

Knowledge Table
Overview :
Knowledge Table is an open-source toolkit designed to streamline the process of extracting and exploring structured data from unstructured documents. It allows users to create structured knowledge representations, such as tables and charts, through a natural language query interface. The toolkit features customizable extraction rules, finely-tuned formatting options, and data provenance displayed through the UI, adapting to a variety of use cases. Its goal is to provide business users with a familiar spreadsheet-like interface while offering developers a flexible and highly configurable backend, ensuring seamless integration with existing Retrieval-Augmented Generation (RAG) workflows.
Target Users :
The target audience includes developers, data scientists, and business analysts who need to extract valuable information from large volumes of unstructured documents and convert it into structured data suitable for analysis and decision-making. Knowledge Table offers an intuitive interface and robust backend support, making this process straightforward and efficient.
Use Cases
Contract Management: Extract key information from contracts, such as party names, effective dates, and renewal dates.
Financial Reporting: Extract financial data from annual reports or earnings statements.
Research Extraction: Pose key questions regarding a series of research reports and extract relevant information.
Metadata Generation: Classify and tag documents by running targeted questions, generating insights about the documents and files.
Features
Extract structured data from unstructured documents using natural language queries.
Create structured knowledge representations like tables and charts.
Customize extraction rules to ensure data quality.
Control the output format of extracted data.
Filter documents based on metadata or extracted data.
Export the extracted data to CSV or Graph triples.
Reference data from previous columns for chained extractions.
Integrate with the Unstructured API to enhance document processing capabilities.
How to Use
1. Visit the Knowledge Table GitHub page and clone the repository.
2. Install the necessary dependencies, including Docker and Docker Compose.
3. Run the Docker containers or local environment as needed.
4. Set up environment variables, such as the OpenAI API key.
5. Define extraction rules and formatting options.
6. Upload unstructured documents and create questions to guide data extraction.
7. Process the data according to the questions and rules to obtain structured outputs.
8. Adjust the questions or rule settings as needed to optimize extraction results.
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