:INFO
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.


:COUNTER.half
10,000 Docs
|
:COUNTER.half
Under 2s

:PATH
Ingest Phase
Collect your source documents. Run them through a chunking and embedding pipeline. Store vectors in a local or cloud vector database.

:PATH
Query Phase
Ask questions in natural language. The retriever fetches relevant chunks and the LLM generates a grounded answer with source references.

:PATH
Refine Phase
Improve retrieval quality by adjusting chunk size, overlap, and embedding model. Add metadata filtering for better precision.

:CHECKLIST
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

:NOTE
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.


:LINK
https://docs.llamaindex.ai
LlamaIndex documentation and quickstart guides