a agR@sdZddlmZddlmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZdd lmZmZdd lmZe d dddGdddeZdS)zCUse a single chain to route an input to one of multiple llm chains.) annotations)AnyDictListOptional) deprecated)BaseLanguageModel)PromptTemplate)ConversationChain)Chain)LLMChain)MultiRouteChain)LLMRouterChainRouterOutputParser)MULTI_PROMPT_ROUTER_TEMPLATEz0.2.12z1.0zPlease see migration guide here for recommended implementation: https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/)ZsinceZremovalmessagec@s>eZdZdZeddddZedddd d dd d d ZdS)MultiPromptChainaA multi-route chain that uses an LLM router chain to choose amongst prompts. This class is deprecated. See below for a replacement, which offers several benefits, including streaming and batch support. Below is an example implementation: .. code-block:: python from operator import itemgetter from typing import Literal from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnableConfig from langchain_openai import ChatOpenAI from langgraph.graph import END, START, StateGraph from typing_extensions import TypedDict llm = ChatOpenAI(model="gpt-4o-mini") # Define the prompts we will route to prompt_1 = ChatPromptTemplate.from_messages( [ ("system", "You are an expert on animals."), ("human", "{input}"), ] ) prompt_2 = ChatPromptTemplate.from_messages( [ ("system", "You are an expert on vegetables."), ("human", "{input}"), ] ) # Construct the chains we will route to. These format the input query # into the respective prompt, run it through a chat model, and cast # the result to a string. chain_1 = prompt_1 | llm | StrOutputParser() chain_2 = prompt_2 | llm | StrOutputParser() # Next: define the chain that selects which branch to route to. # Here we will take advantage of tool-calling features to force # the output to select one of two desired branches. route_system = "Route the user's query to either the animal or vegetable expert." route_prompt = ChatPromptTemplate.from_messages( [ ("system", route_system), ("human", "{input}"), ] ) # Define schema for output: class RouteQuery(TypedDict): """Route query to destination expert.""" destination: Literal["animal", "vegetable"] route_chain = route_prompt | llm.with_structured_output(RouteQuery) # For LangGraph, we will define the state of the graph to hold the query, # destination, and final answer. class State(TypedDict): query: str destination: RouteQuery answer: str # We define functions for each node, including routing the query: async def route_query(state: State, config: RunnableConfig): destination = await route_chain.ainvoke(state["query"], config) return {"destination": destination} # And one node for each prompt async def prompt_1(state: State, config: RunnableConfig): return {"answer": await chain_1.ainvoke(state["query"], config)} async def prompt_2(state: State, config: RunnableConfig): return {"answer": await chain_2.ainvoke(state["query"], config)} # We then define logic that selects the prompt based on the classification def select_node(state: State) -> Literal["prompt_1", "prompt_2"]: if state["destination"] == "animal": return "prompt_1" else: return "prompt_2" # Finally, assemble the multi-prompt chain. This is a sequence of two steps: # 1) Select "animal" or "vegetable" via the route_chain, and collect the answer # alongside the input query. # 2) Route the input query to chain_1 or chain_2, based on the # selection. graph = StateGraph(State) graph.add_node("route_query", route_query) graph.add_node("prompt_1", prompt_1) graph.add_node("prompt_2", prompt_2) graph.add_edge(START, "route_query") graph.add_conditional_edges("route_query", select_node) graph.add_edge("prompt_1", END) graph.add_edge("prompt_2", END) app = graph.compile() result = await app.ainvoke({"query": "what color are carrots"}) print(result["destination"]) print(result["answer"]) z List[str])returncCsdgS)Ntext)selfrrr/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/router/multi_prompt.py output_keysszMultiPromptChain.output_keysNrzList[Dict[str, str]]zOptional[Chain]r)llm prompt_infos default_chainkwargsrcKsdd|D}d|}tj|d}t|dgtd}t||} i} |D]6} | d} | d} t| dgd }t||d }|| | <qJ|pt|d d }|f| | |d |S)zCConvenience constructor for instantiating from destination prompts.cSs"g|]}|dd|dqS)namez: descriptionr).0prrr z1MultiPromptChain.from_prompts.. ) destinationsinput)templateinput_variablesZ output_parserrprompt_template)r&r')rpromptr)rZ output_key) router_chaindestination_chainsr) joinrformatr rrZfrom_llmr r )clsrrrrr$Zdestinations_strZrouter_templateZ router_promptr*r+Zp_inforr(r)chainZ_default_chainrrr from_promptss4     zMultiPromptChain.from_prompts)N)__name__ __module__ __qualname____doc__propertyr classmethodr0rrrrrs  trN)r4 __future__rtypingrrrrZlangchain_core._apirZlangchain_core.language_modelsrZlangchain_core.promptsr Zlangchain.chainsr Zlangchain.chains.baser Zlangchain.chains.llmr Zlangchain.chains.router.baser Z"langchain.chains.router.llm_routerrrZ+langchain.chains.router.multi_prompt_promptrrrrrrs"