Mi^) %SrSSKrSSKrSSKJr SSKJr SSKJr S\ \S\S\4S jr S S \ 4S S \ 4SSS4SS\ 4SS\ 4SS\ 4SSS4SS\ 4SS\ 4S S\ 4S. r \ \ \\ \ \S \444\S!'\R "\"\ 5S"9S#\ S\S \44S$j5rS\ 4S%jrS&\ S\\ \ 44S'jrSS(.S)\ S#\ S-S\\ \ 44S*jjrSS(.S)\ S#\ S-S\S\4S+jjrS,S-/rg).z!Factory functions for embeddings.N)Callable)Any) Embeddingsclskwargsreturnc U"S0UD6$)Nr )rrs s/var/www/eduai.edurigo.com/storigo/production/storigo_env/lib/python3.13/site-packages/langchain/embeddings/base.py_callr s ==zlangchain_azure_ai.embeddingsAzureAIOpenAIApiEmbeddingsModellangchain_openaiAzureOpenAIEmbeddings langchain_awsBedrockEmbeddingsc U"SSU0UD6$)Nmodel_idr r rmodelrs r rsS%B%%B6%Br langchain_cohereCohereEmbeddingslangchain_google_genaiGoogleGenerativeAIEmbeddingslangchain_google_vertexaiVertexAIEmbeddingslangchain_huggingfaceHuggingFaceEmbeddingsc U"SSU0UD6$)N model_namer r rs r rrsS%DE%DV%Dr langchain_mistralaiMistralAIEmbeddingslangchain_ollamaOllamaEmbeddingsOpenAIEmbeddings) azure_ai azure_openaibedrockcohere google_genaigoogle_vertexai huggingface mistralaiollamaopenai._BUILTIN_PROVIDERS)maxsizeprovidercdU[;aSUS[53n[U5e[Uup#n[R"U5n[XS5n[R"XHS 9$![ a=nUR SSS9SRSS5nS US US 3n[ U5UeS nAff=f)a Return a factory function that creates an embeddings model for the given provider. This function is cached to avoid repeated module imports. Args: provider: The name of the model provider (e.g., `'openai'`, `'cohere'`). Must be a key in `_BUILTIN_PROVIDERS`. Returns: A callable that accepts model kwargs and returns an `Embeddings` instance for the specified provider. Raises: ValueError: If the provider is not in `_BUILTIN_PROVIDERS`. ImportError: If the provider's integration package is not installed. Provider 'E' is not supported. Supported providers and their required packages: .)maxsplitr_-zCould not import z5 python package. Please install it with `pip install `N)r) r1_get_provider_list ValueError importlib import_module ImportErrorsplitreplacegetattr functoolspartial) r3msg module_name class_name creator_funcmoduleepkgrs r _get_embeddings_class_creatorrN9s&)) #A!#$ & o,>x,H)K\&((5 & %C   \ 33 &a03;;CE!#&[\_[``ab#A%&sA(( B/28B**B/cVSRS[R555$)z3Get formatted list of providers and their packages. c3\# UH"upSUSUSRSS53v M$ g7f)z - z: rr:r;N)rC).0prMs r %_get_provider_list..bs3=W61$qcCFNN3,-.=Ws*,)joinr1itemsr r r r=r=`s) 99=O=U=U=W r r!cTSU;a%SUS[R53n[U5eURSS5up#UR 5R 5nUR 5nU[;aSUS[ 53n[U5eU(d Sn[U5eX#4$)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) Example: ```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: r8r5r6Model name cannot be empty)r1keysr>rBlowerstripr=)r!rGr3rs r _parse_model_stringr^gs4 *$ZL1$ %7$;$;$=#>  @ o &&sA.OH~~%%'H KKME)) #A!#$ & o *o ?r r3rcUR5(d Sn[U5eUcSU;a[U5upOUnU(d"S[R 53n[U5eU[;aSUS[ 53n[U5eX4$)NrZrYzMust specify either: 1. A model string in format 'provider:model-name' Example: 'openai:text-embedding-3-small' 2. Or explicitly set provider from: r5r6)r]r>r^r1r[r=)rr3rGr!s r _infer_model_and_providerras ;;==*oC5L259*  3"&&()  + o)) #A!#$ & o  r c U(d3[R5nSSRU53n[U5e[ XS9up[ U5"SSU0UD6$)aC Initialize an embedding model from a model name and optional provider. !!! note Requires the integration package for the chosen model provider to be installed. See the `model_provider` parameter below for specific package names (e.g., `pip install langchain-openai`). Refer to the [provider integration's API reference](https://docs.langchain.com/oss/python/integrations/providers) for supported model parameters to use as `**kwargs`. Args: model: The name of the model, e.g. `'openai:text-embedding-3-small'`. You can also specify model and model provider in a single argument using `'{model_provider}:{model}'` format, e.g. `'openai:text-embedding-3-small'`. provider: The model provider if not specified as part of the model arg (see above). Supported `provider` values and the corresponding integration package are: - `openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai) - `azure_ai` -> [`langchain-azure-ai`](https://docs.langchain.com/oss/python/integrations/providers/microsoft) - `azure_openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai) - `bedrock` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws) - `cohere` -> [`langchain-cohere`](https://docs.langchain.com/oss/python/integrations/providers/cohere) - `google_vertexai` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google) - `huggingface` -> [`langchain-huggingface`](https://docs.langchain.com/oss/python/integrations/providers/huggingface) - `mistralai` -> [`langchain-mistralai`](https://docs.langchain.com/oss/python/integrations/providers/mistralai) - `ollama` -> [`langchain-ollama`](https://docs.langchain.com/oss/python/integrations/providers/ollama) **kwargs: Additional model-specific parameters passed to the embedding model. These vary by provider. Refer to the specific model provider's [integration reference](https://reference.langchain.com/python/integrations/) for all available parameters. 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 ???+ example ```python # pip install langchain langchain-openai # 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-...") ``` !!! version-added "Added in `langchain` 0.3.9" z2Must specify model name. Supported providers are: z, r_rr )r1r[rVr>rarN)rr3r providersrGr!s r init_embeddingsrds[P &++- B499YCWBXYo4UNH ( 2 N Nv NNr rrd)__doc__rEr?collections.abcrtypingrlangchain_core.embeddingsrtyper r1dictstrtuple__annotations__ lru_cachelenrNr=r^rard__all__r r r rqs'$0tJ3: 12SUZ[')@%HB "#5u=-/MuU35I5QD ()>F!#5u=!#5u=%MDeChsJ.G$GHHI(, S!345#4C#4HS*_4M#46#4LC4C4E#s(O4t   Dj  38_  H NO NODjNO NO  NOd r