Merge pull request #11 from joshuasundance-swca/dev

langchain-streamlit-demo/app.py

@@ -3,13 +3,13 @@ import os
 import anthropic
 import openai
 import streamlit as st
-from langchain.callbacks.manager import tracing_v2_enabled
 from langchain.callbacks.tracers.langchain import wait_for_all_tracers
 from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
 from langchain.schema.runnable import RunnableConfig
 from langsmith.client import Client
 
 from llm_stuff import (
+ _MEMORY,
  _MODEL_DICT,
  _SUPPORTED_MODELS,
  _DEFAULT_MODEL,
@@ -45,52 +45,54 @@ model = st.sidebar.selectbox(
 )
 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
-
- client = Client(api_key=langsmith_api_key)
- else:
- langsmith_project = None
- client = None
-
- system_prompt = (
- st.sidebar.text_area(
- "Custom Instructions",
- _DEFAULT_SYSTEM_PROMPT,
- help="Custom instructions to provide the language model to determine style, personality, etc.",
- )
- .strip()
- .replace("{", "{{")
- .replace("}", "}}")
+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",
+)
+if langsmith_api_key:
+ 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
 
- temperature = st.sidebar.slider(
- "Temperature",
- min_value=_MIN_TEMPERATURE,
- max_value=_MAX_TEMPERATURE,
- value=_DEFAULT_TEMPERATURE,
- help="Higher values give more random results.",
+ client = Client(api_key=langsmith_api_key)
+else:
+ langsmith_project = None
+ client = None
+
+system_prompt = (
+ st.sidebar.text_area(
+ "Custom Instructions",
+ _DEFAULT_SYSTEM_PROMPT,
+ help="Custom instructions to provide the language model to determine style, personality, etc.",
  )
+ .strip()
+ .replace("{", "{{")
+ .replace("}", "}}")
+)
 
- max_tokens = st.sidebar.slider(
- "Max Tokens",
- min_value=_MIN_TOKENS,
- max_value=_MAX_TOKENS,
- value=_DEFAULT_MAX_TOKENS,
- help="Higher values give longer results.",
- )
+temperature = st.sidebar.slider(
+ "Temperature",
+ 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=_MIN_TOKENS,
+ max_value=_MAX_TOKENS,
+ value=_DEFAULT_MAX_TOKENS,
+ help="Higher values give longer results.",
+)
+chain = None
+if provider_api_key:
  chain = get_llm_chain(
  model,
  provider_api_key,
@@ -99,29 +101,30 @@ if provider_api_key := st.sidebar.text_input(f"{provider} API key", type="passwo
  max_tokens,
  )
 
- run_collector = RunCollectorCallbackHandler()
+run_collector = RunCollectorCallbackHandler()
 
- if st.sidebar.button("Clear message history"):
- print("Clearing message history")
- chain.memory.clear()
- st.session_state.trace_link = None
- st.session_state.run_id = None
+if st.sidebar.button("Clear message history"):
+ print("Clearing message history")
+ st.session_state["langchain_messages"].memory.clear()
+ st.session_state.trace_link = None
+ st.session_state.run_id = None
+
+for msg in _MEMORY.messages:
+ with st.chat_message(
+ msg.type,
+ avatar="🦜" if msg.type in ("ai", "assistant") else None,
+ ):
+ st.markdown(msg.content)
 
- for msg in st.session_state.langchain_messages:
- with st.chat_message(msg.type, avatar="🦜" if msg.type == "assistant" else None):
- st.markdown(msg.content)
 
- if client and st.session_state.trace_link:
- st.sidebar.markdown(
- f'<a href="{st.session_state.trace_link}" target="_blank"><button>Latest Trace: 🛠️</button></a>',
- unsafe_allow_html=True,
- )
+def _reset_feedback():
+ st.session_state.feedback_update = None
+ st.session_state.feedback = None
 
- def _reset_feedback():
- st.session_state.feedback_update = None
- st.session_state.feedback = None
 
- if prompt := st.chat_input(placeholder="Ask me a question!"):
+if chain:
+ prompt = st.chat_input(placeholder="Ask me a question!")
+ if prompt:
  st.chat_message("user").write(prompt)
  _reset_feedback()
 
@@ -133,17 +136,10 @@ if provider_api_key := st.sidebar.text_input(f"{provider} API key", type="passwo
  tags=["Streamlit Chat"],
  )
  try:
- if client and langsmith_project:
- with tracing_v2_enabled(project_name=langsmith_project):
- full_response = chain.invoke(
- {"input": prompt},
- config=runnable_config,
- )["text"]
- else:
- full_response = chain.invoke(
- {"input": prompt},
- config=runnable_config,
- )["text"]
+ full_response = chain.invoke(
+ {"input": prompt},
+ config=runnable_config,
+ )["text"]
  except (openai.error.AuthenticationError, anthropic.AuthenticationError):
  st.error(f"Please enter a valid {provider} API key.", icon="❌")
  st.stop()
@@ -156,10 +152,14 @@ if provider_api_key := st.sidebar.text_input(f"{provider} API key", type="passwo
  wait_for_all_tracers()
  url = client.read_run(run.id).url
  st.session_state.trace_link = url
-
  if client and st.session_state.get("run_id"):
  feedback_component(client)
 
 else:
  st.error(f"Please enter a valid {provider} API key.", icon="❌")
- st.stop()
+
+if client and st.session_state.get("trace_link"):
+ st.sidebar.markdown(
+ f'<a href="{st.session_state.trace_link}" target="_blank"><button>Latest Trace: 🛠️</button></a>',
+ unsafe_allow_html=True,
+ )

langchain-streamlit-demo/llm_stuff.py

@@ -10,6 +10,8 @@ from langchain.memory.chat_memory import BaseChatMemory
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from streamlit_feedback import streamlit_feedback
 
+_MEMORY = StreamlitChatMessageHistory(key="langchain_messages")
+
 _DEFAULT_SYSTEM_PROMPT = "You are a helpful chatbot."
 
 _MODEL_DICT = {
@@ -35,7 +37,7 @@ _MAX_TOKENS = 100000
 
 def get_memory() -> BaseChatMemory:
  return ConversationBufferMemory(
- chat_memory=StreamlitChatMessageHistory(key="langchain_messages"),
+ chat_memory=_MEMORY,
  return_messages=True,
  memory_key="chat_history",
  )
@@ -109,7 +111,7 @@ class StreamHandler(BaseCallbackHandler):
 
 
 def feedback_component(client):
- scores = {"😀": 1, "🙂": 0.0, "😐": 0.5, "🙁": 0.25, "😞": 0}
+ scores = {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0}
  if feedback := streamlit_feedback(
  feedback_type="faces",
  optional_text_label="[Optional] Please provide an explanation",