Code Along กับเราได้ที่
Code Along ↗Retrieval Augmented Generation with LlamaIndex
In this session you'll learn how to get started with Chroma and perform Q&A on some documents using Llama 2, the RAG technique, and LlamaIndex.
Dan Becker
สร้างบัญชีฟรีของคุณ
ฝึกฝนและตรวจสอบความก้าวหน้าของคุณ
ดำเนินการต่อด้วย Googleเมื่อดำเนินการต่อ ถือว่าคุณยอมรับ ข้อกำหนดการใช้งานและ นโยบายความเป็นส่วนตัว และยอมรับว่าข้อมูลของคุณจะถูกจัดเก็บในสหรัฐอเมริกาที่เกี่ยวข้อง
บล็อก
What Is Retrieval Augmented Generation (RAG)? A Complete Guide
Understand how RAG grounds LLM outputs in external data to reduce hallucinations, with a guide to the RAG pipeline, advanced techniques, and real-world applications.
บล็อก
Advanced RAG Techniques
Learn advanced RAG methods like dense retrieval, reranking, or multi-step reasoning to tackle issues like hallucination or ambiguity.
บทแนะนำ
RAG With Llama 3.1 8B, Ollama, and Langchain: Tutorial
Learn to build a RAG application with Llama 3.1 8B using Ollama and Langchain by setting up the environment, processing documents, creating embeddings, and integrating a retriever.

Large language models (LLMs) like Llama 2 are the must-have technology of the year. Unfortunately, LLMs can't accurately answer questions about your business because they lack enough domain knowledge. The solution is to combine the LLM with a vector database like Chroma—a technique known as retrieval augmented generation (RAG). Beyond this, incorporating AI into products is best done with an AI application framework, like LlamaIndex.
In this session you'll learn how to get started with Chroma and perform Q&A on some documents using Llama 2, the RAG technique, and LlamaIndex.
Key Takeaways:
Additional Resources
[COURSE] Dan's course: Introduction to Deep Learning in Python
[CODE-ALONG SERIES] Become a Generative AI developer
[SKILL TRACK] OpenAI Fundamentals
[SKILL TRACK] Deep Learning in Python
[BLOG] The Top 5 Vector Databases
[TUTORIAL] Mastering Vector Databases with Pinecone: A Comprehensive Guide