app.py

import os
import gradio as gr
from langchain_core.prompts import PromptTemplate
from langchain_community.document_loaders import PyPDFLoader
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
from langchain.chains.question_answering import load_qa_chain
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# 使用 LangSmith 实际提供的类或方法

# 配置 Gemini API
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# 加载 Mistral 模型
model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base"
mistral_tokenizer = AutoTokenizer.from_pretrained(model_path)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dtype = torch.bfloat16
mistral_model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)

# 初始化 LangSmith 客户端或其他相关的工具
# client = LangSmithClient(api_key=os.getenv("LANGSMITH_API_KEY")) # 请替换为实际的客户端或工具

def initialize(file_path, question):
    try:
        model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
        prompt_template = """Answer the question as precise as possible using the provided context. If the answer is
                              not contained in the context, say "answer not available in context" \n\n
                              Context: \n {context}?\n
                              Question: \n {question} \n
                              Answer:
                            """
        prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
        
        if os.path.exists(file_path):
            pdf_loader = PyPDFLoader(file_path)
            pages = pdf_loader.load_and_split()
            context = "\n".join(str(page.page_content) for page in pages[:10])
            stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
            stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
            gemini_answer = stuff_answer['output_text']
            
            # 使用 Mistral 模型进行文本生成
            mistral_prompt = f"Based on this answer: {gemini_answer}\nGenerate a follow-up question:"
            mistral_inputs = mistral_tokenizer.encode(mistral_prompt, return_tensors='pt').to(device)
            with torch.no_grad():
                mistral_outputs = mistral_model.generate(mistral_inputs, max_length=100)
            mistral_output = mistral_tokenizer.decode(mistral_outputs[0], skip_special_tokens=True)
            
            combined_output = f"Gemini Answer: {gemini_answer}\n\nMistral Follow-up: {mistral_output}"

            # 在这里使用 LangSmith 进行评估或日志记录
            # evaluation = client.evaluate(gemini_answer, mistral_output, question) # 替换为实际评估方法
            # return combined_output + f"\n\nEvaluation: {evaluation}"
            return combined_output
        else:
            return "Error: Unable to process the document. Please ensure the PDF file is valid."
    except Exception as e:
        return f"An error occurred: {str(e)}"

# 定义 Gradio 接口
input_file = gr.File(label="Upload PDF File")
input_question = gr.Textbox(label="Ask about the document")
output_text = gr.Textbox(label="Answer - Combined Gemini and Mistral")

def pdf_qa(file, question):
    if file is None:
        return "Please upload a PDF file first."
    return initialize(file.name, question)

# 创建 Gradio 界面
gr.Interface(
    fn=pdf_qa,
    inputs=[input_file, input_question],
    outputs=output_text,
    title="RAG Knowledge Retrieval using Gemini API and Mistral Model",
    description="Upload a PDF file and ask questions about the content."
).launch()