Tech stack

• Python 3.8+
• Google Vertex AI
• Google ADK (Agent Development Kit)
• Gemini Models
• PyPDF2 / PyMuPDF

Features

• Multi-Modal Document Processing: Extract and process text, images, and tables from PDF documents
• Intelligent Chunking: Optimized document segmentation with overlap for better context preservation
• Hybrid Search: Combines semantic and keyword-based search with configurable weighting
• LLM Reranking: Advanced result reranking using Gemini models for improved relevance
• Grounding Verification: Automatic verification of response claims against source material
• Context Management: Multi-turn conversation support with context tracking
• Google ADK Integration: Modern agent framework using Google’s Agent Development Kit
• Cloud Storage Integration: Seamless GCS integration for document management

Architecture

 
 PDF Documents RAG Ingestion Vertex AI RAG 
 Pipeline Corpus 
 
 
 
 
 
 
 Google ADK 
 Agent 
 
 
 
 
 Gemini Model 
 (Flash 2.5) 
 

Requirements

• Python 3.8+
• Google Cloud Platform account
• Vertex AI API enabled
• Google Cloud Storage bucket

Installation

1. Clone the repository:

   git clone <repository-url>
   cd ai-rag-engine

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate # On Windows: venvScriptsactivate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:
   Create a .env file with the following:

   GOOGLE_CLOUD_PROJECT=your-project-id
   GOOGLE_CLOUD_LOCATION=us-central1
   GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
   GCS_BUCKET_NAME=your-bucket-name

Usage

Basic Usage

1. Place your PDF documents in the docs/ directory

2. Run the RAG agent:

   python rag_agent.py

Programmatic Usage

from rag_agent import ADKRAGAgent, create_rag_corpus, import_documents_to_corpus

# Initialize the system
corpus_id = create_rag_corpus(
 corpus_name="my-knowledge-base",
 description="Enterprise documentation"
)

# Upload and import documents from local docs folder
from rag_agent import upload_file_to_gcs
import os

# Upload local PDFs to GCS
document_paths = [
 "docs/technical_manual.pdf",
 "docs/product_specs.pdf",
 "docs/user_guide.pdf"
]

gcs_uris = []
for doc_path in document_paths:
 if os.path.exists(doc_path):
 gcs_uri = upload_file_to_gcs(doc_path, os.getenv('GCS_BUCKET_NAME'))
 gcs_uris.append(gcs_uri)

# Import uploaded documents to RAG corpus
import_documents_to_corpus(corpus_id, gcs_uris)

# Create and use the agent
adk_agent = ADKRAGAgent(
 corpus_id=corpus_id,
 project_id="your-project-id",
 location="us-central1"
)

agent = adk_agent.create_agent()
response = adk_agent.chat(agent, "What are the system requirements?")
print(response)

Key Components

RAGAgent

Core RAG functionality including:

• Hybrid search with semantic and keyword matching
• Context management for multi-turn conversations
• Result reranking using LLM scoring
• Grounding verification for hallucination prevention

ADKRAGAgent

Google ADK wrapper that provides:

• Tool-based RAG search functionality
• Native integration with Gemini models
• Automatic tool calling and response synthesis
• Session-based conversation tracking

Document Processing

• PDF text extraction with PyPDF2
• Image extraction with PyMuPDF (fitz)
• Table content parsing and structuring
• Multi-modal embedding creation

Configuration

Retrieval Parameters

Adjust in configure_retrieval_parameters():

• similarity_top_k: Number of results to retrieve (default: 10)
• vector_distance_threshold: Similarity threshold (default: 0.5)
• alpha: Hybrid search weight (default: 0.5)

Chunking Settings

Modify in chunk_document():

• chunk_size: Characters per chunk (default: 1000)
• overlap: Overlap between chunks (default: 200)

Model Configuration

Change models in initialization:

• RAG Agent: gemini-2.5-flash
• ADK Agent: gemini-2.0-flash-001
• Embeddings: text-embedding-004

Project Structure

ai-rag-engine/
 rag_agent.py # Main RAG system implementation
 requirements.txt # Python dependencies
 .env # Environment variables (not in git)
 docs/ # Source PDF documents
 extracted_images/ # Extracted images from PDFs
 README.md # This file

Features in Detail

Hybrid Search

Combines semantic embeddings with keyword matching for optimal retrieval:

results = agent.hybrid_search(
 corpus_id=corpus_id,
 query="your query",
 semantic_weight=0.7, # 70% semantic, 30% keyword
 top_k=10
)

Grounding Verification

Ensures responses are factually grounded in source documents:

verification = agent.verify_grounding(
 response=answer,
 sources=retrieved_docs
)

Multi-Modal Processing

Handle documents with text, images, and tables:

images = agent.extract_images_from_pdf(pdf_path, output_dir)
table_data = agent.process_table_content(table_text)
embedding = agent.create_multimodal_embedding(text, image_path, table_data)

Limitations

• Rate limits: 5 requests/minute for RAG retrieval operations
• Requires 3-minute wait time after corpus document import for indexing
• PDF processing only (extend for other formats as needed)

Contributing

Contributions are welcome! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch
3. Make your changes with clear commit messages
4. Submit a pull request

License

[Add your license information here]

Support

For issues and questions:

• Open an issue on GitHub
• Check Google Vertex AI documentation: https://cloud.google.com/vertex-ai/docs

Acknowledgments

• Built with Google Vertex AI RAG Engine
• Powered by Google ADK and Gemini models
• Uses PyPDF2 and PyMuPDF for document processing