Closed JulianOestreich90 closed 6 months ago
The issue you're encountering, where your agent returns { "type": "assistant", "content": null }
before calling the tools, likely stems from how the tool's output is handled. Specifically, if a tool execution is successful but the tool returns a None
value or an incorrectly formatted ToolOutput
object, this scenario isn't explicitly managed, leading to the null
content issue.
To address this, ensure that every tool returns a ToolOutput
object with a non-null content
field upon successful execution. Additionally, you can modify the call_tool_with_error_handling
function to check for None
or invalid outputs from the tool and handle these cases appropriately. For instance, you could raise an exception or set a default error message if the tool's output is not as expected.
Here's a code snippet to help you modify the call_tool_with_error_handling
function:
def call_tool_with_error_handling(
tool: BaseTool,
input_dict: Dict,
error_message: Optional[str] = None,
raise_error: bool = False,
) -> ToolOutput:
try {
output = tool(**input_dict)
if output.content is None: # Check if the content is None
raise ValueError("Tool returned None content")
return output
} catch (Exception e) {
if raise_error:
throw
error_message = error_message or f"Error: {e!s}"
return ToolOutput(
content=error_message,
tool_name=tool.metadata.name,
raw_input={"kwargs": input_dict},
raw_output=e,
)
}
This modification ensures that if a tool returns a ToolOutput
object with content
set to None
, it's treated as an error, and an appropriate error message is generated. This should prevent the agent from returning invalid responses and mitigate the BadRequestError
you're encountering.
@dosu-bot And where should i call the call_tool_with_error_handling
function within my code?
To integrate the call_tool_with_error_handling
function within your code, you should override the call_tool
method in the OpenAIAgent
class. This method is responsible for calling the tools, and by overriding it, you can ensure that every tool call is processed through your error handling function. Here's a simplified example of how to do it:
class OpenAIAgent(Agent):
# ...
def call_tool(self, tool: BaseTool, input_dict: Dict) -> ToolOutput:
return call_tool_with_error_handling(tool, input_dict)
# ...
This modification directs all tool calls to go through call_tool_with_error_handling
, which checks for None
content and handles errors appropriately. If your tools involve asynchronous operations, you should also apply a similar override to the acall_tool
method for asynchronous handling.
If there are any specific aspects of your call_tool_with_error_handling
function or other parts of your code that you're unsure about, please share those details for more targeted advice.
@dosu-bot I fixed it by changing message.content
to an empty string if its None, by impementing a custom add_message() method for the RedisVectorStore:
from llama_index.agent.openai import OpenAIAgent
from llama_index.storage.chat_store.redis import RedisChatStore
from llama_index.core.memory import ChatMemoryBuffer
class SafeRedisChatStore(RedisChatStore):
def add_message(self, key, message):
print(f"Storing message: {message.content}")
if message.content is None:
message.content = ""
super().add_message(key, message)
chat_store = SafeRedisChatStore(redis_url="redis://localhost:6379", ttl=30000)
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=chat_store,
chat_store_key="user1",
)
top_agent = OpenAIAgent.from_tools(
tool_retriever=obj_index.as_retriever(similarity_top_k=10),
memory=chat_memory,
system_prompt=""" \
You are an agent designed to answer queries about a set of given scientific papers.
Please always use the tools provided to answer a question. Do not rely on prior knowledge. Do not issue a response with \{'content': null\}\
""",
verbose=True,
)
Now i run into a new Error:
Retrying llama_index.llms.openai.base.OpenAI._chat in 0.9066396127181989 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[5].role', 'code': None}}.
How can i fix this?
The error you're encountering, where messages with the role 'tool' must follow a 'tool_calls' message, is a specific requirement from the OpenAI API. This means that in your message flow, every 'tool' message must be directly preceded by a 'tool_calls' message to satisfy the API's constraints.
To address this, you can modify the add_message()
method in your custom SafeRedisChatStore
class to enforce this rule. Before adding a 'tool' message, check if the last message stored has the role 'tool_calls'. If not, you should either adjust the message flow to include a 'tool_calls' message before the 'tool' message or handle the error accordingly.
Here's a conceptual approach to modifying the add_message()
method:
class SafeRedisChatStore(RedisChatStore):
def add_message(self, key, message):
if message.content is None:
message.content = ""
last_message = self.get_last_message(key)
if message.role == 'tool' and (last_message is None or last_message.role != 'tool_calls'):
raise ValueError("A 'tool' message must follow a 'tool_calls' message.")
super().add_message(key, message)
This code snippet includes a check for the last message's role before adding a new 'tool' message. It's a simplified example, and you'll need to implement the get_last_message()
method or an equivalent to retrieve the last message for the given key.
This solution ensures that your message flow adheres to the OpenAI API's requirements, preventing the BadRequestError you're experiencing.
@dosu-bot There are no 'tool_calls' messages added to the chat_history, when using ChatMemoryBuffer
.
To resolve the issue where 'tool_calls' messages are not being added to the chat history when using ChatMemoryBuffer
, it's crucial to ensure that your application correctly handles the sequence and structure of messages according to the OpenAI API's requirements. Specifically, a 'tool' message must directly follow a 'tool_calls' message. Here's a focused approach to address this:
Ensure Proper Message Structure: Verify that messages intended for tool calls (tool_calls
) are correctly structured. This involves setting up messages with the appropriate fields such as id
, type
, function
, etc., to comply with the OpenAI API's expectations.
Update Message Handling Logic: Your application's logic for processing, converting, and storing chat messages must recognize and correctly handle the tool_calls
role. This may require adjustments in enums, validation logic, or storage schemas to accommodate tool_calls
as a valid message role.
Implement Error Handling for Message Roles: Enhance your error handling to manage BadRequestError
related to incorrect message role sequences. This should help in identifying and rectifying issues with message role handling promptly.
Testing and Validation: Extend your testing to cover scenarios that involve tool_calls
, ensuring that your application can handle these messages both when sending to and receiving from the OpenAI API.
For practical examples and more detailed guidance, you might look into the test suites and class definitions within the llama-index repository, specifically focusing on how tool_calls
are structured and handled. This includes looking at test cases for message conversion functions and the OpenAIToolCall
class for encapsulating tool call information.
By following these steps and ensuring that your application adheres to the OpenAI API's message handling requirements, you should be able to mitigate the error related to 'tool' messages needing to follow 'tool_calls' messages in your chat history.
Question Validation
Question
I am using the OpenAI Top Level Agent as in the Multi Documents Agent Example. I made the agent use a persistent Redis memory. After querying something that involves using a tool, it writes the following entries to Redis:
So message 3 is written in the database and it contains a null value, which is not allowed. Further queries using the memory will give me then:
Retrying llama_index.llms.openai.base.OpenAI._chat in 0.699491702621411 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': "Invalid value for 'content': expected a string, got null.", 'type': 'invalid_request_error', 'param': 'messages.[4].content', 'code': None}}.
I know that i could instantiate a CustomMemoryBuffer that checks for null-contents before uploading. But instead i would like to understand why my agent is returning { "type": "assistant", "content": null } before calling the tools?
Here is how i initialized the agents: