224 lines
7.1 KiB
Python
224 lines
7.1 KiB
Python
import logging
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from typing import Dict, List, Optional
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from pydantic import BaseModel
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try:
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import chromadb
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from chromadb.config import Settings
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except ImportError:
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raise ImportError("The 'chromadb' library is required. Please install it using 'pip install chromadb'.")
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from mem0.vector_stores.base import VectorStoreBase
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logger = logging.getLogger(__name__)
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class OutputData(BaseModel):
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id: Optional[str] # memory id
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score: Optional[float] # distance
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payload: Optional[Dict] # metadata
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class ChromaDB(VectorStoreBase):
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def __init__(
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self,
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collection_name: str,
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client: Optional[chromadb.Client] = None,
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host: Optional[str] = None,
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port: Optional[int] = None,
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path: Optional[str] = None,
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):
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"""
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Initialize the Chromadb vector store.
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Args:
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collection_name (str): Name of the collection.
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client (chromadb.Client, optional): Existing chromadb client instance. Defaults to None.
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host (str, optional): Host address for chromadb server. Defaults to None.
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port (int, optional): Port for chromadb server. Defaults to None.
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path (str, optional): Path for local chromadb database. Defaults to None.
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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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self.settings = Settings(anonymized_telemetry=False)
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if host and port:
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self.settings.chroma_server_host = host
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self.settings.chroma_server_http_port = port
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self.settings.chroma_api_impl = "chromadb.api.fastapi.FastAPI"
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else:
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if path is None:
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path = "db"
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self.settings.persist_directory = path
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self.settings.is_persistent = True
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self.client = chromadb.Client(self.settings)
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self.collection_name = collection_name
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self.collection = self.create_col(collection_name)
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def _parse_output(self, data: Dict) -> List[OutputData]:
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"""
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Parse the output data.
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Args:
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data (Dict): Output data.
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Returns:
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List[OutputData]: Parsed output data.
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"""
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keys = ["ids", "distances", "metadatas"]
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values = []
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for key in keys:
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value = data.get(key, [])
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if isinstance(value, list) and value and isinstance(value[0], list):
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value = value[0]
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values.append(value)
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ids, distances, metadatas = values
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max_length = max(len(v) for v in values if isinstance(v, list) and v is not None)
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result = []
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for i in range(max_length):
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entry = OutputData(
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id=ids[i] if isinstance(ids, list) and ids and i < len(ids) else None,
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score=(distances[i] if isinstance(distances, list) and distances and i < len(distances) else None),
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payload=(metadatas[i] if isinstance(metadatas, list) and metadatas and i < len(metadatas) else None),
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)
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result.append(entry)
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return result
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def create_col(self, name: str, embedding_fn: Optional[callable] = None):
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"""
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Create a new collection.
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Args:
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name (str): Name of the collection.
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embedding_fn (Optional[callable]): Embedding function to use. Defaults to None.
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Returns:
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chromadb.Collection: The created or retrieved collection.
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"""
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collection = self.client.get_or_create_collection(
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name=name,
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embedding_function=embedding_fn,
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)
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return collection
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def insert(
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self,
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vectors: List[list],
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payloads: Optional[List[Dict]] = None,
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ids: Optional[List[str]] = None,
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):
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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]): List of vectors to insert.
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payloads (Optional[List[Dict]], optional): List of payloads corresponding to vectors. Defaults to None.
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ids (Optional[List[str]], 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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self.collection.add(ids=ids, embeddings=vectors, metadatas=payloads)
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def search(
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self, query: str, vectors: List[list], limit: int = 5, filters: Optional[Dict] = None
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) -> List[OutputData]:
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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[list]): List of vectors to search.
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limit (int, optional): Number of results to return. Defaults to 5.
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filters (Optional[Dict], optional): Filters to apply to the search. Defaults to None.
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Returns:
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List[OutputData]: Search results.
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"""
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results = self.collection.query(query_embeddings=vectors, where=filters, n_results=limit)
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final_results = self._parse_output(results)
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return final_results
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def delete(self, vector_id: str):
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"""
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Delete a vector by ID.
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Args:
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vector_id (str): ID of the vector to delete.
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"""
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self.collection.delete(ids=vector_id)
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def update(
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self,
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vector_id: str,
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vector: Optional[List[float]] = None,
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payload: Optional[Dict] = None,
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):
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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 (str): ID of the vector to update.
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vector (Optional[List[float]], optional): Updated vector. Defaults to None.
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payload (Optional[Dict], optional): Updated payload. Defaults to None.
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"""
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self.collection.update(ids=vector_id, embeddings=vector, metadatas=payload)
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def get(self, vector_id: str) -> OutputData:
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"""
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Retrieve a vector by ID.
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Args:
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vector_id (str): ID of the vector to retrieve.
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Returns:
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OutputData: Retrieved vector.
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"""
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result = self.collection.get(ids=[vector_id])
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return self._parse_output(result)[0]
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def list_cols(self) -> List[chromadb.Collection]:
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"""
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List all collections.
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Returns:
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List[chromadb.Collection]: List of collections.
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"""
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return self.client.list_collections()
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def delete_col(self):
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"""
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Delete a collection.
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"""
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self.client.delete_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(name=self.collection_name)
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def list(self, filters: Optional[Dict] = None, limit: int = 100) -> List[OutputData]:
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"""
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List all vectors in a collection.
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Args:
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filters (Optional[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[OutputData]: List of vectors.
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"""
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results = self.collection.get(where=filters, limit=limit)
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return [self._parse_output(results)]
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