Mistral 7B RAG Tutorial: Build RAG Application Easily

🚀 In this tutorial, I'm thrilled to take you on a journey through creating a RAG (Retrieval-Augmented Generation) application using the Mistral, Ollama and Llama Index. 🧠💻 You'll learn how to integrate these powerful tools, index data using Quadrant Vector Store, and use the Mistal model to answer queries.

🌟 What You'll Discover:

How to set up and use Mistral Ollama and Llama-Index
Indexing JSON data and saving it using Quadrant Vector Store
Running the Mistal model on an NVIDIA RTX 5000 Ada GPU
Creating a fully functional RAG application

🛠️ Timestamps:
0:00 - Introduction to RAG Application Building
0:22 - Setting Up Mistral Ollama and Llama Index
1:21 - Starting the Ollama Server
1:43 - Installing Required Python Packages
2:14 - Loading JSON Data and Indexing
3:45 - Querying the Indexed Data
4:55 - Loading and Querying Saved Indexes

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[Galmiza]

Thanks for sharing this straight to the point tutorial. A few questions popped out.

Does the input data have to be in a specific or recommanded structure?
Will the code index ALL the "text" it can find?

What are the limitations on the input? Would it work with TB of data?
Could we train the model to query directly a database? (to avoid giving the whole database as the input)

Thank you!

[TaHa-nfvc]

Tell me it's a Mervain video without telling me : THIS IS AMAZING !
I love your content, great value !

[HyperUpscale]

Is it possible to include tho these type of demos webUI and dynamic prompts to these examples - it will take you 2 minutes, but for me it will save me 2 days (or 2 weeks)? I know for you it is not necessary, but for us is a good to have/see.
That will also make the video more impressive, attractive and widen the audience ;)

[legit_phoenix]

if someone has a problem importing: ''' pip install llama-index==0.6.2 ''' helped me

[AustinKang-wkcl]

Could you do one for on ctransformers on Python? Thank you so much!

[KodandocomFaria]

How can I effectively utilize the "three of thoughts" or "chain of thoughts" technique on the Llama Index to prompt the model to search the web for information? For example, if I want to understand the process of converting CO2 into oxygen, how can the model employ this technique to determine if web-based data search is necessary? Additionally, how can the information gathered be stored in a vector database for future reference? I'm aware of the Tools feature in Llama Index but unsure of the specific steps to achieve this.

[neoglacius]

Hu, is posible to train the LLM to return html, e.g to create a section in a website, or what would be the best approach

[jim]

It wasn't clear to me what embedding model you used to create the embeddings that are stored in the vector store. I think you said embedding = local. What embedding tool does that use?

[alqods]

Can it be integrated with an api with json response and not just documents?

[tyessenov]

How can we create effective rag for 1500+ pages docs with a lot of similar words? For example, a state law or the codex?

[Andromeda_]

Can you please create some RAG videos using pgVector as vector database