tj$ddlZddlZddlmZmZmZddlmZmZm Z m Z m Z m Z ddl mZmZmZddlmZmZmZmZmZddlmZmZmZmZddlmZddlmZm Z dd l!m"Z"dd l#m$Z$m%Z%dd l&m'Z'm(Z(dd l)m*Z*dd l+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z;ddlZ>m?Z?ddl@mAZAmBZBeCe;zZDeCeEe;zZFe?de9Gdde>ZGe?de9Gdde;ZHejI5ejJde9d e?d!e9Gd"d#eGZKdddn #1swxYwYejI5ejJde9d e?d$e9Gd%d&eHZLdddn #1swxYwYejI5ejJde9e d'eGeHz(ZMeEeMZNdddn #1swxYwYd)ZOeePzeeMgefzeeMefzZQdLd*eMd+ePd,ed-efd.ZRd/eQdzd-efd0ZS dMd1ed2eed3eTd-eUfd4ZVd1ed-efd5ZWd6eed-dfd7ZXe?d8e9ddddddddddd9d:dd; d1ePezeeMe0e7gefzeeMe0e7geefzeeMe0e7geeeffzeeMe0e7geeeeffzd2eeezeCePefzeBzd/eQdzde dzd?eNdzd@eEedzdAe4dzdBe2dzdCeZePdzdDeZePdzdEeUdFe dGdHePdzdIed-e,f"dJZ[e[Z\gdKZ]dS)NN) AwaitableCallableSequence) AnnotatedAnyLiteralTypeVarcastget_type_hints) BaseChatModelLanguageModelInputLanguageModelLike) AIMessage AnyMessage BaseMessage SystemMessage ToolMessage)RunnableRunnableBindingRunnableConfigRunnableSequence)BaseTool)RunnableCallable RunnableLike)MISSING) ErrorCodecreate_error_message)END StateGraph) add_messages)CompiledStateGraph)RemainingSteps)Runtime) BaseStore) CheckpointerSend)ContextT)LangGraphDeprecatedSinceV10) BaseModel) NotRequired TypedDict deprecated)ToolCallWithContextToolNodezxAgentState has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentState`.categorycPeZdZUdZeeeefed<e e ed<dS) AgentStateThe state of the agent.messagesremaining_stepsN) __name__ __module__ __qualname____doc__rrrr __annotations__r*r"d/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langgraph/prebuilt/chat_agent_executor.pyr2r25sF "!-|;<<<< 000000r<r2zTAgentStatePydantic has been deprecated in favor of AgentState in `langchain.agents`.cHeZdZUdZeeeefed<dZ e ed<dS)AgentStatePydanticr3r4r5N) r6r7r8r9rrrr r:r5r"r;r<r=r?r?AsE "!-|;<<<<&(O^(((((r<r?ignorez1AgentState has been moved to `langchain.agents`.*)r0messagezbAgentStateWithStructuredResponse has been deprecated in favor of AgentState in `langchain.agents`.ceZdZUdZeed<dS) AgentStateWithStructuredResponse2The state of the agent with a structured response.structured_responseNr6r7r8r9StructuredResponser:r;r<r=rDrDT' A@//////r<rDzjAgentStateWithStructuredResponsePydantic has been deprecated in favor of AgentState in `langchain.agents`.ceZdZUdZeed<dS)(AgentStateWithStructuredResponsePydanticrErFNrGr;r<r=rKrKerIr<rK StateSchema)boundPromptstatekeydefaultreturnczt|tr|||nt|||SN) isinstancedictgetgetattr)rOrPrQs r=_get_state_valuerYs= eT " " * #w UC ) )r<promptc,tdt}nttr*t tfdt}nttrtfdt}nt jrtdt}n]trtt}n7ttr}ntdt|S)Nc"t|dSNr4rY)rOs r=z&_get_prompt_runnable..s*5*==r<namecontentc,gt|dzSr]r^)rO_system_messages r=r_z&_get_prompt_runnable..s?+.>uj.Q.QQr<c,gt|dzSr]r^)rOrZs r=r_z&_get_prompt_runnable..s6(%5eZ%H%HHr<z"Got unexpected type for `prompt`: ) rPROMPT_RUNNABLE_NAMErUstrrinspectiscoroutinefunctioncallabler ValueErrortype)rZprompt_runnableres` @r=_get_prompt_runnablerosO ~* = =DX    FC N'4V'D'D'D* Q Q Q Q%    FM * *N* H H H H%     $V , ,N*  %    &  N* %    FH % %N Ld6llLLMMM r<modeltools num_builtinct|trtd|jD|}t|tsdSd|jvrdS|jd}t |t ||z kr2tdt |dt ||z td|D}t}|D]^}| ddkr|dd }n| d r |d }nI| |_||z x}rtd |d d S) Nc3RK|]"}t|ttf|V#dSrTrUrr .0steps r= z%_should_bind_tools..K  d_m$DEE       r<Trqz`Number of tools in the model.bind_tools() and tools passed to create_react_agent must match Got z tools, expected c3$K|] }|jV dSrTr`)rwtools r=ryz%_should_bind_tools..s$114TY111111r<rmfunctionrazMissing tools 'z' in the model.bind_tools()F) rUrnextstepsrkwargslenrlsetrWadd) rprqrr bound_tools tool_namesbound_tool_names bound_toolbound_tool_name missing_toolss r=_should_bind_toolsrs%)**   !K        e_ - -tel""t,w'K 5zzS%% 333 RJJ R R14[1A1AK1O R R   11511111Juu! . . >>& ! !Z / /(4V.rzr<zXExpected `model` to be a ChatModel or RunnableBinding (e.g. model.bind_tools(...)), got ) rUrr~rrrMr TypeErrorrm)rps r= _get_modelrs%)**   !K       %))  e] + +  tgklqgrgr t t    Lr<r4cd|D}d|Dfd|D}|sdStd|dddtj}t|) zLValidate that all tool calls in AIMessages have a corresponding ToolMessage.cPg|]#}t|t|jD]}|$Sr;)rUr tool_calls)rwrB tool_calls r= z*_validate_chat_history..sV  gy ) )!+   r<cFh|]}t|t|jSr;rUr tool_call_id)rwrBs r= z)_validate_chat_history..s:"""!( 7K8X8X""""r<c(g|]}|dv |Sidr;)rwrtool_call_ids_with_resultss r=rz*_validate_chat_history..s3"""  T?"< < <  < < dt@tBdtDf,.fd )dt>dt@tBdtDf+,.fd 'dt>dtFdtHf-fd &dt>dt>ffd (dt>dt@tBdtJdt>f&()+,.fd }dt>dt@tBdtJdt>f&'(,.fd }bttLrdt@tBdtJdt>f)+fd% }dt>dt@tBdtJdt>f'fd& }|stY|'} | -d(t]|||)/| -d*| /d*d(d**nd(*| 0*,| -d+| /d(d+S| -d,t]||| /d+d,n| /d(d,| 1|| | | | -Sdt>dt$ttdzf fd. }!tYpt|'} | -d(t]|||)| -d/|/| -d*| /d*d(d**nd(*| 0*g}"*d/g}#B| -d+|"3d+| /d(d+n|"3d/R| -d,t]|||#3d,nM|"3d,n7|#3thn|"3th@dt>dt$ttdzf*fd0 }$| 5d+|$|#1| 5d(|!|"1dt>dt$f*-fd2 }%-r | 5d/|%*thg1n| /d/*| 1|| | | | -S)3a*Creates an agent graph that calls tools in a loop until a stopping condition is met. !!! warning This function is deprecated in favor of [`create_agent`][langchain.agents.create_agent] from the `langchain` package, which provides an equivalent agent factory with a flexible middleware system. For migration guidance, see [Migrating from LangGraph v0](https://docs.langchain.com/oss/python/migrate/langgraph-v1). Args: model: The language model for the agent. Supports static and dynamic model selection. - **Static model**: A chat model instance (e.g., [`ChatOpenAI`][langchain_openai.ChatOpenAI]) or string identifier (e.g., `"openai:gpt-4"`) - **Dynamic model**: A callable with signature `(state, runtime) -> BaseChatModel` that returns different models based on runtime context If the model has tools bound via `bind_tools` or other configurations, the return type should be a `Runnable[LanguageModelInput, BaseMessage]` Coroutines are also supported, allowing for asynchronous model selection. Dynamic functions receive graph state and runtime, enabling context-dependent model selection. Must return a `BaseChatModel` instance. For tool calling, bind tools using `.bind_tools()`. Bound tools must be a subset of the `tools` parameter. !!! example "Dynamic model" ```python from dataclasses import dataclass @dataclass class ModelContext: model_name: str = "gpt-3.5-turbo" # Instantiate models globally gpt4_model = ChatOpenAI(model="gpt-4") gpt35_model = ChatOpenAI(model="gpt-3.5-turbo") def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI: model_name = runtime.context.model_name model = gpt4_model if model_name == "gpt-4" else gpt35_model return model.bind_tools(tools) ``` !!! note "Dynamic Model Requirements" Ensure returned models have appropriate tools bound via `.bind_tools()` and support required functionality. Bound tools must be a subset of those specified in the `tools` parameter. tools: A list of tools or a `ToolNode` instance. If an empty list is provided, the agent will consist of a single LLM node without tool calling. prompt: An optional prompt for the LLM. Can take a few different forms: - `str`: This is converted to a `SystemMessage` and added to the beginning of the list of messages in `state["messages"]`. - `SystemMessage`: this is added to the beginning of the list of messages in `state["messages"]`. - `Callable`: This function should take in full graph state and the output is then passed to the language model. - `Runnable`: This runnable should take in full graph state and the output is then passed to the language model. response_format: An optional schema for the final agent output. If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key. If not provided, `structured_response` will not be present in the output state. Can be passed in as: - An OpenAI function/tool schema, - A JSON Schema, - A TypedDict class, - A Pydantic class. - A tuple `(prompt, schema)`, where schema is one of the above. The prompt will be used together with the model that is being used to generate the structured response. !!! Important `response_format` requires the model to support `.with_structured_output` !!! Note The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished. This is not the only strategy to get structured responses, see more options in [this guide](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/). pre_model_hook: An optional node to add before the `agent` node (i.e., the node that calls the LLM). Useful for managing long message histories (e.g., message trimming, summarization, etc.). Pre-model hook must be a callable or a runnable that takes in current graph state and returns a state update in the form of ```python # At least one of `messages` or `llm_input_messages` MUST be provided { # If provided, will UPDATE the `messages` in the state "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), ...], # If provided, will be used as the input to the LLM, # and will NOT UPDATE `messages` in the state "llm_input_messages": [...], # Any other state keys that need to be propagated ... } ``` !!! Important At least one of `messages` or `llm_input_messages` MUST be provided and will be used as an input to the `agent` node. The rest of the keys will be added to the graph state. !!! Warning If you are returning `messages` in the pre-model hook, you should OVERWRITE the `messages` key by doing the following: ```python { "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), *new_messages] ... } ``` post_model_hook: An optional node to add after the `agent` node (i.e., the node that calls the LLM). Useful for implementing human-in-the-loop, guardrails, validation, or other post-processing. Post-model hook must be a callable or a runnable that takes in current graph state and returns a state update. !!! Note Only available with `version="v2"`. state_schema: An optional state schema that defines graph state. Must have `messages` and `remaining_steps` keys. Defaults to `AgentState` that defines those two keys. !!! Note `remaining_steps` is used to limit the number of steps the react agent can take. Calculated roughly as `recursion_limit` - `total_steps_taken`. If `remaining_steps` is less than 2 and tool calls are present in the response, the react agent will return a final AI Message with the content "Sorry, need more steps to process this request.". No `GraphRecusionError` will be raised in this case. context_schema: An optional schema for runtime context. checkpointer: An optional checkpoint saver object. This is used for persisting the state of the graph (e.g., as chat memory) for a single thread (e.g., a single conversation). store: An optional store object. This is used for persisting data across multiple threads (e.g., multiple conversations / users). interrupt_before: An optional list of node names to interrupt before. Should be one of the following: `"agent"`, `"tools"`. This is useful if you want to add a user confirmation or other interrupt before taking an action. interrupt_after: An optional list of node names to interrupt after. Should be one of the following: `"agent"`, `"tools"`. This is useful if you want to return directly or run additional processing on an output. debug: A flag indicating whether to enable debug mode. version: Determines the version of the graph to create. Can be one of: - `"v1"`: The tool node processes a single message. All tool calls in the message are executed in parallel within the tool node. - `"v2"`: The tool node processes a tool call. Tool calls are distributed across multiple instances of the tool node using the [Send](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API. name: An optional name for the `CompiledStateGraph`. This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node - particularly useful for building multi-agent systems. !!! warning "`config_schema` Deprecated" The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0. Please use `context_schema` instead to specify the schema for run-scoped context. Returns: A compiled LangChain `Runnable` that can be used for chat interactions. The "agent" node calls the language model with the messages list (after applying the prompt). If the resulting AIMessage contains `tool_calls`, the graph will then call the ["tools"][langgraph.prebuilt.tool_node.ToolNode]. The "tools" node executes the tools (1 tool per `tool_call`) and adds the responses to the messages list as `ToolMessage` objects. The agent node then calls the language model again. The process repeats until no more `tool_calls` are present in the response. The agent then returns the full list of messages as a dictionary containing the key `'messages'`. ``` mermaid sequenceDiagram participant U as User participant A as LLM participant T as Tools U->>A: Initial input Note over A: Prompt + LLM loop while tool_calls present A->>T: Execute tools T-->>A: ToolMessage for each tool_calls end A->>U: Return final state ``` Example: ```python from langgraph.prebuilt import create_react_agent def check_weather(location: str) -> str: '''Return the weather forecast for the specified location.''' return f"It's always sunny in {location}" graph = create_react_agent( "anthropic:claude-3-7-sonnet-latest", tools=[check_weather], prompt="You are a helpful assistant", ) inputs = {"messages": [{"role": "user", "content": "what is the weather in sf"}]} for chunk in graph.stream(inputs, stream_mode="updates"): print(chunk) ``` config_schemazW`config_schema` is deprecated and will be removed. 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CUUUUATUUU U U U +52CTJJ  &""'>"t 1$$$tur<c0&t|dpt|d}d|}nt|d}d|}|t|t|ttrt t r||_n||d<|S)Nllm_input_messagesr4zUExpected input to call_model to have 'llm_input_messages' or 'messages' key, but got z=Expected input to call_model to have 'messages' key, but got )rYrlrrUrm issubclassr)r4)rOr4 error_msgrrs r=_get_model_input_statez2create_react_agent.._get_model_input_state|s  % (<==5!%44 xpuwwII'z::HWPUWW   Y'' 'x((( lD ) ) )jy.Q.Q )%ENN (E*  r<configcd rd}t||} r6 ||}tt|||}n)tt ||} |_||rdt|jdgiSd|giS)NAsync model callable provided but agent invoked synchronously. 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