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35 lines
2.3 KiB
Markdown
35 lines
2.3 KiB
Markdown
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# Powering your products with ChatGPT and your own data
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The Chatbot Kickstarter is a starter repo to get you used to building basic a basic Chatbot using the ChatGPT API and your own knowledge base. The flow you're taken through was originally presented with [these slides](https://drive.google.com/file/d/1dB-RQhZC_Q1iAsHkNNdkqtxxXqYODFYy/view?usp=share_link), which may come in useful to refer to.
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This repo contains one notebook and two basic Streamlit apps:
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- `powering_your_products_with_chatgpt_and_your_data.ipynb`: A notebook containing a step by step process of tokenising, chunking and embedding your data in a vector database, and building simple Q&A and Chatbot functionality on top.
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- `search.py`: A Streamlit app providing simple Q&A via a search bar to query your knowledge base.
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- `chat.py`: A Streamlit app providing a simple Chatbot via a search bar to query your knowledge base.
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To run either version of the app, please follow the instructions in the respective README.md files in the subdirectories.
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## How it works
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The notebook is the best place to start, and is broadly laid out as follows:
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- **Lay the foundations:**
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- Set up the vector database to accept vectors and data
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- Load the dataset, chunk the data up for embedding and store in the vector database
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- **Make it a product:**
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- Add a retrieval step where users provide queries and we return the most relevant entries
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- Summarise search results with GPT-3
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- Test out this basic Q&A app in Streamlit
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- **Build your moat:**
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- Create an Assistant class to manage context and interact with our bot
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- Use the Chatbot to answer questions using semantic search context
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- Test out this basic Chatbot app in Streamlit
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Once you've run the notebook and tried the two Streamlit apps, you should be in a position to strip out any useful snippets and start your own Q&A or Chat application.
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## Limitations
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- This app uses Redis as a vector database, but there are many other options highlighted `../examples/vector_databases` depending on your need.
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- This is a simple starting point - if you hit issues deploying your use case you may need to tune (non-exhaustive list):
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- The prompt and parameters for the model for it to answer accurately
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- Your search to return more relevant results
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- Your chunking/embedding approach to store the most relevant content effectively for retrieval
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