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#duckdb

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Public
Hacker News

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

arxiv.org/abs/2504.01157

arXiv.orgBeyond Quacking: Deep Integration of Language Models and RAG into DuckDBKnowledge-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.
#HackerNews#BeyondQuacking#LanguageModels
Public
Curated Hacker News

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

arxiv.org/abs/2504.01157

arXiv.orgBeyond Quacking: Deep Integration of Language Models and RAG into DuckDBKnowledge-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.
#arxiv#duckdb
Public
GripNews

🌘 我的瀏覽器還沒準備好迎接這個！在真實環境中使用 WebAssembly、DuckDB 和 Web Workers
➤ 瀏覽器資料分析新境界：WebAssembly 的挑戰與潛力
✤ motifanalytics.medium.com/my-b
Motif Analytics 正在開發一款高度互動的瀏覽器內資料分析工具，藉由採用 DuckDB WASM、Apache Arrow 和 Web Workers 等技術，讓使用者無需任何承諾，即可在本地實驗和分析大型資料集。本文探討了這些技術的優缺點，以及在實際應用中的效能表現。雖然 WASM 帶來額外負擔，但透過適當的架構設計和資料處理，可以顯著提升效能，並實現與伺服器端類似的程式碼重用。
+ 這篇文章讓我對在瀏覽器中處理大型資料集的可
#網路技術 #資料分析 #WebAssembly #DuckDB #Web Workers

Medium · My browser WASM’t prepared for this. Using DuckDB, Apache Arrow and Web Workers in real lifeBy Motif Analytics
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