This article details our exploration of Snowflake Cortex Analyst's multi-turn conversation feature, focusing on practical implementation using Streamlit and addressing challenges encountered during development. We'll examine how to effectively incorporate conversation history to enable more natural and nuanced interactions with the Cortex Analyst API.

Understanding Snowflake Cortex Analyst and its Multi-Turn Conversation Feature

Snowflake Cortex Analyst is a powerful feature enabling the creation of applications capable of reliably answering business questions based on structured Snowflake data. It achieves this through a REST API, allowing external applications to leverage Snowflake connection information and receive data-driven responses. A key advantage is its support for multi-turn conversations, significantly enhancing the user experience and enabling more complex analytical workflows.

The multi-turn conversation functionality allows for context preservation across multiple user queries. Instead of treating each query in isolation, Cortex Analyst remembers past interactions, using this historical context to provide more accurate and relevant answers. This is achieved by including the conversation history within the API request.

The crucial aspect of implementing multi-turn conversations lies in the structure of the messages parameter in the API request. This parameter is a JSON array, where each element represents a single message in the conversation. The order is crucial: user message, analyst response, user message, analyst response, and so on. Maintaining this sequential order is vital for the proper functioning of the multi-turn conversation feature.

Example Multi-Turn Conversation:

Let's consider a scenario illustrated in the Snowflake blog post https://www.snowflake.com/en/engineering-blog/cortex-analyst-multi-turn-conversations-support/. A user asks: "What is the month-over-month revenue growth for 2021 in Asia?" Cortex Analyst returns the answer and the corresponding SQL query. The user then follows up with: "What about North America?" Cortex Analyst leverages the previous query's context (geographic focus on revenue growth) to intelligently answer the second question, demonstrating the power of multi-turn conversation.

This seemingly simple interaction hides significant underlying complexity. Cortex Analyst doesn't merely concatenate past queries; it processes and summarizes them before generating a response. This sophisticated approach ensures that the system doesn't become overwhelmed by irrelevant historical data and maintains accuracy and efficiency. The internal workings likely involve several interconnected models working in concert to achieve this, highlighting the sophisticated architecture behind this seemingly simple feature.

The challenge of generating appropriate JOIN statements in SQL queries is particularly noteworthy. Complex business questions often require joining multiple tables, and Cortex Analyst's ability to accurately and efficiently generate these joins showcases its advanced capabilities. The system's performance in this area is surprisingly robust, suggesting a high degree of optimization within its query generation engine.

Implementing Multi-Turn Conversations with Streamlit and Snowflake Cortex Analyst

Our initial attempt involved a simplified Streamlit application that simply POSTed user input to the Cortex Analyst REST API without including past conversation history. While this yielded correct answers for individual queries, the system failed to maintain context across multiple interactions. Subsequent queries were treated as independent requests, effectively forgetting previous interactions.

For example, if a user asked about Asian revenue growth, and then followed up with "and what about that in North America?", the system failed to recognize the reference to "that" (the previous query's results). This failure clearly indicated the necessity of incorporating conversation history.

Addressing the Context Loss Issue: A Revised Implementation

To rectify this shortcoming, we implemented a revised Streamlit application that explicitly includes past conversation history in the messages parameter of each API request. This history is stored in the Streamlit session state, enabling persistent context across multiple user interactions.

The revised implementation involved the following steps:

1. Session State Management: The Streamlit session state was used to store the conversation history (past_messages). This ensures that the conversation history persists across multiple user interactions within the same Streamlit session.

2. API Request Modification: Each user query is appended to the past_messages list, and the entire list is included in the messages parameter of the API request. The correct ordering (user, analyst, user, analyst, etc.) is strictly maintained.

3. Response Handling: The response from the Cortex Analyst API is parsed, and both the answer and the generated SQL (if available) are displayed to the user. The analyst's response is also appended to the past_messages list, updating the conversation history for the next iteration.

4. Handling Suggestions: Cortex Analyst may return suggestions instead of a direct answer and SQL. This indicates that the input was not sufficiently clear or that additional information is required to formulate a complete response. The application handles these suggestions gracefully, informing the user and allowing them to refine their query. This error handling is crucial for providing a robust and user-friendly experience.

This enhanced implementation successfully addresses the context loss issue. Subsequent queries referencing previous conversations now receive appropriate responses, demonstrating the effective utilization of multi-turn conversation capabilities.

Code Example (Illustrative):

The following code snippet illustrates the core logic of our Streamlit application (simplified for clarity):

```python import streamlit as st import requests

... (API key and endpoint configuration) ...

pastmessages = st.sessionstate.get("past_messages", [])

userinput = st.textinput("Enter your query:")

if st.button("Submit"): messages = pastmessages + [{"role": "user", "content": userinput}] response = requests.post(endpoint, json={"messages": messages}, headers=headers) data = response.json()

if "suggestions" in data:
    st.write("Suggestion:", data["suggestions"][0]["content"])
else:
    st.write("Answer:", data["choices"][0]["message"]["content"])
    st.write("SQL:", data["choices"][0]["message"]["sql"])

past_messages.append({"role": "user", "content": user_input})
past_messages.append({"role": "assistant", "content": data["choices"][0]["message"]["content"]})
st.session_state["past_messages"] = past_messages

```

This simplified example demonstrates the core principles. A fully functional application would require more robust error handling, input validation, and potentially more sophisticated UI elements.

Conclusion: Unlocking the Potential of Multi-Turn Conversations in Snowflake Cortex Analyst

By incorporating conversation history into our Streamlit application, we successfully leveraged Snowflake Cortex Analyst's multi-turn conversation capabilities. This significantly improves the user experience, enabling more natural and nuanced interactions with the powerful data analysis capabilities of Snowflake Cortex Analyst. The ability to maintain context across multiple queries unlocks a wide range of possibilities for business intelligence applications, allowing for more complex and iterative analytical workflows. This implementation shows the relative ease with which developers can integrate this powerful feature, expanding the potential applications of Snowflake Cortex Analyst within their existing workflows. The future of interactive data analysis hinges on such seamless and intuitive integrations. The detailed approach outlined here allows developers to replicate and extend these functionalities, pushing the boundaries of what's possible with Snowflake and Streamlit. Further exploration into more sophisticated natural language processing techniques combined with the power of Snowflake Cortex Analyst will likely lead to even more advanced capabilities.