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Merge pull request #13902 from bcdonadio/litellm_ollama_tools_transforms

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Fix Ollama transformations crash when tools are used with non-tool trained models
This commit is contained in:
Krish Dholakia
2025-08-22 23:49:31 -07:00
committed by GitHub
parent f9e9f8c25e 6384595289
commit 558c0940b2
1 changed files with 44 additions and 30 deletions
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litellm/llms/ollama/completion/transformation.py
+44 -30
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@@ -262,38 +262,52 @@ class OllamaConfig(BaseConfig):
## RESPONSE OBJECT
model_response.choices[0].finish_reason = "stop"
if request_data.get("format", "") == "json":
response_content = json.loads(response_json["response"])
# Check if this is a function call format with name/arguments structure
if (
isinstance(response_content, dict)
and "name" in response_content
and "arguments" in response_content
):
# Handle as function call (original behavior)
function_call = response_content
message = litellm.Message(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
else:
# Handle as regular JSON (new behavior)
message = litellm.Message(
content=json.dumps(response_content),
)
# Check if response field exists and is not empty before parsing JSON
response_text = response_json.get("response", "")
if not response_text or not response_text.strip():
# Handle empty response gracefully - set empty content
message = litellm.Message(content="")
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "stop"
else:
try:
response_content = json.loads(response_text)
# Check if this is a function call format with name/arguments structure
if (
isinstance(response_content, dict)
and "name" in response_content
and "arguments" in response_content
):
# Handle as function call (original behavior)
function_call = response_content
message = litellm.Message(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
else:
# Handle as regular JSON (new behavior)
message = litellm.Message(
content=json.dumps(response_content),
)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "stop"
except json.JSONDecodeError:
# If JSON parsing fails, treat as regular text response
message = litellm.Message(content=response_text)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "stop"
else:
model_response.choices[0].message.content = response_json["response"] # type: ignore
model_response.created = int(time.time())