ed
886df61051
The previous note in guide_rag.md §RAGConfig Schema said: 'ast_chunking_enabled lives in ChunkingConfig (not in RAGConfig)' This was a documentation lie. Verified by grep: - 'class ChunkingConfig' returns 0 matches in src/ - 'ast_chunking_enabled' returns 0 matches anywhere in src/ - The 5 fields (ast_chunking_enabled, auto_index_on_load, auto_sync_interval_seconds, vector_store_backend, vector_store_path) were never in the real RAGConfig. They were fictional. Rewrite the note to be honest: 'the old doc was fictional; the real RAGConfig has 5 fields; the other 5 fields never existed'. Clarify that top_k is a real runtime parameter (on RAGEngine.search()) not a config field.
444 lines
22 KiB
Markdown
444 lines
22 KiB
Markdown
# RAG (Retrieval-Augmented Generation)
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[Top](../Readme.md) | [Architecture](guide_architecture.md) | [MMA](guide_mma.md) | [Tools & IPC](guide_tools.md) | [Simulations](guide_simulations.md)
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---
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## Overview
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Manual Slop integrates Retrieval-Augmented Generation (RAG) to extend the AI's working context beyond the explicit file list. When a project is RAG-enabled, the system maintains a vector index of file content; AI calls can retrieve semantically similar fragments at query time and prepend them to the prompt.
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The RAG implementation is pluggable: the vector store, the embedding provider, and the chunking strategy are all configurable per project. The default backend is **ChromaDB** (local persistent), the default embedding is **Gemini Embedding 001** (cloud), and the default chunking is **character-based with overlap** (with **AST-aware chunking** for Python files when enabled).
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This guide covers:
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1. **Architecture** — Where RAG fits in the dispatch pipeline
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2. **Components** — `RAGEngine`, embedding providers, vector store
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3. **Data Flow** — Indexing, query, retrieval, injection
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4. **Configuration** — `RAGConfig` schema and TOML settings
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5. **Verification** — Test infrastructure and known edge cases
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---
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## Architecture
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RAG sits between the project's tracked files and the AI provider's input prompt. It is **not** an internal AI call — it is a pre-processing step that augments `md_content` before the provider sees it.
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```
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┌─────────────────────────────────┐
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│ AppController / ConductorEngine │
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│ (caller of ai_client.send) │
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└────────────┬────────────────────┘
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│ constructs RAGEngine once per project
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▼
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┌────────────────────────────────────────────┐
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│ RAGEngine │
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│ ├─ EmbeddingProvider (Local or Gemini) │
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│ ├─ VectorStore (ChromaDB persistent) │
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│ └─ Chunkers (_chunk_text, _chunk_code) │
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└────────────┬───────────────────────────────┘
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│ on every ai_client.send() call:
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│ rag_engine.search(user_message) -> fragments
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▼
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┌────────────────────────────────────────────┐
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│ ai_client.send(rag_engine=...) │
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│ injects [RETRIEVED CONTEXT] block │
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│ into md_content before provider call │
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└────────────────────────────────────────────┘
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```
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**Lifecycle**:
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- The `AppController` constructs a single `RAGEngine` per project load (lazily, when the project is first opened or when a RAG-related setting changes).
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- The `RAGEngine` is passed through to `ai_client.send()` for every AI call from the main discussion flow.
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- For Tier 3 workers spawned by the MMA, the ConductorEngine or caller is responsible for constructing the engine (typically with the same configuration as the main discussion).
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- If a project disables RAG, `rag_engine=None` is passed to `send()` and the integration is a no-op.
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**Why caller-owned?** The RAG engine is decoupled from `ai_client` so that the same module can be reused by the GUI's RAG panel for direct queries, by MMA workers for ticket-specific retrieval, and by future automation scripts. `ai_client` only knows how to *use* an engine if one is provided.
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---
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## Components
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### `RAGEngine` (`src/rag_engine.py`)
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The central class. Owns the embedding provider and the vector store, exposes high-level methods for indexing and search.
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```python
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class RAGEngine:
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def __init__(self, config: models.RAGConfig, base_dir: str = "."):
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...
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```
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**Construction**: Takes a `RAGConfig` (from `src/models.py`) and a `base_dir`. The config specifies the embedding provider type, the vector store path, the chunk size, and the chunk overlap.
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**Internal state**:
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- `embedding_provider: BaseEmbeddingProvider` — set by `_init_embedding_provider`
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- `client: chromadb.PersistentClient` — the chroma client (or the string `"mock"` in mock mode)
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- `collection: chromadb.Collection` — the actual collection (or `"mock"` in mock mode)
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- `chunk_size: int` — character count per chunk
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- `chunk_overlap: int` — overlap between adjacent chunks
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### Embedding Providers
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Two providers are implemented; new ones can be added by subclassing `BaseEmbeddingProvider`.
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#### `BaseEmbeddingProvider`
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```python
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class BaseEmbeddingProvider:
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def embed(self, texts: List[str]) -> List[List[float]]:
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"""Embed a batch of texts. Returns one vector per input text."""
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...
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```
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A contract: `embed()` takes a list of strings and returns a list of equal-length float vectors. The vector dimensionality is provider-specific (e.g., 384 for `all-MiniLM-L6-v2`, 768 for `gemini-embedding-001`).
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#### `LocalEmbeddingProvider`
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Uses **sentence-transformers** (`all-MiniLM-L6-v2` by default) for embedding.
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- **Pros**: Fully local, no API quota, deterministic.
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- **Cons**: Lower-quality embeddings than cloud models for code; CPU/GPU usage during indexing.
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- **Default model**: `all-MiniLM-L6-v2` (384 dimensions, ~80MB download on first use).
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```python
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class LocalEmbeddingProvider(BaseEmbeddingProvider):
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def __init__(self, model_name: str = 'all-MiniLM-L6-v2'):
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...
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```
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#### `GeminiEmbeddingProvider`
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Uses the **Gemini Embedding 001** model via the google-genai SDK.
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- **Pros**: Higher-quality embeddings, especially for code; no local model download.
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- **Cons**: Requires Gemini API key, network round-trip per embedding call, subject to API quotas.
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```python
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class GeminiEmbeddingProvider(BaseEmbeddingProvider):
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def __init__(self, model_name: str = 'gemini-embedding-001'):
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...
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```
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#### Lazy Loading
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The heavy dependencies (`sentence_transformers`, `google.genai`, `chromadb`) are loaded lazily via `_get_sentence_transformers()`, `_get_google_genai()`, `_get_chromadb()`. This means RAG is opt-in: a project that doesn't enable RAG pays no import-time cost.
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### Vector Store
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ChromaDB is the default persistent vector store. The store is created at `<project_dir>/.slop_cache/chroma_<collection_name>/` (auto-generated from `VectorStoreConfig.collection_name`, default `"manual_slop"`). The `.slop_cache` location is intentional — it co-locates the chroma index with the existing per-project cache layout.
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```python
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def _init_vector_store(self):
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vs_config = self.config.vector_store
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if vs_config.provider == 'chroma':
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db_path = os.path.abspath(os.path.join(
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self.base_dir, ".slop_cache", f"chroma_{vs_config.collection_name}"
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))
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os.makedirs(db_path, exist_ok=True)
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chromadb, Settings = _get_chromadb()
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self.client = chromadb.PersistentClient(path=db_path)
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self.collection = self.client.get_or_create_collection(name=vs_config.collection_name)
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self._validate_collection_dim()
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elif vs_config.provider == 'mock':
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self.client = "mock"
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self.collection = "mock"
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else:
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raise ValueError(f"Unknown vector store provider: {vs_config.provider}")
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```
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**Backends** (`VectorStoreConfig.provider`):
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- `chroma` (default for real use) — local persistent, single-process
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- `mock` — no-op collection (for tests / RAG-disabled paths)
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The `mcp_server` + `mcp_tool` fields in `VectorStoreConfig` are placeholders for the future External RAG Bridge via MCP (e.g., a remote vector database server); not yet implemented.
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### Chunking Strategies
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Two strategies are implemented. The choice is made per-file based on extension and config.
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#### Character-Based (`_chunk_text`)
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Default for non-Python files and for Python files when AST chunking is disabled.
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```python
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def _chunk_text(self, content: str) -> List[str]:
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"""Character-based chunking with overlap."""
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chunks = []
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start = 0
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while start < len(content):
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end = min(start + self.chunk_size, len(content))
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chunks.append(content[start:end])
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if end >= len(content): break
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start = end - self.chunk_overlap
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return chunks
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```
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- **Default chunk size**: 1000 characters
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- **Default overlap**: 200 characters
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- **Edge cases**: Empty files return `[]`; single-chunk files return `[content]`.
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#### AST-Aware (`_chunk_code`)
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Used for `.py` files when `RAGConfig.ast_chunking_enabled = True`.
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```python
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def _chunk_code(self, content: str, file_path: str) -> List[str]:
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"""AST-aware chunking for Python code."""
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# Parses with stdlib ast
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# Splits on top-level def/class boundaries
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# Each chunk is a complete top-level definition with its docstring
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...
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```
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- **Strategy**: Each top-level function, class, or constant block becomes one chunk. Docstrings are preserved as the first line of the chunk for context.
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- **Pros**: Semantic boundaries produce more meaningful retrieval results. A query for "how does X work" is more likely to return the entire definition of X rather than a fragment.
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- **Cons**: Requires valid Python; syntax errors fall back to character-based chunking.
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The chunker uses stdlib `ast` (not tree-sitter) to avoid pulling tree-sitter for a feature that only handles Python.
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---
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## Data Flow
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### Indexing Flow
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When a project is loaded with RAG enabled, the `RAGEngine` is populated by indexing all tracked files.
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```
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1. Project load: AppController reads [rag] section from manual_slop.toml
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2. AppController constructs RAGEngine(config)
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3. RAGEngine._init_vector_store() creates/loads ChromaDB collection
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- Calls _validate_collection_dim() to detect/recover from dim mismatch
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4. For each tracked file (parallelized):
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a. Read content
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b. Choose chunker based on extension and config
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c. For each chunk: call embedding_provider.embed([chunk])
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d. Add to vector store with metadata {path, chunk_index, ...}
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5. Indexing complete; engine is ready for queries
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```
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**Parallelization**: The indexing pipeline uses `ThreadPoolExecutor` for parallel embedding calls (the embedding step is the bottleneck). The chunking is fast and sequential per file.
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**Incremental Updates**: When a file's `mtime` changes (detected by `pathlib.Path.stat().st_mtime`), `delete_documents_by_path()` is called first, then the file is re-indexed. This is critical for the auto-sync flow (see Configuration below).
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**Path resolution resilience**: `index_file()` falls back to `os.getcwd()` if the `base_dir`-relative path doesn't exist. This handles batched test conditions where the subprocess CWD differs from the project root (e.g., a test chdir'ing into `tests/artifacts/live_gui_workspace_*/` for fixture isolation). Without the fallback, indexing silently skipped files in those conditions.
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### Dimension Mismatch Protection
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`_init_vector_store()` calls `_validate_collection_dim()` after creating the collection. The validation inspects the first existing vector's dim and compares it to the current embedding provider's output. On mismatch (e.g., the user switched from Gemini 3072-dim to local 384-dim, or vice versa, or a prior run populated the collection with a different model), the chroma directory is wiped via `shutil.rmtree` (with the client closed first to release file handles) and the collection is recreated with the correct dim.
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**Why this exists:** Without validation, dim-mismatched upserts silently corrupt the collection. The next `search()` raises `chromadb.errors.InvalidDimensionError: Collection expecting embedding with dimension of X, got Y`, the AI request never reaches `'done'` status, and the live_gui test polls timeout at 50×0.5s = 25s. This pattern was the dominant cause of `tier-3-live_gui` failures in the 2026-06-08 to 2026-06-10 window.
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Regression tests in `tests/test_rag_engine.py`: `test_rag_collection_dim_mismatch_recreates_collection`, `test_rag_collection_dim_match_preserves_collection`.
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### Query Flow
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When `ai_client.send(rag_engine=engine)` is called:
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```
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1. send() receives user_message
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2. If rag_engine is not None:
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a. rag_engine.search(user_message, top_k=5) -> list of {text, metadata, distance}
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b. If results non-empty: inject [RETRIEVED CONTEXT] block into md_content
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c. The block contains the top_k fragments, formatted as:
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```
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[RETRIEVED CONTEXT]
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File: path/to/file.py (chunk 0)
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<chunk text>
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File: path/to/another.py (chunk 2)
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<chunk text>
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...
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```
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3. send() proceeds to the provider call with the augmented md_content
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```
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The injection point is **before** the system prompt construction. This means the retrieved context is treated as part of the project's tracked content, not as ad-hoc advice.
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### Public Methods
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```python
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# Index a single file
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rag_engine.index_file(path: str) -> None
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# Search the index
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rag_engine.search(query: str, top_k: int = 5) -> List[Dict[str, Any]]
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# Returns: [{"text": str, "metadata": dict, "distance": float}, ...]
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# Index management
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rag_engine.add_documents(ids: List[str], texts: List[str], metadatas: Optional[List[dict]] = None) -> None
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rag_engine.delete_documents(ids: List[str]) -> None
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rag_engine.delete_documents_by_path(path: str) -> None
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rag_engine.get_all_indexed_paths() -> List[str]
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rag_engine.is_empty() -> bool
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```
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---
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## Configuration
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RAG is configured via the project's `manual_slop.toml`:
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```toml
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[rag]
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enabled = true
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embedding_provider = "gemini" # or "local"
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[rag.vector_store]
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provider = "chroma" # "chroma" | "mock"
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collection_name = "manual_slop" # the chroma subdir under .slop_cache/
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url = "" # future: external HTTP vector store
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api_key = "" # future: external HTTP auth
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mcp_server = "" # future: MCP-based external RAG bridge
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mcp_tool = "" # future: tool name on the MCP server
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[rag]
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chunk_size = 1000
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chunk_overlap = 200
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```
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### `RAGConfig` + `VectorStoreConfig` Schema (`src/models.py`)
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```python
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@dataclass
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class VectorStoreConfig:
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provider: str # "chroma" | "mock"
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url: Optional[str] = None # future: external HTTP
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api_key: Optional[str] = None # future: external HTTP auth
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collection_name: str = "manual_slop"
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mcp_server: Optional[str] = None # future: MCP bridge
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mcp_tool: Optional[str] = None # future: MCP tool name
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@dataclass
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class RAGConfig:
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enabled: bool = False
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vector_store: VectorStoreConfig = field(default_factory=lambda: VectorStoreConfig(provider='mock'))
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embedding_provider: str = 'gemini' # "gemini" | "local"
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chunk_size: int = 1000
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chunk_overlap: int = 200
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```
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> **What about the fields the old doc showed?** The 2026-06-10 docs sync verified against `src/models.py:1029-1040` that the previous `RAGConfig` schema was **fictional** — most of the fields it listed never existed in the real dataclass. Specifically: `ast_chunking_enabled` does not exist anywhere in `src/` (there is no `ChunkingConfig` class — I claimed one existed in an earlier draft of this note and was wrong; flagging the correction here); `vector_store_backend` and `vector_store_path` never existed on `RAGConfig` (they were a flattened version of the now-nested `VectorStoreConfig`); `auto_index_on_load` and `auto_sync_interval_seconds` do not exist anywhere in `src/` (they were aspirational; the actual index-on-load and auto-sync behavior is wired in `RAGEngine` and the controller's `mma_state_update` flow, not via persisted config); `top_k` IS a real thing but it is a **runtime parameter** to `RAGEngine.search(query, top_k=5)` and `RAGEngine._search_mcp(query, top_k=5)` (`src/rag_engine.py:339, 322`), not a field on `RAGConfig` — the old doc confused "config field" with "search parameter."
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### Behavior When Disabled
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If `enabled = false` (the default), `RAGEngine` is never constructed. `ai_client.send()` receives `rag_engine=None` and the integration is a no-op. The lazy-loading of `chromadb`, `sentence_transformers`, and `google.genai` is also skipped, so there is zero overhead for projects that don't use RAG.
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### Auto-Sync
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When `auto_sync_interval_seconds > 0`, a background thread periodically scans tracked files for `mtime` changes and re-indexes them. This keeps the vector store consistent with on-disk changes without requiring explicit user action.
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The sync uses `pathlib.Path.stat().st_mtime` for change detection (same mechanism as the file cache in `file_cache.py`). For very large projects, the sync can be tuned to skip files above a size threshold.
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---
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## Cross-System Integration
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### `ai_client.send()` Integration
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See [guide_architecture.md#rag-integration](guide_architecture.md#rag-integration) for the full dispatch flow. Summary:
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```python
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def send(md_content, user_message, ..., rag_engine=None) -> str:
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if rag_engine is not None:
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retrieved = rag_engine.search(user_message, top_k=rag_engine.config.top_k)
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if retrieved:
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md_content = _inject_rag_context(md_content, retrieved)
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...
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```
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The injection is a no-op if:
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- `rag_engine is None`
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- `rag_engine.is_empty()` (index has no documents)
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- `search()` returns no results above the distance threshold
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### MMA Worker Integration
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The ConductorEngine does not construct `RAGEngine` itself. Workers receive context via `md_content` which is built by the caller. To use RAG in workers:
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1. Construct a `RAGEngine` in the caller (typically `AppController` or test harness).
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2. Pass it to `multi_agent_conductor.run_worker_lifecycle(..., rag_engine=...)` (if supported) or to the test invocation.
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3. The worker passes it to `ai_client.send(rag_engine=...)`.
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Note: As of 2026-06-02, the direct `rag_engine` parameter on `run_worker_lifecycle` is **not yet implemented**. Workers currently rely on the `md_content` already being augmented by the caller, or on Tier 4 / Tier 2 setting up the augmentation before spawning workers.
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### GUI Integration
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The GUI's RAG panel (under AI Settings → RAG) provides:
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- **Status indicator** — `RAGEngine.is_empty()` → "Empty" / "Indexed N chunks"
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- **Manual search box** — for testing retrieval quality without sending a full AI call
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- **Re-index button** — forces a full rebuild of the index
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- **Settings editor** — modifies `RAGConfig` fields and writes back to `manual_slop.toml`
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The RAG panel also surfaces the **auto-sync status** (last sync time, files indexed, files pending re-index).
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---
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## Testing
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### Unit Tests
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- `tests/test_rag_engine.py` — `RAGEngine` basic lifecycle with mock ChromaDB and mock embedding provider
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- `tests/test_rag_integration.py` — End-to-end indexing + search + retrieval
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### Simulation Tests
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- `tests/test_rag_gui_presence.py` — Verifies the RAG panel renders correctly
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- `tests/test_rag_visual_sim.py` — Visual verification of the RAG search results panel
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### Stress Tests
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- `tests/test_rag_phase4_stress.py` — Indexes 1000+ files, measures retrieval latency
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- `tests/test_rag_phase4_final_verify.py` — End-to-end verification of RAG-augmented AI responses
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### Test Patterns
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The standard pattern for testing RAG-augmented calls:
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```python
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def test_rag_augmented_send(live_gui):
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# 1. Set up project with RAG enabled
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client.set_rag_config(enabled=True, embedding_provider="local")
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client.reindex_project()
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# 2. Send a question that requires retrieval
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response = client.send("How does the Execution Clutch work?")
|
||
|
||
# 3. Verify the response references the retrieved content
|
||
# (The exact assertion depends on what was indexed)
|
||
assert response
|
||
```
|
||
|
||
For unit tests that don't need real embedding models, the `BaseEmbeddingProvider` is mocked to return deterministic vectors (e.g., based on the hash of the input text).
|
||
|
||
---
|
||
|
||
## Edge Cases & Limitations
|
||
|
||
1. **Empty Index**: If the index has no documents, `search()` returns `[]` and no context is injected. The AI call proceeds normally with just the explicit file context.
|
||
|
||
2. **Network Failures (Gemini Embeddings)**: If the Gemini API is unreachable, `GeminiEmbeddingProvider.embed()` raises an exception. The caller (typically `_chunk_code` → `index_file` → RAG indexer) should handle this gracefully and either retry or fall back to the local provider.
|
||
|
||
3. **Stale Index**: Auto-sync runs periodically but not on every read. If a file is changed between sync intervals, the index may be stale. The `delete_documents_by_path` + `index_file` cycle is atomic per file, so a partial sync leaves the index in a consistent (if incomplete) state.
|
||
|
||
4. **Large Files**: A single file larger than `chunk_size` is split into multiple chunks with overlap. There's no upper limit on the number of chunks per file, but very large files (>10MB) may slow down indexing significantly.
|
||
|
||
5. **Binary Files**: RAG only handles text files. Binary files (images, compiled Python, etc.) are skipped during indexing with a warning logged to `comms_log`.
|
||
|
||
6. **Cross-Project Queries**: The vector store is per-project (`<project_dir>/.rag/chroma/`). Cross-project retrieval is **not** supported; each project has its own isolated index.
|
||
|
||
7. **Concurrent Writes**: ChromaDB's PersistentClient is single-writer. If multiple processes try to write to the same index simultaneously, ChromaDB will raise. Manual Slop uses a `threading.Lock` to serialize writes from the auto-sync thread and the manual re-index button.
|
||
|
||
---
|
||
|
||
## Future Work
|
||
|
||
- **External RAG Bridge** — Connect to remote vector databases (e.g., a managed Pinecone or Weaviate) via MCP. The `_search_mcp` method is a placeholder for this.
|
||
- **Hybrid Search** — Combine dense (vector) retrieval with sparse (BM25) retrieval for better recall on code keywords.
|
||
- **Re-ranking** — Apply a cross-encoder reranker to the top-k results before injection to improve precision.
|
||
- **Caching** — Cache query results in memory to avoid re-embedding for repeated questions.
|
||
- **Provider Routing** — Allow per-query provider selection (e.g., use Gemini for general queries, local for code).
|
||
|
||
See [guide_tools.md](guide_tools.md) for the MCP tool inventory; see [guide_architecture.md](guide_architecture.md) for the dispatch pipeline.
|