LKi_NSSKJrJrJrJrJr SSKJrJrJ r J r J r J r J r JrJrJrJrJrJrJrJrJrJrJrJr SSKJr SSKJr SSKJr \(aSSK J!r! "SS\S 5r"g ) ) TYPE_CHECKINGOptionalUnionListcast)URICollectionMetadata Embedding PyEmbeddingIncludeIndexingStatusMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany ReadLevel WhereDocument SearchResultmaybe_cast_one_to_many)CollectionCommon)UpdateCollectionConfiguration)Search)AsyncServerAPIc\rSrSrS)S\\S\\\\\\ 4S\\\ S\\\ S\\\ S\\\ S S4S jjrS \4S jrS \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!jr\ RB4S"\\"S#\ S \#4S$jjr$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),AsyncCollection!Nids 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 e/var/www/html/dynamic-report/venv/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)r0_countr2r-r.r3s r5countAsyncCollection.countXsB\\(('';;]])    =AAAc# URRURURURS9IShvN $N7f)aGet the indexing status of this collection. Returns: IndexingStatus: An object containing: - num_indexed_ops: Number of user operations that have been indexed - num_unindexed_ops: Number of user operations pending indexing - total_ops: Total number of user operations in collection - op_indexing_progress: Proportion of user operations that have been indexed as a float between 0 and 1 r:N)r0_get_indexing_statusr2r-r.r<s r5get_indexing_status#AsyncCollection.get_indexing_statusesB\\66'';;]]7    r?wherelimitoffsetwhere_documentincludec # URUUUUS9nURRURUSUSUSUSUUURUR S9 IShvN nUR XSS9$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. `{"$contains": "hello"}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. )r"rDrGrHr"rDrGrH) r,r"rDrGrHrErFr-r.NresponserH)!_validate_and_prepare_get_requestr0_getr2r-r._transform_get_response) r3r"rDrErFrGrH get_request get_resultss r5getAsyncCollection.getus0<<) = !LL--''E"g&&'78 *;;]].   ++ i*@,    sA!A<#A:$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_responser0_peekr2r-r.)r3rEs r5peekAsyncCollection.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. `{"$contains": "hello"}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional. Returns: QueryResult: A QueryResult object containing the results. Raises: ValueError: If you don't provide either query_embeddings, query_texts, or query_images ValueError: If you provide both query_embeddings and query_texts ValueError: If you provide both query_embeddings and query_images ValueError: If you provide both query_texts and query_images ) r[r\r]r^r"r_rDrGrHr"r#r_rDrGrH) r,r"r[r_rDrGrHr-r.NrJ)#_validate_and_prepare_query_requestr0_queryr2r-r._transform_query_response) r3r[r\r]r^r"r_rDrGrH query_request query_resultss r5queryAsyncCollection.querysX@@-#%!)A  #ll11''e$*<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 )r2new_name new_metadatanew_configurationr-r.N)_validate_modify_requestr0_modifyr2r-r."_update_model_after_modify_success)r3rhrirjs r5modifyAsyncCollection.modifysm %%h/ ll""ww!+;;]] #    // N sAA-A+A-rlc# 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,rlr-r.N)clientmodelembedding_function data_loader)r0_forkr2r-r.r _embedding_function _data_loader)r3rlrvs r5forkAsyncCollection.forksmll(('';;]] )   <<#77))    s>A1A//A1searches read_levelc(# [U5nUc/nUVs/sHo@RU5PM nnURRUR[ [ [U5URURUS9IShvN $s snfN 7f)ad 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) read_level: Controls whether to read from the write-ahead log (WAL): - ReadLevel.INDEX_AND_WAL: Read from both the compacted index and WAL (default). All committed writes will be visible. - ReadLevel.INDEX_ONLY: Read only from the compacted index, skipping the WAL. Faster, but recent writes that haven't been compacted may not be visible. 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) # Skip WAL for faster queries (may miss recent uncommitted writes) from chromadb.api.types import ReadLevel result = await collection.search(search, read_level=ReadLevel.INDEX_ONLY) N)r,r~r-r.r) r_embed_search_string_queriesr0_searchr2rrrr-r.)r3r~r searches_listsearchembedded_searchess r5rAsyncCollection.search8sP/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_requestr0_updater2r-r.)r3r"r#r$r%r&r'update_requests r5updateAsyncCollection.update2BB! C ll""''u%%l3$[1$[1';;]]#  r8c # URUUUUUUS9nURRURUSUSUSUSUSURUR S9IShvN gN7f) aUpdate the embeddings, metadatas or documents for provided ids, or create them if they don't exist. Args: ids: The ids of the embeddings to update embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. Returns: None r*r"r#r$r%r'r+N)$_validate_and_prepare_upsert_requestr0_upsertr2r-r.)r3r"r#r$r%r&r'upsert_requests r5upsertAsyncCollection.upsertrr8c # 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. `{"$contains": "hello"}`. Optional. Returns: None Raises: ValueError: If you don't provide either ids, where, or where_document r"rDrG)r,r"rDrGr-r.N)$_validate_and_prepare_delete_requestr0_deleter2r-r.)r3r"rDrGdelete_requests r5deleteAsyncCollection.deletesm&BB  ll""''u% ))*:;;;]] #   sAA%A#A%)NNNNN)rS)NNN)*__name__ __module__ __qualname____firstlineno__rrrrr r rrrrr6intr=r rBrrr rrQrXrrfstrr rrrr|r INDEX_AND_WALrrrrrrr__static_attributes__rr5r r !sf 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 $F F F !/F F ( )F T#15AE OsmO-.O => O  O@    :!* 7 7W F#W W   W B 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + j 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + ^"!%26  c]  !/     rr rN)#typingrrrrrchromadb.api.typesrr r r r r rrrrrrrrrrrrr$chromadb.api.models.CollectionCommonr%chromadb.api.collection_configurationr"chromadb.execution.expression.planr chromadb.apirr rrr5rsP==,BO5+h &'78h r