o g=@sddlZddlmZmZmZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZm Z ddlZeeZerKddlmZGdddedZGd d d eZ dS) N) TYPE_CHECKINGOptionalUnion)CollectionCommon)URICollectionMetadata Embedding IncludeEnum PyEmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocumentr ) ServerAPIc@seZdZdefddZ     d(deedeeee ee fdeee deee d eee d eeeddfd d Zdddddejejgfdeeed eedeedeedeededefddZd)dedefddZdddddddejejejgfdeeee ee fdeee deee deeeded eedeededefddZ d*deedeeddfd d!Z     d(deedeeee ee fdeee deee d eee d eeeddfd"d#Z     d(deedeeee ee fdeee deee d eee d eeeddfd$d%Z   d+dee!d eedeeddfd&d'Z"dS), CollectionreturncCs|jj|j|j|jdS)zThe total number of embeddings added to the database Returns: int: The total number of embeddings added to the database ) collection_idtenantdatabase)_client_countidrr)selfr U/var/www/visachat/venv/lib/python3.10/site-packages/chromadb/api/models/Collection.pycount"s zCollection.countNids embeddings metadatas documentsimagesurisc CP|j||||||d}|jj|j|d|d|d|d|d|j|jddS) a]Add embeddings to the data store. Args: ids: The ids of the embeddings you wish to add embeddings: The embeddings to add. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. uris: The uris of the images to associate with the embeddings. Optional. Returns: None Raises: ValueError: If you don't provide either embeddings or documents ValueError: If the length of ids, embeddings, metadatas, or documents don't match ValueError: If you don't provide an embedding function and don't provide embeddings ValueError: If you provide both embeddings and documents ValueError: If you provide an id that already exists r#r$r%r&r'r(r#r$r%r&r(rr#r$r%r&r(rrN)!_validate_and_prepare_add_requestr_addrrr)rr#r$r%r&r'r( add_requestr r r!add/s$#  zCollection.addwherelimitoffsetwhere_documentincludec CsZ|j||||d}|jj|j|d|d|d|dd|||j|jd }|j||ddS) 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. `{$contains: {"text": "hello"}}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. )r#r0r3r4r#r0r3r4N) rr#r0r3r4sortr1r2rrresponser4)!_validate_and_prepare_get_requestr_getrrr_transform_get_response) rr#r0r1r2r3r4 get_request get_resultsr r r!getfs* zCollection.get cCs ||jj|j||j|jdS)zGet the first few results in the database up to limit Args: limit: The number of results to return. Returns: GetResult: A GetResult object containing the results. )rnrr)_transform_peek_responser_peekrrr)rr1r r r!peeks zCollection.peekquery_embeddings query_texts query_images query_uris n_resultsc Csb|j||||||||d} |jj|j| d| d| d| d| d|j|jd} |j| | ddS) aGet the n_results nearest neighbor embeddings for provided query_embeddings or query_texts. Args: query_embeddings: The embeddings to get the closes neighbors of. Optional. query_texts: The document texts to get the closes neighbors of. Optional. query_images: The images to get the closes neighbors of. Optional. query_uris: The URIs to be used with data loader. Optional. n_results: The number of neighbors to return for each query_embedding or query_texts. Optional. where: A Where type dict used to filter results by. E.g. `{"$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 )rCrDrErFrGr0r3r4r$rGr0r3r4)rrCrGr0r3r4rrr6)#_validate_and_prepare_query_requestr_queryrrr_transform_query_response) rrCrDrErFrGr0r3r4 query_request query_resultsr r r!querys.+  zCollection.querynamemetadatacCs6|||jj|j|||j|jd|||dS)zModify the collection name or metadata Args: name: The updated name for the collection. Optional. metadata: The updated metadata for the collection. Optional. Returns: None )rnew_name new_metadatarrN)_validate_modify_requestr_modifyrrr"_update_model_after_modify_success)rrNrOr r r!modifys zCollection.modifyc Cr)) aUpdate the embeddings, metadatas or documents for provided ids. Args: ids: The ids of the embeddings to update embeddings: The embeddings to update. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. Returns: None r*r#r$r%r&r(r+N)$_validate_and_prepare_update_requestr_updaterrr)rr#r$r%r&r'r(update_requestr r r!update $  zCollection.updatec Cr)) 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_upsertrrr)rr#r$r%r&r'r(upsert_requestr r r!upsert6rZzCollection.upsertcCs<||||}|jj|j|d|d|d|j|jddS)a8Delete the embeddings based on ids and/or a where filter Args: ids: The ids of the embeddings to delete where: A Where type dict used to filter the delection by. E.g. `{"$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#r0r3)rr#r0r3rrN)$_validate_and_prepare_delete_requestr_deleterrr)rr#r0r3delete_requestr r r!deletecs zCollection.delete)NNNNN)r>)NN)NNN)#__name__ __module__ __qualname__intr"rrrrrr r r rrr/r r%r&rrr rr=rB distancesrrMstrrrUrYr^rrbr r r r!r!s<     9   0       F $     5     /rrc@seZdZdZddZdS)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 cCsFddttD}||vrtdt|d|dtd|d)NcSsg|] \}}|ds|qS)_) startswith).0memberrjr r r! s z.CollectionName.__getattr__..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 getmembersrNotImplementedErrorrhAttributeError)ritem!collection_attributes_and_methodsr r r! __getattr__szCollectionName.__getattr__N)rcrdre__doc__rvr r r r!ris ri)!rptypingrrr$chromadb.api.models.CollectionCommonrchromadb.api.typesrrrr r r r r rrrrrrrrlogging getLoggerrclogger chromadb.apirrrhrir r r r!s L  e