Document Q&A

This example demonstrates how to build a Retrieval-Augmented Generation (RAG) system that can answer questions from your documents with accurate, cited responses.

Example Workflow

Here's an example of a document Q&A workflow that retrieves relevant information and generates answers:

Key Features

• Document Loading: Load and process documents from various sources (PDFs, text files, etc.)
• Vector Search: Efficiently search through your document collection using semantic similarity
• Context Retrieval: Retrieve the most relevant document chunks for each query
• Source Citation: Provide citations and references to the source documents
• Accurate Answers: Generate answers based on your actual documents, not general knowledge

Workflow Components

1. QueryNode: Captures user questions about your documents
2. DocumentLoaderNode: Loads and processes documents (PDFs, text files, etc.)
3. KnowledgeBaseRetrievalNode: Searches your vector database for relevant document chunks
4. LanguageModelNode: Generates answers using the retrieved context and original query
5. ResponseNode: Returns the answer along with source citations

Use Cases

• Answer questions from technical documentation
• Provide information from company policies and procedures
• Query research papers and academic documents
• Search through legal documents and contracts
• Answer questions from product manuals and guides

Configuration Tips

Document Loading:
  - Support multiple file formats (PDF, TXT, DOCX, etc.)
  - Configure chunking strategy (size, overlap)
  - Set up metadata extraction

Vector Database:
  - Connect to your vector store (Pinecone, Weaviate, etc.)
  - Configure embedding model
  - Set similarity search parameters

Retrieval Settings:
  - Top K documents to retrieve
  - Similarity threshold
  - Re-ranking options

Response Generation:
  - Include source citations in prompts
  - Configure answer format
  - Set context window limits

Best Practices

• Chunk documents appropriately (typically 500-1000 tokens)
• Include metadata (page numbers, document titles) for better citations
• Use appropriate embedding models for your domain
• Implement re-ranking for better retrieval quality
• Test with various query types to optimize retrieval parameters