q 9iS ddlmZmZmZmZmZmZmZddlm Z ddl m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZddlmZddlmZddl Z ddl!m"Z"erddl#m$Z$e jJe&Z'erdd l(m)Z)Gd d e d Z*y) ) TYPE_CHECKINGOptionalUnionListcastDictAny)CollectionCommon)URICollectionMetadata Embedding PyEmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument SearchResultmaybe_cast_one_to_many)UpdateCollectionConfiguration)SearchN)Function)AttachedFunction) ServerAPIceZdZdefdZ d-deedeeee ee fdeee deee deee d eeeddfd Zdddddddgfdeeed eed eed eedeededefdZd.d edefdZddddddddgdf deeee ee fdeee deee deeedeeeded eedeededefdZ d/deedeedeeddfdZdeddfdZd eedefd!Z d-deedeeee ee fdeee deee deee d eeeddfd"Z d-deedeeee ee fdeee deee deee d eeeddfd#Z! d/dee"d eedeeddfd$Z# d0d%e$ded&ed'ee%ee&fdd(f d)Z'dedd(fd*Z( d1ded+e)de)fd,Z*y)2 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 g/var/www/html/leadgen/airagagent/rag_env/lib/python3.12/site-packages/chromadb/api/models/Collection.pycountzCollection.count(s5||""'';;]]#  Nids 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 r/r0r1r2r3r4r/r0r1r2r4r%r/r0r1r2r4r&r'N)!_validate_and_prepare_add_requestr(_addr*r&r')r+r/r0r1r2r3r4 add_requests r,addzCollection.add5sF<<! =  ''E""<0!+.!+.V$;;]]  r.wherelimitoffsetwhere_documentincludec |j||||}|jj|j|d|d|d|d|||j|j  }|j ||dS)aGet embeddings and their associate data from the data store. If no ids or where filter is provided returns all embeddings up to limit starting at offset. Args: ids: The ids of the embeddings to get. Optional. where: A Where type dict used to filter results by. E.g. `{"$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. )r/r<r?r@r/r<r?r@) r%r/r<r?r@r=r>r&r'responser@)!_validate_and_prepare_get_requestr(_getr*r&r'_transform_get_response) r+r/r<r=r>r?r@ get_request get_resultss r,getzCollection.getls0<<) = ll''''E"g&&'78 *;;]](  ++ +i*@,  r. 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')r+r=s r,peekzCollection.peeksF,, LL  "gg{{    r.)r1r2 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. `{"$contains": "hello"}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional. Returns: QueryResult: A QueryResult object containing the results. Raises: ValueError: If you don't provide either query_embeddings, query_texts, or query_images ValueError: If you provide both query_embeddings and query_texts ValueError: If you provide both query_embeddings and query_images ValueError: If you provide both query_texts and query_images ) rQrRrSrTr/rUr<r?r@r/r0rUr<r?r@) r%r/rQrUr<r?r@r&r'rB)#_validate_and_prepare_query_requestr(_queryr*r&r'_transform_query_response) r+rQrRrSrTr/rUr<r?r@ query_request query_resultss r,queryzCollection.querysZ@@-#%!)A   ++''e$*<8#K0(()9:!),;;]],  --"M),D.  r.namemetadata 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)r+r]r^r_s r,modifyzCollection.modifys] %%h/ ww!+;;]]   //h Nr.rac|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%rar&r')clientmodelembedding_function data_loader)r(_forkr*r&r'r"_embedding_function _data_loader)r+rarjs r,forkzCollection.forks` ""'';;]] #  <<#77))   r.searchesc t|}|g}|Dcgc]}|j|}}|jj|jt t t||j|jScc}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) 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%rqr&r') r_embed_search_string_queriesr(_searchr*rrrr&r')r+rq searches_listsearchembedded_searchess r,rvzCollection.search/s|/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 r6r/r0r1r2r4r7N)$_validate_and_prepare_update_requestr(_updater*r&r')r+r/r0r1r2r3r4update_requests r,updatezCollection.update}2BB! C  ''u%%l3$[1$[1';;]]  r.c |j||||||}|jj|j|d|d|d|d|d|j|j y) aOUpdate the embeddings, metadatas or documents for provided ids, or create them if they don't exist. Args: ids: The ids of the embeddings to update embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. Returns: None r6r/r0r1r2r4r7N)$_validate_and_prepare_upsert_requestr(_upsertr*r&r')r+r/r0r1r2r3r4upsert_requests r,upsertzCollection.upsertr}r.c|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. `{"$contains": "hello"}`. Optional. Returns: None Raises: ValueError: If you don't provide either ids, where, or where_document r/r<r?)r%r/r<r?r&r'N)$_validate_and_prepare_delete_requestr(_deleter*r&r')r+r/r<r?delete_requests r,deletezCollection.deletesb&BB   ''u% ))*:;;;]]  r.functionoutput_collectionparamsrc 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: AttachedFunction: Object representing the attached function Example: >>> from chromadb.api.functions import STATISTICS_FUNCTION >>> attached_fn = collection.attach_function( ... function=STATISTICS_FUNCTION, ... name="mycoll_stats_fn", ... output_collection="mycoll_stats", ... ) ) function_idr]input_collection_idrrr&r') isinstancervaluer(attach_functionr*r&r')r+rr]rrrs r,rzCollection.attach_functionsU4)38X(FhnnH ||++# $/;;]],  r.c||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 )r]rr&r')r(get_attached_functionr*r&r')r+r]s r,rz Collection.get_attached_functions8||11 $;;]] 2  r.delete_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) )r]r delete_outputr&r')r(detach_functionr*r&r')r+r]rs r,rzCollection.detach_function/s;"||++ $2;;]] ,  r.)NNNNN)rJ)NNN)N)F)+__name__ __module__ __qualname__intr-rrrrr rrrrr r;rrrrrIrOrr\strr rrgrprrrvr|rrrrrr rrboolrr.r,r"r"'s  s  * 3737-1)-5 r]5  )$+&(  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( #  # J # 2D ,*/  #'    r.r"r )+typingrrrrrrr $chromadb.api.models.CollectionCommonr chromadb.api.typesr r r rrrrrrrrrrrrrr%chromadb.api.collection_configurationr"chromadb.execution.expression.planrloggingchromadb.api.functionsr$chromadb.api.models.AttachedFunctionr getLoggerrlogger chromadb.apir r"rr.r,rskHHHA&P5+E   8 $&_ !+._ r.