Support sage agent

The implementation of Support Sage as an AI-driven support assistant leverages several advanced technologies and methodologies to enhance the efficiency and effectiveness of customer support operations. Here are some key takeaways from your detailed explanation:

Key Components

1. LLM (Large Language Model) Integration:

   • The core functionality is provided by a large language model, specifically llama3-70b, which is run through Groq's optimized inference engine.
   • This combination ensures rapid response times and high-quality text generation.

2. Memory Layer with Hindsight:

   • A critical component of Support Sage is the memory layer, implemented using Vectorize’s Hindsight service.
   • Hindsight manages a vector database that stores historical support tickets and their resolutions.
   • It provides semantic search capabilities to retrieve relevant past cases based on similarity rather than exact keyword matches.

3. Structured Resolution Template:

   • To ensure high-quality data in the memory layer, a structured resolution template is used when closing support tickets.
   • This template includes fields for issue description, root cause analysis, exact steps taken to resolve the issue, and tags for categorization.

4. Frontend Integration with FastAPI:

   • A FastAPI

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