Support for Anthropics Models

This update enhances the "Second Brain" application by adding support for Anthropics AI models. Users can now use not only OpenAI's GPT-3/4, but also Anthropics' Claude models to store and query their knowledge.

Key changes include:

Added an anthropic_api_key field in the secrets configuration file. Introduced a selection for different AI models including GPT-3, GPT-4, and various versions of Claude. Updated question handling to be model-agnostic, and added support for Anthropics' Claude models in the question processing workflow. Modified the streamlit interface to allow users to input their choice of model, control the "temperature" of the model's responses, and set the max tokens limit. Upgraded requirements.txt file with the latest version of the Anthropics library. This update empowers users to leverage different AI models based on their needs, providing a more flexible and robust tool for knowledge management.

.streamlit/secrets.toml.example

@@ -1,3 +1,4 @@
 supabase_url = "https://lalalala.supabase.co"
 supabase_service_key = "lalalala"
-openai_api_key = "sk-lalalala"
+openai_api_key = "sk-lalalala"
+anthropic_api_key = ""

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.autopep8"
+    },
+    "python.formatting.provider": "none"
+}

main.py

@@ -10,25 +10,30 @@ from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.vectorstores import SupabaseVectorStore
 from supabase import Client, create_client
 
-# supabase_url = "https://fqgpcifsfmamprzldyiv.supabase.co"
 supabase_url = st.secrets.supabase_url
 supabase_key = st.secrets.supabase_service_key
 openai_api_key = st.secrets.openai_api_key
+anthropic_api_key = st.secrets.anthropic_api_key
 supabase: Client = create_client(supabase_url, supabase_key)
 
 embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-vector_store = SupabaseVectorStore(supabase, embeddings, table_name="documents")
+vector_store = SupabaseVectorStore(
+    supabase, embeddings, table_name="documents")
+models = ["gpt-3.5-turbo", "gpt-4"]
+if anthropic_api_key:
+    models += ["claude-v1", "claude-v1.3",
+               "claude-instant-v1-100k", "claude-instant-v1.1-100k"]
 
 # Set the theme
 st.set_page_config(
     page_title="Second Brain",
     layout="wide",
     initial_sidebar_state="expanded",
-
 )
 
 st.title("🧠 Second Brain 🧠")
-st.markdown("Store your knowledge in a vector store and query it with OpenAI's GPT-3/4.")
+st.markdown(
+    "Store your knowledge in a vector store and query it with OpenAI's GPT-3/4.")
 st.markdown("---\n\n")
 
 # Initialize session state variables
@@ -40,31 +45,40 @@ if 'chunk_size' not in st.session_state:
     st.session_state['chunk_size'] = 500
 if 'chunk_overlap' not in st.session_state:
     st.session_state['chunk_overlap'] = 0
+if 'max_tokens' not in st.session_state:
+    st.session_state['max_tokens'] = 256
 
 # Create a radio button for user to choose between adding knowledge or asking a question
-user_choice = st.radio("Choose an action", ('Add Knowledge', 'Chat with your Brain','Forget' ))
+user_choice = st.radio(
+    "Choose an action", ('Add Knowledge', 'Chat with your Brain', 'Forget'))
 
 st.markdown("---\n\n")
 
-
-
 if user_choice == 'Add Knowledge':
     # Display chunk size and overlap selection only when adding knowledge
-    st.sidebar.title("Configuration") 
-    st.sidebar.markdown("Choose your chunk size and overlap for adding knowledge.")
-    st.session_state['chunk_size'] = st.sidebar.slider("Select Chunk Size", 100, 1000, st.session_state['chunk_size'], 50)
-    st.session_state['chunk_overlap'] = st.sidebar.slider("Select Chunk Overlap", 0, 100, st.session_state['chunk_overlap'], 10)
+    st.sidebar.title("Configuration")
+    st.sidebar.markdown(
+        "Choose your chunk size and overlap for adding knowledge.")
+    st.session_state['chunk_size'] = st.sidebar.slider(
+        "Select Chunk Size", 100, 1000, st.session_state['chunk_size'], 50)
+    st.session_state['chunk_overlap'] = st.sidebar.slider(
+        "Select Chunk Overlap", 0, 100, st.session_state['chunk_overlap'], 10)
     file_uploader(supabase, openai_api_key, vector_store)
 elif user_choice == 'Chat with your Brain':
     # Display model and temperature selection only when asking questions
-    st.sidebar.title("Configuration") 
-    st.sidebar.markdown("Choose your model and temperature for asking questions.")
-    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']))
-    st.session_state['temperature'] = st.sidebar.slider("Select Temperature", 0.0, 1.0, st.session_state['temperature'], 0.1)
-    chat_with_doc(openai_api_key, vector_store)
+    st.sidebar.title("Configuration")
+    st.sidebar.markdown(
+        "Choose your model and temperature for asking questions.")
+    st.session_state['model'] = st.sidebar.selectbox(
+        "Select Model", models, index=(models).index(st.session_state['model']))
+    st.session_state['temperature'] = st.sidebar.slider(
+        "Select Temperature", 0.0, 1.0, st.session_state['temperature'], 0.1)
+    st.session_state['max_tokens'] = st.sidebar.slider(
+        "Select Max Tokens", 256, 2048, st.session_state['max_tokens'], 2048)
+    chat_with_doc(st.session_state['model'], vector_store)
 elif user_choice == 'Forget':
     st.sidebar.title("Configuration")
-    
+
     brain(supabase)
 
-st.markdown("---\n\n")
+st.markdown("---\n\n")

question.py

@@ -1,14 +1,35 @@
 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.chat_models import ChatAnthropic
+from langchain.vectorstores import SupabaseVectorStore
 
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+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
+logger = get_logger(__name__)
 
-def chat_with_doc(openai_api_key, vector_store):
-    question = st.text_input("## Ask a question")
+
+def chat_with_doc(model, vector_store: SupabaseVectorStore):
+    question = st.text_area("## Ask a question")
     button = st.button("Ask")
     if button:
-        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)
-        result = qa({"question": question})
-        st.write(result["answer"])
+        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"])

requirements.txt

@@ -8,4 +8,4 @@ StrEnum==0.4.10
 supabase==1.0.3
 tiktoken==0.4.0
 unstructured==0.6.5
-
+anthropic==0.2.8