[docs]@deprecated(since="0.2.0",removal="1.0",alternative_import="langchain_milvus.MilvusVectorStore",)classMilvus(VectorStore):"""`Milvus` vector store. You need to install `pymilvus` and run Milvus. See the following documentation for how to run a Milvus instance: https://milvus.io/docs/install_standalone-docker.md If looking for a hosted Milvus, take a look at this documentation: https://zilliz.com/cloud and make use of the Zilliz vectorstore found in this project. IF USING L2/IP metric, IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA. Args: embedding_function (Embeddings): Function used to embed the text. collection_name (str): Which Milvus collection to use. Defaults to "LangChainCollection". collection_description (str): The description of the collection. Defaults to "". collection_properties (Optional[dict[str, any]]): The collection properties. Defaults to None. If set, will override collection existing properties. For example: {"collection.ttl.seconds": 60}. connection_args (Optional[dict[str, any]]): The connection args used for this class comes in the form of a dict. consistency_level (str): The consistency level to use for a collection. Defaults to "Session". index_params (Optional[dict]): Which index params to use. Defaults to HNSW/AUTOINDEX depending on service. search_params (Optional[dict]): Which search params to use. Defaults to default of index. drop_old (Optional[bool]): Whether to drop the current collection. Defaults to False. auto_id (bool): Whether to enable auto id for primary key. Defaults to False. If False, you needs to provide text ids (string less than 65535 bytes). If True, Milvus will generate unique integers as primary keys. primary_field (str): Name of the primary key field. Defaults to "pk". text_field (str): Name of the text field. Defaults to "text". vector_field (str): Name of the vector field. Defaults to "vector". metadata_field (str): Name of the metadata field. Defaults to None. When metadata_field is specified, the document's metadata will store as json. The connection args used for this class comes in the form of a dict, here are a few of the options: address (str): The actual address of Milvus instance. Example address: "localhost:19530" uri (str): The uri of Milvus instance. Example uri: "http://randomwebsite:19530", "tcp:foobarsite:19530", "https://ok.s3.south.com:19530". host (str): The host of Milvus instance. Default at "localhost", PyMilvus will fill in the default host if only port is provided. port (str/int): The port of Milvus instance. Default at 19530, PyMilvus will fill in the default port if only host is provided. user (str): Use which user to connect to Milvus instance. If user and password are provided, we will add related header in every RPC call. password (str): Required when user is provided. The password corresponding to the user. secure (bool): Default is false. If set to true, tls will be enabled. client_key_path (str): If use tls two-way authentication, need to write the client.key path. client_pem_path (str): If use tls two-way authentication, need to write the client.pem path. ca_pem_path (str): If use tls two-way authentication, need to write the ca.pem path. server_pem_path (str): If use tls one-way authentication, need to write the server.pem path. server_name (str): If use tls, need to write the common name. Example: .. code-block:: python from langchain_community.vectorstores import Milvus from langchain_community.embeddings import OpenAIEmbeddings embedding = OpenAIEmbeddings() # Connect to a milvus instance on localhost milvus_store = Milvus( embedding_function = Embeddings, collection_name = "LangChainCollection", drop_old = True, auto_id = True ) Raises: ValueError: If the pymilvus python package is not installed. """
[docs]def__init__(self,embedding_function:Embeddings,collection_name:str="LangChainCollection",collection_description:str="",collection_properties:Optional[dict[str,Any]]=None,connection_args:Optional[dict[str,Any]]=None,consistency_level:str="Session",index_params:Optional[dict]=None,search_params:Optional[dict]=None,drop_old:Optional[bool]=False,auto_id:bool=False,*,primary_field:str="pk",text_field:str="text",vector_field:str="vector",metadata_field:Optional[str]=None,partition_key_field:Optional[str]=None,partition_names:Optional[list]=None,replica_number:int=1,timeout:Optional[float]=None,num_shards:Optional[int]=None,):"""Initialize the Milvus vector store."""try:frompymilvusimportCollection,utilityexceptImportError:raiseImportError("Could not import pymilvus python package. ""Please install it with `pip install pymilvus`.")# Default search params when one is not provided.self.default_search_params={"IVF_FLAT":{"metric_type":"L2","params":{"nprobe":10}},"IVF_SQ8":{"metric_type":"L2","params":{"nprobe":10}},"IVF_PQ":{"metric_type":"L2","params":{"nprobe":10}},"HNSW":{"metric_type":"L2","params":{"ef":10}},"RHNSW_FLAT":{"metric_type":"L2","params":{"ef":10}},"RHNSW_SQ":{"metric_type":"L2","params":{"ef":10}},"RHNSW_PQ":{"metric_type":"L2","params":{"ef":10}},"IVF_HNSW":{"metric_type":"L2","params":{"nprobe":10,"ef":10}},"ANNOY":{"metric_type":"L2","params":{"search_k":10}},"SCANN":{"metric_type":"L2","params":{"search_k":10}},"AUTOINDEX":{"metric_type":"L2","params":{}},"GPU_CAGRA":{"metric_type":"L2","params":{"itopk_size":128,"search_width":4,"min_iterations":0,"max_iterations":0,"team_size":0,},},"GPU_IVF_FLAT":{"metric_type":"L2","params":{"nprobe":10}},"GPU_IVF_PQ":{"metric_type":"L2","params":{"nprobe":10}},}self.embedding_func=embedding_functionself.collection_name=collection_nameself.collection_description=collection_descriptionself.collection_properties=collection_propertiesself.index_params=index_paramsself.search_params=search_paramsself.consistency_level=consistency_levelself.auto_id=auto_id# In order for a collection to be compatible, pk needs to be varcharself._primary_field=primary_field# In order for compatibility, the text field will need to be called "text"self._text_field=text_field# In order for compatibility, the vector field needs to be called "vector"self._vector_field=vector_fieldself._metadata_field=metadata_fieldself._partition_key_field=partition_key_fieldself.fields:list[str]=[]self.partition_names=partition_namesself.replica_number=replica_numberself.timeout=timeoutself.num_shards=num_shards# Create the connection to the serverifconnection_argsisNone:connection_args=DEFAULT_MILVUS_CONNECTIONself.alias=self._create_connection_alias(connection_args)self.col:Optional[Collection]=None# Grab the existing collection if it existsifutility.has_collection(self.collection_name,using=self.alias):self.col=Collection(self.collection_name,using=self.alias,)ifself.collection_propertiesisnotNone:self.col.set_properties(self.collection_properties)# If need to drop old, drop itifdrop_oldandisinstance(self.col,Collection):self.col.drop()self.col=None# Initialize the vector storeself._init(partition_names=partition_names,replica_number=replica_number,timeout=timeout,)
@propertydefembeddings(self)->Embeddings:returnself.embedding_funcdef_create_connection_alias(self,connection_args:dict)->str:"""Create the connection to the Milvus server."""frompymilvusimportMilvusException,connections# Grab the connection arguments that are used for checking existing connectionhost:str=connection_args.get("host",None)port:Union[str,int]=connection_args.get("port",None)address:str=connection_args.get("address",None)uri:str=connection_args.get("uri",None)user=connection_args.get("user",None)# Order of use is host/port, uri, addressifhostisnotNoneandportisnotNone:given_address=str(host)+":"+str(port)elifuriisnotNone:ifuri.startswith("https://"):given_address=uri.split("https://")[1]elifuri.startswith("http://"):given_address=uri.split("http://")[1]else:logger.error("Invalid Milvus URI: %s",uri)raiseValueError("Invalid Milvus URI: %s",uri)elifaddressisnotNone:given_address=addresselse:given_address=Nonelogger.debug("Missing standard address type for reuse attempt")# User defaults to empty string when getting connection infoifuserisnotNone:tmp_user=userelse:tmp_user=""# If a valid address was given, then check if a connection existsifgiven_addressisnotNone:forconinconnections.list_connections():addr=connections.get_connection_addr(con[0])if(con[1]and("address"inaddr)and(addr["address"]==given_address)and("user"inaddr)and(addr["user"]==tmp_user)):logger.debug("Using previous connection: %s",con[0])returncon[0]# Generate a new connection if one doesn't existalias=uuid4().hextry:connections.connect(alias=alias,**connection_args)logger.debug("Created new connection using: %s",alias)returnaliasexceptMilvusExceptionase:logger.error("Failed to create new connection using: %s",alias)raiseedef_init(self,embeddings:Optional[list]=None,metadatas:Optional[list[dict]]=None,partition_names:Optional[list]=None,replica_number:int=1,timeout:Optional[float]=None,)->None:ifembeddingsisnotNone:self._create_collection(embeddings,metadatas)self._extract_fields()self._create_index()self._create_search_params()self._load(partition_names=partition_names,replica_number=replica_number,timeout=timeout,)def_create_collection(self,embeddings:list,metadatas:Optional[list[dict]]=None)->None:frompymilvusimport(Collection,CollectionSchema,DataType,FieldSchema,MilvusException,)frompymilvus.orm.typesimportinfer_dtype_bydata# Determine embedding dimdim=len(embeddings[0])fields=[]ifself._metadata_fieldisnotNone:fields.append(FieldSchema(self._metadata_field,DataType.JSON))else:# Determine metadata schemaifmetadatas:# Create FieldSchema for each entry in metadata.forkey,valueinmetadatas[0].items():# Infer the corresponding datatype of the metadatadtype=infer_dtype_bydata(value)# Datatype isn't compatibleifdtype==DataType.UNKNOWNordtype==DataType.NONE:logger.error(("Failure to create collection, ""unrecognized dtype for key: %s"),key,)raiseValueError(f"Unrecognized datatype for {key}.")# Dataype is a string/varchar equivalentelifdtype==DataType.VARCHAR:fields.append(FieldSchema(key,DataType.VARCHAR,max_length=65_535))else:fields.append(FieldSchema(key,dtype))# Create the text fieldfields.append(FieldSchema(self._text_field,DataType.VARCHAR,max_length=65_535))# Create the primary key fieldifself.auto_id:fields.append(FieldSchema(self._primary_field,DataType.INT64,is_primary=True,auto_id=True))else:fields.append(FieldSchema(self._primary_field,DataType.VARCHAR,is_primary=True,auto_id=False,max_length=65_535,))# Create the vector field, supports binary or float vectorsfields.append(FieldSchema(self._vector_field,infer_dtype_bydata(embeddings[0]),dim=dim))# Create the schema for the collectionschema=CollectionSchema(fields,description=self.collection_description,partition_key_field=self._partition_key_field,)# Create the collectiontry:ifself.num_shardsisnotNone:# Issue with defaults:# https://github.com/milvus-io/pymilvus/blob/59bf5e811ad56e20946559317fed855330758d9c/pymilvus/client/prepare.py#L82-L85self.col=Collection(name=self.collection_name,schema=schema,consistency_level=self.consistency_level,using=self.alias,num_shards=self.num_shards,)else:self.col=Collection(name=self.collection_name,schema=schema,consistency_level=self.consistency_level,using=self.alias,)# Set the collection properties if they existifself.collection_propertiesisnotNone:self.col.set_properties(self.collection_properties)exceptMilvusExceptionase:logger.error("Failed to create collection: %s error: %s",self.collection_name,e)raiseedef_extract_fields(self)->None:"""Grab the existing fields from the Collection"""frompymilvusimportCollectionifisinstance(self.col,Collection):schema=self.col.schemaforxinschema.fields:self.fields.append(x.name)def_get_index(self)->Optional[dict[str,Any]]:"""Return the vector index information if it exists"""frompymilvusimportCollectionifisinstance(self.col,Collection):forxinself.col.indexes:ifx.field_name==self._vector_field:returnx.to_dict()returnNonedef_create_index(self)->None:"""Create a index on the collection"""frompymilvusimportCollection,MilvusExceptionifisinstance(self.col,Collection)andself._get_index()isNone:try:# If no index params, use a default HNSW based oneifself.index_paramsisNone:self.index_params={"metric_type":"L2","index_type":"HNSW","params":{"M":8,"efConstruction":64},}try:self.col.create_index(self._vector_field,index_params=self.index_params,using=self.alias,)# If default did not work, most likely on Zilliz CloudexceptMilvusException:# Use AUTOINDEX based indexself.index_params={"metric_type":"L2","index_type":"AUTOINDEX","params":{},}self.col.create_index(self._vector_field,index_params=self.index_params,using=self.alias,)logger.debug("Successfully created an index on collection: %s",self.collection_name,)exceptMilvusExceptionase:logger.error("Failed to create an index on collection: %s",self.collection_name)raiseedef_create_search_params(self)->None:"""Generate search params based on the current index type"""frompymilvusimportCollectionifisinstance(self.col,Collection)andself.search_paramsisNone:index=self._get_index()ifindexisnotNone:index_type:str=index["index_param"]["index_type"]metric_type:str=index["index_param"]["metric_type"]self.search_params=self.default_search_params[index_type]self.search_params["metric_type"]=metric_typedef_load(self,partition_names:Optional[list]=None,replica_number:int=1,timeout:Optional[float]=None,)->None:"""Load the collection if available."""frompymilvusimportCollection,utilityfrompymilvus.client.typesimportLoadStatetimeout=self.timeoutortimeoutif(isinstance(self.col,Collection)andself._get_index()isnotNoneandutility.load_state(self.collection_name,using=self.alias)==LoadState.NotLoad):self.col.load(partition_names=partition_names,replica_number=replica_number,timeout=timeout,)
[docs]defadd_texts(self,texts:Iterable[str],metadatas:Optional[List[dict]]=None,timeout:Optional[float]=None,batch_size:int=1000,*,ids:Optional[List[str]]=None,**kwargs:Any,)->List[str]:"""Insert text data into Milvus. Inserting data when the collection has not be made yet will result in creating a new Collection. The data of the first entity decides the schema of the new collection, the dim is extracted from the first embedding and the columns are decided by the first metadata dict. Metadata keys will need to be present for all inserted values. At the moment there is no None equivalent in Milvus. Args: texts (Iterable[str]): The texts to embed, it is assumed that they all fit in memory. metadatas (Optional[List[dict]]): Metadata dicts attached to each of the texts. Defaults to None. should be less than 65535 bytes. Required and work when auto_id is False. timeout (Optional[float]): Timeout for each batch insert. Defaults to None. batch_size (int, optional): Batch size to use for insertion. Defaults to 1000. ids (Optional[List[str]]): List of text ids. The length of each item Raises: MilvusException: Failure to add texts Returns: List[str]: The resulting keys for each inserted element. """frompymilvusimportCollection,MilvusExceptiontexts=list(texts)ifnotself.auto_id:assertisinstance(ids,list),("A list of valid ids are required when auto_id is False.")assertlen(set(ids))==len(texts),("Different lengths of texts and unique ids are provided.")assertall(len(x.encode())<=65_535forxinids),("Each id should be a string less than 65535 bytes.")try:embeddings=self.embedding_func.embed_documents(texts)exceptNotImplementedError:embeddings=[self.embedding_func.embed_query(x)forxintexts]iflen(embeddings)==0:logger.debug("Nothing to insert, skipping.")return[]# If the collection hasn't been initialized yet, perform all steps to do soifnotisinstance(self.col,Collection):kwargs={"embeddings":embeddings,"metadatas":metadatas}ifself.partition_names:kwargs["partition_names"]=self.partition_namesifself.replica_number:kwargs["replica_number"]=self.replica_numberifself.timeout:kwargs["timeout"]=self.timeoutself._init(**kwargs)# Dict to hold all insert columnsinsert_dict:dict[str,list]={self._text_field:texts,self._vector_field:embeddings,}ifnotself.auto_id:insert_dict[self._primary_field]=ids# type: ignore[assignment]ifself._metadata_fieldisnotNone:fordinmetadatas:# type: ignore[union-attr]insert_dict.setdefault(self._metadata_field,[]).append(d)else:# Collect the metadata into the insert dict.ifmetadatasisnotNone:fordinmetadatas:forkey,valueind.items():keys=([xforxinself.fieldsifx!=self._primary_field]ifself.auto_idelse[xforxinself.fields])ifkeyinkeys:insert_dict.setdefault(key,[]).append(value)# Total insert countvectors:list=insert_dict[self._vector_field]total_count=len(vectors)pks:list[str]=[]assertisinstance(self.col,Collection)foriinrange(0,total_count,batch_size):# Grab end indexend=min(i+batch_size,total_count)# Convert dict to list of lists batch for insertioninsert_list=[insert_dict[x][i:end]forxinself.fieldsifxininsert_dict]# Insert into the collection.try:res:Collectiontimeout=self.timeoutortimeoutres=self.col.insert(insert_list,timeout=timeout,**kwargs)pks.extend(res.primary_keys)exceptMilvusExceptionase:logger.error("Failed to insert batch starting at entity: %s/%s",i,total_count)raiseereturnpks
[docs]defsimilarity_search(self,query:str,k:int=4,param:Optional[dict]=None,expr:Optional[str]=None,timeout:Optional[float]=None,**kwargs:Any,)->List[Document]:"""Perform a similarity search against the query string. Args: query (str): The text to search. k (int, optional): How many results to return. Defaults to 4. param (dict, optional): The search params for the index type. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. timeout (int, optional): How long to wait before timeout error. Defaults to None. kwargs: Collection.search() keyword arguments. Returns: List[Document]: Document results for search. """ifself.colisNone:logger.debug("No existing collection to search.")return[]timeout=self.timeoutortimeoutres=self.similarity_search_with_score(query=query,k=k,param=param,expr=expr,timeout=timeout,**kwargs)return[docfordoc,_inres]
[docs]defsimilarity_search_by_vector(self,embedding:List[float],k:int=4,param:Optional[dict]=None,expr:Optional[str]=None,timeout:Optional[float]=None,**kwargs:Any,)->List[Document]:"""Perform a similarity search against the query string. Args: embedding (List[float]): The embedding vector to search. k (int, optional): How many results to return. Defaults to 4. param (dict, optional): The search params for the index type. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. timeout (int, optional): How long to wait before timeout error. Defaults to None. kwargs: Collection.search() keyword arguments. Returns: List[Document]: Document results for search. """ifself.colisNone:logger.debug("No existing collection to search.")return[]timeout=self.timeoutortimeoutres=self.similarity_search_with_score_by_vector(embedding=embedding,k=k,param=param,expr=expr,timeout=timeout,**kwargs)return[docfordoc,_inres]
[docs]defsimilarity_search_with_score(self,query:str,k:int=4,param:Optional[dict]=None,expr:Optional[str]=None,timeout:Optional[float]=None,**kwargs:Any,)->List[Tuple[Document,float]]:"""Perform a search on a query string and return results with score. For more information about the search parameters, take a look at the pymilvus documentation found here: https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md Args: query (str): The text being searched. k (int, optional): The amount of results to return. Defaults to 4. param (dict): The search params for the specified index. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. timeout (float, optional): How long to wait before timeout error. Defaults to None. kwargs: Collection.search() keyword arguments. Returns: List[float], List[Tuple[Document, any, any]]: """ifself.colisNone:logger.debug("No existing collection to search.")return[]# Embed the query text.embedding=self.embedding_func.embed_query(query)timeout=self.timeoutortimeoutres=self.similarity_search_with_score_by_vector(embedding=embedding,k=k,param=param,expr=expr,timeout=timeout,**kwargs)returnres
[docs]defsimilarity_search_with_score_by_vector(self,embedding:List[float],k:int=4,param:Optional[dict]=None,expr:Optional[str]=None,timeout:Optional[float]=None,**kwargs:Any,)->List[Tuple[Document,float]]:"""Perform a search on a query string and return results with score. For more information about the search parameters, take a look at the pymilvus documentation found here: https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md Args: embedding (List[float]): The embedding vector being searched. k (int, optional): The amount of results to return. Defaults to 4. param (dict): The search params for the specified index. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. timeout (float, optional): How long to wait before timeout error. Defaults to None. kwargs: Collection.search() keyword arguments. Returns: List[Tuple[Document, float]]: Result doc and score. """ifself.colisNone:logger.debug("No existing collection to search.")return[]ifparamisNone:param=self.search_params# Determine result metadata fields with PK.output_fields=self.fields[:]output_fields.remove(self._vector_field)timeout=self.timeoutortimeout# Perform the search.res=self.col.search(data=[embedding],anns_field=self._vector_field,param=param,limit=k,expr=expr,output_fields=output_fields,timeout=timeout,**kwargs,)# Organize results.ret=[]forresultinres[0]:data={x:result.entity.get(x)forxinoutput_fields}doc=self._parse_document(data)pair=(doc,result.score)ret.append(pair)returnret
[docs]defmax_marginal_relevance_search(self,query:str,k:int=4,fetch_k:int=20,lambda_mult:float=0.5,param:Optional[dict]=None,expr:Optional[str]=None,timeout:Optional[float]=None,**kwargs:Any,)->List[Document]:"""Perform a search and return results that are reordered by MMR. Args: query (str): The text being searched. k (int, optional): How many results to give. Defaults to 4. fetch_k (int, optional): Total results to select k from. Defaults to 20. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5 param (dict, optional): The search params for the specified index. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. timeout (float, optional): How long to wait before timeout error. Defaults to None. kwargs: Collection.search() keyword arguments. Returns: List[Document]: Document results for search. """ifself.colisNone:logger.debug("No existing collection to search.")return[]embedding=self.embedding_func.embed_query(query)timeout=self.timeoutortimeoutreturnself.max_marginal_relevance_search_by_vector(embedding=embedding,k=k,fetch_k=fetch_k,lambda_mult=lambda_mult,param=param,expr=expr,timeout=timeout,**kwargs,)
[docs]defmax_marginal_relevance_search_by_vector(self,embedding:list[float],k:int=4,fetch_k:int=20,lambda_mult:float=0.5,param:Optional[dict]=None,expr:Optional[str]=None,timeout:Optional[float]=None,**kwargs:Any,)->List[Document]:"""Perform a search and return results that are reordered by MMR. Args: embedding (str): The embedding vector being searched. k (int, optional): How many results to give. Defaults to 4. fetch_k (int, optional): Total results to select k from. Defaults to 20. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5 param (dict, optional): The search params for the specified index. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. timeout (float, optional): How long to wait before timeout error. Defaults to None. kwargs: Collection.search() keyword arguments. Returns: List[Document]: Document results for search. """ifself.colisNone:logger.debug("No existing collection to search.")return[]ifparamisNone:param=self.search_params# Determine result metadata fields.output_fields=self.fields[:]output_fields.remove(self._vector_field)timeout=self.timeoutortimeout# Perform the search.res=self.col.search(data=[embedding],anns_field=self._vector_field,param=param,limit=fetch_k,expr=expr,output_fields=output_fields,timeout=timeout,**kwargs,)# Organize results.ids=[]documents=[]scores=[]forresultinres[0]:data={x:result.entity.get(x)forxinoutput_fields}doc=self._parse_document(data)documents.append(doc)scores.append(result.score)ids.append(result.id)vectors=self.col.query(expr=f"{self._primary_field} in {ids}",output_fields=[self._primary_field,self._vector_field],timeout=timeout,)# Reorganize the results from query to match search order.vectors={x[self._primary_field]:x[self._vector_field]forxinvectors}ordered_result_embeddings=[vectors[x]forxinids]# Get the new order of results.new_ordering=maximal_marginal_relevance(np.array(embedding),ordered_result_embeddings,k=k,lambda_mult=lambda_mult)# Reorder the values and return.ret=[]forxinnew_ordering:# Function can return -1 indexifx==-1:breakelse:ret.append(documents[x])returnret
[docs]defdelete(# type: ignore[no-untyped-def]self,ids:Optional[List[str]]=None,expr:Optional[str]=None,**kwargs:str):"""Delete by vector ID or boolean expression. Refer to [Milvus documentation](https://milvus.io/docs/delete_data.md) for notes and examples of expressions. Args: ids: List of ids to delete. expr: Boolean expression that specifies the entities to delete. kwargs: Other parameters in Milvus delete api. """ifisinstance(ids,list)andlen(ids)>0:ifexprisnotNone:logger.warning("Both ids and expr are provided. Ignore expr and delete by ids.")expr=f"{self._primary_field} in {ids}"else:assertisinstance(expr,str),("Either ids list or expr string must be provided.")returnself.col.delete(expr=expr,**kwargs)# type: ignore[union-attr]
[docs]@classmethoddeffrom_texts(cls,texts:List[str],embedding:Embeddings,metadatas:Optional[List[dict]]=None,collection_name:str="LangChainCollection",connection_args:dict[str,Any]=DEFAULT_MILVUS_CONNECTION,consistency_level:str="Session",index_params:Optional[dict]=None,search_params:Optional[dict]=None,drop_old:bool=False,*,ids:Optional[List[str]]=None,**kwargs:Any,)->Milvus:"""Create a Milvus collection, indexes it with HNSW, and insert data. Args: texts (List[str]): Text data. embedding (Embeddings): Embedding function. metadatas (Optional[List[dict]]): Metadata for each text if it exists. Defaults to None. collection_name (str, optional): Collection name to use. Defaults to "LangChainCollection". connection_args (dict[str, Any], optional): Connection args to use. Defaults to DEFAULT_MILVUS_CONNECTION. consistency_level (str, optional): Which consistency level to use. Defaults to "Session". index_params (Optional[dict], optional): Which index_params to use. Defaults to None. search_params (Optional[dict], optional): Which search params to use. Defaults to None. drop_old (Optional[bool], optional): Whether to drop the collection with that name if it exists. Defaults to False. ids (Optional[List[str]]): List of text ids. Defaults to None. Returns: Milvus: Milvus Vector Store """ifisinstance(ids,list)andlen(ids)>0:auto_id=Falseelse:auto_id=Truevector_db=cls(embedding_function=embedding,collection_name=collection_name,connection_args=connection_args,consistency_level=consistency_level,index_params=index_params,search_params=search_params,drop_old=drop_old,auto_id=auto_id,**kwargs,)vector_db.add_texts(texts=texts,metadatas=metadatas,ids=ids)returnvector_db
[docs]defget_pks(self,expr:str,**kwargs:Any)->List[int]|None:"""Get primary keys with expression Args: expr: Expression - E.g: "id in [1, 2]", or "title LIKE 'Abc%'" Returns: List[int]: List of IDs (Primary Keys) """frompymilvusimportMilvusExceptionifself.colisNone:logger.debug("No existing collection to get pk.")returnNonetry:query_result=self.col.query(expr=expr,output_fields=[self._primary_field])exceptMilvusExceptionasexc:logger.error("Failed to get ids: %s error: %s",self.collection_name,exc)raiseexcpks=[item.get(self._primary_field)foriteminquery_result]returnpks
[docs]defupsert(# type: ignore[override]self,ids:Optional[List[str]]=None,documents:List[Document]|None=None,**kwargs:Any,)->List[str]|None:"""Update/Insert documents to the vectorstore. Args: ids: IDs to update - Let's call get_pks to get ids with expression \n documents (List[Document]): Documents to add to the vectorstore. Returns: List[str]: IDs of the added texts. """frompymilvusimportMilvusExceptionifdocumentsisNoneorlen(documents)==0:logger.debug("No documents to upsert.")returnNoneifidsisnotNoneandlen(ids):kwargs["ids"]=idstry:self.delete(ids=ids)exceptMilvusException:passtry:returnself.add_documents(documents=documents,**kwargs)exceptMilvusExceptionasexc:logger.error("Failed to upsert entities: %s error: %s",self.collection_name,exc)raiseexc