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from config import settings
from llm_provider import get_llm
from tools import ALL_TOOLS
from retrievers import custom_retriever
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_core.messages import SystemMessage, HumanMessage
from prompt.system_prompt import SYSTEM_PROMPT
sys_msg = SystemMessage(content=SYSTEM_PROMPT)
def build_graph():
llm = get_llm(settings.llm_provider)
llm_with_tools = llm.bind_tools(ALL_TOOLS)
def assistant(state: MessagesState):
return {"messages": [llm_with_tools.invoke(state["messages"])]}
def retriever(state: MessagesState):
similar_q = custom_retriever.retrieve(state["messages"][0].content)
example_msg = HumanMessage(content=f"Similar Q&A:\n\n{similar_q}")
return {"messages": [sys_msg] + state["messages"] + [example_msg]}
builder = StateGraph(MessagesState)
builder.add_node("retriever", retriever)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(ALL_TOOLS))
builder.add_edge(START, "retriever")
builder.add_edge("retriever", "assistant")
builder.add_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")
return builder.compile()
if __name__ == "__main__":
graph = build_graph()
question = input("Ask your question: ")
messages = [HumanMessage(content=question)]
results = graph.invoke({"messages": messages})
for m in results["messages"]:
print(m.content)
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