Semantic search

The tutorial catalogue is split into ~850 heading-level passages, embedded with a local fastembed model (ONNX, CPU, no API keys), and stored in an embedded Qdrant index — a real vector database that runs in-process, with no server to operate.

Build the index once (downloads the embedding model on first run):

pip install "pyxel-config-core[rag]"
pyxel-config index

(pyxel-config-index, the older spelling, still works: it prints a deprecation notice and forwards to the same command.)

Then query it:

from pyxel_config_core import semantic_search

for m in semantic_search("how do I add dark current to the detector", limit=5):
    print(f"{m.score:.3f}  {m.group}/{m.model}  §{m.section}")

Once the index is built, the library’s search_tutorials(query) uses it by default and falls back to keyword search if the index or the RAG extra is absent; pass semantic=False to force keywords or semantic=True to insist on the index. The MCP tool search_pyxel_tutorials is keyword search unless it is called with semantic=true, so an assistant gets the same answer on every machine regardless of what has been built there. Point the index elsewhere with PYXEL_CONFIG_CORE_INDEX=/path/to/dir.

Scale note: at ~850 passages a brute-force index would also work; Qdrant is used because it is a standard vector store, scales cleanly, and here runs embedded — so the retrieval design is production-shaped without a production dependency.