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Zipstack
unstract
Python2024–2026
Zipstack / unstract

unstract

LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows

4Distinct people
32Mentions
0Explicit recommenders
7.4KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 1:03:20 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month32 dated mentions

Observed mentions: 2024-03-27 11:18:50 UTC to 2026-03-09 18:02:28 UTC.

02462024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsMar 20242024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 6 mentions; 2 submissions, 4 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMar 20252025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 4 mentions; 3 submissions, 1 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsMar 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC110
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC624
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC211
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC101
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC202
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC202
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC101
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC202
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC202
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC321
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC431
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC101
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC110
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC220
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC000
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC110

What Hacker News says

6 sources

Show HN: LLMWhisperer – Prep complex documents ready for use in LLMs

Hey HN! Over the last few months, we’ve seen many tools here trying to tackle the problem of making complex, unstructured documents ready for LLMs. The complexity primarily includes parsing highly complex documents in terms of format, layout, design, complex tables, checkboxes, etc, with high accuracy and reliability. LLMWhisperer is our take on the problem. LLMwhisperer solves most of the document complexity with reliable accuracy. With our user-friendly playground (https://pg.llmwhisperer.unstract.com/), you can effortlessly test your document use case. No sign-up is required. We eagerly await your valuable feedback. LLMWhisperer is also a part of Unstract, an open-source document processing automation platform. Links: https://llmwhisperer.unstract.com/ Playground: https://pg.llmwhisperer.unstract.com/ Unstract: https://github.com/Zipst
Read the rest
12 points · Open discussion
shuss·Apr 1, 2024Submission

Show HN: Unstract – AGPL, LLM-powered Fivetran/Twilio for unstructured docs

Hello HN! One of the most common uses of LLMs is to go beyond what traditional RPA or IDP can do when it comes to structuring unstructured documents. However, there are a lot of challenges in getting this done right from extraction of text data from PDFs, scanned images or other formats, prompt engineering, evaluation and integration with existing systems. This very specific use case is where Unstract can help teams move really fast, leveraging LLMs. By doing the heavy-lifting in this fast-changing ecosystem it lets engineers concentrate on implementing core business workflow automations. Here's what's special about Unstract: AGPL-licensed: Unstract is available under the AGPL, which is friendly for personal or commercial use. Getting started is simply a matter of running a script, which should take care of everything leveraging Docker Co
Read the rest
mpose. We've also made is very easy to update the container images should you wish to. If you have a Mac or a Linux laptop, you can get started in minutes. LLMWhisperer: Results from LLMs are as good as the input they receive. LLMWhisperer is a cloud service that given PDFs (native text, scanned) returns text data in a way LLMs are best able to "understand". LLMWhisperer is free for up to 100 pages a day. While Unstract supports LLMWhisperer, we're also adding support for unstructured.io and LlamaParse as well (they're both great services!). For more details: https://llmwhisperer.unstract.com Strong, improving ecosystem support: Unstract supports a variety of providers for LLMs, Vector Databases, Embeddings, Cloud File Storage systems and databases/data warehouses. A full list is available on our Github page (https://github.com/Zipstack/unstract). No-code Prompt Studio: You need a way to figure out how well your prompts are working for a set of documents to make sure the prompts you're writing are as generic as possible. This is not easy to do with a regular IDE and a file manager on your laptop. Prompt Studio a purpose-built, no-code environment that makes it easy for you to develop, run and debug prompts while referring to sample documents side-by-side. The whole project is stored as a YAML (export/import options coming soon). Easy integrations: Unstract supports the launching of APIs and ETL Pipelines that take in unstructured documents and produce JSON data or sync data with various data sources like Snowflake, Bigquery, Redshift, PostgreSQL, etc. Prompt Studio no-code projects can be launched as APIs or ETL Pipelines with ease in just minutes letting you leverage LLMs to solve business automation problems. Unstructured data APIs are useful when an existing application needs to automate the processing of unstructured docs. Unstructured data ETL Pipelines are useful when unstructured docs in cloud file or object stores need to be structured and pushed into databases or data warehouses. Quick start: See our documentation (https://docs.unstract.com) to walk through a quick start project structuring standard credit card statements from a couple of different banks. You can also view a 3-min video of Unstract here: https://www.youtube.com/watch?v=8aZh-pRwZh8 Enterprise-only features: Unstract has a couple of enterprise/cloud/managed features that are not part of the open source offering: LLMEval uses more than one LLM to arrive at a consensus on extracted fields. If there is no consensus, the field is set to null (a null value is always better than a wrong value), after which a human can review the extraction results. There are a couple of advanced Prompt Studio features that can cut token usage (and thus costs) by up to 6x (Single Pass and Summary-based Extraction). Otherwise, all other features are available in our open source version.
5 points · Open discussion

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