Content Deep Dive
How We Built a Content Recommendation System With Pgai and Pgvectorscale
Blog post from Tiger Data
Post Details
Company
Date Published
Author
Andreas Nigg
Word Count
4,580
Company Posts That Month
Language
English
Hacker News Points
3
Post removed?
No
Source URL
Summary
Pondhouse Data built a content recommendation system using pgai and pgvectorscale for an SEO-related internal link building project. The company used the tools to create summaries and embeddings from texts, search for similar content, and filter results based on metadata. Key takeaways include the seamless integration of AI functionalities with PostgreSQL, enabling users to leverage the full capabilities of the database while incorporating AI features.
Trends Found in this Post
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 46 | 4,739 | 317 | 104 | +27% |
| LLM | 10 | 4,031 | 525 | 170 | -2% |
| RAG | 6 | 2,256 | 295 | 90 | +14% |
| AI Guardrails | 2 | 293 | 75 | 40 | +94% |
| Kubernetes | 2 | 1,671 | 200 | 79 | +15% |
| Real-time | 2 | 4,664 | 1,039 | 250 | +5% |
| AI Agents | 1 | 603 | 138 | 64 | -22% |
| AI Coding Assistant | 1 | 518 | 108 | 58 | -27% |
Use This Data
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