Gen AI Course | Gen AI Tutorial For Beginners

This generative AI mini course covers essential fundamentals of Gen AI, introduces LangChain—a Python framework for developing generative applications—and culminates in the creation of two comprehensive projects. The first project utilizes a commercial GPT model to develop an equity news research tool, while the second focuses on leveraging open-source large language models (LLMs).

Generative AI, unlike non-generative AI which focuses on decision-making based on existing data (like diagnosing pneumonia from x-rays or assessing loan eligibility), involves the creation of new content. A prime example is ChatGPT, where users can generate resumes, plan trips, or even create imaginative images like Hulk playing. This technology opens up possibilities for innovative applications in various fields.

The Shift from Statistical Learning to Neural Networks Generative AI focuses on creating new content, including text, images, and audio. Initially, AI tackled simpler problems like predicting home prices using structured data features such as area and bedrooms. As technology advanced to image recognition tasks involving complex features (like identifying cats versus dogs), neural networks emerged from statistical machine learning to address these challenges through deep learning techniques.

Advancements in Language Models Through Self-Supervision Recurrent neural networks revolutionized language translation by processing words in sequence while considering previous translations for context. This led to the development of language models capable of predicting subsequent words based on input sequences—an approach exemplified by Gmail's autocomplete feature. The introduction of self-supervised learning allowed training with vast amounts of unlabelled text data without needing extensive labeled datasets; this culminated in large-scale models like GPT-4 that utilize transformer architecture for enhanced performance across various generative tasks.

A large language model (LLM) is akin to a highly advanced parrot, like Buddy, who mimics conversations based on statistical probabilities rather than understanding meaning. Just as Buddy predicts words he hears frequently in his environment, LLMs use neural networks trained on vast datasets—like Wikipedia and news articles—to generate text by predicting the next word or phrase. With enhanced capabilities, these models can produce coherent responses across various subjects due to their extensive training data and complex parameters. Additionally, they employ reinforcement learning from human feedback to refine outputs and minimize toxic language while lacking any emotional comprehension.

Understanding Embeddings and Their Mathematical Power Embeddings are numeric representations of text that capture meaning, allowing for mathematical operations on words and sentences. For instance, embeddings enable calculations like "Paris minus France plus India equals Delhi." Vector databases store these embeddings to facilitate efficient searches using semantic search techniques rather than exact keyword matching. By understanding user intent through context, systems can differentiate between meanings of the same word based on its embedding properties.

Optimizing Search with Vector Databases To manage large volumes of embeddings efficiently, traditional relational databases face challenges due to computational demands during searches. Instead, vector databases utilize indexing methods such as locality sensitive hashing which groups similar-looking vectors into buckets for faster searching within relevant subsets instead of comparing all stored vectors directly. This approach significantly enhances performance in retrieving related documents from extensive datasets while maintaining accuracy in results.

Harnessing Vector Databases for Efficient AI Solutions Vector databases are becoming increasingly popular due to their efficiency in handling retrieval augmented generation (RAG). RAG allows for the integration of private or public datasets with large language models like ChatGPT, enabling tailored AI solutions. An analogy illustrates this: a student can either spend a year studying biology or use an open book during an exam—similarly, RAG leverages existing data sources to provide quick answers without extensive retraining.

Integrating Language Models and Backend Systems Building generative AI applications involves combining powerful language models like GPT-4 with backend servers that process user queries against various data sources. These systems can be hosted on platforms such as Azure OpenAI Service or alternatives like Amazon Bedrock. Developers face choices between commercial and open-source models based on cost considerations; frameworks like LangChain facilitate easy transitions between different model implementations while maintaining application integrity.

To build applications using generative AI, start with a language model (LLM), which can be either commercial like GPT-4 or open-source such as Llama or Mistral. Next, choose a cloud service provider—options include Azure, OpenAI, Amazon Bedrock, and Google Cloud. Incorporate frameworks like LangChain or the Hugging Face Transformer library to access various models. For deep learning tasks, utilize libraries such as PyTorch and TensorFlow.

Understanding LangChain Framework LangChain is a framework designed for building applications on top of large language models (LLMs) like GPT-3.5 and GPT-4. This crash course will cover the fundamentals of LangChain, culminating in creating a restaurant idea generator app using Streamlit that generates names and menu items based on user-inputted cuisines.

Limitations of Direct API Usage When developing an application with LLMs, one can directly call OpenAI's API to access their capabilities; however, this approach has limitations such as cost per token usage and restricted knowledge updates beyond September 2021. Businesses are increasingly interested in custom LLM solutions due to these constraints since they often require integration with internal data sources or real-time information from platforms like Google or Wikipedia.

Setting Up Your Environment To get started with LangChain, create an account at OpenAI’s website to obtain your unique API key necessary for authentication when making requests through the framework. After setting up environment variables securely within your codebase, install required modules including 'langchain' and 'openai'.

Configuring Model Creativity Through Temperature Settings 'Temperature' settings influence how creative responses generated by the model can be—lower values yield safer outputs while higher values encourage more risk-taking creativity. By utilizing prompt templates within LangChain you can streamline input handling without repetitive coding efforts.

Utilizing Chains For Sequential Processing 'Chains' are fundamental components in Lang Chain allowing sequential processing where output from one step serves as input for another task—in this case generating both restaurant names followed by corresponding menu items based on cuisine type selected by users via prompts created earlier.

Building an Equity Research Tool An equity research tool will be developed to analyze news articles and answer questions based on them. The project utilizes LangChain, OpenAI, and Streamlit for implementation. A narrative about Rocky B hiring an analyst sets the stage for creating a chatbot that can assist in investment decisions.

The Role of Equity Analysts Equity analysts like Peter Pande read numerous stock market articles daily to provide insights into companies' performance. This process is tedious due to the volume of information available from various sources such as Moneycontrol or Bloomberg terminals.

Streamlining Information Retrieval To streamline this analysis, a new tool will allow users to input multiple article URLs and retrieve answers by querying those specific texts instead of manually searching through each one. This approach aims at efficiency in data consumption while providing accurate responses.

Limitations of ChatGPT for Financial Analysis 'ChatGPT' could theoretically perform similar tasks but has limitations: copy-pasting text is cumbersome; it lacks an aggregated knowledge base; word limits restrict large inputs which are common in financial reports—hence building a dedicated solution becomes necessary.

Building an Innovative Retail Q&A Tool An end-to-end project is introduced, focusing on building a Retail Q&A Tool for a t-shirt store. The tool will allow users to ask questions in natural language, which the system will convert into SQL queries and execute against a MySQL database. This innovative approach aims to simplify data retrieval for store manager Tony Sharma by eliminating reliance on complex software or manual Excel work.

Understanding Project Requirements The project's requirements are outlined with details about the t-shirt inventory stored in two tables: one for stock quantities and another for discounts. Each brand's products have specific attributes such as color, size, price per unit, and discount percentages that need to be managed effectively within this framework.

Identifying Challenges Faced by Store Manager Tony Sharma often faces challenges when using existing software due to its limitations in handling custom queries related to stock levels or discounts. When he encounters difficulties retrieving information himself—especially during Loki’s absence—he seeks assistance from Peter P., who proposes creating an AI-driven chat interface similar to ChatGPT that can interpret human language into SQL commands.

Technical Architecture Overview The technical architecture of the proposed solution involves utilizing Google Palm through LangChain Framework alongside embedding techniques like few-shot learning. By preparing training datasets containing sample questions paired with their corresponding SQL queries where errors occurred previously, it enhances query accuracy over time while leveraging vector databases like ChromaDB.

Leveraging Few-Shot Learning Techniques. 'Few-shot learning' is explained as crucial preparation involving minimal yet effective examples used during model training; these samples help improve performance without needing extensive datasets typically required by machine-learning models.

Choosing Google Palm Over Other Models. 'Google Palm' offers advantages over other LLMs due mainly because it's free compared with OpenAI's paid services while being easier than Meta’s heavy local installations requiring significant setup efforts before use.