init app code

app.py

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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)

requirements.txt

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+pymilvus
+langchain
+openai
+tiktoken
+gradio
+bs4