iddlZddlmZmZmZmZmZmZmZm Z m Z ddl m Z ddl mZddlmZddlmZmZddlmZmZmZmZmZmZmZmZmZdd lmZdd l m!Z!dd l"m#Z#m$Z$m%Z&m'Z'm(Z(dd l)m*Z+m,Z,dd l-m.Z/m0Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@ddlAm.Z.m>> index.upsert([('id1', [1.0, 2.0, 3.0], {'key': 'value'}), ('id2', [1.0, 2.0, 3.0]) ], namespace='ns1', async_req=True) >>> index.upsert([{'id': 'id1', 'values': [1.0, 2.0, 3.0], 'metadata': {'key': 'value'}}, {'id': 'id2', 'values': [1.0, 2.0, 3.0], 'sparse_values': {'indices': [1, 8], 'values': [0.2, 0.4]}, ]) >>> index.upsert([GRPCVector(id='id1', values=[1.0, 2.0, 3.0], metadata={'key': 'value'}), GRPCVector(id='id2', values=[1.0, 2.0, 3.0]), GRPCVector(id='id3', values=[1.0, 2.0, 3.0], sparse_values=GRPCSparseValues(indices=[1, 2], values=[0.2, 0.4]))]) Args: vectors (Union[List[Vector], List[Tuple]]): A list of vectors to upsert. A vector can be represented by a 1) GRPCVector object, a 2) tuple or 3) a dictionary 1) if a tuple is used, it must be of the form (id, values, metadata) or (id, values). where id is a string, vector is a list of floats, and metadata is a dict. Examples: ('id1', [1.0, 2.0, 3.0], {'key': 'value'}), ('id2', [1.0, 2.0, 3.0]) 2) if a GRPCVector object is used, a GRPCVector object must be of the form GRPCVector(id, values, metadata), where metadata is an optional argument of type Dict[str, Union[str, float, int, bool, List[int], List[float], List[str]]] Examples: GRPCVector(id='id1', values=[1.0, 2.0, 3.0], metadata={'key': 'value'}), GRPCVector(id='id2', values=[1.0, 2.0, 3.0]), GRPCVector(id='id3', values=[1.0, 2.0, 3.0], sparse_values=GRPCSparseValues(indices=[1, 2], values=[0.2, 0.4])) 3) if a dictionary is used, it must be in the form {'id': str, 'values': List[float], 'sparse_values': {'indices': List[int], 'values': List[float]}, 'metadata': dict} Note: the dimension of each vector must match the dimension of the index. async_req (bool): If True, the upsert operation will be performed asynchronously. Cannot be used with batch_size. Defaults to False. See: https://docs.pinecone.io/docs/performance-tuning [optional] namespace (str): The namespace to write to. If not specified, the default namespace is used. [optional] batch_size (int): The number of vectors to upsert in each batch. Cannot be used with async_req=True. If not specified, all vectors will be upserted in a single batch. [optional] show_progress (bool): Whether to show a progress bar using tqdm. Applied only if batch_size is provided. Default is True. Returns: UpsertResponse, contains the number of vectors upserted Nzasync_req is not supported when batch_size is provided.To upsert in parallel, please follow: https://docs.pinecone.io/docs/performance-tuningtimeoutrLrJrQrQresult_transformerrz%batch_size must be a positive integerzUpserted vectorstotaldisabledescupserted_count) ValueErrorpoplistmaprbuild_parse_non_empty_argsr&runnerrunstubUpsertfuturer9r _upsert_batch isinstanceintr lenrangeupdaterZr')rFrJrKrLrMrNkwargsrQ args_dictrequestrfpbartotal_upsertedi batch_results rGupsertzGRPCIndex.upsertOsD /C  **Y-s,22G<= 22[)4L3MNI#KGKyKFKG[[__TYY%5%5%<%_` [optional] async_req (bool): If True, the delete operation will be performed asynchronously. Defaults to False. [optional] Returns: DeleteResponse (contains no data) or a PineconeGrpcFuture object if async_req is True. .. admonition:: Note For any delete call, if namespace is not specified, the default namespace is used. Delete can occur in the following mutual exclusive ways: 1. Delete by ids from a single namespace 2. Delete all vectors from a single namespace by setting delete_all to True 3. Delete all vectors from a single namespace by specifying a metadata filter (note that for this option delete all must be set to False) Examples: .. code-block:: python >>> index.delete(ids=['id1', 'id2'], namespace='my_namespace') >>> index.delete(delete_all=True, namespace='my_namespace') >>> index.delete(filter={'key': 'value'}, namespace='my_namespace', async_req=True) NrrrLrrQrRrSr[) rrar]r(rbrcrdDeleterfr9r) rFrrrLrrKrm filter_structrnrQrorfs rGdeletezGRPCIndex.deletes`  08M M.. z*i(=)   **Y-6)6v6 [[__TYY%5%5%<%>> index.fetch(ids=['id1', 'id2'], namespace='my_namespace') >>> index.fetch(ids=['id1', 'id2']) Args: ids (List[str]): The vector IDs to fetch. namespace (str): The namespace to fetch vectors from. If not specified, the default namespace is used. [optional] Returns: FetchResponse object which contains the list of Vector objects, and namespace name. rQNrLrrRrTrQr[) r]rar*rbrcrdFetchrfr9r) rFrrLrKrmrQrnrorfresponses rGfetchzGRPCIndex.fetch?s2**Y-..i0H/IJ >3>)>v> [[__TYY__%;%;Wg_VF%+? {{tyyQH'1 1rIvectoridtop_kinclude_valuesinclude_metadata sparse_vectorc >| | td| t|} nd} tj|}|j d|fd|fd|fd|fd| fd|fd |fd |fg} t di| } | j d d}| rN|jj|jjj| | }t|t| S|jj|jj| | }tj|}t|dS)an The Query operation searches a namespace, using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores. Examples: .. code-block:: python >>> index.query(vector=[1, 2, 3], top_k=10, namespace='my_namespace') >>> index.query(id='id1', top_k=10, namespace='my_namespace') >>> index.query(vector=[1, 2, 3], top_k=10, namespace='my_namespace', filter={'key': 'value'}) >>> index.query(id='id1', top_k=10, namespace='my_namespace', include_metadata=True, include_values=True) >>> index.query(vector=[1, 2, 3], sparse_vector={'indices': [1, 2], 'values': [0.2, 0.4]}, >>> top_k=10, namespace='my_namespace') >>> index.query(vector=[1, 2, 3], sparse_vector=GRPCSparseValues([1, 2], [0.2, 0.4]), >>> top_k=10, namespace='my_namespace') Args: vector (List[float]): The query vector. This should be the same length as the dimension of the index being queried. Each ``query()`` request can contain only one of the parameters ``id`` or ``vector``.. [optional] id (str): The unique ID of the vector to be used as a query vector. Each ``query()`` request can contain only one of the parameters ``vector`` or ``id``.. [optional] top_k (int): The number of results to return for each query. Must be an integer greater than 1. namespace (str): The namespace to fetch vectors from. If not specified, the default namespace is used. [optional] filter (Dict[str, Union[str, float, int, bool, List, dict]]): The filter to apply. You can use vector metadata to limit your search. See `metadata filtering _` [optional] include_values (bool): Indicates whether vector values are included in the response. If omitted the server will use the default value of False [optional] include_metadata (bool): Indicates whether metadata is included in the response as well as the ids. If omitted the server will use the default value of False [optional] sparse_vector: (Union[SparseValues, Dict[str, Union[List[float], List[int]]]]): sparse values of the query vector. Expected to be either a SparseValues object or a dict of the form: {'indices': List[int], 'values': List[float]}, where the lists each have the same length. Returns: QueryResponse object which contains the list of the closest vectors as ScoredVector objects, and namespace name. Nz%Cannot specify both `id` and `vector`rrrLrrrrrrQrRrF) _check_typer[)r\rrr`rar)r]rbrcrdQueryrfr9rr MessageToDict)rFrrrLrrrrrrKrmrrnrorQrfr json_responses rGqueryzGRPCIndex.querygs1r  ".DE E  08M M+11-@ ..6"r i(% =)!>2#%56 -0   ++**Y- [[__TYY__%;%;Wg_VF%+? {{tyyQH'55h?M' 5I IrI namespacesmetric)cosine euclidean dotproductc  |t|dk(r tdt|dk(r td||nd} t| |} t|} | D cgc]2} |jj |j f|| | ||||dd| 4}} ttt|}t|D]!}| j|j#| j}|Scc} w)Nrz(At least one namespace must be specifiedzQuery vector must not be empty )rrF)rrLrrrrrrK)rjr\r5setthreadpool_executorsubmitrr r rr add_resultsr get_results)rFrrrrrrrrrm overall_topk aggregatortarget_namespacesnsfutures only_futuresr final_resultss rGquery_namespaceszGRPCIndex.query_namespacess  ZA!5GH H v;! => > % 1ur +,vN  O(  ,D $ $ + +  "-!1+    HV,g6 $\2 6H  " "8??#4 5 6#..0 + s7C(values set_metadata sparse_valuesc | t|}nd}|jdd} tj|}|j d|fd|fd|fd|fg} t d d|i| } |rN|j j|jjj| | } t| | t S|j j|jj| | S) a The Update operation updates vector in a namespace. If a value is included, it will overwrite the previous value. If a set_metadata is included, the values of the fields specified in it will be added or overwrite the previous value. Examples: .. code-block:: python >>> index.update(id='id1', values=[1, 2, 3], namespace='my_namespace') >>> index.update(id='id1', set_metadata={'key': 'value'}, namespace='my_namespace', async_req=True) >>> index.update(id='id1', values=[1, 2, 3], sparse_values={'indices': [1, 2], 'values': [0.2, 0.4]}, >>> namespace='my_namespace') >>> index.update(id='id1', values=[1, 2, 3], sparse_values=GRPCSparseValues(indices=[1, 2], values=[0.2, 0.4]), >>> namespace='my_namespace') Args: id (str): Vector's unique id. async_req (bool): If True, the update operation will be performed asynchronously. Defaults to False. [optional] values (List[float]): vector values to set. [optional] set_metadata (Dict[str, Union[str, float, int, bool, List[int], List[float], List[str]]]]): metadata to set for vector. [optional] namespace (str): Namespace name where to update the vector.. [optional] sparse_values: (Dict[str, Union[List[float], List[int]]]): sparse values to update for the vector. Expected to be either a GRPCSparseValues object or a dict of the form: {'indices': List[int], 'values': List[float]} where the lists each have the same length. Returns: UpdateResponse (contains no data) or a PineconeGrpcFuture object if async_req is True. NrQrrrLrrrRrSr[) rr]rr`rar+rbrcrdUpdaterfr9r) rFrrKrrrLrrmset_metadata_structrQrnrorfs rGrlzGRPCIndex.updatesT  #"6|"D "& **Y-+11-@ ..6"!45i( -0    3233 [[__TYY%5%5%<%>> results = index.list_paginated(prefix='99', limit=5, namespace='my_namespace') >>> [v.id for v in results.vectors] ['99', '990', '991', '992', '993'] >>> results.pagination.next eyJza2lwX3Bhc3QiOiI5OTMiLCJwcmVmaXgiOiI5OSJ9 >>> next_results = index.list_paginated(prefix='99', limit=5, namespace='my_namespace', pagination_token=results.pagination.next) Args: prefix (Optional[str]): The id prefix to match. If unspecified, an empty string prefix will be used with the effect of listing all ids in a namespace [optional] limit (Optional[int]): The maximum number of ids to return. If unspecified, the server will use a default value. [optional] pagination_token (Optional[str]): A token needed to fetch the next page of results. This token is returned in the response if additional results are available. [optional] namespace (Optional[str]): The namespace to fetch vectors from. If not specified, the default namespace is used. [optional] Returns: SimpleListResponse object which contains the list of ids, the namespace name, pagination information, and usage showing the number of read_units consumed. rrrLrrQNrR)next)rLrJ paginationr[) rar,r]rbrcrdrrrr#SimpleListResponserLrJ) rFrrrrLrmrnrorQrrs rGlist_paginatedzGRPCIndex.list_paginated3sH..6"% i(#%56   4 4V4**Y-;;??499>>7G?L   8#6#6#;#;r#A#)<)<)A)ABJJ!(((2B2Bz  rIc+Kd}|s |jdi|}t|jdkDr&|jDcgc]}|jc}|j r>|j j r(|jd|j j ind}|syy#t$r}|d}~wwxYwcc}ww)a The list operation accepts all of the same arguments as list_paginated, and returns a generator that yields a list of the matching vector ids in each page of results. It automatically handles pagination tokens on your behalf. Examples: .. code-block:: python >>> for ids in index.list(prefix='99', limit=5, namespace='my_namespace'): >>> print(ids) ['99', '990', '991', '992', '993'] ['994', '995', '996', '997', '998'] ['999'] Args: prefix (Optional[str]): The id prefix to match. If unspecified, an empty string prefix will be used with the effect of listing all ids in a namespace [optional] limit (Optional[int]): The maximum number of ids to return. If unspecified, the server will use a default value. [optional] pagination_token (Optional[str]): A token needed to fetch the next page of results. This token is returned in the response if additional results are available. [optional] namespace (Optional[str]): The namespace to fetch vectors from. If not specified, the default namespace is used. [optional] FNrrTr[)r ExceptionrjrJrrrrl)rFrmdonerevs rGr^zGRPCIndex.listls0 -$--777??#a'%,__5qtt55!!g&8&8&=&= 173E3E3J3JKL  6s:CB)&CB<AC'C) B92B44B99Cc (| t|}nd}|jd|fg}|jdd}tdi|}|jj |j j||}tj|}t|S)a The DescribeIndexStats operation returns statistics about the index's contents. For example: The vector count per namespace and the number of dimensions. Examples: .. code-block:: python >>> index.describe_index_stats() >>> index.describe_index_stats(filter={'key': 'value'}) Args: filter (Dict[str, Union[str, float, int, bool, List, dict]]): If this parameter is present, the operation only returns statistics for vectors that satisfy the filter. See `metadata filtering _` [optional] Returns: DescribeIndexStatsResponse object which contains stats about the index. NrrQrRr[) rrar]r-rbrcrdDescribeIndexStatsr rr) rFrrmrrnrQrorrs rGdescribe_index_statszGRPCIndex.describe_index_statss*  08M M..=0I/JK **Y-+8i8;;??499#?#?RY?Z#11(; #M22rIc |jdd}t|}|jj|jj ||}t |S)a The describe_namespace operation returns information about a specific namespace, including the total number of vectors in the namespace. Examples: .. code-block:: python >>> index.describe_namespace(namespace='my_namespace') Args: namespace (str): The namespace to describe. Returns: NamespaceDescription object which contains information about the namespace. rQNrLrR)r]r1rbrcrdDescribeNamespacerrFrLrmrQrors rGdescribe_namespacezGRPCIndex.describe_namespacesL&**Y-*Y?;;??499#>#>QX?Y*844rIc |jdd}t|}|jj|jj ||}t |S)a The delete_namespace operation deletes a namespace from an index. This operation is irreversible and will permanently delete all data in the namespace. Examples: .. code-block:: python >>> index.delete_namespace(namespace='my_namespace') Args: namespace (str): The namespace to delete. Returns: Empty dictionary indicating successful deletion. rQNrrR)r]r2rbrcrdDeleteNamespacerrs rGdelete_namespacezGRPCIndex.delete_namespacesK&**Y-(9=;;??499#<#>> results = index.list_namespaces_paginated(limit=10) >>> [ns.name for ns in results.namespaces] ['namespace1', 'namespace2', 'namespace3'] >>> results.pagination.next eyJza2lwX3Bhc3QiOiI5OTMiLCJwcmVmaXgiOiI5OSJ9 >>> next_results = index.list_namespaces_paginated(limit=10, pagination_token=results.pagination.next) Args: limit (Optional[int]): The maximum number of namespaces to return. If unspecified, the server will use a default value. [optional] pagination_token (Optional[str]): A token needed to fetch the next page of results. This token is returned in the response if additional results are available. [optional] Returns: ListNamespacesResponse object which contains the list of namespaces and pagination information. rrrQNrRr[)rar]r3rbrcrdListNamespacesr)rFrrrmrnrQrors rGlist_namespaces_paginatedz#GRPCIndex.list_namespaces_paginateds{:..% #%56  **Y-'>)>v>;;??499#;#;Wg?V-h77rIc+vKd}|s |jdd|i|}|jr-t|jdkDr|jD]}||jr>|jj r(|j d|jj ind}|syy#t$r}|d}~wwxYww)a& The list_namespaces operation accepts all of the same arguments as list_namespaces_paginated, and returns a generator that yields each namespace. It automatically handles pagination tokens on your behalf. Args: limit (Optional[int]): The maximum number of namespaces to fetch in each network call. If unspecified, the server will use a default value. [optional] Returns: Returns a generator that yields each namespace. It automatically handles pagination tokens on your behalf so you can easily iterate over all results. The ``list_namespaces`` method accepts all of the same arguments as list_namespaces_paginated Examples: .. code-block:: python >>> for namespace in index.list_namespaces(): >>> print(namespace.name) namespace1 namespace2 namespace3 You can convert the generator into a list by wrapping the generator in a call to the built-in ``list`` function: .. code-block:: python namespaces = list(index.list_namespaces()) You should be cautious with this approach because it will fetch all namespaces at once, which could be a large number of network calls and a lot of memory to hold the results. FrNrrTr[)rrrrjrrrl)rFrrmrrrrLs rGlist_namespaceszGRPCIndex.list_namespaces s@ 8$88OuOO!!c'*<*<&=&A!(!3!3$I#O$!!g&8&8&=&= 173E3E3J3JKL  s.B9B&BB9$B9& B6/B11B66B9argsc@|Dcic] \}}| || c}}Scc}}wNr[)rarg_namevals rGrazGRPCIndex._parse_non_empty_args<s"37K-(C3?# KKKs )FNNT)riTT)NNNNF)NF) NNNNNNNNF)NNNNN)FNNNN)NNNNr)NN)8__name__ __module__ __qualname____doc__propertyrHrrr$rAr=r<boolrstrrir'r9rtrgr staticmethodr~r>r.rrrfloatr0rCr;rrr r4rr?r/rlrrr^DescribeIndexStatsResponserrr rrrrr!rrrrar[rIrGr@r@GsF !! #'$(" d=tF|T*%5tK7H$J__`d=d=C= d= SM d=  d= ~11 2d=LUJ'U4 &2 }00 1 &2T)- #'#,0)-+/ $)[Je%[J SM[JC= [J } [J ) [J![J#4.[J , 02GG H [JD>[J }00 1[JD $,0)-+/RV*U *I*;< * } * ) *!*#4.* &68M&M NO* *^ (,:>#'RVAO AOAOe% AO 67 AO C= AO &68M&M NOAO ~11 2AOJ!%#*.#' 7  7 }7 #3- 7 C= 7  7 r%P373/3 #3B55 55.// c3h//. $*.%8}%8#3-%8 %8%8N-Xc]--^LDsCx$9Ld38nLLrIr@)Vloggingtypingrrrrrrr r r google.protobufr pinecone.utils.tqdmr pinecone.utilsrconcurrent.futuresrrutilsrrrrrrrrrvector_factory_grpcrsparse_values_factoryr$pinecone.core.openapi.db_data.modelsrrrrr r!(pinecone.db_control.models.list_responser"rr#-pinecone.core.grpc.protos.db_data_2025_04_pb2r$rAr%rBr&r'r(r)r*r+r,r-r.r/r0rCr1r2r3pinecone)pinecone.db_data.query_results_aggregatorr4r52pinecone.core.grpc.protos.db_data_2025_04_pb2_grpcr7baser8rfr9 db_data.typesr;r<r=r>r?__all__ getLoggerr_loggerr@r[rIrGrsSSS'$)3   36d$*dP&  '  H %w L w LrI