v0.1.0 Release Notes
Hi folks, based on some feedback a few important changes:
- We have shifted away from the CLI approach to a more flexible API-based solution. For v0.1.0, you'll need to clone the repository and install dependencies using Poetry.
- The env.example file now includes a LOGFIRE_TOKEN. You can obtain one by signing up at https://logfire.pydantic.dev. Logfire is an observability platform developed by the Pydantic team, designed to assist with debugging and monitoring, including LLM calls.
- This version focuses on producing consistent LLM outputs for PII detection and incorporates extensive error handling to create a more production-ready service.
- We've implemented robust validation and error handling throughout the codebase to ensure reliability and ease of debugging.
Start by cloning the repo and installing the dependencies using poetry:
git clone https://github.com/datafog/datafog-instructor.git
cd datafog-instructor
poetry install
You'll also need to create a .env file with the OPENAI_API_KEY and GROQ_API_KEY. You can get these by signing up for accounts at https://openai.com/ and https://www.groq.com/.
Once you have the .env file, you can run the following to start the service:
uvicorn app.main:app --reload
curl -X POST "http://localhost:8000/extract-pii" \
-H "Content-Type: application/json" \
-d '{"content": "My name is John Doe and my email is [email protected]. My phone number is 123-456-7890."}'
Contributions to the DataFog Instructor SDK are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License.
If you encounter any problems or have any questions, please open an issue on the GitHub repository or join our Discord community at https://discord.gg/bzDth394R4.
- Logfire: https://logfire.pydantic.dev
- Pydantic: https://pydantic.dev
- Instructor: https://github.com/jxnl/instructor
- Homepage: https://datafog.ai
- Documentation: https://docs.datafog.ai
- Twitter: https://twitter.com/datafoginc
- GitHub: https://github.com/datafog/datafog-instructor