docuquery-ai 0.1.1

A powerful document query system using RAG and LLM technologies

DocuQuery AI

A powerful document query system that combines RAG (Retrieval-Augmented Generation) with structured data handling capabilities. Upload documents and interact with them through natural language queries.

Features

• Document Processing: Supports PDF, DOCX, PPTX, TXT, MD files
• Structured Data: Handles CSV, XLS, XLSX with direct data operations
• Vector Search: Automatic text chunking and embedding with FAISS
• Natural Language Queries: RAG for unstructured documents
• Google Vertex AI: Integration with Google's LLM and embedding models
• CLI Interface: Command-line tool for easy document management
• Python API: Clean programmatic interface for integration

Installation

Install from PyPI:

pip install docuquery-ai

For development with optional dependencies:

pip install docuquery-ai[dev,web]

For GPU acceleration (if you have CUDA):

pip install docuquery-ai[gpu]

Quick Start

1. Set up Google Cloud credentials

export GOOGLE_API_KEY="your-google-api-key"
export GOOGLE_PROJECT_ID="your-google-project-id"

2. Python API Usage

from docuquery_ai import DocumentQueryClient

# Initialize the client
client = DocumentQueryClient(
    google_api_key="your-api-key",
    google_project_id="your-project-id"
)

# Upload a document
result = client.upload_document("path/to/document.pdf")
print(f"Uploaded: {result['filename']}")

# Query the document
response = client.query("What are the main topics discussed?")
print(f"Answer: {response.answer}")

# List all documents
documents = client.list_documents()
for doc in documents:
    print(f"- {doc['filename']} ({doc['file_type']})")

3. CLI Usage

Initialize DocuQuery AI:

docuquery init

Upload a document:

docuquery upload document.pdf

Query your documents:

docuquery query "What are the key findings?"

List uploaded documents:

docuquery list

Get help:

docuquery --help

Supported File Types

• Text Documents: PDF, DOCX, PPTX, TXT, MD
• Structured Data: CSV, XLS, XLSX
• Archives: Processing of multiple files

Advanced Usage

Custom Configuration

from docuquery_ai import DocumentQueryClient

client = DocumentQueryClient(
    google_api_key="your-api-key",
    google_project_id="your-project-id",
    vector_store_path="./custom_vector_db",
    temp_upload_folder="./custom_temp"
)

Query Specific Files

# Upload multiple files
file1_result = client.upload_document("report1.pdf")
file2_result = client.upload_document("data.xlsx")

# Query specific files
response = client.query(
    "Compare the metrics between reports",
    file_ids=[file1_result['file_id'], file2_result['file_id']]
)

Using with Different Users

# Upload documents for different users
client.upload_document("doc1.pdf", user_id="user_123")
client.upload_document("doc2.pdf", user_id="user_456")

# Query documents for specific user
response = client.query("Summarize the content", user_id="user_123")

Architecture

The system uses a hybrid approach:

• RAG Pipeline: For unstructured documents (PDF, DOCX, etc.)
• Direct Data Operations: For structured files (CSV, Excel)
• Vector Store: FAISS for semantic search
• LLM Integration: Google Vertex AI for query understanding and response generation
• Database: SQLite for metadata and file tracking

CLI Commands

• docuquery init - Initialize configuration
• docuquery upload <file> - Upload and process a document
• docuquery query "<question>" - Query uploaded documents
• docuquery list - List all uploaded documents
• docuquery delete <file_id> - Delete a document
• docuquery --help - Show help information

Development

Installing for Development

git clone https://github.com/saichowdary007/DocuQuery-AI.git
cd DocuQuery-AI
pip install -e .[dev]

Running Tests

pytest

Code Formatting

black src/
isort src/

Requirements

• Python 3.8+
• Google Cloud credentials (API key or service account)
• Internet connection for Google Vertex AI API calls

Environment Variables

• GOOGLE_API_KEY - Google API key for Vertex AI
• GOOGLE_PROJECT_ID - Google Cloud project ID
• GOOGLE_LOCATION - Google Cloud location (default: us-central1)

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• GitHub Issues
• Documentation

Acknowledgments

• LangChain for RAG implementation
• Google Vertex AI for ML capabilities
• FAISS for vector search
• FastAPI for the web framework