Gen AI Course | Gen AI Tutorial For Beginners

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The text discusses the fundamentals of Generative AI, highlighting projects using commercial and open-source models, the evolution of AI from statistical to deep learning, Transformers, vector databases, embeddings, and the Lang chain framework for AI applications, emphasizing the importance of building secure and efficient solutions for real-world industry applications. It also details the process of using Lang chain to load, split, embed, and retrieve text data, along with creating applications like a restaurant name generator and a news research tool for equity analysts by combining various components like loaders, splitters, embeddings, and retrieval methods efficiently.

Insights

Generative AI creates new content, unlike non-generative AI that uses existing data for decision-making.
Large language models (LLMs) trained on extensive datasets like Wikipedia enable predictive text generation.
Vector databases offer faster search capabilities and optimal storage, becoming popular for AI applications.
Agents in Lang chain connect with external tools like Google Search and Wikipedia, enhancing AI models' reasoning capabilities.
Lang chain aids in building real-life industry applications like a news research tool for equity analysts using semantic search techniques.

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What is Generative AI?
Generative AI creates new content.

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Summary

00:00

"Exploring Gen AI: Fundamentals, Models, Applications"

The course begins with an introduction to Gen AI fundamentals.
Lang chain, a Python framework for building Gen applications, is discussed.
Two Gen AI projects are outlined: one using a commercial GPT model for an equity news research tool and the other using an open-source LLM model for a Q&A tool in the retail industry.
Generative AI is explained as the creation of new content, contrasting with non-generative AI that makes decisions based on existing data.
Chat GPT is highlighted as a classic example of generative AI, allowing tasks like resume writing and trip planning.
The evolution of AI from statistical machine learning to deep learning and recurrent neural networks is detailed.
The introduction of Transformers, a powerful neural network architecture, is discussed, leading to models like BERT and GPT.
Large language models (LLMs) are explained as trained on vast data sets like Wikipedia, enabling predictive text generation.
The analogy of a parrot mimicking conversations is used to explain LLMs' predictive capabilities based on past data.
Embeddings and vector databases are described as numeric representations of text, allowing for mathematical operations and semantic search capabilities.

17:30

"Advanced Techniques in Word Embeddings and AI"

Video explanation available for generating handcrafted features for word embeddings using complex statistical techniques
Different techniques used for representing words, sentences, and documents into embeddings, with Transformer-based techniques gaining popularity
Use of relational databases to store embedding vectors for search queries and retrieval of relevant documents using cosine similarity
Challenges arise with millions of records in databases, leading to the need for smarter approaches like locality-sensitive hashing
Vector databases offer faster search and optimal storage, gaining popularity for AI applications
Retrieval Augmented Generation (RAG) concept explained as a method to fine-tune large language models on specific datasets for AI applications
Comparison between full-fledged training and open-book exam analogy for RAG-based applications
Tools required for building AI applications like Chat GPT, including large language models, cloud services, frameworks like Lang chain, and deep learning libraries
Overview of Lang chain framework for building applications on large language models, addressing limitations of using open AI API directly
Importance of frameworks like Lang chain for integrating various models, accessing different data sources, and building applications using large language models

34:25

"Secure Keys, Creative Names: Lang Chain Application"

The key provided is in the format SK-something, resembling a password, to be used in the code for Lang chain.
Separate keys can be created for different projects, ensuring security and organization.
Keys should be stored securely as they cannot be accessed after copying, necessitating deletion and creation of new keys.
Import the OS module and create an environment variable with the key, stored in a Python file for confidentiality.
Install the Lang chain module and the Open AI module to proceed with the code.
Utilize the Open AI model with a specified temperature setting to control creativity levels.
Input any question into the Open AI model to generate creative outputs, such as restaurant names for different cuisines.
Create a prompt template using variables like Cuisine to generate customized responses efficiently.
Implement the concept of chain in Lang chain to streamline the process of generating restaurant names and menu items.
Develop a streamlit-based application for a restaurant name generator, allowing users to select a cuisine and receive corresponding restaurant names and menu items.

53:23

Creating Secret Key for Lang Chain Helper

Import the module and call the function, focusing on Lang chain helper.
Copy and paste code from the notebook to create a file for the secret key.
Create a secret key file and input the key obtained, ensuring it remains private.
Utilize the key by importing the variable from the Python file directly.
Copy and paste the code for the sequential chain from the notebook to create a restaurant name chain and manual items chain.
Create a main function to test the code, generating an Italian food restaurant name and manual items.
Notice extra characters in the output and decide to remove them using a function in the streamlit code.
Run the streamlit application to generate and view the restaurant name and manual items.
Introduce the concept of agents in Lang chain, powerful tools that connect with external tools and utilize reasoning capabilities.
Agents use tools like Google Search, math tools, and Wikipedia to perform tasks beyond the knowledge limit of the AI model.

01:11:18

"Lang Chain: Enhancing Conversational Memory Efficiency"

In a conversation window, history and input are distinguished, with history representing past interactions and input for upcoming questions.
The default conversation chain object includes a memory buffer to store the conversation's transcript.
To manage costs and prevent excessive token usage, limit the conversation buffer to the last 10 or 20 exchanges using the from_lang_chain.memory object.
Demonstrating the K parameter, the memory buffer retains only the most recent question-answer pair, leading to short-term memory loss for older exchanges.
The Lang chain fundamentals are utilized to create two end-to-end projects in finance and retail domains, emphasizing real-life industry applications.
The finance project involves building a news research tool using Lang chain, OpenAI, and Streamlet, enhancing it with storytelling elements.
Equity research analysts rely on news articles for stock analysis, highlighting the need for tools to aggregate and retrieve relevant information efficiently.
Semantic search techniques, such as word embedding and vector databases, aid in identifying and retrieving pertinent chunks of text for specific questions.
The technical architecture involves a document loader, chunk splitter, and vector database to streamline the retrieval process based on the given question.
The project progresses from a proof of concept in Streamlet to a long-term architecture with a database ingestion system and a chatbot interface for enhanced functionality.

01:28:08

Creating Technical Architecture for Lang Chain Library

When working as an NLP engineer or data scientist, start by brainstorming with your team to create a technical architecture before coding.
Before discussing text loaders, ensure you have watched the Lang chain crash course for a basic understanding of the Lang chain Library.
Install Lang chain by running "pip install Lang chain" to access the library.
Import the text loader class from Lang chain to load data from a text file, such as "nvda_new1.txt."
The text loader class returns a data object containing the text content and metadata of the loaded file.
Lang chain offers various loader classes, including text loader and CSV loader for loading data from CSV files.
The CSV loader class loads data from a CSV file, like "movies.csv," providing access to the content and metadata of each record.
Utilize the unstructured URL loader class to load text content directly from URLs, such as news articles, into Jupyter notebook.
Perform text splitting using Lang chain's text splitter classes, like character text splitter, to divide text into manageable chunks based on specified parameters.
Recursive character text splitter allows for more advanced text splitting by using multiple separators and rules to ensure chunks are within the token limit for efficient processing.

01:46:45

Optimizing Text Chunk Splitting for Efficiency

The text discusses the process of splitting chunks of text based on character count, merging them for optimization.
It explains how chunks are divided based on a size of 200, creating one chunk of 200 characters and another of 10.
The splitting process ensures words remain intact, with examples showing the division of a 210-character chunk into 199 and 10.
The text introduces the use of a recursive text splitter for news research tools, emphasizing the importance of maintaining word integrity.
It transitions to discussing Vector databases, highlighting the use of a lightweight in-memory database called "phase" for faster vector searches.
"Phase" stands for Facebook AI similarity search, allowing quick searches within a set of vectors.
The process involves converting text chunks into embeddings using various techniques like open ey embedding or hugging face embeddings.
These embeddings are then stored in a vector database like "phase" for efficient search operations.
The text demonstrates how "phase" efficiently searches for similar vectors within a database, aiding in information retrieval.
It concludes by discussing retrieval QA methods like the "stuff" method and "map reduce" method for handling token limits and optimizing search processes.

02:04:07

Building Open API Embedding Retrieval System

To create an open API embedding, use the "embeddings" class and call the "from_documents" method in the "Phase" class.
The "from_documents" method in "Phase" requires documents or chunks and the chosen embedding method, such as open API embedding.
Save the resulting Vector index into a file using the code provided, like writing it to a pickle file named "Vector_index.pickle."
Load the saved Vector index from the pickle file into memory using the code snippet shared.
Create a retrieval QA chain object with the "llm" and "retriever" arguments, where "retriever" specifies the Vector database retrieval method.
Ask a sample question, like "What is the price of Thiago icng," to test the retrieval process.
The retrieval process involves retrieving similar chunks from the Vector database and generating answers based on the questions asked.
Utilize the "fc1," "fc2," "fc3," and "fc4" answers generated by the retrieval process for further analysis.
Combine the individual answers into a summary chunk and query the "llm" for a final answer to the question.
Assemble all the components, including loaders, splitters, embeddings, and retrieval methods, to build the final project efficiently.

02:22:35

"Answer and Sources: Tool for Equity Analysts"

The result of the process will be a dictionary with two elements: "answer" and "sources."
The "answer" element will contain the response to the query.
Utilize "St. header" and "St. subheader" to display the answer in the UI.
The tool can provide answers based on questions from articles.
It can also display the source URLs from where the answer was retrieved.
The tool can summarize articles upon request.
The summarized article will include the source reference.
The tool is beneficial for equity research analysts.
Long-term project components include a Data Injection System and a Vector Database.
The technical architecture involves using Google Palm for converting questions to SQL queries.

02:39:29

"Import Google Pal model, use open EI"

Import Google Pal model from Lang chain
Use open EI for all models
Create an object for the LLM model and pass the Google API key
Ensure to use your own API key to avoid issues
Install all required libraries using "pip install -r requirements.txt"
Create an SQL database object by importing a specific class
Define the URI for the database with details like host, username, password, and database name
Form the URI using a specific syntax
Create a SQL database chain object using Lang chain experimental module
Run queries using the chain object, ensuring to specify parameters like verbose for detailed output

02:59:09

Generating String from List in Python

The text discusses generating a string from a list in Python using the `join` function.
It introduces the concept of a vector database, specifically Chroma, for a project.
The process involves importing Chroma, supplying text and embeddings, and providing metadata.
The Vector store is created to convert input questions into embeddings for similarity matching.
To enable similarity matching, the Semantic Similarity Example Selector class is imported.
The class requires the Vector store and a parameter `K` to pull similar examples.
The mechanism involves passing input sentences to the Vector database to retrieve similar queries.
Instructions are provided for customizing MySQL prompts to avoid errors in queries.
A prompt template is created for MySQL queries, including prefixes and suffixes.
The process involves creating a few short prompt template with parameters like example selector, prompt, prefix, suffix, and input variables.

03:18:25

SQL tool tracks t-shirt stock for Nike.

The tool allows users to run SQL queries to determine the quantity of t-shirts in stock for Nike, Exess, and White, providing a useful resource for store managers like Tony Sharma to obtain direct answers to various questions.

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