110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
"""
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Qdrant vector store wrapper.
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"""
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import logging
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import os
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from typing import List, Optional, Dict, Any
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from langchain_core.documents import Document
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from langchain_qdrant import QdrantVectorStore as LangchainQdrantVS
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from qdrant_client import QdrantClient
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from qdrant_client.http import models
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from qdrant_client.http.models import Distance, VectorParams
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from .embedders import LlamaCppEmbedder
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logger = logging.getLogger(__name__)
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class QdrantVectorStore:
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"""Wrapper for Qdrant vector database operations."""
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def __init__(
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self,
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collection_name: str,
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embeddings: Optional[Any] = None,
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qdrant_url: Optional[str] = None,
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api_key: Optional[str] = None,
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):
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self.collection_name = collection_name
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self.qdrant_url = qdrant_url or os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
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self.api_key = api_key
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# Embeddings
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if embeddings is None:
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embedder = LlamaCppEmbedder()
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self.embeddings = embedder.as_langchain_embeddings()
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else:
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self.embeddings = embeddings
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# Qdrant client
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self.client = QdrantClient(url=self.qdrant_url, api_key=self.api_key)
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# LangChain vector store
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self.vector_store = LangchainQdrantVS(
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client=self.client,
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collection_name=self.collection_name,
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embeddings=self.embeddings,
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)
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def create_collection(self, vector_size: Optional[int] = None, force_recreate: bool = False):
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"""Create collection with appropriate vector size."""
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if vector_size is None:
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embedder = LlamaCppEmbedder()
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vector_size = embedder.get_embedding_dimension()
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collections = self.client.get_collections().collections
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exists = any(c.name == self.collection_name for c in collections)
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if exists and force_recreate:
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self.client.delete_collection(self.collection_name)
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exists = False
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if not exists:
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self.client.create_collection(
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collection_name=self.collection_name,
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vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
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)
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logger.info("Collection '%s' created (dim=%d)", self.collection_name, vector_size)
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else:
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logger.info("Collection '%s' already exists", self.collection_name)
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def add_documents(self, documents: List[Document], batch_size: int = 100):
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"""Add documents to vector store."""
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if not documents:
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return []
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self.create_collection()
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ids = self.vector_store.add_documents(documents, batch_size=batch_size)
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logger.info("Added %d documents to '%s'", len(ids), self.collection_name)
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return ids
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def similarity_search(self, query: str, k: int = 5) -> List[Document]:
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return self.vector_store.similarity_search(query, k=k)
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def similarity_search_with_score(self, query: str, k: int = 5) -> List[tuple[Document, float]]:
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return self.vector_store.similarity_search_with_score(query, k=k)
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def delete_collection(self):
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self.client.delete_collection(self.collection_name)
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logger.info("Collection '%s' deleted", self.collection_name)
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def get_collection_info(self) -> Dict[str, Any]:
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info = self.client.get_collection(self.collection_name)
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return {
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"name": info.name,
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"vectors_count": info.vectors_count,
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"status": info.status,
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"vector_size": info.config.params.vectors.size,
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}
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def as_langchain_vectorstore(self):
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return self.vector_store
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def get_langchain_vectorstore(self):
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"""返回 LangChain Qdrant 向量存储对象（别名）"""
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return self.vector_store
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def get_qdrant_client(self):
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"""返回原生 Qdrant 客户端（如需手动管理 collection）"""
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return self.client |