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| import streamlit as st | |
| from llama_index import VectorStoreIndex, ServiceContext, set_global_service_context | |
| from llama_index.llms import AzureOpenAI | |
| from llama_index.embeddings import OpenAIEmbedding | |
| import json | |
| import os | |
| from llama_index import SimpleDirectoryReader | |
| # Initialize message history | |
| st.header("Chat with André's research 💬 📚") | |
| if "messages" not in st.session_state.keys(): # Initialize the chat message history | |
| st.session_state.messages = [ | |
| {"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) | |
| def load_data(): | |
| 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=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 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=config_details["ENGINE_EMBEDDING"], | |
| api_key=os.getenv("OPENAI_API_KEY"), | |
| api_base=config_details['OPENAI_API_BASE'], | |
| api_type="azure", | |
| api_version=config_details['OPENAI_API_VERSION'], | |
| ) | |
| service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model) | |
| set_global_service_context(service_context) | |
| index = VectorStoreIndex.from_documents(documents) #, service_context=service_context) | |
| return index | |
| 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 | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| for message in st.session_state.messages: # Display the prior chat messages | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| # If last message is not from assistant, generate a new response | |
| if st.session_state.messages[-1]["role"] != "assistant": | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| response = chat_engine.chat(prompt) | |
| st.write(response.response) | |
| message = {"role": "assistant", "content": response.response} | |
| st.session_state.messages.append(message) # Add response to message history | |
| if __name__ == "__main__": | |
| main() | |