Added README instructions; refactored app

README.md

@@ -1,18 +1,31 @@
-# custom-chatbot-streamlit
+# chatbot-streamlit-demo
 
 This application demonstrates how to setup a simple ChatBot with Azure OpenAI and StreamLit.
+
 The ChatBot enables you to talk with your own data - in this case, to learn about André's research.
 
 ## Getting Started
 
-1.Setup virtual environment and install dependencies:
+1. Setup virtual environment and install dependencies:
 ```
 python -m venv venv/
 source venv/bin/activate
 pip install -r requirements.txt
 ```
 
-2. Download test data
+2. Set OPENAI_API_KEY:
+```
+export OPENAI_API_KEY=<insert key here>
+```
+
+3. Download test data:
+```
+gdown https://drive.google.com/drive/folders/1uDSAWtLvp1YPzfXUsK_v6DeWta16pq6y -O ./data/ --folder
+```
 
-## 
+3. Launch the app:
+```
+streamlit run app.py
+```
 
+You can then access the app in your browser at `http://localhost:8501`

app.py

@@ -7,10 +7,6 @@ import os
 from llama_index import SimpleDirectoryReader
 
 
-# Load config values
-with open(r'config.json') as config_file:
-    config_details = json.load(config_file)
-
 # Initialize message history
 st.header("Chat with André's research 💬 📚")
 
@@ -19,25 +15,29 @@ if "messages" not in st.session_state.keys(): # Initialize the chat message hist
         {"role": "assistant", "content": "Ask me a question about André's research!"}
     ]
 
+# Load config values
+with open(r'config.json') as config_file:
+    config_details = json.load(config_file)
+
 
 @st.cache_resource(show_spinner=False)
 def load_data():
-    with st.spinner(text="Loading and indexing the Streamlit docs – hang tight! This should take 1-2 minutes."):
+    with st.spinner(text="Loading and indexing the provided dataset – hang tight! This may take a few seconds."):
         documents = SimpleDirectoryReader(input_dir="./data", recursive=True).load_data()
         llm = AzureOpenAI(
             model="gpt-3.5-turbo",
-            engine="chatbot-streamlit",
+            engine=config_details["ENGINE"],
             temperature=0.5,
             api_key=os.getenv("OPENAI_API_KEY"),
             api_base=config_details['OPENAI_API_BASE'],
             api_type="azure",
             api_version=config_details['OPENAI_API_VERSION'],
-            system_prompt="You are an expert on the Streamlit Python library and your job is to answer technical questions. Assume that all questions are related to the Streamlit Python library. Keep your answers technical and based on facts – do not hallucinate features."
+            system_prompt="You are an expert on André's research and your job is to answer technical questions. Assume that all questions are related to André's research. Keep your answers technical and based on facts – do not hallucinate features."
         )
         # You need to deploy your own embedding model as well as your own chat completion model
         embed_model = OpenAIEmbedding(
             model="text-embedding-ada-002",
-            deployment_name="chatbot-streamlit-embedding",
+            deployment_name=config_details["ENGINE_EMBEDDING"],
             api_key=os.getenv("OPENAI_API_KEY"),
             api_base=config_details['OPENAI_API_BASE'],
             api_type="azure",
@@ -51,7 +51,6 @@ def load_data():
 
 def main():
     index = load_data()
-
     chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)
 
     if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history

requirements.txt

@@ -3,3 +3,4 @@ openai
 llama-index
 nltk
 pypdf
+gdown