i6$ddlmZmZmZmZmZmZddlmZddl m Z m Z m Z m Z ddlmZmZddlmZmZmZerddlmZGdd Zy ) )OptionalDictListUnionAny TYPE_CHECKING)AsyncioInferenceApi)EmbeddingsList RerankResult ModelInfoList ModelInfo)require_kwargsparse_non_empty_args)InferenceRequestBuilder EmbedModel RerankModel ModelAsyncioc2eZdZdZeZeZddZ dde de e e e e e fde e e efdefdZedd Zd gd ddfde d e d e e e e e e effde e dede ede e e efdefdZedddde e de e defdZede defdZy)AsyncioInferencea The `AsyncioInference` class configures and uses the Pinecone Inference API to generate embeddings and rank documents. This class 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 PineconeAsyncio pc = PineconeAsyncio() embeddings = await 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 D||_ d|_ t||_yN) api_client_modelr _AsyncioInference__inference_api)selfrkwargss v/var/www/catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/pinecone/inference/inference_asyncio.py__init__zAsyncioInference.__init__+s&$8< 2:>modelinputs parameterscKtj|||}|jj|d{}t |S7w)a0 Generates embeddings for the provided inputs using the specified model and (optional) parameters. :param model: The model to use for generating embeddings. :type model: str, required :param inputs: A list of items to generate embeddings for. :type inputs: list, required :param parameters: A dictionary of parameters to use when generating embeddings. :type parameters: dict, optional :return: EmbeddingsList object with keys `data`, `model`, and `usage`. The `data` key contains a list of `n` embeddings, where `n` = len(inputs) and type(n) = Embedding. Precision of returned embeddings is either float16 or float32, with float32 being the default. `model` key is the model used to generate the embeddings. `usage` key contains the total number of tokens used at request-time. Example: >>> inputs = ["Who created the first computer?"] >>> outputs = await pc.inference.embed(model="multilingual-e5-large", inputs=inputs, parameters={"input_type": "passage", "truncate": "END"}) >>> print(outputs) EmbeddingsList( model='multilingual-e5-large', data=[ {'values': [0.1, ...., 0.2]}, ], usage={'total_tokens': 6} ) )r#r$r%) embed_requestN)rr'rembedr )rr#r$r% request_bodyresps r r(zAsyncioInference.embed5sOF/<<: ))//l/KKd##Ls8A A A cn|jddlm}||j|_|jS)a Model is a resource that describes models available in the Pinecone Inference API. Curently you can get or list models. ```python async with PineconeAsyncio() as pc: # List all models models = await pc.inference.model.list() # List models, with model type filtering models = await pc.inference.model.list(type="embed") models = await pc.inference.model.list(type="rerank") # List models, with vector type filtering models = await pc.inference.model.list(vector_type="dense") models = await pc.inference.model.list(vector_type="sparse") # List models, with both type and vector type filtering models = await pc.inference.model.list(type="rerank", vector_type="dense") # Get details on a specific model model = await pc.inference.model.get("text-embedding-3-small") ``` r r) inference_api)rresources.asyncio.modelrr)rModelAsyncioResources r r#zAsyncioInference.model^s-6 ;;  U.T=Q=QRDK{{r"textTquery documents rank_fieldsreturn_documentstop_nc Ktj|||||||}|jj|d{} t| S7w)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 = await 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#r0r1r2r3r4r%)rerank_requestN)rrerankrr ) rr#r0r1r2r3r4r%r6r*s r r7zAsyncioInference.reranks]F177#-! ))000OOD!!Pss1AAA model_nameclK|jj|d{}t|S7w)af Get details on a specific model. ```python async with PineconeAsyncio() as pc: model = await pc.inference.get_model(model_name="text-embedding-3-small") ``` :param model_name: The name of the model to get details on. :type model_name: str, required :return: A ModelInfo object. )r>N)r get_modelr)rr>r*s r r@zAsyncioInference.get_models4))33z3JJKs 424)rNr)rr.)__name__ __module__ __qualname____doc__EmbedModelEnumrRerankModelEnumrr!strrrrrrr r(propertyr#boolintr r7rr r<rr@r;r"r rrsw, J!K04 '$'$c4:tCy01'$T#s(^, '$  '$RJ#)!%#/3M"M"M"cDc3h$889 M" #Y M"  M"}M"T#s(^,M" M"^'+$#}#:B3-# ##$#)r"rN)typingrrrrrr1pinecone.core.openapi.inference.api.inference_apir modelsr r r rpinecone.utilsrrinference_request_builderrrrErrFr-rr.rr;r"r rPs5BBQJJ? 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