rag-implementation

What problem does it solve?

Large Language Models (LLMs) can "hallucinate" or lack up-to-date information. Retrieval-Augmented Generation (RAG) systems solve this by grounding LLM responses in external, factual knowledge bases, ensuring accuracy and reducing misinformation.

Core Features & Use Cases

• Vector Databases & Embeddings: Guides on selecting and configuring vector stores (Pinecone, Weaviate, Chroma) and embedding models.
• Retrieval Strategies: Covers dense, sparse, hybrid, multi-query, and contextual compression techniques.
• Chunking & Reranking: Provides strategies for optimal document chunking and improving retrieval quality with reranking.
• Use Case: Develop a chatbot that answers questions about your company's internal documentation, ensuring all responses are accurate and cite specific sources from your knowledge base.

Quick Start

Example: Basic RAG setup with Langchain

This example demonstrates loading documents, splitting, embedding, and querying.

from langchain.document_loaders import DirectoryLoader from langchain.text_splitters import RecursiveCharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chains import RetrievalQA from langchain.llms import OpenAI

Load, split, embed, and query documents

... (code omitted for brevity, see SKILL.md for full example)