247 lines
7.5 KiB
Python
247 lines
7.5 KiB
Python
import os
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import sys
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import asyncio
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import json
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from typing import List, Dict, Any, Optional
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import chromadb
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from chromadb.config import Settings
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from src import models
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from src import mcp_client
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try:
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from sentence_transformers import SentenceTransformer
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except ImportError:
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SentenceTransformer = None
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from google import genai
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from google.genai import types
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from src import ai_client
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class BaseEmbeddingProvider:
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def embed(self, texts: List[str]) -> List[List[float]]:
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raise NotImplementedError()
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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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if SentenceTransformer is None:
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raise ImportError("sentence-transformers is not installed")
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self.model = SentenceTransformer(model_name)
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def embed(self, texts: List[str]) -> List[List[float]]:
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embeddings = self.model.encode(texts)
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return embeddings.tolist()
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class GeminiEmbeddingProvider(BaseEmbeddingProvider):
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def __init__(self, model_name: str = 'text-embedding-004'):
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self.model_name = model_name
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def embed(self, texts: List[str]) -> List[List[float]]:
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ai_client._ensure_gemini_client()
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client = ai_client._gemini_client
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if not client:
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raise ValueError("Gemini client not initialized")
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# For text-embedding-004, we can embed a batch
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res = client.models.embed_content(
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model=self.model_name,
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contents=texts,
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config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")
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)
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return [e.values for e in res.embeddings]
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class RAGEngine:
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def __init__(self, config: models.RAGConfig, base_dir: str = "."):
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self.config = config
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self.base_dir = base_dir
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self.client = None
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self.collection = None
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self.embedding_provider = None
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if not self.config.enabled:
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return
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self._init_embedding_provider()
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self._init_vector_store()
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def _init_embedding_provider(self):
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if self.config.embedding_provider == 'gemini':
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self.embedding_provider = GeminiEmbeddingProvider()
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elif self.config.embedding_provider == 'local':
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self.embedding_provider = LocalEmbeddingProvider()
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else:
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raise ValueError(f"Unknown embedding provider: {self.config.embedding_provider}")
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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.join(self.base_dir, ".slop_cache", "chroma_db")
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os.makedirs(db_path, exist_ok=True)
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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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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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def is_empty(self) -> bool:
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if not self.config.enabled:
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return True
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if self.config.vector_store.provider == 'mock' or self.collection == "mock":
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return True
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if self.collection is None:
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return True
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return self.collection.count() == 0
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def add_documents(self, ids: List[str], texts: List[str], metadatas: Optional[List[Dict[str, Any]]] = None):
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if not self.config.enabled or self.collection == "mock":
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return
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embeddings = self.embedding_provider.embed(texts)
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self.collection.upsert(
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ids=ids,
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embeddings=embeddings,
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documents=texts,
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metadatas=metadatas
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)
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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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if not content:
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return chunks
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chunk_size = self.config.chunk_size
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overlap = self.config.chunk_overlap
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start = 0
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while start < len(content):
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end = start + chunk_size
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chunks.append(content[start:end])
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if end >= len(content):
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break
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start += (chunk_size - overlap)
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return chunks
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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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try:
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from src.file_cache import ASTParser
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parser = ASTParser("python")
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tree = parser.parse(content)
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chunks = []
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# Capture classes and top-level functions
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for node in tree.root_node.children:
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if node.type in ("function_definition", "class_definition"):
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chunks.append(content[node.start_byte:node.end_byte])
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# Fallback if no structural chunks found or if file is small
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if not chunks or len(content) < self.config.chunk_size:
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return self._chunk_text(content)
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return chunks
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except Exception:
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return self._chunk_text(content)
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def index_file(self, file_path: str):
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"""Reads, chunks, and indexes a file into the vector store."""
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if not self.config.enabled or self.collection == "mock":
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return
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full_path = os.path.join(self.base_dir, file_path)
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if not os.path.exists(full_path):
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return
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try:
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mtime = os.path.getmtime(full_path)
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except Exception:
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return
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# Incremental check: see if we already have this file with the same mtime
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try:
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res = self.collection.get(where={"path": file_path}, limit=1, include=["metadatas"])
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if res and res["metadatas"] and res["metadatas"][0]:
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if res["metadatas"][0].get("mtime") == mtime:
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return
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except Exception:
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pass
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try:
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with open(full_path, "r", encoding="utf-8", errors="ignore") as f:
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content = f.read()
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except Exception:
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return
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# Remove old entries for this file
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self.collection.delete(where={"path": file_path})
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if file_path.lower().endswith(".py"):
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chunks = self._chunk_code(content, file_path)
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else:
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chunks = self._chunk_text(content)
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if not chunks:
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return
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ids = [f"{file_path}_{i}" for i in range(len(chunks))]
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metadatas = [{"path": file_path, "chunk": i, "mtime": mtime} for i in range(len(chunks))]
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self.add_documents(ids, chunks, metadatas)
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def _search_mcp(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
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async def _async_search_mcp():
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tool_name = self.config.vector_store.mcp_tool or "rag_search"
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args = {"query": query, "top_k": top_k}
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res_str = await mcp_client.async_dispatch(tool_name, args)
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try:
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data = json.loads(res_str)
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if isinstance(data, list):
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return data
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elif isinstance(data, dict) and "results" in data:
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return data["results"]
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return []
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except:
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return []
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return asyncio.run(_async_search_mcp())
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def search(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
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if not self.config.enabled:
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return []
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if self.config.vector_store.provider == 'mcp':
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return self._search_mcp(query, top_k)
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if self.collection == "mock":
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return []
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query_embedding = self.embedding_provider.embed([query])[0]
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results = self.collection.query(
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query_embeddings=[query_embedding],
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n_results=top_k
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)
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ret = []
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if results and results["ids"] and results["ids"][0]:
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for i in range(len(results["ids"][0])):
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ret.append({
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"id": results["ids"][0][i],
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"document": results["documents"][0][i],
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"metadata": results["metadatas"][0][i] if results["metadatas"] else {},
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"distance": results["distances"][0][i] if "distances" in results and results["distances"] else 0.0
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})
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return ret
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def delete_documents(self, ids: List[str]):
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if not self.config.enabled or self.collection == "mock":
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return
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self.collection.delete(ids=ids)
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def get_all_indexed_paths(self) -> List[str]:
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if not self.config.enabled or self.collection == "mock":
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return []
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res = self.collection.get(include=["metadatas"])
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if not res or not res["metadatas"]:
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return []
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return list(set(m.get("path") for m in res["metadatas"] if m.get("path")))
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def delete_documents_by_path(self, file_paths: List[str]):
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if not self.config.enabled or self.collection == "mock":
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return
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for path in file_paths:
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self.collection.delete(where={"path": path})
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