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NL querying of larger datasets

30 Apr 2025 - 30 Apr 2025
Open in Logseq

    • State of the Art in Natural Language Querying of Large Datasets The field of natural language querying (NLQ) for large datasets has seen significant advancements recently. Here's a concise overview of the current state of the art as of October 2024:

    • Core Technologies and Approaches

    • LLM-powered Database Interfaces
      • Large language models are now commonly used to translate natural language into SQL, SPARQL, or other query languages
      • Systems like AI2's Panoramic, Microsoft's Semantic Kernel, and DataChat have transformed how users interact with structured data
        • TODO check em out
      • My Enflame work and the BRUCE NLP thing fit this category (or rather I need to actually do the enflame nlp integration I keep dreaming about)
    • Vector-based Retrieval Systems
      • Embedding-based approaches allow semantic search over both structured and unstructured data
      • Hybrid search combines vector similarity with traditional filtering for more precise results
    • Multi-modal Data Understanding
      • Systems now handle queries that reference both tabular data and unstructured content (text, images)
        • TODO indeed they do but I need to deepen my understanding of this
      • Context-aware querying understands relationships between different data types

    • Leading Frameworks and Tools

    • LangChain, LlamaIndex: Framework ecosystems for building LLM-powered data applications
    • RAG-SQL: Retrieval-augmented generation for SQL queries
    • Chroma, Weaviate, Pinecone: Vector databases optimized for semantic search
    • TextSQL, SQLCoder: Specialized models for text-to-SQL conversion
    • Modelscape, MindsDB: Integrate ML models directly into database systems

    • Recent Innovations

    • Zero-shot schema understanding: Systems can now query unfamiliar databases without prior training
      • TODO look this up, sounds like something I reinvented in Traverse
    • Conversational context maintenance: Follow-up queries understood in context of previous interactions
      • TODO expand
    • Self-verification techniques: Query verification through multiple approaches to ensure correctness
      • also sounds like something I've been reinventing or at least researching....
    • Federated querying: Natural language interfaces that span multiple data sources simultaneously

    • Limitations and Challenges

    • Handling complex joins and nested queries remains challenging
    • Querying extremely large datasets still faces latency issues
    • Consistency in understanding business logic and domain-specific terminology
    • Verifying the correctness of generated queries Would you like me to elaborate on any particular aspect of natural language querying for large datasets?