3iHSSKJrJrJrJrJr SSKJrJrJ r J r J r J r J r JrJrJrJrJrJrJrJrJrJr SSKJr SSKJr SSKJr \(aSSKJr "SS\S 5r g ) ) TYPE_CHECKINGOptionalUnionListcast)URICollectionMetadata Embedding PyEmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument SearchResultmaybe_cast_one_to_many)CollectionCommon)UpdateCollectionConfiguration)Search)AsyncServerAPIcb\rSrSrS'S\\S\\\\\\ 4S\\\ S\\\ S\\\ S\\\ S S4S jjrS \4S jrSSSSSSS/4S\\\S \\S \\S\\S\\S\S \4SjjrS(S \S \4SjjrSSSSSSSS/SQ4 S\\\\\\ 4S\\\ S\\\ S\\\ S\\\S\S \\S\\S\S \4SjjrS)S\\S\\S\\S S4SjjrS\S S4S jrS!\\S \4S"jr S'S\\S\\\\\\ 4S\\\ S\\\ S\\\ S\\\ S S4S#jjr!S'S\\S\\\\\\ 4S\\\ S\\\ S\\\ S\\\ S S4S$jjr"S)S\\#S \\S\\S S4S%jjr$S&r%g)*AsyncCollectionNids embeddings metadatas documentsimagesurisreturnc # URUUUUUUS9nURRURUSUSUSUSUSURUR S9IShvN gN7f) aAdd 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$r%r r!r"r#r% collection_idr r!r"r#r%tenantdatabaseN)!_validate_and_prepare_add_request_client_addidr+r,)selfr r!r"r#r$r% add_requests ]/home/james-whalen/.local/lib/python3.13/site-packages/chromadb/api/models/AsyncCollection.pyaddAsyncCollection.add sD<<! = ll''E""<0!+.!+.V$;;]]  A%A/'A-(A/c# URRURURURS9IShvN $N7f)z~The total number of embeddings added to the database Returns: int: The total number of embeddings added to the database )r*r+r,N)r._countr0r+r,)r1s r3countAsyncCollection.countVsB\\(('';;]])    s=AAAwherelimitoffsetwhere_documentincludec # URUUUUS9nURRURUSUSUSUSUUURUR S9 IShvN nUR XSS9$N7f) atGet 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. `{"$contains": "hello"}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. )r r;r>r?r r;r>r?) r*r r;r>r?r<r=r+r,Nresponser?)!_validate_and_prepare_get_requestr._getr0r+r,_transform_get_response) r1r r;r<r=r>r? get_request get_resultss r3getAsyncCollection.getcs0<<) = !LL--''E"g&&'78 *;;]].   ++ i*@,    sA!A<#A:$A< c # URURRURUURUR S9IShvN 5$N7f)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*nr+r,N)_transform_peek_responser._peekr0r+r,)r1r<s r3peekAsyncCollection.peeksS,,,,$$"gg{{ %    sA A A A)r"r# distancesquery_embeddings query_texts query_images query_uris n_resultsc # URUUUUUUUUU S9 n URRURU SU SU SU SU SU SURUR S9 IS hvN n UR XSS 9$N7f) a.Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts. Args: query_embeddings: The embeddings to get the closes neighbors of. Optional. query_texts: The document texts to get the closes neighbors of. Optional. query_images: The images to get the closes neighbors of. Optional. ids: A subset of ids to search within. Optional. n_results: The number of neighbors to return for each query_embedding or query_texts. Optional. where: A Where type dict used to filter results by. E.g. `{"$and": [{"color" : "red"}, {"price": {"$gte": 4.20}}]}`. Optional. where_document: A WhereDocument type dict used to filter by the documents. E.g. `{"$contains": "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 ) rRrSrTrUr rVr;r>r?r r!rVr;r>r?) r*r rRrVr;r>r?r+r,NrA)#_validate_and_prepare_query_requestr._queryr0r+r,_transform_query_response) r1rRrSrTrUr rVr;r>r? query_request query_resultss r3queryAsyncCollection.querysX@@-#%!)A  #ll11''e$*<8#K0(()9:!),;;]]2   --"),D.    sA,B.B/Bnamemetadata configurationc # URU5 URRURUUUURUR S9IShvN UR XU5 gN7f)zModify the collection name or metadata Args: name: The updated name for the collection. Optional. metadata: The updated metadata for the collection. Optional. Returns: None )r0new_name new_metadatanew_configurationr+r,N)_validate_modify_requestr._modifyr0r+r,"_update_model_after_modify_success)r1r_r`ras r3modifyAsyncCollection.modifysm %%h/ ll""ww!+;;]] #    // N sAA-A+A-rcc# URRURUURURS9IShvN n[ URUUR URS9$N.7f)axFork the current collection under a new name. The returning collection should contain identical data to the current collection. This is an experimental API that only works for Hosted Chroma for now. Args: new_name: The name of the new collection. Returns: Collection: A new collection with the specified name and containing identical data to the current collection. )r*rcr+r,N)clientmodelembedding_function data_loader)r._forkr0r+r,r_embedding_function _data_loader)r1rcrms r3forkAsyncCollection.fork smll(('';;]] )   <<#77))    s>A1A//A1searchesc&# [U5nUc/nUVs/sHo0RU5PM nnURRUR[ [ [U5URURS9IShvN $s snfN 7f)a%Perform hybrid search on the collection. This is an experimental API that only works for Hosted Chroma for now. Args: searches: A single Search object or a list of Search objects, each containing: - where: Where expression for filtering - rank: Ranking expression for hybrid search (defaults to Val(0.0)) - limit: Limit configuration for pagination (defaults to no limit) - select: Select configuration for keys to return (defaults to empty) Returns: SearchResult: Column-major format response with: - ids: List of result IDs for each search payload - documents: Optional documents for each payload - embeddings: Optional embeddings for each payload - metadatas: Optional metadata for each payload - scores: Optional scores for each payload - select: List of selected keys for each payload Raises: NotImplementedError: For local/segment API implementations Examples: # Using builder pattern with Key constants from chromadb.execution.expression import ( Search, Key, K, Knn, Val ) # Note: K is an alias for Key, so K.DOCUMENT == Key.DOCUMENT search = (Search() .where((K("category") == "science") & (K("score") > 0.5)) .rank(Knn(query=[0.1, 0.2, 0.3]) * 0.8 + Val(0.5) * 0.2) .limit(10, offset=0) .select(K.DOCUMENT, K.SCORE, "title")) # Direct construction from chromadb.execution.expression import ( Search, Eq, And, Gt, Knn, Limit, Select, Key ) search = Search( where=And([Eq("category", "science"), Gt("score", 0.5)]), rank=Knn(query=[0.1, 0.2, 0.3]), limit=Limit(offset=0, limit=10), select=Select(keys={Key.DOCUMENT, Key.SCORE, "title"}) ) # Single search result = await collection.search(search) # Multiple searches at once searches = [ Search().where(K("type") == "article").rank(Knn(query=[0.1, 0.2])), Search().where(K("type") == "paper").rank(Knn(query=[0.3, 0.4])) ] results = await collection.search(searches) N)r*rur+r,) r_embed_search_string_queriesr._searchr0rrrr+r,)r1ru searches_listsearchembedded_searchess r3rzAsyncCollection.search&s|/x8  MER DQ& - -f 5M  \\))''$v,(9:;;]] *     sBB ABB Bc # URUUUUUUS9nURRURUSUSUSUSUSURUR S9IShvN gN7f) a:Update 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)$_validate_and_prepare_update_requestr._updater0r+r,)r1r r!r"r#r$r%update_requests r3updateAsyncCollection.updatet2BB! C ll""''u%%l3$[1$[1';;]]#  r6c # URUUUUUUS9nURRURUSUSUSUSUSURUR S9IShvN gN7f) aUpdate 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)$_validate_and_prepare_upsert_requestr._upsertr0r+r,)r1r r!r"r#r$r%upsert_requests r3upsertAsyncCollection.upsertrr6c # URXU5nURRURUSUSUSURUR S9IShvN gN7f)aDelete 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. `{"$contains": "hello"}`. Optional. Returns: None Raises: ValueError: If you don't provide either ids, where, or where_document r r;r>)r*r r;r>r+r,N)$_validate_and_prepare_delete_requestr._deleter0r+r,)r1r r;r>delete_requests r3deleteAsyncCollection.deletesm&BB  ll""''u% ))*:;;;]] #   sAA%A#A%)NNNNN)rJ)NNN)&__name__ __module__ __qualname____firstlineno__rrrrr r r rrrr4intr9rrr rrHrOrr]strr rrirsrrrzrrrr__static_attributes__rr3rrsD 3737-1)-4 r]4  )$+&(  4 Ih/04 Ih/04 5)*4 y~&4  4 l  S  (,!%# $26'5- im $- - } -  - !/ - -  - ^  Y 2 5937/3'+!%26 F " )$+&(  F i12F y/0F Ys^,F im $F F F !/F F ( )F T#15AE OsmO-.O => O  O@    4L F#L  L l 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + j 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + ^"!%26  c]  !/     rrrN)!typingrrrrrchromadb.api.typesrr r r r r rrrrrrrrrrr$chromadb.api.models.CollectionCommonr%chromadb.api.collection_configurationr"chromadb.execution.expression.planr chromadb.apirrrrr3rsK==(BO5+M &'78M r