Refactor app.py to use async context for SmartQuery Agent, enhance error handling, and streamline message streaming. Add comprehensive documentation in chainlit.md and update prompt_templates.py to improve user query responses. Update README.md with project metadata.

README.md

@@ -1 +1,6 @@
-# SmartQuery
+title: SmartQuery
+emoji: 🔍
+colorFrom: blue
+colorTo: yellow
+sdk: docker
+pinned: false

app.py

@@ -2,9 +2,6 @@ import chainlit as cl
 from langchain.schema.runnable.config import RunnableConfig
 from sql_agent import SQLAgent
 
-# Test the agent
-# agent.invoke({"input": "How many artists are there?"})
-
 # ChainLit Integration
 @cl.on_chat_start
 async def on_chat_start():
@@ -16,15 +13,12 @@ async def on_message(message: cl.Message):
     cb = cl.AsyncLangchainCallbackHandler(stream_final_answer=True)
     config = RunnableConfig(callbacks=[cb])
 
-    result = await agent.ainvoke(message.content, config=config)
-
-    msg = cl.Message(content="")
-
-    async for chunk in result:
-        await msg.stream_token(chunk)
+    async with cl.Step(name="SmartQuery Agent", root=True) as step:
+        step.input = message.content
+        result = await agent.ainvoke(message.content, config=config)
 
-    await msg.send()
+        # Assuming the result is a dictionary with a key 'output' containing the final answer
+        final_answer = result.get('output', 'No answer returned')
 
-# Run the app
-if __name__ == "__main__":
-    cl.run()
+        # Stream the final answer as a token to the step
+        await step.stream_token(final_answer)

chainlit.md

@@ -0,0 +1,15 @@
+# Welcome to SmartQuery! 🔍📊
+
+Hello and welcome to SmartQuery, your intelligent assistant designed to help you interact with your database effortlessly. Whether you're a business analyst, data scientist, or a curious user, our tool will help you get answers from your database without writing a single line of SQL.
+
+## Getting Started
+
+## Sample Questions to Try:
+
+## How It Works 🧠
+
+## The Tech Behind It 💡🤖
+
+## Ready to Query?
+
+With SmartQuery, transform your database interactions into a seamless experience. Dive into your data, uncover insights, and make data-driven decisions more effectively than ever before. Happy querying!

prompt_templates.py

@@ -55,6 +55,19 @@ You MUST double check your query before executing it. If you get an error while
 
 DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
 
-If the question does not seem related to the database, just return "I don't know" as the answer.
+If the question does not seem related to the database, just return "Sorry, the query is not related to the database, I can't answe" as the answer.
 
 Here are some examples of user inputs and their corresponding SQL queries:"""
+
+
+
+
+
+
+
+
+
+
+
+
+# DO NOT include code formatted text in FINAL response to the user. For example, avoid this: "\[ \text{Proportion} = \left( \frac{1233.54}{2328.60} \right) \times 100 \approx 52.98\% \]"