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arXiv:2504.01157 (cs)
[Submitted on 1 Apr 2025]

Title:Beyond Quacking: Deep Integration of Language Models and RAG
into DuckDB

Authors:Anas Dorbani, Sunny Yasser, Jimmy Lin, Amine Mhedhbi
View a PDF of the paper titled Beyond Quacking: Deep Integration of
Language Models and RAG into DuckDB, by Anas Dorbani and 3 other
authors
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    Abstract:Knowledge-intensive analytical applications retrieve
    context from both structured tabular data and unstructured,
    text-free documents for effective decision-making. Large language
    models (LLMs) have made it significantly easier to prototype such
    retrieval and reasoning data pipelines. However, implementing
    these pipelines efficiently still demands significant effort and
    has several challenges. This often involves orchestrating
    heterogeneous data systems, managing data movement, and handling
    low-level implementation details, e.g., LLM context management.
    To address these challenges, we introduce FlockMTL: an extension
    for DBMSs that deeply integrates LLM capabilities and
    retrieval-augmented generation (RAG). FlockMTL includes
    model-driven scalar and aggregate functions, enabling chained
    predictions through tuple-level mappings and reductions. Drawing
    inspiration from the relational model, FlockMTL incorporates: (i)
    cost-based optimizations, which seamlessly apply techniques such
    as batching and caching; and (ii) resource independence, enabled
    through novel SQL DDL abstractions: PROMPT and MODEL, introduced
    as first-class schema objects alongside TABLE. FlockMTL
    streamlines the development of knowledge-intensive analytical
    applications, and its optimizations ease the implementation
    burden.

Subjects: Databases (cs.DB); Information Retrieval (cs.IR)
Cite as:  arXiv:2504.01157 [cs.DB]
          (or arXiv:2504.01157v1 [cs.DB] for this version)
          https://doi.org/10.48550/arXiv.2504.01157
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Anas Dorbani [view email]
[v1] Tue, 1 Apr 2025 19:48:17 UTC (446 KB)
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