Merge branch 'main' of https://github.com/holoviz-topics/panel-chat-e…

…xamples into enhancement/news-summarizer

docs/examples/langchain/langchain_lcel.py

@@ -0,0 +1,34 @@
+"""
+Demonstrates how to use the `ChatInterface` to create a chatbot using
+[LangChain Expression Language](https://python.langchain.com/docs/expression_language/) (LCEL).
+"""
+
+import panel as pn
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI
+
+pn.extension()
+
+
+async def callback(contents, user, instance):
+    message = ""
+    async for token in chain.astream(contents):
+        message += token
+        yield message
+
+
+prompt = ChatPromptTemplate.from_template("Tell me a top-notch joke about {topic}")
+model = ChatOpenAI(model="gpt-3.5-turbo")
+output_parser = StrOutputParser()
+chain = {"topic": RunnablePassthrough()} | prompt | model | output_parser
+
+chat_interface = pn.chat.ChatInterface(
+    pn.chat.ChatMessage(
+        "Offer a topic and ChatGPT will respond with a joke!", user="System"
+    ),
+    callback=callback,
+    callback_user="ChatGPT",
+)
+chat_interface.servable()

docs/examples/langchain/langchain_streaming_lcel_with_memory.py

@@ -0,0 +1,56 @@
+"""
+Demonstrates how to use the `ChatInterface` to create a chatbot using
+[LangChain Expression Language](https://python.langchain.com/docs/expression_language/) (LCEL)
+with streaming and memory.
+"""
+
+from operator import itemgetter
+
+import panel as pn
+from langchain.memory import ConversationSummaryBufferMemory
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain_openai import ChatOpenAI
+
+pn.extension()
+
+SYSTEM_PROMPT = "Try to be a silly comedian."
+
+
+async def callback(contents, user, instance):
+    message = ""
+    inputs = {"input": contents}
+    async for token in chain.astream(inputs):
+        message += token
+        yield message
+    memory.save_context(inputs, {"output": message})
+
+
+model = ChatOpenAI(model="gpt-3.5-turbo")
+memory = ConversationSummaryBufferMemory(return_messages=True, llm=model)
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", SYSTEM_PROMPT),
+        MessagesPlaceholder(variable_name="history"),
+        ("human", "{input}"),
+    ]
+)
+output_parser = StrOutputParser()
+chain = (
+    RunnablePassthrough.assign(
+        history=RunnableLambda(memory.load_memory_variables) | itemgetter("history")
+    )
+    | prompt
+    | model
+    | output_parser
+)
+
+chat_interface = pn.chat.ChatInterface(
+    pn.chat.ChatMessage(
+        "Offer a topic and ChatGPT will try to be funny!", user="System"
+    ),
+    callback=callback,
+    callback_user="ChatGPT",
+)
+chat_interface.servable()

docs/external_resources.md

@@ -92,12 +92,22 @@ Links: [Medium](https://sophiamyang.medium.com/building-ai-chatbots-with-mistral
 
 The news summarizer app by [Eduardo Blancas](https://github.com/edublancas) is a RAG built *from scratch* without any LLM framework. Its deployed to [Ploomber Cloud](https://ploomber.io/cloud/). The methodology is nicely described in [rag-from-scratch.ipynb](https://github.com/ploomber/doc/blob/main/notebooks/rag-from-scratch.ipynb).
 
+Try asking `What is the latest sports news?`.
+
 <iframe
 src="https://ancient-wind-3019.ploomberapp.io/app"
 frameborder="0"
-style="width:100%;height:800px"
+style="width:100%;height:600px"
 ></iframe>
 
+### Talk with Plot
+
+Authors: [Andrew Huang](https://twitter.com/IAteAnDrew1) | [Sophia Yang](https://twitter.com/sophiamyang)
+
+Links: [Blog Post](https://blog.holoviz.org/posts/tweak-mpl-chat/) | [Hugging Face Spaces](https://huggingface.co/spaces/ahuang11/tweak-mpl-chat)
+
+![Talk to Plot](https://blog.holoviz.org/posts/tweak-mpl-chat/images/app1.gif)
+
 ## Ragna
 
 [Ragna](https://ragna.chat/) is an open source RAG orchestration framework. The UI is built on top of Panel.

overrides/main.html

@@ -12,13 +12,13 @@
   <meta name="twitter:creator" content="@Panel_org" />
   <meta name="twitter:title" content="{{ title }}" />
   <meta name="twitter:description" content="{{ config.site_description }}" />
-  <meta name="twitter:image" content="https://holoviz-topics.github.io/panel-chat-examples/assets/images/panel-chat-examples-card-800x418.png?version=2" />
+  <meta name="twitter:image" content="https://holoviz-topics.github.io/panel-chat-examples/assets/images/panel-chat-examples-card.png?version=1" />
   <meta property="og:type" content="website" />
   <meta property="og:title" content="{{ title }}" />
   <meta property="og:description" content="{{ config.site_description }}" />
   <meta property="og:url" content="https://holoviz-topics.github.io/panel-chat-examples" />
-  <meta property="og:image" content="https://holoviz-topics.github.io/panel-chat-examples/assets/images/panel-chat-examples-card-1600x837.png?version=2" />
+  <meta property="og:image" content="https://holoviz-topics.github.io/panel-chat-examples/assets/images/panel-chat-examples-card.png?version=1" />
   <meta property="og:image:type" content="image/png" />
-  <meta property="og:image:width" content="1600" />
-  <meta property="og:image:height" content="837" />
+  <meta property="og:image:width" content="1024" />
+  <meta property="og:image:height" content="1024" />
 {% endblock %}

pyproject.toml

@@ -99,6 +99,7 @@ dependencies = [
     "tabulate",
     "tiktoken",
     "mistralai",
+    "langchain-openai",
 ]
 
 [project.urls]

tests/ui/user.py

@@ -169,6 +169,20 @@ def langchain_pdf_assistant(page: Page):
     page.wait_for_timeout(10000)
 
 
+def langchain_lcel(page: Page):
+    chat = ChatInterface(page)
+    chat.send("Python")
+    page.wait_for_timeout(5000)
+
+
+def langchain_streaming_lcel_with_memory(page: Page):
+    chat = ChatInterface(page)
+    chat.send("Remember this number: 8. Be concise.")
+    page.wait_for_timeout(10000)
+    chat.send("What number did I just ask you to remember?")
+    page.wait_for_timeout(10000)
+
+
 def mistral_and_llama(page: Page):
     chat = ChatInterface(page)
     chat.send("What do you think about HoloViz in a single sentence?")