FlashLearn simplifies integrating Large Language Models (LLMs) into existing workflows and ETL pipelines, targeting developers and data scientists. It enables LLM-powered data transformations, classifications, and multi-step tasks with a familiar fit/predict pattern, outputting structured JSON for seamless integration.

How It Works

FlashLearn uses a "skill" abstraction, defined in JSON, which encapsulates LLM instructions and output validation schemas. These skills can be learned from sample data or defined manually, allowing users to treat LLM operations like standard ML transformers. The library handles parallel execution of tasks across various LLM providers (OpenAI, LiteLLM, Ollama, etc.) and ensures consistent, structured JSON outputs.

Quick Start & Requirements

• Install via pip: pip install flashlearn
• Requires API keys for chosen LLM providers (e.g., OPENAI_API_KEY) set in .env.
• Supports multiple LLM providers including OpenAI, LiteLLM, Ollama, and OpenAI-compatible clients.
• Full Documentation: https://github.com/Pravko-Solutions/FlashLearn#readme

Highlighted Details

• Supports up to 1000 LLM calls per minute with built-in concurrency.
• Enables "learning" custom skills via learn_skill with sample data and instructions.
• Provides pre-built skills for common tasks like classification and text humorization.
• Offers cost estimation for LLM tasks.

Maintenance & Community

• Licensed under the MIT License.
• Encourages community contributions for new skills, bug fixes, and examples.
• Aims for robust LLM workflows accessible to startups.

Licensing & Compatibility

• MIT License.
• Permissive for commercial use and integration with closed-source projects.

Limitations & Caveats

The README mentions enterprise solutions for higher loads, implying the current open-source version may have throughput limitations beyond the stated 1000 calls/min. Some examples require API keys to be manually set in the script, which might be less secure than environment variables for production.