Update app.py

app.py

@@ -34,23 +34,33 @@ try:
         Document(content="Victor, a retired astronaut in Houston, is contacted by an alien intelligence through a mysterious transmission. As he seeks to uncover the truth, he is drawn into an interstellar adventure that reveals the universe's greatest mysteries and humanity's place among the stars.")
     ]
     
+    # Initializing a document store in MongoDB Atlas to store the narrative documents.
     document_store = MongoDBAtlasDocumentStore(
-        database_name="sample_mflix",
-        collection_name="haystack_embedded_movies",
-        vector_search_index="default",
+        database_name="sample_mflix",  # Specifies the database name.
+        collection_name="haystack_embedded_movies",  # Specifies the collection name where documents will be stored.
+        vector_search_index="default",  # The search index for vectorized content.
     )
+    
+    # Setting up a document writer to handle the insertion of documents into the MongoDB collection.
     doc_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)
+    
+    # Initializing a document embedder to convert text content into vectorized form.
     doc_embedder = OpenAIDocumentEmbedder()
-
+    
+    # Creating a pipeline for indexing documents. The pipeline includes embedding and writing documents.
     indexing_pipe = Pipeline()
     indexing_pipe.add_component(instance=doc_embedder, name="doc_embedder")
     indexing_pipe.add_component(instance=doc_writer, name="doc_writer")
     
+    # Connecting the components of the pipeline for document flow.
     indexing_pipe.connect("doc_embedder.documents", "doc_writer.documents")
+    
+    # Running the pipeline with the list of documents to index them in MongoDB.
     indexing_pipe.run({"doc_embedder": {"documents": documents}})
-
+    
+    # Template for generating prompts for a movie recommendation engine.
     prompt_template = """
-        You are a movie recommendation engine use the following cotext documents.\nDocuments:
+        You are a movie recommendation engine use the following context documents.\nDocuments:
         {% for doc in documents %}
             {{ doc.content }}
         {% endfor %}
@@ -58,20 +68,28 @@ try:
         \Query: {{query}}
         \nAnswer:
     """
-
+    
+    # Setting up a retrieval-augmented generation (RAG) pipeline for generating responses.
     rag_pipeline = Pipeline()
     rag_pipeline.add_component("text_embedder", OpenAITextEmbedder())
-
     
+    # Adding a component for retrieving related documents from MongoDB based on the query embedding.
     rag_pipeline.add_component(instance=MongoDBAtlasEmbeddingRetriever(document_store=document_store,top_k=15), name="retriever")
+    
+    # Building prompts based on retrieved documents to be used for generating responses.
     rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
+    
+    # Adding a language model generator to produce the final text output.
     rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
+    
+    # Connecting the components of the RAG pipeline to ensure proper data flow.
     rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
     rag_pipeline.connect("retriever", "prompt_builder.documents")
     rag_pipeline.connect("prompt_builder", "llm")
-
-except Exception as e:
-    print("An error occurred: \n" + error_message)
+    
+    # Exception handling to catch and display errors during the pipeline execution.
+    except Exception as e:
+        print("An error occurred: \n" + error_message)
     
 
 def get_movies(message, history):