Pebblo Gen-AI Data Analyzer
Project description
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.
- Pebblo Daemon - a REST api application with topic-classifier, entity-classifier and reporting features
- Pebblo Safe DataLoader - a thin wrapper to Gen-AI framework's data loaders
Pebblo Daemon
Installation
Pre-requisites
Mac OSX
brew install pango
Linux (debian/ubuntu)
sudo apt-get install libpango-1.0-0 libpangoft2-1.0-0
Install Pebblo Daemon
pip install pebblo
Run Pebblo daemon
pebblo
Pebblo daemon now listens to localhost:8000
to accept Gen-AI application data snippets for inspection and reporting.
Pebblo Safe DataLoader for Langchain
Pebblo Safe DataLoader
currently supports Langchain framework.
Installation
Install pebblo-langchain
package in the Python environment where the RAG application is running. Add it as one of the dependencies in pyproject.toml
or any other methods used for dependency management.
pip install pebblo-langchain
Enable Pebblo in Langchain
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 pebblo_langchain.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.
Contribution
Pebblo is a open-source community project. If you want to contribute see Contributor Guidelines for more details.
License
Pebblo is released under the MIT License
Project details
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