Semantic Search

name: semantic-search description: Semantic search the CasareRPA codebase using the local ChromaDB index and MCP server. Use when: locating code by intent, finding classes/functions without exact names, exploring patterns across layers.

Semantic Search

Use the local ChromaDB index for intent-based discovery. Prefer semantic search for concepts and rg for exact symbols.

Quick Start

1. Build or refresh the index: python scripts/index_codebase.py
2. Query via the MCP tool: python scripts/chroma_search_mcp.py
3. Call search_codebase(query, top_k) with a natural-language query.

Direct Python Query

from fastembed import TextEmbedding
from casare_rpa.infrastructure.ai.vector_store import get_vector_store

store = get_vector_store(persist_path=".chroma")
model = TextEmbedding("sentence-transformers/all-MiniLM-L6-v2")
emb = list(model.embed(["browser automation click"]))[0].tolist()
results = await store.search(
    query="browser automation click",
    collection="casare_codebase",
    top_k=5,
    query_embedding=emb,
)

Notes

• Index scope: src/ Python files only.
• Index location: .chroma/ in repo root.
• Re-run indexing if results look stale.