Add Claude from Bedrock

.gitignore

@@ -6,4 +6,6 @@ secrets.toml
 archived_logs/
 
 build/
-snowchat.egg-info/
+snowchat.egg-info/
+
+chroma_db

chain.py

@@ -1,161 +1,141 @@
+from typing import Any, Callable, Dict, Optional
+
+import boto3
 import streamlit as st
 from langchain.chains import ConversationalRetrievalChain, LLMChain
 from langchain.chains.question_answering import load_qa_chain
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.llms import OpenAI, Replicate
-from langchain.prompts.prompt import PromptTemplate
+from langchain.llms.bedrock import Bedrock
 from langchain.vectorstores import SupabaseVectorStore
+from pydantic import BaseModel, validator
 from supabase.client import Client, create_client
 
-template = """You are an AI chatbot having a conversation with a human.
-
-Chat History:\"""
-{chat_history}
-\"""
-Human Input: \"""
-{question}
-\"""
-AI:"""
-
-condense_question_prompt = PromptTemplate.from_template(template)
-
-TEMPLATE = """ 
-You're an AI assistant specializing in data analysis with Snowflake SQL. When providing responses, strive to exhibit friendliness and adopt a conversational tone, similar to how a friend or tutor would communicate.
-
-When asked about your capabilities, provide a general overview of your ability to assist with data analysis tasks using Snowflake SQL, instead of performing specific SQL queries. 
-
-Based on the question provided, if it pertains to data analysis or SQL tasks, generate SQL code that is compatible with the Snowflake environment. Additionally, offer a brief explanation about how you arrived at the SQL code. If the required column isn't explicitly stated in the context, suggest an alternative using available columns, but do not assume the existence of any columns that are not mentioned. Also, do not modify the database in any way (no insert, update, or delete operations). You are only allowed to query the database. Refrain from using the information schema.
-**You are only required to write one SQL query per question.**
-
-If the question or context does not clearly involve SQL or data analysis tasks, respond appropriately without generating SQL queries. 
-
-When the user expresses gratitude or says "Thanks", interpret it as a signal to conclude the conversation. Respond with an appropriate closing statement without generating further SQL queries.
-
-If you don't know the answer, simply state, "I'm sorry, I don't know the answer to your question."
-
-Write your response in markdown format.
-
-Question: ```{question}```
-{context}
-
-Answer:
-"""
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-LLAMA_TEMPLATE = """
-You're specialized with Snowflake SQL. When providing answers, strive to exhibit friendliness and adopt a conversational tone, similar to how a friend or tutor would communicate.
-
-If the question or context does not clearly involve SQL or data analysis tasks, respond appropriately without generating SQL queries. 
-
-If you don't know the answer, simply state, "I'm sorry, I don't know the answer to your question."
-
-Write SQL code for this Question based on the below context details:  {question}
-
-<<CONTEXT>>
-context: \n {context}
-<</CONTEXT>>
-
-write responses in markdown format
-
-Answer:
-
-"""
-
-LLAMA_TEMPLATE = B_INST + B_SYS + LLAMA_TEMPLATE + E_SYS + E_INST
-
-QA_PROMPT = PromptTemplate(template=TEMPLATE, input_variables=["question", "context"])
-LLAMA_PROMPT = PromptTemplate(
-    template=LLAMA_TEMPLATE, input_variables=["question", "context"]
-)
+from template import CONDENSE_QUESTION_PROMPT, LLAMA_PROMPT, QA_PROMPT
 
 supabase_url = st.secrets["SUPABASE_URL"]
 supabase_key = st.secrets["SUPABASE_SERVICE_KEY"]
 supabase: Client = create_client(supabase_url, supabase_key)
 
-VERSION = "be553392065353425e0f0193d2a896d6a5ff201549f5d7cd9180c8dfdeac39ed"
+VERSION = "1f01a52ff933873dff339d5fb5e1fd6f24f77456836f514fa05e91c1a42699c7"
 LLAMA = "meta/codellama-13b-instruct:{}".format(VERSION)
 
-
-def get_chain_replicate(vectorstore, callback_handler=None):
-    """
-    Get a chain for chatting with a vector database.
-    """
-    q_llm = Replicate(
-        model=LLAMA,
-        input={"temperature": 0.2, "max_length": 200, "top_p": 1},
-        replicate_api_token=st.secrets["REPLICATE_API_TOKEN"],
-    )
-    llm = Replicate(
-        streaming=True,
-        callbacks=[callback_handler],
-        model=LLAMA,
-        input={"temperature": 0.2, "max_length": 300, "top_p": 1},
-        replicate_api_token=st.secrets["REPLICATE_API_TOKEN"],
-    )
-
-    question_generator = LLMChain(llm=q_llm, prompt=condense_question_prompt)
-
-    doc_chain = load_qa_chain(llm=llm, chain_type="stuff", prompt=QA_PROMPT)
-    conv_chain = ConversationalRetrievalChain(
-        callbacks=[callback_handler],
-        retriever=vectorstore.as_retriever(),
-        combine_docs_chain=doc_chain,
-        question_generator=question_generator,
-    )
-
-    return conv_chain
-
-
-def get_chain_gpt(vectorstore, callback_handler=None):
-    """
-    Get a chain for chatting with a vector database.
-    """
-    q_llm = OpenAI(
-        temperature=0.1,
-        openai_api_key=st.secrets["OPENAI_API_KEY"],
-        model_name="gpt-3.5-turbo-16k",
-        max_tokens=500,
-    )
-
-    llm = ChatOpenAI(
-        model_name="gpt-3.5-turbo",
-        temperature=0.5,
-        openai_api_key=st.secrets["OPENAI_API_KEY"],
-        max_tokens=500,
-        callbacks=[callback_handler],
-        streaming=True,
-    )
-    question_generator = LLMChain(llm=q_llm, prompt=condense_question_prompt)
-
-    doc_chain = load_qa_chain(llm=llm, chain_type="stuff", prompt=QA_PROMPT)
-    conv_chain = ConversationalRetrievalChain(
-        retriever=vectorstore.as_retriever(),
-        combine_docs_chain=doc_chain,
-        question_generator=question_generator,
-    )
-
-    return conv_chain
+class ModelConfig(BaseModel):
+    model_type: str
+    secrets: Dict[str, Any]
+    callback_handler: Optional[Callable] = None
+
+    @validator("model_type", pre=True, always=True)
+    def validate_model_type(cls, v):
+        if v not in ["code-llama", "gpt", "claude"]:
+            raise ValueError(f"Unsupported model type: {v}")
+        return v
+
+
+class ModelWrapper:
+    def __init__(self, config: ModelConfig):
+        self.model_type = config.model_type
+        self.secrets = config.secrets
+        self.callback_handler = config.callback_handler
+        self.setup()
+
+    def setup(self):
+        if self.model_type == "code-llama":
+            self.setup_llama()
+        elif self.model_type == "gpt":
+            self.setup_gpt()
+        elif self.model_type == "claude":
+            self.setup_claude()
+
+    def setup_llama(self):
+        self.q_llm = Replicate(
+            model=LLAMA,
+            input={"temperature": 0.2, "max_length": 200, "top_p": 1},
+            replicate_api_token=self.secrets["REPLICATE_API_TOKEN"],
+        )
+        self.llm = Replicate(
+            streaming=True,
+            callbacks=[self.callback_handler],
+            model=LLAMA,
+            input={"temperature": 0.2, "max_length": 300, "top_p": 1},
+            replicate_api_token=self.secrets["REPLICATE_API_TOKEN"],
+        )
+
+    def setup_gpt(self):
+        self.q_llm = OpenAI(
+            temperature=0.1,
+            openai_api_key=self.secrets["OPENAI_API_KEY"],
+            model_name="gpt-3.5-turbo-16k",
+            max_tokens=500,
+        )
+
+        self.llm = ChatOpenAI(
+            model_name="gpt-3.5-turbo-16k",
+            temperature=0.5,
+            openai_api_key=self.secrets["OPENAI_API_KEY"],
+            max_tokens=500,
+            callbacks=[self.callback_handler],
+            streaming=True,
+        )
+
+    def setup_claude(self):
+        bedrock_runtime = boto3.client(
+            service_name="bedrock-runtime",
+            aws_access_key_id=self.secrets["AWS_ACCESS_KEY_ID"],
+            aws_secret_access_key=self.secrets["AWS_SECRET_ACCESS_KEY"],
+            region_name="us-east-1",
+        )
+        parameters = {
+            "max_tokens_to_sample": 1000,
+            "stop_sequences": [],
+            "temperature": 0,
+            "top_p": 0.9,
+        }
+        self.q_llm = Bedrock(
+            model_id="anthropic.claude-instant-v1", client=bedrock_runtime
+        )
+        self.llm = Bedrock(
+            model_id="anthropic.claude-instant-v1",
+            client=bedrock_runtime,
+            callbacks=[self.callback_handler],
+            streaming=True,
+            model_kwargs=parameters,
+        )
+
+    def get_chain(self, vectorstore):
+        if not self.q_llm or not self.llm:
+            raise ValueError("Models have not been properly initialized.")
+        question_generator = LLMChain(llm=self.q_llm, prompt=CONDENSE_QUESTION_PROMPT)
+        doc_chain = load_qa_chain(llm=self.llm, chain_type="stuff", prompt=QA_PROMPT)
+        conv_chain = ConversationalRetrievalChain(
+            retriever=vectorstore.as_retriever(),
+            combine_docs_chain=doc_chain,
+            question_generator=question_generator,
+        )
+        return conv_chain
 
 
 def load_chain(model_name="GPT-3.5", callback_handler=None):
-    """
-    Load the chain from the local file system
-
-    Returns:
-        chain (Chain): The chain object
-
-    """
-
     embeddings = OpenAIEmbeddings(
         openai_api_key=st.secrets["OPENAI_API_KEY"], model="text-embedding-ada-002"
     )
     vectorstore = SupabaseVectorStore(
-        embedding=embeddings, client=supabase, table_name="documents"
+        embedding=embeddings,
+        client=supabase,
+        table_name="documents",
+        query_name="v_match_documents",
     )
-    return (
-        get_chain_gpt(vectorstore, callback_handler=callback_handler)
-        if "GPT-3.5" in model_name
-        else get_chain_replicate(vectorstore, callback_handler=callback_handler)
+
+    if "claude" in model_name.lower():
+        model_type = "claude"
+    elif "GPT-3.5" in model_name:
+        model_type = "gpt"
+    else:
+        model_type = "code-llama"
+
+    config = ModelConfig(
+        model_type=model_type, secrets=st.secrets, callback_handler=callback_handler
     )
+    model = ModelWrapper(config)
+    return model.get_chain(vectorstore)

main.py

@@ -17,7 +17,7 @@
 st.caption("Talk your way through data")
 model = st.radio(
     "",
-    options=["✨ GPT-3.5", "🐐 code-LLama"],
+    options=["✨ GPT-3.5", "🐐 code-LLama", "♾️ Claude"],
     index=0,
     horizontal=True,
 )
@@ -94,8 +94,6 @@ def get_sql(text):
 
 def append_message(content, role="assistant", display=False):
     message = {"role": role, "content": content}
-    if model == "LLama-2":  # unable to get streaming working with LLama-2
-        message_func(content, False, display)
     st.session_state.messages.append(message)
     if role != "data":
         append_chat_history(st.session_state.messages[-2]["content"], content)
@@ -138,11 +136,11 @@ def execute_sql(query, conn, retries=2):
         result = chain(
             {"question": content, "chat_history": st.session_state["history"]}
         )["answer"]
-        # print(result)
+        print(result)
         append_message(result)
-        if get_sql(result):
-            conn = SnowflakeConnection().get_session()
-            df = execute_sql(get_sql(result), conn)
-            if df is not None:
-                callback_handler.display_dataframe(df)
-                append_message(df, "data", True)
+        # if get_sql(result):
+        #     conn = SnowflakeConnection().get_session()
+        #     df = execute_sql(get_sql(result), conn)
+        #     if df is not None:
+        #         callback_handler.display_dataframe(df)
+        #         append_message(df, "data", True)

requirements.txt

@@ -1,12 +1,13 @@
-langchain==0.0.266
+langchain==0.0.305
 pandas==1.5.0
 pydantic==1.10.8
 snowflake_snowpark_python==1.5.0
 snowflake-snowpark-python[pandas]
-streamlit==1.24.0
+streamlit==1.27.1
 supabase==1.0.3
 unstructured==0.7.12
 tiktoken==0.4.0
 openai==0.27.8
 black==23.3.0
-replicate==0.8.4
+replicate==0.8.4
+boto3==1.28.57