Advanced RAG Techniques with @LlamaIndex
3 هزار بار بازدید -
4 ماه پیش
-
Retrieval-Augmented Generation (RAG) is a
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?
Join 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.
🛠 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
📌 Free trial of Timescale Vector ⇒ https://tsdb.co/webinar-signup
📌 Presentation slides ⇒ https://tsdb.co/llamaindex-rag-slides
📌 Getting started with LlamaIndex and Timescale Vector tutorial ⇒ https://tsdb.co/llamaindex-tutorial
📌 RAG with time-based retrieval ⇒ LlamaIndex Webinar: Time-based retrie...
🐯 𝗔𝗯𝗼𝘂𝘁 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲
Timescale a mature cloud PostgreSQL platform engineered for demanding workloads like time-series, vector, events and analytics data.
💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲!
🔍 Website ⇒ https://tsdb.co/homepage
🔍 Slack ⇒ https://slack.timescale.com
🔍 GitHub ⇒ https://github.com/timescale
🔍 Twitter ⇒ Twitter: timescaledb
🔍 Twitch ⇒ Twitch: timescaledb
🔍 LinkedIn ⇒ LinkedIn: timescaledb
🔍 Timescale Blog ⇒ https://tsdb.co/blog
🔍 Timescale Documentation ⇒ https://tsdb.co/docs
📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀
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
Join 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.
🛠 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
📌 Free trial of Timescale Vector ⇒ https://tsdb.co/webinar-signup
📌 Presentation slides ⇒ https://tsdb.co/llamaindex-rag-slides
📌 Getting started with LlamaIndex and Timescale Vector tutorial ⇒ https://tsdb.co/llamaindex-tutorial
📌 RAG with time-based retrieval ⇒ LlamaIndex Webinar: Time-based retrie...
🐯 𝗔𝗯𝗼𝘂𝘁 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲
Timescale a mature cloud PostgreSQL platform engineered for demanding workloads like time-series, vector, events and analytics data.
💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲!
🔍 Website ⇒ https://tsdb.co/homepage
🔍 Slack ⇒ https://slack.timescale.com
🔍 GitHub ⇒ https://github.com/timescale
🔍 Twitter ⇒ Twitter: timescaledb
🔍 Twitch ⇒ Twitch: timescaledb
🔍 LinkedIn ⇒ LinkedIn: timescaledb
🔍 Timescale Blog ⇒ https://tsdb.co/blog
🔍 Timescale Documentation ⇒ https://tsdb.co/docs
📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀
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
4 ماه پیش
در تاریخ 1403/01/14 منتشر شده
است.
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