ti/ddlmZmZmZddlZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZddlZejeZerddlmZGddedZdS) ) TYPE_CHECKINGOptionalUnionN)CollectionCommon)URICollectionMetadata EmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument) ServerAPIceZdZdefdZ d deedeeee ee j fdeee deee deeed eeeddfd Zdddddddgfdeeed eed eed eedeededefdZd!d edefdZdddddddgdfdeeee ee j fdeee deeedeeeded eedeededefdZ d"deedeeddfdZ d deedeeee ee j fdeee deee deeed eeeddfdZ d deedeeee ee j fdeee deee deeed eeeddfdZ d#deed eedeeddfdZdS)$ CollectionreturncB|j|jS)zThe total number of embeddings added to the database Returns: int: The total number of embeddings added to the database ) collection_id)_client_countid)selfs Q/var/www/icac/venv/lib/python3.11/site-packages/chromadb/api/models/Collection.pycountzCollection.counts|"""999Nids embeddings metadatas documentsimagesurisc|||||||\}}}}}|j||j||||dS)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_setr_addrrr"r#r$r%r&r's raddzCollection.add(scP  4 4 Y 64        #tw Iy$OOOOOr!wherelimitoffsetwhere_documentincludec |||||\}}} } |j|j||d||| | } || |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. `{$contains: {"text": "hello"}}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. N)r0r1)!_validate_and_prepare_get_requestr_getr_transform_get_response) rr"r-r.r/r0r1 valid_ids valid_wherevalid_where_document valid_include get_resultss rgetzCollection.getVs|:  2 23~w W W    l'' G     /!(   ++KAAAr! cB|j|j|S)zGet the first few results in the database up to limit Args: limit: The number of results to return. Returns: GetResult: A GetResult object containing the results. )r_peekr)rr.s rpeekzCollection.peeks|!!$'5111r!)r$r% distancesquery_embeddings query_texts query_images query_uris n_resultsc |||||||||\} } } } |j|j| | | | |} || |S)aGet the n_results nearest neighbor embeddings for provided query_embeddings or query_texts. Args: query_embeddings: The embeddings to get the closes neighbors of. Optional. query_texts: The document texts to get the closes neighbors of. Optional. query_images: The images to get the closes neighbors of. Optional. n_results: The number of neighbors to return for each query_embedding or query_texts. Optional. where: A Where type dict used to filter results by. E.g. `{"$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 )rrArEr-r0r1)#_validate_and_prepare_query_requestr_queryr_transform_query_response)rrArBrCrDrEr-r0r1valid_query_embeddingsvalid_n_resultsr7r8 query_resultss rqueryzCollection.querysV  4 4          "    ++'3%/ ,  --mWEEEr!namemetadatac|||j|j|||||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 )rnew_name new_metadataN)_validate_modify_requestr_modifyr"_update_model_after_modify_success)rrNrOs rmodifyzCollection.modifysU %%h/// $XNNN //h?????r!c|||||||\}}}}}|j|j|||||dS)aUpdate the embeddings, metadatas or documents for provided ids. Args: ids: The ids of the embeddings to update embeddings: The embeddings to update. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. Returns: None N)$_validate_and_prepare_update_requestr_updaterr+s rupdatezCollection.updatesb>  5 5 Y 64        TWc:y)TRRRRRr!c|||||||\}}}}}|j|j|||||dS)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 )rr"r#r$r%r'N)$_validate_and_prepare_upsert_requestr_upsertrr+s rupsertzCollection.upsert sw>  5 5 Y 64        '!      r!c||||\}}}|j|j|||dS)a8Delete 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)rr"r-r0s rdeletezCollection.delete5sM&(,'P'P ( ( $e^ TWc5.AAAAAr!)NNNNN)r<)NN)NNN) __name__ __module__ __qualname__intr rrrrr npndarrayr r r rr,rrr rr;r?rrMstrrrVrZr^rrbr!rrrs:s::::" 3737-1)-,P,P r],P )$"*%'  ,PIh/0,PIh/0,P5)*,Py~&,P ,P,P,P,P`(,!%# $26'5*B*B im $*B*B} *B  *B !/ *B*B *B*B*B*BX 2 2# 2y 2 2 2 2$ 5937/3!%26BBB?F?F" )$"*%'  ?Fi12?Fy/0?FYs^,?F?F?F!/?F?F ?F?F?F?FDTX@@SM@4<=O4P@ @@@@< 3737-1)-#S#S r]#S )$"*%'  #SIh/0#SIh/0#S5)*#Sy~&#S #S#S#S#SZ 3737-1)-* * r]*  )$"*%'  * Ih/0* Ih/0* 5)** y~&*  * * * * \"!%26 BB c]BB!/ B  BBBBBBr!rr)typingrrrnumpyrg$chromadb.api.models.CollectionCommonrchromadb.api.typesrrr r r r r rrrrrrrlogging getLoggerrclogger chromadb.apirrrjr!rrssf1111111111AAAAAA"  8 $ $'&&&&&&nBnBnBnBnB!+.nBnBnBnBnBr!