Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB
Bibliographic record
Abstract
Knowledge-intensive analytical applications retrieve context from both structured tabular data and unstructured free text documents for effective decision-making. Large language models (LLMs) have significantly simplified the prototyping of such retrieval and reasoning data pipelines. However, implementing them efficiently remains challenging and demands significant effort. Developers must often orchestrate heterogeneous systems, manage data movement, and handle low-level concerns such as LLM context management. To address these challenges, we introduce FlockMTL: an extension for DBMSs that integrates LLM capabilities and enables retrieval-augmented generation (RAG) within SQL. FlockMTL provides LLM-powered scalar and aggregate functions, enabling chained predictions over tuples. It further provides data fusion functions to support hybrid search. Drawing inspiration from the relational model, FlockMTL incorporates: (i) seamless optimizations such as batching and meta-prompting; and (ii) resource independence through novel SQL DDL abstractions: PROMPT and MODEL, introduced as first-class schema objects alongside TABLE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".