ti00ddlmZmZmZddlZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZddlmZerddlmZGddedZdS) ) TYPE_CHECKINGOptionalUnionN)URICollectionMetadata EmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument)CollectionCommon)AsyncServerAPIceZdZ d deedeeeeeej fdeee deee deee deee ddfd Zdefd Zdddddddgfdeeed eed eed eedeededefdZd!d edefdZdddddddgdfdeeeeeej fdeee deee deee ded eedeededefdZ d"deedeeddfdZ d deedeeeeeej fdeee deee deee deee ddfdZ d deedeeeeeej fdeee deee deee deee ddfdZ d#deed eedeeddfdZdS)$AsyncCollectionNids embeddings metadatas documentsimagesurisreturncK|||||||\}}}}}|j||j||||d{VdS)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 N)#_validate_and_prepare_embedding_set_client_addidselfrrrrrrs V/var/www/icac/venv/lib/python3.11/site-packages/chromadb/api/models/AsyncCollection.pyaddzAsyncCollection.add szP  4 4 Y 64        lTWj)YPTUUUUUUUUUUUcRK|j|jd{VS)zThe total number of embeddings added to the database Returns: int: The total number of embeddings added to the database ) collection_idN)r!_countr#)r%s r&countzAsyncCollection.countNs5\((tw(?????????r(wherelimitoffsetwhere_documentincludec K|||||\}}} } |j|j||d||| | d{V} || | S)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. `{$contains: {"text": "hello"}}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. N)r0r1)!_validate_and_prepare_get_requestr!_getr#_transform_get_response) r%rr-r.r/r0r1 valid_ids valid_wherevalid_where_document valid_include get_resultss r&getzAsyncCollection.getWs:  2 23~w W W    !L-- G     /!.         ++KGGGr( cRK|j|j|d{VS)zGet the first few results in the database up to limit Args: limit: The number of results to return. Returns: GetResult: A GetResult object containing the results. N)r!_peekr#)r%r.s r&peekzAsyncCollection.peeks4\''777777777r()rr distancesquery_embeddings query_texts query_images query_uris n_resultsc K|||||||||\} } } } |j|j| | | | |d{V} || |S)a}Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts. Args: query_embeddings: The embeddings to get the closes neighbors of. Optional. query_texts: The document texts to get the closes neighbors of. Optional. query_images: The images to get the closes neighbors of. Optional. n_results: The number of neighbors to return for each query_embedding or query_texts. Optional. where: A Where type dict used to filter results by. E.g. `{"$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 )r*rArEr-r0r1N)#_validate_and_prepare_query_requestr!_queryr#_transform_query_response)r%rArBrCrDrEr-r0r1valid_query_embeddingsvalid_n_resultsr7r8 query_resultss r&queryzAsyncCollection.querysV  4 4          "   #l11'3%/ 2        --mWEEEr(namemetadatacK|||j|j||d{V|||dS)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%rNrOs r&modifyzAsyncCollection.modifysk %%h/// l""dg8"TTTTTTTTT //h?????r(cK|||||||\}}}}}|j|j|||||d{VdS)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 N)$_validate_and_prepare_update_requestr!_updater#r$s r&updatezAsyncCollection.updatesy>  5 5 Y 64        l""47CY SWXXXXXXXXXXXr(cK|||||||\}}}}}|j|j|||||d{VdS)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*rrrrrN)$_validate_and_prepare_upsert_requestr!_upsertr#r$s r&upsertzAsyncCollection.upsert s>  5 5 Y 64        l""'! #           r(cK||||\}}}|j|j|||d{VdS)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. `{$contains: {"text": "hello"}}`. Optional. Returns: None Raises: ValueError: If you don't provide either ids, where, or where_document N)$_validate_and_prepare_delete_requestr!_deleter#)r%rr-r0s r&deletezAsyncCollection.delete6sc&(,'P'P ( ( $e^l""47CGGGGGGGGGGGr()NNNNN)r<)NN)NNN) __name__ __module__ __qualname__rrrrrnpndarrayr r r rr'intr,r rr rr;r?rrMstrrrVrZr^rrbr(r&rrs 3737-1)-,V,V r],V )$"*%'  ,VIh/0,VIh/0,V5)*,Vy~&,V ,V,V,V,V\@S@@@@(,!%# $26'5*H*H im $*H*H} *H  *H !/ *H*H *H*H*H*HX 8 8 8Y 8 8 8 8$ 5937/3!%26BBB?F?F" )$"*%'  ?Fi12?Fy/0?FYs^,?F?F?F!/?F?F ?F?F?F?FDTX@@SM@4<=O4P@ @@@@< 3737-1)-#Y#Y r]#Y )$"*%'  #YIh/0#YIh/0#Y5)*#Yy~&#Y #Y#Y#Y#YZ 3737-1)-* * r]*  )$"*%'  * Ih/0* Ih/0* 5)** y~&*  * * * * \"!%26 HH c]HH!/ H  HHHHHHr(rr)typingrrrnumpyrfchromadb.api.typesrrrr r r r r rrrrrr$chromadb.api.models.CollectionCommonr chromadb.apirrrjr(r&rpsc "BAAAAA,++++++nHnHnHnHnH&'78nHnHnHnHnHr(