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80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
import anthropic
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import streamlit as st
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from streamlit.logger import get_logger
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from langchain.chains import ConversationalRetrievalChain
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from langchain.memory import ConversationBufferMemory
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from langchain.llms import OpenAI
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from langchain.chat_models import ChatAnthropic
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from langchain.vectorstores import SupabaseVectorStore
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from stats import add_usage
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memory = ConversationBufferMemory(
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memory_key="chat_history", return_messages=True)
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openai_api_key = st.secrets.openai_api_key
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anthropic_api_key = st.secrets.anthropic_api_key
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logger = get_logger(__name__)
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def count_tokens(question, model):
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count = f'Words: {len(question.split())}'
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if model.startswith("claude"):
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count += f' | Tokens: {anthropic.count_tokens(question)}'
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return count
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def chat_with_doc(model, vector_store: SupabaseVectorStore, stats_db):
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if 'chat_history' not in st.session_state:
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st.session_state['chat_history'] = []
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question = st.text_area("## Ask a question")
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columns = st.columns(3)
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with columns[0]:
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button = st.button("Ask")
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with columns[1]:
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count_button = st.button("Count Tokens", type='secondary')
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with columns[2]:
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clear_history = st.button("Clear History", type='secondary')
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if clear_history:
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st.session_state['chat_history'] = []
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st.experimental_rerun()
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if button:
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qa = None
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if not st.session_state["overused"]:
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add_usage(stats_db, "chat", "prompt" + question, {"model": model, "temperature": st.session_state['temperature']})
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if model.startswith("gpt"):
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logger.info('Using OpenAI model %s', model)
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qa = ConversationalRetrievalChain.from_llm(
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OpenAI(
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model_name=st.session_state['model'], openai_api_key=openai_api_key, temperature=st.session_state['temperature'], max_tokens=st.session_state['max_tokens']), vector_store.as_retriever(), memory=memory, verbose=True)
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elif anthropic_api_key and model.startswith("claude"):
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logger.info('Using Anthropics model %s', model)
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qa = ConversationalRetrievalChain.from_llm(
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ChatAnthropic(
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model=st.session_state['model'], anthropic_api_key=anthropic_api_key, temperature=st.session_state['temperature'], max_tokens_to_sample=st.session_state['max_tokens']), vector_store.as_retriever(), memory=memory, verbose=True, max_tokens_limit=102400)
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st.session_state['chat_history'].append(("You", question))
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# Generate model's response and add it to chat history
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model_response = qa({"question": question})
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logger.info('Result: %s', model_response)
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st.session_state['chat_history'].append(("Quivr", model_response["answer"]))
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# Display chat history
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st.empty()
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for speaker, text in st.session_state['chat_history']:
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st.markdown(f"**{speaker}:** {text}")
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else:
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st.error("You have used all your free credits. Please try again later or self host.")
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if count_button:
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st.write(count_tokens(question, model))
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