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import os
import queries
import pinecone
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
import streamlit as st
import openai
import time
from tqdm.auto import tqdm
from typing import Tuple
# from dotenv import load_dotenv,find_dotenv,dotenv_values
# load_dotenv(find_dotenv(),override=True)
from canopy.tokenizer import Tokenizer
from canopy.knowledge_base import KnowledgeBase
from canopy.context_engine import ContextEngine
from canopy.chat_engine import ChatEngine
from canopy.llm.openai import OpenAILLM
# from canopy.llm.models import ModelParams
from canopy.models.data_models import Document, Messages, UserMessage, AssistantMessage
from canopy.models.api_models import ChatResponse
def chat(new_message: str, history: Messages) -> Tuple[str, Messages, ChatResponse]:
messages = history + [UserMessage(content=new_message)]
response = chat_engine.chat(messages)
assistant_response = response.choices[0].message.content
return assistant_response, messages + [AssistantMessage(content=assistant_response)], response
# Set secrets
# PINECONE_ENVIRONMENT=db.secrets.get('PINECONE_ENVIRONMENT')
# PINECONE_API_KEY=db.secrets.get('PINECONE_API_KEY')
PINECONE_ENVIRONMENT=os.getenv('PINECONE_ENVIRONMENT')
PINECONE_API_KEY=os.getenv('PINECONE_API_KEY')
# Set the page title
st.set_page_config(
page_title='Aerospace Chatbot: AMS w/Langchain',
)
st.title('Aerospace Mechanisms Chatbot')
with st.expander('''What's under the hood?'''):
st.markdown('''
This chatbot will look up from all Aerospace Mechanism Symposia in the following location: https://github.com/dsmueller3760/aerospace_chatbot/tree/main/data/AMS
* Source code: https://github.com/dsmueller3760/aerospace_chatbot/blob/main/scripts/setup_page_canopy.py
* Uses pinecone canopy: https://www.pinecone.io/blog/canopy-rag-framework/
* **Response time ~45 seconds per prompt**
''')
# Add a sidebar for input options
st.title('Input')
st.sidebar.title('Input Options')
# Add input fields in the sidebar
model_name=st.sidebar.selectbox('Model', ['gpt-3.5-turbo''gpt-3.5-turbo-16k','gpt-3.5-turbo','gpt-3.5-turbo-1106','gpt-4','gpt-4-32k'], index=1)
model_list={'gpt-3.5-turbo':4096,
'gpt-3.5-turbo-16k':16385,
'gpt-3.5-turbo-1106':16385,
'gpt-4':8192,
'gpt-4-32k':32768}
temperature = st.sidebar.slider('Temperature', min_value=0.0, max_value=2.0, value=0.0, step=0.1)
n=None # Not used. How many chat completion choices to generate for each input message.
top_p=None # Not used. Only use this or temperature. Where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
k=st.sidebar.number_input('Number document chunks per query', min_value=1, step=1, value=15)
output_level=st.sidebar.selectbox('Level of Output', ['Concise', 'Detailed', 'No Limit'], index=2)
max_prompt_tokens=model_list[model_name]
# Vector databases
st.sidebar.title('Vector Database')
index_name=st.sidebar.selectbox('Index name', ['canopy--ams'], index=0)
# Embeddings
st.sidebar.title('Embeddings')
embedding_type=st.sidebar.selectbox('Embedding type', ['Openai'], index=0)
embedding_name=st.sidebar.selectbox('Embedding name', ['text-embedding-ada-002'], index=0)
# Add a section for secret keys
st.sidebar.title('Secret Keys')
OPENAI_API_KEY = st.sidebar.text_input('OpenAI API Key', type='password')
if OPENAI_API_KEY:
openai.api_key = OPENAI_API_KEY
embeddings_model = OpenAIEmbeddings(model=embedding_name,openai_api_key=OPENAI_API_KEY)
# Set up chat history
qa_model_obj = st.session_state.get('qa_model_obj',[])
message_id = st.session_state.get('message_id', 0)
history = st.session_state.get('history',[])
if 'messages' not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message['role']):
st.markdown(message['content'])
# Process some items
if output_level == 'Concise':
out_token = 50
else:
out_token = 516
# Display assistant response in chat message container
if prompt := st.chat_input('Prompt here'):
st.session_state.messages.append({'role': 'user', 'content': prompt})
with st.chat_message('user'):
st.markdown(prompt)
with st.chat_message('assistant'):
message_placeholder = st.empty()
with st.status('Generating response...') as status:
t_start=time.time()
message_id += 1
st.write('Message: '+str(message_id))
# Process some items
if output_level == 'Concise':
max_generated_tokens = 50
elif output_level == 'Detailed':
max_generated_tokens = 516
else:
max_generated_tokens = None
# Inialize canopy
Tokenizer.initialize()
pinecone.init(
api_key=PINECONE_API_KEY,
environment=PINECONE_ENVIRONMENT
)
kb = KnowledgeBase(index_name=index_name,
default_top_k=k)
kb.connect()
context_engine = ContextEngine(kb)
llm=OpenAILLM(model_name=model_name)
chat_engine = ChatEngine(context_engine,
llm=llm,
max_generated_tokens=max_generated_tokens,
max_prompt_tokens=max_prompt_tokens)
st.write('Searching vector database, generating prompt...')
response, history, chat_response = chat(prompt, history)
message_placeholder.markdown(response)
t_delta=time.time() - t_start
status.update(label='Prompt generated in '+"{:10.3f}".format(t_delta)+' seconds', state='complete', expanded=False)
st.session_state['history'] = history
st.session_state['qa_model_obj'] = qa_model_obj
st.session_state['message_id'] = message_id
st.session_state.messages.append({'role': 'assistant', 'content': response})
else:
st.warning('No API key found. Add your API key in the sidebar under Secret Keys. Find it or create one here: https://platform.openai.com/api-keys')
st.info('Your API-key is not stored in any form by this app. However, for transparency it is recommended to delete your API key once used.') |