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import os
from langchain_core.output_parsers import StrOutputParser
from langchain_groq import ChatGroq
from langchain_core.prompts import PromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from typing import List
from typing_extensions import TypedDict
from typing import Annotated
from langgraph.graph.message import AnyMessage, add_messages
from langchain_core.messages import HumanMessage, AIMessage
from langgraph.graph import END, StateGraph, START
from langgraph.checkpoint.memory import MemorySaver
from langchain_community.document_loaders import DirectoryLoader
from langchain_text_splitters import CharacterTextSplitter
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import datetime
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class Request(BaseModel):
query : str
id : str
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.5)
memory = MemorySaver()
glob_pattern="./*.md"
directory_path = "./documents"
loader = DirectoryLoader(directory_path, glob=glob_pattern)
cv = loader.load()
prompt = PromptTemplate.from_template("""
Tu dois uniquement répondre aux questions posées à propos de Ninon Roche.
Pour t'aider dans tes réponses, voici un texte qui comporte des informations sur Ninon Roche :
{document}
Voici l'historique de la conversation :
{historical}
Et enfin, la question posée par l'utilisateur :
{question}
Voici la date du jour : {date}
""")
chain = prompt | llm | StrOutputParser()
def format_historical(hist):
historical = []
for i in range(0,len(hist)-2,2):
historical.append("Utilisateur : "+hist[i].content)
historical.append("Assistant : "+hist[i+1].content)
return "\n".join(historical[-10:])
class GraphState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
def chatbot(state : GraphState):
response = chain.invoke({'document': cv, 'historical': format_historical(state['messages']), 'question' : state['messages'][-1].content, 'date': datetime.date.today()})
return {"messages": [AIMessage(content=response)]}
workflow = StateGraph(GraphState)
workflow.add_node('chatbot', chatbot)
workflow.add_edge(START,'chatbot')
workflow.add_edge('chatbot', END)
app_chatbot = workflow.compile(checkpointer=memory)
@app.post('/request')
def request(req: Request):
config = {"configurable": {"thread_id": req.id}}
rep = app_chatbot.invoke({"messages": [HumanMessage(content=req.query)]},config, stream_mode="values")
return {"response":rep['messages'][-1].content}
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