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Implementing a Chatbot using Streamlit and OpenAI in Python

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In this blog post, we will explore how to implement a chatbot using Streamlit and OpenAI in Python. The chatbot will be designed to answer questions about the contents of the Plan and Building Law. This is a practical application of Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI) that focuses on the interaction between computers and humans through natural language.

The implementation of the chatbot involves several steps. First, we import the necessary libraries. The main libraries used in this script are Streamlit, OpenAI, and Chroma. Streamlit is a fast, easy, and fun way to build web apps in Python. OpenAI's GPT-4 model is used for language understanding and generation. Chroma is used for document retrieval.

The script starts by setting up the Streamlit page configuration and defining the system messages for the chatbot. The system messages provide the initial instructions for the chatbot. The script then defines a function to configure the document retriever. The document retriever is responsible for retrieving the relevant sections or chapters from the Plan and Building Law based on the user's questions.

The script also defines a function to reload the language model. The language model can be configured to use different models, temperatures, system messages, retriever types, and granularities. The script includes a sidebar for reloading the language model.

Finally, the script sets up a chat interface for interacting with the chatbot. The chat interface includes a chat input for the user to enter their questions and a chat message for the chatbot to display its responses. The chatbot's responses are generated by the OpenAI GPT-4 model based on the user's questions and the retrieved sections or chapters from the Plan and Building Law.

In conclusion, this script demonstrates how to implement a chatbot using Streamlit and OpenAI in Python. The chatbot provides a practical application of NLP and AI, and it can be easily adapted to answer questions about other topics or documents. So, if you are interested in implementing your own chatbot, this script can serve as a good starting point.