Upload app.py

app.py

@@ -4,7 +4,7 @@ import os
 import asyncio
 from pymongo import MongoClient
 from langchain_community.vectorstores import MongoDBAtlasVectorSearch
-from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_openai import OpenAIEmbeddings
 from langchain_community.llms import OpenAI
 # from langchain_community.prompts import PromptTemplate
 # from langchain.chains import LLMChain
@@ -15,7 +15,7 @@ import json
 MONGODB_ATLAS_CLUSTER_URI = os.getenv('MONGODB_ATLAS_CLUSTER_URI')
 client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
 db_name = 'sample_mflix'
-collection_name = 'movies'
+collection_name = 'embedded_movies'
 collection = client[db_name][collection_name]
 
 ## Create a vector search index
@@ -38,11 +38,18 @@ print ('Creating vector search index')
 vector_store = MongoDBAtlasVectorSearch(embedding=OpenAIEmbeddings(), collection=collection, index_name='vector_index', text_key='plot', embedding_key='plot_embedding')
 
 def get_movies(message, history):
-    movies = vector_store.similarity_search(message, 3)
+    # Use AsyncIO to run the similarity search in the background
+   # movies =  vector_store.similarity_search(message, 3)
+    print ('Searching for: ' + message)
+    movies =  vector_store.similarity_search(message, 3)
+    retrun_text = ''
     for movie in movies:
-        for i in range(len(movie.metadata['title'])):
-            time.sleep(0.05)
-            yield "Movie " + i + " :  Title - " + movie.metadata['title'][: i+1]
+        retrun_text = retrun_text + movie.metadata['title'] + '\n------------\n' + movie.page_content + '\n\n'
+
+    for i in range(len(retrun_text)):
+        time.sleep(0.05)
+        yield "Found: " + "\n\n" + retrun_text[: i+1]
+    
 
 demo = gr.ChatInterface(get_movies, examples=["What movies are scary?", "Find me a comedy", "Movies for kids"], title="Movies Atlas Vector Search", submit_btn="Search").queue()