Flash Learn - LLM Integration for Any Pipeline

What is the project about?

FlashLearn is a Python library that simplifies the integration of Agent LLMs (Large Language Models) into data pipelines and workflows. It provides a straightforward interface and orchestration for tasks like data transformation, classification, summarization, and more, leveraging the power of LLMs.

What problem does it solve?

Integrating LLMs into existing data pipelines can be complex and require significant custom coding. FlashLearn simplifies this process, allowing users to easily incorporate LLM capabilities into their workflows without needing deep expertise in LLM technology. It handles the orchestration of LLM calls, making it efficient and scalable.

What are the features of the project?

Simple Interface: Uses a familiar "fit/predict" pattern similar to standard ML libraries.
Skill Learning: Allows users to "learn" custom skills by providing instructions and sample data, defining how the LLM should process information.
JSON-Based Definition: Each step and task has a compact JSON definition, making pipelines easy to understand, maintain, and version control.
Structured Results: Provides outputs in a structured JSON format, making it easy to integrate with downstream tasks.
Parallel Execution: Supports parallel processing of tasks for high throughput (up to 1000 calls/min).
Cost Estimation: Provides a way to estimate the token usage and cost before running tasks.
Multiple LLM Providers: Supports LiteLLM, Ollama, OpenAI, DeepSeek, and other OpenAI-compatible clients.
Prebuilt Skills: Includes prebuilt skills for common tasks like classification and text rewriting.
Customizable: Allows for the creation of custom skills tailored to specific needs.
High Throughput: Can process a large number of tasks quickly.
Image and Text Classification: Provides "Hello World" examples for both image and text classification.

What are the technologies used in the project?

Python: The core language of the library.
LLMs: Leverages various LLMs through providers like OpenAI, LiteLLM, Ollama, and DeepSeek.
JSON: Used for defining skills, tasks, and storing results.
OpenAI API: Used for interacting with OpenAI models.
LiteLLM: Integration for managing API keys and environment variables.
Ollama: Support for local LLM models.

What are the benefits of the project?

Simplified LLM Integration: Makes it easy to incorporate LLMs into existing workflows.
Increased Efficiency: Parallel processing and optimized orchestration improve performance.
Scalability: Handles high volumes of requests.
Maintainability: JSON-based definitions make pipelines easy to understand and maintain.
Cost-Effectiveness: Cost estimation helps manage expenses.
Flexibility: Supports multiple LLM providers and custom skill creation.
Structured Output: JSON output simplifies integration with other tools and systems.
Open Source: MIT License allows for free use and modification.

What are the use cases of the project?

Customer Service: Classifying customer tickets, analyzing feedback.
Finance: Parsing financial reports, extracting data.
Marketing: Customer segmentation, sentiment analysis.
Personal Assistant: Research tasks, summarizing information.
Product Intelligence: Discovering trends in product reviews, user behavior analysis.
Sales: Personalized cold emails, lead scoring.
Software Development: Automated PR reviews, code summarization.
Data Transformation: Rewriting text, summarizing content, extracting information.
Data Classification: Categorizing data based on content.
Any ETL Pipeline: Where you need to process text or images using LLM capabilities.
Image Classification: Classifying the content of images.