Fix LangChain4j naming

Author: kdubois

documentation/modules/ROOT/pages/17_prompts.adoc

@@ -1,13 +1,13 @@
 = Working with prompts
 
-:project-ai-name: quarkus-langchain-app
+:project-ai-name: quarkus-langchain4j-app
 
 The Quarkus LangChain4j extension seamlessly integrates Large Language Models (LLMs) into Quarkus applications. LLMs are AI-based systems designed to understand, generate, and manipulate human language, showcasing advanced natural language processing capabilities. Thanks to this extensions we can enable the harnessing of LLM capabilities for the development of more intelligent applications. 
 
 In this first chapter we'll explore the simplest of interactions with an LLM: Prompting. It essentially means just asking questions to an LLM and receiving an answer in natural language from a given Model, such as OpenAI, Mistral, Hugging Face, Ollama, etc.
 
 
-== Creating a Quarkus & LangChain Application
+== Creating a Quarkus & LangChain4j Application
 
 [tabs%sync]
 ====

documentation/modules/ROOT/pages/18_chains_memory.adoc

@@ -1,6 +1,6 @@
 = Chains and Memory
 
-:project-ai-name: quarkus-langchain-app
+:project-ai-name: quarkus-langchain4j-app
 
 So far we explored how to use prompts with LLMs, however to really leverage the power of LLMs it is essential that you 
 can build a conversation by referring to previous questions and answers and manage concurrent interactions. 

documentation/modules/ROOT/pages/19_agents_tools.adoc

@@ -1,6 +1,6 @@
 = Agents/Tools
 
-:project-ai-name: quarkus-langchain-app
+:project-ai-name: quarkus-langchain4j-app
 
 Things become more interesting when you can bring the AI LLM into your application and get it to interact with specific functions you build for it. 
 

documentation/modules/ROOT/pages/20_embed_documents.adoc

@@ -1,6 +1,6 @@
 = Embedding Documents
 
-:project-ai-name: quarkus-langchain-app
+:project-ai-name: quarkus-langchain4j-app
 
 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. 
 Through this technique you can combine your own business data (or whatever data!) with the LLM to enrich the possibilities offered by your application.