To keep the pipeline fast and prevent Gemma's context window from overflowing,
the REST API needs a highly efficient way to filter your existing routines
before the LLM orchestration layer ever sees them.
Here is the architectural blueprint for implementing a low-latency vector
search directly within your API.
1. Choose a Lightweight Embedding Model
You do not need a massive model to calculate similarity between code routines.
You need a fast, local embedding model that runs in milliseconds without
external API calls.
 * The Model: Use something like all-MiniLM-L6-v2 or nomic-embed-text. These
models convert text into dense vector arrays (usually 384 or 768 dimensions)
and are small enough to keep fully loaded in RAM.
 * What to Embed: Don't just embed the raw code of the existing routines.
Embed a concatenated string of the routine's name, description, input/output
signature, and core dependencies.
2. High-Performance Storage & Retrieval
You can skip bloated, off-the-shelf vector databases if you want maximum
throughput and minimal infrastructure overhead.
 * The Native Approach: If your REST endpoints run on FastAPI or Node.js, you
can manage the index in-memory. For a catalog of a few thousand routines, an
HNSW (Hierarchical Navigable Small World) index loaded via a library like
FAISS is incredibly fast.
 * The Custom Engine Approach: If you are managing your data through a custom
C-based columnar database engine, adding vector search is just a matter of
adding a new column type for float32 arrays. By leveraging POSIX mmap to map
the vector column directly into memory and applying AVX2 vectorization to
handle the dot-product (cosine similarity) math, you can scan hundreds of
thousands of candidate embeddings in fractions of a millisecond.
3. The API Routing Logic
When the REST API receives a request to fetch candidates, the workflow should
execute like this:
 * Parse the Spec: The API receives the new idea/need (ideally formatted as a
structured Markdown specification or AST node).
 * Live Embedding: The API passes this text through the local embedding model
to generate a single query vector.
 * Similarity Scan: The API calculates the cosine similarity 
   between the query vector and the pre-computed vectors of all existing
routines.
 * Threshold & Truncate: Sort the results by similarity score. Drop anything
below a strict relevance threshold (e.g., < 0.65), and strictly truncate the
list to a maximum of 15 to 25 candidates.
 * Return Payload: The API returns only the text descriptions and IDs of these
top candidates to the Gemma cluster for the final semantic evaluation.