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import streamlit as st
try:
import dotenv
except ImportError:
pass
dotenv.load_dotenv()
import openai
import os
import streamlit.components.v1 as components
import requests
import asyncio
openai.api_key = os.getenv("OPENAI_API_KEY")
embedbase_api_key = os.getenv("EMBEDBASE_API_KEY")
URL = "https://embedbase-hosted-usx5gpslaq-uc.a.run.app"
local_history = []
async def add_to_dataset(dataset_id: str, data: str):
response = requests.post(
f"{URL}/v1/{dataset_id}",
headers={
"Content-Type": "application/json",
"Authorization": "Bearer " + embedbase_api_key,
},
json={
"documents": [
{
"data": data,
},
],
},
)
response.raise_for_status()
return response.json()
async def search_dataset(dataset_id: str, query: str, limit: int = 3):
response = requests.post(
f"{URL}/v1/{dataset_id}/search",
headers={
"Content-Type": "application/json",
"Authorization": "Bearer " + embedbase_api_key,
},
json={
"query": query,
"top_k": limit,
},
)
response.raise_for_status()
return response.json()
async def chat(user_input: str, conversation_name: str) -> str:
local_history.append(user_input)
history = await search_dataset(
f"infinite-pt-{conversation_name}",
# searching using last 4 messages from local history
"\n\n---\n\n".join(local_history[-4:]),
limit=3,
)
print("history", history)
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{
"role": "system",
"content": "You are a helpful assistant.",
},
*[
{
"role": "assistant",
"content": h["data"],
}
for h in history["similarities"][-5:]
],
{"role": "user", "content": user_input},
],
)
message = response.choices[0]["message"]
await add_to_dataset(f"infinite-pt-{conversation_name}", message["content"])
local_history.append(message)
return message["content"]
from datetime import datetime
# conversation name is date like ddmmyy_hhmmss
# conversation_name = datetime.now().strftime("%d%m%y_%H%M%S")
conversation_name = st.text_input("Conversation name", "purpose")
# eg not local dev
if not os.getenv("OPENAI_API_KEY"):
embedbase_api_key = st.text_input(
"Your Embedbase key", "get it here <https://app.embedbase.xyz/signup>"
)
openai_key = st.text_input(
"Your OpenAI key", "get it here <https://platform.openai.com/account/api-keys>"
)
openai.api_key = openai_key
user_input = st.text_input("You", "How can I reach maximum happiness this year?")
if st.button("Send"):
infinite_pt_response = asyncio.run(chat(user_input, conversation_name))
st.markdown(
f"""
Infinite-PT
"""
)
st.write(infinite_pt_response)
components.html(
"""
<script>
const doc = window.parent.document;
buttons = Array.from(doc.querySelectorAll('button[kind=primary]'));
const send = buttons.find(el => el.innerText === 'Send');
doc.addEventListener('keydown', function(e) {
switch (e.keyCode) {
case 13:
send.click();
break;
}
});
</script>
""",
height=0,
width=0,
)
st.markdown(
"""
[Source code](https://huggingface.co/spaces/louis030195/infinite-memory-chatgpt)
"""
)
st.markdown(
"""
Built with ❤️ by [louis030195](https://louis030195.com).
"""
)
st.markdown(
"""
Powered by [Embedbase](https://embedbase.xyz).
"""
)
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