agent.py

import operator
import os
import time
from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage, AnyMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langgraph.graph import add_messages, START, END, StateGraph
from langchain_core.tools import tool
from langgraph.prebuilt import ToolNode


from typing_extensions import TypedDict, Annotated



class State(TypedDict):
    messages: Annotated[list, add_messages]
    content_type: str
    content: str
    aggregate: Annotated[list, operator.add]
    # graph_state: str


def get_llm():
    os.getenv("GROQ_API_KEY")
    # return init_chat_model("llama-3.3-70b-versatile", model_provider="groq")

    return init_chat_model("gemini-2.0-flash", model_provider="google_genai")

def get_graph(llm):
    with open('prompts/system_prompt.md', 'r', encoding='utf-8') as markdown_file:
        system_prompt = markdown_file.read()

    prompt_template = ChatPromptTemplate.from_messages(
        [
            ("system", system_prompt),
            MessagesPlaceholder(variable_name="messages"),
        ]
    )

    from langchain_community.retrievers import WikipediaRetriever
    from langchain_community.retrievers import TavilySearchAPIRetriever

    # Wikipedia retriever
    wiki_retriever = WikipediaRetriever(load_max_docs =20)
    # Tavily retriever
    tavily_retriever = TavilySearchAPIRetriever(k=3)

    @tool
    def retrieve(query: str):
        """
        This function retrieves Wikipedia entries based on the query.
        """
        print("\n-------------------- Tool (Wikipedia) has been called --------------------\n")
        print("The query is: ", query)
        docs = wiki_retriever.invoke(query)
        serialized = "\n\n".join(
            (f"\nContent:\n{doc.page_content}")
            for doc in docs
        )

        return serialized

    @tool
    def online_search(query: str):
        """
        This function does a web search based on the query.
        """
        print("\n-------------------- Tool (Tavily) has been called --------------------\n")
        print("The query is: ", query)
        docs = tavily_retriever.invoke(query)
        serialized = "\n\n".join(
            (f"\nContent:\n{doc.page_content}")
            for doc in docs
        )

        return serialized



    tools = [retrieve, online_search]
    tool_node = ToolNode(tools)
    llm_with_tools = llm.bind_tools(tools)

    def make_plan(state: State):

        print("\n-------------------- Starting to create a plan --------------------\n")
        print("Content is: ", state["content_type"])
        # get all messages from the state
        messages = state["messages"]
        # append planning message
        messages.append(HumanMessage(content="Write a plan how to solve this qustion?"))
        # create prompt
        prompt = prompt_template.invoke(messages)
        # invoke LLM
        response = llm.invoke(prompt)
        print("The plan is: ", response.content)
        return {"messages": [response], "aggregate": ["Plan"]}




    def call_model(state: State):
        print("\n-------------------- Agent has been called -----------------------------------\n")
        # get all messages from the state
        messages = state["messages"]
        # append instruction message
        messages.append(HumanMessage(content="Please provide me the answer to the question in detail."))
        # create prompt
        prompt_answer = prompt_template.invoke(messages)
        # invoke LLM
        response = llm_with_tools.invoke(prompt_answer)
        print("Agent has made a decision:\n", response.content, response.tool_calls)
        print("Waiting for 4 seconds...")
        time.sleep(4)

        return {"messages": [response], "aggregate": ["Agent"]}

    def get_answer(state: State):
        # get all messages from the state
        messages = state["messages"]
        # add prompt message
        messages.append(HumanMessage(content="Please provide me just the plain answer to the question"))
        # create prompt
        prompt_answer = prompt_template.invoke(messages)
        # invoke LLM
        response = llm.invoke(prompt_answer)
        print("The final answer is: ", response.content)
        return {"messages": [response], "aggregate": ["Answer"]}

    def should_continue(state: State):
        print("\n-------------------- Decision of forwarding has been made --------------------\n")
        messages = state["messages"]
        print("This is round: ",len(state["aggregate"]))
        print("The last message is: ", messages[-1])

        if len(state["aggregate"]) < 8:
            last_message = messages[-1]
            if last_message.tool_calls:

                return "tools"
            return "Answer"
        else:
            return "Answer"

    # Build graph
    builder = StateGraph(State)
    builder.add_node("tools", tool_node)
    builder.add_node("Plan", make_plan)
    builder.add_node("Agent", call_model)
    builder.add_node("Answer", get_answer)



    # Logic
    builder.add_edge(START, "Plan")
    builder.add_edge("Plan", "Agent")
    builder.add_conditional_edges("Agent", should_continue, ["tools", "Answer"])
    builder.add_edge("tools", "Agent")
    builder.add_edge("Answer", END)

    return builder.compile()