Building a RAG Knowledge Base for Research
Retrieval-augmented generation lets you query your own documents using natural language. LlamaIndex and LangChain make it possible to build a personal research assistant over any collection of PDFs, notes, or web pages.
10,000Docs
Under2s
Ingest Phase
Collect your source documents. Run them through a chunking and embedding pipeline. Store vectors in a local or cloud vector database.
Query Phase
Ask questions in natural language. The retriever fetches relevant chunks and the LLM generates a grounded answer with source references.
Refine Phase
Improve retrieval quality by adjusting chunk size, overlap, and embedding model. Add metadata filtering for better precision.
RAG Setup Checklist
Choose framework: LlamaIndex or LangChain
Select embedding model
Choose vector store: Chroma, Qdrant, or Pinecone
Ingest first batch of documents
Test with 10 research questions
Evaluate answer accuracy and source quality
Add new documents on a regular schedule
Chunk size has a big impact on retrieval quality. Start with 512 tokens and an overlap of 50 tokens. Smaller chunks improve precision; larger chunks improve context for complex answers.
