Open AI tutorials and use cases

OpenAI Tutorials and Use Cases

This guide compiles methods for building automated business workflows, semantic search applications, and autonomous research agents using OpenAI and related tools. Readers will find comprehensive details covering data extraction, vector storage, and web interface deployment.

Automated Data Extraction and Workflows

GPT function calling can be used to convert unstructured inbound emails into structured JSON data containing company names, product details, categories, next steps, and priority scores [1]. To build this, developers use Visual Studio to create a .env file containing the OpenAI API key and a main.py file utilizing the GPT-4 July 13 model for email information extraction and priority scoring [1]. A local FastAPI application handles POST requests with Pydantic base models, and the local server is tested using Uvicorn and HTTP requests [1]. Projects are uploaded to GitHub via a requirements.txt file and deployed on Render.com, where the live URL is tested after adding the OpenAI API key to the environment settings [1]. Zapier connects Gmail to the Render API endpoint via webhooks to automatically save extracted lead data into a Google Sheet [1]. Alternatively, autonomous agents integrate with Make.com to automate business workflows such as triggering research from incoming emails and sending prioritized leads to AirTable [3].

Semantic Search Applications

Semantic search can be built in a Ruby on Rails application using OpenAI, LangChain.rb, and Postgres with pgvector to perform nearest neighbor searches based on meaning rather than exact text matches [2]. Developers add the Matrix, lang_chain.rb, and neighbor gems to the project bundle and run a neighbor vector migration to enable the Postgres vector extension [2]. A polymorphic chunk model with a vector embedding column matching model dimensions is generated alongside a chunkable concern to handle text chunking and embedding generation [2]. LangChain::Chunker::RecursiveText splits lengthy articles or transcripts, while the text-embedding-ada-002 model stores text embeddings as 1,536-dimensional vectors in PostgreSQL with pgvector [2]. Nearest neighbor lookups and cosine similarity query and retrieve semantically relevant records, which are wired into the Rails controller and UI [2].

Autonomous AI Research Agents

Autonomous AI research agents execute online research through an iterative process of planning, executing, and reprioritizing tasks without hallucinating [3]. These agents rely on three main components: a large language model as a decision engine, a memory system to maintain context and task lists, and a set of execution tools [3]. Development begins in Visual Studio Code by creating a project folder and an app.py file alongside an ENV file containing API keys for OpenAI, Serper, and Browserless [3]. A search tool function uses Serper to pull Google search results, while a scraping tool function uses Browserless and Beautiful Soup to extract text from website HTML [3]. Token limits are managed using a map-reduce summarization method with GPT-3.5-turbo-16k, which splits large content into chunks and summarizes them relative to the research objective [3]. Alternatively, another autonomous researcher searches the internet via Serp, selects the best three article URLs using GPT-3.5 Turbo, scrapes content using LangChain's document loader, and splits text into chunks using a character text splitter with a chunk size of 3,000 [4]. These text chunks are summarized by the LLM and combined to generate a final viral Twitter thread [4]. LangChain is chosen over Flowise and LangFlow due to limitations in controlling agent outputs like returning valid URLs [4]. Streamlit builds the user interface with text inputs and collapsible expanders to display results step-by-step [4], or to input queries and display research results for the Make.com-integrated agent [3]. The agent can be deployed as a web service via Render.com [3]. Relevance AI is also presented as a no-code alternative allowing users to build the exact same autonomous researcher workflow in about 10 minutes [4].

What is not covered

Specific pricing costs for API usage and detailed error-handling protocols for failed webhook requests are not provided across the sources.