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import gradio as gr |
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from openai import OpenAI |
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from qdrant_client import QdrantClient |
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import os |
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qclient = QdrantClient( |
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url="https://68106439-3d00-42df-880f-a5519695f677.us-east4-0.gcp.cloud.qdrant.io:6333", |
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api_key=os.getenv("QDRANT_API_KEY"), |
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) |
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client = OpenAI( |
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base_url="https://openrouter.ai/api/v1", |
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api_key=os.getenv("OPENROUTER_API_KEY"), |
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) |
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def chat(prompt: str) -> str: |
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message = client.chat.completions.create( |
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model="anthropic/claude-3-haiku", |
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messages=[ |
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{"role": "user", "content": prompt} |
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], |
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).choices[0].message.content |
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return message |
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def question_answer(chat_history, question): |
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import requests |
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API_URL = "https://api-inference.huggingface.co/models/BAAI/bge-large-zh-v1.5" |
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headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"} |
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payload = { |
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"inputs": question, |
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} |
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response = requests.post(API_URL, headers=headers, json=payload) |
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e = response.json() |
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search_result = qclient.search( |
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collection_name="test_collection", query_vector=e, limit=20 |
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) |
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txt = '\n'.join([r.payload['text'] for r in search_result]) |
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print(txt) |
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prompt = f"现在你是一个资深的工程师管家，我将相关的信息已经从数据库中通过向量搜索给你了，如下\n{txt}\n, 根据这些信息回答我的这个问题\n{question}\n,"\ |
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"尽量简短以及用数值去说明,如果并没有答案，请回答我不知道。" |
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answer = chat(prompt) |
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chat_history.append([question, answer]) |
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return chat_history |
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with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo: |
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gr.Markdown(f'<center><h3>Demo</h3></center>') |
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with gr.Row(): |
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with gr.Group(): |
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question = gr.Textbox(label='Enter your question here') |
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btn = gr.Button(value='Submit') |
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with gr.Group(): |
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chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot") |
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btn.click( |
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question_answer, |
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inputs=[chatbot, question], |
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outputs=[chatbot], |
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api_name="predict", |
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) |
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demo.launch(share=True) |
