Inspector

Inspector is a Proof of Concept (POC) for a Python-based web application designed to analyze documents using cutting-edge AI technologies.

Application

Test the application here

Key Features

Insight Extraction

• Large Language Models (LLMs): Utilizes advanced LLMs for user-driven document analysis, seamlessly integrated with Azure OpenAI services.
• Retrieval-Augmented Generation (RAG): Enhances information retrieval by combining retrieval mechanisms with generative models, enabling more accurate and contextually relevant insights.
• Prompting Techniques like Tree-of-Thought Prompting to facilitates complex reasoning processes to improve the quality of generated responses.
• Secure Data Handling: Protects sensitive information by identifying and masking personal data (e.g., names, document numbers) before any transmission to external services, ensuring confidentiality and compliance. (coming soon)

Integration

• Apache Tika Integration: Efficiently extracts and processes information from a wide variety of document formats, ensuring versatile data handling.
• Redis Vector Database: Employs Redis for high-performance vector storage and retrieval, optimizing data management and access speeds.

Documentation

The documentation was created using the mkdocs-material and mkdocs-string libraries. https://andrecorumba.github.io/inspector/

Libraries Used

This project leverages the following Python libraries and tools:

• python-dotenv: For environment variable management.
• openai: For interaction with Azure OpenAI’s GPT models.
• numpy: For numerical operations.
• langchain-text-splitters: For efficient text chunking.
• pytest: For testing.
• tiktoken: For tokenizing text for LLM interactions.
• fastapi: For building the backend REST API.
• uvicorn: ASGI server for FastAPI.
• streamlit: For building the frontend interface.
• streamlit-option-menu: For adding navigation menus to Streamlit apps.
• redis: For caching and database operations.
• redisvl: Vector library for similarity search in Redis.
• tika: For extracting content from diverse document formats.
• aiohttp: For asynchronous HTTP requests.
• python-multipart: For handling file uploads.

Infrastructure

The project uses Docker for deployment, and services are orchestrated using docker-compose. The following services are included:

1. FastAPI: Port: 8997
   • Backend REST API
2. Streamlit: Port: 8998
   • Frontend of the POC
3. Redis: Port: 8999
   • Data volume: redis-data-inspector
4. MkDocs: Port: 8996
   • Serves project documentation.
5. Tika: Port: 8995
   • Extracts information from documents.

Azure OpenAI Integration

The project utilizes Azure OpenAI services for generating insights and embeddings. Ensure the following environment variables .env file are configured:

# If you use Azure services
# AZURE_OPENAI_API_KEY = "your_api_key_here"
# OPENAI_API_TYPE = "azure"
# AZURE_OPENAI_ENDPOINT = "https://your_azure_endpointservices.com/"
# OPENAI_API_VERSION = "api_version"
# AZURE_DEPLOYMENT = "your_azure_azure_chat_deployment_name"
# AZURE_EMBEDDING_DEPLOYMENT = "your_azure_embedding_deployment_name"

# If you use Openai Services
OPENAI_API_KEY="your_api_key_here"
MODEL_NAME = "gpt-4o-mini" # e.g

# Services
API_HOST=fastapi
API_PORT=8000
REDIS_HOST=redis
REDIS_PORT=6379
REDIS_DB=0
STREAMLIT_PORT=8501
TIKA_SERVER_ENDPOINT='http://tika:9998/'

These environment variables enable seamless interaction with Azure OpenAI endpoints for document analysis and embedding operations.

Local Development

For local development, this project uses Poetry to manage dependencies.

Setup

1. Install Dependencies: This command reads the pyproject.toml file and installs the required libraries into a new virtual environment.

   poetry install

2. Activate Virtual Environment: To start using the project's dependencies, activate the virtual environment.

   eval $(poetry env activate)

3. Update Dependencies: This command updates the project's libraries to the latest versions allowed in pyproject.toml and updates the poetry.lock file.

   poetry update

How to Run (Docker)

1. Clone the repository and navigate to the project directory:

git clone https://github.com/andrecorumba/inspector.git
cd inspector

2. Ensure you have Docker and Docker Compose installed.
3. Build and start the services:

docker-compose up --build

4. Access the application:

• FastAPI Backend: http://localhost:8997
• Streamlit Frontend: http://localhost:8998
• MkDocs Documentation: http://localhost:8996

Contributing

Feel free to submit issues or pull requests. Contributions are welcome!

License

This project is licensed under the MIT License.

Author: André Rocha Version: 0.2.0