Introduction

• A framework for building app that use LLMs.
• As a toolkit for making LLMs “smarter” in real-world apps.
• Not only sending and receiving text, it structuring interaction of LLM with other data, APIs, or tools.
  • Searching a database
  • Calling an API
  • Writing to or reading from files
  • Reasoning step by step (chains of thought)
  • Keeping context in multi-step conversations

Components

1. LLMs
   • The “brain” that generates text.
   • Can be OpenAI GPT, Hugging Face models, or others.
2. Prompts
   • Templates that structure what you send to the model.
   • Can include dynamic variables, instructions, or context.
3. Chains
   • Sequences of actions or prompts.
   • Example: Extract info → summarize → answer.
4. Agents
   • Special chains that decide actions dynamically.
   • Can choose which tool to call based on user input.
   • Example: If the user asks for weather, the agent decides to call a weather API.
5. Memory
   • Keeps track of previous interactions.
   • Useful for chatbots that remember context.
6. Data Connectors
   • Connect LLMs to external data: databases, Google Sheets, APIs, PDFs, websites, etc.

Typical Use Cases

• Chatbots with context or long-term memory
• Document QA: Ask questions about PDFs or websites
• Personal assistants that can perform tasks or fetch info
• Reasoning applications: step-by-step decision-making