no env

.env-example

@@ -1,10 +0,0 @@
-APP_PORT=8181
-
-LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
-LANGCHAIN_API_KEY=ls__...
-LANGCHAIN_TRACING_V2="true"
-LANGCHAIN_PROJECT="streamlit_chatbot"
-
-ANYSCALE_API_KEY=secret_...
-OPENAI_API_KEY=sk-...
-ANTHROPIC_API_KEY="sk-..."

docker-compose.yml

@@ -4,8 +4,6 @@ services:
  streamlit-chat:
  image: streamlit-chat:latest
  build: .
- env_file:
- - .env
  ports:
  - "8000:8000"
  volumes:

langchain-streamlit-demo/app.py

@@ -1,8 +1,10 @@
+import os
 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 openai.error import AuthenticationError
 
 from llm_stuff import (
  _DEFAULT_SYSTEM_PROMPT,
@@ -30,83 +32,125 @@ st.sidebar.markdown(
 """,
 )
 
-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("}", "}}")
-)
-
-memory = get_memory()
-
-chain = get_llm_chain(memory, system_prompt)
+openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
+st.session_state.openai_api_key = openai_api_key
 
-client = get_langsmith_client()
-
-run_collector = RunCollectorCallbackHandler()
-
-
-if st.sidebar.button("Clear message history"):
- print("Clearing message history")
- memory.clear()
- st.session_state.trace_link = None
- st.session_state.run_id = None
+langsmith_api_key = st.sidebar.text_input("LangSmith API Key", type="password")
+st.session_state.langsmith_api_key = langsmith_api_key
 
 
-# Display chat messages from history on app rerun
-# NOTE: This won't be necessary for Streamlit 1.26+, you can just pass the type directly
-# https://github.com/streamlit/streamlit/pull/7094
-def _get_openai_type(msg):
- if msg.type == "human":
- return "user"
- if msg.type == "ai":
- return "assistant"
- return msg.role if msg.type == "chat" else msg.type
-
-
-for msg in st.session_state.langchain_messages:
- streamlit_type = _get_openai_type(msg)
- avatar = "🦜" if streamlit_type == "assistant" else None
- with st.chat_message(streamlit_type, avatar=avatar):
- st.markdown(msg.content)
-
-if 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,
+if st.session_state.openai_api_key.startswith("sk-"):
+ 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("}", "}}")
  )
 
+ temperature = st.sidebar.slider(
+ "Temperature",
+ min_value=0.0,
+ max_value=1.0,
+ value=0.7,
+ help="Higher values give more random results.",
+ )
 
-def _reset_feedback():
- st.session_state.feedback_update = None
- st.session_state.feedback = None
-
+ memory = get_memory()
 
-if prompt := st.chat_input(placeholder="Ask me a question!"):
- st.chat_message("user").write(prompt)
- _reset_feedback()
+ chain = get_llm_chain(memory, system_prompt, temperature)
 
- with st.chat_message("assistant", avatar="🦜"):
- message_placeholder = st.empty()
- stream_handler = StreamHandler(message_placeholder)
- runnable_config = RunnableConfig(
- callbacks=[run_collector, stream_handler],
- tags=["Streamlit Chat"],
+ if st.session_state.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"] = st.session_state.langsmith_api_key
+ os.environ["LANGCHAIN_TRACING_V2"] = "true"
+ os.environ["LANGCHAIN_PROJECT"] = langsmith_project
+
+ client = get_langsmith_client()
+ else:
+ langsmith_project = None
+ client = None
+
+ run_collector = RunCollectorCallbackHandler()
+
+ if st.sidebar.button("Clear message history"):
+ print("Clearing message history")
+ memory.clear()
+ st.session_state.trace_link = None
+ st.session_state.run_id = None
+
+ # Display chat messages from history on app rerun
+ # NOTE: This won't be necessary for Streamlit 1.26+, you can just pass the type directly
+ # https://github.com/streamlit/streamlit/pull/7094
+ def _get_openai_type(msg):
+ if msg.type == "human":
+ return "user"
+ if msg.type == "ai":
+ return "assistant"
+ return msg.role if msg.type == "chat" else msg.type
+
+ for msg in st.session_state.langchain_messages:
+ streamlit_type = _get_openai_type(msg)
+ avatar = "🦜" if streamlit_type == "assistant" else None
+ with st.chat_message(streamlit_type, avatar=avatar):
+ st.markdown(msg.content)
+
+ if 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,
  )
- full_response = chain.invoke({"input": prompt}, config=runnable_config)["text"]
- message_placeholder.markdown(full_response)
-
- run = run_collector.traced_runs[0]
- run_collector.traced_runs = []
- st.session_state.run_id = run.id
- wait_for_all_tracers()
- url = client.read_run(run.id).url
- st.session_state.trace_link = url
-
 
-if st.session_state.get("run_id"):
- feedback_component(client)
+ def _reset_feedback():
+ st.session_state.feedback_update = None
+ st.session_state.feedback = None
+
+ if prompt := st.chat_input(placeholder="Ask me a question!"):
+ st.chat_message("user").write(prompt)
+ _reset_feedback()
+
+ with st.chat_message("assistant", avatar="🦜"):
+ message_placeholder = st.empty()
+ stream_handler = StreamHandler(message_placeholder)
+ runnable_config = RunnableConfig(
+ callbacks=[run_collector, stream_handler],
+ 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"]
+ except AuthenticationError:
+ st.error("Please enter a valid OpenAI API key.", icon="❌")
+ st.stop()
+ message_placeholder.markdown(full_response)
+
+ if client:
+ run = run_collector.traced_runs[0]
+ run_collector.traced_runs = []
+ st.session_state.run_id = run.id
+ 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("Please enter a valid OpenAI API key.", icon="❌")
+ st.stop()

langchain-streamlit-demo/llm_stuff.py

@@ -6,14 +6,16 @@ from langchain.callbacks.base import BaseCallbackHandler
 from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory, StreamlitChatMessageHistory
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from streamlit_feedback import streamlit_feedback
 from langsmith.client import Client
+from streamlit_feedback import streamlit_feedback
 
 _DEFAULT_SYSTEM_PROMPT = "You are a helpful chatbot."
 
 
 def get_langsmith_client():
- return Client()
+ return Client(
+ api_key=st.session_state.langsmith_api_key,
+ )
 
 
 def get_memory() -> ConversationBufferMemory:
@@ -27,6 +29,7 @@ def get_memory() -> ConversationBufferMemory:
 def get_llm_chain(
  memory: ConversationBufferMemory,
  system_prompt: str = _DEFAULT_SYSTEM_PROMPT,
+ temperature: float = 0.7,
 ) -> LLMChain:
  """Return a basic LLMChain with memory."""
  prompt = ChatPromptTemplate.from_messages(
@@ -39,7 +42,11 @@ def get_llm_chain(
  ("human", "{input}"),
  ],
  ).partial(time=lambda: str(datetime.now()))
- llm = ChatOpenAI(temperature=0.7, streaming=True)
+ llm = ChatOpenAI(
+ temperature=temperature,
+ streaming=True,
+ openai_api_key=st.session_state.openai_api_key,
+ )
  return LLMChain(prompt=prompt, llm=llm, memory=memory or get_memory())