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Delete diff.txt
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shaun
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diff.txt
163
diff.txt
@ -1,163 +0,0 @@
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diff --git a/.streamlit/secrets.toml.example b/.streamlit/secrets.toml.example
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index 093c5bf..4c405dc 100644
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--- a/.streamlit/secrets.toml.example
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+++ b/.streamlit/secrets.toml.example
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@@ -1,3 +1,4 @@
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supabase_url = "https://lalalala.supabase.co"
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supabase_service_key = "lalalala"
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-openai_api_key = "sk-lalalala"
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\ No newline at end of file
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+openai_api_key = "sk-lalalala"
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+anthropic_api_key = ""
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diff --git a/main.py b/main.py
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index 6ed2560..88af1c5 100644
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--- a/main.py
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+++ b/main.py
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@@ -10,25 +10,30 @@ from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores import SupabaseVectorStore
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from supabase import Client, create_client
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-# supabase_url = "https://fqgpcifsfmamprzldyiv.supabase.co"
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supabase_url = st.secrets.supabase_url
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supabase_key = st.secrets.supabase_service_key
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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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supabase: Client = create_client(supabase_url, supabase_key)
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embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
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-vector_store = SupabaseVectorStore(supabase, embeddings, table_name="documents")
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+vector_store = SupabaseVectorStore(
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+ supabase, embeddings, table_name="documents")
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+models = ["gpt-3.5-turbo", "gpt-4"]
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+if anthropic_api_key:
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+ models += ["claude-v1", "claude-v1.3",
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+ "claude-instant-v1-100k", "claude-instant-v1.1-100k"]
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# Set the theme
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st.set_page_config(
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page_title="Second Brain",
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layout="wide",
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initial_sidebar_state="expanded",
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-
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)
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st.title("🧠 Second Brain 🧠")
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-st.markdown("Store your knowledge in a vector store and query it with OpenAI's GPT-3/4.")
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+st.markdown(
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+ "Store your knowledge in a vector store and query it with OpenAI's GPT-3/4.")
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st.markdown("---\n\n")
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# Initialize session state variables
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@@ -40,31 +45,40 @@ if 'chunk_size' not in st.session_state:
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st.session_state['chunk_size'] = 500
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if 'chunk_overlap' not in st.session_state:
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st.session_state['chunk_overlap'] = 0
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+if 'max_tokens' not in st.session_state:
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+ st.session_state['max_tokens'] = 256
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# Create a radio button for user to choose between adding knowledge or asking a question
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-user_choice = st.radio("Choose an action", ('Add Knowledge', 'Chat with your Brain','Forget' ))
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+user_choice = st.radio(
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+ "Choose an action", ('Add Knowledge', 'Chat with your Brain', 'Forget'))
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st.markdown("---\n\n")
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-
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-
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if user_choice == 'Add Knowledge':
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# Display chunk size and overlap selection only when adding knowledge
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- st.sidebar.title("Configuration")
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- st.sidebar.markdown("Choose your chunk size and overlap for adding knowledge.")
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- st.session_state['chunk_size'] = st.sidebar.slider("Select Chunk Size", 100, 1000, st.session_state['chunk_size'], 50)
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- st.session_state['chunk_overlap'] = st.sidebar.slider("Select Chunk Overlap", 0, 100, st.session_state['chunk_overlap'], 10)
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+ st.sidebar.title("Configuration")
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+ st.sidebar.markdown(
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+ "Choose your chunk size and overlap for adding knowledge.")
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+ st.session_state['chunk_size'] = st.sidebar.slider(
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+ "Select Chunk Size", 100, 1000, st.session_state['chunk_size'], 50)
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+ st.session_state['chunk_overlap'] = st.sidebar.slider(
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+ "Select Chunk Overlap", 0, 100, st.session_state['chunk_overlap'], 10)
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file_uploader(supabase, openai_api_key, vector_store)
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elif user_choice == 'Chat with your Brain':
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# Display model and temperature selection only when asking questions
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- st.sidebar.title("Configuration")
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- st.sidebar.markdown("Choose your model and temperature for asking questions.")
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- st.session_state['model'] = st.sidebar.selectbox("Select Model", ["gpt-3.5-turbo", "gpt-4"], index=("gpt-3.5-turbo", "gpt-4").index(st.session_state['model']))
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- st.session_state['temperature'] = st.sidebar.slider("Select Temperature", 0.0, 1.0, st.session_state['temperature'], 0.1)
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- chat_with_doc(openai_api_key, vector_store)
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+ st.sidebar.title("Configuration")
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+ st.sidebar.markdown(
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+ "Choose your model and temperature for asking questions.")
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+ st.session_state['model'] = st.sidebar.selectbox(
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+ "Select Model", models, index=(models).index(st.session_state['model']))
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+ st.session_state['temperature'] = st.sidebar.slider(
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+ "Select Temperature", 0.0, 1.0, st.session_state['temperature'], 0.1)
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+ st.session_state['max_tokens'] = st.sidebar.slider(
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+ "Select Max Tokens", 256, 2048, st.session_state['max_tokens'], 2048)
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+ chat_with_doc(st.session_state['model'], vector_store)
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elif user_choice == 'Forget':
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st.sidebar.title("Configuration")
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-
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+
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brain(supabase)
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-st.markdown("---\n\n")
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\ No newline at end of file
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+st.markdown("---\n\n")
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diff --git a/question.py b/question.py
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index 8e875f6..6f8e9d3 100644
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--- a/question.py
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+++ b/question.py
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@@ -1,14 +1,35 @@
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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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-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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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 chat_with_doc(openai_api_key, vector_store):
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- question = st.text_input("## Ask a question")
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+
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+def chat_with_doc(model, vector_store: SupabaseVectorStore):
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+ question = st.text_area("## Ask a question")
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button = st.button("Ask")
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if button:
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- qa = ConversationalRetrievalChain.from_llm(OpenAI(model_name=st.session_state['model'], openai_api_key=openai_api_key, temperature=st.session_state['temperature']), vector_store.as_retriever(), memory=memory)
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- result = qa({"question": question})
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- st.write(result["answer"])
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\ No newline at end of file
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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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+ result = qa({"question": question})
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+ logger.info('Result: %s', result)
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+ st.write(result["answer"])
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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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+ result = qa({"question": question})
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+ logger.info('Result: %s', result)
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+ st.write(result["answer"])
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diff --git a/requirements.txt b/requirements.txt
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index 981d00b..ea8f1f6 100644
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--- a/requirements.txt
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+++ b/requirements.txt
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@@ -8,4 +8,4 @@ StrEnum==0.4.10
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supabase==1.0.3
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tiktoken==0.4.0
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unstructured==0.6.5
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-
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+anthropic=0.2.8
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