Show HN: TeletextSignals – Local RAG over 25 Years of Swiss Teletext News (github.com)

A new project, TeletextSignals, highlights a local retrieval-augmented generation (RAG) approach to processing over 25 years of Swiss Teletext news articles, totaling more than 500,000 entries. This initiative focuses on building a proof of concept for local data processing, ensuring that users' queries and documents remain on-device, making it ideal for handling sensitive information. By employing a combination of dense and full-text semantic retrieval methods, the system uses the multilingual E5 model for efficient querying, with results ranked through a cross-encoder that evaluates the relevance of documents for given queries. The significance of TeletextSignals lies in its innovative architecture that showcases the potential for local machine learning solutions, particularly for large text corpora. The integration of PostgreSQL with pgvector for storage allows the system to manage complex vector spaces effectively. With a structured workflow for chunking and embedding texts, the platform enhances the retrieval process for multilingual and succinct news summaries. Additionally, the use of reinforced learning techniques like agentic RAG further boosts its capability, enabling the system to adaptively refine its queries, thus offering a promising avenue for semantic search and precise information retrieval in AI applications.