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Local Knowledge Library Example

This example searches an auditable AI-infrastructure knowledge bundle containing News, Earnings, and a pre-V1 LegacyPaper tree. New paper ingestion uses PaperSemanticResult and LocalKnowledgeLibrary.put_paper(); the legacy type is retained only so this existing bundle remains readable.

Run

Put OPENAI_API_KEY in the repository .env, then run from the repository root:

python examples/library/semantic_search.py

The bundled SQLite database already contains six 1536-dimensional text-embedding-3-small document targets. Running the example embeds only the query, then uses LocalKnowledgeLibrary.open(), search(), and get() to show ranked evidence and tree paths.

Files

  • semantic_search.py is the user-facing query example.
  • data/ai_infrastructure.json is the canonical, reviewable source bundle.
  • data/ai_infrastructure.db is the generated SQLite library with canonical records and model-specific embeddings. Do not edit it directly.

Rebuild the bundle

Regenerate the database after changing the source JSON, library schema, retrieval-target rules, embedding model, or dimension:

python scripts/examples/build_ai_infrastructure_bundle.py

The build validates every source item as canonical QuantMind knowledge and removes an incomplete database if embedding generation fails.

See the library guide for storage, retrieval-grain, and financial-time semantics.