17 Python Libraries Every AI Engineer Should Know

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AI Summary

Summary of Essential Python Libraries for AI Engineers

AI Engineer Role Clarification

• AI engineers focus on integrating pre-trained models into applications, not training models from scratch.

Essential Python Libraries

1. Pydantic
   • Data validation library superior to Python’s dataclasses.
   • Structures and validates data for reliable AI systems.
2. Pydantic Settings
   • Part of Pydantic ecosystem for structuring application settings.
   • Validates important information like API keys.
3. Python-dotenv
   • Manages environment variables and keeps sensitive information like API keys out of version control.

Backend Components

2. FastAPI
   • Preferred over Flask for building APIs.
   • Integrates with Pydantic for data validation at endpoints.
3. Celery
   • Task queue library for distributing work across threads or machines.
   • Ensures API endpoints remain available during heavy processing.

Data Management

4. Databases
   • PostgreSQL (SQL) and MongoDB (NoSQL) are common options.
   • Libraries: psycopg2 for PostgreSQL, pymongo for MongoDB.
5. SQLAlchemy
   • Simplifies operations with SQL databases.
6. Alembic
   • Manages database migrations in pure Python.
7. Pandas
   • Data manipulation and structuring in a human-readable format.

AI Integration

8. Model Providers
   • Understand APIs from OpenAI, Hugging Face, Google, etc.
   • Dive beyond quick starts into detailed API documentation.
9. InstructGPT
   • Preferred library for structured output from models.
   • Uses Pydantic for complex data validation.
10. Frameworks (Lang Chain, Llama Index)

• Controversial but essential to be familiar with.

• Abstract core components for easy application building.

11. Vector Databases

• For retrieval-augmented generation.
• Options: Pinecone, Weaviate, PG VectorScale.

Observability and Monitoring

12. Lang Fuse, Lang Smith

• Track interactions with language models.
• Essential for maintaining and debugging applications.

Specialized Tasks

13. DSPy

• Optimizes prompts and weights for modular AI systems.

14. Document Extraction

• Libraries like PyMuPDF and Py2PDF.

• Services like Amazon Textract for more powerful extraction.

15. Jinja

• Templating engine for dynamic prompts.

Additional Resources

• Generative AI Launchpad repository and course for deploying AI applications.
• Prompts managed using Jinja templates.

Conclusion

• The video provides a comprehensive list of Python libraries and tools for AI engineers to build robust and reliable AI applications.
• Emphasizes the importance of understanding and integrating these libraries into AI projects.