PanelCallbackHandler

Open this notebook in Jupyterlite | Download this notebook from GitHub (right-click to download).

---

The Langchain 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!

Parameters:

Core

• instance (ChatFeed | ChatInterface): The ChatFeed or ChatInterface instance to render or stream to.

• user (str): Name of the user who sent the message.

• avatar (str | BinaryIO): The avatar to use for the user. Can be a single character text, an emoji, or anything supported by pn.pane.Image. If not set, uses the first character of the name.

---

Basics

To get started:

1. Pass the instance of a ChatFeed or ChatInterface to PanelCallbackHandler.

2. Pass the callback_handler as a list into callbacks when constructing or using Langchain objects.

import panel as pn
from langchain.llms import OpenAI

pn.extension()

llm = OpenAI(temperature=0)

def callback(contents, user, instance):
    callback_handler = pn.chat.langchain.PanelCallbackHandler(instance)    
    return llm.predict(contents, callbacks=[callback_handler])

pn.chat.ChatInterface(callback=callback).servable()

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

async can also be used. This will make your app more responsive and enable it to support more users.

import panel as pn
from langchain.llms import OpenAI

pn.extension()

llm = OpenAI(temperature=0)

async def callback(contents, user, instance):
    callback_handler = pn.chat.langchain.PanelCallbackHandler(instance)
    return await llm.apredict(contents, callbacks=[callback_handler])

pn.chat.ChatInterface(callback=callback).servable()

Streaming

To stream tokens from the LLM, simply set streaming=True when constructing the LLM.

Note, async is not required to stream, but it is more efficient.

import panel as pn
from langchain.llms import OpenAI

pn.extension()

llm = OpenAI(temperature=0, streaming=True)

async def callback(contents, user, instance):
    callback_handler = pn.chat.langchain.PanelCallbackHandler(instance)
    return await llm.apredict(contents, callbacks=[callback_handler])

pn.chat.ChatInterface(callback=callback).servable()

Agents with Tools

Tools can be used and it’ll also be streamed by passing callback_handler to callbacks.

from langchain.llms import OpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
import panel as pn

llm = OpenAI(temperature=0, streaming=True)
tools = load_tools(["ddg-search"])
agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, 
)

def callback(contents, user, instance):
    callback_handler = pn.chat.langchain.PanelCallbackHandler(instance)
    return agent.run(contents, callbacks=[callback_handler])

pn.chat.ChatInterface(callback=callback).servable()

Chains with Retrievers

Again, async is not required, but more efficient.

from uuid import uuid4

import requests

from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma

import panel as pn

TEXT = "https://raw.githubusercontent.com/langchain-ai/langchain/master/docs/docs/modules/state_of_the_union.txt"

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

{context}

Question: {question}
"""

pn.extension(design="material")

prompt = ChatPromptTemplate.from_template(TEMPLATE)


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


db = get_vector_store()


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

    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 callback(contents, user, instance):
    callback_handler = pn.chat.langchain.PanelCallbackHandler(instance)
    chain = get_chain(callbacks=[callback_handler])
    response = await chain.ainvoke(contents)
    return response.content


pn.chat.ChatInterface(callback=callback).servable()

Please note that we use the hack because retrievers currently do not call any CallbackHandlers. See LangChain Issue#7290.

---

Open this notebook in Jupyterlite | Download this notebook from GitHub (right-click to download).