Skip to main content

Panel

Panel is an open-source Python library designed to streamline the development of robust tools, dashboards, and complex applications entirely within Python. With a comprehensive philosophy, Panel integrates seamlessly with the PyData ecosystem, offering powerful, interactive data tables, visualizations, and much more, to unlock, visualize, share, and collaborate on your data for efficient workflows.

In this guide, we will go over how to setup the PanelCallbackHandler. The PanelCallbackHandler is useful for rendering and streaming the chain of thought from Langchain objects like Tools, Agents, and Chains. It inherits from Langchain’s BaseCallbackHandler.

Check out the panel-chat-examples docs to see more examples on how to use PanelCallbackHandler. If you have an example to demo, we’d love to add it to the panel-chat-examples gallery!

Installation and Setup​

pip install langchain panel

See full instructions in Panel's Getting started documentation.

Basic chat with an LLM​

To get started:

  1. Define a chat callback, like respond here.
  2. Pass the instance of a ChatFeed or ChatInterface to PanelCallbackHandler.
  3. Pass the callback_handler as a list into callbacks when constructing or using Langchain objects like ChatOpenAI here.
import panel as pn
from langchain_community.callbacks import PanelCallbackHandler
from langchain_openai import ChatOpenAI

pn.extension()


def respond(contents):
    llm.invoke(contents)


chat_interface = pn.chat.ChatInterface(callback=respond)
callback = PanelCallbackHandler(chat_interface)
llm = ChatOpenAI(model_name="gpt-4o-mini", streaming=True, callbacks=[callback])
chat_interface

This example shows the response from the LLM only. A LLM by it self does not show any chain of thought. Later we will build an agent that uses tools. This will show chain of thought.

Async chat with an LLM​

Using async prevents blocking the main thread, enabling concurrent interactions with the app. This improves responsiveness and user experience.

To do so:

  1. Prefix the function with async
  2. Prefix the call with await
  3. Use ainvoke instead of invoke
import panel as pn
from langchain_community.callbacks import PanelCallbackHandler
from langchain_openai import ChatOpenAI

pn.extension()


async def respond(contents):
    await llm.ainvoke(contents)


chat_interface = pn.chat.ChatInterface(callback=respond)
callback = PanelCallbackHandler(chat_interface)
llm = ChatOpenAI(model_name="gpt-4o-mini", streaming=True, callbacks=[callback])
chat_interface

Agents with Tools​

Agents and tools can also be used. Simply pass callback to the AgentExecutor and its invoke method.

import panel as pn
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent, load_tools
from langchain_community.callbacks import PanelCallbackHandler
from langchain_openai import ChatOpenAI

pn.extension()


def respond(contents):
    agent_executor.invoke({"input": contents}, {"callbacks": [callback]})


chat_interface = pn.chat.ChatInterface(callback=respond)
callback = PanelCallbackHandler(chat_interface)
llm = ChatOpenAI(model_name="gpt-4o-mini", streaming=True, callbacks=[callback])
tools = load_tools(["ddg-search"])
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, callbacks=[callback])

chat_interface

Chain with Retrievers​

RAG is also possible; simply pass callback again. Then ask it what the secret number is!

from uuid import uuid4

import panel as pn
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain_community.callbacks import PanelCallbackHandler
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

TEXT = "The secret number is 888."

TEMPLATE = """Answer the question based only on the following context:

{context}

Question: {question}
"""

pn.extension(design="material")


@pn.cache
def get_vector_store():
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    texts = text_splitter.split_text(TEXT)
    embeddings = OpenAIEmbeddings()
    db = Chroma.from_texts(texts, embeddings)
    return db


def get_chain(callbacks):
    retriever = db.as_retriever(callbacks=callbacks)
    model = ChatOpenAI(callbacks=callbacks, streaming=True)

    def format_docs(docs):
        text = "\n\n".join([d.page_content for d in docs])
        return text

    def hack(docs):
        # https://github.com/langchain-ai/langchain/issues/7290
        for callback in callbacks:
            callback.on_retriever_end(docs, run_id=uuid4())
        return docs

    return (
        {"context": retriever | hack | format_docs, "question": RunnablePassthrough()}
        | prompt
        | model
    )


async def respond(contents):
    chain = get_chain(callbacks=[callback])
    await chain.ainvoke(contents)


db = get_vector_store()
prompt = ChatPromptTemplate.from_template(TEMPLATE)
chat_interface = pn.chat.ChatInterface(callback=respond)
callback = PanelCallbackHandler(chat_interface)

chat_interface

Was this page helpful?