Update app.py

app.py

@@ -6,8 +6,12 @@ from pymongo import MongoClient
 from langchain_community.vectorstores import MongoDBAtlasVectorSearch
 from langchain_openai import OpenAIEmbeddings
 from langchain_community.llms import OpenAI
-# from langchain_community.prompts import PromptTemplate
-# from langchain.chains import LLMChain
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+output_parser = StrOutputParser()
+
 import json
 
 
@@ -18,49 +22,38 @@ db_name = 'sample_mflix'
 collection_name = 'embedded_movies'
 collection = client[db_name][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)
-
 try:
     vector_store = MongoDBAtlasVectorSearch(embedding=OpenAIEmbeddings(), collection=collection, index_name='vector_index', text_key='plot', embedding_key='plot_embedding')
+    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}")
+     ])
+    chain = prompt | llm | output_parser
 
 except:
-    # If open ai key is wrong
+    #If open ai key is wrong
     print ('Open AI key is wrong')
     vector_store = None
 
 def get_movies(message, history):
-    # Use AsyncIO to run the similarity search in the background
-   # movies =  vector_store.similarity_search(message, 3)
-    print ('Searching for: ' + message)
+
     try:
         movies =  vector_store.similarity_search(message, 3)
-        retrun_text = ''
+        return_text = ''
         for movie in movies:
-            retrun_text = retrun_text + 'Title : ' +  movie.metadata['title'] + '\n------------\n' + 'Plot: ' + movie.page_content + '\n\n'
-
-        for i in range(len(retrun_text)):
+            return_text = return_text + 'Title : ' +  movie.metadata['title'] + '\n------------\n' + 'Plot: ' + movie.page_content + '\n\n'
+    
+        print_llm_text = chain.invoke({"input": return_text})
+    
+        for i in range(len(print_llm_text)):
             time.sleep(0.05)
-            yield "Found: " + "\n\n" + retrun_text[: i+1]
+            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 UR 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 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"
     
 
-demo = gr.ChatInterface(get_movies, examples=["What movies are scary?", "Find me a comedy", "Movies for kids"],description="Uses the sample_mflix.embedded_movies collection from [sample_data](https://www.mongodb.com/docs/atlas/sample-data/sample-mflix/#sample_mflix.embedded_movies) require a [vector index](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-tutorial/#create-the-atlas-vector-search-index) on `plot_embedding` field", title="Movies Atlas Vector Search", submit_btn="Search").queue()
+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__":
     demo.launch()