Alternatives
Products that do what LlamaExtract, a tool to automatically extract schema from documents does
We build LlamaExtract, a tool that allow you to automatically extract a data model from a collection of documents, and then reusing this datamodels (JSON Schema) to extract data from documents. Available as a Python library and as an API. Announcement blog: https://www.llamaindex.ai/blog/introducing-llamaextract-beta...
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Extract web data into structured JSON, no scraper required.
Jun 2026
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Hi HN! I’m Alex from Parabola (https://parabola.io). Parabola is a visual programming tool for creating functional data flows that everyone can use. It’s entirely drag-and-drop, handles data sizes much larger than a traditional spreadsheet, calculates everything live, and can run your flows on a schedule of your choosing. I used to work in strategy consulting, doing data analytics for SMBs and Fortune 500 companies. The amount of time wasted on menial tasks was astounding. Things like cleaning data, generating custom reports, creating human workflows to solve shortcomings in third…
2018 · parabola.io
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2017 · link.fish
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2016 · github.com
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2023 · github.com
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2019 · apify.com
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Hey! We’ve built a data extraction tool to flexibly automate data and document processing. You’ve probably seen a few of these, so have we! A few of us have been varyingly stuck trying to automate the extraction of borrower financials for the past 5 years. We think that there are a few missing features of most data extraction tools. * They are usually too complex to quickly get up and running * They are overly constrained in terms of what workflows and documents they support We’ve always felt like speed and flexibility were sticking points, so we went slightly orthogonal to the alternatives.…
2024 · go.sea.dev
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2018 · github.com
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I built a CLI tool in Go that extracts structured data (JSON, CSV, Parquet) from messy PDFs and HTML pages. The core idea: LLMs are great at understanding structure but wasteful for bulk data extraction. So smelt uses a two-pass architecture: 1. A fast Go capture layer parses the document and detects table-like regions 2. Those regions (not the whole document) get sent to Claude for schema inference — column names, types, nesting 3. The Go layer then does deterministic extraction using the inferred schema This means the LLM is never in the hot path of actual data processing. It figures out…
Mar 2026 · github.com
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This project originally started out with a focus on easily generating embeddings from Llama2 and other llama_cpp (gguf) models and storing them in a database, all exposed via a convenient REST api (hence why it was originally called the not-very-catchy name of "llama_embeddings_fastapi_service" when I submitted it a few weeks ago). But since then, I've added a lot more functionality: 1) New endpoint for generating text completions (including specifying custom grammars, like JSON). 2) Get all the embeddings for an entire document--can be any kind of document (plaintext, PDFs, doc/.docx,…
2023 · github.com
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