reqs_embed
Stores a requirement's embedding vector in the local tracker for later similarity searches via reqs_search_similar.
Instructions
Store an embedding vector for a requirement.
YOU compute the embedding, in your own process, and pass the finished numbers here. This tool never receives the requirement's text. codebugs stores the vector in its own local SQLite file and sends it nowhere. (Scope, stated precisely rather than loudly, because a promise wider than its check is worse than no promise. The route above is the claim. A test enforces two narrower things beside it: this package's own source imports none of the socket-opening modules that test lists, and it imports nothing at all from outside the package and the standard library without a declared, reasoned entry — so a network client nobody anticipated is still refused. Neither says "codebugs cannot reach the network": the MCP transport your client is talking over is a separate layer, and it is not covered.)
Because there is no embedding provider inside codebugs, nothing here knows the "right" dimensionality — it is whatever the first stored vector had. So the vector is refused if it is empty, contains a non-number, contains NaN or infinity, or has a different number of components than the vectors already stored in this tracker. Each of those would otherwise break reqs_search_similar: a mismatched width makes it unable to score the other rows, and a NaN makes a row drop out of every result with no error at all.
Once a tracker holds vectors of one width you cannot switch embedding model: there is no clear-and-re-embed operation in this package. reqs_embedding_stats reports which widths are actually present.
Args:
req_id: Requirement ID
embedding: Float vector. Any dimensionality, but the SAME one for every requirement in a given tracker.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| req_id | Yes | ||
| embedding | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||