f624ad489a
**TL;DR much of the provided `Makefile` targets were broken, and any time I wanted to preview changes locally I either had to refer to a command Chester gave me or try waiting on a Vercel preview deployment. With this PR, everything should behave like normal.** Significant updates to the `Makefile` and documentation files, focusing on improving usability, adding clear messaging, and fixing/enhancing documentation workflows. ### Updates to `Makefile`: #### Enhanced build and cleaning processes: - Added informative messages (e.g., "📚 Building LangChain documentation...") to makefile targets like `docs_build`, `docs_clean`, and `api_docs_build` for better user feedback during execution. - Introduced a `clean-cache` target to the `docs` `Makefile` to clear cached dependencies and ensure clean builds. #### Improved dependency handling: - Modified `install-py-deps` to create a `.venv/deps_installed` marker, preventing redundant/duplicate dependency installations and improving efficiency. #### Streamlined file generation and infrastructure setup: - Added caching for the LangServe README download and parallelized feature table generation - Added user-friendly completion messages for targets like `copy-infra` and `render`. #### Documentation server updates: - Enhanced the `start` target with messages indicating server start and URL for local documentation viewing. --- ### Documentation Improvements: #### Content clarity and consistency: - Standardized section titles for consistency across documentation files. [[1]](diffhunk://#diff-9b1a85ea8a9dcf79f58246c88692cd7a36316665d7e05a69141cfdc50794c82aL1-R1) [[2]](diffhunk://#diff-944008ad3a79d8a312183618401fcfa71da0e69c75803eff09b779fc8e03183dL1-R1) - Refined phrasing and formatting in sections like "Dependency management" and "Formatting and linting" for better readability. [[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L6-R6) [[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L84-R82) #### Enhanced workflows: - Updated instructions for building and viewing documentation locally, including tips for specifying server ports and handling API reference previews. [[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L60-R94) [[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126) - Expanded guidance on cleaning documentation artifacts and using linting tools effectively. [[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126) [[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142) #### API reference documentation: - Improved instructions for generating and formatting in-code documentation, highlighting best practices for docstring writing. [[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142) [[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L144-R186) --- ### Minor Changes: - Added support for a new package name (`langchain_v1`) in the API documentation generation script. - Fixed minor capitalization and formatting issues in documentation files. [[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L40-R40) [[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L166-R160) --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
Looking for the JS/TS version? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to speak with our sales team.
Quick Install
pip install langchain
or
pip install langsmith && conda install langchain -c conda-forge
🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
❓ Question answering with RAG
- Documentation
- End-to-end Example: Chat LangChain and repo
🧱 Extracting structured output
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
📖 Documentation
Please see here for full documentation on:
- Getting started (installation, setting up the 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 five main areas that LangChain is designed to help with. These are, in increasing order of complexity:
📃 Models and Prompts:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with chat models and LLMs.
🔗 Chains:
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
📚 Retrieval Augmented Generation:
Retrieval Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
🤖 Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
🧐 Evaluation:
[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
For more information on these concepts, please see our full documentation.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.