service_context.llama_logger.get_logs() but when using create_llama_chat_agent (AgentExecutor)create_llama_chat_agent, you can use the LlamaLogger class. First, create an instance of LlamaLogger and pass it to the ServiceContext. Then, use this ServiceContext when creating the QueryEngine. Finally, pass the toolkit containing the QueryEngine to the create_llama_chat_agent function. Here's an example based on the information extracts:from llama_index.logger import LlamaLogger
from llama_index import ServiceContext
from llama_index.langchain_helpers.agents import create_llama_chat_agent, LlamaToolkit, IndexToolConfig, LlamaIndexTool
# Create LlamaLogger instance
llama_logger = LlamaLogger()
service_context = ServiceContext.from_defaults(llama_logger=llama_logger)
# Create QueryEngine with the service_context containing the LlamaLogger
query_engine = index.as_query_engine(
service_context=service_context,
similarity_top_k=2,
)
# Create LlamaIndexTool and LlamaToolkit
tool_config = IndexToolConfig(
query_engine=query_engine,
name=f"Vector Index",
description=f"useful for when you want to answer queries about X",
tool_kwargs={"return_direct": True}
)
tool = LlamaIndexTool.from_tool_config(tool_config)
toolkit = LlamaToolkit(index_configs=index_configs)
# Create the chat agent
agent_chain = create_llama_chat_agent(
toolkit,
llm,
memory=memory,
verbose=True
)
# Run the chat agent
agent_chain.run(input="Query about X")
# Get logs
logs = service_context.llama_logger.get_logs()LlamaLogger and uses it with the create_llama_chat_agent function. After running the chat agent, you can obtain the logs using service_context.llama_logger.get_logs().Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:LlamaLogger is associated with the specific QueryEngine and ServiceContext used in the tool. If the tool isn't used, the LlamaLogger won't capture any logs related to the query.Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:LlamaToolkit containing the LlamaIndexTool and pass it to the create_llama_chat_agent function. This way, the chat agent will use the provided toolkit for all queries. Here's an example based on the information extracts:from llama_index.langchain_helpers.agents import create_llama_chat_agent, LlamaToolkit, IndexToolConfig, LlamaIndexTool
# Create QueryEngine
query_engine = index.as_query_engine()
# Create LlamaIndexTool and LlamaToolkit
tool_config = IndexToolConfig(
query_engine=query_engine,
name=f"Vector Index",
description=f"useful for when you want to answer queries about X",
tool_kwargs={{"return_direct": True}}
)
tool = LlamaIndexTool.from_tool_config(tool_config)
toolkit = LlamaToolkit(index_configs=[tool_config])
# Create the chat agent
agent_chain = create_llama_chat_agent(
toolkit,
llm,
memory=memory,
verbose=True
)
# Run the chat agent
agent_chain.run(input="Query about X")LlamaToolkit contains only the LlamaIndexTool created with the desired QueryEngine. By passing this toolkit to the create_llama_chat_agent function, the chat agent will use the provided toolkit for all queries.Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: