From 58b0a6735524d65abb2ae09358db45c54f4d9229 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sim=C3=B3n=20Fishman?= Date: Wed, 27 Sep 2023 14:14:51 -0700 Subject: [PATCH] sort tools and libraries list alphabetically (#740) --- related_resources.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/related_resources.md b/related_resources.md index 84a3e51..fcac252 100644 --- a/related_resources.md +++ b/related_resources.md @@ -2,34 +2,34 @@ People are writing great tools and papers for improving outputs from GPT. Here are some cool ones we've seen: -## Prompting libraries & tools +## Prompting libraries & tools (in alphabetical order) -- [Guidance](https://github.com/microsoft/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control. -- [LangChain](https://github.com/hwchase17/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts. -- [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices. +- [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc. - [Chainlit](https://docs.chainlit.io/overview): A Python library for making chatbot interfaces. +- [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices. - [Guardrails.ai](https://shreyar.github.io/guardrails/): A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs. -- [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning. -- [YiVal](https://github.com/YiVal/YiVal): An open-source GenAI-Ops tool for tuning and evaluating prompts, retrieval configurations, and model parameters using customizable datasets, evaluation methods, and evolution strategies. -- [Prompttools](https://github.com/hegelai/prompttools): Open-source Python tools for testing and evaluating models, vector DBs, and prompts. +- [Guidance](https://github.com/microsoft/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control. +- [Haystack](https://github.com/deepset-ai/haystack): Open-source LLM orchestration framework to build customizable, production-ready LLM applications in Python. +- [LangChain](https://github.com/hwchase17/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts. +- [LlamaIndex](https://github.com/jerryjliu/llama_index): A Python library for augmenting LLM apps with data. +- [LMQL](https://lmql.ai): A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools. +- [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts. - [Outlines](https://github.com/normal-computing/outlines): A Python library that provides a domain-specific language to simplify prompting and constrain generation. - [Promptify](https://github.com/promptslab/Promptify): A small Python library for using language models to perform NLP tasks. -- [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps. - [PromptPerfect](https://promptperfect.jina.ai/prompts): A paid product for testing and improving prompts. +- [Prompttools](https://github.com/hegelai/prompttools): Open-source Python tools for testing and evaluating models, vector DBs, and prompts. +- [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps. +- [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning. - [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments. -- [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts. -- [LlamaIndex](https://github.com/jerryjliu/llama_index): A Python library for augmenting LLM apps with data. -- [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc. -- [LMQL](https://lmql.ai): A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools. -- [Haystack](https://github.com/deepset-ai/haystack): Open-source LLM orchestration framework to build customizable, production-ready LLM applications in Python. +- [YiVal](https://github.com/YiVal/YiVal): An open-source GenAI-Ops tool for tuning and evaluating prompts, retrieval configurations, and model parameters using customizable datasets, evaluation methods, and evolution strategies. ## Prompting guides - [Brex's Prompt Engineering Guide](https://github.com/brexhq/prompt-engineering): Brex's introduction to language models and prompt engineering. -- [promptingguide.ai](https://www.promptingguide.ai/): A prompt engineering guide that demonstrates many techniques. -- [OpenAI Cookbook: Techniques to improve reliability](https://github.com/openai/openai-cookbook/blob/main/techniques_to_improve_reliability.md): A slightly dated (Sep 2022) review of techniques for prompting language models. -- [Lil'Log Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/): An OpenAI researcher's review of the prompt engineering literature (as of March 2023). - [learnprompting.org](https://learnprompting.org/): An introductory course to prompt engineering. +- [Lil'Log Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/): An OpenAI researcher's review of the prompt engineering literature (as of March 2023). +- [OpenAI Cookbook: Techniques to improve reliability](https://github.com/openai/openai-cookbook/blob/main/techniques_to_improve_reliability.md): A slightly dated (Sep 2022) review of techniques for prompting language models. +- [promptingguide.ai](https://www.promptingguide.ai/): A prompt engineering guide that demonstrates many techniques. ## Video courses