Update tutorials/how-to-implement-rag-generativeapis/index.mdx

Co-authored-by: Jessica <[email protected]>

Author: fpagny

tutorials/how-to-implement-rag-generativeapis/index.mdx

@@ -239,6 +239,7 @@ Then, we will embed them as vectors and store these vectors in your PostgreSQL d
    for r in rag_chain.stream("Provide the CLI command to shut down a scaleway instance. Its instance-uuid is example-28f3-4e91-b2af-4c3502562d72"):
        print(r, end="", flush=True)
    ```
+
    - `hub.pull("rlm/rag-prompt")` uses a standard RAG template. This ensures documents content retrieved will be passed as context along your prompt to the LLM using a compatible format.
    - `vector_store.as_retriever()` configures your vector store as additional context to retrieve before calling the LLM. By default, the 4 closest document chunks are retrieved using vector `similarity` score.
    - `rag_chain` defines a workflow performing the following steps in order: Retrieve relevant documents, Prompt LLM with document as context, and final output parsing.