239 lines
7.9 KiB
Python
239 lines
7.9 KiB
Python
import logging
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from typing import Any, Dict, List, Optional, Tuple, Union
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from embedchain.config import ZillizDBConfig
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from embedchain.helpers.json_serializable import register_deserializable
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from embedchain.vectordb.base import BaseVectorDB
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try:
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from pymilvus import (
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Collection,
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CollectionSchema,
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DataType,
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FieldSchema,
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MilvusClient,
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connections,
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utility,
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)
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except ImportError:
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raise ImportError(
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"Zilliz requires extra dependencies. Install with `pip install --upgrade embedchain[milvus]`"
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) from None
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@register_deserializable
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class ZillizVectorDB(BaseVectorDB):
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"""Base class for vector database."""
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def __init__(self, config: ZillizDBConfig = None):
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"""Initialize the database. Save the config and client as an attribute.
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:param config: Database configuration class instance.
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:type config: ZillizDBConfig
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"""
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if config is None:
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self.config = ZillizDBConfig()
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else:
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self.config = config
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self.client = MilvusClient(
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uri=self.config.uri,
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token=self.config.token,
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)
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self.connection = connections.connect(
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uri=self.config.uri,
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token=self.config.token,
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)
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super().__init__(config=self.config)
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def _initialize(self):
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"""
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This method is needed because `embedder` attribute needs to be set externally before it can be initialized.
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So it's can't be done in __init__ in one step.
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"""
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self._get_or_create_collection(self.config.collection_name)
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def _get_or_create_db(self):
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"""Get or create the database."""
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return self.client
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def _get_or_create_collection(self, name):
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"""
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Get or create a named collection.
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:param name: Name of the collection
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:type name: str
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"""
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if utility.has_collection(name):
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logging.info(f"[ZillizDB]: found an existing collection {name}, make sure the auto-id is disabled.")
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self.collection = Collection(name)
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else:
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fields = [
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FieldSchema(name="id", dtype=DataType.VARCHAR, is_primary=True, max_length=512),
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FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=2048),
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FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=self.embedder.vector_dimension),
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]
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schema = CollectionSchema(fields, enable_dynamic_field=True)
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self.collection = Collection(name=name, schema=schema)
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index = {
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"index_type": "AUTOINDEX",
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"metric_type": self.config.metric_type,
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}
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self.collection.create_index("embeddings", index)
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return self.collection
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def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
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"""
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Get existing doc ids present in vector database
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:param ids: list of doc ids to check for existence
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:type ids: List[str]
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:param where: Optional. to filter data
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:type where: Dict[str, Any]
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:param limit: Optional. maximum number of documents
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:type limit: Optional[int]
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:return: Existing documents.
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:rtype: Set[str]
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"""
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if ids is None or len(ids) == 0 or self.collection.num_entities == 0:
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return {"ids": []}
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if not self.collection.is_empty:
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filter_ = f"id in {ids}"
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results = self.client.query(
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collection_name=self.config.collection_name, filter=filter_, output_fields=["id"]
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)
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results = [res["id"] for res in results]
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return {"ids": set(results)}
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def add(
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self,
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embeddings: List[List[float]],
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documents: List[str],
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metadatas: List[object],
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ids: List[str],
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**kwargs: Optional[Dict[str, any]],
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):
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"""Add to database"""
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embeddings = self.embedder.embedding_fn(documents)
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for id, doc, metadata, embedding in zip(ids, documents, metadatas, embeddings):
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data = {**metadata, "id": id, "text": doc, "embeddings": embedding}
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self.client.insert(collection_name=self.config.collection_name, data=data, **kwargs)
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self.collection.load()
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self.collection.flush()
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self.client.flush(self.config.collection_name)
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def query(
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self,
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input_query: List[str],
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n_results: int,
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where: Dict[str, any],
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citations: bool = False,
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**kwargs: Optional[Dict[str, Any]],
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) -> Union[List[Tuple[str, Dict]], List[str]]:
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"""
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Query contents from vector database based on vector similarity
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:param input_query: list of query string
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:type input_query: List[str]
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:param n_results: no of similar documents to fetch from database
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:type n_results: int
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:param where: to filter data
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:type where: str
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:raises InvalidDimensionException: Dimensions do not match.
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:param citations: we use citations boolean param to return context along with the answer.
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:type citations: bool, default is False.
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:return: The content of the document that matched your query,
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along with url of the source and doc_id (if citations flag is true)
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:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
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"""
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if self.collection.is_empty:
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return []
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if not isinstance(where, str):
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where = None
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output_fields = ["*"]
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input_query_vector = self.embedder.embedding_fn([input_query])
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query_vector = input_query_vector[0]
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query_result = self.client.search(
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collection_name=self.config.collection_name,
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data=[query_vector],
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limit=n_results,
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output_fields=output_fields,
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**kwargs,
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)
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query_result = query_result[0]
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contexts = []
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for query in query_result:
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data = query["entity"]
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score = query["distance"]
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context = data["text"]
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if "embeddings" in data:
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data.pop("embeddings")
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if citations:
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data["score"] = score
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contexts.append(tuple((context, data)))
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else:
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contexts.append(context)
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return contexts
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def count(self) -> int:
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"""
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Count number of documents/chunks embedded in the database.
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:return: number of documents
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:rtype: int
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"""
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return self.collection.num_entities
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def reset(self, collection_names: List[str] = None):
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"""
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Resets the database. Deletes all embeddings irreversibly.
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"""
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if self.config.collection_name:
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if collection_names:
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for collection_name in collection_names:
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if collection_name in self.client.list_collections():
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self.client.drop_collection(collection_name=collection_name)
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else:
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self.client.drop_collection(collection_name=self.config.collection_name)
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self._get_or_create_collection(self.config.collection_name)
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def set_collection_name(self, name: str):
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"""
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Set the name of the collection. A collection is an isolated space for vectors.
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:param name: Name of the collection.
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:type name: str
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"""
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if not isinstance(name, str):
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raise TypeError("Collection name must be a string")
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self.config.collection_name = name
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def delete(self, keys: Union[list, str, int]):
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"""
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Delete the embeddings from DB. Zilliz only support deleting with keys.
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:param keys: Primary keys of the table entries to delete.
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:type keys: Union[list, str, int]
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"""
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self.client.delete(
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collection_name=self.config.collection_name,
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pks=keys,
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)
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