β½οΈ Prompt Engineering Libraries
Abstract
This chapter covers some of the popular libraries related to Generative AI and Prompt Engineering.
β³οΈ OpenAI Python Library
The OpenAI Python library offers developers access to a range of OpenAI models, including GPT-3.5, GPT-4, Whisper, DALL-E, and more. This library is an essential tool for developers looking to harness the power of OpenAI models in their Python-based projects.
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π’ Google GenerativeAI Library
The google-generativeai library provides Python developers with access to Google's cutting-edge generative AI models, such as Gemini and PaLM. In essence, the google-generativeai library serves as a powerful tool for developers seeking to seamlessly integrate advanced Google AI models into their projects.
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β³οΈ LiteLLM
Some of the popular LLM providers are OpenAI, Azure, Cohere, Anthropic, HuggingFace etc. Each of these LLM providers offers access to LLMs using their APIs. Each of these APIs has a different format which makes it difficult to use multiple LLMs in one project.
LiteLLM library addresses this issue by offering a unified OpenAI format i.e., LiteLLM library allows you to call all LLM APIs using the OpenAI format. LiteLLM supports over 100 LLM providers like Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate etc.
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π’ Promptify
Promptify is an open-source library designed for prompt engineering to solve NLP problems using large language models (LLMs) like GPT, PALM, and others. Promptify covers a wide range of NLP tasks like multilabel text classification, question-answering, summarization etc.
To summarize, the promptify library simplifies the task of solving NLP problems using LLMs by offering ready-made prompt templates.
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β³οΈ PromptBench
Microsoft researchers have introduced PromptBench, a library designed for evaluating large language models. It is a Python package based on PyTorch, providing easy-to-use APIs that allow researchers to thoroughly assess the performance of LLMs.
In summary, PromptBench serves as an excellent tool for conducting comprehensive evaluations of large language models.
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π’ LangChain
LangChain is a framework for developing applications powered by language models. This framework consists of several parts namely
- LangChain Libraries: The Python and JavaScript libraries.
- LangChain Templates: A collection of easily deployable reference architectures for a wide variety of tasks.
- LangServe: A library for deploying LangChain chains as a REST API. and
- LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
To summarize, the LangChain library simplifies the process of LLM app development and deployment.
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β³οΈ EmbedChain
EmbedChain is a new framework to easily create LLM-powered chatbots over any dataset. Without EmbedChain building a chatbot involves many steps like loading the dataset, chunking it into the appropriate size, deciding on an embedding model, choosing a vector database and writing a retrieval function. With EmbedChain building a chatbot involves creating app instance, adding dataset and querying.
To summarize, the embedchain library abstracts several steps and simplifies the process of creating RAG-based chatbots in just a few lines of code.
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π’ LlamaIndex
The Llama Index is a versatile data framework specifically designed to external data into large language models (LLMs) and enhance their performance and factual correctness. applications. Llama Index is user-friendly, providing high-level APIs for beginners to manage data in just a few lines of code, while also offering lower-level APIs for advanced users seeking customization and extension capabilities.
To summarize, the Llama Index is a powerful library for building RAG-based LLM apps.
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β³οΈ Streamlit
Streamlit is a powerful Python library that allows to creation and sharing of interactive web apps for data science and machine learning projects.
Key features of Streamlit library are
rapid app development-Β you write pure Python code, and Streamlit automatically transforms it into a web application, eliminating the need for extensive web development expertise.interactive elements-Β you can effortlessly incorporate various elements such as text, images, plots, data tables, and widgets like sliders, buttons, and text inputs to create engaging and dynamic user experiences.seamless integration- Streamlit seamlessly works with popular data science libraries like Pandas, NumPy, Matplotlib, as well as machine learning frameworks like TensorFlow and PyTorch.easy sharing-Β you can easily share your apps locally, through cloud platforms, or via Streamlit's free Community Cloud platform, facilitating effortless collaboration and deployment.
To summarize, the streamlit library greatly simplifies the task of developing data web apps without writing any HTML code.
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π’ Chainlit
Chainlit, an open-source Python library simplifies building production-ready chat apps. It supports integration with a variety of tools and services like OpenAI, AnthropiΡ, LangChain, LlamaIndex, ChromaDB, Pinecone etc.
To summarize, Chainlit helps to build LLM apps in minutes with just a few lines of code and supports various LLM providers and vector databases.
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β³οΈ Gradio
Gradio is a Python library that is open-source and enables the rapid creation of web applications or demos for machine learning models, APIs, or any Python function. It has been designed to be user-friendly, eliminating the need for expertise in JavaScript, CSS, or web hosting.
To summarize, Gradio is an ideal tool for showcasing machine learning models and other Python-based functionalities in an interactive and accessible manner.
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