PageIndex RAG
No embeddings, no vector database, no infrastructure. Documents are tokenized into a BM25Okapi index persisted as a pickle file. Retrieval is pure lexical scoring — fast, deterministic, and runnable on a laptop.
Stage-by-Stage Data Flow Explorer
Select a phase from the controller below, then click individual step nodes to view their technical role, inputs, outputs, and mockup diagnostics data stream.
Indexing
Chunks are tokenized and scored into a classic BM25 index — saved as a single file.
> INGEST_STREAM: "financial_report_2026.pdf" (Size: 2.4 MB)
> DECODING_META: { mime: "application/pdf", pages: 12 }
> READOUT: "Ragiment Corp Annual Report 2026. EBITDA grew 18% to $4.6M. Product lines expanded by..."Best suited for
Small corpora and zero-infrastructure deployments — runnable entirely on a laptop.
Complexity
No database, no embeddings, no GPU — just a BM25 index serialized to a file. The simplest RAG you can ship.
Relevance today
A classic information-retrieval technique that's still genuinely useful for prototypes, edge/offline apps, and exact-keyword search.
Where it's used
Prototypes & demos
Stand up a working RAG pipeline in minutes with no database to provision.
Edge & offline apps
Run fully local — no network, no GPU, no external dependencies.
Compliance-restricted setups
No data leaves the machine; the index is a single file on disk.
Why it matters
- Zero infrastructure — no vector database, no embeddings, no GPU.
- Deterministic and instant; the BM25 index serializes to one pickle file.
- Excellent at exact-keyword, code, and identifier search.
Trade-offs & considerations
- Purely lexical — it misses paraphrases and semantic matches a vector search would catch.
- Doesn't scale to very large corpora as gracefully as vector retrieval.
- Relevance is keyword-frequency based; there is no semantic ranking.
Alternatives to consider
When PageIndex RAG isn't the right fit, reach for one of these instead.