iJ LUdZddlmZddlZddlZddlZddlmZmZm Z m Z m Z m Z ddl mZmZddlmZmZddlmZmZddlmZmZmZdd lmZer,dd lmZmZmZm Z dd l!m"Z"dd l#m$Z$dd l%m&Z&ddl'm(Z(m)Z)ddl*m+Z+d~dZ,iddde,fddde,fddde,fd d!d"e,fd#dd$e,fd%dd&e,fd'd(d)e,fd*d+d,e,fd-d.d/e,fd0d1d2e,fd3d4d5e,fd6d7d8e,fd9d:d;e,fdd?fd@dAdBdCfdDdEdFe,fdGdHdIe,fdJdKe,fd!dLe,fdMdNe,fdOdPe,fdQdRe,fdSdTe,fdUdVe,fdWZ-dXe.dY< dd^Z/ej0e1e-_ddbZ2e ddddcddjZ3e dddddcddlZ3e dddmddcddoZ3 dddddcddrZ3ddsddtZ4ddvZ5ddxZ6dd{Z7d|Z8Gd}dkeeefZ9dS)z"Factory functions for chat models.) annotationsN) TYPE_CHECKINGAnyLiteral TypeAliascastoverload) BaseChatModelLanguageModelInput) AIMessage AnyMessage)ChatPromptValueConcreteStringPromptValue)RunnableRunnableConfig ensure_config)override) AsyncIteratorCallableIteratorSequence) ModuleType) StreamEvent)BaseTool)RunLog RunLogPatch) BaseModelclstype[BaseChatModel]kwargsrreturnr c |di|SNr$)rr s C:\Users\Dell Inspiron 16\Desktop\tws\AgrotaPowerBi\back-agrota-powerbi\mcp-client-agrota\venv\Lib\site-packages\langchain/chat_models/base.py_callr&!s 3==== anthropiclangchain_anthropic ChatAnthropicanthropic_bedrock langchain_awsChatAnthropicBedrockazure_aizlangchain_azure_ai.chat_modelsAzureAIChatCompletionsModel azure_openailangchain_openaiAzureChatOpenAIbedrock ChatBedrockbedrock_converseChatBedrockConversecoherelangchain_cohere ChatCoheredeepseeklangchain_deepseek ChatDeepSeek fireworkslangchain_fireworks ChatFireworksgoogle_anthropic_vertexz&langchain_google_vertexai.model_gardenChatAnthropicVertex google_genailangchain_google_genaiChatGoogleGenerativeAIgoogle_vertexailangchain_google_vertexai ChatVertexAIgroqlangchain_groqChatGroq huggingfacelangchain_huggingfaceChatHuggingFacec  |jdd|i|SNmodel_idr$) from_model_idrmodelr s r%rT;s%6S%6%P%P%P%P%Pr'ibm langchain_ibm ChatWatsonxc |dd|i|SrOr$rRs r%rTrT@sSS%B%B%%B6%B%Br' mistralailangchain_mistralai ChatMistralAInvidialangchain_nvidia_ai_endpoints ChatNVIDIAlangchain_ollama ChatOllama ChatOpenAIlangchain_openrouterChatOpenRouterlangchain_perplexityChatPerplexitylangchain_together ChatTogetherlangchain_upstage ChatUpstage langchain_xaiChatXAI)ollamaopenai openrouter perplexitytogetherupstagexaiz8dict[str, tuple[str, str, Callable[..., BaseChatModel]]]_BUILTIN_PROVIDERSmodulestr class_namerc tj|S#t$rR}|ddddd}d|d|d |d }t||d }~wwxYw) aImport a module by name. Args: module: The fully qualified module name to import (e.g., `'langchain_openai'`). class_name: The name of the class being imported, used for error messages. Returns: The imported module. Raises: ImportError: If the module cannot be imported, with a message suggesting the pip package to install. .maxsplitr_-z Initializing z requires the z. package. Please install it with `pip install `N) importlib import_module ImportErrorsplitreplace)rtrvepkgmsgs r%_import_moduleres &&v... &&&ll3l++A.66sC@@ (J ( (c ( (!$ ( ( ( #A%&s A2A A--A2)maxsizeproviderCallable[..., BaseChatModel]c|tvrCdt}d|d|}t|t|\}}} t ||}nA#t $r4}|dkr t d|}n#t $r|dwxYwYd}~nd}~wwxYwt ||}tj||S)aJReturn a factory function that creates a chat 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'`, `'anthropic'`). Must be a key in `_BUILTIN_PROVIDERS`. Returns: A callable that accepts model kwargs and returns a `BaseChatModel` 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. , zUnsupported provider=z". Supported model providers are: rlzlangchain_community.chat_modelsN)r) rsjoinkeys ValueErrorrrgetattr functoolspartial) r supportedrrrv creator_funcrtrrs r%_get_chat_model_creatorrs*)))II0557788 WXWWIWWoo$6x$@!C\ Z00     x    #$EzRRFF      FFFF  &* % %C  \s 3 3 33s0A00 B.:B)BB)B!!B))B.)model_providerconfigurable_fields config_prefixrSr str | NonerNonerc dSNr$rSrrrr s r%init_chat_modelrs Cr'_ConfigurableModelc dSrr$rs r%rr r'.,Literal['any'] | list[str] | tuple[str, ...]c dSrr$rs r%rrrr'3Literal['any'] | list[str] | tuple[str, ...] | None"BaseChatModel | _ConfigurableModelc L| `openai` - `claude...` -> `anthropic` - `amazon...` -> `bedrock` - `gemini...` -> `google_vertexai` - `command...` -> `cohere` - `accounts/fireworks...` -> `fireworks` - `mistral...` -> `mistralai` - `deepseek...` -> `deepseek` - `grok...` -> `xai` - `sonar...` -> `perplexity` - `solar...` -> `upstage` model_provider: The model provider if not specified as part of the model arg (see above). Supported `model_provider` values and the corresponding integration package are: - `openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai) - `anthropic` -> [`langchain-anthropic`](https://docs.langchain.com/oss/python/integrations/providers/anthropic) - `azure_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) - `google_vertexai` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google) - `google_genai` -> [`langchain-google-genai`](https://docs.langchain.com/oss/python/integrations/providers/google) - `anthropic_bedrock` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws) - `bedrock` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws) - `bedrock_converse` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws) - `cohere` -> [`langchain-cohere`](https://docs.langchain.com/oss/python/integrations/providers/cohere) - `fireworks` -> [`langchain-fireworks`](https://docs.langchain.com/oss/python/integrations/providers/fireworks) - `together` -> [`langchain-together`](https://docs.langchain.com/oss/python/integrations/providers/together) - `mistralai` -> [`langchain-mistralai`](https://docs.langchain.com/oss/python/integrations/providers/mistralai) - `huggingface` -> [`langchain-huggingface`](https://docs.langchain.com/oss/python/integrations/providers/huggingface) - `groq` -> [`langchain-groq`](https://docs.langchain.com/oss/python/integrations/providers/groq) - `ollama` -> [`langchain-ollama`](https://docs.langchain.com/oss/python/integrations/providers/ollama) - `google_anthropic_vertex` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google) - `deepseek` -> [`langchain-deepseek`](https://docs.langchain.com/oss/python/integrations/providers/deepseek) - `ibm` -> [`langchain-ibm`](https://docs.langchain.com/oss/python/integrations/providers/ibm) - `nvidia` -> [`langchain-nvidia-ai-endpoints`](https://docs.langchain.com/oss/python/integrations/providers/nvidia) - `xai` -> [`langchain-xai`](https://docs.langchain.com/oss/python/integrations/providers/xai) - `openrouter` -> [`langchain-openrouter`](https://docs.langchain.com/oss/python/integrations/providers/openrouter) - `perplexity` -> [`langchain-perplexity`](https://docs.langchain.com/oss/python/integrations/providers/perplexity) - `upstage` -> [`langchain-upstage`](https://docs.langchain.com/oss/python/integrations/providers/upstage) configurable_fields: Which model parameters are configurable at runtime: - `None`: No configurable fields (i.e., a fixed model). - `'any'`: All fields are configurable. **See security note below.** - `list[str] | Tuple[str, ...]`: Specified fields are configurable. Fields are assumed to have `config_prefix` stripped if a `config_prefix` is specified. If `model` is specified, then defaults to `None`. If `model` is not specified, then defaults to `("model", "model_provider")`. !!! warning "Security note" Setting `configurable_fields="any"` means fields like `api_key`, `base_url`, etc., can be altered at runtime, potentially redirecting model requests to a different service/user. Make sure that if you're accepting untrusted configurations that you enumerate the `configurable_fields=(...)` explicitly. config_prefix: Optional prefix for configuration keys. Useful when you have multiple configurable models in the same application. If `'config_prefix'` is a non-empty string then `model` will be configurable at runtime via the `config["configurable"]["{config_prefix}_{param}"]` keys. See examples below. If `'config_prefix'` is an empty string then model will be configurable via `config["configurable"]["{param}"]`. **kwargs: Additional model-specific keyword args to pass to the underlying chat model's `__init__` method. Common parameters include: - `temperature`: Model temperature for controlling randomness. - `max_tokens`: Maximum number of output tokens. - `timeout`: Maximum time (in seconds) to wait for a response. - `max_retries`: Maximum number of retry attempts for failed requests. - `base_url`: Custom API endpoint URL. - `rate_limiter`: A [`BaseRateLimiter`][langchain_core.rate_limiters.BaseRateLimiter] instance to control request rate. Refer to the specific model provider's [integration reference](https://reference.langchain.com/python/integrations/) for all available parameters. Returns: A `BaseChatModel` corresponding to the `model_name` and `model_provider` specified if configurability is inferred to be `False`. If configurable, a chat model emulator that initializes the underlying model at runtime once a config is passed in. Raises: ValueError: If `model_provider` cannot be inferred or isn't supported. ImportError: If the model provider integration package is not installed. ???+ example "Initialize a non-configurable model" ```python # pip install langchain langchain-openai langchain-anthropic langchain-google-vertexai from langchain.chat_models import init_chat_model o3_mini = init_chat_model("openai:o3-mini", temperature=0) claude_sonnet = init_chat_model("anthropic:claude-sonnet-4-5-20250929", temperature=0) gemini_2-5_flash = init_chat_model("google_vertexai:gemini-2.5-flash", temperature=0) o3_mini.invoke("what's your name") claude_sonnet.invoke("what's your name") gemini_2-5_flash.invoke("what's your name") ``` ??? example "Partially configurable model with no default" ```python # pip install langchain langchain-openai langchain-anthropic from langchain.chat_models import init_chat_model # (We don't need to specify configurable=True if a model isn't specified.) configurable_model = init_chat_model(temperature=0) configurable_model.invoke("what's your name", config={"configurable": {"model": "gpt-4o"}}) # Use GPT-4o to generate the response configurable_model.invoke( "what's your name", config={"configurable": {"model": "claude-sonnet-4-5-20250929"}}, ) ``` ??? example "Fully configurable model with a default" ```python # pip install langchain langchain-openai langchain-anthropic from langchain.chat_models import init_chat_model configurable_model_with_default = init_chat_model( "openai:gpt-4o", configurable_fields="any", # This allows us to configure other params like temperature, max_tokens, etc at runtime. config_prefix="foo", temperature=0, ) configurable_model_with_default.invoke("what's your name") # GPT-4o response with temperature 0 (as set in default) configurable_model_with_default.invoke( "what's your name", config={ "configurable": { "foo_model": "anthropic:claude-sonnet-4-5-20250929", "foo_temperature": 0.6, } }, ) # Override default to use Sonnet 4.5 with temperature 0.6 to generate response ``` ??? example "Bind tools to a configurable model" You can call any chat model declarative methods on a configurable model in the same way that you would with a normal model: ```python # pip install langchain langchain-openai langchain-anthropic from langchain.chat_models import init_chat_model from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") class GetPopulation(BaseModel): '''Get the current population in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") configurable_model = init_chat_model( "gpt-4o", configurable_fields=("model", "model_provider"), temperature=0 ) configurable_model_with_tools = configurable_model.bind_tools( [ GetWeather, GetPopulation, ] ) configurable_model_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?" ) # Use GPT-4o configurable_model_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?", config={"configurable": {"model": "claude-sonnet-4-5-20250929"}}, ) # Use Sonnet 4.5 ``` Nz6`model` must be a string (e.g., 'openai:gpt-4o'), got zp. If you've already constructed a chat model object, use it directly instead of passing it to init_chat_model().)rSrzconfig_prefix=z has been set but no fields are configurable. Set `configurable_fields=(...)` to specify the model params that are configurable.) stacklevelrurrS)default_configrr) isinstancerutype__name__ TypeErrorwarningswarn_init_chat_model_helperrr)rSrrrr rs r%rrs;p E3!7!7 SE{{# S S S nn :,:9!'RM 0   }          &     )      w2#1 #/   r')rc Zt||\}}t|}|dd|i|S)NrSr$) _parse_modelr)rSrr rs r%rrs@ )??E>*>::L < . .e .v . ..r' model_namec6|tfddDrdSdrdSdrdSdrd Sd rd Sd rd SdrdSdrdSdrdSdrdSdrdSdS)zAttempt to infer model provider from model name. Args: model_name: The name of the model to infer provider for. Returns: The inferred provider name, or `None` if no provider could be inferred. c3BK|]}|VdSr) startswith).0pre model_lowers r% z0_attempt_infer_model_provider..sE    s##      r')zgpt-o1o3chatgptz text-davincirmclauder(commandr7zaccounts/fireworksr=geminirE)zamazon.z anthropic.zmeta.r3)mistralmixtralrYr:grokrrsonarrosolarrqN)loweranyr)rrs @r%_attempt_infer_model_providerrs""$$K           xh''{i((x233{h''!  @AAy455{j))zf%%ug&&|g&&y 4r'tuple[str, str]c|sud|vrq|dddtvrM|ddd}d|ddd}|pt|}|s?dt t}d|d|d }t ||d d }||fS) z?Parse model name and provider, inferring provider if necessary.:ryrzrNrz)Unable to infer model provider for model=zB. Please specify 'model_provider' directly. 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? ?*-*G     r'rcVttztzttzS)z'Get the input type for this `Runnable`.)rurrrr )rs r% InputTypez_ConfigurableModel.InputTypes &&)@@4 CSSSr'inputr c H||j|fd|i|SNr)rinvokerr rr s r%r z_ConfigurableModel.invokes0*t{{6"")%III&IIIr'cXK||j|fd|i|d{VSr )rainvoker s r%rz_ConfigurableModel.ainvokesF1T[[((0PPvPPPPPPPPPPr' Any | None Iterator[Any]c+\K||j|fd|i|Ed{VdSr )rstreamr s r%rz_ConfigurableModel.streamsL.4;;v&&-eMMFMfMMMMMMMMMMMr'AsyncIterator[Any]c lK||j|fd|i|23d{V}|WV 6dSr )rastreamrr rr xs r%rz_ConfigurableModel.astreamso3t{{6**25RRR6RR       !GGGGGSRR3F)return_exceptionsinputslist[LanguageModelInput],RunnableConfig | list[RunnableConfig] | Nonerbool list[Any]c |pd}|(t|tst|dkrAt|tr|d}||j|f||d|St j|f||d|SNryrrr)rrlenrrbatchsuperrrrrr __class__s r%r$z_ConfigurableModel.batchs4 >Z55>V9I9I&$'' #,4;;v&&,"3  uww}  /      r'c8K|pd}|(t|tst|dkrGt|tr|d}||j|f||d|d{VSt j|f||d|d{VSr!)rrr#rrabatchr%r&s r%r)z_ConfigurableModel.abatchs4 >Z55>V9I9I&$'' #3V,,3"3  $UWW^  /            r'Sequence[LanguageModelInput]0RunnableConfig | Sequence[RunnableConfig] | None%Iterator[tuple[int, Any | Exception]]c+\K|pd}|(t|tst|dkrWt|tr|d}|t d|j|f||d|Ed{VdStj|f||d|Ed{VdSNryrrr")rrr#rrrbatch_as_completedr%r&s r%r/z%_ConfigurableModel.batch_as_completed.s%4 >Z55>V9I9I&$'' #Ut{{4(8&#A#ABBU"3          2uww1"3          r'AsyncIterator[tuple[int, Any]]c |K|pd}|(t|tst|dkr_t|tr|d}|t d|j|f||d|23d{V}|WV 6dStj|f||d|23d{V}|WV 6dSr.)rrr#rrrabatch_as_completedr%)rrrrr rr's r%r2z&_ConfigurableModel.abatch_as_completedKsZ4 >Z55>V9I9I&$'' #"4;;%v..!"3          a75776"3        a  s B.B;Iterator[LanguageModelInput]c+\K||j|fd|i|Ed{VdSr )r transformr s r%r5z_ConfigurableModel.transformlsL14;;v&&0PPvPPPPPPPPPPPPr'!AsyncIterator[LanguageModelInput]c lK||j|fd|i|23d{V}|WV 6dSr )r atransformrs r%r8z_ConfigurableModel.atransformuso6t{{6**5eUUFUfUU       !GGGGGVUUrT)diffwith_streamed_output_list include_names include_types include_tags exclude_names exclude_types exclude_tagsr9 Literal[True]r:r;Sequence[str] | Noner<r=r>r?r@AsyncIterator[RunLogPatch]c dSrr$ rr rr9r:r;r<r=r>r?r@r s r% astream_logz_ConfigurableModel.astream_logs &)Sr')r:r;r<r=r>r?r@Literal[False]AsyncIterator[RunLog]c dSrr$rEs r%rFz_ConfigurableModel.astream_logs !$r'2AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]c |K||j|f||||||| | |d | 23d{V} | WV 6dS)N) rr9r:r;r<r=r@r?r>)rrF) rr rr9r:r;r<r=r>r?r@r rs r%rFz_ConfigurableModel.astream_logs 7t{{6**6   &?''%%''            !GGGGG    s;v2)versionr;r<r=r>r?r@rMLiteral['v1', 'v2']AsyncIterator[StreamEvent]c zK||j|f|||||| ||d| 23d{V} | WV 6dS)N)rrMr;r<r=r@r?r>)rastream_events) rr rrMr;r<r=r>r?r@r rs r%rQz!_ConfigurableModel.astream_eventss:t{{6**9   ''%%''            !GGGGG    s:toolsJSequence[dict[str, Any] | type[BaseModel] | Callable[..., Any] | BaseTool]'Runnable[LanguageModelInput, AIMessage]c :|d|fi|S)Nrr)rrRr s r%rz_ConfigurableModel.bind_toolss* .t --e>>v>>>r'schema dict[str, Any] | type[BaseModel]8Runnable[LanguageModelInput, dict[str, Any] | BaseModel]c :|d|fi|S)NrrV)rrWr s r%rz)_ConfigurableModel.with_structured_outputs+ :t 899&KKFKKKr') rrrrrrurrr!r)rrur!rr)rrr!r)rrr!r)rrr rr!r)r!r)r r rrr rr!r)r r rrr rr!r)r r rrr rr!r) rrrrrrr rr!r) rr*rr+rrr rr!r,) rr*rr+rrr rr!r0)r r3rrr rr!r)r r6rrr rr!r)r rrrr9rAr:rr;rBr<rBr=rBr>rBr?rBr@rBr rr!rC)r rrrr9rGr:rr;rBr<rBr=rBr>rBr?rBr@rBr rr!rH)r rrrr9rr:rr;rBr<rBr=rBr>rBr?rBr@rBr rr!rJ)r rrrrMrNr;rBr<rBr=rBr>rBr?rBr@rBr rr!rO)rRrSr rr!rT)rWrXr rr!rY)r __module__ __qualname__rrrrrpropertyrrr rrrr$r)r/r2r5r8r rFrQrr __classcell__)r's@r%rr_sE15LQ_a       .""""<    )-     B TTTXXT)-JJJJXJ)-QQQQXQ)-NNNNXN)-X@D #(         >@D #(         >DH #( @DH #( B)-QQQQXQ)-X )-) #*..2.2-1.2.2-1)))))XX)  )-$ +/.2.2-1.2.2-1$$$$$XX$ )- *..2.2-1.2.2-1X<)- (,.2.2-1.2.2-1X:????LLLLLLLLr')rrr rr!r )rtrurvrur!r)rrur!r) rSrurrrrrrr rr!r r) rSrrrrrrrr rr!r) rSrrrrrrrr rr!r) rSrrrrrrrr rr!r)rSrurrr rr!r )rrur!r)rSrurrr!r)rrurrur!ru):__doc__ __future__rrrrtypingrrrrrr langchain_core.language_modelsr r langchain_core.messagesr r langchain_core.prompt_valuesrrlangchain_core.runnablesrrrtyping_extensionsrcollections.abcrrrrtypesrlangchain_core.runnables.schemarlangchain_core.toolsrlangchain_core.tracersrrpydanticrr&rs__annotations__r lru_cacher#rrrrrrrrr$r'r%ros(((""""""MLLLLLLL99999999SSSSSSSSLLLLLLLLLL&&&&&&#KKKKKKKKKKKK      ;;;;;;------::::::::"""""" %P'%@%P/+A5I%P13PRWX%P'):EB %P 6 %P *?G %P !<7%P%~u=%P'%@%P0  %P-/GO%P 3^UK!%P" z5 1#%P$PP%%P. 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