From 337c21ad1f014a322c8991714725632617e447ab Mon Sep 17 00:00:00 2001 From: Ed_ Date: Mon, 4 May 2026 05:39:21 -0400 Subject: [PATCH] conductor(plan): Mark Phase 1 complete and Phase 2 In Progress --- conductor/tracks/rag_support_20260308/plan.md | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/conductor/tracks/rag_support_20260308/plan.md b/conductor/tracks/rag_support_20260308/plan.md index 62fa3a9..b33fc97 100644 --- a/conductor/tracks/rag_support_20260308/plan.md +++ b/conductor/tracks/rag_support_20260308/plan.md @@ -1,29 +1,29 @@ # 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)