talltree commited on
Commit
2a576c8
1 Parent(s): 4eb2e1f

Update DESCRIPTION.md

Browse files
Files changed (1) hide show
  1. DESCRIPTION.md +4 -1
DESCRIPTION.md CHANGED
@@ -1,7 +1,10 @@
1
  # Development of a Virtual Assistant Powered by the GPT-4 language model
2
 
3
- This project is an innovative initiative by [Tall Tree Health](https://www.talltreehealth.ca/) to enhance the customer experience by integrating cutting-edge conversational Artificial Intelligence (AI) technologies. The goal is to build an advanced question-answering (Q&A) chatbot that seamlessly responds to real-time queries, providing users with personalized guidance as they navigate the healthcare landscape offered by Tall Tree. The Retrieval Augmented Generation (RAG) technique is used to improve the accuracy and reliability of chatbot responses by fetching relevant and up-to-date information from the Tall Tree database.
4
 
5
  # Chatbot Application Development Overview
6
 
7
  The application has been developed in pure Python. The back-end architecture has been built using [LangChain](https://github.com/langchain-ai/langchain), while the front-end, i.e., the chat interface, has been created with [Streamlit](https://streamlit.io/). At the core of our chatbot lies [OpenAI's GPT-4](https://openai.com/gpt-4) (Generative Pre-trained Transformer 4), a state-of-the-art language model known for its advanced comprehension and generation capabilities. For storage of the embeddings and efficient similarity search, the [Qdrant](https://qdrant.tech/) vector database is used. LangChain offers seamless integration with both the OpenAI API and Qdrant, ensuring a smooth workflow.
 
 
 
 
1
  # Development of a Virtual Assistant Powered by the GPT-4 language model
2
 
3
+ This project is an innovative initiative by [Tall Tree Health](https://www.talltreehealth.ca/) to enhance the customer experience by integrating cutting-edge conversational Artificial Intelligence (AI) technologies. This question-answering (Q&A) chatbot responds to real-time queries, providing users with personalized guidance as they navigate the healthcare landscape offered by Tall Tree. The Retrieval Augmented Generation (RAG) technique is used to improve the accuracy and reliability of chatbot responses by fetching relevant and up-to-date information from the Tall Tree database.
4
 
5
  # Chatbot Application Development Overview
6
 
7
  The application has been developed in pure Python. The back-end architecture has been built using [LangChain](https://github.com/langchain-ai/langchain), while the front-end, i.e., the chat interface, has been created with [Streamlit](https://streamlit.io/). At the core of our chatbot lies [OpenAI's GPT-4](https://openai.com/gpt-4) (Generative Pre-trained Transformer 4), a state-of-the-art language model known for its advanced comprehension and generation capabilities. For storage of the embeddings and efficient similarity search, the [Qdrant](https://qdrant.tech/) vector database is used. LangChain offers seamless integration with both the OpenAI API and Qdrant, ensuring a smooth workflow.
8
+
9
+ ## Note:
10
+ This AI assistant is designed to provide guidance and general information about the services offered by Tall Tree Health. It is not intended for seeking medical advice and should not be used as such. The information provided by this generative AI technology cannot replace the advice of qualified healthcare professionals.