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from langchain_community.document_loaders import PyPDFLoader |
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from langchain_text_splitters import RecursiveCharacterTextSplitter |
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from langchain_core.messages import AIMessage, HumanMessage |
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from fastapi import FastAPI |
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from pydantic import BaseModel |
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import os |
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from rag import Rag |
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from storePDF import get_documents_from_path |
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folder_path = "files" |
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all_documents = get_documents_from_path(folder_path) |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, add_start_index=True) |
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texts = text_splitter.split_documents(all_documents) |
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rag_llm = Rag(); |
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rag_llm.createRagChain() |
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chat_history = [] |
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class ChatInput(BaseModel): |
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question: str |
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app = FastAPI() |
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@app.get("/") |
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async def root(): |
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return {"message": "Hello World"} |
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@app.post("/generatechat/") |
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async def generateResponse(chat_input: ChatInput): |
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ai_msg = rag_llm.generateResponse(chat_input.question, chat_history) |
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chat_history.extend( |
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[ |
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HumanMessage(content=chat_input.question), |
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AIMessage(content=ai_msg["answer"]), |
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] |
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) |
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return {"response": ai_msg} |
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def run_server(): |
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import uvicorn |
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uvicorn.run(app, host="127.0.0.1", port=8000) |
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print("Server is running") |
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if __name__ == "__main__": |
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run_server() |
