LightRAG for graph-assisted document retrieval

How it works

LightRAG builds graph information from document content and uses graph and vector retrieval in its query modes. That gives the application more than one way to gather context.

What you need

Prepare a small corpus and configure model and storage dependencies for indexing and querying. Record which model produces the graph so an index can be rebuilt after a change.

What to try first

Index a small collection with named entities that recur across files. Ask one fact question and one relationship question, then inspect the context each mode supplies.

Remove one source that supplied an important relationship. Check how the index changes and whether the graph continues to surface outdated evidence.

Where to look for failures

Extraction errors can create a misleading relationship and propagate into later queries. Track where entities and edges came from and budget for index construction.

Related Franklin pages

Use a question that needs evidence from several sources and another that needs an exact quotation. This helps show when building intermediate relationships or summaries is worth the effort.

• Build entity relationships and summaries that support questions across many documents. Microsoft GraphRAG for synthesizing a corpus
• Turn inputs into connected knowledge and retrieve that context for AI applications. Cognee for building memory from documents

For the full path from source documents to generated answers, start with Franklin’s glossary. Retrieval-augmented generation

Project documentation

• Current project README
• README at the reviewed revision