langchain[patch]: fix deprecation warning (#28535)

libs/langchain/langchain/chains/router/multi_prompt.py

@@ -20,9 +20,8 @@ from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMP
     since="0.2.12",
     removal="1.0",
     message=(
-        "Use RunnableLambda to select from multiple prompt templates. See example "
-        "in API reference: "
-        "https://api.python.langchain.com/en/latest/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html"  # noqa: E501
+        "Please see migration guide here for recommended implementation: "
+        "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/"  # noqa: E501
     ),
 )
 class MultiPromptChain(MultiRouteChain):
@@ -37,60 +36,109 @@ class MultiPromptChain(MultiRouteChain):
 
             from operator import itemgetter
             from typing import Literal
-            from typing_extensions import TypedDict
 
             from langchain_core.output_parsers import StrOutputParser
             from langchain_core.prompts import ChatPromptTemplate
-            from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+            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", "{query}"),
+                    ("human", "{input}"),
                 ]
             )
             prompt_2 = ChatPromptTemplate.from_messages(
                 [
                     ("system", "You are an expert on vegetables."),
-                    ("human", "{query}"),
+                    ("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", "{query}"),
+                    ("human", "{input}"),
                 ]
             )
 
 
+            # Define schema for output:
             class RouteQuery(TypedDict):
-                \"\"\"Route query to destination.\"\"\"
+                \"\"\"Route query to destination expert.\"\"\"
+
                 destination: Literal["animal", "vegetable"]
 
 
-            route_chain = (
-                route_prompt
-                | llm.with_structured_output(RouteQuery)
-                | itemgetter("destination")
-            )
+            route_chain = route_prompt | llm.with_structured_output(RouteQuery)
 
-            chain = {
-                "destination": route_chain,  # "animal" or "vegetable"
-                "query": lambda x: x["query"],  # pass through input query
-            } | RunnableLambda(
-                # if animal, chain_1. otherwise, chain_2.
-                lambda x: chain_1 if x["destination"] == "animal" else chain_2,
-            )
 
-            chain.invoke({"query": "what color are carrots"})
+            # 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"])
     """  # noqa: E501
 
     @property