ug6ddlmZmZmZddlmZmZmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZddlmZerddlmZGddedZy) ) TYPE_CHECKINGOptionalUnion)URICollectionMetadata Embedding IncludeEnum PyEmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument)CollectionCommon)AsyncServerAPIcTeZdZ ddeedeeeeeefdeee deee deee deee ddfd Z defd Zdddddej"ej$gfdeeed eed eed eedeededefdZd d edefdZdddddddej"ej$ej2gfdeeeeeefdeee deee deee ded eedeededefdZ d!deedeeddfdZ ddeedeeeeeefdeee deee deee deee ddfdZ ddeedeeeeeefdeee deee deee deee ddfdZ d"dee!d eedeeddfdZ"y)#AsyncCollectionNids embeddings metadatas documentsimagesurisreturnc K|j||||||}|jj|j|d|d|d|d|d|j|j d{y7w) a]Add embeddings to the data store. Args: ids: The ids of the embeddings you wish to add embeddings: The embeddings to add. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. uris: The uris of the images to associate with the embeddings. Optional. Returns: None Raises: ValueError: If you don't provide either embeddings or documents ValueError: If the length of ids, embeddings, metadatas, or documents don't match ValueError: If you don't provide an embedding function and don't provide embeddings ValueError: If you provide both embeddings and documents ValueError: If you provide an id that already exists rrrrrrrrrrr collection_idrrrrrtenantdatabaseN)!_validate_and_prepare_add_request_client_addidr%r&)selfrrrrrr add_requests X/var/www/openai/venv/lib/python3.12/site-packages/chromadb/api/models/AsyncCollection.pyaddzAsyncCollection.addsD<<! = ll''E""<0!+.!+.V$;;]]  A+A5-A3.A5cK|jj|j|j|jd{S7w)zThe total number of embeddings added to the database Returns: int: The total number of embeddings added to the database )r$r%r&N)r(_countr*r%r&)r+s r-countzAsyncCollection.countSsC\\(('';;]])    sAA AA wherelimitoffsetwhere_documentincludec K|j||||}|jj|j|d|d|d|dd|||j|j  d{}|j ||dS7w) aGet embeddings and their associate data from the data store. If no ids or where filter is provided returns all embeddings up to limit starting at offset. Args: ids: The ids of the embeddings to get. Optional. where: A Where type dict used to filter results by. E.g. `{"$and": ["color" : "red", "price": {"$gte": 4.20}]}`. Optional. limit: The number of documents to return. Optional. offset: The offset to start returning results from. Useful for paging results with limit. Optional. where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$contains: {"text": "hello"}}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. )rr3r6r7rr3r6r7N) r$rr3r6r7sortr4r5r%r&responser7)!_validate_and_prepare_get_requestr(_getr*r%r&_transform_get_response) r+rr3r4r5r6r7 get_request get_resultss r-getzAsyncCollection.get`s0<<) = !LL--''E"g&&'78 *;;]].   ++ +i*@,    sA(B*B+B cK|j|jj|j||j|j d{S7w)zGet the first few results in the database up to limit Args: limit: The number of results to return. Returns: GetResult: A GetResult object containing the results. )r$nr%r&N)_transform_peek_responser(_peekr*r%r&)r+r4s r-peekzAsyncCollection.peeksT,,,,$$"gg{{ %    sA AA Aquery_embeddings query_texts query_images query_uris n_resultsc K|j||||||||} |jj|j| d| d| d| d| d|j|j d{} |j | | d S7w) a}Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts. Args: query_embeddings: The embeddings to get the closes neighbors of. Optional. query_texts: The document texts to get the closes neighbors of. Optional. query_images: The images to get the closes neighbors of. Optional. n_results: The number of neighbors to return for each query_embedding or query_texts. Optional. where: A Where type dict used to filter results by. E.g. `{"$and": ["color" : "red", "price": {"$gte": 4.20}]}`. Optional. where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$contains: {"text": "hello"}}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional. Returns: QueryResult: A QueryResult object containing the results. Raises: ValueError: If you don't provide either query_embeddings, query_texts, or query_images ValueError: If you provide both query_embeddings and query_texts ValueError: If you provide both query_embeddings and query_images ValueError: If you provide both query_texts and query_images )rHrIrJrKrLr3r6r7rrLr3r6r7)r$rHrLr3r6r7r%r&Nr:)#_validate_and_prepare_query_requestr(_queryr*r%r&_transform_query_response) r+rHrIrJrKrLr3r6r7 query_request query_resultss r-queryzAsyncCollection.querysT@@-#%!)A  #ll11''*<8#K0(()9:!),;;]]2   --"M),D.    sA-B /B 0B namemetadatacK|j||jj|j||d{|j ||y7w)zModify the collection name or metadata Args: name: The updated name for the collection. Optional. metadata: The updated metadata for the collection. Optional. Returns: None )r*new_name new_metadataN)_validate_modify_requestr(_modifyr*"_update_model_after_modify_success)r+rTrUs r-modifyzAsyncCollection.modifysQ %%h/ ll""dgg8"TTT //h? Us=AAAc K|j||||||}|jj|j|d|d|d|d|d|j|j d{y7w) aUpdate the embeddings, metadatas or documents for provided ids. Args: ids: The ids of the embeddings to update embeddings: The embeddings to update. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. Returns: None r"rrrrrr#N)$_validate_and_prepare_update_requestr(_updater*r%r&)r+rrrrrrupdate_requests r-updatezAsyncCollection.update2BB! C ll""''u%%l3$[1$[1';;]]#  r/c K|j||||||}|jj|j|d|d|d|d|d|j|j d{y7w) aOUpdate the embeddings, metadatas or documents for provided ids, or create them if they don't exist. Args: ids: The ids of the embeddings to update embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. Returns: None r"rrrrrr#N)$_validate_and_prepare_upsert_requestr(_upsertr*r%r&)r+rrrrrrupsert_requests r-upsertzAsyncCollection.upsert)rbr/cK|j|||}|jj|j|d|d|d|j|j d{y7w)a4Delete the embeddings based on ids and/or a where filter Args: ids: The ids of the embeddings to delete where: A Where type dict used to filter the delection by. E.g. `{"$and": ["color" : "red", "price": {"$gte": 4.20}]}`. Optional. where_document: A WhereDocument type dict used to filter the deletion by the document content. E.g. `{$contains: {"text": "hello"}}`. Optional. Returns: None Raises: ValueError: If you don't provide either ids, where, or where_document rr3r6)r$rr3r6r%r&N)$_validate_and_prepare_delete_requestr(_deleter*r%r&)r+rr3r6delete_requests r-deletezAsyncCollection.deleteVsp&BB  ll""''u% ))*:;;;]] #   sAA)!A'"A))NNNNN)rB)NN)NNN)#__name__ __module__ __qualname__rrrrrr r r rrr.intr2r rrrrr rrArG distancesrrSstrrr\rargrrlr-rrs 3737-1)-4 r]4  )$+&(  4 Ih/04 Ih/04 5)*4 y~&4  4 l  S  (,!%# $26'11;3H3HI. im $. . } .  . !/ . .  . `  Y 2 5937/3!%26  ! !  ! !  ! ! B " )$+&(  B i12B y/0B Ys^,B B B !/B B & 'B JTX@SM@4<=O4P@ @< 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + j 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + ^"!%26  c]  !/    rtrrN)typingrrrchromadb.api.typesrrrr r r r r rrrrrrrr$chromadb.api.models.CollectionCommonr chromadb.apirrrsrtr-rysE11&B+X &'78X rt