Add pictures to the documentation

docs/modules/ROOT/pages/index.adoc

@@ -16,6 +16,8 @@ For instance, an application utilizing this extension can:
 This extension is built upon the https://github.com/langchain4j/langchain4j[LangChain4j library].
 It offers a declarative approach to interact with diverse LLMs like OpenAI, Hugging Face, or Ollama. It facilitates LLM-invoked functions within Quarkus applications and allows document loading within the LLM "context".
 
+image::llms-big-picture.png[width=600,align="center"]
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 == Quick Overview
 
 To incorporate this extension, add the `io.quarkiverse.langchain4j:quarkus-langchain4j` extension to your build file.

docs/modules/ROOT/pages/retrievers.adoc

@@ -4,6 +4,7 @@ include::./includes/attributes.adoc[]
 
 Many applications involving Large Language Models (LLMs) often require user-specific data beyond their training set, such as CSV files, data from various sources, or reports. To achieve this, the process of Retrieval Augmented Generation (RAG) is commonly employed.
 
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 == Understanding the Data Ingestion Journey
 
 Before delving into the RAG process, it's crucial to delineate two distinct phases:
@@ -12,6 +13,8 @@ The Data Ingestion Process:: Involves data collection, cleaning, transformation,
 
 The RAG Process:: Before engaging with the LLM, it seeks relevant documents in the database and passes them for model augmentation.
 
+image::chatbot-architecture.png[width=1000,align="center"]
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 == Unveiling Embeddings
 
 Embeddings denote data representations, typically in a lower-dimensional space (arrays of floats), preserving essential characteristics of the original data. In the realm of language or text, word embeddings represent words as numerical vectors in a continuous space.