chatbot-rag-app: polishes source file style and formatting

example-apps/chatbot-rag-app/.gitignore

@@ -1,7 +1,7 @@
 frontend/build
 frontend/node_modules
 api/__pycache__
-api/.env
 .venv
 venv
-.DS_Store
+.DS_Store
+.env

example-apps/chatbot-rag-app/api/app.py

@@ -1,9 +1,10 @@
-from flask import Flask, jsonify, request, Response
-from flask_cors import CORS
-from uuid import uuid4
-from chat import ask_question
 import os
 import sys
+from uuid import uuid4
+
+from chat import ask_question
+from flask import Flask, Response, jsonify, request
+from flask_cors import CORS
 
 app = Flask(__name__, static_folder="../frontend/build", static_url_path="/")
 CORS(app)

example-apps/chatbot-rag-app/api/chat.py

@@ -1,19 +1,16 @@
 import json
 import os
 
-from elasticsearch import Elasticsearch
+from elasticsearch_client import (
+    elasticsearch_client,
+    get_elasticsearch_chat_message_history,
+)
 from flask import current_app, render_template, stream_with_context
 from langchain_elasticsearch import (
-    ElasticsearchChatMessageHistory,
     ElasticsearchStore,
     SparseVectorStrategy,
 )
-from langchain_openai import ChatOpenAI
 from llm_integrations import get_llm
-from elasticsearch_client import (
-    elasticsearch_client,
-    get_elasticsearch_chat_message_history,
-)
 
 INDEX = os.getenv("ES_INDEX", "workplace-app-docs")
 INDEX_CHAT_HISTORY = os.getenv(

example-apps/chatbot-rag-app/api/elasticsearch_client.py

@@ -1,8 +1,8 @@
+import os
+
 from elasticsearch import Elasticsearch
 from langchain_elasticsearch import ElasticsearchChatMessageHistory
 
-import os
-
 ELASTICSEARCH_URL = os.getenv("ELASTICSEARCH_URL")
 ELASTICSEARCH_USER = os.getenv("ELASTICSEARCH_USER")
 ELASTICSEARCH_PASSWORD = os.getenv("ELASTICSEARCH_PASSWORD")

example-apps/chatbot-rag-app/api/llm_integrations.py

@@ -1,12 +1,11 @@
-from langchain_openai import AzureChatOpenAI
-from langchain_openai import ChatOpenAI
-from langchain_google_vertexai import ChatVertexAI
-from langchain_cohere import ChatCohere
-from langchain_mistralai import ChatMistralAI
-from langchain_aws import ChatBedrock
-
 import os
+
 import vertexai
+from langchain_aws import ChatBedrock
+from langchain_cohere import ChatCohere
+from langchain_google_vertexai import ChatVertexAI
+from langchain_mistralai import ChatMistralAI
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
 
 LLM_TYPE = os.getenv("LLM_TYPE", "openai")
 
@@ -79,7 +78,7 @@ def init_cohere_chat(temperature):
 
 
 def get_llm(temperature=0):
-    if not LLM_TYPE in MAP_LLM_TYPE_TO_CHAT_MODEL:
+    if LLM_TYPE not in MAP_LLM_TYPE_TO_CHAT_MODEL:
         raise Exception(
             "LLM type not found. Please set LLM_TYPE to one of: "
             + ", ".join(MAP_LLM_TYPE_TO_CHAT_MODEL.keys())