LangChain & AI Training

❮ Previous Next ❯ Creating a Custom AI Agent You can design your own agent to match a specific use case—like a financial advisor, medical assistant, or travel planner. 🧱 Steps to Build Your Custom Agent: 1. Choose an LLM Example: GPT-4, Claude, or a local model like LLaMA. 2. Define Tools Examples:    o 

❮ Previous Next ❯ What are LangChain Agents? Think of LangChain Agents as decision-making brains that sit on top of a language model like GPT-4. Instead of just answering a question, they decide how to answer it—and what tools to use. 🔍 Real-World Analogy Imagine you asked a personal assistant: “Can you book a table

❮ Previous Next ❯ Testing and Debugging API Responses Use tools like Postman, Insomnia, or Django’s built-in testing tools to: •  Send test requests•  Debug response behavior•  Fix issues in prompt formatting or input handling You can also log the input and output for analysis: Wrap-Up By the end of this module, you’ll be able

❮ Previous Next ❯ Adding User Input Handling Make sure the API properly handles incoming user messages using serializers: Update your view to use this serializer for validation: Handling API requests & responses This setup ensures:    •  Clean request validation   •  Modular response generation   •  JSON formatted output Sample request: Sample response: Processing user

❮ Previous Next ❯ Integrating LangChain with Django Rest Framework Django Rest Framework (DRF) is a powerful toolkit for building Web APIs using Django. In this module, we will connect LangChain to DRF so users can interact with an AI model via HTTP requests. LangChain will handle the prompt logic and LLM integration, while DRF