RAG Implementation

Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources.

Overview

RAG (Retrieval-Augmented Generation) enhances AI applications by retrieving relevant information from knowledge bases and incorporating it into AI responses, reducing hallucinations and providing accurate, grounded answers.

When to Use

Use this skill when:

• Building Q&A systems over proprietary documents
• Creating chatbots with current, factual information
• Implementing semantic search with natural language queries
• Reducing hallucinations with grounded responses
• Enabling AI systems to access domain-specific knowledge
• Building documentation assistants
• Creating research tools with source citation
• Developing knowledge management systems

Instructions

Step 1: Choose Vector Database

Select an appropriate vector database based on your requirements:

1. For production scalability: Use Pinecone or Milvus
2. For open-source requirements: Use Weaviate or Qdrant
3. For local development: Use Chroma or FAISS
4. For hybrid search needs: Use Weaviate with BM25 support

Step 2: Select Embedding Model

Choose an embedding model based on your use case:

1. General purpose: text-embedding-ada-002 (OpenAI)
2. Fast and lightweight: all-MiniLM-L6-v2
3. Multilingual support: e5-large-v2
4. Best performance: bge-large-en-v1.5

Step 3: Implement Document Processing Pipeline

1. Load documents from your source (file system, database, API)
2. Clean and preprocess documents (remove formatting artifacts, normalize text)
3. Split documents into chunks using appropriate chunking strategy
4. Generate embeddings for each chunk
5. Store embeddings in your vector database with metadata

Step 4: Configure Retrieval Strategy

1. Dense Retrieval: Use semantic similarity via embeddings for most use cases
2. Hybrid Search: Combine dense + sparse retrieval for better coverage
3. **Metada...