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app.py

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+import time
+import gradio as gr
+
+import asyncio
+from pymongo import MongoClient
+from langchain.vectorstores import MongoDBAtlasVectorSearch
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+import json
+
+
+## Connect to MongoDB Atlas local cluster
+MONGODB_ATLAS_CLUSTER_URI = os.getenv('MONGODB_ATLAS_CLUSTER_URI')
+client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
+db_name = 'sample_mflix'
+collection_name = 'movies'
+collection = client[db_name][collection_name]
+
+## Create a collection and insert data
+print ('Creating collection movies')
+client[db_name].create_collection(collection_name)
+
+## Create a vector search index
+print ('Creating vector search index')
+collection.create_search_index(model={"definition": {"mappings":{
+  "dynamic":True,
+  "fields": {
+    "plot_embedding": {
+      "type": "knnVector",
+      "dimensions": 1536,
+      "similarity": "euclidean"
+    }
+  }
+}}, "name":'default'})
+
+# sleep for minute
+print ('Waiting for vector index on field "embedding" to be created')
+time.sleep(60)
+
+vector_store = MongoDBAtlasVectorSearch(embedding=OpenAIEmbeddings(), collection=collection, index_name='default', text_key='plot', embedding_key='plot_embedding')
+
+def get_movies(message, history):
+    movies = vector_store.similarity_search(message, 3)
+    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]
+
+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()
+
+if __name__ == "__main__":
+    demo.launch()

requirments.txt

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+ pymongo 
+ langchain
+ openai