Rhubarb is a light-weight Python framework that makes it easy to build document understanding applications using Multi-modal Large Language Models (LLMs) and Embedding models. Rhubarb is created from the ground up to work with Amazon Bedrock and supports multiple foundation models including Anthropic Claude V3 Multi-modal Language Models and Amazon Nova models for document processing, along with Amazon Titan Multi-modal Embedding model for embeddings.
Visit Rhubarb documentation.
Rhubarb can do multiple document processing tasks such as
- ✅ Document Q&A
- ✅ Streaming chat with documents (Q&A)
- ✅ Document Summarization
- 🚀 Page level summaries
- 🚀 Full summaries
- 🚀 Summaries of specific pages
- 🚀 Streaming Summaries
- ✅ Structured data extraction
- ✅ Extraction Schema creation assistance
- ✅ Named entity recognition (NER)
- 🚀 With 50 built-in common entities
- ✅ PII recognition with built-in entities
- ✅ Figure and image understanding from documents
- 🚀 Explain charts, graphs, and figures
- 🚀 Perform table reasoning (as figures)
- ✅ Document Classification with vector sampling using multi-modal embedding models
- ✅ Logs token usage to help keep track of costs
Rhubarb comes with built-in system prompts that makes it easy to use it for a number of different document understanding use-cases. You can customize Rhubarb by passing in your own system prompts. It supports exact JSON schema based output generation which makes it easy to integrate into downstream applications.
- Supports PDF, TIFF, PNG, JPG, DOCX files (support for Excel, PowerPoint, CSV, Webp, eml files coming soon)
- Performs document to image conversion internally to work with the multi-modal models
- Works on local files or files stored in S3
- Supports specifying page numbers for multi-page documents
- Supports chat-history based chat for documents
- Supports streaming and non-streaming mode
- Supports Converse API
- Supports Cross-Region Inference
Start by installing Rhubarb using pip.
pip install pyrhubarb
Create a boto3 session.
import boto3
session = boto3.Session()Local file
from rhubarb import DocAnalysis
da = DocAnalysis(file_path="./path/to/doc/doc.pdf",
boto3_session=session)
resp = da.run(message="What is the employee's name?")
respWith file in Amazon S3
from rhubarb import DocAnalysis
da = DocAnalysis(file_path="s3://path/to/doc/doc.pdf",
boto3_session=session)
resp = da.run(message="What is the employee's name?")
respFor more usage examples see cookbooks.
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.
