Extralit is a UI interface and platform for LLM-based document data extraction that integrates human and model feedback loops for continuous LLM refinement and collaborative data extraction.

With a Python SDK and adaptable UI, you can create human and model-in-the-loop workflows for:

• Schema-driven extraction: Ensures high specificity, contextual relevance, and automated validation of the extracted data.
• Advanced PDF preprocessing: AI optical character recoginition (OCR) algorithms to detect and correct table structures within documents.
• User-friendly interface: Facilitates easy verification and correction of extracted data.
• Data flywheel: Continuous data collection of table extractions and LLM outputs to monitor performance and build datasets.

Install the client package

pip install extralit

After installing this client package, you can manage your extraction workspace through the CLI.

extralit login --api-url http://<path_to_the_webserver>
# You will be prompted an API key to login to your account, which can be obtained from User Settings in the web interface

Argilla is built on 5 core components:

• Python SDK: A Python SDK which is installable with pip install extralit. To interact with the Argilla Server and the Argilla UI. It provides an API to manage the data, configuration and annotation workflows.
• FastAPI Server: The core of Argilla is a Python FastAPI server that manages the data, by pre-processing it and storing it in the vector database. Also, it stores application information in the relational database. It provides a REST API to interact with the data from the Python SDK and the Argilla UI. It also provides a web interface to visualize the data.
• Relational Database: A relational database to store the metadata of the records and the annotations. SQLite is used as the default built-in option and is deployed separately with the Argilla Server but a separate PostgreSQL can be used too.
• Vector Database: A vector database to store the records data and perform scalable vector similarity searches and basic document searches. We currently support ElasticSearch and AWS OpenSearch and they can be deployed as separate Docker images.
• Vue.js UI: A web application to visualize and annotate your data, users and teams. It is built with Vue.js and is directly deployed alongside the Argilla Server within our Argilla Docker image.