3i>PSSKJrJrJrJrJrJrJr SSKJ r SSK J r J r J r JrJrJrJrJrJrJrJrJrJrJrJrJrJr SSKJr SSKJr SSK r \ RB"\"5r#\(aSSK$J%r% "SS \ S 5r&g) ) TYPE_CHECKINGOptionalUnionListcastDictAny)CollectionCommon)URICollectionMetadata Embedding PyEmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument SearchResultmaybe_cast_one_to_many)UpdateCollectionConfiguration)SearchN) ServerAPIc\rSrSrS\4SjrS.S\\S\\ \\ \\ 4S\\\ S\\\ S \\\S \\\SS4S jjrSSSSSSS/4S\\\S \\S \\S\\S\\S\S\4SjjrS/S \S\4SjjrSSSSSSSS/SQ4 S\\ \\ \\ 4S\\\ S\\\S\\\S\\\S\S \\S\\S\S\4SjjrS0S\\S\\S\\SS4SjjrS\SS4S jrS!\\S\4S"jr S.S\\S\\ \\ \\ 4S\\\ S\\\ S \\\S \\\SS4S#jjr!S.S\\S\\ \\ \\ 4S\\\ S\\\ S \\\S \\\SS4S$jjr"S0S\\#S \\S\\SS4S%jjr$S1S&\S'\S(\S)\\%\\&4S\'\(\44 S*jjr)S2S&\S+\(S\(4S,jjr*S-r+g)3 Collection"returnctURRURURURS9$)z~The total number of embeddings added to the database Returns: int: The total number of embeddings added to the database ) collection_idtenantdatabase)_client_countidr%r&)selfs X/home/james-whalen/.local/lib/python3.13/site-packages/chromadb/api/models/Collection.pycountCollection.count#s5||""'';;]]#  Nids embeddings metadatas documentsimagesurisc URUUUUUUS9nURRURUSUSUSUSUSURUR S9 g) aAdd embeddings to the data store. Args: ids: The ids of the embeddings you wish to add embeddings: The embeddings to add. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. uris: The uris of the images to associate with the embeddings. Optional. Returns: None Raises: ValueError: If you don't provide either embeddings or documents ValueError: If the length of ids, embeddings, metadatas, or documents don't match ValueError: If you don't provide an embedding function and don't provide embeddings ValueError: If you provide both embeddings and documents ValueError: If you provide an id that already exists r/r0r1r2r3r4r/r0r1r2r4r$r/r0r1r2r4r%r&N)!_validate_and_prepare_add_requestr'_addr)r%r&)r*r/r0r1r2r3r4 add_requests r+addCollection.add0sF<<! =  ''E""<0!+.!+.V$;;]]  r.wherelimitoffsetwhere_documentincludec URUUUUS9nURRURUSUSUSUSUUURUR S9 nUR XSS9$)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. `{"$contains": "hello"}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional. Returns: GetResult: A GetResult object containing the results. )r/r=r@rAr/r=r@rA) r$r/r=r@rAr>r?r%r&responserA)!_validate_and_prepare_get_requestr'_getr)r%r&_transform_get_response) r*r/r=r>r?r@rA get_request get_resultss r+getCollection.getgs0<<) = ll''''E"g&&'78 *;;]](  ++ i*@,  r. c URURRURUURUR S95$)zGet the first few results in the database up to limit Args: limit: The number of results to return. Returns: GetResult: A GetResult object containing the results. )r$nr%r&)_transform_peek_responser'_peekr)r%r&)r*r>s r+peekCollection.peeksF,, LL  "gg{{    r.)r1r2 distancesquery_embeddings query_texts query_images query_uris n_resultsc URUUUUUUUUU S9 n URRURU SU SU SU SU SU SURUR S9 n UR XSS 9$) anGet the n_results nearest neighbor embeddings for provided query_embeddings or query_texts. Args: query_embeddings: The embeddings to get the closes neighbors of. Optional. query_texts: The document texts to get the closes neighbors of. Optional. query_images: The images to get the closes neighbors of. Optional. query_uris: The URIs to be used with data loader. Optional. ids: A subset of ids to search within. Optional. n_results: The number of neighbors to return for each query_embedding or query_texts. Optional. where: A Where type dict used to filter results by. E.g. `{"$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 ) rTrUrVrWr/rXr=r@rAr/r0rXr=r@rA) r$r/rTrXr=r@rAr%r&rC)#_validate_and_prepare_query_requestr'_queryr)r%r&_transform_query_response) r*rTrUrVrWr/rXr=r@rA query_request query_resultss r+queryCollection.querysZ@@-#%!)A   ++''e$*<8#K0(()9:!),;;]],  --"),D.  r.namemetadata configurationc URU5 URRURUUUURUR S9 UR XU5 g)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_metadatanew_configurationr%r&N)_validate_modify_requestr'_modifyr)r%r&"_update_model_after_modify_success)r*rarbrcs r+modifyCollection.modifys[ %%h/ ww!+;;]]   // Nr.recURRURUURURS9n[ URUUR URS9$)axFork the current collection under a new name. The returning collection should contain identical data to the current collection. This is an experimental API that only works for Hosted Chroma for now. Args: new_name: The name of the new collection. Returns: Collection: A new collection with the specified name and containing identical data to the current collection. )r$rer%r&)clientmodelembedding_function data_loader)r'_forkr)r%r&r _embedding_function _data_loader)r*reros r+forkCollection.forks` ""'';;]] #  <<#77))   r.searchesc [U5nUc/nUVs/sHo0RU5PM nnURRUR[ [ [U5URURS9$s snf)aPerform hybrid search on the collection. This is an experimental API that only works for Hosted Chroma for now. Args: searches: A single Search object or a list of Search objects, each containing: - where: Where expression for filtering - rank: Ranking expression for hybrid search (defaults to Val(0.0)) - limit: Limit configuration for pagination (defaults to no limit) - select: Select configuration for keys to return (defaults to empty) Returns: SearchResult: Column-major format response with: - ids: List of result IDs for each search payload - documents: Optional documents for each payload - embeddings: Optional embeddings for each payload - metadatas: Optional metadata for each payload - scores: Optional scores for each payload - select: List of selected keys for each payload Raises: NotImplementedError: For local/segment API implementations Examples: # Using builder pattern with Key constants from chromadb.execution.expression import ( Search, Key, K, Knn, Val ) # Note: K is an alias for Key, so K.DOCUMENT == Key.DOCUMENT search = (Search() .where((K("category") == "science") & (K("score") > 0.5)) .rank(Knn(query=[0.1, 0.2, 0.3]) * 0.8 + Val(0.5) * 0.2) .limit(10, offset=0) .select(K.DOCUMENT, K.SCORE, "title")) # Direct construction from chromadb.execution.expression import ( Search, Eq, And, Gt, Knn, Limit, Select, Key ) search = Search( where=And([Eq("category", "science"), Gt("score", 0.5)]), rank=Knn(query=[0.1, 0.2, 0.3]), limit=Limit(offset=0, limit=10), select=Select(keys={Key.DOCUMENT, Key.SCORE, "title"}) ) # Single search result = collection.search(search) # Multiple searches at once searches = [ Search().where(K("type") == "article").rank(Knn(query=[0.1, 0.2])), Search().where(K("type") == "paper").rank(Knn(query=[0.3, 0.4])) ] results = collection.search(searches) )r$rwr%r&) r_embed_search_string_queriesr'_searchr)rrrr%r&)r*rw searches_listsearchembedded_searchess r+r|Collection.search*s|/x8  MER DQ& - -f 5M  ||##''$v,(9:;;]] $   sBc URUUUUUUS9nURRURUSUSUSUSUSURUR S9 g) 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 r6r/r0r1r2r4r7N)$_validate_and_prepare_update_requestr'_updater)r%r&)r*r/r0r1r2r3r4update_requests r+updateCollection.updatex2BB! C  ''u%%l3$[1$[1';;]]  r.c URUUUUUUS9nURRURUSUSUSUSUSURUR S9 g) aUpdate the embeddings, metadatas or documents for provided ids, or create them if they don't exist. Args: ids: The ids of the embeddings to update embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. Returns: None r6r/r0r1r2r4r7N)$_validate_and_prepare_upsert_requestr'_upsertr)r%r&)r*r/r0r1r2r3r4upsert_requests r+upsertCollection.upsertrr.c URXU5nURRURUSUSUSURUR S9 g)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)$_validate_and_prepare_delete_requestr'_deleter)r%r&)r*r/r=r@delete_requests r+deleteCollection.deletes`&BB   ''u% ))*:;;;]]  r. task_name operator_nameoutput_collection_nameparamsc |URRUUURUUURURS9$)akCreate a recurring task that processes this collection. Args: task_name: Unique name for this task instance operator_name: Built-in operator name (e.g., "record_counter") output_collection_name: Name of the collection where task output will be stored params: Optional dictionary with operator-specific parameters Returns: tuple: (success: bool, task_id: str) Example: >>> success, task_id = collection.create_task( ... task_name="count_docs", ... operator_name="record_counter", ... output_collection_name="doc_counts", ... params={"threshold": 100} ... ) )rrinput_collection_idrrr%r&)r' create_taskr)r%r&)r*rrrrs r+rCollection.create_tasksA4||''' $#9;;]](  r. delete_outputcxURRUURUURURS9$)a-Delete a task and prevent any further runs. Args: task_name: Name of the task to remove delete_output: Whether to also delete the output collection. Defaults to False. Returns: bool: True if successful Example: >>> success = collection.remove_task("count_docs", delete_output=True) )rrrr%r&)r' remove_taskr)r%r&)r*rrs r+rCollection.remove_tasks;"||'' $';;]] (  r.)NNNNN)rL)NNN)N)F),__name__ __module__ __qualname____firstlineno__intr,rrrrr rrrrr r;rrrrrJrQrr_strr rrkrurrr|rrrrrr tupleboolrr__static_attributes__rr.r+r r "s  s  * 3737-1)-5 r]5  )$+&(  5 Ih/05 Ih/05 5)*5 y~&5  5 r(,!%# $26'5, im $, , } ,  , !/ , ,  , \ # y 2 5937/3'+!%26 G " )$+&(  G i12G y/0G Ys^,G im $G G G !/G G ( )G V#15AE OsmO-.O => O  O@    4L F#L  L l 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + j 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + ^"!%26  c]  !/    J,0 " " " !$ " c3h( " tSy  " N$       r.r r)'typingrrrrrrr $chromadb.api.models.CollectionCommonr chromadb.api.typesr r r rrrrrrrrrrrrrr%chromadb.api.collection_configurationr"chromadb.execution.expression.planrlogging getLoggerrlogger chromadb.apirr rr.r+rs`HHHA&P5   8 $&K !+.K r.