Talk to Your Documents, Powered by Llama-Index

In this video, we will build a Chat with your document system using Llama-Index. I will explain concepts related to llama index with a focus on understanding Vector Store Index.

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Timestamps:
[00:00] What is Llama-index
[01:10] System Architecture
[02:54] Llama-index Setup
[04:54] Loading Documents
[05:42] Creating the Vector Store Index
[06:16] Creating Query Engine
[07:06] Q&A Over Documents
[09:00] How to Persist the Index
[10:20] What is inside the Index?
[11:38] How to change the default LLM
[13:25] Change the Chunk Size
[14:26] Use Open Source LLM with Llama Index

[mlg]

The SINGLE MOST valuable YT video I have come across on this topic. BRAVO!! And thank you!

[rehberim]

Again a great video. While I was trying to figure out how to learn this technology and where I could find reliable sources, it was lucky for me to find such up-to-date information.

[gregorykarsten]

Excellent. I was wondering what the difference between langchain and lama index was. I also thought lama index is very powerful with its indexing functionality. This can bridge the gap between semantic and index search

[regonzalezayala]

🎯 Key points for quick navigation:

00:00 *💡 Introduction to Llama-Index*
- Introduction to the task: building a document Q&A system using Llama-Index,
- Comparison with LangChain and brief overview of functionalities,
- Emphasis on fine-tuning embedding models for better performance.
01:19 *📑 Document Processing and Embedding*
- Explanation of converting documents into chunks and computing embeddings,
- Process of creating a semantic index and storing embeddings,
- Introduction to querying the document by computing embeddings for user questions.
02:57 *🛠️ Initial Setup and Code Implementation*
- Installing necessary packages: Llama-Index, OpenAI, Transformers, Accelerate,
- Setting up the environment and loading the document using Simple Directory Reader,
- Overview of creating vector stores and relevant indexing.
05:17 *🧩 Implementing Query Engine and Basic Queries*
- Description of building a query engine,
- Implementation of querying the documents with sample questions,
- Obtaining and displaying responses from the model.
08:42 *🛠️ Customizing Configuration and Parameters*
- Explanation of customizing chunk sizes, LLM models, and other parameters,
- Process of persisting vector stores for future use,
- Detailed look at embedding and document storage components.
11:43 *🔧 Advanced Customization and LLM Usage*
- Methods for changing the LLM model, including GPT-3.5 Turbo and Google Palm,
- Instructions on setting chunk sizes and overlaps,
- Using open-source LLMs from Hugging Face and configuring corresponding parameters.
16:37 *🚀 Conclusion and Future Prospects*
- Summary of using Llama-Index for document Q&A systems,
- Mention of advanced features and future tutorial plans,
- Encouragement to check out additional resources and support via Patreon.

Made with HARPA AI

[dario]

Finally a good tutorial on the subject! Thanks so much!

[s.moneebahnoman]

Amazing! I haven't seen enough videos talking about persisting the index especially in beginner level tutorials. I think its such a crucial concept that I found out much later. Love the flow for this and its perfectly explained! Liked and subbed!

[abdullahiahmad]

to be honest this is the best tutorial i see in 2023

[aseemasthana]

Perfect pace and level of knowledge. Loved the video.

[ChronozOdP]

Another excellent video. Easy to follow and up to date. Thank you and keep it up!

[vladimirgorea]

what's the difference in using Llamaindex and just using openai embeddings?

[rajmankad]

The explanation is so clear! Thank you.

[KinoInsight]

I liked your explanation. You are a good story teller. You explained the details in a simple way and yet easy to implement manner. Thank you. I look forward to your next video.

But how do we ensure the latest data is fed to the LLM in real time? In this case, we need to provide the data to the Llama. And the response is limited to the data provided.

[HarmeetSingh-ryfm]

Hi prompt as you mentioned in this video that this is a system prompt for StableLM, I want to know is there a way I can find prompt format for different LLM for example mixtral 8x7b/22b or llama 3

[adamduncan]

Excellent videos! Really helping out with my work. Curious what tool you are using to draw the system architecture? I really like the way it renders the architectures.

[henkhbit]

Nice intro about llma-index👍. I think for small amount of documents the default llma-index embedding in json is sufficient. I suppose u can also use chromadb or weaviate or other vectorstores. Would be nice to see with the non default vector store...

[jamesvictor]

Great video. The notebook fails at the first hurdle for me: ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
llmx 0.0.15a0 requires cohere, which is not installed.
tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.

[MikewasG]

Thanks for your sharing! It's very helpful.

[MarshallMelnychuk]

Great explanation and comparison very useful thank you

[CaesarEduBiz-lzcg]

Is it better to use Llama Index or RAG (Retrieval Augmented Generation) ?

[nishkarve]

Excellent. Is there a video you are planning to make on a multi modal RAG? I have a PDF which is an instruction manual. It has text and images. When a user asks a question, for example, "How to connect the TV to external speakers?", it should show the steps and the images associated with those steps. Everywhere I see are examples of image "generation". I don't want to generate images. I just want to show what's in the PDF based on the question.