guangliang.yin commited on
Commit
6978c3c
1 Parent(s): 5fb9ac1

openai key 和 智谱的 key 分开

Browse files
Files changed (1) hide show
  1. app.py +5 -4
app.py CHANGED
@@ -6,14 +6,13 @@ from langchain.vectorstores import Zilliz
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  from langchain.document_loaders import WebBaseLoader
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  from langchain.text_splitter import CharacterTextSplitter
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  from langchain.chains import RetrievalQAWithSourcesChain
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- from langchain.llms import OpenAI
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  import uuid
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  from project.llm.zhipuai_llm import ZhipuAILLM
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  chain: Optional[Callable] = None
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- def web_loader(url_list, openai_key, zilliz_uri, user, password):
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  if not url_list:
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  return "please enter url list"
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  loader = WebBaseLoader(url_list.split())
@@ -45,7 +44,7 @@ def web_loader(url_list, openai_key, zilliz_uri, user, password):
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  global chain
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  chain = RetrievalQAWithSourcesChain.from_chain_type(
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- ZhipuAILLM(model="glm-3-turbo", temperature=0.1, zhipuai_api_key=openai_key),
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  chain_type="map_reduce",
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  retriever=docsearch.as_retriever(),
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  )
@@ -70,7 +69,7 @@ if __name__ == "__main__":
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  <h1><center>Langchain And Zilliz Cloud Example</center></h1>
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  This is how to use Zilliz Cloud as vector store in LangChain.
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  The purpose of this example is to allow you to input multiple URLs (separated by newlines) and then ask questions about the content of the corresponding web pages.
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-
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  ## 📋 Prerequisite:
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  1. 🔑 To obtain an OpenAI key, please visit https://platform.openai.com/account/api-keys.
@@ -93,6 +92,7 @@ if __name__ == "__main__":
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  placeholder="https://milvus.io/docs/overview.md",
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  )
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  openai_key_text = gr.Textbox(label="openai api key", type="password", placeholder="sk-******")
 
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  with gr.Row():
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  zilliz_uri_text = gr.Textbox(
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  label="zilliz cloud uri",
@@ -109,6 +109,7 @@ if __name__ == "__main__":
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  inputs=[
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  url_list_text,
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  openai_key_text,
 
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  zilliz_uri_text,
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  user_text,
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  password_text,
 
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  from langchain.document_loaders import WebBaseLoader
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  from langchain.text_splitter import CharacterTextSplitter
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  from langchain.chains import RetrievalQAWithSourcesChain
 
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  import uuid
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  from project.llm.zhipuai_llm import ZhipuAILLM
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  chain: Optional[Callable] = None
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+ def web_loader(url_list, openai_key, puzhiai_key, zilliz_uri, user, password):
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  if not url_list:
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  return "please enter url list"
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  loader = WebBaseLoader(url_list.split())
 
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  global chain
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  chain = RetrievalQAWithSourcesChain.from_chain_type(
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+ ZhipuAILLM(model="glm-3-turbo", temperature=0.1, zhipuai_api_key=puzhiai_key),
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  chain_type="map_reduce",
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  retriever=docsearch.as_retriever(),
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  )
 
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  <h1><center>Langchain And Zilliz Cloud Example</center></h1>
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  This is how to use Zilliz Cloud as vector store in LangChain.
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  The purpose of this example is to allow you to input multiple URLs (separated by newlines) and then ask questions about the content of the corresponding web pages.
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+ v.2.26.19.47
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  ## 📋 Prerequisite:
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  1. 🔑 To obtain an OpenAI key, please visit https://platform.openai.com/account/api-keys.
 
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  placeholder="https://milvus.io/docs/overview.md",
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  )
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  openai_key_text = gr.Textbox(label="openai api key", type="password", placeholder="sk-******")
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+ puzhiai_key_text = gr.Textbox(label="puzhi api key", type="password", placeholder="******")
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  with gr.Row():
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  zilliz_uri_text = gr.Textbox(
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  label="zilliz cloud uri",
 
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  inputs=[
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  url_list_text,
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  openai_key_text,
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+ puzhiai_key_text,
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  zilliz_uri_text,
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  user_text,
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  password_text,