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Udpate Spring AI Built-in Advisors documentation

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Mark Pollack
2024-10-08 12:39:04 +02:00
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spring-ai-docs/src/main/antora/modules/ROOT/pages/api/advisors.adoc
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@@ -307,12 +307,35 @@ public class ReReadingAdvisor implements CallAroundAdvisor, StreamAroundAdvisor
<4> You can control the order of execution by setting the order value. Lower values execute first.
<5> Provides a unique name for the advisor.
==== Spring AI built-in Advisors
==== Spring AI Built-in Advisors
You can also explore the built-in advisors provided by the Spring AI framework.
For example the `MessageChatMemoryAdvisor`, `PromptChatMemoryAdvisor` and `VectorStoreChatMemoryAdvisor` advisors provide different strategies the conversation chat history in a chat memory store and the `QuestionAnswerAdvisor` uses a vector store to provide question-answering capabilities (e.g. implements the RAG pattern).
Spring AI framework provides several built-in advisors to enhance your AI interactions. Here's an overview of the available advisors:
===== Chat Memory Advisors
These advisors manage conversation history in a chat memory store:
* `MessageChatMemoryAdvisor`
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Retrieves memory and adds it as a collection of messages to the prompt. This approach maintains the structure of the conversation history. Note, not all AI Models support this approach.
* `PromptChatMemoryAdvisor`
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Retrieves memory and incorporates it into the prompt's system text.
* `VectorStoreChatMemoryAdvisor`
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Retrieves memory from a VectorStore and adds it into the prompt's system text. This advisor is useful for efficiently searching and retrieving relevant information from large datasets.
===== Question Answering Advisor
* `QuestionAnswerAdvisor`
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This advisor uses a vector store to provide question-answering capabilities, implementing the RAG (Retrieval-Augmented Generation) pattern.
===== Content Safety Advisor
* `SafeGuardAdvisor`
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A simple advisor designed to prevent the model from generating harmful or inappropriate content.
The `SafeGuardAdvisor` is another, simple, built-in advisor that can be used to prevent the model from generating harmful or inappropriate content.
=== Streaming vs Non-Streaming