q 9iHddlmZmZmZmZmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZddlmZddlmZddlmZerddlmZGdded Z y ) ) TYPE_CHECKINGOptionalUnionListcast)URICollectionMetadata Embedding PyEmbeddingIncludeMetadataDocumentImageWhereIDs GetResult QueryResultID OneOrMany WhereDocument SearchResultmaybe_cast_one_to_many)CollectionCommon)UpdateCollectionConfiguration)Search)AsyncServerAPIc4eZdZ d%deedeeeeeefdeee deee deee deee ddfd Z defd Zdddddddgfdeeed eed eed eedeededefdZd&d edefdZddddddddgdf deeeeeefdeee deee deee deeeded eedeededefdZ d'deedeedeeddfdZdeddfdZd eedefd!Z d%deedeeeeeefdeee deee deee deee ddfd"Z d%deedeeeeeefdeee deee deee deee ddfd#Z! d'dee"d eedeeddfd$Z#y)(AsyncCollectionNids embeddings metadatas documentsimagesurisreturnc K|j||||||}|jj|j|d|d|d|d|d|j|j d{y7w) a]Add embeddings to the data store. Args: ids: The ids of the embeddings you wish to add embeddings: The embeddings to add. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. uris: The uris of the images to associate with the embeddings. Optional. Returns: None Raises: ValueError: If you don't provide either embeddings or documents ValueError: If the length of ids, embeddings, metadatas, or documents don't match ValueError: If you don't provide an embedding function and don't provide embeddings ValueError: If you provide both embeddings and documents ValueError: If you provide an id that already exists rr r!r"r#r$rr r!r"r$ collection_idrr r!r"r$tenantdatabaseN)!_validate_and_prepare_add_request_client_addidr*r+)selfrr r!r"r#r$ add_requests l/var/www/html/leadgen/airagagent/rag_env/lib/python3.12/site-packages/chromadb/api/models/AsyncCollection.pyaddzAsyncCollection.add sD<<! = ll''E""<0!+.!+.V$;;]]  A+A5-A3.A5cK|jj|j|j|jd{S7w)zThe total number of embeddings added to the database Returns: int: The total number of embeddings added to the database )r)r*r+N)r-_countr/r*r+)r0s r2countzAsyncCollection.countVsC\\(('';;]])    sAA AA wherelimitoffsetwhere_documentincludec K|j||||}|jj|j|d|d|d|d|||j|j  d{}|j ||dS7w) aGet embeddings and their associate data from the data store. If no ids or where filter is provided returns all embeddings up to limit starting at offset. Args: ids: The ids of the embeddings to get. Optional. where: A Where type dict used to filter results by. E.g. `{"$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. )rr8r;r<rr8r;r<) r)rr8r;r<r9r:r*r+Nresponser<)!_validate_and_prepare_get_requestr-_getr/r*r+_transform_get_response) r0rr8r9r:r;r< get_request get_resultss r2getzAsyncCollection.getcs0<<) = !LL--''E"g&&'78 *;;]].   ++ +i*@,    sA'B)B*B cK|j|jj|j||j|j d{S7w)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_responser-_peekr/r*r+)r0r9s r2peekzAsyncCollection.peeksT,,,,$$"gg{{ %    sA AA A)r!r" distancesquery_embeddings query_texts query_images query_uris n_resultsc &K|j|||||||||  } |jj|j| d| d| d| d| d| d|j|j  d {} |j | | d S7w) 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. 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. `{"$contains": "hello"}`. Optional. include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional. Returns: QueryResult: A QueryResult object containing the results. Raises: ValueError: If you don't provide either query_embeddings, query_texts, or query_images ValueError: If you provide both query_embeddings and query_texts ValueError: If you provide both query_embeddings and query_images ValueError: If you provide both query_texts and query_images ) rMrNrOrPrrQr8r;r<rr rQr8r;r<) r)rrMrQr8r;r<r*r+Nr>)#_validate_and_prepare_query_requestr-_queryr/r*r+_transform_query_response) r0rMrNrOrPrrQr8r;r< query_request query_resultss r2queryzAsyncCollection.querysX@@-#%!)A  #ll11''e$*<8#K0(()9:!),;;]]2   --"M),D.    sA2B4B5Bnamemetadata configurationcK|j||jj|j||||j|j d{|j |||y7w)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)r0rYrZr[s r2modifyzAsyncCollection.modifysp %%h/ ll""ww!+;;]] #    //h N sAA1A/A1r]cK|jj|j||j|jd{}t |j||j |jS71w)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)r]r*r+N)clientmodelembedding_function data_loader)r-_forkr/r*r+r_embedding_function _data_loader)r0r]rfs r2forkzAsyncCollection.fork snll(('';;]] )   <<#77))    sAA7A52A7searchesc&Kt|}|g}|Dcgc]}|j|}}|jj|jt t t||j|jd{Scc}w7 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 = 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) N)r)rmr*r+) r_embed_search_string_queriesr-_searchr/rrrr*r+)r0rm searches_listsearchembedded_searchess r2rrzAsyncCollection.search&s|/x8  MER :@D - -f 5  \\))''$v,(9:;;]] *     sBB ABB Bc K|j||||||}|jj|j|d|d|d|d|d|j|j d{y7w) aUpdate the embeddings, metadatas or documents for provided ids. Args: ids: The ids of the embeddings to update embeddings: The embeddings to update. If None, embeddings will be computed based on the documents or images using the embedding_function set for the Collection. Optional. metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional. documents: The documents to associate with the embeddings. Optional. images: The images to associate with the embeddings. Optional. Returns: None r'rr r!r"r$r(N)$_validate_and_prepare_update_requestr-_updater/r*r+)r0rr r!r"r#r$update_requests r2updatezAsyncCollection.updatet2BB! C ll""''u%%l3$[1$[1';;]]#  r4c K|j||||||}|jj|j|d|d|d|d|d|j|j d{y7w) 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'rr r!r"r$r(N)$_validate_and_prepare_upsert_requestr-_upsertr/r*r+)r0rr r!r"r#r$upsert_requests r2upsertzAsyncCollection.upsertryr4cK|j|||}|jj|j|d|d|d|j|j d{y7w)a0Delete the embeddings based on ids and/or a where filter Args: ids: The ids of the embeddings to delete where: A Where type dict used to filter the delection by. E.g. `{"$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 rr8r;)r)rr8r;r*r+N)$_validate_and_prepare_delete_requestr-_deleter/r*r+)r0rr8r;delete_requests r2deletezAsyncCollection.deletesp&BB  ll""''u% ))*:;;;]] #   sAA)!A'"A))NNNNN)rF)NNN)$__name__ __module__ __qualname__rrrrr r r rrrr3intr7rrr rrErKrrXstrr rrcrlrrrrrxr~rrr2rrs? 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 $F F F !/F F ( )F T#15AE OsmO-.O => O  O@    4L F#L  L l 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + j 3737-1)-+ r]+  )$+&(  + Ih/0+ Ih/0+ 5)*+ y~&+  + ^"!%26  c]  !/    rrrN)!typingrrrrrchromadb.api.typesrr r r r r rrrrrrrrrrr$chromadb.api.models.CollectionCommonr%chromadb.api.collection_configurationr"chromadb.execution.expression.planr chromadb.apirrrrr2rsK==(BO5+M &'78M r