ChatGPT for my lecture slides
Built with Streamlit, powered by LlamaIndex and LangChain.
Uses the latest ChatGPT API from OpenAI.
Inspired by AthensGPT
demo.mp4
- Parses pdf with pypdf
- Index Construction with LlamaIndex's
GPTSimpleVectorIndex
- the
text-embedding-ada-002
model is used to create embeddings - see vector store index page to learn more
- here's a sample index
- the
- indexes and files are stored on s3
- Query the index
- uses the latest ChatGPT model
gpt-3.5-turbo
- uses the latest ChatGPT model
- configure aws (quickstart)
aws configure
-
create an s3 bucket with a unique name
-
Change the bucket name in the codebase (look for
bucket_name = "classgpt"
to whatever you created. -
rename [.env.local.example] to
.env
and add your openai credentials
- create python env
conda create -n classgpt python=3.9
conda activate classgpt
- install dependencies
pip install -r requirements.txt
- run streamlit app
cd app/
streamlit run app/01_❓_Ask.py
Alternative, you can use Docker
docker compose up
Then open up a new tab and navigate to http://localhost:8501/
- local mode for app (no s3)
- global variable use_s3 to toggle between local and s3 mode
- deploy app to streamlit cloud
- have input box for openai key
- uses pyarrow local FS to store files
- update code for new langchain update
- Custom prompts and tweak settings
- create a settings page for tweaking model parameters and provide custom prompts example
- Add ability to query on multiple files
- Compose indices of multiple lectures and query on all of them
- loop through all existing index, create the ones that haven't been created, and compose them together
- references
Tokens can be thought of as pieces of words. Before the API processes the prompts, the input is broken down into tokens. These tokens are not cut up exactly where the words start or end - tokens can include trailing spaces and even sub-words. Here are some helpful rules of thumb for understanding tokens in terms of lengths:
- 1 token ~= 4 chars in English
- 1 token ~= ¾ words
- 100 tokens ~= 75 words
- 1-2 sentence ~= 30 tokens
- 1 paragraph ~= 100 tokens
- 1,500 words ~= 2048 tokens
Try the OpenAI Tokenizer tool
An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.
For text-embedding-ada-002
, cost is $0.0004 / 1k tokens or 3000 pages/dollar
For gpt-3.5-turbo
model (ChatGPTAPI) cost is $0.002 / 1K tokens
For text-davinci-003
model, cost is $0.02 / 1K tokens
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