small changes

.env-example

@@ -1,18 +1,10 @@
-JUPYTER_PORT=8181
+APP_PORT=8181
 
 LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
-LANGCHAIN_API_KEY=...
+LANGCHAIN_API_KEY=ls__...
 LANGCHAIN_TRACING_V2="true"
-LANGCHAIN_PROJECT="AI_GIS_language"
+LANGCHAIN_PROJECT="streamlit_chatbot"
 
-ANYSCALE_API_KEY=...
+ANYSCALE_API_KEY=secret_...
 OPENAI_API_KEY=sk-...
-ANTHROPIC_API_KEY=...
-
-#https://github.com/streamlit/app-starter-kit
-#https://github.com/langchain-ai/streamlit-agent
-#https://github.com/hwchase17/langchain-streamlit-template
-#https://github.com/dataprofessor/langchain-ask-the-doc
-#https://github.com/dataprofessor/langchain-ask-the-data
-#https://github.com/dataprofessor/langchain-blog-outline-generator
-#https://github.com/dataprofessor/langchain-text-summarization
+ANTHROPIC_API_KEY="sk-..."

.github/pull_request_template.md

@@ -1,4 +1,4 @@
-Thank you for contributing to `AI_chatbot`.
+Thank you for contributing!
 Before submitting this PR, please make sure:
 
 - [ ] Your code builds clean without any errors or warnings

.idea/.name

@@ -0,0 +1 @@
+langchain-streamlit-demo

.idea/{AI_chatbot.iml → langchain-streamlit-demo.iml}

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
  <content url="file://$MODULE_DIR$" />
- <orderEntry type="inheritedJdk" />
+ <orderEntry type="jdk" jdkName="Remote Python 3.11.4 Docker (&lt;none&gt;:&lt;none&gt;) (5)" jdkType="Python SDK" />
  <orderEntry type="sourceFolder" forTests="false" />
  </component>
 </module>

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
- <component name="ProjectRootManager" version="2" project-jdk-name="arcgis-pc (2)" project-jdk-type="Python SDK" />
+ <component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.11.4 Docker (&lt;none&gt;:&lt;none&gt;) (5)" project-jdk-type="Python SDK" />
 </project>

docker-compose.yml

@@ -9,6 +9,12 @@ services:
  ports:
  - "8000:8000"
  volumes:
- - .:/home/appuser/app:rw
+ - .:/home/appuser/app:rw
  working_dir: /home/appuser/app/
- entrypoint: ["python", "-m", "streamlit", "run", "${APP}", "--server.port", "8000", "--server.enableCORS", "false", "--server.address", "0.0.0.0"]
+ command: [
+ "python", "-m",
+ "streamlit", "run",
+ "/home/appuser/app/langchain-streamlit-demo/app.py",
+ "--server.port", "8000",
+ "--server.address", "0.0.0.0"
+ ]

{AI_chatbot → langchain-streamlit-demo}/app.py

@@ -1,42 +1,23 @@
-from datetime import datetime
-
 import streamlit as st
-from langchain import LLMChain
-from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.tracers.langchain import wait_for_all_tracers
 from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import StreamlitChatMessageHistory, ConversationBufferMemory
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.schema.runnable import RunnableConfig
-from langsmith import Client
-from streamlit_feedback import streamlit_feedback
+
+
+from llm_stuff import (
+ _DEFAULT_SYSTEM_PROMPT,
+ get_memory,
+ get_llm_chain,
+ StreamHandler,
+ feedback_component,
+ get_langsmith_client,
+)
 
 st.set_page_config(
  page_title="Chat LangSmith",
  page_icon="🦜",
 )
 
-
-def get_llm_chain(system_prompt: str, memory: ConversationBufferMemory) -> LLMChain:
- """Return a basic LLMChain with memory."""
- prompt = ChatPromptTemplate.from_messages(
- [
- (
- "system",
- system_prompt + "\nIt's currently {time}.",
- ),
- MessagesPlaceholder(variable_name="chat_history"),
- ("human", "{input}"),
- ],
- ).partial(time=lambda: str(datetime.now()))
- llm = ChatOpenAI(temperature=0.7, streaming=True)
- return LLMChain(prompt=prompt, llm=llm, memory=memory)
-
-
-client = Client()
-
-
 # "# Chat🦜🛠️"
 # Initialize State
 if "trace_link" not in st.session_state:
@@ -49,21 +30,25 @@ st.sidebar.markdown(
 """,
 )
 
-_DEFAULT_SYSTEM_PROMPT = "You are a helpful chatbot."
-
-system_prompt = st.sidebar.text_area(
- "Custom Instructions",
- _DEFAULT_SYSTEM_PROMPT,
- help="Custom instructions to provide the language model to determine style, personality, etc.",
-)
-system_prompt = system_prompt.strip().replace("{", "{{").replace("}", "}}")
-memory = ConversationBufferMemory(
- chat_memory=StreamlitChatMessageHistory(key="langchain_messages"),
- return_messages=True,
- memory_key="chat_history",
+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("}", "}}")
 )
 
-chain = get_llm_chain(system_prompt, memory)
+memory = get_memory()
+
+chain = get_llm_chain(memory, system_prompt)
+
+client = get_langsmith_client()
+
+run_collector = RunCollectorCallbackHandler()
+
 
 if st.sidebar.button("Clear message history"):
  print("Clearing message history")
@@ -96,19 +81,6 @@ if st.session_state.trace_link:
  )
 
 
-class StreamHandler(BaseCallbackHandler):
- def __init__(self, container, initial_text=""):
- self.container = container
- self.text = initial_text
-
- def on_llm_new_token(self, token: str, **kwargs) -> None:
- self.text += token
- self.container.markdown(self.text)
-
-
-run_collector = RunCollectorCallbackHandler()
-
-
 def _reset_feedback():
  st.session_state.feedback_update = None
  st.session_state.feedback = None
@@ -135,59 +107,6 @@ if prompt := st.chat_input(placeholder="Ask me a question!"):
  url = client.read_run(run.id).url
  st.session_state.trace_link = url
 
-# Simple feedback section
-# Optionally add a thumbs up/down button for feedback
+
 if st.session_state.get("run_id"):
- # feedback = streamlit_feedback(
- # feedback_type="thumbs",
- # key=f"feedback_{st.session_state.run_id}",
- # )
- # scores = {"👍": 1, "👎": 0}
- scores = {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0}
- feedback = streamlit_feedback(
- feedback_type="faces",
- optional_text_label="[Optional] Please provide an explanation",
- key=f"feedback_{st.session_state.run_id}",
- )
- if feedback:
- score = scores[feedback["score"]]
- feedback = client.create_feedback(
- st.session_state.run_id,
- feedback["type"],
- score=score,
- comment=feedback.get("text", None),
- )
- st.session_state.feedback = {"feedback_id": str(feedback.id), "score": score}
- st.toast("Feedback recorded!", icon="📝")
-
-
-# # Prompt for more information, if feedback was submitted
-# if st.session_state.get("feedback"):
-# feedback = st.session_state.get("feedback")
-# feedback_id = feedback["feedback_id"]
-# score = feedback["score"]
-# if score == 0:
-# if correction := st.text_input(
-# label="What would the correct or preferred response have been?",
-# key=f"correction_{feedback_id}",
-# ):
-# st.session_state.feedback_update = {
-# "correction": {"desired": correction},
-# "feedback_id": feedback_id,
-# }
-# elif score == 1:
-# if comment := st.text_input(
-# label="Anything else you'd like to add about this response?",
-# key=f"comment_{feedback_id}",
-# ):
-# st.session_state.feedback_update = {
-# "comment": comment,
-# "feedback_id": feedback_id,
-# }
-# # Update the feedback if additional information was provided
-# if st.session_state.get("feedback_update"):
-# feedback_update = st.session_state.get("feedback_update")
-# feedback_id = feedback_update.pop("feedback_id")
-# client.update_feedback(feedback_id, **feedback_update)
-# # Clear the comment or correction box
-# _reset_feedback()
+ feedback_component(client)

langchain-streamlit-demo/llm_stuff.py

@@ -0,0 +1,71 @@
+from datetime import datetime
+
+import streamlit as st
+from langchain import LLMChain
+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
+
+_DEFAULT_SYSTEM_PROMPT = "You are a helpful chatbot."
+
+
+def get_langsmith_client():
+ return Client()
+
+
+def get_memory() -> ConversationBufferMemory:
+ return ConversationBufferMemory(
+ chat_memory=StreamlitChatMessageHistory(key="langchain_messages"),
+ return_messages=True,
+ memory_key="chat_history",
+ )
+
+
+def get_llm_chain(
+ memory: ConversationBufferMemory,
+ system_prompt: str = _DEFAULT_SYSTEM_PROMPT,
+) -> LLMChain:
+ """Return a basic LLMChain with memory."""
+ prompt = ChatPromptTemplate.from_messages(
+ [
+ (
+ "system",
+ system_prompt + "\nIt's currently {time}.",
+ ),
+ MessagesPlaceholder(variable_name="chat_history"),
+ ("human", "{input}"),
+ ],
+ ).partial(time=lambda: str(datetime.now()))
+ llm = ChatOpenAI(temperature=0.7, streaming=True)
+ return LLMChain(prompt=prompt, llm=llm, memory=memory or get_memory())
+
+
+class StreamHandler(BaseCallbackHandler):
+ def __init__(self, container, initial_text=""):
+ self.container = container
+ self.text = initial_text
+
+ def on_llm_new_token(self, token: str, **kwargs) -> None:
+ self.text += token
+ self.container.markdown(self.text)
+
+
+def feedback_component(client):
+ scores = {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0}
+ if feedback := streamlit_feedback(
+ feedback_type="faces",
+ optional_text_label="[Optional] Please provide an explanation",
+ key=f"feedback_{st.session_state.run_id}",
+ ):
+ score = scores[feedback["score"]]
+ feedback = client.create_feedback(
+ st.session_state.run_id,
+ feedback["type"],
+ score=score,
+ comment=feedback.get("text", None),
+ )
+ st.session_state.feedback = {"feedback_id": str(feedback.id), "score": score}
+ st.toast("Feedback recorded!", icon="📝")