Semantic Search in Historical Archives Using AI

Paměť novin (Newspaper memory) is an innovative application designed for advanced search and analysis of historical periodicals from the turn of the 19th and 20th centuries. Unlike conventional full-text search engines, which only work with exact keyword matches, this system uses extensive language models (LLM) to understand the meaning and context of a query. Thus, it allows users to discover events, relationships and period contexts in natural language, thereby greatly streamlining the work with large digital archives.

Our solution uses vector indexing technology and AI to enable the system to answer specific user queries while providing direct backlinks to source documents. Newspaper memory doesn't just search for words, but interprets the content of articles, is able to summarize long texts and link events across different newspaper titles. In this way, we transform a static digital archive into an active history guide that can identify key themes and trends over time without the need for complex user training.

After all, try it yourself: https://www.pametnovin.cz/

Technical Specification

The basis of the application is an intuitive interface that allows you to ask questions similar to when talking to a specialist, with each statement of the system substantiated by a specific scan from the digital library. The integration of artificial intelligence ensures more accurate search results even with incomplete or inaccurate queries, opening up the archives to the wider public and students alike.

• ‍Technology: Use of Large Language Models (LLM) and vision models for advanced analysis and comparison of historical texts.‍
• Data Integration: Connection to Kramerius digital library indexes and use of vector databases for semantic search.‍
• Analytical modules: Features for summarizing articles, automatic translations of historical texts and generating interpretations with an emphasis on factual accuracy.‍
• Validation: The system continuously verifies responses against primary sources (grounding) to minimize the risk of AI hallucinations

The use of semantic questioning in corporate practice

The core application, built on vector indexing and large language models (LLM), enables efficient work with large sets of unstructured documents. Instead of a static site or an unreliable chatbot, you get a system that interprets the meaning of the query in natural language and immediately substantiates the answers with a quote from a specific in-house source.

And where will this technology find application?

• Management of corporate know-how: Instant access to information in thousands of contracts, directives or technical sheets without the need for manual search.
• Intelligent Customer Support: A web presentation that actually answers a customer's query about a product or service based on the entire history of your documents.
• Data autonomy of management: The ability to analyze the contents of archives and search for connections in the data independently, without waiting for the preparation of scripts from the IT department.
• Processing of professional texts: Effective searches in case law archives, project documentation or medical studies where keyword searches fail.

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