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Search Evolution - Keeping up with the hype?

Search Evolution - Keeping up with the hype?

This talk is an introduction into search and focuses on the recent search trends in the last years. Search is not a solved problem. We start with the basics like relevance, extending over to learning to rank and vector search and will - of course - also cover LLMs and what might come in the future.

Alexander Reelsen

May 25, 2023
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  1. Learn about the trends in search engines Understand that this

    is a highly volatile market in the coming years Today's goal
  2. Text search Enterprise search Ecommerce search Log search Analytics Dashboards

    NLP Generative/Conversational Search Evolution of Use-Cases
  3. SQL: Does row r match query q ? Answer: /

    How well matches query q document d ? Answer: [0..∞] Scoring based on formula: TF/IDF , BM25 Dependent on corpus Relevancy
  4. Scoring/Relevancy based on machine learning model Common: Reranking after first

    filtering Machine Learning models trained independently Learning to rank
  5. Vector search engines: translates content into vectors QDrant, Milvus, Weaviate,

    Pinecone, Deeplake, nucliadb Best model wins... Going hybrid: Will search engines add vector support or vector engines add search support? Vector Search
  6. SQLite: vector extension, FTS3/4 extension Postgres: PostgresML - full model

    management and querying in Postgres! Don't sleep on SQL engines!
  7. Distributed search across regions Search on your browser Search on

    your phone Check out OramaSearch Search on the edge
  8. blue dress with white stripes that has been shown on

    the last fashion week in milan summarize the quarterly earnings call, focus on numbers that differ strongly from the last three quarters Convert the following CDK snippet from Java to python Generative/Conversational search
  9. blue dress with white stripes requires image extraction last fashion

    week in milan requires external knowledge Your own dataset is not enough for a good search! Generative search - context
  10. futuristic skyline in neon colors with a futuristic looking tesla

    model 3 in the foreground Stable diffusion
  11. Large size, trained on massive datasets Open Source: Langchain Prompt

    engineering Classification, Question Answering, Summarization, Fill-mask, Translation Hallucination & Model bias Conversational memory Learning from queries (dangerous?) Agents for LLMs (execute a calculator, SQL query, use mechanical turk) LLMs
  12. Search becomes hybrid: Will the existing search engines adapt? Search

    customization is expensive - A brief history of code search at GitHub Search engine becomes the commodity Rent your industry specific LLM! Privacy LLMs might be a thing Expect a lot of movement, lots of "AI integrations" and even more hot air... Summary