DSPy: MOST Advanced AI RAG Framework with Auto Reasoning and Prompting
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2 ماه پیش
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👋 Welcome to our in-depth
👋 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:
Sponsor a Video: https://mer.vin/contact/
Do a Demo of Your Product: https://mer.vin/contact/
Patreon: Patreon: MervinPraison
Ko-fi: https://ko-fi.com/mervinpraison
Discord: Discord: discord
Twitter / X : Twitter: mervinpraison
Code: https://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
👉 Don't forget to subscribe for more insightful AI content and hit the bell icon to stay updated!
🔍 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:
Sponsor a Video: https://mer.vin/contact/
Do a Demo of Your Product: https://mer.vin/contact/
Patreon: Patreon: MervinPraison
Ko-fi: https://ko-fi.com/mervinpraison
Discord: Discord: discord
Twitter / X : Twitter: mervinpraison
Code: https://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
👉 Don't forget to subscribe for more insightful AI content and hit the bell icon to stay updated!
2 ماه پیش
در تاریخ 1403/02/19 منتشر شده
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