How can I pass a llama_debug = LlamaDebugHandler(print

TTennisPrestigious624

@kapa.ai How can I pass a
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = CallbackManager([llama_debug])
to the create_llama_chat_agent functino?

5 comments

kkapa.ai

Please stand by @TennisPrestigious624 as I search the knowledge base. We are experiencing especially high traffic right now and might take longer than usual. :wave:

kkapa.ai

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To pass the LlamaDebugHandler and CallbackManager to the create_llama_chat_agent function, you need to set up a ServiceContext with the callback_manager and pass it as an argument. Here's how you can do it:

First, create the LlamaDebugHandler and CallbackManager:

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from llama_index.callbacks import CallbackManager, LlamaDebugHandler

llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = CallbackManager([llama_debug])

Set up a ServiceContext with the callback_manager:

Plain Text

from llama_index import ServiceContext

service_context = ServiceContext.from_defaults(callback_manager=callback_manager)

Pass the service_context to the create_llama_chat_agent function:

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from llama_index.langchain_helpers.agents import create_llama_chat_agent
from langchain.chat_models import ChatOpenAI
from llama_index import LLMPredictor

llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0)
llm_predictor = LLMPredictor(llm=llm)

agent_executor = create_llama_chat_agent(toolkit, llm_predictor, callback_manager=callback_manager)

Replace toolkit with the appropriate LlamaToolkit instance. For more information on the create_llama_chat_agent function, refer to the documentation (https://gpt-index.readthedocs.io/en/latest/reference/langchain_integrations/base.html).
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TTennisPrestigious624

@kapa.ai you are not passing any service_context to the create_llama_chat_agent

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Apologies for the confusion in my previous response. You're right, I didn't pass the service_context to the create_llama_chat_agent function. Unfortunately, the provided extracts do not contain information about a create_llama_chat_agent function that accepts a service_context parameter.

However, you can still use the LlamaDebugHandler and CallbackManager with a chat engine like SimpleChatEngine or ReAct mode. Here's an example using SimpleChatEngine:

First, create the LlamaDebugHandler and CallbackManager:

Plain Text

from llama_index.callbacks import CallbackManager, LlamaDebugHandler

llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = CallbackManager([llama_debug])

Set up a ServiceContext with the callback_manager:

Plain Text

from llama_index import ServiceContext
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0)

service_context = ServiceContext.from_defaults(llm=llm, callback_manager=callback_manager)

Create a SimpleChatEngine with the service_context:

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from llama_index.chat_engine import SimpleChatEngine

chat_engine = SimpleChatEngine.from_defaults(service_context=service_context)

Now you can use the chat_engine to interact with the model, and the LlamaDebugHandler and CallbackManager will be used during the process.

For more information on using SimpleChatEngine, refer to the documentation (https://gpt-index.readthedocs.io/en/latest/examples/chat_engine/chat_engine_repl.html).
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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