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feat(history): now chat history

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This commit is contained in:
Stan Girard 2023-05-16 11:50:47 +02:00
parent 766a13ed9b
commit a06d505920
2 changed files with 36 additions and 9 deletions
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2
files.py
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@ -61,7 +61,7 @@ def filter_file(file, supabase, vector_store):
return False return False
def url_uploader(supabase, openai_key, vector_store): def url_uploader(supabase, openai_key, vector_store):
url = st.text_area("## Add an url",placeholder="https://www.quivr.app") url = st.text_area("**Add an url**",placeholder="https://www.quivr.app")
button = st.button("Add the URL to the database") button = st.button("Add the URL to the database")
if button: if button:
html = get_html(url) html = get_html(url)

43
question.py
View File

@ -22,26 +22,53 @@ def count_tokens(question, model):
def chat_with_doc(model, vector_store: SupabaseVectorStore): def chat_with_doc(model, vector_store: SupabaseVectorStore):
if 'chat_history' not in st.session_state:
st.session_state['chat_history'] = []
question = st.text_area("## Ask a question") question = st.text_area("## Ask a question")
button = st.button("Ask") columns = st.columns(3)
count_button = st.button("Count Tokens", type='secondary') with columns[0]:
button = st.button("Ask")
with columns[1]:
count_button = st.button("Count Tokens", type='secondary')
with columns[2]:
clear_history = st.button("Clear History", type='secondary')
for speaker, text in st.session_state['chat_history']:
st.markdown(f"**{speaker}:** {text}")
if clear_history:
st.session_state['chat_history'] = []
st.experimental_rerun()
if button: if button:
qa = None
if model.startswith("gpt"): if model.startswith("gpt"):
logger.info('Using OpenAI model %s', model) logger.info('Using OpenAI model %s', model)
qa = ConversationalRetrievalChain.from_llm( qa = ConversationalRetrievalChain.from_llm(
OpenAI( OpenAI(
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) 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)
result = qa({"question": question})
logger.info('Result: %s', result)
st.write(result["answer"])
elif anthropic_api_key and model.startswith("claude"): elif anthropic_api_key and model.startswith("claude"):
logger.info('Using Anthropics model %s', model) logger.info('Using Anthropics model %s', model)
qa = ConversationalRetrievalChain.from_llm( qa = ConversationalRetrievalChain.from_llm(
ChatAnthropic( ChatAnthropic(
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) 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)
result = qa({"question": question})
logger.info('Result: %s', result)
st.write(result["answer"]) st.session_state['chat_history'].append(("You", question))
# Generate model's response and add it to chat history
model_response = qa({"question": question})
logger.info('Result: %s', model_response)
st.session_state['chat_history'].append(("Quivr", model_response["answer"]))
# Display chat history
for speaker, text in st.session_state['chat_history']:
st.empty()
st.markdown(f"**{speaker}:** {text}")
if count_button: if count_button:
st.write(count_tokens(question, model)) st.write(count_tokens(question, model))