app.py

import gradio as gr
import getpass
import os

def _set_if_undefined(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"Please provide your {var}")

_set_if_undefined("OPENAI_API_KEY")
_set_if_undefined("LANGCHAIN_API_KEY")
_set_if_undefined("TAVILY_API_KEY")

# Optional, add tracing in LangSmith
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = "Multi-agent Collaboration"

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

tavily_tool = TavilySearchResults(max_results=5)

# This executes code locally, which can be unsafe
python_repl_tool = PythonREPLTool()

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

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)]}

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

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()
)

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


# 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


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()

###

def invoke(openai_api_key, topic, word_count=500):
    if (openai_api_key == ""):
        raise gr.Error("OpenAI API Key is required.")
    if (topic == ""):
        raise gr.Error("Topic is required.")
        
    #agentops.init(os.environ["AGENTOPS_API_KEY"])

    os.environ["OPENAI_API_KEY"] = openai_api_key

    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 result

gr.close_all()

demo = gr.Interface(fn = invoke, 
                    inputs = [gr.Textbox(label = "OpenAI API Key", type = "password", lines = 1),
                              gr.Textbox(label = "Topic", value="TODO", lines = 1),
                              gr.Number(label = "Word Count", value=1000, minimum=500, maximum=5000)],
                    outputs = [gr.Markdown(label = "Generated Blog Post", value="TODO")],
                    title = "Multi-Agent RAG: Blog Post Generation",
                    description = "TODO")

demo.launch()