langchain

Introduce various applications using LangChain

Contents

• API
• LangChain
  • Models
  • Promts
  • Indexes
  • Memory
  • Chains
  • Agents
• Use case
  • Model Comparison
  • Text Generation
  • Chat Model
  • Q&A
  • Summary
  • Utility
• Reference

How to set up the API

Set the following environment variables

LANGCHAIN_HANDLER="langchain"
OPENAI_API_KEY=""
SERPAPI_API_KEY=""
HUGGINGFACEHUB_API_TOKEN=""
GPLACES_API_KEY=""

# 上記の内容を.envファイルに書き込み以下を実行
source .env

• OpenAI
• Google Search API
• News API
• The Movie Database API
• Hugging Face API
• CohereAI API
• Google Maps API

LangChain

Models

• LLMs

  • OpenAI
  • GPT4all
  • llama-cpp
  • RWKV-4

• text-embedding-models

  • Aleph Alpha
  • Jina

Prompts

Indexes

• document loader

  • Email
  • URL

• retrievers

  • ElasticSearch BM25
  • TF-IDF Retriever

• vectorstores

  • Qdrant
  • Zilliz

Memory

• Custom Agent with Tool Retrieval
• Custom LLM Agent

Chains

Agents

• Custom Agent with Tool Retrieval
• Custom LLM Agent

Use case

Model Comparison

• Model Comparison

Text Generation

• SequentialChain
• QA Generation

Chat Model

• simple chat model
  Simple chat model, you can even pretend to be something else and answer the questions.

• translate chat model
  Translate chat model

• search chat model
  Searching for information and answering questions through search chat model

• conversation chat model
  It is also possible to set up chat models for conversations, memory retention methods, etc.

• customize chat model
  Different ways to customize the chat model

Q&A

• QA for document
• QA for document with source

Summary

• text summary

Utility

• LLMMathChain
• PALChain
• LLMBashChaion
• LLMCheckerChain
• LLMRequestsChain
• OpenAIModerationChain

Reference

• https://python.langchain.com/en/latest/index.html