Hunter Gerlach 482611f426 unit test / code coverage improvements (#322)
This PR has two contributions:

1. Add test for when stop token is found in middle of text

2. Add code coverage tooling and instructions
- Add pytest-cov via poetry
- Add necessary config files
- Add new make instruction for `coverage`
- Update README with coverage guidance
- Update minor README formatting/spelling

Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
2022-12-13 05:48:53 -08:00
2022-10-24 14:51:15 -07:00
2022-11-01 21:12:02 -07:00

🦜🔗 LangChain

Building applications with LLMs through composability

lint test License: MIT Twitter

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

  1. LLM and Prompts
  2. Chains
  3. Agents
  4. Memory

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 infra, or better documentation.

For detailed information on how to contribute, see here.

Description
Building applications with LLMs through composability
Readme MIT 4.8 GiB
Languages
Python 82.1%
omnetpp-msg 16.8%
Jupyter Notebook 0.5%
Makefile 0.4%
Shell 0.1%