Solutions

Chatbot with Snowflake Cortex

Using Cortex AI on Snowflake to build robust Chatbots

AI capabilities within Cortex

Leverage the power of Snowflake's data platform, the simplicity of Cortex AI, and Streamlit's robust capabilities to create a customizable chatbot that enhances your data interaction capabilities.

How Does it Work?

Data Preparation: Upload relevant files (e.g., PDFs) to Snowflake.
Data Chunking: Create a Snowflake function to read and chunk the PDFs into manageable data segments.
Data Vectorization: Use Snowflake's Cortex AI and vectorization functionality to vectorize each chunk, storing the data in a Snowflake table.
Interface Development: Build a basic interface in Streamlit that allows users to query the uploaded documents.
Question Processing: When a user asks a question, it is vectorized and matched against the data vectors to find the most relevant context.
Response Generation: The question and context are fed to an LLM, which generates a response based on the information available.

Use Cases

Customer Support: Enable customers to self-serve information that is buried in extensive documentation.
Internal Helpdesks: Assist employees in quickly finding information without sifting through numerous documents.
Knowledge Management: Provide instant answers to queries based on a vast repository of company knowledge.

Key Features

Advanced AI Integration: Utilize retrieval-augmented generation with large language models (LLMs) to deliver accurate and relevant responses.
Seamless Data Handling: Keep all data securely within the Snowflake platform, ensuring enhanced security and reduced ingress/egress costs.
Customizable User Interface: Build and deploy user-friendly applications using the Python-based Streamlit framework.
Flexible Deployment Options: Deploy your Streamlit apps in Streamlit Community Cloud, Docker, Kubernetes, or natively within Snowflake.
Comprehensive Tracking and Billing: Maintain consolidated billing and utilization tracking under one platform, simplifying management and optimization of your AI/ML compute costs.