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LLMFlows - Simple, Explicit, and Transparent LLM Apps


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Source code: https://github.com/stoyan-stoyanov/llmflows
Twitter: https://twitter.com/LLMFlows
Substack: https://llmflows.substack.com/

🤖 About LLM Flows

LLMFlows is a framework for building simple, explicit, and transparent LLM(Large Language Model) applications such as chatbots, question-answering systems, and agents.

At its core, LLMFlows provides a minimalistic set of abstractions that allow you to utilize LLMs and vector stores and build well-structured and explicit apps that don't have hidden prompts or LLM calls. LLM Flows ensures complete transparency for each component, making monitoring, maintenance, and debugging easy.

📦 Installation

pip install llmflows

▶️ Live Demo

Check out LLM-99 - a demo app that uses LLMs to explain superconductors in simple terms. The app is built with LLMFlows, and FastAPI and uses Pinecone to store document embeddings created from Wikipedia articles. You can find the source code for this demo app and other examples in our examples folder.

🧪 Getting Started

After installing llmflows with pip, your go-to destination should be our User Guide. Each page helps new users gradually learn LLMFlow starting from simple concepts, going through all main abstractions, and ending with complex use cases where LLMFlows shines.

Each page has a TL;DR code snippet to copy, paste and move on. If you already have significant experience with LLMs and prompt engineering, or if you are in a rush, feel free to use the TL;DR section and the API reference when needed.

If you are new to LLMs and prompt engineering, feel free to go through the user guide in the specified order where you can learn about concepts, abstractions, and patterns when creating LLM-powered applications.

If you have any questions check out our FAQ section or feel free to join our Github Discussions page.

We hope you find this documentation helpful and look forward to your feedback on how we can improve it.

🧭 Philosophy

Simple

Our goal is to build a simple, well-documented framework with minimal abstractions that allow users to build flexible LLM-powered apps without compromising on capabilities.

Explicit

We want to create an explicit API enabling users to write clean and readable code while easily creating complex flows of LLMs interacting with each other. LLMFlows' classes give users full control and do not have any hidden prompts or LLM calls.

Transparent

We aim to help users have full transparency on their LLM-powered apps by providing traceable flows and complete information for each app component, making it easy to monitor, maintain, and debug.

🛠️ Features

LLMs

  • Utilize LLMs such as OpenAI's ChatGPT to generate natural language text.
  • Configure LLM classes easily, choosing specific models, parameters, and settings.
  • Benefit from automatic retries when model calls fail, ensuring reliable LLM interactions.

Prompt Templates

  • Create dynamic prompts using Prompt Templates, providing flexible and customizable text generation.
  • Define variables within prompts to generate prompt strings tailored to specific inputs.

Flows and FlowSteps

  • Structure LLM applications using Flows and FlowSteps, providing a clear and organized framework for executing LLM interactions.
  • Connect flow steps to pass outputs as inputs, facilitating seamless data flow and maintaining a transparent LLM pipeline.
  • Leverage Async Flows to run LLMs in parallel when all their inputs are available, optimizing performance and efficiency.
  • Incorporate custom string manipulation functions directly into flows, allowing specialized text transformations without relying solely on LLM calls.

VectorStore Integrations

  • Integrate with vector databases like Pinecone using the VectorStoreFlowStep, empowering efficient and scalable storage and retrieval of vector embeddings.
  • Leverage vector databases for seamless storage and querying of vectors, enabling straightforward integration with LLM-powered applications.

Callbacks

  • Execute callback functions at different stages within flow steps, enabling enhanced customization, logging, tracing, or other specific integrations.
  • Utilize callbacks to comprehensively control and monitor LLM-powered apps, ensuring clear visibility into the execution process.

Explicit API and Full Transparency

With LLMFlows you have the full control to create explicit applications without any hidden prompts or predefined behaviors.

In addition LLMFlows allows you to answer questions such as:

  • When was a particular flowstep run?
  • How much time did it take?
  • What were the input variables?
  • What was the prompt template?
  • What did the prompt look like?
  • What was the exact configuration of the model?
  • How many times did we retry the request?
  • What was the raw data the API returned?
  • How many tokens were used?
  • What was the final result?

📃 License

LLMFlows is covered by the MIT license. For more information, check LICENCE.

❤️ How you can help?

If you like the project please consider giving it a star, sharing it with friends or on social media.

If you've tried LLMFlows and have some issues, feedback or ideas feel free to open an issue or reach out!

If you find LLMFlows exciting and you are considering contributing, please check CONTRIBUTING.md.