LKiuW,SSKJrJrJrJrJrJrJrJr SSK J r SSK J r J r JrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJr SSKJ r SSK!J"r" SSK#r#SSK$J%r% \(aSSK&J'r' \#RP"\)5r*\(aSS K+J,r, "S S \ S 5r-g) ) TYPE_CHECKINGOptionalUnionListcastDictAnyTuple)CollectionCommon)URICollectionMetadata Embedding PyEmbeddingIncludeIndexingStatusMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany ReadLevel WhereDocument SearchResultmaybe_cast_one_to_many)UpdateCollectionConfiguration)SearchN)Function)AttachedFunction) ServerAPIc\rSrSrS\4SjrS\4SjrS1S\\ S\ \ \\ \\ 4S\ \\S \ \\S \ \\S \ \\SS4S jjrSSSSSSS /4S\ \\ S \ \S\ \S\ \S\ \S\S\4SjjrS2S\S\4SjjrSSSSSSSS/SQ4 S\ \ \\ \\ 4S\ \\S\ \\S\ \\S\ \\ S\S \ \S\ \S\S\4SjjrS3S\ \S\ \S\ \SS4SjjrS \SS4S!jr\ RB4S"\\"S#\ S\#4S$jjr$S1S\\ S\ \ \\ \\ 4S\ \\S \ \\S \ \\S \ \\SS4S%jjr%S1S\\ S\ \ \\ \\ 4S\ \\S \ \\S \ \\S \ \\SS4S&jjr&S3S\ \'S \ \S\ \SS4S'jjr(S4S(\)S\S)\S*\ \*\\+4S\,S+\-44 S,jjr.S\SS+4S-jr/S5S\S.\-S\-4S/jjr0S0r1g)6 Collection)returnctURRURURURS9$)z/Return the number of records in the collection. collection_idtenantdatabase)_client_countidr+r,selfs `/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/chromadb/api/models/Collection.pycountCollection.count*s3||""'';;]]#  ctURRURURURS9$)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))r-_get_indexing_statusr/r+r,r0s r2get_indexing_statusCollection.get_indexing_status2s5||00'';;]]1  r5Nids embeddings metadatas documentsimagesurisc URUUUUUUS9nURRURUSUSUSUSUSURUR S9 g) a0Add records to the collection. Args: ids: Record IDs to add. embeddings: Embeddings to add. If None, embeddings are computed. metadatas: Optional metadata for each record. documents: Optional documents for each record. images: Optional images for each record. uris: Optional URIs for loading images. Raises: ValueError: If embeddings and documents are both missing. ValueError: If embeddings and documents are both provided. ValueError: If lengths of provided fields do not match. ValueError: If an ID already exists. r:r;r<r=r>r?r:r;r<r=r?r*r:r;r<r=r?r+r,N)!_validate_and_prepare_add_requestr-_addr/r+r,)r1r:r;r<r=r>r? add_requests r2addCollection.addBs><<! =  ''E""<0!+.!+.V$;;]]  r5wherelimitoffsetwhere_documentincludec URUUUUS9nURRURUSUSUSUSUUURUR S9 nUR XSS9$)aRetrieve records from the collection. If no filters are provided, returns records up to ``limit`` starting at ``offset``. Args: ids: If provided, only return records with these IDs. where: A Where filter used to filter based on metadata values. limit: Maximum number of results to return. offset: Number of results to skip before returning. where_document: A WhereDocument filter used to filter based on K.DOCUMENT. include: Fields to include in results. Can contain "embeddings", "metadatas", "documents", "uris". Defaults to "metadatas" and "documents". Returns: GetResult: Retrieved records and requested fields as a GetResult object. )r:rHrKrLr:rHrKrL) r*r:rHrKrLrIrJr+r,responserL)!_validate_and_prepare_get_requestr-_getr/r+r,_transform_get_response) r1r:rHrIrJrKrL get_request get_resultss r2getCollection.getus2<<) = ll''''E"g&&'78 *;;]](  ++ i*@,  r5 c URURRURUURUR S95$)zReturn the first ``limit`` records from the collection. Args: limit: Maximum number of records to return. Returns: GetResult: Retrieved records and requested fields. )r*nr+r,)_transform_peek_responser-_peekr/r+r,)r1rIs r2peekCollection.peeksF,, LL  "gg{{    r5)r<r= distancesquery_embeddings query_texts query_images query_uris n_resultsc URUUUUUUUUU S9 n URRURU SU SU SU SU SU SURUR S9 n UR XSS 9$) a+Query for the K nearest neighbor records in the collection. This is a batch query API. Multiple queries can be performed at once by providing multiple embeddings, texts, or images. >>> query_1 = [0.1, 0.2, 0.3] >>> query_2 = [0.4, 0.5, 0.6] >>> results = collection.query( >>> query_embeddings=[query_1, query_2], >>> n_results=10, >>> ) If query_texts, query_images, or query_uris are provided, the collection's embedding function will be used to create embeddings before querying the API. The `ids`, `where`, `where_document`, and `include` parameters are applied to all queries. Args: query_embeddings: Raw embeddings to query for. query_texts: Documents to embed and query against. query_images: Images to embed and query against. query_uris: URIs to be loaded and embedded. ids: Optional subset of IDs to search within. n_results: Number of neighbors to return per query. where: Metadata filter. where_document: Document content filter. include: Fields to include in results. Can contain "embeddings", "metadatas", "documents", "uris", "distances". Defaults to "metadatas", "documents", "distances". Returns: QueryResult: Nearest neighbor results. Raises: ValueError: If no query input is provided. ValueError: If multiple query input types are provided. ) r_r`rarbr:rcrHrKrLr:r;rcrHrKrL) r*r:r_rcrHrKrLr+r,rN)#_validate_and_prepare_query_requestr-_queryr/r+r,_transform_query_response) r1r_r`rarbr:rcrHrKrL query_request query_resultss r2queryCollection.querysv@@-#%!)A   ++''e$*<8#K0(()9:!),;;]],  --"),D.  r5namemetadata configurationc URU5 URRURUUUURUR S9 UR XU5 g)zUpdate collection name, metadata, or configuration. Args: name: New collection name. metadata: New metadata for the collection. configuration: New configuration for the collection. )r/new_name new_metadatanew_configurationr+r,N)_validate_modify_requestr-_modifyr/r+r,"_update_model_after_modify_success)r1rlrmrns r2modifyCollection.modify s[ %%h/ ww!+;;]]   // Nr5rpcURRURUURURS9n[ URUUR URS9$)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*rpr+r,)clientmodelembedding_function data_loader)r-_forkr/r+r,r%_embedding_function _data_loader)r1rprzs r2forkCollection.fork+s` ""'';;]] #  <<#77))   r5searches read_levelc [U5nUc/nUVs/sHo@RU5PM nnURRUR[ [ [U5URURUS9$s snf)ab Perform hybrid search on the collection. This is an experimental API that only works for distributed and 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 = 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) # Skip WAL for faster queries (may miss recent uncommitted writes) from chromadb.api.types import ReadLevel result = collection.search(search, read_level=ReadLevel.INDEX_ONLY) )r*rr+r,r) r_embed_search_string_queriesr-_searchr/rrr r+r,)r1rr searches_listsearchembedded_searchess r2rCollection.searchEsP/x8  MER DQ& - -f 5M  ||##''$v,(9:;;]]! $   sBc URUUUUUUS9nURRURUSUSUSUSUSURUR S9 g) aUpdate existing records by ID. Records are provided in columnar format. If provided, the `embeddings`, `metadatas`, `documents`, and `uris` lists must be the same length. Entries in each list correspond to the same record. >>> ids = ["id1", "id2", "id3"] >>> embeddings = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]] >>> metadatas = [{"key": "value"}, {"key": "value"}, {"key": "value"}] >>> documents = ["document1", "document2", "document3"] >>> uris = ["uri1", "uri2", "uri3"] >>> collection.update(ids, embeddings, metadatas, documents, uris) If `embeddings` are not provided, the embeddings will be computed based on `documents` using the collection's embedding function. Args: ids: Record IDs to update. embeddings: Updated embeddings. If None, embeddings are computed. metadatas: Updated metadata. documents: Updated documents. images: Updated images. uris: Updated URIs for loading images. rAr:r;r<r=r?rBN)$_validate_and_prepare_update_requestr-_updater/r+r,)r1r:r;r<r=r>r?update_requests r2updateCollection.updatesHBB! C  ''u%%l3$[1$[1';;]]  r5c URUUUUUUS9nURRURUSUSUSUSUSURUR S9 g) a2Create or update records by ID. Args: ids: Record IDs to upsert. embeddings: Embeddings to add or update. If None, embeddings are computed. metadatas: Metadata to add or update. documents: Documents to add or update. images: Images to add or update. uris: URIs for loading images. rAr:r;r<r=r?rBN)$_validate_and_prepare_upsert_requestr-_upsertr/r+r,)r1r:r;r<r=r>r?upsert_requests r2upsertCollection.upserts0BB! C  ''u%%l3$[1$[1';;]]  r5c URXU5nURRURUSUSUSURUR S9 g)a)Delete records by ID or filters. All documents that match the `ids` or `where` and `where_document` filters will be deleted. Args: ids: Record IDs to delete. where: Metadata filter. where_document: Document content filter. Raises: ValueError: If no IDs or filters are provided. r:rHrK)r*r:rHrKr+r,N)$_validate_and_prepare_delete_requestr-_deleter/r+r,)r1r:rHrKdelete_requests r2deleteCollection.deletes`$BB   ''u% ))*:;;;]]  r5functionoutput_collectionparamsr"c [U[5(a UROUnURR UUUR UUUR URS9$)aUAttach a function to this collection. Args: function: A Function enum value (e.g., STATISTICS_FUNCTION, RECORD_COUNTER_FUNCTION) name: Unique name for this attached function output_collection: Name of the collection where function output will be stored params: Optional dictionary with function-specific parameters Returns: Tuple of (AttachedFunction, created) where created is True if newly created, False if already existed (idempotent request) Example: >>> from chromadb.api.functions import STATISTICS_FUNCTION >>> attached_fn = collection.attach_function( ... function=STATISTICS_FUNCTION, ... name="mycoll_stats_fn", ... output_collection="mycoll_stats", ... ) >>> if created: ... print("New function attached") ... else: ... print("Function already existed") ) function_idrlinput_collection_idrrr+r,) isinstancer!valuer-attach_functionr/r+r,)r1rrlrrrs r2rCollection.attach_function!sX>)38X(F(FhnnH ||++# $/;;]],  r5cvURRUURURURS9$)zGet an attached function by name for this collection. Args: name: Name of the attached function Returns: AttachedFunction: The attached function object Raises: NotFoundError: If the attached function doesn't exist )rlrr+r,)r-get_attached_functionr/r+r,)r1rls r2r Collection.get_attached_functionKs8||11 $;;]] 2  r5delete_output_collectioncxURRUURUURURS9$)aFDetach a function from this collection. Args: name: The name of the attached function delete_output_collection: Whether to also delete the output collection. Defaults to False. Returns: bool: True if successful Example: >>> success = collection.detach_function("my_function", delete_output_collection=True) )rlr delete_outputr+r,)r-detach_functionr/r+r,)r1rlrs r2rCollection.detach_function^s;"||++ $2;;]] ,  r5)NNNNN)rW)NNN)N)F)2__name__ __module__ __qualname____firstlineno__intr3rr8rrrrrrrrrr rFrrrrrUr\rrjstrr rrvrr INDEX_AND_WALr rrrrrrr!rr r boolrrr__static_attributes__rr5r2r%r%)s s  ^ 0 3737-1)-1 r]1  )$+&(  1 Ih/01 Ih/01 5)*1 y~&1  1 j(,!%# $26'5- im $- - } -  - !/ - -  - ^ # y 2 5937/3'+!%26 U " )$+&(  U i12U y/0U Ys^,U im $U U U !/U U ( )U r#15AE OsmO-.O => O  O<    :!* 7 7W F#W W   W B 3737-1)-6 r]6  )$+&(  6 Ih/06 Ih/06 5)*6 y~&6  6 @ 3737-1)-* r]*  )$+&(  * Ih/0* Ih/0* 5)** y~&*  * \"!%26  c]  !/    H,0 ( ( (  ( c3h( ( !4' ( ( T # 2D ,*/  #'     r5r%r#).typingrrrrrrr r $chromadb.api.models.CollectionCommonr chromadb.api.typesr r rrrrrrrrrrrrrrrrr%chromadb.api.collection_configurationr"chromadb.execution.expression.planr loggingchromadb.api.functionsr!$chromadb.api.models.AttachedFunctionr" getLoggerrlogger chromadb.apir#r%rr5r2rsnOOOA*P5+E   8 $&L  !+.L  r5