eiBZddlmZmZmZmZmZmZmZmZddl m Z ddl m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZddlm Z ddl!m"Z"ddl#Z#ddl$m%Z%erddl&m'Z'e#jPe)Z*erdd l+m,Z,Gd d e d Z-y) ) TYPE_CHECKINGOptionalUnionListcastDictAnyTuple)CollectionCommon)URICollectionMetadata Embedding PyEmbeddingIncludeIndexingStatusMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany ReadLevel WhereDocument SearchResultmaybe_cast_one_to_many)UpdateCollectionConfiguration)SearchN)Function)AttachedFunction) ServerAPIceZdZdefdZdefdZ d/deede e ee ee fde ee de eed e eed e eeddfd Zdddddddgfde eed e ed e ede ede ededefdZd0d edefdZddddddddgdf de e ee ee fde eede eede eede eeded e ede ededefdZ d1de ede ede eddfdZdeddfd Zej@fd!ee!d"ede"fd#Z# d/deede e ee ee fde ee de eed e eed e eeddfd$Z$ d/deede e ee ee fde ee de eed e eed e eeddfd%Z% d1de e&d e ede eddfd&Z' d2d'e(ded(ed)e e)ee*fde+d*e,ff d+Z-dedd*fd,Z. d3ded-e,de,fd.Z/y)4 Collectionreturncz|jj|j|j|jS)zThe 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 h/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/chromadb/api/models/Collection.pycountzCollection.count*s5||""'';;]]#  cz|jj|j|j|jS)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+r/s r1get_indexing_statuszCollection.get_indexing_status7s5||00'';;]]1  r3Nids embeddings metadatas documentsimagesurisc |j||||||}|jj|j|d|d|d|d|d|j|j y) 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 r7r8r9r:r;r<r7r8r9r:r<r)r7r8r9r:r<r*r+N)!_validate_and_prepare_add_requestr,_addr.r*r+)r0r7r8r9r:r;r< add_requests r1addzCollection.addGsF<<! =  ''E""<0!+.!+.V$;;]]  r3wherelimitoffsetwhere_documentincludec |j||||}|jj|j|d|d|d|d|||j|j  }|j ||dS)aGet embeddings and their associate data from the data store. If no ids or where filter is provided returns all embeddings up to limit starting at offset. Args: ids: The ids of the embeddings to get. Optional. where: A Where type dict used to filter results by. E.g. `{"$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": "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. )r7rDrGrHr7rDrGrH) r)r7rDrGrHrErFr*r+responserH)!_validate_and_prepare_get_requestr,_getr.r*r+_transform_get_response) r0r7rDrErFrGrH get_request get_resultss r1getzCollection.get~s0<<) = ll''''E"g&&'78 *;;]](  ++ +i*@,  r3 c|j|jj|j||j|j S)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+)r0rEs r1peekzCollection.peeksF,, LL  "gg{{    r3)r9r: distancesquery_embeddings query_texts query_images query_uris n_resultsc  |j|||||||||  } |jj|j| d| d| d| d| d| d|j|j  } |j | | d S) 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. 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. `{"$contains": "hello"}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional. Returns: QueryResult: A QueryResult object containing the results. Raises: ValueError: If you don't provide either query_embeddings, query_texts, or query_images ValueError: If you provide both query_embeddings and query_texts ValueError: If you provide both query_embeddings and query_images ValueError: If you provide both query_texts and query_images ) rYrZr[r\r7r]rDrGrHr7r8r]rDrGrH) r)r7rYr]rDrGrHr*r+rJ)#_validate_and_prepare_query_requestr,_queryr.r*r+_transform_query_response) r0rYrZr[r\r7r]rDrGrH query_request query_resultss r1queryzCollection.querysZ@@-#%!)A   ++''e$*<8#K0(()9:!),;;]],  --"M),D.  r3namemetadata configurationc|j||jj|j||||j|j |j |||y)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)r0rerfrgs r1modifyzCollection.modifys] %%h/ ww!+;;]]   //h Nr3ric|jj|j||j|j}t |j||j |jS)aFork 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)rir*r+)clientmodelembedding_function data_loader)r,_forkr.r*r+r%_embedding_function _data_loader)r0rirrs r1forkzCollection.fork's` ""'';;]] #  <<#77))   r3searches read_levelc t|}|g}|Dcgc]}|j|}}|jj|jt t t||j|j|Scc}w)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) 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)ryr*r+rz) r_embed_search_string_queriesr,_searchr.rrr r*r+)r0ryrz searches_listsearchembedded_searchess r1rzCollection.searchAsP/x8  MER :@D - -f 5  ||##''$v,(9:;;]]! $   sBc |j||||||}|jj|j|d|d|d|d|d|j|j y) 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>r7r8r9r:r<r?N)$_validate_and_prepare_update_requestr,_updater.r*r+)r0r7r8r9r:r;r<update_requests r1updatezCollection.update2BB! C  ''u%%l3$[1$[1';;]]  r3c |j||||||}|jj|j|d|d|d|d|d|j|j y) aOUpdate the embeddings, metadatas or documents for provided ids, or create them if they don't exist. Args: ids: The ids of the embeddings to update embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. Returns: None r>r7r8r9r:r<r?N)$_validate_and_prepare_upsert_requestr,_upsertr.r*r+)r0r7r8r9r:r;r<upsert_requests r1upsertzCollection.upsertrr3c|j|||}|jj|j|d|d|d|j|j y)a0Delete 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. `{"$contains": "hello"}`. Optional. Returns: None Raises: ValueError: If you don't provide either ids, where, or where_document r7rDrG)r)r7rDrGr*r+N)$_validate_and_prepare_delete_requestr,_deleter.r*r+)r0r7rDrGdelete_requests r1deletezCollection.deletesb&BB   ''u% ))*:;;;]]  r3functionoutput_collectionparamsr"c t|tr |jn|}|jj |||j |||j |jS)aAttach 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_idreinput_collection_idrrr*r+) isinstancer!valuer,attach_functionr.r*r+)r0rrerrrs r1rzCollection.attach_functionsU>)38X(FhnnH ||++# $/;;]],  r3c||jj||j|j|jS)aGet 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 )rerr*r+)r,get_attached_functionr.r*r+)r0res r1rz Collection.get_attached_function>s8||11 $;;]] 2  r3delete_output_collectionc~|jj||j||j|jS)aDetach 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) )rer delete_outputr*r+)r,detach_functionr.r*r+)r0rers r1rzCollection.detach_functionQs;"||++ $2;;]] ,  r3)NNNNN)rR)NNN)N)F)0__name__ __module__ __qualname__intr2rr6rrrrrrrrrr rCrrrrrQrWrrdstrr rrorxr INDEX_AND_WALr rrrrrrr!rr r boolrrrr3r1r%r%)s  s   ^ 0 3737-1)-5 r]5  )$+&(  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 $G G G !/G G ( )G V#15AE OsmO-.O => O  O@    :!* 7 7W F#W W   W B 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + j 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + ^"!%26  c]  !/    J,0 ( ( (  ( c3h( ( !4' ( ( T # 2D ,*/  #'    r3r%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%rr3r1rspOOOA*P5+E   8 $& !+. r3