Create app.py

app.py

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+import os
+import gradio as gr
+from langchain.chat_models import AzureChatOpenAI
+from langchain.schema import format_document
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
+from operator import itemgetter
+# import socks
+# import socket
+# import requests
+
+# # 设置 SOCKS5 代理和认证信息
+# socks.set_default_proxy(socks.SOCKS5, "sftp-v-proxy.szh.internet.bosch.com", 1080, True, 'zfn3wx_ftp', 'Bosch@123')
+#
+# # 将 socket 的默认连接重定向到 SOCKS5 代理
+# socket.socket = socks.socksocket
+
+os.environ["OPENAI_API_KEY"] = '8b3bb832d6ef4a019a6fbddb4986cb9b'
+os.environ["OPENAI_API_TYPE"] = 'azure'
+os.environ["OPENAI_API_VERSION"] = '2023-07-01-preview'
+os.environ["OPENAI_API_BASE"] = 'https://ostingpteu.openai.azure.com/'
+
+llm = AzureChatOpenAI(deployment_name='OstinAIEU', model_name="gpt-35-turbo")
+import time
+from langchain.vectorstores import Weaviate
+import weaviate
+
+WEAVIATE_URL = 'http://40.81.20.137:8080'
+client = weaviate.Client(
+    url=WEAVIATE_URL
+)
+
+embedding = OpenAIEmbeddings(deployment="ostinembedding")
+vectordb = Weaviate(client=client, index_name="GS_data", text_key="text")
+
+from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
+from langchain.schema import Document
+
+# 定义元数据的过滤条件
+retriever = WeaviateHybridSearchRetriever(
+    client=client,
+    index_name="GS_data",
+    text_key="text",
+    attributes=['title', 'update_time', 'source_name', 'url'],
+    create_schema_if_missing=True,
+    k=5,
+)
+
+from typing import List
+
+
+def _format_docs(docs: List[Document]) -> str:
+    buffer = ''
+    for doc in docs:
+        # Start with the document's title if available
+        # doc_string = f"Title: {doc.metadata.get('title', 'No Title')}\n"
+        # Iterate over all metadata key-value pairs
+        doc_string = ''
+        for key, value in doc.metadata.items():
+            doc_string += f"{key.capitalize()}: {value}\n"
+
+        # Adding this document's string to the buffer
+        buffer += doc_string + '\n'  # Added an extra newline for separation between documents
+
+    return buffer
+
+
+DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
+
+
+def _combine_documents(
+        docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
+):
+    doc_strings = [format_document(doc, document_prompt) for doc in docs]
+    return document_separator.join(doc_strings)
+
+
+
+template = """"You are an expert, tasked to answer any question about Global Business Services (GS) . Using the 
+provided context, answer the user's question to the best of your ability using the resources provided. Generate a 
+comprehensive and informative answer (but no more than 80 words) for a given question based solely on the context. 
+Use an unbiased and journalistic tone. Combine search results together into a coherent answer. Do not repeat text 
+If there is nothing in the context relevant to the question at hand, just say "Sorry, I'm not sure. Could you provide
+more information?" Don't try to make up an answer. You should use bullet points in your answer for readability."
+{context}
+
+Question: {question}
+"""
+ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
+
+
+def ans_format(ans) -> str:
+    answer = ans['answer']
+    sources = ans['sources']
+    return f"{answer} \n\n \n\nHere are the sources:\n{sources}"
+
+# Now we retrieve the documents
+retrieved_documents = RunnablePassthrough.assign(docs=itemgetter('question') | retriever)
+
+# Now we construct the inputs for the final prompt
+final_inputs = {
+    "context": lambda x: _combine_documents(x["docs"]),
+    "question": itemgetter("question"),
+}
+
+# And finally, we do the part that returns the answers
+answer = {
+    "answer": final_inputs | ANSWER_PROMPT | llm,
+    "docs": itemgetter("docs"),
+}
+
+organized_ans = {
+    'ans': {
+               'answer': lambda x: x["answer"].content,
+               'sources': lambda x: _format_docs(x["docs"]),
+           }
+           | RunnableLambda(ans_format)
+           | StrOutputParser()
+
+}
+
+# And now we put it all together!
+final_chain = retrieved_documents | answer | organized_ans | RunnablePassthrough()
+
+
+def response(msg: str) -> str:
+    inp = {'question': msg}
+    return final_chain.invoke(inp)['ans']
+
+
+gr.Interface(fn=response, inputs=gr.Textbox(lines=2, placeholder="Ask Here..."), outputs="text").launch()