Set Up and Customize OpenAI GPT Plugin

Set up a custom gpt for me. These are the instructions I got from chat gpt.. Setting up a system with the OpenAI Retrieval Plugin involves several steps, integrating your data sources and customizing it to your needs. Here’s a step-by-step guide to get started: --- 1. Requirements to Begin OpenAI API Access: Ensure you have an OpenAI API key and access to use plugins. OpenAI offers the plugin feature primarily for GPT-4. Knowledge Base or Data Sources: Identify sources you want the plugin to retrieve information from: Research papers (e.g., PubMed, Elsevier). Your presentations, case notes, or patient FAQs (e.g., from OneDrive, Google Drive, or a custom database). Public medical databases or search engines. Basic Programming Knowledge: You or a developer can set up the plugin using Python or similar languages. --- 2. Set Up OpenAI Retrieval Plugin Step 1: Install Required Tools You’ll need a development environment: Python: Install Python if it’s not already installed. Dependencies: Install openai, faiss-cpu (for search indexing), and langchain for structured data retrieval: pip install openai faiss-cpu langchain Step 2: Build or Connect Your Data Source For Custom Files: Upload your files (PDFs, Word documents, PPTs, etc.) to a storage service like OneDrive, Google Drive, or AWS S3. Use tools like PyPDF2 or langchain.document_loaders to process these documents. For Medical Journals: Use APIs like PubMed or Elsevier to fetch research articles. Example for PubMed API: Sign up for an API key from PubMed. Use libraries like requests to retrieve articles dynamically. Step 3: Create a Retrieval Index Index your documents to make them searchable. Use FAISS for efficient indexing: from [login to view URL] import FAISS from [login to view URL] import OpenAIEmbeddings # Load documents and embed them embeddings = OpenAIEmbeddings() vectorstore = FAISS.load_local("path_to_your_index", embeddings) Step 4: Connect the Retrieval Plugin Configure GPT to use the retrieval index. Here’s a basic example: from [login to view URL] import RetrievalQA from langchain.chat_models import ChatOpenAI # Load vector store retriever = vectorstore.as_retriever() # Set up GPT with retrieval chat = ChatOpenAI(model="gpt-4") qa = RetrievalQA.from_chain_type(chat, retriever=retriever) # Query for a topic query = "What are the latest advancements in endoscopic spine surgery?" response = [login to view URL](query) print(response) --- 3. Customization for Your Needs Platform-Specific Scripts Use templates to generate scripts tailored to YouTube and Instagram: query = "What is endoscopic discectomy, and what are its advantages?" response = [login to view URL](query) youtube_script = f""" Introduction: - "Are you suffering from chronic back pain? Let me introduce you to a groundbreaking procedure: Endoscopic Discectomy." Body: - "{response} This minimally invasive procedure is ideal for quick recovery and minimal discomfort." CTA: - "Subscribe for more spine health tips!" """ print(youtube_script) Real-Time Updates Add a scheduled job (using tools like cron or APScheduler) to: Regularly fetch the latest research. Update your vector index for new topics. Language and Tone Modify output to use Telugu and Indian English based on the script's audience: query = "What are the benefits of endoscopic spine surgery?" response = [login to view URL](query) instagram_script = f""" Caption: "సైటికా వల్ల ఇబ్బందులు పడుతున్నారా? ఎండోస్కోపిక్ స్పైన్ సర్జరీ గురించి తెలుసుకోండి. ఇది తక్కువ నొప్పి మరియు వేగవంతమైన కోలుకోవడం కోసం ఉత్తమ ఎంపిక!" Hashtags: #EndoscopicSurgery #SciaticaRelief #HyderabadDoctor """ print(instagram_script) --- 4. Automate the Workflow Input Sources: Automate fetching journal articles, FAQs, and patient concerns from Google Trends, Reddit, or PubMed. Output Generation: Generate scripts automatically and store them in a folder or directly upload them to your content management system. Notifications: Use tools like Slack or email to notify you when new scripts or updates are ready. --- 5. Deploy the Plugin Local Use: Run scripts locally for personal use. Cloud Deployment: Use platforms like AWS, Azure, or Google Cloud to deploy your solution. User Interface: Create a simple web interface (using Flask or Streamlit) to: Input queries. View generated scripts. Fetch recent research. --- 6. Tools and Resources LangChain: Framework for integrating language models with data. FAISS: Library for fast vector similarity search. PubMed API: Access research articles. OpenAI API: Core GPT functionality. Streamlit: For building a user-friendly UI. --- Would you like help setting up a specific part of this system, like automating journal article fetching or designing the script templates?