AI Chat (RAG)

The AI Chat feature lets you ask natural language questions about your codebase. It uses Retrieval-Augmented Generation (RAG) to find relevant code and provide accurate, source-referenced answers.

How It Works

1. Code graph is built for the repository (functions, classes, modules)
2. Embeddings are generated for each code symbol using an LLM embedding model
3. When you ask a question, your query is embedded and compared against code embeddings
4. The top 8 most relevant code snippets are retrieved
5. These snippets are sent as context to the LLM along with your question
6. The LLM generates a response grounded in your actual code

Getting Started

1. Select a Repository

Navigate to AI Chat in the sidebar. You'll see a grid of repository cards. Click one to open the chat interface.

2. Build Embeddings

Before chatting, you need to build embeddings for the repository:

1. Click Build Embeddings
2. Wait for the process to complete — a progress bar shows X/Y chunks
3. Once the status shows Embeddings ready, the chat input is enabled

INFO

Embedding builds require:

• A code graph already built for the repository (via the Graph feature)
• A configured embedding model (LITELLM_EMBED_MODEL)

The default model is text-embedding-3-small.

3. Ask Questions

Type your question in the input area and press Enter (or click Send). Examples:

• "How does authentication work in this codebase?"
• "What functions handle database connections?"
• "Explain the error handling pattern used in this project"
• "Where are the API routes defined?"
• "What does the process_scan function do?"

Understanding Responses

Answer

The AI response is a natural language answer to your question, grounded in the actual source code of your repository.

Source References

Below each response, you'll see source references showing exactly which code was used to generate the answer:

• Symbol name — The qualified name of the function/class/module
• File path — Where the code is located, with line range
• Code snippet — The first ~10 lines of the relevant code
• Relevance score — How closely the code matched your question (0.0 to 1.0)

Conversation Context

The chat maintains conversation history within a session. You can ask follow-up questions that reference previous answers. The system sends the last 10 messages as context to maintain coherence.

Configuration

Variable	Description	Default
LITELLM_URL	LiteLLM proxy URL	http://localhost:4000
LITELLM_API_KEY	API key for the LLM provider	—
LITELLM_MODEL	Model for chat responses	gpt-4o
LITELLM_EMBED_MODEL	Model for code embeddings	text-embedding-3-small

Tips

• Be specific — "How does the JWT validation middleware work?" is better than "Tell me about auth"
• Reference filenames — "What does server.rs do?" helps the retrieval find relevant code
• Ask about patterns — "What error handling pattern does this project use?" works well with RAG
• Rebuild after changes — If the repository has been updated significantly, rebuild embeddings to include new code