added the new chatbot

app.py

@@ -1,63 +1,102 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+import os
+from upstash_vector import Index
+from openai import OpenAI
+import dotenv
+
+# Load environment variables
+dotenv.load_dotenv()
+
+# Services (keep the same as in your original code)
+class ConfigService:
+    @staticmethod
+    def load_config():
+        return {
+            "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"),
+            "UPSTASH_VECTOR_REST_URL": os.getenv("UPSTASH_VECTOR_REST_URL"),
+            "UPSTASH_VECTOR_REST_TOKEN": os.getenv("UPSTASH_VECTOR_REST_TOKEN"),
+        }
+
+class ClientService:
+    @staticmethod
+    def initialize_clients(config):
+        return {
+            "index": Index(url=config["UPSTASH_VECTOR_REST_URL"], token=config["UPSTASH_VECTOR_REST_TOKEN"]),
+            "openai": OpenAI(api_key=config["OPENAI_API_KEY"]),
+        }
+
+class EmbeddingService:
+    @staticmethod
+    def get_embedding(content, client):
+        response = client.embeddings.create(
+            input=content,
+            model="text-embedding-3-large"
+        )
+        return response.data[0].embedding
+
+class VectorSearchService:
+    @staticmethod
+    def search(index, vector, top_k=2):
+        return index.query(vector=vector, top_k=top_k, include_metadata=True)
+
+class PromptService:
+    @staticmethod
+    def load_system_prompt(file_path):
+        with open(file_path, 'r') as file:
+            return file.read().strip()
+
+    @staticmethod
+    def create_chat_prompt(question, context):
+        return f"Question: {question}\n\nContext: {context}"
+
+class ChatService:
+    def __init__(self, clients, system_prompt):
+        self.clients = clients
+        self.messages = [{"role": "system", "content": system_prompt}]
+
+    def ask_question(self, question):
+        question_embedding = EmbeddingService.get_embedding(question, self.clients["openai"])
+        search_results = VectorSearchService.search(self.clients["index"], question_embedding)
+        
+        context = []
+        for r in search_results:
+            if r.score > 0.7:
+                context.append(r.metadata['content'])
+        
+        context = "\n".join(context)
+        final_prompt = PromptService.create_chat_prompt(question, context)
+        self.messages.append({"role": "user", "content": final_prompt})
+        
+        chat_completion = self.clients["openai"].chat.completions.create(
+            model="gpt-4o-mini",
+            messages=self.messages
+        )
+        response_text = chat_completion.choices[0].message.content
+        self.messages.append({"role": "assistant", "content": response_text})
+        return response_text
+
+# Initialize services
+config = ConfigService.load_config()
+clients = ClientService.initialize_clients(config)
+system_prompt = PromptService.load_system_prompt("prompts/sys.md")
+chat_service = ChatService(clients, system_prompt)
+
+# Gradio interface
+def chatbot(message, history):
+    response = chat_service.ask_question(message)
+    return response
+
 demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+    fn=chatbot,
+    title="Vector Database Cloud Chatbot",
+    description="Ask questions about Vector Database Cloud",
+    theme="default",
+    examples=[
+        "What is Vector Database Cloud?",
+        "How does one-click deployment work?",
+        "What vector databases are supported?",
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()