LLMFlows: Simplifying Large Language Model Application Development
About LLMFlows
LLMFlows is a cutting-edge framework designed to simplify the creation of applications utilizing Large Language Models (LLMs), such as chatbots, question-answering systems, and AI agents. Its primary goal is to provide developers with tools that make the development process straightforward, clear, and efficient without hiding any prompts or LLM processes.
Installation
To get started with LLMFlows, simply install it using pip:
pip install llmflows
Philosophy
Simple
LLMFlows aims to deliver a simple and well-documented framework with minimal layers of abstraction. This simplicity ensures users can create flexible applications without trading off advanced capabilities.
Explicit
The framework offers an explicit API, allowing developers to write clean, readable, and maintainable code to create complex LLM interactions easily. It emphasizes full control over processes, with no concealed operations.
Transparent
Transparency is key in LLMFlows. It enables full visibility over each component of your application, facilitating easy monitoring, maintenance, and debugging.
Features and Capabilities
Working with LLMs
LLMFlows enhances the use of LLMs, such as those from OpenAI, by providing robust classes to manage and configure model interactions. The framework provides methods for retrying calls and formatting responses, enhancing reliability.
PromptTemplates
The PromptTemplate class empowers developers to create adaptable string templates with variables that can be replaced dynamically, ensuring easy customization of text generation.
Flows and FlowSteps
LLMFlows uses Flows and FlowSteps to manage LLM application structures clearly and systematically. Developers can connect steps, passing outputs as inputs, to maintain a transparent data flow, and use Async Flows to optimize performance by running operations in parallel.
VectorStore Integrations
The framework integrates with vector stores like Pinecone to efficiently manage and retrieve vector embeddings, supporting LLM-based applications with seamless data storage and querying.
Callbacks
Developers can define callback functions at various stages within a flow step, enabling advanced monitoring, logging, and tracing functionalities to ensure controlled and transparent operations.
Getting Started
LLMFlows provides comprehensive guides to help you start using LLMs efficiently within your applications. You can begin with basic setups and move onto creating more complex applications like chatbots, or those requiring question-answering capabilities, utilizing the various classes and methods LLMFlows offers.
Live Demo
An exemplary application built using LLMFlows is LLM-99, which simplifies the explanation of superconductors. The application leverages LLMFlows, FastAPI, and Pinecone, showcasing how the framework can be used in real-world scenarios.
User Guide
LLMFlows offers a detailed user guide covering topics from basic introductions to more advanced concepts like Async Flows and integrating vector stores. This comprehensive guide is designed to help developers at all levels make the most of the framework.
Contribution and Contact
LLMFlows is an open-source project, and contributions are warmly welcomed. If you're interested in contributing or providing feedback, you can reach out through the project's GitHub repository or connect on social media platforms like LinkedIn and Twitter. Your engagement is appreciated and helps in continually enhancing the project.