fix(genapi): remove file redundant annotations in comments

Author: fpagny

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

@@ -95,11 +95,9 @@ In this tutorial, you will learn how to implement RAG using LangChain, a leading
 
 ### Configure embeddings client
 
-4. Configure [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html) class from LangChain to use your API Secret Key, Generative APIs Endpoint URL and a supported model (`bge-multilingual-gemma2` in our example).
+4. Edit `embed.py` to configure [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html) class from LangChain to use your API Secret Key, Generative APIs Endpoint URL and a supported model (`bge-multilingual-gemma2` in our example).
 
 ```python
-# embed.py
-
 embeddings = OpenAIEmbeddings(
     openai_api_key=os.getenv("SCW_SECRET_KEY"),
     openai_api_base=os.getenv("SCW_GENERATIVE_APIs_ENDPOINT"),
@@ -110,11 +108,9 @@ embeddings = OpenAIEmbeddings(
 
 ### Configure vector store client
 
-5. Configure connection to your Managed Database for PostgreSQL Instance storing vectors:
+5. Edit `embed.py` to configure connection to your Managed Database for PostgreSQL Instance storing vectors:
 
 ```python
-# embed.py
-
 connection_string = f'postgresql+psycopg2://{os.getenv("SCW_DB_USER")}:{os.getenv("SCW_DB_PASSWORD")}@{os.getenv("SCW_DB_HOST")}:{os.getenv("SCW_DB_PORT")}/{os.getenv("SCW_DB_NAME")}'
 vector_store = PGVector(connection=connection_string, embeddings=embeddings)
 ```
@@ -135,8 +131,6 @@ Then, we will embed them as vectors and store these vectors in your PostgreSQL d
 7. Edit the beginning of `embed.py` to import `S3DirectoryLoader` and `RecursiveCharacterTextSplitter`:
 
    ```python
-   #embed.py
-   
    from langchain_community.document_loaders import S3DirectoryLoader
    from langchain.text_splitter import RecursiveCharacterTextSplitter
 
@@ -147,8 +141,6 @@ Then, we will embed them as vectors and store these vectors in your PostgreSQL d
 8. Edit `embed.py` to list objects:
 
    ```python
-   # embed.py
-   
    session = boto3.session.Session()
    client_s3 = session.client(
        service_name='s3', 
@@ -165,8 +157,6 @@ Then, we will embed them as vectors and store these vectors in your PostgreSQL d
 9. Edit `embed.py` to load all files in your bucket using `S3DirectoryLoader`, split them into chunks of 500 characters using `RecursiveCharacterTextSplitter` and embed them and store them into your PostgreSQL database using `PGVector`.
 
    ```python
-   # embed.py
-   
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0, add_start_index=True, length_function=len, is_separator_regex=False)
    file_loader = S3DirectoryLoader(
        bucket=os.getenv("SCW_BUCKET_NAME", ""),
@@ -224,7 +214,6 @@ Then, we will embed them as vectors and store these vectors in your PostgreSQL d
 2. Edit `rag.py` to load `.env` file, and configure Embeddings format and Vector store:
 
    ```python
-   
    load_dotenv()
 
    embeddings = OpenAIEmbeddings(
@@ -247,8 +236,6 @@ Then, we will embed them as vectors and store these vectors in your PostgreSQL d
 3. Edit `rag.py` to configure LLM client using `ChatOpenAI` and create a simple RAG pipeline:
 
    ```python
-   #rag.py
-   
    llm = ChatOpenAI(
            base_url=os.getenv("SCW_GENERATIVE_APIs_ENDPOINT"),
            api_key=os.getenv("SCW_SECRET_KEY"),