Build your first RAG system using Llamaindex
IntermediateLevel
2 Hrs Duration
About this Course
- Build your first RAG system with LlamaIndex, covering data ingestion, indexing, and response synthesis for a seamless learning experience.
- Gain practical skills by setting up retrieval configurations and using a query engine to generate relevant, coherent responses.
- Master the process of building an efficient RAG system, from loading data to synthesizing outputs, and apply these techniques confidently.
Learning Outcomes
Understand NLP Evolution
Explore NLP evolution and advancements in language models.
Build LLMs for NLP Tasks
Learn to build and LLMs to solve real-world NLP challenges.
Practical Application
Apply concepts with hands-on exercises to build an application.
Course Curriculum
Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications
1. Welcome to this course
2. Why RAG is important?
3. What is RAG system
4. Overview of RAG Framework
1. Introduction to LlamaIndex
2. Components of LlamaIndex
3. Reading Material: How to get your API Key
4. How to get Open AI Keys - 2 min - Website go through
5. Build Your First RAG system using LlamaIndex
1. Data Loaders
2. Data Loaders Implementation
3. Chunking - Tokenization
4. Chunking - Tokenization Implementation
5. Node Parser
6. Embeddings
7. Embeddings Implementation
Meet the instructor
Our instructor and mentors carry years of experience in data industry
Get this Course Now
With this course you’ll get
- 2 Hours
Duration
- Prashant Sahu
Instructor
- Intermediate
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
LLMs are AI models trained on vast amounts of text to understand and generate human-like language. They power applications like chatbots, content creation, and code generation.
NLP has progressed through several phases: Symbolic NLP (rule-based methods), Statistical NLP (probabilistic models), Deep Learning NLP (neural networks), and now Transformers and Large Language Models (LLMs), which provide state-of-the-art performance.
Transformers introduced the self-attention mechanism, allowing models to analyze entire text sequences at once rather than processing them sequentially. This led to faster, more accurate, and context-aware language models like GPT, BERT, and T5.
LangGraph is an advanced framework built on LangChain, focusing on structured workflows, graph-based chaining, and improved modularity for building complex LLM-driven systems.
Hallucination: Generating incorrect or misleading information. Bias: Learning biases from training data , Compute Costs: High energy and hardware requirements , Compute Costs: High energy and hardware requirements
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