Add doc concepts diagrams

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

@@ -144,9 +144,11 @@ Also, asking "`for JSON`" as part of the prompt is not 100% accurate.
 
 This intricacy has led to the emergence of a specialized field involving the creation of prompts to yield the intended output, followed by converting the resulting simple string into a usable data structure for application integration.
 
+image::structured-output-architecture.jpg[Structured Output Converter Architecture, width=900, align="center"]
+
 The xref:api/structured-output-converter.adoc#_structuredoutputconverter[Structured output conversion] employs meticulously crafted prompts, often necessitating multiple interactions with the model to achieve the desired formatting.
 
-== Bringing Your Data to the AI model
+== Bringing Your Data & APIs to the AI Model
 
 How can you equip the AI model with information on which it has not been trained?
 
@@ -187,6 +189,8 @@ The next phase in RAG is processing user input.
 When a user's question is to be answered by an AI model, the question and all the "`similar`" document pieces are placed into the prompt that is sent to the AI model.
 This is the reason to use a vector database. It is very good at finding similar content.
 
+image::spring-ai-rag.jpg[Spring AI RAG, width=1000, align="center"]
+
 There are several concepts that are used in implementing RAG.
 The concepts map onto classes in Spring AI:
 
@@ -196,10 +200,13 @@ The concepts map onto classes in Spring AI:
 * `DocumentWriter`: Lets you persist the Documents into a database (most commonly in the AI stack, a vector database).
 * `Embedding`: A representation of your data as a `List<Double>` that is used by the vector database to compute the "`similarity`" of a user's query to relevant documents.
 
+
 == Function Calling
 
 Large Language Models (LLMs) are frozen after training, leading to stale knowledge and they are unable to access or modify external data.
 
+image::function-calling-basic-flow.jpg[Function calling, width=800, align="center"]
+
 The `Function Calling` mechanism addresses these shortcomings.
 It allows you to register your own functions to connect the large language models to the APIs of external systems.
 These systems can provide LLMs with real-time data and perform data processing actions on their behalf.