codebase-rag

Search indexed code repositories semantically using Qdrant and OpenAI embeddings.

2|Updated Mar 5, 2026

Search Tags:#codebase #semantic search #embeddings #rag #code search #qdrant

Ready to run

npx skills add https://github.com/Art-of-Technology/agent-factory --skill codebase-rag

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: codebase-rag
Source: https://github.com/Art-of-Technology/agent-factory/tree/main/codebase-rag
Command: npx skills add https://github.com/Art-of-Technology/agent-factory --skill codebase-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables AI agents to semantically search through indexed code repositories, making it easy to find specific code implementations, understand architecture, and answer questions about where functionality resides.

Core Features & Use Cases

Semantic Code Search: Query your codebase using natural language.
Vector Database Integration: Indexes code into Qdrant for efficient similarity searches.
Use Case: An agent needs to understand how user authentication is handled across a large project. It can use this Skill to search for "how is user authentication implemented?" and receive relevant code snippets.

Quick Start

Use the codebase-rag skill to search for "how is risk score calculated" in the 'my-repo' collection.

Frequently Asked Questions about codebase-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I search a codebase using natural language queries?▼

Semantic code search lets you query indexed repositories using natural language. This Skill uses OpenAI embeddings and a Qdrant vector database to locate code implementations and answer where specific functionality resides within a project.

What do I need to set up semantic code search with Qdrant?▼

To set up semantic code search, you need a running Qdrant vector database instance, the codebase-rag CLI, and an OpenAI API key for generating embeddings. These components index repository code and execute similarity searches.

Can I find how a specific feature is implemented across a large repository?▼

Yes, you can find feature implementations by querying the indexed codebase. The Skill searches for natural language questions like "how is user authentication implemented?" and returns relevant code snippets showing architectural patterns and locations.

How does vector database integration work for code search?▼

Vector database integration for code search works by converting code snippets into OpenAI embeddings and storing them in Qdrant. When you search, the query is embedded and matched against stored vectors to retrieve similar code implementations.

What are the limitations of using embeddings for code search?▼

Using embeddings for code search requires an OpenAI API key and a Qdrant instance, adding external dependencies. The effectiveness depends on how well the codebase is indexed, and semantic matches may not always capture exact variable or function names.

Is semantic code search better than standard grep for understanding architecture?▼

Semantic code search outperforms standard grep for understanding architecture by matching intent rather than exact strings. While grep finds literal text, semantic search with embeddings answers conceptual questions about how functionality is structured across files.