241 lines
8.0 KiB
Python
241 lines
8.0 KiB
Python
import logging
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import os
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import shutil
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from qdrant_client import QdrantClient
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from qdrant_client.models import (
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Distance,
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FieldCondition,
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Filter,
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MatchValue,
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PointIdsList,
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PointStruct,
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Range,
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VectorParams,
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)
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from mem0.vector_stores.base import VectorStoreBase
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logger = logging.getLogger(__name__)
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class Qdrant(VectorStoreBase):
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def __init__(
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self,
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collection_name: str,
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embedding_model_dims: int,
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client: QdrantClient = None,
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host: str = None,
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port: int = None,
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path: str = None,
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url: str = None,
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api_key: str = None,
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on_disk: bool = False,
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):
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"""
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Initialize the Qdrant vector store.
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Args:
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collection_name (str): Name of the collection.
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embedding_model_dims (int): Dimensions of the embedding model.
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client (QdrantClient, optional): Existing Qdrant client instance. Defaults to None.
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host (str, optional): Host address for Qdrant server. Defaults to None.
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port (int, optional): Port for Qdrant server. Defaults to None.
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path (str, optional): Path for local Qdrant database. Defaults to None.
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url (str, optional): Full URL for Qdrant server. Defaults to None.
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api_key (str, optional): API key for Qdrant server. Defaults to None.
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on_disk (bool, optional): Enables persistent storage. Defaults to False.
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"""
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if client:
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self.client = client
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else:
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params = {}
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if api_key:
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params["api_key"] = api_key
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if url:
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params["url"] = url
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if host and port:
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params["host"] = host
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params["port"] = port
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if not params:
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params["path"] = path
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if not on_disk:
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if os.path.exists(path) and os.path.isdir(path):
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shutil.rmtree(path)
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self.client = QdrantClient(**params)
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self.collection_name = collection_name
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self.embedding_model_dims = embedding_model_dims
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self.on_disk = on_disk
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self.create_col(embedding_model_dims, on_disk)
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def create_col(self, vector_size: int, on_disk: bool, distance: Distance = Distance.COSINE):
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"""
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Create a new collection.
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Args:
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vector_size (int): Size of the vectors to be stored.
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on_disk (bool): Enables persistent storage.
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distance (Distance, optional): Distance metric for vector similarity. Defaults to Distance.COSINE.
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"""
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# Skip creating collection if already exists
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response = self.list_cols()
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for collection in response.collections:
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if collection.name == self.collection_name:
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logging.debug(f"Collection {self.collection_name} already exists. Skipping creation.")
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return
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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, on_disk=on_disk),
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)
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def insert(self, vectors: list, payloads: list = None, ids: list = None):
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"""
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Insert vectors into a collection.
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Args:
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vectors (list): List of vectors to insert.
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payloads (list, optional): List of payloads corresponding to vectors. Defaults to None.
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ids (list, optional): List of IDs corresponding to vectors. Defaults to None.
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"""
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logger.info(f"Inserting {len(vectors)} vectors into collection {self.collection_name}")
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points = [
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PointStruct(
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id=idx if ids is None else ids[idx],
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vector=vector,
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payload=payloads[idx] if payloads else {},
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)
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for idx, vector in enumerate(vectors)
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]
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self.client.upsert(collection_name=self.collection_name, points=points)
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def _create_filter(self, filters: dict) -> Filter:
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"""
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Create a Filter object from the provided filters.
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Args:
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filters (dict): Filters to apply.
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Returns:
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Filter: The created Filter object.
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"""
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conditions = []
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for key, value in filters.items():
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if isinstance(value, dict) and "gte" in value and "lte" in value:
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conditions.append(FieldCondition(key=key, range=Range(gte=value["gte"], lte=value["lte"])))
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else:
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conditions.append(FieldCondition(key=key, match=MatchValue(value=value)))
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return Filter(must=conditions) if conditions else None
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def search(self, query: str, vectors: list, limit: int = 5, filters: dict = None) -> list:
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"""
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Search for similar vectors.
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Args:
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query (str): Query.
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vectors (list): Query vector.
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limit (int, optional): Number of results to return. Defaults to 5.
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filters (dict, optional): Filters to apply to the search. Defaults to None.
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Returns:
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list: Search results.
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"""
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query_filter = self._create_filter(filters) if filters else None
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hits = self.client.query_points(
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collection_name=self.collection_name,
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query=vectors,
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query_filter=query_filter,
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limit=limit,
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)
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return hits.points
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def delete(self, vector_id: int):
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"""
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Delete a vector by ID.
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Args:
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vector_id (int): ID of the vector to delete.
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"""
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self.client.delete(
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collection_name=self.collection_name,
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points_selector=PointIdsList(
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points=[vector_id],
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),
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)
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def update(self, vector_id: int, vector: list = None, payload: dict = None):
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"""
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Update a vector and its payload.
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Args:
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vector_id (int): ID of the vector to update.
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vector (list, optional): Updated vector. Defaults to None.
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payload (dict, optional): Updated payload. Defaults to None.
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"""
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point = PointStruct(id=vector_id, vector=vector, payload=payload)
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self.client.upsert(collection_name=self.collection_name, points=[point])
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def get(self, vector_id: int) -> dict:
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"""
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Retrieve a vector by ID.
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Args:
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vector_id (int): ID of the vector to retrieve.
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Returns:
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dict: Retrieved vector.
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"""
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result = self.client.retrieve(collection_name=self.collection_name, ids=[vector_id], with_payload=True)
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return result[0] if result else None
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def list_cols(self) -> list:
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"""
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List all collections.
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Returns:
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list: List of collection names.
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"""
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return self.client.get_collections()
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def delete_col(self):
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"""Delete a collection."""
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self.client.delete_collection(collection_name=self.collection_name)
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def col_info(self) -> dict:
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"""
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Get information about a collection.
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Returns:
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dict: Collection information.
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"""
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return self.client.get_collection(collection_name=self.collection_name)
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def list(self, filters: dict = None, limit: int = 100) -> list:
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"""
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List all vectors in a collection.
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Args:
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filters (dict, optional): Filters to apply to the list. Defaults to None.
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limit (int, optional): Number of vectors to return. Defaults to 100.
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Returns:
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list: List of vectors.
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"""
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query_filter = self._create_filter(filters) if filters else None
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result = self.client.scroll(
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collection_name=self.collection_name,
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scroll_filter=query_filter,
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limit=limit,
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with_payload=True,
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with_vectors=False,
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)
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return result
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def reset(self):
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"""Reset the index by deleting and recreating it."""
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logger.warning(f"Resetting index {self.collection_name}...")
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self.delete_col()
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self.create_col(self.embedding_model_dims, self.on_disk)
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