Safely load and retrieve data
in your Gen AI applications

What is Pebblo?

Pebblo enables developers to safely load and retrieve data to promote their Gen AI app to deployment without worrying about the organization’s compliance and security requirements. The Pebblo SafeLoader identifies semantic topics and entities found in the loaded data and the Pebblo SafeRetriever (coming soon on this page) enforces identity and semantic controls on the retrieved context. The results are summarized on the UI or a PDF report.

Highlights
Identify semantic topics and entities in your data loaded in RAG applications
Prevent restricted data access by unauthorized RAG app users
Accelerate time-to-production by effortlessly meeting your organization’s data compliance requirements
Comply with regulations such as the EU AI Act with custom reports and data records
Support for a wide range of Gen AI development frameworks and data loaders
Flexible reporting options on the UI, PDF, JSON and more

Pebblo SafeLoader

Check on Github

Lorm ipsum reviews tools you need simplicity

Pebblo enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance.
Pebblo enables Pebblo developers to safely load data @pebblo and promote their Gen AI app to deployment without worrying about the organization’s compliance and # pebblo security requirements.
John Doe
Sr. Product Manager
Pebblo enables Pebblo developers to safely load data @pebblo and promote their Gen AI app to deployment without worrying about the organization’s compliance and # pebblo security requirements.
John Doe
Sr. Product Manager
Pebblo enables Pebblo developers to safely load data @pebblo and promote their Gen AI app to deployment without worrying about the organization’s compliance and # pebblo security requirements.
John Doe
Sr. Product Manager

Steps to deploy

1. Install and Start Pebblo daemon
Install Pebblo Server
pip install pebblo --extra-index-url https://packages.daxa.ai/simple/
Run Pebblo daemon
pebblo
2. Change your code and deploy the app
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 RAG app",# Description(Optional)
)

documents = loader.load()
vectordb = Chroma.from_documents(documents, OpenAIEmbeddings())
3. Get Data reports (UI or PDF)

Pebblo Safe Retriever

Check on Github

Lorm ipsum reviews tools you need simplicity

Pebblo enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance.
Pebblo enables Pebblo developers to safely load data @pebblo and promote their Gen AI app to deployment without worrying about the organization’s compliance and # pebblo security requirements.
John Doe
Sr. Product Manager
Pebblo enables Pebblo developers to safely load data @pebblo and promote their Gen AI app to deployment without worrying about the organization’s compliance and # pebblo security requirements.
John Doe
Sr. Product Manager
Pebblo enables Pebblo developers to safely load data @pebblo and promote their Gen AI app to deployment without worrying about the organization’s compliance and # pebblo security requirements.
John Doe
Sr. Product Manager

Steps to deploy

1. Add Identity-aware Safe Loader
self.loader = PebbloSafeLoader(            
GoogleDriveLoader(                
 folder_id=folder_id,                
  token_path="./google_token.json",
  recursive=True,
  file_loader_cls=UnstructuredFileIOLoader,
  file_loader_kwargs={"mode": "elements"},
  load_auth=True,
 ),            
 name=self.app_name,  # App name (Mandatory)
 owner="Joe Smith",  # Owner (Optional)
 description="Identity enabled SafeLoader and SafeRetrival app using  Pebblo",  # Description (Optional)        
)
2. Add SafeRetrieval code
self.retrieval_chain = PebbloRetrievalQA.from_chain_type(        llm=self.llm, 
    app_name=self.retrieval_app_name,
    owner="Joe Smith",
    description="Identity enabled SafeRetrival app using  Pebblo",        chain_type="stuff",
    retriever=self.vectordb.as_retriever(),
    verbose=True,
)
auth_context = {
    "user_id": "[email protected]",
    "user_auth":
[
        "[email protected]",
        "[email protected]",
        "[email protected]",
 
 ],
}
self.retrieval_chain.invoke({"auth_context": auth_context})
3. Get User Data Report

Start exploring Pebblo