refactor(rag): simplify configuration and initialization

gptme.toml

@@ -1,2 +1,5 @@
 files = ["README.md", "Makefile"]
 #files = ["README.md", "Makefile", "gptme/cli.py", "docs/*.rst", "docs/*.md"]
+
+[rag]
+enabled = true

gptme/tools/_rag_context.py

@@ -4,9 +4,7 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from pathlib import Path
-from typing import (
-    Any,
-)
+from typing import Any
 
 from ..config import get_project_config
 from ..message import Message
@@ -64,7 +62,7 @@ def get_context(self, query: str, max_tokens: int = 1000) -> list[Context]:
 class RAGManager:
     """Manages RAG functionality for both context enhancement and tool use."""
 
-    def __init__(self, index_path: Path | None = None, collection: str | None = None):
+    def __init__(self):
         if not _HAS_RAG:
             raise ImportError("gptme-rag not installed")
 
@@ -73,10 +71,8 @@ def __init__(self, index_path: Path | None = None, collection: str | None = None
         self.config = config.rag if config and config.rag else {}
 
         # Use config values if not overridden by parameters
-        self.index_path = Path(
-            index_path or self.config.get("index_path", "~/.cache/gptme/rag")
-        ).expanduser()
-        self.collection = collection or self.config.get("collection", "default")
+        self.index_path = Path("~/.cache/gptme/rag").expanduser()
+        self.collection = "default"
 
         # Initialize the indexer
         self.indexer = gptme_rag.Indexer(

gptme/tools/rag.py

@@ -14,40 +14,26 @@
 Configure RAG in your ``gptme.toml``::
 
     [rag]
-    # Storage configuration
-    index_path = "~/.cache/gptme/rag"  # Where to store the index
-    collection = "gptme_docs"          # Collection name for documents
-
-    # Context enhancement settings
-    max_tokens = 2000                  # Maximum tokens for context window
-    auto_context = true               # Enable automatic context enhancement
-    min_relevance = 0.5               # Minimum relevance score for including context
-    max_results = 5                   # Maximum number of results to consider
-
-    # Cache configuration
-    [rag.cache]
-    max_embeddings = 10000            # Maximum number of cached embeddings
-    max_searches = 1000               # Maximum number of cached search results
-    embedding_ttl = 86400             # Embedding cache TTL in seconds (24h)
-    search_ttl = 3600                # Search cache TTL in seconds (1h)
+    enabled = true
 
 .. rubric:: Features
 
 1. Manual Search and Indexing
+
    - Index project documentation with ``rag_index``
    - Search indexed documents with ``rag_search``
    - Check index status with ``rag_status``
 
 2. Automatic Context Enhancement
+
    - Automatically adds relevant context to user messages
    - Retrieves semantically similar documents
    - Manages token budget to avoid context overflow
    - Preserves conversation flow with hidden context messages
 
 3. Performance Optimization
+
    - Intelligent caching system for embeddings and search results
-   - Configurable cache sizes and TTLs
-   - Automatic cache invalidation
    - Memory-efficient storage
 
 .. rubric:: Benefits
@@ -59,12 +45,13 @@
 """
 
 import logging
+from dataclasses import replace
 from pathlib import Path
 
 from ..config import get_project_config
 from ..util import get_project_dir
+from ._rag_context import _HAS_RAG, RAGManager
 from .base import ToolSpec, ToolUse
-from ._rag_context import RAGManager, _HAS_RAG
 
 logger = logging.getLogger(__name__)
 
@@ -119,20 +106,31 @@ def rag_status() -> str:
     return f"Index contains {rag_manager.get_document_count()} documents"
 
 
+_init_run = False
+
+
 def init() -> ToolSpec:
     """Initialize the RAG tool."""
-    if not _HAS_RAG:
+    global _init_run
+    if _init_run:
+        return tool
+    _init_run = True
+
+    if not tool.available:
         return tool
 
     project_dir = get_project_dir()
-    index_path = Path("~/.cache/gptme/rag").expanduser()
-    collection = "default"
     if project_dir and (config := get_project_config(project_dir)):
-        index_path = Path(config.rag.get("index_path", index_path)).expanduser()
-        collection = config.rag.get("collection", project_dir.name)
+        enabled = config.rag.get("enabled", False)
+        if not enabled:
+            logger.debug("RAG not enabled in the project configuration")
+            return replace(tool, available=False)
+    else:
+        logger.debug("Project configuration not found, not enabling")
+        return replace(tool, available=False)
 
     global rag_manager
-    rag_manager = RAGManager(index_path=index_path, collection=collection)
+    rag_manager = RAGManager()
     return tool