From 8189459e5bd4b0a896403a8ee0c9da32a443806b Mon Sep 17 00:00:00 2001 From: Mark Pollack Date: Thu, 10 Aug 2023 13:15:54 -0400 Subject: [PATCH] fix more typos --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 1a2e52b91..773b2194f 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ Let's make your `@Beans` intelligent! ## Overview Despite the extensive history of AI, Java's role in this domain has been relatively minor. -This is mainly due to the historical reliance on efficient algorithms developed in languages such as C/C++, with Python services as bridges to access these libraries. +This is mainly due to the historical reliance on efficient algorithms developed in languages such as C/C++, with Python serving as a bridge to access these libraries. The majority of ML/AI tools were built around the Python ecosystem. However, recent progress in Generative AI, spurred by innovations like OpenAI's ChatGPT, has popularized the interaction with pre-trained models via HTTP. This eliminates much of the dependency on C/C++/Python libraries and opens the door to the use of programming languages such as Java. @@ -23,7 +23,7 @@ These Python libraries share foundational themes with Spring projects, such as: * Extensibility * Reduction of boilerplate code * Integration with diverse data sources -* Prebuilt solutions for common use cases such as Question-Answering, Text Generation, and Summarization. +* Prebuilt solutions for common use cases Taking inspiration from these libraries, the Spring AI project aims to provide a similar experience for Spring developers in the AI domain. @@ -40,16 +40,15 @@ Features without links are part of our future roadmap. A common API enables you to develop an application targeting OpenAI's ChatGPT HTTP interface and easily switch to Azure's OpenAI service, as an example. **Prompts:** At the center of LLM interaction is the Prompt - a set of instructions for the LLM to respond to. -Creating an effective part is part art and part science, giving rise to the discipline of Prompt Engineering. +Creating an effective Prompt is part art and part science, giving rise to the discipline of Prompt Engineering. Prompts utilize a templating engine, enabling easy replacement of data within prompt text placeholders. -**Output Parsers:** LLM responses are typically a raw `java.lang.String`. -Output Parsers transform the raw String into structured formats like CSV or JSON, to make the output usable in a programming environment. -Output Parser may also do additional post-processing on the response String. +**Output Parsers:** LLM responses are typically a raw `java.lang.String`. Output Parsers transform the raw String into structured formats like CSV or JSON, to make the output usable in a programming environment. +Output Parsers may also do additional post-processing on the response String. ### Incorporating your data -**Data Management:** A significant innovation in Generative AI involves enabling LLMs to understand your proprietary data without having to retrain the model's weights. Retraining a model is a complex and compute-intensive task. +**Data Management:** A significant innovation in Generative AI involves enabling LLMs to understand your proprietary data without having to retrain the model's weights. Retraining a model is a complex and compute-intensive task. Recent Generative AI models have billions of parameters that require specialized hard-to-find hardware making it practically impossible to retrain the largest of models. Instead, the 'In-context' learning technique lets you more easily incorporate your data into the pre-trained model. This data can be from text files, HTML, database results, etc. @@ -65,6 +64,7 @@ Spring IO abstracts these databases, allowing easy swapping of implementations. **Chains:** Many AI solutions require multiple LLM interactions to respond to a single user input. "Chains" organize these interactions, offering modular AI workflows that promote reusability. While you can create custom Chains tailored to your specific use case, pre-configured use-case-specific Chains are provided to accelerate your development. +Use-cases such as Question-Answering, Text Generation, and Summarization are examples. ### Memory