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Parent(s):
5fb9ac1
openai key 和 智谱的 key 分开
Browse files
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())
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@@ -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=
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chain_type="map_reduce",
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retriever=docsearch.as_retriever(),
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)
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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.
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@@ -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",
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@@ -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,
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