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AI Engineering Bootcamp: Building AI Applications with LLM APIs, LangChain + much more!Using Jupyter Notebook
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Using Jupyter Notebook
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Using Virtual Environments (venv)
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Getting Started with the requests and httpx Libraries in Python
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Handling HTTP Errors
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Managing HTTP Authentication and Headers (OpenAI API)
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Setting Up the Environment: Jupyter Notebook and Pandas
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Introduction to Pandas: Series and DataFrames
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Importing and Exporting Data: Working with CSV Files
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Exporting Data to Different Formats: Excel, JSON, SQL, YAML
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Modifying Data: Adding and Dropping Columns and Rows
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Accessing Data: Using df.iloc[] and df.loc[]
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Sampling and Previewing Data: Using df.sample() and df.head()
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Filtering Data: Masks and pandas.Series.between()
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Sorting Data: Understanding Pandas Sorting Methods
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Handling Missing Data
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Aggregations and Grouping Data
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Project: Analyzing Website Traffic Data
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Time Series Data Manipulation in Pandas
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Foundations of LLMs and Generative AI
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Tokens, Context Windows and Cost
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Exploring LLM APIs: AI as a Service
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OpenAI Playground, Google AI Studio, and Anthropic Workbench
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Challenges and Limitations of LLMs
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The State of AI: Present and Future – The Good and the Bad
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Pretraining Data (Internet)
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Tokenization
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Training the Neural Network
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Post-Training: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)
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Reinforcement Learning (RL)
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Becoming Better than Humans: AGI and ASI with RL
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Reinforcement Learning with Human Feedback (RLHF)
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How to Deal With Hallucinations
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Using Tools: Internet Search, Interpreter, and Deep Search
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Big Ideas Recap (Core Summary)
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Authenticating to OpenAI using Python Dotenv
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Chat Completions Endpoint
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Developer Message
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Streaming API Responses
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Using Local Base64 Images as Input
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Using Online Images as Input
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Chat Completion API Parameters: Temperature and Seed
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Chat Completion API Parameters: Top P, Max_Tokens, Penalties
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Diving into OpenAI’s Reasoning Models (o1 and o3)
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Best Practices for Prompting Reasoning Models
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Transcriptions with Whisper
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Translations with Whisper
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Text-to-Speech (TTS) API
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Generating Original Images Using the DALL-E 3
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Creating Variations of Images with DALL-E
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Editing Images with DALL-E
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Intro to Prompt Engineering
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Tactic 1: Position Instruction Clearly with Delimiters
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Tactic 2: Provide Detailed Instructions for the Context
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Tactic 3: Use the Rich Text Format (RTF)
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Tactic 4: Few Shot Prompting
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Tactic 5: Specify the Steps Required to Complete a Task
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Tactic 6: Give Models Time to Think
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Other Tactics and Principles for Better Prompting
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Avoid Hallucinations Using Guarding
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Summary
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Project Introduction
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Creating a Daily Meal Plan Using OpenAI API
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Creating the Prompt
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Running the Program
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Generating Original Images for the Recipes using DALL-E
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Narrate the Meals using the Text-to-Speech Model
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Setting Up the Python SDK and Authenticating for Gemini API
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Generating Text From Text Prompts
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Streaming Gemini Responses
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Generating Text From Images
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Gemini API Generation Parameters: Controlling How the Model Generates Responses
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Gemini API Generation Parameters Explained
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Building Chat Conversations
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Project: Building a Conversational Agent Using Gemini Pro
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System Instructions
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The File API: Prompting with Media Files
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Tokens
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Prompting with Audio
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Project Requirements
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Building the Application
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Testing the Application
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Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps
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Creating the Web App Layout With Streamlit
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Saving and Displaying the History Using the Streamlit Session State
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Exercise: Imposter Syndrome
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Project Introduction
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Getting Images Using a Generator
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Renaming Images Using Gemini
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LangChain Demo
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Introduction to LangChain
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Working with the OpenAI Models
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Caching LLM Responses
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LLM Streaming
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Prompt Templates
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ChatPromptTemplate
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Understanding Chains
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Installing the Python Libraries for Gemini and Authenticating to Gemini
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Integrating Gemini with LangChain
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Using a System Prompt and Enabling Streaming
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Multimodal AI With Gemini
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LangChain Tools: DuckDuckGo and Wikipedia
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Creating a React Agent
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Testing the React Agent
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Intro to OpenAI's Text Embeddings
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Generating Simple Embeddings
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Embedding the Dataset for Similarity Searches
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Estimating Embedding Costs With Tiktoken
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Performing Semantic Searches
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Project Introduction
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Loading Your Custom (Private) PDF Documents
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Loading Different Document Formats
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Public and Private Service Loaders
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Chunking Strategies and Splitting the Documents
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Intro to Vector Stores and Authenticating to Pinecone
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Working with Pinecone Indexes
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Working with Vectors
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Pinecone Namespaces
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Embedding and Uploading to a Vector Database (Pinecone)
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Asking and Getting Answers
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Using Chroma as a Vector DB
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Adding Memory to the RAG System (Chat History)
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Using a Custom Prompt
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Introduction to Agents and ReAct
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Creating the Agent Class
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Creating the ReAct Prompt
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Creating the Tools
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Testing the Agent
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Automating the Agent
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LangGraph Concepts and Core Components
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Building a Chatbot
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Visualizing the Graph
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Running the Chatbot
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Tavily AI
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Enhancing the ChatBot with Tools
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Adding Memory to the Chatbot
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Intro to Reflection
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Generate
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Reflect and Repeat
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Define the Graph - Part 1
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Define the Graph - Part 2
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Running the App
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Intro to LangSmith
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Setting Up LangSmith
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Tracing with LangSmith
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Tracing the Reflective Agentic App
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Project Overview
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Defining the Agent State and the Prompts
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Implementing Agents and Nodes
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Defining the Conditional Edge
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Defining the Graph
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Running the App
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Tracing the App with LangSmith
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Note
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Application Overview
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Extracting Data from ArXiv with Pandas
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Downloading Research Papers
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Loading, Splitting and Expanding Data
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Building a Knowledge Base for RAG
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Creating a Pinecone Index
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Loading the Knowledge Base and Deploying to Pinecone
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Developing Custom Tools
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Implementing the ArXiv Fetch Tool
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Unlocking Web Search with Google SerpAPI
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Building Google SerpAPI Tools
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Creating RAG Tools
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Implementing the Final Answer Generation Tool
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06_14 Initializing the Oracle LLM
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Testing the Ecosystem
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Building a Decision-Making Pipeline
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Defining the Agent State
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Defining the Graph
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Generating Reports
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Building the Final Research Report
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Concluding the Project
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Understanding Python Modules
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The OS Module
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Advanced Import Techniques and Best Practices
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Using __name__ == '__main__' for Modular and Reusable Code
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Mastering Python Package Management with pip
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Thank You!
