RAG with Llama-Index: Vector Stores

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In this third video of our series on Llama-index, we will explore how to use different vector stores in llama-index while building RAG applications. We will look at self-hosted solution (chroma-DB) and cloud-based solution (Pinecone).

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Timestamps:
[00:00] Intro
[00:21] Vector stores in llamaIndex
[01:24] Basic Setup
[02:45] Upload files
[03:45] Self-Hosted Vector Store
[09:00] Cloud based Vector Store

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engineerprompt

This tutorial series is great ! Best one I found so far. Thank you for sharing this.

AchiniHewawitharana

Great work. Excellent topic. Llama index opens up so much more possibility for RAG. Im very much interested in building a knowledge base. That gets added to on a daily basis. What do think of knowledge graph in this context

gregorykarsten

great video. Thanks. waiting for addition of Local LLM in the same code

anilshinde

Awesome Work, Like Always.
Can you refer to documentation or video "on how to update the chromadb in this context"

hassentangier

Super interesting, looking forward to the video

vitalis

This was great, love this kind of content! ❤❤❤

fuba

please make the video comparing different embedding models

arkodeepchatterjee

Why is OpenAI API Key needed when it does not use OpenAI? Thanks!

kdlin

Great I understood most of the explanation in video but Where is the RAG implementation in it ? I have also created a vector_store, storage_context, index etc when I was implementing chatBot with my data, but I am confused on how to implement RAG as an added functionality ?

Rahul-zqep

Would like to have a video on local download model ( llama2 ggml/gguf ) using llamaindex to build rag pipeline with chormadb. Thank you for videos its helps a lot.

srikanth

I always seem to run into the problem of exclusions when using vector similarity search for RAG. I.e. when you run a query for "Tell me everything you know about dogs other then Labradors." guess which documents will be returned as first 10 (assuming you have a lot of chunks)? Yes, about Labradors. Has anyone figured a way around that yet?

I've been attempting to filter out results if queries include exclusions with additional LLM passes, but only GPT4 seems to have enough brains to do it correctly. PaLM 2 gets it right in 50% of cases.

smoq

Is there a way to bypass the rate limit error for openai api?
Additionally, why is the openai being used even after specifically mentioning the service context?

saikashyapcheruku

I have 2 million data chunks of text, i was used db chroma but it didn't work. Can you help me?

toannn

Can you share the full architecture diagram

shubhamanand

I keep receiving this error :
cannot import name 'Doc' from 'typing_extensions'
I am trying to run your codes on jupyter notebook environment. Can you please help and let me know how to create a vector db?

scorpionrevenge

is this a LangChain competitor library?

hiramcoriarodriguez

hey i have question as we have injested our data to the vector db how do retrive answer without runnin the injestion code all the time

memsSudar

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