Retrieval-Augmented Generation (RAG) is a useful method to enhance LLMs with external knowledge, leading to more relevant answers. But how does one go from a RAG demo to a production RAG application? What are the key factors, frameworks, and techniques to keep in mind?
โJoin Timescale and special guest presenter Laurie Voss, VP DevRel at @LlamaIndex for a deep dive as we go beyond the basics and explore advanced techniques for implementing RAG when building AI applications.
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๐ Free trial of Timescale Vector โ
๐ Presentation slides โ
๐ Getting started with LlamaIndex and Timescale Vector tutorial โ
๐ RAG with time-based retrieval โ
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Timescale a mature cloud PostgreSQL platform engineered for demanding workloads like time-series, vector, events and analytics data.
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๐ Website โ
๐ Slack โ
๐ GitHub โ
๐ Twitter โ
๐ Twitch โ
๐ LinkedIn โ
๐ Timescale Blog โ
๐ Timescale Documentation โ
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00:00 Introduction
02:07 RAG Challenges: Accuracy, Faithfulness, Recency, Provenance
03:44 How to perform RAG: Vector search, hybrid search
06:05 What is LlamaIndex? (Overview)
07:52 Data Ingestion
09:46 Data embedding (vectorization)
10:26 Vector embedding storage
10:49 Embedding querying
12:46 Advanced RAG Strategies
12:51 Sub Question Query Engine
13:54 Small to big retrieval
15:23 Node preprocessing (metadata filtered search)
16:28 Hybrid search
17:21 Time filtered search (time-series)
17:29 Dealing with Complex documents
19:48 Text to SQL
21:50 Agents
23:40 Production deployment
25:04 Recap and Summary
26:21 Demo: Chat with Github Commits
31:52 Questions and Answers
32:34 Nodes vs Indexes in LlamaIndex
33:45 What LLM should I use for my task? (Small vs large models)
36:14 Gemini Support in LlamaIndex
36:38 RAG and SQL
38:36 Security with RAG and SQL database access
39:54 Knowledge Graphs and RAG
41:14 Agents and custom input
42:22 Node Post Processing in LlamaIndex
44:34 Data Schema for vector tables in PostgreSQL and Timescale
45:59 Document Scoring in RAG
46:59 Conclusion and Resources
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