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Project ACI - Augmented Codebase Indexer

by apertureplus

A Python tool for semantic code search with precise line-level location results. Requires external Qdrant vector database which can be auto-started via Docker if not running. Configuration is done via .env file or environment variables.

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Repository-wide counts · Cached 2025-12-19

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Overview

The Project ACI - Augmented Codebase Indexer MCP server is a publicly available project. Review the upstream repository for installation instructions, supported tools, compatibility, permissions, and current maintenance status.

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Open the Project ACI - Augmented Codebase Indexer repository to read the latest documentation.

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FROM THE SOURCE

Repository README

Build-time snapshot · Retrieved 2026-10-05

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ACI — Augmented Codebase Indexer

Tests Python linux.do

Language: English | 简体中文

Ask your codebase a question. Get a precise answer — down to the line.

ACI indexes your code with embeddings and Tree-sitter AST parsing, then lets you search it with natural language. Results come back with exact file paths and line numbers, not just fuzzy matches.

$ aci search "function that validates JWT tokens"

src/auth/middleware.py:42  verify_token(token: str) -> Claims
src/auth/utils.py:118      decode_and_validate(raw: str) -> dict

Why ACI?

Most code search tools give you grep or a fuzzy filename match. ACI gives you semantic understanding:

  • You describe intent, it finds the implementation
  • Hybrid search combines embeddings with keyword/grep for precision
  • Multi-level indexing: raw chunks, function summaries, class summaries, file summaries
  • Incremental updates — only re-indexes what changed
  • Works with Python, JavaScript/TypeScript, Go, Java, C, C++

Get Started

# Install
uv sync

# Configure (add your embedding API key)
cp .env.example .env

# Index your codebase
aci index /path/to/your/project

# Search
aci search "error handling in the HTTP layer"

That's it. See Installation for full setup details.

Interfaces

Interface Command Use case
CLI aci <command> Day-to-day search and indexing
Interactive shell aci shell Iterative exploration sessions
HTTP API aci serve Integrate with other tools
MCP server aci-mcp LLM / agent integration

Documentation

MCP — Let Your LLM Search the Code

ACI ships a first-class MCP server so agents can index and search your codebase directly.

{
  "mcpServers": {
    "aci": {
      "command": "uv",
      "args": ["run", "aci-mcp"],
      "cwd": "/path/to/your/project"
    }
  }
}

For Docker-based deployment (recommended for agentic tools), see MCP Integration.

Requirements

  • Python 3.10+
  • Qdrant (auto-started locally via Docker, or point to Qdrant Cloud)
  • Any OpenAI-compatible embedding API (OpenAI, SiliconFlow, etc.)

Development governance: AGENTS.md