Easily Create Autonomous AI App from Scratch
AI Summary
Summary: AutoRAG with Fire Data
Introduction to AutoRAG
- AutoRAG is an advanced feature for large language models.
- RAG (Retrieval-Augmented Generation) uses accessible information as context to generate responses.
- AutoRAG decides whether to use memory (previous history) or search a knowledge base (current knowledge from files/URLs) or external tools (like web search) to provide information.
Components of AutoRAG
- Uses Postgres as memory storage for conversation history.
- Utilizes PG Vector to store knowledge as soon as a file is uploaded.
- Employs DuckDuckGo search to search the web.
Building AutoRAG
- Guide on creating AutoRAG from scratch using Fire data.
- Setting up a user interface to add URLs and files to the knowledge base.
- Covers setup for models like GPT-4, Grok Llama 370B, and Llama Hermis 2 Pro.
Implementation Steps
- Setup Assistant: Create a function to set up the assistant with a large language model, storage, and knowledge base.
- Add Document to Knowledge Base: Define a function to upload and convert PDFs to embeddings in the knowledge base.
- Run Query: Create a function to ask questions and retrieve responses from the model.
Running the Code
- Install Fire data and other components.
- Export OpenAI API key.
- Create
app.pyand define necessary imports and functions.- Set up the assistant with storage, knowledge base, and search tools.
- Add documents to the knowledge base.
- Query the assistant and print out responses.
User Interface and Testing
- Navigate to the appropriate folder and install requirements.
- Run the application with Streamlit.
- Test adding URLs and files, asking questions, and receiving responses.
- Demonstrate integration with Grok and open-source models like Hermis.
Conclusion
- Successfully created an AutoRAG application with three components.
- Showcased how to integrate the application with different models.
- Encouraged viewers to like, share, subscribe, and stay tuned for more AI-related content.