from fastapi import FastAPI from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings from langgraph.checkpoint.memory import MemorySaver from langgraph.graph import START, StateGraph, END from langgraph.prebuilt import tools_condition, ToolNode from langchain_text_splitters import CharacterTextSplitter from langchain.tools.retriever import create_retriever_tool from langchain import hub from langchain_openai import ChatOpenAI from dotenv import load_dotenv from langgraph.graph import MessagesState import os

load_dotenv()

llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"))

loader = TextLoader("./graph/data.txt") documents = loader.load()

prompt = hub.pull("hwchase17/openai-tools-agent")

text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=50) texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

db = FAISS.from_documents(texts, embeddings) retriever = db.as_retriever()

info_retriever = create_retriever_tool( retriever, "hotel_information_sender", "Searches information about hotel from provided vector and return accurare as you can", )

tools = [info_retriever]

llm_with_tools = llm.bind_tools(tools)

sys_msg = ( "You are Alexandra Hotel's virtual assistant, trained to assist customers with any queries related to the hotel. " "Your primary responsibility is to provide accurate, helpful, and friendly responses. " "You have access to a specialized tool for retrieving detailed and up-to-date information about the hotel, " "such as amenities, room availability, pricing, dining options, events, and policies. Use this tool effectively to provide precise answers. " "If a query is beyond your scope or requires external actions (e.g., booking confirmation, cancellations), " "politely inform the user and guide them to contact the hotel's staff for further assistance. " "Maintain a professional yet approachable tone at all times." )

#defining assistant it will call the llm_with_tools with the last 10 messages def assistant(state: MessagesState): return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"][-10:])]}

#defining the nodes and edges of the graph builder = StateGraph(MessagesState) builder.add_node("assistant", assistant) builder.add_node("tools", ToolNode(tools))

builder.add_edge(START, "assistant") builder.add_conditional_edges("assistant", tools_condition) builder.add_edge("tools", "assistant")

#here is the graph's memory memory = MemorySaver()

#building up the graph agent = builder.compile(checkpointer=memory)

app = FastAPI()

@app.get("/chat/{query}") def get_content(query: str): print(query) try: config = {"configurable": {"thread_id": "1"}} result = agent.invoke({"messages": [("user", query)]}, config) return result except Exception as e: return {"output": str(e)}