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import gradio as gr
from openai import OpenAI
from qdrant_client import QdrantClient
import os
qclient = QdrantClient(
url="https://68106439-3d00-42df-880f-a5519695f677.us-east4-0.gcp.cloud.qdrant.io:6333",
api_key=os.getenv("QDRANT_API_KEY"),
)
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.getenv("OPENROUTER_API_KEY"),
)
def chat(prompt: str) -> str:
message = client.chat.completions.create(
model="anthropic/claude-3-haiku",
messages=[
{"role": "user", "content": prompt}
],
).choices[0].message.content
return message
def question_answer(chat_history, question):
import requests
API_URL = "https://api-inference.huggingface.co/models/BAAI/bge-large-zh-v1.5"
headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
payload = {
"inputs": question,
}
response = requests.post(API_URL, headers=headers, json=payload)
e = response.json()
search_result = qclient.search(
collection_name="test_collection", query_vector=e, limit=20
)
txt = '\n'.join([r.payload['text'] for r in search_result])
print(txt)
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,"\
"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。"
answer = chat(prompt)
chat_history.append([question, answer])
return chat_history
with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo:
gr.Markdown(f'<center><h3>Demo</h3></center>')
with gr.Row():
with gr.Group():
# with gr.Accordion("pdf file"):
# file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
question = gr.Textbox(label='Enter your question here')
btn = gr.Button(value='Submit')
with gr.Group():
chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot")
btn.click(
question_answer,
inputs=[chatbot, question],
outputs=[chatbot],
api_name="predict",
)
demo.launch(share=True) |