LangChain RAG Agent with Memory

Build a Retrieval-Augmented Generation (RAG) agent using LangChain.

Components

1. Document Loading & Chunking

• Load documents from various sources
• Use RecursiveCharacterTextSplitter
• Optimal chunk size (e.g., 1000 chars with 200 overlap)

2. Vector Store

• Options: Pinecone, Chroma, Weaviate
• Store document embeddings
• Efficient similarity search

3. Embeddings

• Options: OpenAI, HuggingFace
• Convert text to vectors
• Consistent embedding model

4. Retrieval Chain

• Strategies:
  • MMR (Maximal Marginal Relevance) - Diverse results
  • Similarity search - Most relevant results
• Configure k (number of documents to retrieve)

5. Conversation Memory

• Use ConversationBufferMemory
• Maintain chat context
• Remember previous questions/answers

6. Prompt Template

• Custom system prompt
• Instruct model to:
  • Use provided context
  • Cite sources
  • Admit when unsure

7. Streaming Responses

• Real-time token streaming
• Better user experience
• Show progressive answers

8. Error Handling

• Fallback responses
• Handle API failures
• Rate limiting

9. Source Attribution

• Return source documents
• Include page numbers/URLs
• Transparent information sources

Implementation

Provide code for:

Indexing Phase

1. Load documents
2. Chunk documents
3. Create embeddings
4. Store in vector database

Query Phase

1. Accept user question
2. Retrieve relevant documents
3. Generate answer with context
4. Stream response
5. Return sources

Language

Use TypeScript or Python

Requirements

• Production-ready code
• Type safety
• Error handling
• Well-documented