🦜️🔗 LangChain

⚡ Building applications with LLMs through composability ⚡

Looking for the JS/TS version? Check out LangChain.js.

Production Support: As you move your LangChains into production, we'd love to offer more hands-on support. Fill out this form to share more about what you're building, and our team will get in touch.

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 over specific documents

Documentation
End-to-end Example: Question Answering over Notion Database

💬 Chatbots

Documentation
End-to-end Example: Chat-LangChain

🤖 Agents

Documentation
End-to-end Example: GPT+WolframAlpha

📖 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 six main areas that LangChain is designed to help with. These are, in increasing order of complexity:

📃 LLMs and Prompts:

This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with 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.

📚 Data Augmented Generation:

Data 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.

🧠 Memory:

Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.

🧐 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 here.

Metadata

Release files for langchain 0.0.294

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for langchain 0.0.294
File	Size	Uploaded
langchain-0.0.294.tar.gz	1.1 MB	Sep 18, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for langchain 0.0.294
File	Interpreter	ABI	Platform	Reset
langchain-0.0.294-py3-none-any.whl 1.7 MB Sep 18, 2023	Python 3	none	any	Details

Total release size: 2.8 MB

Release files / langchain-0.0.294.tar.gz

Download URL	langchain-0.0.294.tar.gz
Size	1.1 MB
Tags	Source
SHA-256 checksum How to use checksums	`e4aa2a3a60aa65ae747c411c5201e5208b14a5c62008315ce29c9b6de29e97f7`
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Upload date	Sep 18, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/4.0.2 CPython/3.11.5`

Release files / langchain-0.0.294-py3-none-any.whl

Download URL	langchain-0.0.294-py3-none-any.whl
Size	1.7 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`03389b1f64e20d288aafb8ff9937e8f21ebaf785397f0d4d9ccacf4e4c7b277e`
BLAKE2b-256 checksum How to use checksums	`b211a056c02e2d4bf894162686f2ee33a7d53d8bdbf393fb429bab614ba4440a`
Upload date	Sep 18, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/4.0.2 CPython/3.11.5`