---
type: source
title: "The PROVEN Solution for Unbelievable RAG Performance (LightRAG Guide)"
description: "Enhancing traditional vector RAG with a knowledge graph via the open-source LightRAG framework dramatically improves retrieval accuracy, especially as knowledge bases scale."
youtube_id: Fx3J8k--U3E
url: https://www.youtube.com/watch?v=Fx3J8k--U3E
slug: the-proven-solution-for-unbelievable-rag-performance-lightrag-guide
published: 2025-04-06
duration: "0:23:05"
recency_rank: 102
raw: "../raw/the-proven-solution-for-unbelievable-rag-performance-lightrag-guide.md"
tags: [rag, knowledge-graphs, retrieval, lightrag, pydantic-ai]
created: 2026-07-21
updated: 2026-07-21
---

Cole tackles the most common question he gets: how to make RAG AI agents more accurate. Basic RAG only reaches roughly 35-75% accuracy on relevant-information retrieval, which is not good enough for real solutions, so simply wiring up a naive n8n or LangChain RAG pipeline will not cut it. His recommended fix is LightRAG, an open-source framework that both vectorizes documents (like traditional RAG) and builds a knowledge graph connecting the topics, ideas, and concepts across those documents for deeper contextual understanding.

LightRAG has three parts: initialize a RAG instance (choosing embedding plus LLM models), insert data with rag.insert (it auto-chunks and populates both the vector DB and the knowledge graph), and query with rag.query using a search mode. Modes range from naive (basic vector RAG) to hybrid to "mix," which searches both the vector store and the graph so you lose nothing from basic RAG and gain graph context. It supports any OpenAI-compatible API, Ollama for local models, and storage backends like Neo4j (graph) and Postgres with Apache AGE (both vector and graph). Its own benchmarks show it beating naive RAG and even Microsoft's heavier GraphRAG while being simpler and faster.

Cole demonstrates with a downloadable repo containing two Pydantic AI agents sharing the same knowledge base, the entire Pydantic AI docs pulled from a single llms.txt URL: a traditional RAG agent on ChromaDB and a LightRAG agent. Asked to build a Brave web-search agent, the ChromaDB agent hallucinates (uses DuckDuckGo instead of Brave from wrong retrieved context) while the LightRAG agent produces cleaner, correctly-scoped code. The LightRAG ingest script is only 53 lines versus 177 for the manual ChromaDB chunking and inserting, though the initial graph build takes about 20 minutes. One caveat: RAG and knowledge graphs struggle with constantly changing real-time data since you must re-ingest and recompute; for that Cole points to Graphiti, which maintains evolving relationships and historical context and powers Zep's memory layer. He closes noting LightRAG mostly shines at thousands-plus documents, and that agentic RAG, query expansion, and reranking are other accuracy strategies.

## Concepts covered

- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)
- [Traditional RAG](../concepts/traditional-rag.md)
- [Knowledge Graphs](../concepts/knowledge-graphs.md)
- [Hybrid Search](../concepts/hybrid-search.md)
- [Chunking](../concepts/chunking.md)
- [Vector Embeddings](../concepts/vector-embeddings.md)
- [Memory Systems](../concepts/memory-systems.md)
- [Agentic RAG](../concepts/agentic-rag.md)
- [Query Expansion](../concepts/query-expansion.md)
- [Reranking](../concepts/reranking.md)

## Entities

- [LightRAG](../entities/tools/lightrag.md)
- [Graphiti](../entities/tools/graphiti.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [Chroma](../entities/tools/chroma.md)
- [Neo4j](../entities/tools/neo4j.md)
- [Postgres](../entities/tools/postgres-pgvector.md)
- [OpenAI](../entities/organizations/openai.md)
- [Archon](../entities/tools/archon.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:00]** The core question: how to make RAG AI agents more accurate; basic RAG cited at 35-75% accuracy
- **[0:01:08]** Introduces LightRAG: vectorizes docs AND builds a knowledge graph connecting concepts
- **[0:02:35]** LightRAG's three parts: init RAG instance, rag.insert (auto-chunk both stores), rag.query with a mode
- **[0:03:20]** Search modes: naive (basic RAG), hybrid, and mix (vector + knowledge graph together)
- **[0:04:21]** Storage options: Neo4j for graph, Postgres with Apache AGE for both vector and graph
- **[0:05:08]** LightRAG benchmarks beat naive RAG and Microsoft's GraphRAG while simpler and faster
- **[0:07:55]** Downloadable repo: two agents (ChromaDB traditional RAG vs LightRAG) over the Pydantic AI docs
- **[0:08:53]** Knowledge base built from Pydantic AI llms.txt, all docs in one text file
- **[0:09:38]** Limitation: RAG struggles with real-time data, must re-ingest and recompute the graph
- **[0:09:57]** Graphiti (sponsor) for real-time evolving knowledge graphs; powers Zep's memory layer
- **[0:11:34]** ChromaDB agent hallucinates (uses DuckDuckGo not Brave) from wrong retrieved context
- **[0:12:53]** LightRAG agent produces cleaner, correctly-scoped Brave code, LightRAG wins
- **[0:16:33]** LightRAG ingest script is 53 lines vs 177 for manual ChromaDB chunking/insert
- **[0:22:27]** Other accuracy strategies to explore: agentic RAG, query expansion, reranking

## Transcript

[Raw transcript](../raw/the-proven-solution-for-unbelievable-rag-performance-lightrag-guide.md)
