Agentic and graph-based retrieval
The agent decides how to search; a knowledge graph answers the global questions.
Use it when
Multi-hop questions, comparisons across many documents, or questions about themes across a whole corpus where chunk retrieval alone returns fragments.
The parts, top to bottom
across every level
Hover or tap any part to see what it does. The light shows the order a request moves through.
What happens, in order
- 1Question analysed
- 2Agent chooses strategy: search, graph, SQL, or ask
- 3Runs several retrievals
- 4Verifies & consolidates evidence
- 5Answers with a reasoning trail
What we typically build it with
Trade-offs
Agentic retrieval costs about 3.3× the input tokens and 1.5× the latency of a strong enhanced pipeline, and only around one in ten queries benefits, so we route by question type. LazyGraphRAG indexes at roughly 0.1% of full GraphRAG's cost, which makes graph retrieval affordable for real corpora.
Problems this architecture solves
Generic problem statements with the flow and the outcomes the industry has documented.
Answers buried in ten systems
Policies live in SharePoint, decisions in email, procedures in Confluence and history in tickets. New staff take months to become useful and experts answer the same questions daily.
How the system works
- Connect sources
- Chunk with context, embed + index
- Hybrid retrieval + rerank
- Cited answer with permissions
- Feedback into evals
Outcome: Contextual chunking with hybrid search and reranking cuts retrieval failures by 67% versus plain vector search, which is the difference between an assistant people trust and one they abandon. Anthropic: Contextual Retrieval
ArchitectureOther retrieval patterns
Hybrid RAG with reranking and citations
Keyword plus vector search, fused and reranked, answered with sources.
Use it when: Questions over a body of documents, tickets or records where exact terms matter as much as meaning, and where every answer must be checkable and permission-aware.
Flow
- 1Ingest & chunk by structure
- 2Enrich with context & metadata
- 3Embed + keyword index
- 4Query: BM25 + vectors, fuse (RRF)
- 5Rerank top candidates
Visual document retrieval (late interaction)
Search scanned forms, slides, drawings and table-heavy PDFs as images, with no OCR pipeline to maintain.
Use it when: Corpora where layout carries meaning: engineering drawings, slide decks, scanned contracts, invoices and financial tables that OCR-based pipelines flatten and lose.
Flow
- 1Rasterise each page
- 2Encode into patch embeddings (ColPali / ColQwen)
- 3Store multi-vectors with MaxSim support
- 4Query encoded token by token
- 5Top pages passed to a vision model
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