Pebblo enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance and security requirements. The project identifies semantic topics and entities found in the loaded data and summarizes them on the UI or a PDF report.

Pebblo has two components.

1. Pebblo Daemon - a REST api application with topic-classifier, entity-classifier and reporting features
2. Pebblo Safe DataLoader - a thin wrapper to Gen-AI framework's data loaders

brew install pango

sudo apt-get install libpango-1.0-0 libpangoft2-1.0-0

pip install pebblo

pebblo

see troubleshooting guide for troubleshooting info.

Pebblo daemon now listens to localhost:8000 to accept Gen-AI application data snippets for inspection and reporting.

• --config <file>: Specifies a custom configuration file in yaml format.

pebblo --config config.yaml

Pebblo Safe DataLoader is natively supported in Langchain framework. It is available in Langchain versions >=0.1.7

Add PebbloSafeLoader wrapper to the existing Langchain document loader(s) used in the RAG application. PebbloSafeLoader is interface compatible with Langchain BaseLoader. The application can continue to use load() and lazy_load() methods as it would on an Langchain document loader.

Here is the snippet of Lanchain RAG application using CSVLoader before enabling PebbloSafeLoader.

    from langchain.document_loaders.csv_loader import CSVLoader

    loader = CSVLoader(file_path)
    documents = loader.load()
    vectordb = Chroma.from_documents(documents, OpenAIEmbeddings())

The Pebblo SafeLoader can be enabled with few lines of code change to the above snippet.

    from langchain.document_loaders.csv_loader import CSVLoader
    from langchain_community.document_loaders.pebblo import PebbloSafeLoader

    loader = PebbloSafeLoader(
                CSVLoader(file_path),
                name="acme-corp-rag-1", # App name (Mandatory)
                owner="Joe Smith", # Owner (Optional)
                description="Support productivity RAG application", # Description (Optional)
    )
    documents = loader.load()
    vectordb = Chroma.from_documents(documents, OpenAIEmbeddings())

See here for samples with Pebblo enabled RAG applications and this document for more details.

Pebblo is a open-source community project. If you want to contribute see Contributor Guidelines for more details.

Pebblo is released under the MIT License