rag_langgraph.py

from typing import Annotated, List, Tuple, Union

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.tools import tool
from langchain_experimental.tools import PythonREPLTool

from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_openai import ChatOpenAI

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser

import operator
from typing import Annotated, Any, Dict, List, Optional, Sequence, TypedDict
import functools

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langgraph.graph import StateGraph, END

def create_agent(llm: ChatOpenAI, tools: list, system_prompt: str):
    # Each worker node will be given a name and some tools.
    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                system_prompt,
            ),
            MessagesPlaceholder(variable_name="messages"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),
        ]
    )
    agent = create_openai_tools_agent(llm, tools, prompt)
    executor = AgentExecutor(agent=agent, tools=tools)
    return executor

def agent_node(state, agent, name):
    result = agent.invoke(state)
    return {"messages": [HumanMessage(content=result["output"], name=name)]}

# The agent state is the input to each node in the graph
class AgentState(TypedDict):
    # The annotation tells the graph that new messages will always
    # be added to the current states
    messages: Annotated[Sequence[BaseMessage], operator.add]
    # The 'next' field indicates where to route to next
    next: str
    
def run_multi_agent(prompt):
    tavily_tool = TavilySearchResults(max_results=5)
    repl = PythonREPL()

    members = ["Researcher", "Coder"]
    system_prompt = (
        "You are a supervisor tasked with managing a conversation between the"
        " following workers:  {members}. Given the following user request,"
        " respond with the worker to act next. Each worker will perform a"
        " task and respond with their results and status. When finished,"
        " respond with FINISH."
    )
    # Our team supervisor is an LLM node. It just picks the next agent to process
    # and decides when the work is completed
    options = ["FINISH"] + members
    # Using openai function calling can make output parsing easier for us
    function_def = {
        "name": "route",
        "description": "Select the next role.",
        "parameters": {
            "title": "routeSchema",
            "type": "object",
            "properties": {
                "next": {
                    "title": "Next",
                    "anyOf": [
                        {"enum": options},
                    ],
                }
            },
            "required": ["next"],
        },
    }
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_prompt),
            MessagesPlaceholder(variable_name="messages"),
            (
                "system",
                "Given the conversation above, who should act next?"
                " Or should we FINISH? Select one of: {options}",
            ),
        ]
    ).partial(options=str(options), members=", ".join(members))
    
    llm = ChatOpenAI(model="gpt-4-1106-preview")
    
    supervisor_chain = (
        prompt
        | llm.bind_functions(functions=[function_def], function_call="route")
        | JsonOutputFunctionsParser()
    )

    research_agent = create_agent(llm, [tavily_tool], "You are a web researcher.")
    research_node = functools.partial(agent_node, agent=research_agent, name="Researcher")
    
    # NOTE: THIS PERFORMS ARBITRARY CODE EXECUTION. PROCEED WITH CAUTION
    code_agent = create_agent(
        llm,
        [python_repl_tool],
        "You may generate safe python code to analyze data and generate charts using matplotlib.",
    )
    code_node = functools.partial(agent_node, agent=code_agent, name="Coder")
    
    workflow = StateGraph(AgentState)
    workflow.add_node("Researcher", research_node)
    workflow.add_node("Coder", code_node)
    workflow.add_node("supervisor", supervisor_chain)

    for member in members:
        # We want our workers to ALWAYS "report back" to the supervisor when done
        workflow.add_edge(member, "supervisor")
    # The supervisor populates the "next" field in the graph state
    # which routes to a node or finishes
    conditional_map = {k: k for k in members}
    conditional_map["FINISH"] = END
    workflow.add_conditional_edges("supervisor", lambda x: x["next"], conditional_map)
    # Finally, add entrypoint
    workflow.set_entry_point("supervisor")
    
    graph = workflow.compile()

    for s in graph.stream(
        {
            "messages": [
                HumanMessage(content="Code hello world and print it to the terminal")
            ]
        }
    ):
        if "__end__" not in s:
            print(s)
            print("----")
        
    return "DONE"