ug=ddlZddlmZmZmZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZm Z ddlZej2eZerddlmZGddedZGd d eZ y) N) TYPE_CHECKINGOptionalUnion)CollectionCommon)URICollectionMetadata Embedding IncludeEnum PyEmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocumentr ) ServerAPIcTeZdZdefdZ ddeedeeee ee fdeee deee deee d eeeddfd Zdddddej"ej$gfdeeed eed eed eedeededefdZd d edefdZdddddddej"ej$ej2gfdeeee ee fdeee deee deeeded eedeededefdZ d!deedeeddfdZ ddeedeeee ee fdeee deee deee d eeeddfdZ ddeedeeee ee fdeee deee deee d eeeddfdZ d"dee!d eedeeddfdZ"y)# Collectionreturncz|jj|j|j|jS)zThe total number of embeddings added to the database Returns: int: The total number of embeddings added to the database ) collection_idtenantdatabase)_client_countidrr)selfs S/var/www/openai/venv/lib/python3.12/site-packages/chromadb/api/models/Collection.pycountzCollection.count"s5||""'';;]]#  Nids embeddings metadatas documentsimagesurisc |j||||||}|jj|j|d|d|d|d|d|j|j y) 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 r&r'r(r)r*r+r&r'r(r)r+rr&r'r(r)r+rrN)!_validate_and_prepare_add_requestr_addr!rr)r"r&r'r(r)r*r+ add_requests r#addzCollection.add/sF<<! =  ''E""<0!+.!+.V$;;]]  r%wherelimitoffsetwhere_documentincludec |j||||}|jj|j|d|d|d|dd|||j|j  }|j ||dS) aGet embeddings and their associate data from the data store. If no ids or where filter is provided returns all embeddings up to limit starting at offset. Args: ids: The ids of the embeddings to get. Optional. where: A Where type dict used to filter results by. E.g. `{"$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. )r&r3r6r7r&r3r6r7N) rr&r3r6r7sortr4r5rrresponser7)!_validate_and_prepare_get_requestr_getr!rr_transform_get_response) r"r&r3r4r5r6r7 get_request get_resultss r#getzCollection.getfs0<<) = ll''''E"g&&'78 *;;]](  ++ +i*@,  r% c|j|jj|j||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. )rnrr)_transform_peek_responser_peekr!rr)r"r4s r#peekzCollection.peeksF,, LL  "gg{{    r%query_embeddings query_texts query_images query_uris n_resultsc |j||||||||} |jj|j| d| d| d| d| d|j|j } |j | | dS) 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. query_uris: The URIs to be used with data loader. 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. `{$contains: {"text": "hello"}}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional. Returns: QueryResult: A QueryResult object containing the results. Raises: ValueError: If you don't provide either query_embeddings, query_texts, or query_images ValueError: If you provide both query_embeddings and query_texts ValueError: If you provide both query_embeddings and query_images ValueError: If you provide both query_texts and query_images )rHrIrJrKrLr3r6r7r'rLr3r6r7)rrHrLr3r6r7rrr:)#_validate_and_prepare_query_requestr_queryr!rr_transform_query_response) r"rHrIrJrKrLr3r6r7 query_request query_resultss r#queryzCollection.querysV@@-#%!)A   ++''*<8#K0(()9:!),;;]],  --"M),D.  r%namemetadatac|j||jj|j|||j|j |j ||y)zModify the collection name or metadata Args: name: The updated name for the collection. Optional. metadata: The updated metadata for the collection. Optional. Returns: None )r!new_name new_metadatarrN)_validate_modify_requestr_modifyr!rr"_update_model_after_modify_success)r"rTrUs r#modifyzCollection.modifysX %%h/ ww!;;]]   //h?r%c |j||||||}|jj|j|d|d|d|d|d|j|j y) aUpdate the embeddings, metadatas or documents for provided ids. Args: ids: The ids of the embeddings to update embeddings: The embeddings to update. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. Returns: None r-r&r'r(r)r+r.N)$_validate_and_prepare_update_requestr_updater!rr)r"r&r'r(r)r*r+update_requests r#updatezCollection.update 2BB! C  ''u%%l3$[1$[1';;]]  r%c |j||||||}|jj|j|d|d|d|d|d|j|j y) 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-r&r'r(r)r+r.N)$_validate_and_prepare_upsert_requestr_upsertr!rr)r"r&r'r(r)r*r+upsert_requests r#upsertzCollection.upsert6rbr%c|j|||}|jj|j|d|d|d|j|j y)a8Delete the embeddings based on ids and/or a where filter Args: ids: The ids of the embeddings to delete where: A Where type dict used to filter the delection by. E.g. `{"$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 r&r3r6)rr&r3r6rrN)$_validate_and_prepare_delete_requestr_deleter!rr)r"r&r3r6delete_requests r#deletezCollection.deletecsb&BB   ''u% ))*:;;;]]  r%)NNNNN)rB)NN)NNN)#__name__ __module__ __qualname__intr$rrrrr r r rrrr2r r(r)rrr rrArG distancesrrSstrrr\rargrrlr%r#rr!s  s  * 3737-1)-5 r]5  )$+&(  5 Ih/05 Ih/05 5)*5 y~&5  5 r(,!%# $26'11;3H3HI. im $. . } .  . !/ . .  . ` # y 2 5937/3!%26  ! !  ! !  ! ! C " )$+&(  C i12C y/0C Ys^,C C C !/C C & 'C LTX@SM@4<=O4P@ @H 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + j 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + ^"!%26  c]  !/    r%rrceZdZdZdZy)CollectionNamea A string wrapper to supply users with indicative message about list_collections only returning collection names, in lieu of Collection object. When a user will try to access an attribute on a CollectionName string, the __getattribute__ method of str is invoked first. If a valid str method or property is found, it will be used. Otherwise, the fallback __getattr__ defined here is invoked next. It will error if the requested attribute is a Collection method or property. For example: collection_name = client.list_collections()[0] # collection_name = "test" collection_name.startsWith("t") # Evaluates to True. # __getattribute__ is invoked first, selecting startsWith from str. collection_name.add(ids=[...], documents=[...]) # Raises the error defined below # __getattribute__ is invoked first, not finding a match in str. # __getattr__ from this class is invoked and raises an error ctjtDcgc]\}}|jds|}}}||vrt dt |d|dt d|dcc}}w)N_z\In Chroma v0.6.0, list_collections only returns collection names. Use Client.get_collection(z ) to access zK. See https://docs.trychroma.com/deployment/migration for more information.z*'CollectionName' object has no attribute '')inspect getmembersr startswithNotImplementedErrorrrAttributeError)r"itemmemberrw!collection_attributes_and_methodss r# __getattr__zCollectionName.__getattr__s%// ;- ; $$S) ; *- 4 4%--0YK|D6J\]  I$qQRR- sA+N)rmrnro__doc__rrsr%r#rurus *Sr%ru)!rytypingrrr$chromadb.api.models.CollectionCommonrchromadb.api.typesrrr r r r r rrrrrrrrrlogging getLoggerrmlogger chromadb.apirrrrrursr%r#rsj11A(   8 $&` !+.` F $SS$Sr%