Merge pull request #4373 from CorrM/main

[Fix-Improve] Improve Ollama prompt input and fix Ollama function calling key error

litellm/llms/ollama.py

@@ -126,7 +126,7 @@ class OllamaConfig:
             )
             and v is not None
         }
-    
+
     def get_required_params(self) -> List[ProviderField]:
         """For a given provider, return it's required fields with a description"""
         return [
@@ -451,7 +451,7 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
                         {
                             "id": f"call_{str(uuid.uuid4())}",
                             "function": {
-                                "name": function_call["name"],
+                                "name": function_call.get("name", function_call.get("function", None)),
                                 "arguments": json.dumps(function_call["arguments"]),
                             },
                             "type": "function",

litellm/llms/ollama_chat.py

@@ -434,7 +434,7 @@ async def ollama_async_streaming(
                         {
                             "id": f"call_{str(uuid.uuid4())}",
                             "function": {
-                                "name": function_call["name"],
+                                "name": function_call.get("name", function_call.get("function", None)),
                                 "arguments": json.dumps(function_call["arguments"]),
                             },
                             "type": "function",

litellm/llms/prompt_templates/factory.py

@@ -135,7 +135,7 @@ def convert_to_ollama_image(openai_image_url: str):
 
 
 def ollama_pt(
-    model, messages
+        model, messages
 ):  # https://github.com/ollama/ollama/blob/af4cf55884ac54b9e637cd71dadfe9b7a5685877/docs/modelfile.md#template
     if "instruct" in model:
         prompt = custom_prompt(
@@ -172,14 +172,36 @@ def ollama_pt(
                             images.append(base64_image)
         return {"prompt": prompt, "images": images}
     else:
-        prompt = "".join(
-            (
-                m["content"]
-                if isinstance(m["content"], str) is str
-                else "".join(m["content"])
-            )
-            for m in messages
-        )
+        prompt = ""
+        for message in messages:
+            role = message["role"]
+            content = message.get("content", "")
+
+            if "tool_calls" in message:
+                tool_calls = []
+
+                for call in message["tool_calls"]:
+                    call_id: str = call["id"]
+                    function_name: str = call["function"]["name"]
+                    arguments = json.loads(call["function"]["arguments"])
+
+                    tool_calls.append({
+                        "id": call_id,
+                        "type": "function",
+                        "function": {
+                            "name": function_name,
+                            "arguments": arguments
+                        }
+                    })
+
+                prompt += f"### Assistant:\nTool Calls: {json.dumps(tool_calls, indent=2)}\n\n"
+
+            elif "tool_call_id" in message:
+                prompt += f"### User:\n{message["content"]}\n\n"
+
+            elif content:
+                prompt += f"### {role.capitalize()}:\n{content}\n\n"
+
     return prompt
 
 
@@ -710,7 +732,7 @@ def convert_to_anthropic_tool_result_xml(message: dict) -> str:
 
     """
     Anthropic tool_results look like:
-    
+
     [Successful results]
     <function_results>
     <result>