![Build a RAG Knowledge Base for Research](https://cdn.slatesource.com/5/d/e/5de854de-0b69-4f4a-98d2-f18253119bdb.jpg)

# Build a RAG Knowledge Base for Research

- [Made in Slatesource](https://slatesource.com/u/kairenner/build-a-rag-knowledge-base-for-research-922)
- By [KaiRenner](https://slatesource.com/u/KaiRenner)
- Science & Technology
- Created on Mar 21, 2026

## 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.

**10,000** Docs

**Under** 2s

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

0%

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.

[LlamaIndex documentation and quickstart guides](https://docs.llamaindex.ai/?utm_source=slatesource)