tj dUdZddlmZddlZddlZddlZddlmZmZm 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+ddZ,iddde,fddde,fddde,fd d!d"e,fd#d$d%e,fd&dd'e,fd(dd)e,fd*d+d,e,fd-d.d/e,fd0d1d2e,fd3d4d5e,fd6d7d8e,fd9d:d;e,fde,fd?d@dAdBfdCdDdEdFfdGdHdIe,fdJdKe,fdLdMe,fdNdOe,fdPdQe,fd!dRe,fdSdTe,fdUdVe,fdWdXe,fdYdZe,fd[d\e,fd] Z-d^e.d_< dddZ/ej0e1e-eddhZ2e ddddiddpZ3e dddddiddrZ3e dddsddidduZ3 dddddiddxZ3ddyddzZ4dd|Z5dd~Z6ddZ7dZ8GddqeeefZ9dS)z"Factory functions for chat models.) annotationsN) TYPE_CHECKINGAnyLiteral TypeAliascastoverload) BaseChatModelLanguageModelInput) AIMessage AnyMessage)ChatPromptValueConcreteStringPromptValue)RunnableRunnableConfig ensure_config)override) AsyncIteratorCallableIteratorSequence) ModuleType) StreamEvent)BaseTool)RunLog RunLogPatch) BaseModelclstype[BaseChatModel]kwargsrreturnr c |di|SNr$)rr s X/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain/chat_models/base.py_callr&!s 3==== anthropiclangchain_anthropic ChatAnthropicanthropic_bedrock langchain_awsChatAnthropicBedrockazure_aizlangchain_azure_ai.chat_modelsAzureAIOpenAIApiChatModel azure_openailangchain_openaiAzureChatOpenAIbasetenlangchain_baseten ChatBasetenbedrock 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$) from_model_idrmodelr s r%rW<s%6S%6%P%P%P%P%Pr'ibm langchain_ibm ChatWatsonxc |dd|i|SrRr$rUs r%rWrWAsSS%B%B%%B6%B%Br'litellmlangchain_litellm ChatLiteLLMlangchain_meta ChatMetaModellangchain_mistralai ChatMistralAIlangchain_nvidia_ai_endpoints ChatNVIDIAlangchain_ollama ChatOllama ChatOpenAIlangchain_openrouterChatOpenRouterlangchain_perplexityChatPerplexitylangchain_together ChatTogetherlangchain_upstage ChatUpstage langchain_xaiChatXAI) meta mistralainvidiaollamaopenai openrouter perplexitytogetherupstagexaiz8dict[str, tuple[str, str, Callable[..., BaseChatModel]]]_BUILTIN_PROVIDERSmodulestr class_namerc tj|S#t$rR}|ddddd}d|d|d |d }t||d }~wwxYw) aImport a module by name. Args: module: The fully qualified module name to import (e.g., `'langchain_openai'`). class_name: The name of the class being imported, used for error messages. Returns: The imported module. Raises: ImportError: If the module cannot be imported, with a message suggesting the pip package to install. .maxsplitr_-z Initializing z requires the z. package. Please install it with `pip install `N) importlib import_module ImportErrorsplitreplace)r}repkgmsgs r%_import_modulerhs &&v... &&&ll3l++A.66sC@@ (J ( (c ( (!$ ( ( ( #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: ruzlangchain_community.chat_modelsN)r) r|joinkeys ValueErrorrrgetattr functoolspartial) r supportedrrr creator_funcr}rrs r%_get_chat_model_creatorrs*)))II0557788 WXWWIWWoo$6x$@!C\ Z00     x    #$EzRRFF      FFFF  &* % %C  \s 3 3 33s0A00 B.:B)BB)B!!B))B.)model_providerconfigurable_fields config_prefixrVr str | NonerNonerc dSNr$rVrrrr 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....` | `anthropic....` | `meta....` -> `bedrock` - `gemini...` -> `google_vertexai` (default changes in next major; pass `model_provider` to lock in) - `command...` -> `cohere` - `accounts/fireworks...` -> `fireworks` - `mistral...` | `mixtral...` -> `mistralai` - `deepseek...` -> `deepseek` - `grok...` -> `xai` - `sonar...` -> `perplexity` - `solar...` -> `upstage` - `chatgpt...` | `text-davinci...` -> `openai` (legacy) model_provider: Provider of the model, passed separately instead of as a prefix on `model`. Equivalent to the prefix form — e.g., `model='claude-sonnet-4-5', model_provider='anthropic'` behaves the same as `model='anthropic:claude-sonnet-4-5'`. Prefer the prefix form on `model` for most usage. Reach for this kwarg when: - The provider is dynamic (read from config or an env var) and you'd otherwise concatenate strings. - You want `model` and `model_provider` to be independently swappable at runtime via `configurable_fields` (e.g., to route the same model name to a different host). Supported values and the integration package each requires: - `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) - `baseten` -> [`langchain-baseten`](https://docs.langchain.com/oss/python/integrations/providers/baseten) - `litellm` -> [`langchain-litellm`](https://docs.langchain.com/oss/python/integrations/providers/litellm) - `meta` -> [`langchain-meta`](https://pypi.org/project/langchain-meta) 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 from langchain.chat_models import init_chat_model gpt_5 = init_chat_model("openai:gpt-5.5", temperature=0) gpt_5.invoke("what's your name") ``` ??? example "Partially configurable model with no default" ```python # pip install langchain langchain-openai 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) # Use GPT-5.5 to generate the response configurable_model.invoke( "what's your name", config={"configurable": {"model": "gpt-5.5"}}, ) ``` ??? 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-5.5", 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-5.5 response with temperature 0 (as set in default) # Invoke overriding model and temperature at runtime via config. # Note the use of the "foo_" prefix on the config keys, which matches # the config_prefix we set when initializing the model. configurable_model_with_default.invoke( "what's your name", config={ "configurable": { "foo_model": "anthropic:claude-opus-4-7", "foo_temperature": 0.6, } }, ) ``` ??? 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-5.5", 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-5.5 configurable_model_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?", config={"configurable": {"model": "claude-opus-4-7"}}, ) # Use Opus 4.7 ``` Nz7`model` must be a string (e.g., 'openai:gpt-5.5'), got zp. If you've already constructed a chat model object, use it directly instead of passing it to init_chat_model().)rVrzconfig_prefix=z has been set but no fields are configurable. Set `configurable_fields=(...)` to specify the model params that are configurable. stacklevelr~rrV)default_configrr) isinstancer~type__name__ TypeErrorwarningswarn_init_chat_model_helperrr)rVrrrr rs r%rrs;T E3!7!7 SE{{# S S S nn :,:9!'RM 0   }          &     )      w2#1 #/   r')rc Zt||\}}t|}|dd|i|S)NrVr$) _parse_modelr)rVrr rs r%rrs@ )??E>*>::L < . .e .v . ..r' model_namec||tfddDrdSdrdSdrdSdrd Sd r%tjd |d |d t ddSdrdSdrdSdrdSdrdSdrdSdrdSdS)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-davincirvclauder(commandr:zaccounts/fireworksr@geminiz1Inferred `model_provider='google_vertexai'` from z. This default will change to 'google_genai' in the next major release.To keep current behavior, pass `model_provider='google_vertexai'` (or use the prefix form, e.g. 'google_vertexai:z;'); for AI Studio / Gemini API, use 'google_genai' instead.rrH)zamazon.z anthropic.zmeta.r6)mistralmixtralrsr=grokr{sonarrxsolarrzN)loweranyrrrDeprecationWarning)rrs @r%_attempt_infer_model_providerr s""$$K           xh''{i((x233{h'' !  F  F F?I F F F     ! @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.:rrrNrz)Unable to infer model provider for model=zB. 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