Improve the Easy RAG documentation

docs/modules/ROOT/pages/easy-rag.adoc

@@ -4,20 +4,21 @@ include::./includes/attributes.adoc[]
 
 The Quarkus LangChain4j project provides a separate extension named
 `quarkus-langchain4j-easy-rag` which provides an extremely easy way to get a
-RAG pipeline up and running. All that is needed to ingest documents into an
-embedding store is a single configuration property,
-`quarkus.langchain4j.easy-rag.path`, which denotes a path in the local
-filesystem where your documents are stored. On startup, Quarkus will
-automatically scan all files in the directory and ingest them into an
-embedding store.
+RAG pipeline up and running. After adding this extension to your
+application, all that is needed to ingest documents into an embedding store
+is to add a dependency that provides an embedding model, and provide a
+single configuration property, `quarkus.langchain4j.easy-rag.path`, which
+denotes a path in the local filesystem where your documents are stored. On
+startup, Quarkus will automatically scan all files in the directory and
+ingest them into an in-memory embedding store.
 
 Apache Tika, a library for parsing various file formats, is used under the
 hood, so your documents can be in any of its supported formats (plain text,
 PDF, DOCX, HTML, etc), including images with text, which will be parsed
 using OCR (OCR requires to have the Tesseract library installed in your
 system - see https://cwiki.apache.org/confluence/display/TIKA/TikaOCR).
 
-You also don't even need to add a "real" embedding store - the
+You also don't even need to add a persistent embedding store - the
 `quarkus-langchain4j-easy-rag` extension will automatically register a
 simple in-memory store for you if no other store is detected. You also don't
 have to provide an implementation of `RetrievalAugmentor`, a basic default
@@ -30,14 +31,27 @@ still possible to use a persistent embedding store, such as Redis, by adding
 the relevant extension (like `quarkus-langchain4j-redis`) to your
 application.
 
-To see this extension in action, use the project `samples/chatbot-easy-rag`
-in the `quarkus-langchain4j` repository. Simply clone the repository,
-compile it with `mvn clean install -DskipTests`, navigate to
-`samples/chatbot-easy-rag`, and then start the project using `mvn
-quarkus:dev`. When the application starts, navigate to `localhost:8080` and
-ask any questions that the bot can answer (look into the files in
-`samples/chatbot-easy-rag/src/main/resources/documents` to see what
-documents the bot has ingested). For example, ask:
+You have to add an extension that provides an embedding model. For that, you
+can choose from the plethora of extensions like
+`quarkus-langchain4j-openai`, `quarkus-langchain4j-ollama`, or import an
+xref:in-process-embedding.adoc[in-process embedding model] - these have the
+advantage of not having to send data over the wire.
+
+NOTE: If you add two or more artifacts that provide embedding models,
+Quarkus will ask you to choose one of them using the
+`quarkus.langchain4j.embedding-model.provider` property.
+
+== Getting started with a ready-to-use example
+
+To see Easy RAG in action, use the project `samples/chatbot-easy-rag` in the
+https://github.com/quarkiverse/quarkus-langchain4j[quarkus-langchain4j
+repository]. Simply clone the repository, navigate to
+`samples/chatbot-easy-rag`, declare the `QUARKUS_LANGCHAIN4J_OPENAI_API_KEY`
+environment variable containing your OpenAI API key and then start the
+project using `mvn quarkus:dev`. When the application starts, navigate to
+`localhost:8080` and ask any questions that the bot can answer (look into
+the files in `samples/chatbot-easy-rag/src/main/resources/documents` to see
+what documents the bot has ingested). For example, ask:
 
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