Odysseus v1.0
This commit is contained in:
pewdiepie-archdaemon
496
src/rag_vector.py
Normal file
496
src/rag_vector.py
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@@ -0,0 +1,496 @@
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"""
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rag_vector.py
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Vector-based RAG using ChromaDB for storage and API-based embeddings.
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Features: persistent storage, hybrid search (vector + keyword), sentence-aware chunking,
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configurable embedding endpoint via EMBEDDING_URL env var.
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"""
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import os
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import re
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import logging
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import numpy as np
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from typing import List, Dict, Any, Optional, Set
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from pathlib import Path
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logger = logging.getLogger(__name__)
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DEFAULT_FILE_EXTENSIONS: Set[str] = {
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'.txt', '.md', '.py', '.json', '.yaml', '.yml',
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'.csv', '.html', '.css', '.js', '.pdf'
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}
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VECTOR_WEIGHT = 0.7
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KEYWORD_WEIGHT = 0.3
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COLLECTION_NAME = "odysseus_rag"
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class VectorRAG:
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"""RAG system using ChromaDB vector storage with hybrid search."""
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def __init__(self, persist_directory: str = "data/chroma"):
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self.persist_directory = persist_directory
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self._collection = None
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self._model = None
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self._healthy = False
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Path(self.persist_directory).mkdir(parents=True, exist_ok=True)
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self._initialize_system()
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# ------------------------------------------------------------------
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# Initialization
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# ------------------------------------------------------------------
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def _initialize_system(self) -> bool:
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try:
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from src.chroma_client import get_chroma_client
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from src.embeddings import get_embedding_client
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self._model = get_embedding_client()
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if self._model is None:
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raise RuntimeError("No embedding backend available")
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logger.info(f"Embedding: {self._model.url} model={self._model.model}")
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client = get_chroma_client()
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self._collection = client.get_or_create_collection(
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name=COLLECTION_NAME,
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metadata={"hnsw:space": "cosine"},
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)
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count = self._collection.count()
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logger.info(f"VectorRAG ready ({count} docs)")
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self._healthy = True
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return True
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except Exception as e:
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logger.error(f"VectorRAG init failed: {e}")
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self._healthy = False
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return False
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def _embed(self, texts: List[str]) -> List[List[float]]:
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vecs = self._model.encode(texts, normalize_embeddings=True)
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return np.array(vecs, dtype=np.float32).tolist()
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# ------------------------------------------------------------------
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# Properties
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# ------------------------------------------------------------------
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@property
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def healthy(self) -> bool:
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return self._healthy and self._collection is not None
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@property
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def collection(self):
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"""Expose the ChromaDB collection for direct access by personal_routes etc."""
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return self._collection
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# ------------------------------------------------------------------
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# Document operations
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# ------------------------------------------------------------------
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def add_document(self, text: str, metadata: Dict[str, Any]) -> bool:
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if not self.healthy:
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logger.error("Collection not initialized")
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return False
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if not text or not isinstance(text, str):
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return False
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if not metadata or not isinstance(metadata, dict):
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return False
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try:
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doc_id = f"doc_{hash(text) % 10**16}"
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# Check if already exists
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existing = self._collection.get(ids=[doc_id])
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if existing["ids"]:
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return True # already exists
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embeddings = self._embed([text])
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self._collection.add(
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ids=[doc_id],
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embeddings=embeddings,
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documents=[text],
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metadatas=[metadata],
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)
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return True
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except Exception as e:
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logger.error(f"add_document failed: {e}")
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return False
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def add_documents_batch(self, docs: List[tuple]) -> Dict[str, Any]:
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if not self.healthy:
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return {"success": False, "message": "Collection not initialized"}
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if not docs:
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return {"success": False, "message": "Empty document list"}
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valid = [
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(t, m) for t, m in docs
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if t and isinstance(t, str) and m and isinstance(m, dict)
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]
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if not valid:
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return {"success": False, "message": "No valid documents"}
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try:
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# Get existing IDs to avoid duplicates
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new_texts = []
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new_metas = []
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new_ids = []
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for t, m in valid:
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doc_id = f"doc_{hash(t) % 10**16}"
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existing = self._collection.get(ids=[doc_id])
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if not existing["ids"]:
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new_texts.append(t)
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new_metas.append(m)
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new_ids.append(doc_id)
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if new_texts:
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# Batch in chunks of 100
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for i in range(0, len(new_texts), 100):
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batch_texts = new_texts[i:i + 100]
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batch_ids = new_ids[i:i + 100]
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batch_metas = new_metas[i:i + 100]
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embeddings = self._embed(batch_texts)
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self._collection.add(
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ids=batch_ids,
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embeddings=embeddings,
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documents=batch_texts,
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metadatas=batch_metas,
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)
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return {
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"success": True,
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"added_count": len(new_texts),
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"total_count": len(docs),
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"failed_count": len(docs) - len(valid),
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}
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except Exception as e:
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logger.error(f"add_documents_batch failed: {e}")
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return {"success": False, "message": str(e)}
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# ------------------------------------------------------------------
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# Search — hybrid: vector similarity + keyword overlap
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# ------------------------------------------------------------------
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def search(self, query: str, k: int = 5, owner: Optional[str] = None) -> List[Dict[str, Any]]:
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if not self.healthy:
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return []
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if not query or not isinstance(query, str):
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return []
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if self._collection.count() == 0:
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return []
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try:
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# Fetch extra candidates when owner-filtering
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fetch_k = min(k * 3, max(k, 20), self._collection.count())
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if owner:
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fetch_k = min(fetch_k * 2, self._collection.count())
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query_embeddings = self._embed([query])
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# Use ChromaDB where filter for owner if specified
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where_filter = {"owner": owner} if owner else None
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results = self._collection.query(
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query_embeddings=query_embeddings,
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n_results=fetch_k,
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where=where_filter,
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include=["documents", "metadatas", "distances"],
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)
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query_words = set(query.lower().split())
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candidates = []
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for idx in range(len(results["ids"][0])):
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doc_id = results["ids"][0][idx]
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distance = results["distances"][0][idx]
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doc_text = results["documents"][0][idx]
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meta = results["metadatas"][0][idx]
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# ChromaDB cosine distance = 1 - cosine_similarity
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vector_sim = 1.0 - distance
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# Keyword overlap score
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doc_words = set(doc_text.lower().split())
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overlap = len(query_words & doc_words)
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keyword_score = overlap / len(query_words) if query_words else 0.0
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hybrid_score = (VECTOR_WEIGHT * vector_sim) + (KEYWORD_WEIGHT * keyword_score)
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candidates.append({
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"id": doc_id,
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"document": doc_text,
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"metadata": meta,
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"distance": round(distance, 4),
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"similarity": round(hybrid_score, 4),
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"vector_similarity": round(vector_sim, 4),
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"keyword_score": round(keyword_score, 4),
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})
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candidates.sort(key=lambda c: c["similarity"], reverse=True)
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top = candidates[:k]
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logger.info(f"Hybrid search for '{query[:60]}': {len(top)} results")
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return top
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except Exception as e:
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logger.error(f"search failed: {e}")
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return self._keyword_search_fallback(query, k, owner=owner)
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def _keyword_search_fallback(self, query: str, k: int = 5, owner: Optional[str] = None) -> List[Dict[str, Any]]:
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try:
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if self._collection.count() == 0:
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return []
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# Fetch all documents for keyword search fallback
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all_docs = self._collection.get(include=["documents", "metadatas"])
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if not all_docs["ids"]:
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return []
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query_words = query.lower().split()
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scored = []
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for i, doc in enumerate(all_docs["documents"]):
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meta = all_docs["metadatas"][i]
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if owner:
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doc_owner = meta.get("owner")
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if doc_owner and doc_owner != owner:
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continue
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doc_lower = doc.lower()
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score = sum(1 for w in query_words if w in doc_lower)
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if score > 0:
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scored.append({
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"id": all_docs["ids"][i],
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"document": doc,
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"metadata": meta,
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"distance": 0,
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"similarity": score,
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"search_type": "keyword_fallback",
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})
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scored.sort(key=lambda x: x["similarity"], reverse=True)
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return scored[:k]
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except Exception as e:
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logger.error(f"keyword fallback failed: {e}")
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return []
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# ------------------------------------------------------------------
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# Index management
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# ------------------------------------------------------------------
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def rebuild_index(self) -> bool:
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try:
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from src.chroma_client import get_chroma_client
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client = get_chroma_client()
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try:
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client.delete_collection(COLLECTION_NAME)
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except Exception:
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pass
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self._collection = client.get_or_create_collection(
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name=COLLECTION_NAME,
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metadata={"hnsw:space": "cosine"},
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)
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self._healthy = True
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return True
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except Exception as e:
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logger.error(f"rebuild_index failed: {e}")
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self._healthy = False
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return False
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def get_stats(self) -> Dict[str, Any]:
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if not self.healthy:
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return {"error": "Collection not initialized"}
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try:
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return {
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"document_count": self._collection.count(),
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"embedding_model": f"{self._model.model} @ {self._model.url}" if self._model else "N/A",
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"persist_directory": self.persist_directory,
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"collection_name": COLLECTION_NAME,
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"healthy": True,
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}
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except Exception as e:
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logger.error(f"get_stats failed: {e}")
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return {"error": str(e), "healthy": False}
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# ------------------------------------------------------------------
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# Directory indexing
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# ------------------------------------------------------------------
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def index_personal_documents(
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self, directory: str, file_extensions: Optional[set] = None, owner: Optional[str] = None
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) -> Dict[str, Any]:
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if file_extensions is None:
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file_extensions = DEFAULT_FILE_EXTENSIONS
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indexed = 0
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failed = 0
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try:
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for root, _, files in os.walk(directory):
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for fname in files:
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fpath = os.path.join(root, fname)
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ext = Path(fname).suffix.lower()
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if ext not in file_extensions:
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continue
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try:
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if ext == '.pdf':
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from src.personal_docs import extract_pdf_text
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content = extract_pdf_text(fpath)
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else:
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with open(fpath, 'r', encoding='utf-8') as f:
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content = f.read()
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if not content or not content.strip():
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continue
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meta = {
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'source': fpath,
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'filename': fname,
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'directory': root,
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'type': ext,
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}
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if owner:
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meta['owner'] = owner
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for i, chunk in enumerate(self._split_into_chunks(content)):
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if self.add_document(chunk, {**meta, 'chunk_id': i}):
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indexed += 1
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else:
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failed += 1
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except Exception as e:
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logger.error(f"index {fpath}: {e}")
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failed += 1
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return {
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'success': True,
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'indexed_count': indexed,
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'failed_count': failed,
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'message': f'Indexed {indexed} chunks from {directory}',
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}
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except Exception as e:
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logger.error(f"index_personal_documents {directory}: {e}")
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return {'success': False, 'indexed_count': indexed, 'failed_count': failed, 'message': str(e)}
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def remove_directory(self, directory: str) -> Dict[str, Any]:
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"""Remove all chunks from a directory. O(1) per chunk via ChromaDB."""
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if not self.healthy:
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return {"success": False, "message": "Collection not initialized"}
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try:
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# Use ChromaDB where filter to find all docs from this directory
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results = self._collection.get(
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where={"source": {"$contains": directory}} if "/" in directory else {"directory": directory},
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include=["metadatas"],
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)
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if not results['ids']:
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return {"success": True, "removed_count": 0, "message": "No docs found"}
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self._collection.delete(ids=results['ids'])
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n = len(results['ids'])
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logger.info(f"Removed {n} chunks from {directory}")
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return {"success": True, "removed_count": n, "message": f"Removed {n} chunks"}
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except Exception as e:
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logger.error(f"remove_directory {directory}: {e}")
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return {"success": False, "message": str(e)}
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def reindex_directory(
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self, directory: str, file_extensions: Optional[set] = None
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) -> Dict[str, Any]:
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remove_result = self.remove_directory(directory)
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if not remove_result.get("success"):
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return remove_result
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index_result = self.index_personal_documents(directory, file_extensions)
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return {
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"success": index_result.get("success", False),
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"message": (
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f"Re-index for {directory}: removed {remove_result.get('removed_count', 0)}, "
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f"{index_result.get('message', '')}"
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),
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"removed_count": remove_result.get("removed_count", 0),
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"indexed_count": index_result.get("indexed_count", 0),
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"failed_count": index_result.get("failed_count", 0),
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}
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# ------------------------------------------------------------------
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# Sentence-boundary-aware chunking
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# ------------------------------------------------------------------
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def _split_into_chunks(
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self, text: str, chunk_size: int = 1000, overlap: int = 200
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) -> List[str]:
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if not text:
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return []
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if len(text) <= chunk_size:
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return [text]
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# Split into sentences first
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sentences = re.split(r'(?<=[.!?])\s+|\n{2,}', text)
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sentences = [s.strip() for s in sentences if s.strip()]
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chunks: List[str] = []
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current_chunk: List[str] = []
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current_len = 0
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for sentence in sentences:
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sent_len = len(sentence)
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# If a single sentence exceeds chunk_size, split it by character
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if sent_len > chunk_size:
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# Flush current chunk first
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if current_chunk:
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chunks.append(' '.join(current_chunk))
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current_chunk = []
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current_len = 0
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# Hard-split the long sentence
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for start in range(0, sent_len, chunk_size - overlap):
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chunks.append(sentence[start:start + chunk_size])
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continue
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if current_len + sent_len + 1 > chunk_size and current_chunk:
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chunks.append(' '.join(current_chunk))
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# Keep last few sentences for overlap
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overlap_sentences: List[str] = []
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overlap_len = 0
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for s in reversed(current_chunk):
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if overlap_len + len(s) > overlap:
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break
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overlap_sentences.insert(0, s)
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overlap_len += len(s) + 1
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current_chunk = overlap_sentences
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current_len = sum(len(s) for s in current_chunk) + max(0, len(current_chunk) - 1)
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current_chunk.append(sentence)
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current_len += sent_len + (1 if current_len > 0 else 0)
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if current_chunk:
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chunks.append(' '.join(current_chunk))
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return chunks if chunks else [text]
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# ------------------------------------------------------------------
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# Delete by metadata
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# ------------------------------------------------------------------
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def delete_by_source(self, source: str) -> int:
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"""Remove all chunks whose metadata['source'] matches *source*.
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Returns the number of removed chunks."""
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if not self.healthy:
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return 0
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try:
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results = self._collection.get(
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where={"source": source},
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include=[],
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)
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ids = results.get("ids", [])
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if not ids:
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return 0
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self._collection.delete(ids=ids)
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logger.info(f"Deleted {len(ids)} chunks for source={source}")
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return len(ids)
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except Exception as e:
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logger.error(f"delete_by_source failed: {e}")
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return 0
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# ------------------------------------------------------------------
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# Convenience
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# ------------------------------------------------------------------
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def retrieve(self, query: str, k: int = 5) -> List[str]:
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return [r['document'] for r in self.search(query, k)]
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Reference in New Issue
Block a user