A hands-on collection of Databricks notebooks exploring Generative AI and Mosaic AI capabilities, including Foundation Models, Embeddings, Retrieval-Augmented Generation (RAG), AI Assistants, Function Calling, AI Agents, Vector Search, and AI Gateway.
This repository contains practical notebooks created while learning and experimenting with Databricks Mosaic AI. The goal was to understand how modern LLM-powered applications are built using Databricks Foundation Models and related AI services.
The notebooks progress from basic model inference to more advanced concepts such as semantic search, retrieval pipelines, tool calling, agents, and AI governance.
- Calling Databricks Foundation Models
- Prompt-based interactions
- Model inference using OpenAI-compatible APIs
- Generating text embeddings
- Understanding vector representations
- Semantic similarity concepts
- Basic RAG workflow
- Context-based question answering
- Embedding generation and retrieval
- Similarity search using cosine similarity
- System prompts
- Custom AI assistants
- Multi-turn conversations
- Context retention across interactions
- Tool definitions
- External function execution
- Structured outputs from LLMs
- Multi-tool workflows
- Dynamic decision-making using LLMs
- Agent-style task execution
- Semantic document retrieval
- Query embeddings
- Similarity matching
- Context extraction
- Request rate limiting
- Request logging
- Governance and monitoring concepts
| Notebook | Description |
|---|---|
| 01_Foundation_Model_API | Foundation Model inference using Databricks serving endpoints |
| 02_Embeddings | Generating embeddings with Databricks embedding models |
| 03_RAG_Basics | Introduction to Retrieval-Augmented Generation |
| 04_AI_assistant | Building a custom AI assistant |
| 05_Multi-turn AI assistant | Multi-turn conversational AI |
| 06_Ai_function calling | Function calling implementation |
| 07_Dynamic user input | Dynamic prompt handling |
| 08_Dynamic Chatbot | Interactive chatbot workflow |
| 09_RAG | End-to-end RAG implementation |
| 10_Ai agent | Agent-based AI workflow |
| 11_Vector search | Semantic retrieval using vector search concepts |
| 12_Ai gateway rate limiting | AI Gateway rate limiting simulation |
| 13_Ai gateway logging | AI Gateway request logging simulation |
- Databricks
- Mosaic AI
- Foundation Models API
- OpenAI Compatible SDK
- Python
- Vector Embeddings
- RAG
- Vector Search
- AI Gateway
By completing these notebooks, I gained practical experience in:
- Working with Databricks Foundation Models
- Generating and using embeddings
- Building Retrieval-Augmented Generation (RAG) pipelines
- Creating AI assistants and chatbots
- Implementing function calling workflows
- Developing basic AI agents
- Understanding vector search and semantic retrieval
- Applying AI governance concepts such as rate limiting and logging
This repository was created for learning, experimentation, and understanding Databricks Mosaic AI concepts through hands-on practice.