Update app.py

app.py

@@ -20,6 +20,11 @@ _ = load_dotenv(find_dotenv())
 
 #openai.api_key = os.environ["OPENAI_API_KEY"]
 
+mongodb_atlas_cluster_uri = os.environ["MONGODB_ATLAS_CLUSTER_URI"]
+client = MongoClient(mongodb_atlas_cluster_uri)
+MONGODB_COLLECTION = client["langchain_db"]["gpt-4"]
+MONGODB_INDEX_NAME = "default"
+
 template = """If you don't know the answer, just say that you don't know, don't try to make up an answer. Keep the answer as concise as possible. Always say 
               "🧠 Thanks for using the app - Bernd" at the end of the answer. """
 
@@ -64,19 +69,19 @@ def document_loading_splitting():
     return splits
 
 def document_storage_chroma(splits):
-    vector_db = Chroma.from_documents(documents = splits, 
-                                      embedding = OpenAIEmbeddings(disallowed_special = ()), 
-                                      persist_directory = CHROMA_DIR)
+    Chroma.from_documents(documents = splits, 
+                          embedding = OpenAIEmbeddings(disallowed_special = ()), 
+                          persist_directory = CHROMA_DIR)
 
 def document_storage_mongodb(splits):
-    #TODO
-    vector_db = Chroma.from_documents(documents = splits, 
-                                      embedding = OpenAIEmbeddings(disallowed_special = ()), 
-                                      persist_directory = CHROMA_DIR)
+    MongoDBAtlasVectorSearch.from_documents(documents = splits,
+                                            embedding = OpenAIEmbeddings(disallowed_special = ()),
+                                            collection = MONGODB_COLLECTION,
+                                            index_name = MONGODB_INDEX_NAME)
 
 def document_retrieval_chroma(llm, prompt):
     db = Chroma(embedding_function = OpenAIEmbeddings(),
-                       persist_directory = CHROMA_DIR)
+                persist_directory = CHROMA_DIR)
     return db
 
 def document_retrieval_mongodb(llm, prompt):
@@ -109,12 +114,13 @@ def invoke(openai_api_key, rag_option, prompt):
         llm = ChatOpenAI(model_name = MODEL_NAME, 
                          openai_api_key = openai_api_key, 
                          temperature = 0)
-        #splits = document_loading_splitting()
         if (rag_option == "Chroma"):
+            #splits = document_loading_splitting()
             #document_storage_chroma(splits)
             db = document_retrieval_chroma(llm, prompt)
             result = rag_chain(llm, prompt, db)
         elif (rag_option == "MongoDB"):
+            #splits = document_loading_splitting()
             #document_storage_mongodb(splits)
             db = document_retrieval_mongodb(llm, prompt)
             result = rag_chain(llm, prompt, db)