chunkrDocument intelligence API in Rust: layout analysis, OCR with bounding boxes, and semantic chunking that turn PDFs, PPTs and Word docs into RAG-ready structured chunks.
Why switchBoth are self-hostable document-intelligence services, split by output: Chunkr produces layout-aware RAG-ready chunks; Unstract produces schema-shaped structured fields via prompt-defined extraction, deployable as APIs and ETL.
Full comparison → contextgemDeclarative LLM extraction from documents: describe Aspects and Concepts in plain language, get structured values back with paragraph- or sentence-level references and built-in justifications.
Why switchBoth turn documents into structured data with LLMs. Unstract is a platform — no-code Prompt Studio, deployable APIs and ETL around it; ContextGem is a library you compose in code, and it goes further on provenance: every value carries a paragraph- or sentence-level reference and a justification.
Full comparison → receipt-ocrReceipt-to-JSON in one pip install: CLI, Python API and FastAPI service that send a receipt image to any OpenAI-compatible model and return merchant, totals and line items, plus a Tesseract module.
Why switchBoth turn documents into structured data with LLMs and expose an API. Unstract is a no-code platform with Prompt Studio, ETL deployment and enterprise licensing; receipt-ocr is a 50-line integration you can read in full.
Full comparison → fenicSemantic DataFrames: PySpark-style select, filter and join alongside AI operators — extract, classify, summarize, embed, semantic join — compiled on an engine built for inference.
Why switchBoth turn unstructured input into structured rows you can deploy. Unstract is a no-code Prompt Studio with an API/ETL deploy path; fenic is code — typed DataFrame operators for engineers who want lineage, caching and cost accounting in the query model.
Full comparison → MinerUHeavyweight document-to-markdown/JSON parser — PDFs plus Office (docx/pptx/xlsx) through layout analysis and OCR into LLM-ready output for RAG and agentic pipelines. 73k stars, self-hostable.
Why switchParser vs platform: MinerU converts documents to LLM-ready markdown/JSON; Unstract sits a level up — prompt-engineered field extraction with deployment, connectors and pipeline management around it. Many stacks front Unstract-style extraction with a MinerU-style parser.
Full comparison → docling-graphDocuments to validated knowledge graphs: Docling parses, an LLM or VLM fills Pydantic schemas, and you get a directed NetworkX graph with provenance, Cypher/CSV export and HTML views.
Why switchSame starting point — unstructured documents — different deliverable: Unstract gives you structured records behind an API or ETL job, docling-graph gives you a typed graph with explicit relationships you can export as Cypher.
Full comparison →