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import os
import octoai
octoai_client = octoai.client.Client(token=os.getenv('OCTOML_KEY'))
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key=os.getenv('PINECONE_API_KEY'))
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_source_node
from llama_index.llms.octoai import OctoAI
octoai = OctoAI(
token=os.getenv('OCTOML_KEY'),
model="meta-llama-3-70b-instruct",
max_tokens=512,
temperature=0.1,
)
from llama_index.core.memory import ChatMemoryBuffer
import gradio as gr
from io import StringIO
def get_credit_dist(history):
_out = StringIO()
print("Disabled momentarily...", file=_out)
return _out.getvalue()
with gr.Blocks() as demo:
chatbot = gr.Chatbot(height=800)
msg = gr.Textbox()
clear = gr.Button("Clear")
credit_box = gr.Textbox(label="Credit distribution", lines=20, autoscroll=False)
credit_btn = gr.Button("Credit response")
def get_chat_engine():
vector_store = PineconeVectorStore(pinecone_index=pc.Index('prorata-postman-ds-256'))
vindex = VectorStoreIndex.from_vector_store(vector_store)
memory = ChatMemoryBuffer.from_defaults(token_limit=5000)
return vindex.as_chat_engine(
chat_mode="context",
llm=octoai,
memory=memory,
system_prompt="You are a chatbot, able to have normal interactions, as well as talk about news events provided in the context of the conversation.",
)
chat_engine_var = gr.State(get_chat_engine)
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history, chat_engine):
response = chat_engine.stream_chat(history[-1][0])
history[-1][1] = ""
for token in response.response_gen:
history[-1][1] += token
yield history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, chat_engine_var], chatbot)
clear.click(lambda x: x.reset(), chat_engine_var, chatbot, queue=False)
credit_btn.click(get_credit_dist, chatbot, credit_box)
if __name__ == "__main__":
demo.queue()
demo.launch()
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