Spaces:
Sleeping
Sleeping
import streamlit as st | |
import chromadb | |
from chromadb.utils import embedding_functions | |
from sentence_transformers import SentenceTransformer | |
from openai import OpenAI | |
# CONSTANTS | |
client = chromadb.PersistentClient(path="./chromadb/") | |
MODEL_NAME: str = "mixedbread-ai/mxbai-embed-large-v1" # ~ 0.5 gb | |
COLLECTION_NAME: str = "scheme" | |
EMBEDDING_FUNC = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=MODEL_NAME) | |
schemer = client.get_collection( | |
name=COLLECTION_NAME, | |
embedding_function=EMBEDDING_FUNC, | |
) | |
DATA_AVAL: bool = schemer.count() > 0 | |
APP_NAME: str = "Groove-GPT" | |
history = [] | |
# INFO | |
st.title(APP_NAME) | |
st.header("What is Groovy-GPT?") | |
st.write("Groovy-GPT is a RAG (Retrieval-Augmented Generation) model that uses ChromaDB to retrieve relevant documents and then uses OpenAI's models to generate a response.") | |
st.write("The model is trained on the MIT Scheme textbook and a handful of Discrete Math and Paradigms related content that Professor Troeger posted") | |
st.write("Data Avaliable: ", DATA_AVAL) | |
# INPUTS | |
user_question: str = st.text_area("Enter your groovy questions here") | |
remember_chat_history = st.toggle("Remember This Chat's History") | |
temperature = st.slider(label="Creativity of Model", min_value=0.0, max_value=2.0, value=0.8) | |
st.markdown("*High creativity will make it go crazy - keep it low*") | |
num_samples = st.slider(label="Amount of References to Give to Model", min_value=10, max=100, value=10) | |
st.markdown("*High amount will make it slow and expensive (and may not be relevant) - keep it low*") | |
access_key: str = st.text_input("Enter your gpt key here", type="password") | |
st.markdown("*For more information about how to get an access key, read [this article](https://platform.openai.com/api-keys). Make sure it has money in it ☠️*", unsafe_allow_html=True) | |
gpt_type: str = st.selectbox(label="Choose GPT Type", options=["gpt-3.5-turbo", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-0125", "gpt-4-32k-0613", "gpt-4-0613", "gpt-4-0125-preview"], index=0) | |
st.markdown("*For more information about GPT types, read [this article](https://platform.openai.com/docs/models).*", unsafe_allow_html=True) | |
st.divider() | |
# ON BUTTON CLICK | |
if st.button('Start Scheming') & (access_key != "") & (user_question != ""): | |
openai_client = OpenAI(api_key=access_key) | |
with st.spinner('Loading...'): | |
# Perform the Chromadb query. | |
results = schemer.query( | |
query_texts=[user_question], | |
n_results=num_samples, | |
include = ['documents'] | |
) | |
documents = results["documents"] | |
response = openai_client.chat.completions.create( | |
model="gpt-3.5-turbo", | |
messages=[ | |
{"role": "system", "content": "You are an expert in functional programming in Scheme, with great knowledge on programming paradigms. You wish to teach the user everything you know about programming paradigms in scheme - so you explain everything thoroughly. Surround Latex equations in dollar signs as such Inline equation: $equation$ & Display equation: $$equation$$"}, | |
{"role": "user", "content": user_question}, | |
{"role": "assistant", "content": str(documents)}, | |
{"role": "user", "content": f"Conversation History: {history}"} | |
], | |
temperature=temperature | |
) | |
history.append({user_question : response.choices[0].message.content} if remember_chat_history else {}) | |
st.header("Prof Says ...") | |
st.write(response.choices[0].message.content) | |
else: | |
st.write("Please provide an input and (valid) API key") | |