A deep dive into Retrieval-Augmented Generation with Llamaindex

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In this talk we cover:
* What LlamaIndex is
* What LlamaHub and create-llama are
* The stages of Retrieval-Augmented Generation (RAG)
* LlamaIndex's ingestion pipeline with caching
* The set of vector stores, LLMs and embedding models available in LlamaIndex
* Inspecting and customizing your prompts
* And then seven advanced querying strategies, including
* SubQuestionQueryEngine for complex questions
* Small-to-big retrieval for improved precision
* Metadata filtering, also for improved precision
* Hybrid search including traditional search engine techniques
* Recursive Retrieval for complex documents
* Text to SQL
* Multi-document agents that can combine all of these techniques

LlamaIndex

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have gone through 100s of videos for llama index - and this is the best one

yashshukla

Best High level coverage on LLamaIndex .. Thank you .. Good job

HazemAzim

Best overview I've seen. Thank you!

bruinaly

Thanks for the clear introduction into the possibilities of llama-index

cgintrallying

I think we’re the lucky few who discovered this.

jasonliu

your reading text killled the mojo of learning...as if old news reader.... But Hats off to the efforts!

hydtechietalks

thanks for this very clear overview and explanation!

johanteekens

Just found your repo today and have been doing this with some custom code to convert sdk docs and code into a graphdb. This looks like it'll be extremely helpful. Thank you

f

I would like to see a complete script in which you show all these functionalities, I hope you publish it soon. Thank you.

randradefonseca

could you share the slide please, I would be appreciated

nguyenne

Performance makes key differentiator apart from novel techniques. I heard some criticism on langchain. Hoping you guys do better in both performance and most reliable RAG with intuitive implementation

explorer

Hi! Is Langchain integratable/compatible with redshift/databricks? (especially the text-to-sql framework)? Thank you.

ragsAI

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