DSPy: MOST Advanced AI RAG Framework with Auto Reasoning and Prompting
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- Опубліковано 9 чер 2024
- 👋 Welcome to our in-depth exploration of DSPY, a groundbreaking technology developed by Stanford NLP University designed to redefine the way we approach RAG applications in AI. In this video, we'll explore the essentials of DSPY, its advantages, and provide a comprehensive guide on setting it up. Whether you're an AI enthusiast or a seasoned developer, this video will equip you with the knowledge to integrate DSPY into your projects effectively.
🔍 What You Will Learn:
Why DSPY? Understanding its necessity in modern AI.
Advantages of DSPY: How it simplifies workflows and enhances AI reasoning.
Setup Guide: Step-by-step instructions on configuring and utilising DSPY.
Practical Demonstration: Creating a chatbot and implementing Chain of Thoughts.
Introduction to DSPy:
DSPy (Differentiable Prompting for Your AI) is a framework created by Stanford NLP that allows you to build and optimize retrieval-augmented generation (RAG) applications. It automatically optimizes prompts, enables auto-reasoning, and has built-in evaluation capabilities.
Advantages of DSPy:
DSPy addresses common issues in RAG applications, such as inadequate retrieval coverage, retrieval of irrelevant information, lack of context and relevance in generated responses, and inconsistency and incoherence. It automates prompt optimization, auto-reasoning, adapts to the pipeline, and has auto-weight optimization and built-in evaluation.
Key Components of DSPy:
The three key components of DSPy are Signatures (defining the input and output structure), Modules (prompting techniques and language models), and Optimizer (automatic evaluation and optimization of generated responses and retrieved context).
Getting Started with DSPy:
The video covers installing the required packages (dpy-ai, openai, and rich), setting up the OpenAI API key, and creating the app.py file. It also demonstrates configuring DSPy, loading data (HotpotQA dataset), and printing examples.
Creating a Basic Chatbot:
The video shows how to create a basic question-answering chatbot using DSPy's signature and prediction capabilities.
Adding Chain of Thought:
To improve the chatbot's reasoning abilities, the video demonstrates incorporating the Chain of Thought module, which provides a series of intermediate reasoning steps.
Creating a RAG Application:
The video walks through the process of creating a full-fledged RAG application in DSPy, including defining the signature, creating the RAG module, setting up the optimizer, and executing the RAG pipeline.
Evaluating Different Approaches:
The video compares and evaluates the performance of different approaches, including the basic RAG, uncompiled Bailing RAG, and compiled Bailing RAG with the optimizer.
Summary and Comparison of Results:
The video summarizes and compares the results of the different approaches, highlighting the improvement in accuracy achieved by using the compiled Bailing RAG with the optimizer.
🔗 Resources:
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Code: mer.vin/2024/05/dspy-beginner...
🚀 Timestamps:
00:00 - Introduction to DSPy
00:23 - Advantages of DSPy
00:43 - Key Components of DSPy
01:55 - Getting Started with DSPy
05:41 - Initial Setup
06:40 - Configuration and Data Loading
08:21 - Creating a Basic Chatbot (Question Answering)
09:40 - Adding Chain of Thought
11:14 - Creating a RAG Application
11:33 - Defining Signature for RAG
11:51 - Creating RAG Module
13:04 - Creating Optimizer
13:39 - Executing the RAG Pipeline
14:35 - Evaluating Different Approaches
14:51 - Evaluating Basic RAG
15:29 - Evaluating Uncompiled Baleen RAG
17:10 - Evaluating Compiled Baleen RAG with Optimizer
18:31 - Summary and Comparison of Results
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This is very similar to other Dspy videos. I have yet to see it tested on non-canned examples. The video is perfectly fine but I would like to see more independent evaluation.
Thank you for another great video.
How do we use it with our own documents? Which are the Vector DBs options? Can we use it with local LLMs (i.e. Ollama, LM Studio)?
Your UA-cam channel is one of the absolute best I am following. You deserve to have soooo many more sub's than you currently have. It won't be long until we get to see that gold UA-cam plaque on the wall behind you ❤
Keep it up!! You are changing life's with your channel. Thank you ❤️
Thanks for the content. It would be great to see more and more the use of open source models like Llama3, they have quality enough now to handle those tasks and we do not need to sell our souls to anyone
simple , clear explantions , Good job Mr Mervin
You are Amazing!
I have learned more form you than I can say.
Thank you for always providing the code.. I break your code examples over and over again and truly learn by getting my hands dirty trying to modify for a current need or just to grasp a concept. I refer back to all of it frequently. Thank you so much!
Your videos are of great help to me! Thank you so much! Looking forward to your other videos.
Very happy to see a long video!
Great Stuff! Reminds me of adding new layers to a model, I will try this with llama3.
Mind blowing! Great!
Way to go Mervin, now we are cooking.... very nice.
Thanks a lot this is very informative.
cool. still trying to understand some of that but it certainly looks good.
You should do a 'tour' of dspy only then, its capabilities can be related to RAG and other things
im consuming your #vids - nice work bro
Nice !
Nice
Thank you so much for the content. It seems very impressive. I still need to see the papers for deep insight but I wonder how this could integrate with agents using crewai or langgraph.
Hi Mervin, you are one the best professors, your explanations made more easy the complicated things. What do you think about weaviate or pgvecto rs for hybrid search on RAG app? Can you do an introduction to that? please ...
Great discovery and presentation. How can this be used with praisonai ? Is that a deprecated idea or will this be incorporated into it ?
what do u recommend where i can talk to a repo?
This plus Autogen to fire off multiple, context explicit, complicated pipelines. Lots of ideas..
Do you have any playlist to learn concepts before this? I don't understand this code, I need more context
This is really interesting, I will try to implement it 100% locally. can you please modify your talking head, it gets in the way of the code. Thank you.
Well presented. What does the Baleen score actually mean??
If I were to create a RAG application and provide the user a Baleen score for each answer I output, what would 58 mean for them??
How to do it with langchain
Can I use llama 3 instead of gpt 3.5 turbo??
You explained it nicely but your circle is blocking the text my friend.
Can i use gemini instead of openai for building rag based chatbot in the above case
Is this similar to crew ai
Thanks Mervin. Can you avoid using openai keys? I want to use llama3 or other models, not support openai’s attempts to lock us into their (paid) ecosystem.
is learning code worth in AI era ?
Whatever you do it seems you need curated data (q and a) so that you have something to evaluate against.
does the Dataset should be in an particular structrure !!! ? , i was checking wheather it can be implemented on LogAnomaly