diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/images/function-calling-basic-flow.jpg b/spring-ai-docs/src/main/antora/modules/ROOT/images/function-calling-basic-flow2.jpg
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rename from spring-ai-docs/src/main/antora/modules/ROOT/images/function-calling-basic-flow.jpg
rename to spring-ai-docs/src/main/antora/modules/ROOT/images/function-calling-basic-flow2.jpg
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/images/spring-ai-rag.jpg b/spring-ai-docs/src/main/antora/modules/ROOT/images/spring-ai-rag.jpg
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diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/functions.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/functions.adoc
index b351d4b59..47ccfe140 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/functions.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/functions.adoc
@@ -3,7 +3,7 @@
 
 The integration of function support in AI models, permits the model to request the execution of client-side functions, thereby accessing necessary information or performing tasks dynamically as required.
 
-image::function-calling-basic-flow.jpg[Function calling, width=500, align="center"]
+image::function-calling-basic-flow2.jpg[Function calling, width=500, align="center"]
 
 Spring AI currently supports Function invocation for the following AI Models
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/index.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/index.adoc
index 1163ddc8b..b37df4eb3 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/index.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/index.adoc
@@ -26,7 +26,7 @@ Portable `Vector Store API` across multiple providers, including a novel `SQL-li
 
 `Function calling`. Spring AI makes it easy to have the AI model invoke your POJO `java.util.Function` object.
 
-image::function-calling-basic-flow.jpg[Function calling, width=500, align="center"]
+image::function-calling-basic-flow2.jpg[Function calling, width=500, align="center"]
 
 Check the Spring AI xref::api/functions.adoc[Function Calling] documentation.
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/concepts.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/concepts.adoc
index 4bb4313c2..af5be6fd8 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/concepts.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/concepts.adoc
@@ -189,7 +189,7 @@ 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"]
+image::spring-ai-rag2.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:
@@ -205,7 +205,7 @@ The concepts map onto classes in Spring AI:
 
 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"]
+image::function-calling-basic-flow2.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.