4",
+   "metadata": {},
+   "source": [
+    "### Examples in the Config\n",
+    "This shows an example of referencing the examples directly in the config."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6cd781ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"_type\": \"few_shot\",\r\n",
+      "    \"input_variables\": [\"adjective\"],\r\n",
+      "    \"prefix\": \"Write antonyms for the following words.\",\r\n",
+      "    \"example_prompt\": {\r\n",
+      "        \"input_variables\": [\"input\", \"output\"],\r\n",
+      "        \"template\": \"Input: {input}\\nOutput: {output}\"\r\n",
+      "    },\r\n",
+      "    \"examples\": [\r\n",
+      "        {\"input\": \"happy\", \"output\": \"sad\"},\r\n",
+      "        {\"input\": \"tall\", \"output\": \"short\"}\r\n",
+      "    ],\r\n",
+      "    \"suffix\": \"Input: {adjective}\\nOutput:\"\r\n",
+      "}   \r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat few_shot_prompt_examples_in.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "533ab8a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Write antonyms for the following words.\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: funny\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = load_prompt(\"few_shot_prompt_examples_in.json\")\n",
+    "print(prompt.format(adjective=\"funny\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e86139e",
+   "metadata": {},
+   "source": [
+    "### Example Prompt from a File\n",
+    "This shows an example of loading the PromptTemplate that is used to format the examples from a separate file. Note that the key changes from `example_prompt` to `example_prompt_path`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0b6dd7b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"input_variables\": [\"input\", \"output\"],\r\n",
+      "    \"template\": \"Input: {input}\\nOutput: {output}\" \r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat example_prompt.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "76a1065d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"_type\": \"few_shot\",\r\n",
+      "    \"input_variables\": [\"adjective\"],\r\n",
+      "    \"prefix\": \"Write antonyms for the following words.\",\r\n",
+      "    \"example_prompt_path\": \"example_prompt.json\",\r\n",
+      "    \"examples\": \"examples.json\",\r\n",
+      "    \"suffix\": \"Input: {adjective}\\nOutput:\"\r\n",
+      "}   \r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat few_shot_prompt_example_prompt.json "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "744d275d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Write antonyms for the following words.\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: funny\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = load_prompt(\"few_shot_prompt_example_prompt.json\")\n",
+    "print(prompt.format(adjective=\"funny\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcfc7176",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/prompts/simple_prompt.json

@@ -1,5 +1,4 @@
 {
-    "_type": "prompt",
     "input_variables": ["adjective", "content"],
     "template": "Tell me a {adjective} joke about {content}."
 }

docs/examples/prompts/simple_prompt.yaml

@@ -1,7 +1,4 @@
-_type: prompt
 input_variables:
-    ["adjective"]
-partial_variables:
-    content: dogs
+    ["adjective", "content"]
 template: 
     Tell me a {adjective} joke about {content}.

docs/examples/prompts/simple_prompt_with_template_file.json

@@ -1,5 +1,4 @@
 {
-    "_type": "prompt",
     "input_variables": ["adjective", "content"],
     "template_path": "simple_template.txt"
 }