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| import anthropic | |
| import streamlit as st | |
| from streamlit.logger import get_logger | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.llms import OpenAI | |
| from langchain.llms import HuggingFaceEndpoint | |
| from langchain.chat_models import ChatAnthropic | |
| from langchain.vectorstores import SupabaseVectorStore | |
| from stats import add_usage | |
| memory = ConversationBufferMemory( | |
| memory_key="chat_history", return_messages=True) | |
| openai_api_key = st.secrets.openai_api_key | |
| anthropic_api_key = st.secrets.anthropic_api_key | |
| hf_api_key = st.secrets.hf_api_key | |
| logger = get_logger(__name__) | |
| def count_tokens(question, model): | |
| count = f'Words: {len(question.split())}' | |
| if model.startswith("claude"): | |
| count += f' | Tokens: {anthropic.count_tokens(question)}' | |
| return count | |
| def chat_with_doc(model, vector_store: SupabaseVectorStore, stats_db): | |
| if 'chat_history' not in st.session_state: | |
| st.session_state['chat_history'] = [] | |
| question = st.text_area("## Ask a question") | |
| 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') | |
| if clear_history: | |
| # Clear memory in Langchain | |
| memory.clear() | |
| st.session_state['chat_history'] = [] | |
| st.experimental_rerun() | |
| if button: | |
| qa = None | |
| if not st.session_state["overused"]: | |
| add_usage(stats_db, "chat", "prompt" + question, {"model": model, "temperature": st.session_state['temperature']}) | |
| 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) | |
| 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) | |
| elif hf_api_key and model.startswith("llama"): | |
| logger.info('Using Llama model %s', model) | |
| # print(st.session_state['max_tokens']) | |
| endpoint_url = ("https://api-inference.huggingface.co/models/meta-llama/Llama-2-70b-chat-hf") | |
| model_kwargs = {"temperature" : st.session_state['temperature'], | |
| "max_new_tokens" : st.session_state['max_tokens'], | |
| "return_full_text" : False} | |
| hf = HuggingFaceEndpoint( | |
| endpoint_url=endpoint_url, | |
| task="text-generation", | |
| huggingfacehub_api_token=hf_api_key, | |
| model_kwargs=model_kwargs | |
| ) | |
| qa = ConversationalRetrievalChain.from_llm(hf, retriever=vector_store.as_retriever(), memory=memory, verbose=True) | |
| 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(("meraKB", model_response["answer"])) | |
| # Display chat history | |
| st.empty() | |
| for speaker, text in st.session_state['chat_history']: | |
| st.markdown(f"**{speaker}:** {text}") | |
| else: | |
| st.error("You have used all your free credits. Please try again later or self host.") | |
| if count_button: | |
| st.write(count_tokens(question, model)) | |