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())