parameterization

langchain-streamlit-demo/app.py

@@ -10,7 +10,16 @@ from langchain.schema.runnable import RunnableConfig
 from langsmith.client import Client
 
 from llm_stuff import (
+    _MODEL_DICT,
+    _SUPPORTED_MODELS,
+    _DEFAULT_MODEL,
     _DEFAULT_SYSTEM_PROMPT,
+    _DEFAULT_TEMPERATURE,
+    _MIN_TEMPERATURE,
+    _MAX_TEMPERATURE,
+    _DEFAULT_MAX_TOKENS,
+    _MIN_TOKENS,
+    _MAX_TOKENS,
     get_llm_chain,
     StreamHandler,
     feedback_component,
@@ -21,60 +30,40 @@ st.set_page_config(
     page_icon="🦜",
 )
 
+st.sidebar.markdown("# Menu")
+
 # Initialize State
 if "trace_link" not in st.session_state:
     st.session_state.trace_link = None
 if "run_id" not in st.session_state:
     st.session_state.run_id = None
 
-st.sidebar.markdown("# Menu")
-models = [
-    "gpt-3.5-turbo",
-    "gpt-4",
-    "claude-instant-v1",
-    "claude-2",
-    "meta-llama/Llama-2-7b-chat-hf",
-    "meta-llama/Llama-2-13b-chat-hf",
-    "meta-llama/Llama-2-70b-chat-hf",
-]
-model = st.sidebar.selectbox(label="Chat Model", options=models, index=0)
-
-if model.startswith("gpt"):
-    provider = "OpenAI"
-elif model.startswith("claude"):
-    provider = "Anthropic"
-elif model.startswith("meta-llama"):
-    provider = "Anyscale"
-else:
-    st.stop()
-
-if not model:
-    st.error("Please select a model and provide an API key.", icon="❌")
-    st.stop()
-
-provider_api_key = st.sidebar.text_input(f"{provider} API key", type="password")
-
-langsmith_api_key = st.sidebar.text_input(
-    "LangSmith API Key (optional)",
-    type="password",
+model = st.sidebar.selectbox(
+    label="Chat Model",
+    options=_SUPPORTED_MODELS,
+    index=_SUPPORTED_MODELS.index(_DEFAULT_MODEL),
 )
+provider = _MODEL_DICT[model]
+
+if provider_api_key := st.sidebar.text_input(f"{provider} API key", type="password"):
+    if langsmith_api_key := st.sidebar.text_input(
+        "LangSmith API Key (optional)",
+        type="password",
+    ):
+        langsmith_project = st.sidebar.text_input(
+            "LangSmith Project Name",
+            value="langchain-streamlit-demo",
+        )
+        os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+        os.environ["LANGCHAIN_API_KEY"] = langsmith_api_key
+        os.environ["LANGCHAIN_TRACING_V2"] = "true"
+        os.environ["LANGCHAIN_PROJECT"] = langsmith_project
 
-if langsmith_api_key.startswith("ls__"):
-    langsmith_project = st.sidebar.text_input(
-        "LangSmith Project Name",
-        value="langchain-streamlit-demo",
-    )
-    os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
-    os.environ["LANGCHAIN_API_KEY"] = langsmith_api_key
-    os.environ["LANGCHAIN_TRACING_V2"] = "true"
-    os.environ["LANGCHAIN_PROJECT"] = langsmith_project
-
-    client = Client(api_key=langsmith_api_key)
-else:
-    langsmith_project = None
-    client = None
+        client = Client(api_key=langsmith_api_key)
+    else:
+        langsmith_project = None
+        client = None
 
-if provider_api_key:
     system_prompt = (
         st.sidebar.text_area(
             "Custom Instructions",
@@ -88,17 +77,17 @@ if provider_api_key:
 
     temperature = st.sidebar.slider(
         "Temperature",
-        min_value=0.0,
-        max_value=1.0,
-        value=0.7,
+        min_value=_MIN_TEMPERATURE,
+        max_value=_MAX_TEMPERATURE,
+        value=_DEFAULT_TEMPERATURE,
         help="Higher values give more random results.",
     )
 
     max_tokens = st.sidebar.slider(
         "Max Tokens",
-        min_value=0,
-        max_value=8000,
-        value=1000,
+        min_value=_MIN_TOKENS,
+        max_value=_MAX_TOKENS,
+        value=_DEFAULT_MAX_TOKENS,
         help="Higher values give longer results.",
     )
 

langchain-streamlit-demo/llm_stuff.py

@@ -12,6 +12,26 @@ from streamlit_feedback import streamlit_feedback
 
 _DEFAULT_SYSTEM_PROMPT = "You are a helpful chatbot."
 
+_MODEL_DICT = {
+    "gpt-3.5-turbo": "OpenAI",
+    "gpt-4": "OpenAI",
+    "claude-instant-v1": "Anthropic",
+    "claude-2": "Anthropic",
+    "meta-llama/Llama-2-7b-chat-hf": "Anyscale Endpoints",
+    "meta-llama/Llama-2-13b-chat-hf": "Anyscale Endpoints",
+    "meta-llama/Llama-2-70b-chat-hf": "Anyscale Endpoints",
+}
+_SUPPORTED_MODELS = list(_MODEL_DICT.keys())
+_DEFAULT_MODEL = "gpt-3.5-turbo"
+
+_DEFAULT_TEMPERATURE = 0.7
+_MIN_TEMPERATURE = 0.0
+_MAX_TEMPERATURE = 1.0
+
+_DEFAULT_MAX_TOKENS = 1000
+_MIN_TOKENS = 1
+_MAX_TOKENS = 100000
+
 
 def get_memory() -> BaseChatMemory:
     return ConversationBufferMemory(
@@ -25,9 +45,9 @@ def get_llm(
     model: str,
     provider_api_key: str,
     temperature: float,
-    max_tokens: int = 1000,
+    max_tokens: int = _DEFAULT_MAX_TOKENS,
 ) -> BaseChatModel:
-    if model.startswith("gpt"):
+    if _MODEL_DICT[model] == "OpenAI":
         return ChatOpenAI(
             model=model,
             openai_api_key=provider_api_key,
@@ -35,7 +55,7 @@ def get_llm(
             streaming=True,
             max_tokens=max_tokens,
         )
-    elif model.startswith("claude"):
+    elif _MODEL_DICT[model] == "Anthropic":
         return ChatAnthropic(
             model_name=model,
             anthropic_api_key=provider_api_key,
@@ -43,7 +63,7 @@ def get_llm(
             streaming=True,
             max_tokens_to_sample=max_tokens,
         )
-    elif model.startswith("meta-llama"):
+    elif _MODEL_DICT[model] == "Anyscale Endpoints":
         return ChatAnyscale(
             model=model,
             anyscale_api_key=provider_api_key,
@@ -59,8 +79,8 @@ def get_llm_chain(
     model: str,
     provider_api_key: str,
     system_prompt: str = _DEFAULT_SYSTEM_PROMPT,
-    temperature: float = 0.7,
-    max_tokens: int = 1000,
+    temperature: float = _DEFAULT_TEMPERATURE,
+    max_tokens: int = _DEFAULT_MAX_TOKENS,
 ) -> LLMChain:
     """Return a basic LLMChain with memory."""
     prompt = ChatPromptTemplate.from_messages(
@@ -89,7 +109,7 @@ class StreamHandler(BaseCallbackHandler):
 
 
 def feedback_component(client):
-    scores = {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0}
+    scores = {"😀": 1, "🙂": 0.0, "😐": 0.5, "🙁": 0.25, "😞": 0}
     if feedback := streamlit_feedback(
         feedback_type="faces",
         optional_text_label="[Optional] Please provide an explanation",