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| from typing import Callable, Optional | |
| import gradio as gr | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import Zilliz | |
| from langchain.document_loaders import WebBaseLoader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.chains import RetrievalQAWithSourcesChain | |
| from langchain.llms import OpenAI | |
| chain: Optional[Callable] = None | |
| def web_loader(url_list, openai_key, zilliz_uri, user, password): | |
| if not url_list: | |
| return "please enter url list" | |
| loader = WebBaseLoader(url_list.split()) | |
| docs = loader.load() | |
| text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=0) | |
| docs = text_splitter.split_documents(docs) | |
| embeddings = OpenAIEmbeddings(model="ada", openai_api_key=openai_key) | |
| docsearch = Zilliz.from_documents( | |
| docs, | |
| embedding=embeddings, | |
| connection_args={ | |
| "uri": zilliz_uri, | |
| "user": user, | |
| "password": password, | |
| "secure": True, | |
| }, | |
| ) | |
| global chain | |
| chain = RetrievalQAWithSourcesChain.from_chain_type( | |
| OpenAI(temperature=0, openai_api_key=openai_key), | |
| chain_type="map_reduce", | |
| retriever=docsearch.as_retriever(), | |
| ) | |
| return "success to load data" | |
| def query(question): | |
| global chain | |
| # "What is milvus?" | |
| if not chain: | |
| return "please load the data first" | |
| return chain(inputs={"question": question}, return_only_outputs=True).get( | |
| "answer", "fail to get answer" | |
| ) | |
| if __name__ == "__main__": | |
| block = gr.Blocks() | |
| with block as demo: | |
| gr.Markdown( | |
| """ | |
| <h1><center>Langchain And Zilliz Cloud Example</center></h1> | |
| This is how to use Zilliz Cloud as vector store in LangChain. | |
| 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. | |
| ## 📋 Prerequisite: | |
| 1. 🔑 To obtain an OpenAI key, please visit https://platform.openai.com/account/api-keys. | |
| 2. 💻 Create a Zilliz Cloud account to get free credits for usage by visiting https://cloud.zilliz.com. | |
| 3. 🗄️ Create a database in Zilliz Cloud. | |
| ## 📝 Steps for usage: | |
| 1. 🖋️ Fill in the url list input box with multiple URLs. | |
| 2. 🔑 Fill in the OpenAI API key in the openai api key input box. | |
| 3. 🌩️ Fill in the Zilliz Cloud connection parameters, including the connection URL, corresponding username, and password. | |
| 4. 🚀 Click the Load Data button to load the data. When the load status text box prompts that the data has been successfully loaded, proceed to the next step. | |
| 5. ❓ In the question input box, enter the relevant question about the web page. | |
| 6. 🔍 Click the Generate button to search for the answer to the question. The final answer will be displayed in the question answer text box. | |
| """ | |
| ) | |
| url_list_text = gr.Textbox( | |
| label="url list", | |
| lines=3, | |
| placeholder="https://milvus.io/docs/overview.md", | |
| ) | |
| openai_key_text = gr.Textbox(label="openai api key", type="password", placeholder="sk-******") | |
| with gr.Row(): | |
| zilliz_uri_text = gr.Textbox( | |
| label="zilliz cloud uri", | |
| placeholder="https://<instance-id>.<cloud-region-id>.vectordb.zillizcloud.com:<port>", | |
| ) | |
| user_text = gr.Textbox(label="username", placeholder="db_admin") | |
| password_text = gr.Textbox( | |
| label="password", type="password", placeholder="******" | |
| ) | |
| loader_output = gr.Textbox(label="load status") | |
| loader_btn = gr.Button("Load Data") | |
| loader_btn.click( | |
| fn=web_loader, | |
| inputs=[ | |
| url_list_text, | |
| openai_key_text, | |
| zilliz_uri_text, | |
| user_text, | |
| password_text, | |
| ], | |
| outputs=loader_output, | |
| api_name="web_load", | |
| ) | |
| question_text = gr.Textbox( | |
| label="question", | |
| lines=3, | |
| placeholder="What is milvus?", | |
| ) | |
| query_output = gr.Textbox(label="question answer", lines=3) | |
| query_btn = gr.Button("Generate") | |
| query_btn.click( | |
| fn=query, | |
| inputs=[question_text], | |
| outputs=query_output, | |
| api_name="generate_answer", | |
| ) | |
| demo.queue().launch(server_name="0.0.0.0", share=False) | |