agents.py

"""agents.py — Supervisor + 4 workers. Zero if/else/for/while/try/except."""
import os

def build_agent():
    """Build the full supervisor graph. Called once at startup."""
    from langchain_openai import ChatOpenAI
    from langgraph.prebuilt import create_react_agent
    from langgraph_supervisor import create_supervisor
    from langgraph.checkpoint.memory import MemorySaver
    from tools import search_openalex, search_tavily, search_scopus, validate_papers, run_bertopic, upload_to_storage

    llm = ChatOpenAI(
        model="Qwen/Qwen2.5-72B-Instruct",
        base_url="https://router.huggingface.co/v1/",
        api_key=os.getenv("HF_TOKEN"),
        temperature=0.01,
    )

    oa = create_react_agent(llm, tools=[search_openalex], name="openalex_agent",
        prompt="You search OpenAlex. Extract BOTH the query and the chat_id from the user prompt. Call your tool once with the query and chat_id, return only the raw output, do nothing else.")
    tv = create_react_agent(llm, tools=[search_tavily], name="tavily_agent",
        prompt="You search Tavily. Extract BOTH the query and the chat_id from the user prompt. Call your tool once with the query and chat_id, return only the raw output, do nothing else.")
    sc = create_react_agent(llm, tools=[search_scopus], name="scopus_agent",
        prompt="You search Scopus. Extract BOTH the query and the chat_id from the user prompt. Call your tool once with the query and chat_id, return only the raw output, do nothing else.")
    val = create_react_agent(llm, tools=[validate_papers], name="validation_agent",
        prompt="You validate papers and check if they are relevant to the original query. Extract BOTH the query and the chat_id from the user prompt. Call your tool once with the query and chat_id, return only the raw output, do nothing else.")
    an = create_react_agent(llm, tools=[run_bertopic, upload_to_storage], name="analysis_agent",
        prompt="You run analysis. Extract the chat_id from the user prompt. First call run_bertopic with the chat_id, then call upload_to_storage with the chat_id. Return only the raw output, do nothing else.")

    workflow = create_supervisor(
        [oa, tv, sc, val, an], model=llm,
        prompt=("You are a research supervisor. For every query, you must run all 5 agents sequentially in this exact order: "
                "1) openalex_agent 2) tavily_agent 3) scopus_agent 4) validation_agent 5) analysis_agent. "
                "Always use ALL 5 agents."),
        output_mode="full_history")

    return workflow.compile(checkpointer=MemorySaver())