Y%hddlZddlmZmZmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZmZdd lmZmZmZej2eZ Gd d eZy) N)OptionalDictListUnionAny) ApiClient) InferenceApi)EmbeddingsList RerankResult) API_VERSION)setup_openapi_client PluginAware)InferenceRequestBuilder EmbedModel RerankModelceZdZdZeZeZddZ dde ee fde e e e e e fde e e efdefdZd gd ddfde ee fd e d e e e e e e effd e e dede ede e e efdefdZy) Inferencea The `Inference` class configures and uses the Pinecone Inference API to generate embeddings and rank documents. It is generally not instantiated directly, but rather accessed through a parent `Pinecone` client object that is responsible for managing shared configurations. ```python from pinecone import Pinecone pc = Pinecone() embeddings = pc.inference.embed( model="text-embedding-3-small", inputs=["Hello, world!"], parameters={"input_type": "passage", "truncate": "END"} ) ``` :param config: A `pinecone.config.Config` object, configured and built in the Pinecone class. :type config: `pinecone.config.Config`, required returnNc ||_ ||_ |jdd|_ t t t |||jddt|_|j|j|j|jy)N pool_threadsr )api_client_klass api_klassconfigopenapi_configr api_version)rrr) rrgetrrrr r _Inference__inference_api load_plugins)selfrrkwargss /var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/pinecone/data/features/inference/inference.py__init__zInference.__init__/s ,"JJ~q93&")NA6#   ;;t/B/BQUQbQb  modelinputs parametersctj|||}|jj|}t |S)a* Generates embeddings for the provided inputs using the specified model and (optional) parameters. :param model: The model to use for generating embeddings. :type model: str, required :param inputs: A list of items to generate embeddings for. :type inputs: list, required :param parameters: A dictionary of parameters to use when generating embeddings. :type parameters: dict, optional :return: EmbeddingsList object with keys `data`, `model`, and `usage`. The `data` key contains a list of `n` embeddings, where `n` = len(inputs) and type(n) = Embedding. Precision of returned embeddings is either float16 or float32, with float32 being the default. `model` key is the model used to generate the embeddings. `usage` key contains the total number of tokens used at request-time. Example: >>> inputs = ["Who created the first computer?"] >>> outputs = pc.inference.embed(model="multilingual-e5-large", inputs=inputs, parameters={"input_type": "passage", "truncate": "END"}) >>> print(outputs) EmbeddingsList( model='multilingual-e5-large', data=[ {'values': [0.1, ...., 0.2]}, ], usage={'total_tokens': 6} ) )r%r&r') embed_request)rr)rembedr )r r%r&r' request_bodyresps r"r*zInference.embedFsBF/<<: ##)) )Ed##r$textTquery documents rank_fieldsreturn_documentstop_nc tj|||||||}|jj|} t| S)a Rerank documents with associated relevance scores that represent the relevance of each document to the provided query using the specified model. :param model: The model to use for reranking. :type model: str, required :param query: The query to compare with documents. :type query: str, required :param documents: A list of documents or strings to rank. :type documents: list, required :param rank_fields: A list of document fields to use for ranking. Defaults to ["text"]. :type rank_fields: list, optional :param return_documents: Whether to include the documents in the response. Defaults to True. :type return_documents: bool, optional :param top_n: How many documents to return. Defaults to len(documents). :type top_n: int, optional :param parameters: A dictionary of parameters to use when ranking documents. :type parameters: dict, optional :return: RerankResult object with keys `data` and `usage`. The `data` key contains a list of `n` documents, where `n` = `top_n` and type(n) = Document. The documents are sorted in order of relevance, with the first being the most relevant. The `index` field can be used to locate the document relative to the list of documents specified in the request. Each document contains a `score` key representing how close the document relates to the query. Example: >>> result = pc.inference.rerank( model="bge-reranker-v2-m3", query="Tell me about tech companies", documents=[ "Apple is a popular fruit known for its sweetness and crisp texture.", "Software is still eating the world.", "Many people enjoy eating apples as a healthy snack.", "Acme Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.", "An apple a day keeps the doctor away, as the saying goes.", ], top_n=2, return_documents=True, ) >>> print(result) RerankResult( model='bge-reranker-v2-m3', data=[ { index=3, score=0.020980744, document={text="Acme Inc. has rev..."} }, { index=1, score=0.00034015716, document={text="Software is still..."} } ], usage={'rerank_units': 1} ) )r%r.r/r0r1r2r')rerank_request)rrerankrr ) r r%r.r/r0r1r2r'r4r,s r"r5zInference.rerankosPF177#-! ##**.*ID!!r$)rN)N)__name__ __module__ __qualname____doc__EmbedModelEnumrRerankModelEnumrr#rstrrrrrr r*boolintr r5r$r"rrs, J!K 604 '$^S()'$c4:tCy01'$T#s(^, '$  '$\#)!%#/3M"_c)*M"M"cDc3h$889 M" #Y M"  M"}M"T#s(^,M" M"r$r)loggingtypingrrrrrpinecone.openapi_supportr$pinecone.core.openapi.inference.apisr modelsr r pinecone.core.openapi.inferencer pinecone.utilsrrinference_request_builderrrr:rr; getLoggerr6loggerrr?r$r"rJsL33.=07<   8 $g" g"r$