o Zh~@s&ddlZddlmZddlmZmZmZmZmZddl m Z ddl m Z ddl mZddd d d d d ddZdefddZdedeeeffddZdddedeedeeeffddZejeeddeddfddZe dddedeededee eeeefffdd Zd d!gZdS)"N)util)AnyListOptionalTupleUnion)beta) Embeddings)Runnablelangchain_openai langchain_awslangchain_coherelangchain_google_vertexailangchain_huggingfacelangchain_mistralailangchain_ollama) azure_openaibedrockcoheregoogle_vertexai huggingface mistralaiollamaopenaireturncCsdddtDS)z3Get formatted list of providers and their packages. css,|]\}}d|d|ddVqdS)z - z: _-N)replace).0ppkgr"P/var/www/html/lang_env/lib/python3.10/site-packages/langchain/embeddings/base.py s z%_get_provider_list..)join_SUPPORTED_PROVIDERSitemsr"r"r"r#_get_provider_lists r( model_namecCsvd|vrt}td|d||dd\}}|}|}|tvr1td|dt|s7td||fS)aParse a model string into provider and model name components. The model string should be in the format 'provider:model-name', where provider is one of the supported providers. Args: model_name: A model string in the format 'provider:model-name' Returns: A tuple of (provider, model_name) .. code-block:: python _parse_model_string("openai:text-embedding-3-small") # Returns: ("openai", "text-embedding-3-small") _parse_model_string("bedrock:amazon.titan-embed-text-v1") # Returns: ("bedrock", "amazon.titan-embed-text-v1") Raises: ValueError: If the model string is not in the correct format or the provider is unsupported :zInvalid model format 'z'. Model name must be in format 'provider:model-name' Example valid model strings: - openai:text-embedding-3-small - bedrock:amazon.titan-embed-text-v1 - cohere:embed-english-v3.0 Supported providers:  Provider 'E' is not supported. Supported providers and their required packages: Model name cannot be empty)r& ValueErrorsplitlowerstripr()r) providersprovidermodelr"r"r#_parse_model_strings& r6r4r5r4cCsr|std|durd|vrt|\}}n|}|}|s&t}td||tvr5td|dt||fS)Nr.r*zMust specify either: 1. A model string in format 'provider:model-name' Example: 'openai:text-embedding-3-small' 2. Or explicitly set provider from: r,r-)r2r/r6r&r()r5r4r)r3r"r"r#_infer_model_and_providerOs(r8)maxsizer!cCs$t|std|d|ddS)z Check if a package is installed.zCould not import z5 python package. Please install it with `pip install `N)r find_spec ImportError)r!r"r"r# _check_pkgms r=kwargscKs|st}tdd|t||d\}}t|}t||dkr3ddlm}|dd|i|S|dkrFdd lm}|dd|i|S|d krYdd l m }|dd|i|S|d krldd l m } | dd|i|S|dkrddl m} | dd|i|S|dkrddlm} | dd|i|S|dkrddlm} | dd|i|S|dkrddlm} | dd|i|Std|dt)aInitialize an embeddings model from a model name and optional provider. **Note:** Must have the integration package corresponding to the model provider installed. Args: model: Name of the model to use. Can be either: - A model string like "openai:text-embedding-3-small" - Just the model name if provider is specified provider: Optional explicit provider name. If not specified, will attempt to parse from the model string. Supported providers and their required packages: {_get_provider_list()} **kwargs: Additional model-specific parameters passed to the embedding model. These vary by provider, see the provider-specific documentation for details. Returns: An Embeddings instance that can generate embeddings for text. Raises: ValueError: If the model provider is not supported or cannot be determined ImportError: If the required provider package is not installed .. dropdown:: Example Usage :open: .. code-block:: python # Using a model string model = init_embeddings("openai:text-embedding-3-small") model.embed_query("Hello, world!") # Using explicit provider model = init_embeddings( model="text-embedding-3-small", provider="openai" ) model.embed_documents(["Hello, world!", "Goodbye, world!"]) # With additional parameters model = init_embeddings( "openai:text-embedding-3-small", api_key="sk-..." ) .. versionadded:: 0.3.9 z2Must specify model name. Supported providers are: z, r7rr)OpenAIEmbeddingsr5r)AzureOpenAIEmbeddingsr)VertexAIEmbeddingsr)BedrockEmbeddingsZmodel_idr)CohereEmbeddingsr)MistralAIEmbeddingsr)HuggingFaceEmbeddingsr)r)OllamaEmbeddingsr,r-Nr")r&keysr/r%r8r=r r?r@rrAr rBr rCrrDrrErrFr()r5r4r>r3r)r!r?r@rArBrCrDrErFr"r"r#init_embeddingswsJ8        rHr ) functools importlibrtypingrrrrrZlangchain_core._apirZlangchain_core.embeddingsr Zlangchain_core.runnablesr r&strr(r6r8 lru_cachelenr=floatrH__all__r"r"r"r#sR     4    j