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"""LangGraph Agent"""
import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_core.messages import SystemMessage, HumanMessage
from prompts import SYS_PROMPT
from tools import tools
from retriever import vector_store
from langchain_openai import ChatOpenAI
load_dotenv()
# System message
sys_msg = SystemMessage(content=SYS_PROMPT)
# Build graph function
def build_graph():
"""Build the graph"""
llm = ChatOpenAI(temperature=0.1, model="gpt-4o", openai_api_key=os.getenv("OPENAI_API_KEY"))
# Bind tools to LLM
llm_with_tools = llm.bind_tools(tools)
# Node
def assistant(state: MessagesState):
"""Assistant node"""
return {"messages": [llm_with_tools.invoke(state["messages"])]}
def retriever(state: MessagesState):
"""Retriever node"""
similar_question = vector_store.similarity_search(state["messages"][0].content, k=3)
similar_question_content = "\n".join([f"{idx+1}. {doc.page_content}" for idx, doc in enumerate(similar_question)])
example_msg = HumanMessage(
content=f"Here I provide some similar questions and answer for reference in case you can't find answer from tool result: \n\n{similar_question_content}",
)
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(tools))
builder.add_edge(START, "retriever")
builder.add_edge("retriever", "assistant")
builder.add_conditional_edges(
"assistant",
tools_condition,
)
builder.add_edge("tools", "assistant")
# Compile graph
return builder.compile()
# test
if __name__ == "__main__":
question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
# Build the graph
graph = build_graph()
# Run the graph
messages = [HumanMessage(content=question)]
messages = graph.invoke({"messages": messages})
answer = messages['messages'][-1].content
for m in messages["messages"]:
m.pretty_print() |