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conductor(plan): Mark Phase 1 complete and Phase 2 In Progress

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Edward R. Gonzalez
2026-05-04 05:39:21 -04:00
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conductor/tracks/rag_support_20260308/plan.md
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# 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 1: Foundation & Vector Store Integration [checkpoint: dd042d9]
- [x] Task: Define the RAG architecture and configuration schema. e80cd6b
- [x] Update `src/models.py` to include `RAGConfig` and `VectorStoreConfig`. e80cd6b
- [x] Implement configuration loading/saving in `AppController`. e80cd6b
- [x] Task: Integrate a local vector store. e80cd6b
- [x] Add `chromadb` or `qdrant-client` to `requirements.txt`. e80cd6b
- [x] Create `src/rag_engine.py` to manage the vector database lifecycle (init, add, search, delete). e80cd6b
- [x] Task: Implement embedding providers. e80cd6b
- [x] Implement Gemini embedding wrapper in `src/rag_engine.py`. e80cd6b
- [x] Implement local embedding wrapper (e.g., using `sentence-transformers`) in `src/rag_engine.py`. e80cd6b
- [x] Task: Write unit tests for vector store operations and embedding generation. e80cd6b
- [x] Task: Conductor - User Manual Verification 'Phase 1: Foundation & Vector Store' (Protocol in workflow.md) dd042d9
## 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: 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)