§ ™Štj³ãóä—dZddlmZddlmZddlmZddlmZddl m Z ddl m Z ddl mZdd lmZdd lmZdd lmZdd lmZmZdd lmZedddd¬¦«Gd„de¦«¦«ZdS)zCUse a single chain to route an input to one of multiple llm chains.é)Ú annotations)ÚAny)Ú deprecated)ÚBaseLanguageModel)ÚPromptTemplate)Úoverride)ÚConversationChain)ÚChain)ÚLLMChain)ÚMultiRouteChain)ÚLLMRouterChainÚRouterOutputParser)ÚMULTI_PROMPT_ROUTER_TEMPLATEz0.2.12z2.0.0zlangchain.agents.create_agentz˜Build routing logic with `create_agent` (e.g. with subagents or prompt-selection middleware). See https://docs.langchain.com/oss/python/langchain/agents)ÚsinceÚremovalÚ alternativeÚaddendumcóV—eZdZdZeedd„¦«¦«Ze ddd„¦«ZdS)ÚMultiPromptChainamA 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: ```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 model = 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 | model | StrOutputParser() chain_2 = prompt_2 | model | 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 | model.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"]) ``` Úreturnú list[str]có—dgS)NÚtext©)Úselfs új/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_classic/chains/router/multi_prompt.pyÚ output_keyszMultiPromptChain.output_keysšs €ðˆxˆóNÚllmrÚ prompt_infosúlist[dict[str, str]]Ú default_chainú Chain | NoneÚkwargsrc ó’—d„|D¦«}d |¦«}tj|¬¦«}t|dgt ¦«¬¦«}t j||¦«} i} |D]:} | d} | d} t| dg¬¦«}t||¬ ¦«}|| | <Œ;|pt|d ¬ ¦«}|d | | |d œ|¤ŽS)zCConvenience constructor for instantiating from destination prompts.có4—g|]}|d›d|d›‘ŒS)Únamez: Ú descriptionr)Ú.0Úps rú z1MultiPromptChain.from_prompts..¨s/€ÐQÐQÐQ¸q˜1˜Vœ9Ð:Ð:¨¨-Ô(8Ð:Ð:ÐQÐQÐQrú )Ú destinationsÚinput)ÚtemplateÚinput_variablesÚ output_parserr'Úprompt_template)r/r0)rÚpromptr)rÚ output_key)Ú router_chainÚdestination_chainsr"r) ÚjoinrÚformatrrr Úfrom_llmr r )Úclsrr r"r$r-Údestinations_strÚrouter_templateÚ router_promptr5r6Úp_infor'r2r3ÚchainÚ_default_chains rÚ from_promptszMultiPromptChain.from_promptsŸs&€ðRÐQÀLÐQÑQÔQˆ ØŸ9š9 \Ñ2Ô2ÐÝ6Ô=Ø)ð ñ ô ˆõ'Ø$Ø$˜IÝ,Ñ.Ô.ð ñ ô ˆ õ &Ô.¨s°MÑBÔBˆ ØÐØ"ð -ð -ˆFؘ&”>ˆDØ$Ð%6Ô7ˆOÝ#¨_ÈwÈiÐXÑXÔXˆFÝ ¨VÐ4Ñ4Ô4ˆEØ',Ð ˜tÑ $Ð $Ø&ÐWÕ*;ÀÐPVÐ*WÑ*WÔ*WˆØˆsð Ø%Ø1Ø(ð ð ðð  ð ð r)rr)N) rrr r!r"r#r$rrr) Ú__name__Ú __module__Ú __qualname__Ú__doc__ÚpropertyrrÚ classmethodrArrrrrsu€€€€€ðtðtðlØ ðððñ„Xñ„Xððð '+ð  ð  ð  ð  ñ„[ð  ð  ð  rrN)rEÚ __future__rÚtypingrÚlangchain_core._apirÚlangchain_core.language_modelsrÚlangchain_core.promptsrÚtyping_extensionsrÚlangchain_classic.chainsr Úlangchain_classic.chains.baser Úlangchain_classic.chains.llmr Ú$langchain_classic.chains.router.baser Ú*langchain_classic.chains.router.llm_routerr rÚ3langchain_classic.chains.router.multi_prompt_promptrrrrrúrTsrðØIÐIà"Ð"Ð"Ð"Ð"Ð"àÐÐÐÐÐà*Ð*Ð*Ð*Ð*Ð*Ø<Ð<Ð<Ð<Ð<Ð<Ø1Ð1Ð1Ð1Ð1Ð1Ø&Ð&Ð&Ð&Ð&Ð&à6Ð6Ð6Ð6Ð6Ð6Ø/Ð/Ð/Ð/Ð/Ð/Ø1Ð1Ð1Ð1Ð1Ð1Ø@Ð@Ð@Ð@Ð@Ð@ððððððððððððððð  €Ø Ø Ø/ð Að ñ ô ð] ð] ð] ð] ð] ñ] ô] ñ ô ð] ð] ð] r