This is a submission for the
Tools Used
- Timescale Cloud: Database hosting
- pgai through TimescaleDB for AI
- pgVector: For efficient data storage and retrieval of embeddings
- Ollama: For generating embeddings and handling response generation using Mistral and LLama
- React.js: Used for building the user interface
- Flask API ; - For backend API Handling
- Postman : - Testing Queries
Technologies Used : -
Ollama with Nomic Embeddings: Utilizes Nomic embeddings for accurate text representation, supporting semantic search and relevance scoring for research queries.
PostgreSQL with PG Vector and Psycopg2: Stores embeddings in PostgreSQL using pgvector, enabling efficient similarity searches, with psycopg2 managing database connections.
Llama Model via Ollama: Leverages the Llama model for natural language processing, generating precise responses and summaries based on user queries.
Flask API for Backend: Manages backend processing and data flow between the frontend and AI models, ensuring smooth interaction.
React for Frontend: Provides a responsive, intuitive interface for query input, viewing results, and accessing summaries.
Final Thoughts
This is my first time participating in a Dev.to Hackathon, and it’s been an incredible learning experience! Building this AI application has opened my eyes to so many new technologies, from working with Large Language Models and embeddings to managing backend integrations. I’m still figuring out deployment and improving the application’s functionality, so I’d love to connect with anyone willing to help or share advice. As a 4th-year student currently interning with React, this project marked my first foray into advanced AI and NLP, and I’m grateful for the opportunity to showcase my work and grow through this experience. Thank you for creating such an encouraging platform!
Prize Categories:
Open-source Models from Ollama: AI Bot leverages Ollama’s open-source models for embedding and understanding text-based queries.
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