examples/

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github.com/dotcommander/reliquary

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README ¶

Retrieval examples

Run an example from the repository root:

GOWORK=off go run ./examples/quickstart-rag

quickstart-rag: in-memory retrieval facade.
search-pipeline: batch search with RRF, external reranking, MMR, and typed explanations.
provider-openai-wiring: injected OpenAI embedding adapter.
postgres-index: explicit PostgreSQL retrieval migration.
rag-ingest-retrieve: manual hybrid retrieval and MMR.
retrieval-calibration-tune: retrieval evaluation and weight tuning.

Run the complete facade search pipeline after the quickstart:

GOWORK=off go run ./examples/search-pipeline

Directories ¶

Path	Synopsis
embed-server-quickstart Command embed-server-quickstart wires a local OpenAI-compatible embedding server into the reliquary facade.	Command embed-server-quickstart wires a local OpenAI-compatible embedding server into the reliquary facade.
internal
examplekit Package examplekit holds demo-only helpers for examples.	Package examplekit holds demo-only helpers for examples.
judgment-quickstart Command judgment-quickstart wires the Embed→Judge stack end to end.	Command judgment-quickstart wires the Embed→Judge stack end to end.
judgment-semantic-quickstart Command judgment-semantic-quickstart wires semantic judgment memory over a local OpenAI-compatible embedding server.	Command judgment-semantic-quickstart wires semantic judgment memory over a local OpenAI-compatible embedding server.
postgres-index
provider-openai-wiring
quickstart-rag Command quickstart-rag is the short, copyable retrieval path.	Command quickstart-rag is the short, copyable retrieval path.
rag-ingest-retrieve Command rag-ingest-retrieve demonstrates an end-to-end retrieval pipeline assembled from reliquary packages the way a real consumer would:	Command rag-ingest-retrieve demonstrates an end-to-end retrieval pipeline assembled from reliquary packages the way a real consumer would:
retrieval-calibration-tune Command retrieval-calibration-tune demonstrates the retrieval quality-control layer: captured runs, threshold checks, score-reference calibration, and deterministic weight tuning.	Command retrieval-calibration-tune demonstrates the retrieval quality-control layer: captured runs, threshold checks, score-reference calibration, and deterministic weight tuning.
search-pipeline

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