teddynote-langchain-rag

What problem does it solve?

This Skill provides a framework for public-safe reasoning about RAG and LangChain pipelines, covering document loading, splitting, embedding, vector stores, retrievers, prompts, chains, evaluation, deployment, and troubleshooting.

Core Features & Use Cases

• Document Loading and Splitting: Efficiently load and split documents into manageable chunks.
• Embedding and Retrieval: Utilize various embedding models and retrieval strategies.
• Prompt Design and Chains: Craft prompts and define chains for effective interaction.
• Evaluation and Deployment: Monitor and troubleshoot the RAG and LangChain pipeline.
• Use Case: Imagine you need to build a search engine that can retrieve relevant information from a large corpus of documents. Use this Skill to construct the pipeline, evaluate its performance, and deploy it for production use.

Quick Start

Load the references and start the LangChain pipeline with the following command: langchain_rag_load_references