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jtsnr
2023-12-12 07:28:41 +00:00
committed by Mark Pollack
parent 2c0da77a5c
commit 7f151c562e
2 changed files with 3 additions and 3 deletions
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4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/aiclient.adoc
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@@ -72,7 +72,7 @@ For instance, OpenAI recognizes message categories for distinct conversational r
While the term, `MessageType`, might imply a specific message format, in this context, it effectively designates the role a message plays in the dialogue.
For AI models that do not use specific roles, the `UserMessage` implementation acts as a standard category, typically representing user-generated inquiries or instructions.
To understand the practical application and the relationship between `Prompt` and `Message`, especially in the context of these roles or message categories, see the detailed explanations in the <<Prompts>> section.
To understand the practical application and the relationship between `Prompt` and `Message`, especially in the context of these roles or message categories, see the detailed explanations in the xref:api/prompt.adoc[Prompts] section.
=== AiResponse
@@ -123,7 +123,7 @@ Others are welcome. The list is not at all closed.
== OpenAI-Compatible Models
A variety of models compatible with the OpenAI API are available, including those that can be operated locally, such as [LocalAI](https://github.com/mudler/LocalAI). The standard configuration for connecting to the OpenAI API is through the `spring.ai.openai.baseUrl` property, which defaults to `https://api.openai.com`.
A variety of models compatible with the OpenAI API are available, including those that can be operated locally, such as https://github.com/mudler/LocalAI[LocalAI]. The standard configuration for connecting to the OpenAI API is through the `spring.ai.openai.baseUrl` property, which defaults to `https://api.openai.com`.
To link the OpenAI client to a compatible model that uses the OpenAI API, you should adjust the `spring.ai.openai.baseUrl` property to the corresponding URL of the model you wish to connect to.

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spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs.adoc
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@@ -12,7 +12,7 @@ Vector databases are used to integrate your data with AI models.
The first step in their usage is to load your data into a vector database.
Then, when a user query is to be sent to the AI model, a set of similar documents is first retrieved.
These documents then serve as the context for the user's question and are sent to the AI model, along with the user's query.
This technique is known as <<concept-rag,Retrieval Augmented Generation (RAG)>>.
This technique is known as xref:concepts.adoc#concept-rag[Retrieval Augmented Generation (RAG)].
The following sections describe the Spring AI interface for using multiple vector database implementations and some high-level sample usage.