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hoyyeva/anthropic-local-image-path
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Reference: github-starred/ollama#33264
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Originally created by @CassidyMabey on GitHub (Jul 11, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/11370
What is the issue?
I created a project where i wanted to use an AI model stored locally to do stuff such as create files, directories and create code much like cursor and github copilot. However, when passing large amounts of code i experienced an error. After more digging this resulted in me realising that it was these non printable characters which were in the code were not being able to be processed. And as it was an issue with the arguments, i couldn't parse the text or anything as the AI calls it from its tool function interface which can't be intercepted.
I have provided the logs below.
TLDR: The issue here is that these non printable charactars can't be passed into a tool resulting in the tool not calling as there is an error.
I have also attempted a pull request so if you want to read it here: https://github.com/ollama/ollama/pull/11372
Relevant log output
OS
macOS
GPU
Apple
CPU
Apple
Ollama version
0.9.5
@rick-github commented on GitHub (Jul 11, 2025):
The error is that the assistant message content is missing a double-quote on
path:How is this message being created?
Error in tool processingis not an ollama error, what framework are you using?@CassidyMabey commented on GitHub (Jul 11, 2025):
Im using llama3.2:1b
Plus, this is not the issue which ollama is having. It was the issue which made me notice that if you send Non Printable Characters into a tool, the tool can have an error before any parsing happens to remove the non printable characters in the tool as there is nothing removing it before it is being passed into the tool.
Sorry if im not being very clear. I should have specified
@rick-github commented on GitHub (Jul 11, 2025):
What framework?
@CassidyMabey commented on GitHub (Jul 11, 2025):
The ollama python library i presume is what you mean by framework.
But ill give you everything for reference:
Model: llama3.2:1b (however same issues occured with qwen2.5-coder:latest and granite3-dense:latest)
@rick-github commented on GitHub (Jul 11, 2025):
Can you show the code where you define the tools?
@CassidyMabey commented on GitHub (Jul 11, 2025):
@rick-github commented on GitHub (Jul 11, 2025):
And where are the non printable characters, in the prompt?
@CassidyMabey commented on GitHub (Jul 11, 2025):
in the response from the AI. often appears in large prompts or code blocks. Then it instantly calls the tools from the tool function interface before these characters can be removed.
@rick-github commented on GitHub (Jul 11, 2025):
ollama doesn't have a "tool function interface", that's a function of the client.
As I don't know the specifics of the client, the dataflow is not really clear. Based on the log snippets, it looks like the client is trying to process the
contentfield from ollama as a tool call, rather than thetool_callsfield which is normally used. For example:The response from ollama has
content(" The `Ball` class ...") but notool_calls(None). The client extracts thecontentfield ("assistant message content") and then tries to parse it as a tool call. It skips the leading space in the content but then throws an error when the next character ("T") is not the start of a valid JSON structure that would signify a tool call.This can be seen in the first DEBUG statement where the parser processes the content until it gets to the missing double-quote.
So, I'm guessing that the client is expecting a response from ollama that is a tool call, and when it can't find a tool call in
tool_calls, it assumes that the tool call must be incontent, tries to parse it and fails because it's not a tool call. The first response is almost a tool call, the second and third responses are definitely not.Failing to return a tool call when one is expected is not unusual for models, particularly small ones like llama3.2:1b. A bigger, more recent model might give you better results - qwen3 is quite a good tool user. The client could handle the case of failed tool calls by retrying the prompt that was supposed to generate the tool call.
As to the non-ASCII characters, if it seems to occur when you use large prompts, it might be due to context buffer overrun. A buffer overrun could also explain why the tool calls sometimes fail - the tools are at the head of the buffer, and exceeding the size of the buffer causes ollama to shift the buffer contents to make room for new tokens, resulting in the tools being lost. See here for ways to manage the size of the context buffer.
@CassidyMabey commented on GitHub (Jul 11, 2025):
Thank you for the response. Yes, you are right the error definitely occured due to the smaller model, however in this scenario, the AI produces a response which contains JSON to call the tools.
Then to be able to read it you have to decode it where it is then trying to decode non printable characters and potentially other english / natural language than the JSON format giving an error. Im just saying this can be fixed by removing these non printable characters from being added into the arguments of a tool.
@rick-github commented on GitHub (Jul 11, 2025):
Decoding
contentis the wrong way to do it. The client should be checkingtool_callsand retrying the request if no tools calls were found if one was expected. There are no non-printable characters in the responses in the supplied log snippet.@CassidyMabey commented on GitHub (Jul 11, 2025):
I understand. Thank you.
However, this is about the wider part where the tools in the response do not get sanitsed before they are parsed into the tools. This results in lots of issues down the road.
As you can see here ollama tries to sanitise the json to help smaller models, however it is not nearly as smart to solve these issues.
Thanks for your help