minor docs improvments

Author: tzolov

spring-ai-docs/src/main/antora/modules/ROOT/pages/index.adoc

@@ -1,17 +1,18 @@
 [[introduction]]
 = Spring AI
 
-The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity.
+The `Spring AI` project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity.
 
 The project draws inspiration from notable Python projects, such as LangChain and LlamaIndex, but Spring AI is not a direct port of those projects.
 The project was founded with the belief that the next wave of Generative AI applications will not be only for Python developers but will be ubiquitous across many programming languages.
 
 At its core, Spring AI provides abstractions that serve as the foundation for developing AI applications.
 These abstractions have multiple implementations, enabling easy component swapping with minimal code changes.
-For example, Spring AI introduces the `ChatClient` interface with implementations for OpenAI and Azure OpenAI.
+For example, Spring AI introduces the `ChatClient`/`StreamingChatClient` interfaces with implementations for OpenAI and Azure OpenAI, Ollama, VertexAI, Huggingface, Bedrock/Llama2, Bedrock/Anthropic, Bedrock/Titan, Bedrock/Cohere etc.
+Similarly abstractions, such as `EmbeddingClient`, `ImageClient` and various model implementations for them are provided.
 
 In addition to these core abstractions, Spring AI aims to provide higher-level functionalities to address common use cases such as "`Q&A over your documentation`" or "`Chat with your documentation.`"
-As the complexity of the use cases increases, the Spring AI project will integrate with other projects in the Spring Ecosystem, such as Spring Integration, Spring Batch, and Spring Data.
+As the complexity of the use cases increases, the Spring AI project will integrate with other projects in the Spring Ecosystem, such as Spring Integration, Spring Batch, and Spring Data, Spring Cloud GCP, Spring Cloud, etc.
 
 To simplify setup, Spring Boot starters are available to help set up essential dependencies and classes.
 There is also a collection of sample applications to help you explore the project's features. Lastly, the new Spring CLI project also enables you to get started quickly by using the `spring boot new ai` command for new projects or `spring boot add ai` for adding AI capabilities to your existing application.