Codebase Search

Builds a persistent ChromaDB vector index over Python source files and enables semantic search with natural language queries.

Quick Start

1. Install the scripts

Copy scripts/code_chunker.py and scripts/code_index.py into your project. They have no dependencies beyond chromadb (install with pip install chromadb).

2. Build the index

import asyncio
from code_index import CodebaseIndex

index = CodebaseIndex(repo_root="/path/to/repo")
count = asyncio.run(index.build())
print(f"Indexed {count} symbols")

The index persists to {repo_root}/.codebase_index/ and survives restarts. Subsequent calls to build() are fast — only new/changed files are indexed.

3. Search

results = asyncio.run(index.search("token payment handling", top_k=5))
for r in results:
    print(f"[{r.score:.2f}] {r.symbol_name} ({r.symbol_type}) — {r.filepath}:{r.start_line}")

Convenience API (when integrated into a project)

If code_chunker.py and code_index.py are in the project as a module, use the singleton helper:

from prsm.compute.nwtn.corpus import search_codebase

results = await search_codebase("circuit breaker", top_k=3)

Key Options

Default excludes: __pycache__, .venv, migrations, tests, scripts, .git, node_modules, .codebase_index

How It Works

1. Chunking — CodeChunker uses Python's ast module to extract every top-level class and function from each .py file. Captures name, type, docstring, line numbers, and source.
2. Indexing — ChromaDB stores each chunk as a document: "{symbol_name}: {docstring or first 300 chars of source}". Uses ChromaDB's default embedding function (no API key needed).
3. Search — Cosine similarity query returns ranked SearchResult objects with filepath, symbol name, line numbers, docstring, and relevance score.

.gitignore

Always add .codebase_index/ to .gitignore — it's a local artifact, not source code.

Reference

See references/integration.md for integration patterns, including how to wire semantic search into sub-agent delegation prompts.