https://github.com/bigmb/mb_rag
RAG files for mb
https://github.com/bigmb/mb_rag
Last synced: 8 days ago
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RAG files for mb
- Host: GitHub
- URL: https://github.com/bigmb/mb_rag
- Owner: bigmb
- Created: 2024-07-25T15:34:40.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-05-06T15:18:31.000Z (16 days ago)
- Last Synced: 2025-05-06T16:46:35.184Z (16 days ago)
- Language: Jupyter Notebook
- Size: 13.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MB-RAG: Modular Building Blocks for Retrieval-Augmented Generation
MB-RAG is a flexible Python package that provides modular building blocks for creating RAG (Retrieval-Augmented Generation) applications. It integrates multiple LLM providers, embedding models, and utility functions to help you build powerful AI applications.
## Features
- **Multiple LLM Support**:
- OpenAI (GPT-4, GPT-3.5)
- Anthropic (Claude)
- Google (Gemini)
- Ollama (Local models)
- Groq
- **RAG Capabilities**:
- Text splitting and chunking
- Multiple embedding models
- Vector store integration
- Conversation history management
- Context-aware retrieval
- **Image Processing**:
- Bounding box generation with Gemini Vision
- Custom image annotations
- Multiple output formats
- Batch processing capabilities
## Installation
1. Basic Installation:
```bash
pip install mb_rag
```
## Quick Start
### Basic Chat Examples
```python
from mb_rag.chatbot.basic import ModelFactory, ConversationModel
# 1. Simple Query with ModelFactory
model = ModelFactory(model_type="openai", model_name="gpt-4o")
response = model.invoke_query("What is artificial intelligence?")
print(response)
# 2. Image Analysis
model = ModelFactory(model_type="openai", model_name="gpt-4o")
response = model.invoke_query(
"What's in these images?",
images=["image1.jpg", "image2.jpg"]
)
print(response)
# 3. Conversation with Context
conversation = ConversationModel(
model_name="gpt-4o",
model_type="openai"
)
# Initialize conversation with context
conversation.initialize_conversation(
question="What is machine learning?",
context="You are an AI expert. Provide clear, concise explanations."
)
# Continue the conversation
response = conversation.add_message("How is it different from deep learning?")
print(response)
# Access conversation history
print("\nAll messages:")
for message in conversation.all_messages_content:
print(message)
# Save conversation
conversation.save_conversation("chat_history.txt")
# 4. Using Different Models
# Anthropic Claude
claude_model = ModelFactory(
model_type="anthropic",
model_name="claude-3-opus-20240229"
)
response = claude_model.invoke_query("Explain quantum computing")
# Google Gemini
gemini_model = ModelFactory(
model_type="google",
model_name="gemini-1.5-pro-latest"
)
response = gemini_model.invoke_query("Describe the solar system")
# Local Ollama
ollama_model = ModelFactory(
model_type="ollama",
model_name="llama3.1"
)
response = ollama_model.invoke_query("What is the meaning of life?")
```
### Embeddings and RAG Example
```python
from mb_rag.rag.embeddings import embedding_generator
# Initialize embedding generator
em_gen = embedding_generator(
model="openai",
model_type="text-embedding-3-small",
vector_store_type="chroma"
)
# Generate embeddings from text files
em_gen.generate_text_embeddings(
text_data_path=['./data.txt'],
chunk_size=500,
chunk_overlap=5,
folder_save_path='./embeddings'
)
# Load embeddings and create retriever
em_loading = em_gen.load_embeddings('./embeddings')
em_retriever = em_gen.load_retriever(
'./embeddings',
search_params=[{"k": 2, "score_threshold": 0.1}]
)
# Generate RAG chain for conversation
rag_chain = em_gen.generate_rag_chain(retriever=em_retriever)
# Have a conversation with context
response = em_gen.conversation_chain(
"What is this document about?",
rag_chain,
file='conversation_history.txt' # Optional: Save conversation
)
# Query specific information
results = em_gen.query_embeddings(
"What are the key points discussed?",
em_retriever
)
# Add new data to existing embeddings
em_gen.add_data(
'./embeddings',
['new_data.txt'],
chunk_size=500
)
# Web scraping and embedding
db = em_gen.firecrawl_web(
website="https://github.com",
mode="scrape",
file_to_save='./web_embeddings'
)
```
### Image Processing with Bounding Boxes
```python
from mb_rag.utils.bounding_box import BoundingBoxProcessor, BoundingBoxConfig
# Initialize processor with configuration
config = BoundingBoxConfig(
model_name="gemini-1.5-pro-latest",
api_key="your-api-key" # Or use environment variable GOOGLE_API_KEY
)
processor = BoundingBoxProcessor(config)
# Generate bounding boxes
boxes = processor.generate_bounding_boxes(
"image.jpg",
prompt="Return bounding boxes of objects"
)
# Add boxes to image with custom styling
processed_img = processor.add_bounding_boxes(
"image.jpg",
boxes,
color=(0, 255, 0), # Green color
thickness=2,
font_scale=0.5,
show=True # Display result
)
# Save processed image
processor.save_image(processed_img, "output.jpg")
# Complete processing pipeline
result = processor.process_image(
"image.jpg",
output_path="result.jpg",
show=True
)
# Batch processing
def batch_process_images(processor, image_paths, output_dir, **kwargs):
"""Process multiple images with same settings."""
import os
os.makedirs(output_dir, exist_ok=True)
results = []
for img_path in image_paths:
try:
output_path = os.path.join(
output_dir,
f"processed_{os.path.basename(img_path)}"
)
result = processor.process_image(
img_path,
output_path=output_path,
**kwargs
)
results.append((img_path, output_path, True))
except Exception as e:
results.append((img_path, None, False))
print(f"Error processing {img_path}: {e}")
return results
# Example batch processing
images = ["image1.jpg", "image2.jpg", "image3.jpg"]
results = batch_process_images(
processor,
images,
"./batch_output",
show=False
)
```
## Package Structure
```
mb_rag/
├── rag/
│ └── embeddings.py # RAG and embedding functionality
├── chatbot/
│ ├── basic.py # Basic chatbot implementations
│ └── chains.py # LangChain integration
├── agents/
│ ├── run_agent.py # Agent execution
│ └── web_browser_agent.py # Web browsing capabilities, Added WebAgent with langgraph
└── utils/
├── bounding_box.py # Image processing utilities
└── extra.py # Additional utilities
```
## Dependencies
Core dependencies:
- langchain-core
- langchain-community
- langchain
- python-dotenv
Optional dependencies by feature:
- Language Models: langchain-openai, langchain-anthropic, langchain-google-genai, langchain-ollama
- Image Processing: Pillow, opencv-python, google-generativeai
- Vector Stores: chromadb
- Web Tools: firecrawl
See `requirements.txt` for a complete list.
## Environment Setup
Create a `.env` file in your project root:
```env
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
GOOGLE_API_KEY=your_google_key
```