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cloud.google.com·6h ago
Data Science
Analytics Vidhya·17d ago·5 min read
10 Solved Generative AI Projects to Boost your Profile
TL;DR
An AI-powered search engine was developed to provide direct, source-backed answers using web search, embeddings, and a large language model (LLM). This project enhances search capabilities by offering specialized modes and citations, making it a valuable addition to any AI portfolio.
✦ Why It Matters
Start building your own AI-powered search engine to enhance your portfolio and demonstrate practical skills.
Key Takeaways
- Explore using embeddings for improved search accuracy.
- Implement specialized search modes for diverse queries.
- Utilize Python and Next.js for building the application.
- Incorporate source citations to enhance credibility.
Full Summary
The project focuses on creating an AI-driven search engine that integrates various technologies to deliver precise answers rather than a list of links. By utilizing embeddings, which are numerical representations of data, and a large language model (LLM), the search engine can understand and process queries more effectively.
It supports multiple search modes, including academic and YouTube results, and provides source citations for transparency. The architecture is inspired by Perplexica, allowing for fast searches and a deeper research mode.
Tools like Python, Next.js, and SearXNG are employed to build the system, ensuring a robust and scalable solution. This project not only showcases technical skills but also addresses real-world search challenges.