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Covers getting started, repositories, scanning, findings, configuration,
SBOM, code graph, impact analysis, DAST, AI chat, issue tracker integration,
Docker deployment, environment variables, Keycloak auth, and OpenTelemetry.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

docs/features/ai-chat.md

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+# 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