# Implementation Plan: RAG Support

## Phase 1: Foundation & Vector Store Integration
- [ ] Task: Define the RAG architecture and configuration schema.
    - [ ] Update `src/models.py` to include `RAGConfig` and `VectorStoreConfig`.
    - [ ] Implement configuration loading/saving in `AppController`.
- [ ] Task: Integrate a local vector store.
    - [ ] Add `chromadb` or `qdrant-client` to `requirements.txt`.
    - [ ] Create `src/rag_engine.py` to manage the vector database lifecycle (init, add, search, delete).
- [ ] Task: Implement embedding providers.
    - [ ] Implement Gemini embedding wrapper in `src/rag_engine.py`.
    - [ ] Implement local embedding wrapper (e.g., using `sentence-transformers`) in `src/rag_engine.py`.
- [ ] Task: Write unit tests for vector store operations and embedding generation.
- [ ] Task: Conductor - User Manual Verification 'Phase 1: Foundation & Vector Store' (Protocol in workflow.md)

## Phase 2: Indexing & Retrieval Logic
- [ ] Task: Implement the indexing pipeline.
    - [ ] Implement file chunking strategies (e.g., character-based, AST-aware) in `src/rag_engine.py`.
    - [ ] Create a background indexing task in `AppController`.
    - [ ] Implement auto-indexing logic triggered by Context Hub changes.
- [ ] Task: Implement the retrieval pipeline.
    - [ ] Implement similarity search with configurable top-k and threshold.
    - [ ] Implement "Native Retrieval" logic for Gemini (leveraging `ai_client.py`).
- [ ] Task: Update `ai_client.py` to support RAG.
    - [ ] Add a `retrieve_context()` step to the `send()` loop.
    - [ ] Format and inject retrieved fragments into the model's system prompt or context block.
- [ ] Task: Write integration tests for the indexing and retrieval flow.
- [ ] Task: Conductor - User Manual Verification 'Phase 2: Indexing & Retrieval Logic' (Protocol in workflow.md)

## Phase 3: GUI Integration & Visualization
- [ ] Task: Implement the RAG Settings panel in `src/gui_2.py`.
    - [ ] Add UI controls for choosing the RAG source, embedding model, and retrieval parameters.
    - [ ] Add a "Rebuild Index" button and status progress bar.
- [ ] Task: Implement retrieval visualization in the Discussion history.
    - [ ] Display "Retrieved Context" blocks with expandable summaries.
    - [ ] Add "Source" buttons to each block that open the file at the specific chunk's location.
- [ ] Task: Implement auto-start/indexing status indicators in the GUI.
- [ ] Task: Write visual regression tests or simulation scripts to verify the RAG UI components.
- [ ] Task: Conductor - User Manual Verification 'Phase 3: GUI Integration & Visualization' (Protocol in workflow.md)

## Phase 4: Refinement & Advanced RAG
- [ ] Task: Implement support for external RAG APIs/MCP servers.
    - [ ] Create a bridge in `src/rag_engine.py` to call external RAG tools via the MCP interface.
- [ ] Task: Optimize indexing performance for large projects (e.g., incremental updates, parallel chunking).
- [ ] Task: Perform a final end-to-end verification with a large codebase.
- [ ] Task: Conductor - User Manual Verification 'Phase 4: Refinement & Advanced RAG' (Protocol in workflow.md)