The Complete OpenAI and GPT Course in Python w/ Q&A Chatbot

Set up a Python virtual environment, clone the starter Financial Starter project, install OpenAI and Pinecone dependencies, and launch a Streamlit app to prototype a ChatGPT clone.

Build a ChatGPT clone in Streamlit in about 50 lines of code, with a text input and persistent chat history, plus OpenAI API integration.

Finish OpenAI API integration in a Streamlit ChatGPT clone by creating a secrets file with your OpenAI API key, restarting the server, and testing the text-davinci-003 model.

Explore how ChatGPT, GPT-3, and InstructGPT work as language models that generate human-like text, with emphasis on GPT-3.5, attention mechanisms, and API applications.

Master prompt engineering and advanced GPT parameters to shape outputs, control randomness and word probabilities, and tailor completions for reliable app development with GPT.

Discover how GPT becomes a multi-task, few-shot learner with strong intent understanding, enabling one model to replace many narrow models through prompts and easy fine-tuning.

Develop an intent classifier for a financial assistant in a Streamlit app using GPT-3.5 to categorize stock questions, earnings summaries, or other prompts, extract tickers, and route to handlers.

Learn to build end-to-end intent classification with GPT in Python using a Google Colab notebook, comparing zero-shot and few-shot prompts, and evaluating with test data and class probabilities.

Apply GPT3.5 to classify stock questions and earnings, generate test data, and evaluate with a classification report, achieving high accuracy. Explore a multitask prompt to extract tickers.

Integrate the intent classifier into the app by centralizing prompts and utilities, routing by intent to stock question, earnings summary, or other handlers, and testing end-to-end with OpenAI.

Examine the five fundamental limitations of GPT models, including token limits, hallucination and imperfect memory, knowledge cut-off, and data gaps, and apply practical strategies like recursive summarization and prompt engineering.

Explore the limits of GPT reasoning and math, and how chain-of-thought prompting with step-by-step prompts boosts performance. Discuss data privacy and semantic search and question answering.

Learn how semantic search uses embeddings and cosine similarity to retrieve context for answers, reduce hallucinations, and implement a retriever-generator QA workflow with Pinecone and OpenAI embeddings.

Explore Python word and sentence embeddings, compute cosine similarity, and build a top ten similar words tool using a 10,000-word MIT dataset and a 768-d vector model.

Build a semantic search engine in Python using OpenAI embeddings and a vector database to retrieve 2,286 financial news articles by semantic similarity.

Implement semantic search by embedding articles with OpenAI's API and uploading vectors to Pinecone for fast nearest-neighbor queries in a vector database.

Embed queries as vectors with get embeddings OpenAI, call Pinecone REST API to fetch top K matches, and render article previews in the stock question handler.

Build a GPT-powered Q&A assistant that answers questions from custom data using semantic search, prompt engineering, and fine tuning. Balance accuracy and hallucinations with discriminators and model selection.

Build a Q&A system using semantic search and a retriever-generator model with contextual articles. Reduce hallucinations with prompt engineering and discriminator fine-tuning, and integrate the Q&A function into the app.

Explore prompt engineering to reduce hallucinations in QA, using a discriminator and a refined template, and construct training data with generative labeling of positive and negative samples for fine-tuning.

Fine-tune a GPT model on a prepared training and validation set with OpenAI's API, then evaluate using accuracy and F-score, and use a discriminator with semantic search for safe Q&A.

Learn to generate earnings call summaries with GPT using recursive summarization with chunking, embeddings, and pinecone-based semantic search, including bullet points and quotes for fast Q&A.

Build an earnings call summarizer in a Google Colab notebook, creating chunked transcripts, generating summaries with GPT-3.5, embedding into Pinecone, and enabling semantic search and Q&A in the AI assistant.

integrate the earnings summary feature by fetching summaries for tickers via semantic search, update utils.py with namespace and filter, render results in streamlit, and test with various tickers.

Add real-time financial charting to your AI assistant by pulling data from Yahoo Finance with the finance module, and render Plotly candlestick charts in Streamlit for multiple tickers.

Deploy a streamlit app to streamlit cloud by pushing to github, linking app.py, and configuring secrets like pinecone api key; set python version and deploy to obtain a URL.