Merge pull request #15 from Shaunwei/support-anthropic-100k

Support for Anthropics Models

.streamlit/secrets.toml.example

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

@@ -10,23 +10,28 @@ 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="Quiver",
     layout="wide",
     initial_sidebar_state="expanded",
-
 )
 
+
 st.title("🧠 Quiver - Your second brain 🧠")
 st.markdown("Store your knowledge in a vector store and query it with OpenAI's GPT-3/4.")
 st.markdown("---\n\n")
@@ -40,28 +45,37 @@ 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.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.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")
 

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)
+        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