Inspector is a Proof of Concept (POC) for a Python-based web application designed to analyze documents using cutting-edge AI technologies.
Test the application here
- 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)
- 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.
The documentation was created using the mkdocs-material and mkdocs-string libraries. https://andrecorumba.github.io/inspector/
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.
The project uses Docker for deployment, and services are orchestrated using docker-compose. The following services are included:
- FastAPI: Port: 8997
- Backend REST API
- Streamlit: Port: 8998
- Frontend of the POC
- Redis: Port: 8999
- Data volume: redis-data-inspector
- MkDocs: Port: 8996
- Serves project documentation.
- Tika: Port: 8995
- Extracts information from documents.
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.
- Clone the repository and navigate to the project directory:
git clone https://github.com/andrecorumba/inspector.git
cd inspector
- Ensure you have Docker and Docker Compose installed.
- Build and start the services:
docker-compose up --build
- Access the application:
- FastAPI Backend: http://localhost:8997
- Streamlit Frontend: http://localhost:8998
- MkDocs Documentation: http://localhost:8996
Feel free to submit issues or pull requests. Contributions are welcome!
This project is licensed under the MIT License.
Author: André Rocha Version: 0.2.0
Let me know if there are additional details or edits you'd like!