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112 lines
4.8 KiB
Python
112 lines
4.8 KiB
Python
# services/retriever.py
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import logging
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import numpy as np # Make sure numpy is imported
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from typing import List, Dict, Any
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from sqlalchemy.orm import Session
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from pkg.rag.knowledge.services.base_service import BaseService
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from pkg.rag.knowledge.services.database import Chunk, SessionLocal
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from pkg.rag.knowledge.services.embedding_models import BaseEmbeddingModel, EmbeddingModelFactory
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from pkg.rag.knowledge.services.chroma_manager import ChromaIndexManager
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logger = logging.getLogger(__name__)
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class Retriever(BaseService):
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def __init__(self, model_type: str, model_name_key: str, chroma_manager: ChromaIndexManager):
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super().__init__()
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self.logger = logging.getLogger(self.__class__.__name__)
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self.model_type = model_type
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self.model_name_key = model_name_key
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self.chroma_manager = chroma_manager
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self.embedding_model: BaseEmbeddingModel = self._load_embedding_model()
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def _load_embedding_model(self) -> BaseEmbeddingModel:
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self.logger.info(
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f'Loading retriever embedding model: type={self.model_type}, name_key={self.model_name_key}...'
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)
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try:
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model = EmbeddingModelFactory.create_model(self.model_type, self.model_name_key)
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self.logger.info(
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f"Retriever embedding model '{self.model_name_key}' loaded. Output dimension: {model.embedding_dimension}"
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)
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return model
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except Exception as e:
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self.logger.error(f"Failed to load retriever embedding model '{self.model_name_key}': {e}")
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raise
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async def retrieve(self, query: str, k: int = 5) -> List[Dict[str, Any]]:
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if not self.embedding_model:
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raise RuntimeError('Retriever embedding model not loaded. Please check Retriever initialization.')
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self.logger.info(f"Retrieving for query: '{query}' with k={k} using {self.model_name_key}")
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query_embedding: List[float] = await self.embedding_model.embed_query(query)
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query_embedding_np = np.array([query_embedding], dtype=np.float32)
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chroma_results = await self._run_sync(self.chroma_manager.search_sync, query_embedding_np, k)
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# 'ids' is always returned by ChromaDB, even if not explicitly in 'include'
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matched_chroma_ids = chroma_results.get('ids', [[]])[0]
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distances = chroma_results.get('distances', [[]])[0]
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chroma_metadatas = chroma_results.get('metadatas', [[]])[0]
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chroma_documents = chroma_results.get('documents', [[]])[0]
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if not matched_chroma_ids:
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self.logger.info('No relevant chunks found in Chroma.')
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return []
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db_chunk_ids = []
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for metadata in chroma_metadatas:
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if 'chunk_id' in metadata:
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db_chunk_ids.append(metadata['chunk_id'])
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else:
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self.logger.warning(f"Metadata missing 'chunk_id': {metadata}. Skipping this entry.")
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if not db_chunk_ids:
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self.logger.warning('No valid chunk_ids extracted from Chroma results metadata.')
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return []
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self.logger.info(f'Fetching {len(db_chunk_ids)} chunk details from relational database...')
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chunks_from_db = await self._run_sync(
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lambda cids: self._db_get_chunks_sync(
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SessionLocal(), cids
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), # Ensure SessionLocal is passed correctly for _db_get_chunks_sync
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db_chunk_ids,
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)
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chunk_map = {chunk.id: chunk for chunk in chunks_from_db}
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results_list: List[Dict[str, Any]] = []
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for i, chroma_id in enumerate(matched_chroma_ids):
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try:
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# Ensure original_chunk_id is int for DB lookup
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original_chunk_id = int(chroma_id.split('_')[-1])
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except (ValueError, IndexError):
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self.logger.warning(f'Could not parse chunk_id from Chroma ID: {chroma_id}. Skipping.')
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continue
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chunk_text_from_chroma = chroma_documents[i]
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distance = float(distances[i])
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file_id_from_chroma = chroma_metadatas[i].get('file_id')
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chunk_from_db = chunk_map.get(original_chunk_id)
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results_list.append(
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{
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'chunk_id': original_chunk_id,
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'text': chunk_from_db.text if chunk_from_db else chunk_text_from_chroma,
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'distance': distance,
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'file_id': file_id_from_chroma,
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}
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)
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self.logger.info(f'Retrieved {len(results_list)} chunks.')
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return results_list
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def _db_get_chunks_sync(self, session: Session, chunk_ids: List[int]) -> List[Chunk]:
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self.logger.debug(f'Fetching {len(chunk_ids)} chunk details from database (sync).')
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chunks = session.query(Chunk).filter(Chunk.id.in_(chunk_ids)).all()
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session.close()
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return chunks
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