Update app.py

app.py

@@ -15,7 +15,7 @@ output_parser = StrOutputParser()
 import json
 
 
-## Connect to MongoDB Atlas local cluster
+## Connect to MongoDB Atlas 
 MONGODB_ATLAS_CLUSTER_URI = os.getenv('MONGODB_ATLAS_CLUSTER_URI')
 client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
 db_name = 'sample_mflix'
@@ -23,12 +23,17 @@ collection_name = 'embedded_movies'
 collection = client[db_name][collection_name]
 
 try:
+    ## Vector store init
     vector_store = MongoDBAtlasVectorSearch(embedding=OpenAIEmbeddings(), collection=collection, index_name='vector_index', text_key='plot', embedding_key='plot_embedding')
+
+    ## LLM init
     llm = ChatOpenAI(temperature=0)
     prompt = ChatPromptTemplate.from_messages([
     ("system", "You are a movie recommendation engine which post a concise and short summary on relevant movies."),
     ("user", "List of movies: {input}")
      ])
+
+    ## RAG Chain
     chain = prompt | llm | output_parser
 
 except:
@@ -39,20 +44,23 @@ except:
 def get_movies(message, history):
 
     try:
+        ### Get top 3 picks
         movies =  vector_store.similarity_search(message, 3)
         return_text = ''
         for movie in movies:
             return_text = return_text + 'Title : ' +  movie.metadata['title'] + '\n------------\n' + 'Plot: ' + movie.page_content + '\n\n'
-    
+
+        ## Invoke RAG on the located documents
         print_llm_text = chain.invoke({"input": return_text})
     
         for i in range(len(print_llm_text)):
             time.sleep(0.05)
             yield "Found: " + "\n\n" + print_llm_text[: i+1]
     except:
-        yield "Please clone the repo and add your open ai key as well as your MongoDB Atlas URI in the Secret Section of you Space\n OPENAI_API_KEY (your Open AI key) and MONGODB_ATLAS_CLUSTER_URI (0.0.0.0/0 whitelisted instance with Vector index created) \n\n For more information : https://mongodb.com/products/platform/atlas-vector-search"
+        yield "Please clone the space and add your open ai key as well as your MongoDB Atlas URI in the Secret Section of you Space\n OPENAI_API_KEY (your Open AI key) and MONGODB_ATLAS_CLUSTER_URI (0.0.0.0/0 whitelisted instance with Vector index created) \n\n For more information : https://mongodb.com/products/platform/atlas-vector-search"
     
 
+## Start gradio chat interface
 demo = gr.ChatInterface(get_movies, examples=["What movies are scary?", "Find me a comedy", "Movies for kids"], title="Movies Atlas Vector Search",description="This small chat uses a similarity search to find relevant movies, it uses an MongoDB Atlase Vector Search read more here: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-tutorial",submit_btn="Search").queue()
 
 if __name__ == "__main__":