Unlock AI Agents, Function Calls and Multi-Step RAG with LLMWare

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AI Summary

Summary: Launch of Slims

• Introduction
  • Announced the launch of Slims.
  • Video aims to explain Slims, their uses, and their role in advanced use cases.
• What are Slims?
  • Slims are Structured Language Instruction Models.
  • Specialized function-calling LLMs fine-tuned for structured outputs.
  • Outputs include Python dictionaries, JSON, and SQL for programmatic handling.
• Use Case Example
  • Traditional LLMs generate text summaries from inputs like customer feedback.
  • Slims enable multi-step processes and structured reports with key-value pairs.
  • Aim to integrate with enterprise processes and data, privately in the cloud.
• The 2024 Vision
  • Multi-step processes deployed through high-level APIs conceptualized as agents.
  • LLMs as function calls with structured outputs.
  • Shift from open-ended reports to structured, actionable reports.
• Workflow with Slims
  • Incoming text is processed through a series of Slim models.
  • Models extract information, classify sentiment/intent, and answer questions.
  • The process ends with a structured report, integrating into enterprise workflows.
• Technical Underpinnings
  • AI-ready knowledge base with text and SQL data.
  • Slim models (represented as diamonds) orchestrate complex, multi-step automation.
  • All processing can run on a local cloud.
• Demo Overview
  • Demonstrated using Slims in LLmware for a multi-key structured dictionary report.
  • Showcased an agent framework for orchestrating function calls with Slim models.
  • Models run locally, producing a structured Python dictionary report.
• Demo Details
  • Tools are loaded and run through an agent, processing sentiment, emotions, intent, and more.
  • Results are displayed in a structured report with confidence scores and metadata.
  • The process is visualized step by step for clarity and debugging.
• Conclusion
  • Upcoming videos and examples will detail Slim models and their technical aspects.
  • Encouraged viewers to engage with the community on Discord for questions.