Source code for langchain_community.vectorstores.azure_cosmos_db
from__future__importannotationsimportloggingfromenumimportEnumfromtypingimport(TYPE_CHECKING,Any,Dict,Generator,Iterable,List,Optional,Tuple,Union,)importnumpyasnpfromlangchain_core.documentsimportDocumentfromlangchain_core.vectorstoresimportVectorStorefromlangchain_community.vectorstores.utilsimportmaximal_marginal_relevanceifTYPE_CHECKING:fromlangchain_core.embeddingsimportEmbeddingsfrompymongo.collectionimportCollection# Before Python 3.11 native StrEnum is not available
[docs]classCosmosDBSimilarityType(str,Enum):"""Cosmos DB Similarity Type as enumerator."""COS="COS""""CosineSimilarity"""IP="IP""""inner - product"""L2="L2""""Euclidean distance"""
[docs]classCosmosDBVectorSearchType(str,Enum):"""Cosmos DB Vector Search Type as enumerator."""VECTOR_IVF="vector-ivf""""IVF vector index"""VECTOR_HNSW="vector-hnsw""""HNSW vector index"""VECTOR_DISKANN="vector-diskann""""DISKANN vector index"""
[docs]classAzureCosmosDBVectorSearch(VectorStore):"""`Azure Cosmos DB for MongoDB vCore` vector store. To use, you should have both: - the ``pymongo`` python package installed - a connection string associated with a MongoDB VCore Cluster Example: . code-block:: python from langchain_community.vectorstores import AzureCosmosDBVectorSearch from langchain_community.embeddings.openai import OpenAIEmbeddings from pymongo import MongoClient mongo_client = MongoClient("<YOUR-CONNECTION-STRING>") collection = mongo_client["<db_name>"]["<collection_name>"] embeddings = OpenAIEmbeddings() vectorstore = AzureCosmosDBVectorSearch(collection, embeddings) """
[docs]def__init__(self,collection:Collection,embedding:Embeddings,*,index_name:str="vectorSearchIndex",text_key:str="textContent",embedding_key:str="vectorContent",application_name:str="LangChain-CDBMongoVCore-VectorStore-Python",):"""Constructor for AzureCosmosDBVectorSearch Args: collection: MongoDB collection to add the texts to. embedding: Text embedding model to use. index_name: Name of the Atlas Search index. text_key: MongoDB field that will contain the text for each document. embedding_key: MongoDB field that will contain the embedding for each document. """self._collection=collectionself._embedding=embeddingself._index_name=index_nameself._text_key=text_keyself._embedding_key=embedding_keyself._application_name=application_name
[docs]defget_index_name(self)->str:"""Returns the index name Returns: Returns the index name """returnself._index_name
[docs]@classmethoddeffrom_connection_string(cls,connection_string:str,namespace:str,embedding:Embeddings,application_name:str="LangChain-CDBMongoVCore-VectorStore-Python",**kwargs:Any,)->AzureCosmosDBVectorSearch:"""Creates an Instance of AzureCosmosDBVectorSearch from a Connection String Args: connection_string: The MongoDB vCore instance connection string namespace: The namespace (database.collection) embedding: The embedding utility application_name: The user agent for telemetry **kwargs: Dynamic keyword arguments Returns: an instance of the vector store """try:frompymongoimportMongoClientexceptImportError:raiseImportError("Could not import pymongo, please install it with ""`pip install pymongo`.")appname=application_nameclient:MongoClient=MongoClient(connection_string,appname=appname)db_name,collection_name=namespace.split(".")collection=client[db_name][collection_name]returncls(collection,embedding,**kwargs)
[docs]defindex_exists(self)->bool:"""Verifies if the specified index name during instance construction exists on the collection Returns: Returns True on success and False if no such index exists on the collection """cursor=self._collection.list_indexes()index_name=self._index_nameforresincursor:current_index_name=res.pop("name")ifcurrent_index_name==index_name:returnTruereturnFalse
[docs]defdelete_index(self)->None:"""Deletes the index specified during instance construction if it exists"""ifself.index_exists():self._collection.drop_index(self._index_name)
# Raises OperationFailure on an error (e.g. trying to drop# an index that does not exist)
[docs]defcreate_index(self,num_lists:int=100,dimensions:int=1536,similarity:CosmosDBSimilarityType=CosmosDBSimilarityType.COS,kind:str="vector-ivf",m:int=16,ef_construction:int=64,max_degree:int=32,l_build:int=50,)->dict[str,Any]:"""Creates an index using the index name specified at instance construction Setting the numLists parameter correctly is important for achieving good accuracy and performance. Since the vector store uses IVF as the indexing strategy, you should create the index only after you have loaded a large enough sample documents to ensure that the centroids for the respective buckets are faily distributed. We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount) for more than 1 million documents. As the number of items in your database grows, you should tune numLists to be larger in order to achieve good latency performance for vector search. If you're experimenting with a new scenario or creating a small demo, you can start with numLists set to 1 to perform a brute-force search across all vectors. This should provide you with the most accurate results from the vector search, however be aware that the search speed and latency will be slow. After your initial setup, you should go ahead and tune the numLists parameter using the above guidance. Args: kind: Type of vector index to create. Possible options are: - vector-ivf - vector-hnsw: available as a preview feature only, to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features - vector-diskann: available as a preview feature only num_lists: This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data. We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount) for more than 1 million documents. Using a numLists value of 1 is akin to performing brute-force search, which has limited performance dimensions: Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000 similarity: Similarity metric to use with the IVF index. Possible options are: - CosmosDBSimilarityType.COS (cosine distance), - CosmosDBSimilarityType.L2 (Euclidean distance), and - CosmosDBSimilarityType.IP (inner product). m: The max number of connections per layer (16 by default, minimum value is 2, maximum value is 100). Higher m is suitable for datasets with high dimensionality and/or high accuracy requirements. ef_construction: the size of the dynamic candidate list for constructing the graph (64 by default, minimum value is 4, maximum value is 1000). Higher ef_construction will result in better index quality and higher accuracy, but it will also increase the time required to build the index. ef_construction has to be at least 2 * m max_degree: Max number of neighbors. Default value is 32, range from 20 to 2048. Only vector-diskann search supports this for now. l_build: l value for index building. Default value is 50, range from 10 to 500. Only vector-diskann search supports this for now. Returns: An object describing the created index """# check the kind of vector search to be performed# prepare the command accordinglycreate_index_commands={}ifkind==CosmosDBVectorSearchType.VECTOR_IVF:create_index_commands=self._get_vector_index_ivf(kind,num_lists,similarity,dimensions)elifkind==CosmosDBVectorSearchType.VECTOR_HNSW:create_index_commands=self._get_vector_index_hnsw(kind,m,ef_construction,similarity,dimensions)elifkind==CosmosDBVectorSearchType.VECTOR_DISKANN:create_index_commands=self._get_vector_index_diskann(kind,max_degree,l_build,similarity,dimensions)# retrieve the database objectcurrent_database=self._collection.database# invoke the command from the database objectcreate_index_responses:dict[str,Any]=current_database.command(create_index_commands)returncreate_index_responses
[docs]defcreate_filter_index(self,property_to_filter:str,index_name:str,)->dict[str,Any]:command={"createIndexes":self._collection.name,"indexes":[{"key":{property_to_filter:1},"name":index_name,}],}# retrieve the database objectcurrent_database=self._collection.database# invoke the command from the database objectcreate_index_responses:dict[str,Any]=current_database.command(command)returncreate_index_responses
def_insert_texts(self,texts:List[str],metadatas:List[Dict[str,Any]])->List:"""Used to Load Documents into the collection Args: texts: The list of documents strings to load metadatas: The list of metadata objects associated with each document Returns: """# If the text is empty, then exit earlyifnottexts:return[]# Embed and create the documentsembeddings=self._embedding.embed_documents(texts)to_insert=[{self._text_key:t,self._embedding_key:embedding,"metadata":m}fort,m,embeddinginzip(texts,metadatas,embeddings)]# insert the documents in Cosmos DBinsert_result=self._collection.insert_many(to_insert)# type: ignorereturninsert_result.inserted_ids
[docs]@classmethoddeffrom_texts(cls,texts:List[str],embedding:Embeddings,metadatas:Optional[List[dict]]=None,collection:Optional[Collection]=None,**kwargs:Any,)->AzureCosmosDBVectorSearch:ifcollectionisNone:raiseValueError("Must provide 'collection' named parameter.")vectorstore=cls(collection,embedding,**kwargs)vectorstore.add_texts(texts,metadatas=metadatas)returnvectorstore
[docs]defdelete(self,ids:Optional[List[str]]=None,**kwargs:Any)->Optional[bool]:ifidsisNone:raiseValueError("No document ids provided to delete.")fordocument_idinids:self.delete_document_by_id(document_id)returnTrue
[docs]defdelete_document_by_id(self,document_id:Optional[str]=None)->None:"""Removes a Specific Document by Id Args: document_id: The document identifier """try:frombson.objectidimportObjectIdexceptImportErrorase:raiseImportError("Unable to import bson, please install with `pip install bson`.")fromeifdocument_idisNone:raiseValueError("No document id provided to delete.")self._collection.delete_one({"_id":ObjectId(document_id)})
def_similarity_search_with_score(self,embeddings:List[float],k:int=4,kind:CosmosDBVectorSearchType=CosmosDBVectorSearchType.VECTOR_IVF,pre_filter:Optional[Dict]=None,ef_search:int=40,score_threshold:float=0.0,l_search:int=40,with_embedding:bool=False,)->List[Tuple[Document,float]]:"""Returns a list of documents with their scores Args: embeddings: The query vector k: the number of documents to return kind: Type of vector index to create. Possible options are: - vector-ivf - vector-hnsw: available as a preview feature only, to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features - vector-diskann: available as a preview feature only ef_search: The size of the dynamic candidate list for search (40 by default). A higher value provides better recall at the cost of speed. score_threshold: (Optional[float], optional): Maximum vector distance between selected documents and the query vector. Defaults to None. Only vector-ivf search supports this for now. l_search: l value for index searching. Default value is 40, range from 10 to 10000. Only vector-diskann search supports this. Returns: A list of documents closest to the query vector """pipeline:List[dict[str,Any]]=[]ifkind==CosmosDBVectorSearchType.VECTOR_IVF:pipeline=self._get_pipeline_vector_ivf(embeddings,k,pre_filter)elifkind==CosmosDBVectorSearchType.VECTOR_HNSW:pipeline=self._get_pipeline_vector_hnsw(embeddings,k,ef_search,pre_filter)elifkind==CosmosDBVectorSearchType.VECTOR_DISKANN:pipeline=self._get_pipeline_vector_diskann(embeddings,k,l_search,pre_filter)cursor=self._collection.aggregate(pipeline)docs=[]forresincursor:score=res.pop("similarityScore")ifscore<score_threshold:continuedocument_object_field=res.pop("document")text=document_object_field.pop(self._text_key)metadata=document_object_field.pop("metadata",{})metadata["_id"]=document_object_field.pop("_id")# '_id' is in new positionifwith_embedding:metadata[self._embedding_key]=document_object_field.pop(self._embedding_key)docs.append((Document(page_content=text,metadata=metadata),score))returndocsdef_get_pipeline_vector_ivf(self,embeddings:List[float],k:int=4,pre_filter:Optional[Dict]=None)->List[dict[str,Any]]:params={"vector":embeddings,"path":self._embedding_key,"k":k,}ifpre_filter:params["filter"]=pre_filterpipeline:List[dict[str,Any]]=[{"$search":{"cosmosSearch":params,"returnStoredSource":True,}},{"$project":{"similarityScore":{"$meta":"searchScore"},"document":"$$ROOT",}},]returnpipelinedef_get_pipeline_vector_hnsw(self,embeddings:List[float],k:int=4,ef_search:int=40,pre_filter:Optional[Dict]=None,)->List[dict[str,Any]]:params={"vector":embeddings,"path":self._embedding_key,"k":k,"efSearch":ef_search,}ifpre_filter:params["filter"]=pre_filterpipeline:List[dict[str,Any]]=[{"$search":{"cosmosSearch":params,}},{"$project":{"similarityScore":{"$meta":"searchScore"},"document":"$$ROOT",}},]returnpipelinedef_get_pipeline_vector_diskann(self,embeddings:List[float],k:int=4,l_search:int=40,pre_filter:Optional[Dict]=None,)->List[dict[str,Any]]:params={"vector":embeddings,"path":self._embedding_key,"k":k,"lSearch":l_search,}ifpre_filter:params["filter"]=pre_filterpipeline:List[dict[str,Any]]=[{"$search":{"cosmosSearch":params,}},{"$project":{"similarityScore":{"$meta":"searchScore"},"document":"$$ROOT",}},]returnpipeline
[docs]defmax_marginal_relevance_search_by_vector(self,embedding:List[float],k:int=4,fetch_k:int=20,lambda_mult:float=0.5,kind:CosmosDBVectorSearchType=CosmosDBVectorSearchType.VECTOR_IVF,pre_filter:Optional[Dict]=None,ef_search:int=40,score_threshold:float=0.0,l_search:int=40,with_embedding:bool=False,**kwargs:Any,)->List[Document]:# Retrieves the docs with similarity scores# sorted by similarity scores in DESC orderdocs=self._similarity_search_with_score(embedding,k=fetch_k,kind=kind,pre_filter=pre_filter,ef_search=ef_search,score_threshold=score_threshold,l_search=l_search,with_embedding=with_embedding,)# Re-ranks the docs using MMRmmr_doc_indexes=maximal_marginal_relevance(np.array(embedding),[doc.metadata[self._embedding_key]fordoc,_indocs],k=k,lambda_mult=lambda_mult,)mmr_docs=[docs[i][0]foriinmmr_doc_indexes]returnmmr_docs
[docs]defmax_marginal_relevance_search(self,query:str,k:int=4,fetch_k:int=20,lambda_mult:float=0.5,kind:CosmosDBVectorSearchType=CosmosDBVectorSearchType.VECTOR_IVF,pre_filter:Optional[Dict]=None,ef_search:int=40,score_threshold:float=0.0,l_search:int=40,with_embedding:bool=False,**kwargs:Any,)->List[Document]:# compute the embeddings vector from the query stringembeddings=self._embedding.embed_query(query)docs=self.max_marginal_relevance_search_by_vector(embeddings,k=k,fetch_k=fetch_k,lambda_mult=lambda_mult,kind=kind,pre_filter=pre_filter,ef_search=ef_search,score_threshold=score_threshold,l_search=l_search,with_embedding=with_embedding,)returndocs