AI-powered data pipelines just got louder. The trio datagen, Extrai, and REFRAG are showing how synthetic data, LLM-backed extraction, and vector-aware decoding can reshape relational-plus-vector workflows [1][2][3].
• datagen - Generates coherent synthetic data at scale across relational DBMS, JSON stores, or CSV uploads; users describe shapes in a .dg DSL, then transpile to Go to generate data [1].
• Extrai - An open-source tool to fight LLM randomness in data extraction. It runs multiple LLMs to converge on consistent results, supports SQLModel schema generation, and stores outputs in a DB with analytics [2].
• REFRAG - Pre-computes vectors for RAG-based decoding, letting LLMs consume pre-fetched vectors for longer contexts and faster inference. Works with vector databases like Weaviate, with insights from Xiaoqiang Lin of Meta on faster decoding (TTFT 31x, TTIT 3x) [3].
Together, they map a practical data loop: datagen populates relational storage, Extrai feeds clean extractions into SQLModel schemas, and REFRAG pushes pre-computed vectors to a vector DB like Weaviate for long-context LLM throughput [1][2][3].
Watch this space: production-minded teams will likely graft synthetic data, precise extraction, and vector-core acceleration into end-to-end data stacks in 2025 and beyond.
References
Show HN: Generate coherent, synthetic data at scale
Tool datagen generates coherent synthetic data across services via a DSL; supports relational and document stores and outputs Go code.
View sourceShow HN: Extrai – An open-source tool to fight LLM randomness in data extraction
Extrai blends open-source multi-LLM extraction with SQLModel storage, enabling structured DB schemas, nested data handling, and analytics.
View sourceInterview with the lead author of REFRAG (Meta)
Interview discusses REFRAG from Meta, vector pre-computation for LLMs, improving long-context throughput in vector databases like Weaviate, and RAG-based decoding.
View source