o 1XxiG0@sddlmZmZmZddlZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZddlZeeZerEddlmZGdddedZdS) ) TYPE_CHECKINGOptionalUnionN)CollectionCommon)URICollectionMetadata EmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument) ServerAPIc@seZdZdefddZ     d)deedeeee ee j fdeee deee d eeed eeeddfd d Zdddddddgfdeeed eedeedeedeededefddZd*dedefddZdddddddgdfdeeee ee j fdeee deeedeeeded eedeededefddZ d+deed eeddfd!d"Z     d)deedeeee ee j fdeee deee d eeed eeeddfd#d$Z     d)deedeeee ee j fdeee deee d eeed eeeddfd%d&Z   d,deed eedeeddfd'd(ZdS)- CollectionreturncCs|jj|jdS)zThe total number of embeddings added to the database Returns: int: The total number of embeddings added to the database ) collection_id)_client_countid)selfr]/home/air/biblejyuku/back/venv/lib/python3.10/site-packages/chromadb/api/models/Collection.pycountszCollection.countNids embeddings metadatas documentsimagesuriscCs:|||||||\}}}}}|j||j||||dS)a]Add embeddings to the data store. Args: ids: The ids of the embeddings you wish to add embeddings: The embeddings to add. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. uris: The uris of the images to associate with the embeddings. Optional. Returns: None Raises: ValueError: If you don't provide either embeddings or documents ValueError: If the length of ids, embeddings, metadatas, or documents don't match ValueError: If you don't provide an embedding function and don't provide embeddings ValueError: If you provide both embeddings and documents ValueError: If you provide an id that already exists N)#_validate_and_prepare_embedding_setr_addrrrr r!r"r#r$rrradd(s( zCollection.addwherelimitoffsetwhere_documentincludec CsB|||||\}}} } |jj|j||d||| | 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. `{$contains: {"text": "hello"}}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. N)r,r-)!_validate_and_prepare_get_requestr_getr_transform_get_response) rrr)r*r+r,r- valid_ids valid_wherevalid_where_document valid_include get_resultsrrrgetVs" zCollection.get cCs|j|j|S)zGet the first few results in the database up to limit Args: limit: The number of results to return. Returns: GetResult: A GetResult object containing the results. )r_peekr)rr*rrrpeeks zCollection.peek)r!r" distancesquery_embeddings query_texts query_images query_uris n_resultsc  CsF|||||||||\} } } } |jj|j| | | | |d} || |S)aGet the n_results nearest neighbor embeddings for provided query_embeddings or query_texts. Args: query_embeddings: The embeddings to get the closes neighbors of. Optional. query_texts: The document texts to get the closes neighbors of. Optional. query_images: The images to get the closes neighbors of. Optional. n_results: The number of neighbors to return for each query_embedding or query_texts. Optional. where: A Where type dict used to filter results by. E.g. `{"$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 )rr;r?r)r,r-)#_validate_and_prepare_query_requestr_queryr_transform_query_response)rr;r<r=r>r?r)r,r-valid_query_embeddingsvalid_n_resultsr2r3 query_resultsrrrquerys0+  zCollection.querynamemetadatacCs.|||jj|j||d|||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)rrGrHrrrmodifys zCollection.modifycCs:|||||||\}}}}}|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)$_validate_and_prepare_update_requestr_updaterr'rrrupdates zCollection.updatecCs<|||||||\}}}}}|jj|j|||||ddS)aOUpdate the embeddings, metadatas or documents for provided ids, or create them if they don't exist. Args: ids: The ids of the embeddings to update embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. Returns: None )rrr r!r"r$N)$_validate_and_prepare_upsert_requestr_upsertrr'rrrupsert s"  zCollection.upsertcCs,||||\}}}|j|j|||dS)a8Delete the embeddings based on ids and/or a where filter Args: ids: The ids of the embeddings to delete where: A Where type dict used to filter the delection by. E.g. `{"$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)rrr)r,rrrdelete5s zCollection.delete)NNNNN)r7)NN)NNN) __name__ __module__ __qualname__intrrrrrrnpndarrayr r r rr(r rr rr6r9rrFstrrrNrQrTrrWrrrrrs6     0  ,       B      -     .rr)typingrrrnumpyr\$chromadb.api.models.CollectionCommonrchromadb.api.typesrrrr r r r r rrrrrrlogging getLoggerrXlogger chromadb.apirrrrrrs @