ragger-python-sdk 0.1.3

Python SDK for ragger.ai RAG API

Ragger Python SDK

Python SDK for the Ragger RAG (Retrieval Augmented Generation) API. Build powerful AI applications that can search and answer questions using your own documents.

Features

• Easy Integration: Simple Python client. With a few lines of code, you can build a complete RAG application.
• Document Upload: Support for PDF, Word, text, markdown, and other formats
• Vector Indexing: Create searchable embeddings from your documents
• Natural Language Queries: Ask questions and get AI-powered answers with advanced reasoning
• Conversation Memory: Maintain context across multiple queries
• Multi-tenant: Organization and project-based document isolation
• Async Processing: Handle large documents with background processing

Installation

pip install ragger-python-sdk

Quick Start

1. Initialize the client

from ragger_sdk import RaggerClient
client = RaggerClient(
    base_url="https://your-ragger-server.com/rag/api/v1",
    token="your-api-token"
)

2. Upload a document

upload_result = client.documents.upload(
    organization="my-company",
    project="knowledge-base",
    name="user-manual",
    file_path="/path/to/manual.pdf"
)

3. Create searchable index

index_result = client.index.index(
    organization="my-company",
    project="knowledge-base"
)
task_id = index_result['task_id']

# Wait for indexing to complete
status = client.index.status(task_id, organization="my-company")

4. Ask questions

answer = client.query.ask(
    query="How do I reset my password?",
    organization="my-company",
    project="knowledge-base",
    user="[e-mail]"
)

print(f"Answer: {answer['answer']}")
# Assistant: "To reset your password, go to the login page and click 'Forgot Password'..."

print(f"Source: {answer['metadata']['sources']}")
# "Source: {'https://intranet.company.com/policy', 'https://help.company.com/reset-password'}"

print(f"Session ID: {answer['session_id']}")
# 123456789abcdef

5. Maintain conversation context

followup_answer = client.query.ask(
    query="Sorry, I meant username recovery",
    organization="my-company",
    project="knowledge-base",
    user="[e-mail]",
    session_id="123456789abcdef"
)
# Answer: "Unlike password resets, username recovery usually requires contacting support..."

Core Concepts

• Organization: Top-level container for your projects
• Project: Collection of related documents and their index
• Document: Individual files (PDF, Word, text, etc.) containing your data
• Index: Searchable vector representation of your documents
• Query: Natural language questions answered using your documents

API Reference

Client Initialization

client = RaggerClient(
    base_url="https://api.ragger.ai/v1",  # Your Ragger server URL
    token="your-api-token",               # API authentication token
    timeout=30,                           # Request timeout in seconds
    verify_ssl=True                       # SSL certificate verification
)

Document Management

# Upload from file
response = client.documents.upload(
    organization="org-name",
    project="project-name",
    name="document-name",
    file_path="/path/to/file.pdf",
    metadata={"author": "John Doe", "department": "Engineering"},
    system_prompt="You are a helpful assistant for company policies",
    text_search_config="english"
)

# Upload from text content
response = client.documents.upload(
    organization="org-name",
    project="project-name",
    name="document-name",
    content="Your text content here...",
    content_type="text/markdown",
    metadata={"source": "manual_entry"}
)

# Check processing status
status = client.documents.status(
    task_id=response['task_id'],
    organization="org-name"
)

Index Management

# Create/update index
response = client.index.index(
    organization="org-name",
    project="project-name",
    force_overwrite=False
)

# Check indexing status
task_id = response['task_id']
status = client.index.status(
    task_id=task_id,
    organization="org-name"
)

Querying

# Basic query
answer = client.query.ask(
    query="What is the return policy?",
    organization="org-name",
    project="project-name",
    user="[e-mail]"
)

# Query with session (maintains conversation context)
answer = client.query.ask(
    query="Tell me more about that",
    organization="org-name",
    project="project-name",
    user="[e-mail]",
    session_id="conversation-123"
)

Deleting Documents

# Delete a single document by name
response = client.documents.delete(
    organization="org-name",
    project="project-name",
    name="document-name"
)

# Delete all documents in a project (use with caution!)
response = client.documents.delete(
    organization="org-name",
    project="project-name",
    delete_all=True
)

Chat History

# Get all sessions for a user
sessions = client.chat_history.sessions(
    organization="org-name",
    project="project-name",
    user="[e-mail]"
)

# Get specific session details
session = client.chat_history.session(
    organization="org-name",
    project="project-name",
    user="[e-mail]",
    session_id="session-123"
)

Error Handling

The SDK raises RaggerAPIError for API-related issues. You can inspect the error type:

try:
    result = client.some_operation()
except RaggerAPIError as e:
    if e.is_validation_error():
        # Handle parameter validation
    elif e.is_not_found():
        # Handle missing resources
    elif e.is_conflict():
        # Handle resource conflicts

Examples

See the examples/ directory for complete usage examples:

• documents_from_file_example.py - File upload and processing
• index_example.py - Vector index creation
• query_example.py - Natural language querying
• chat_history_example.py - Conversation management

Requirements

• Python 3.8+
• requests >= 2.25.0

License

MIT License - see LICENSE file for details.

Support

• Documentation: GitHub README
• Issues: GitHub Issues
• Source: GitHub Repository

Development

To contribute or modify the SDK, clone the repository and install dependencies:

git clone <repository-url>
cd ragger-sdk
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

Create a tagged release:

git tag -a vX.Y.Z -m "Release version X.Y.Z"
git push origin vX.Y.Z