Semantic search: embed query, return top-k matching code chunks.

Each result carries score + payload (path, name, kind, line range, docstring, text).

Structural search: expand the graph from names up to hops away.

Returns neighbor nodes (callers/callees/imports) with hop distance. Requires the repo to have been ingested (graph.json present).

Ingest a repo: parse, chunk, embed + upsert to Qdrant, build + save graph.

Appends to the existing collection by default so multiple repos coexist; pass reset=True to drop + recreate the collection first. Every chunk is tagged with its repo name.

Return store stats: vector count (Qdrant) + node/edge counts (graph).

Self-diagnose prerequisites before any org-facing setup.

The onboarding agent calls this first to learn what is already healthy and what is missing, so it only asks the org about the things that actually need a human decision. Non-destructive: never writes, never creates a collection, never pulls a model.

Checks:

• Qdrant reachable on the configured URL.

• Target collection exists and its vector count.

• Ollama reachable and the configured embed model is pulled.

• Graph file exists with node and edge counts.

Returns: Per-check status (ok bool + detail) and an overall healthy flag.

Two-stage retrieval in one call: semantic search + graph expansion.

Runs vector search for query (top-k), takes the symbol names from those hits, expands the graph one hop from them, and returns a combined result: similar_chunks (the vector hits) plus related_symbols (graph neighbors).