QuivrHQ/quivr
1
1
Fork 0
mirror of https://github.com/QuivrHQ/quivr.git synced 2024-08-17 02:10:31 +03:00
Code Issues Packages Projects Releases Wiki Activity

feat(history): now chat history

Browse Source
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
files.py
View File

@ -61,7 +61,7 @@ def filter_file(file, supabase, vector_store):
return False
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")
if button:
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):
if 'chat_history' not in st.session_state:
st.session_state['chat_history'] = []
question = st.text_area("## Ask a question")
button = st.button("Ask")
count_button = st.button("Count Tokens", type='secondary')
columns = st.columns(3)
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:
qa = None
if model.startswith("gpt"):
logger.info('Using OpenAI model %s', model)
qa = ConversationalRetrievalChain.from_llm(
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)
result = qa({"question": question})
logger.info('Result: %s', result)
st.write(result["answer"])
elif anthropic_api_key and model.startswith("claude"):
logger.info('Using Anthropics model %s', model)
qa = ConversationalRetrievalChain.from_llm(
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)
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:
st.write(count_tokens(question, model))