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Sidchat95 c5e50c40c9
Fix Document Similarity Check with passed Threshold (#6845)
Converting the Similarity obtained in the
similarity_search_with_score_by_vector method whilst comparing to the
passed
threshold. This is because the passed threshold is a number between 0 to
1 and is already in the relevance_score_fn format.
As of now, the function is comparing two different scoring parameters
and that wouldn't work.

Dependencies
None

Issue:
Different scores being compared in
similarity_search_with_score_by_vector method in FAISS.

Tag maintainer
@hwchase17



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---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-13 01:30:47 -04:00
.devcontainer Update dev container (#6189) 2023-06-16 15:42:14 -07:00
.github codespell: workflow, config + some (quite a few) typos fixed (#6785) 2023-07-12 16:20:08 -04:00
docs Adds OpenAI functions powered document metadata tagger (#7521) 2023-07-13 01:12:41 -04:00
langchain Fix Document Similarity Check with passed Threshold (#6845) 2023-07-13 01:30:47 -04:00
tests Add new types of document transformers (#7379) 2023-07-12 23:53:30 -04:00
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.gitattributes Update dev container (#6189) 2023-06-16 15:42:14 -07:00
.gitignore Fix make docs_build and related scripts (#7276) 2023-07-11 22:05:14 -04:00
.gitmodules Doc refactor (#6300) 2023-06-16 11:52:56 -07:00
.readthedocs.yaml Page per class-style api reference (#6560) 2023-06-30 09:23:32 -07:00
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Dockerfile make ARG POETRY_HOME available in multistage (#3882) 2023-05-01 20:57:41 -07:00
LICENSE
Makefile codespell: workflow, config + some (quite a few) typos fixed (#6785) 2023-07-12 16:20:08 -04:00
poetry.lock WhyLabsCallbackHandler updates (#7621) 2023-07-12 23:46:56 -04:00
poetry.toml
pyproject.toml WhyLabsCallbackHandler updates (#7621) 2023-07-12 23:46:56 -04:00
README.md Del linkcheck readme (#6317) 2023-06-16 16:18:45 -07:00

🦜🔗 LangChain

Building applications with LLMs through composability

Release Notes lint test Downloads License: MIT Twitter Open in Dev Containers Open in GitHub Codespaces GitHub star chart Dependency Status Open Issues

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 comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.

Quick Install

pip install langchain or 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

💬 Chatbots

🤖 Agents

📖 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.