VLM extracts data from diverse documents for RAG pipelines

A new approach to building retrieval-augmented generation (RAG) pipelines uses a vision-language model (VLM) to extract information from diverse document formats, including images and spreadsheets. This method converts each document page into an image, which is then processed by a VLM to generate summaries and keywords. The extracted text is embedded and stored in a PostgreSQL database with pgvector for semantic search. The pipeline was optimized by switching from the Qwen3.5:20B model to Qwen3.5:4B to reduce inference latency. AI

IMPACT Enables more robust data extraction for RAG systems dealing with varied document types.

RANK_REASON The item describes a technical implementation and optimization of an AI-powered pipeline for data extraction and retrieval, rather than a new model release or significant industry event.

Read on dev.to — LLM tag →

• Ollama
• pgvector
• PostgreSQL
• Qwen3.5:20B
• Qwen3.5:4B
• retrieval-augmented generation
• vision-language model

• product
• infra

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