fix(main.py): fix azure ai cohere tool calling

litellm/llms/prompt_templates/factory.py

@@ -1903,6 +1903,26 @@ def azure_text_pt(messages: list):
     return prompt
 
 
+###### AZURE AI #######
+def stringify_json_tool_call_content(messages: List) -> List:
+    """
+
+    - Check 'content' in tool role -> convert to dict (if not) -> stringify
+
+    Done for azure_ai/cohere calls to handle results of a tool call
+    """
+
+    for m in messages:
+        if m["role"] == "tool" and isinstance(m["content"], str):
+            # check if content is a valid json object
+            try:
+                json.loads(m["content"])
+            except json.JSONDecodeError:
+                m["content"] = json.dumps({"result": m["content"]})
+
+    return messages
+
+
 ###### AMAZON BEDROCK #######
 
 from litellm.types.llms.bedrock import ContentBlock as BedrockContentBlock

litellm/main.py

@@ -113,6 +113,7 @@ from .llms.prompt_templates.factory import (
     function_call_prompt,
     map_system_message_pt,
     prompt_factory,
+    stringify_json_tool_call_content,
 )
 from .llms.text_completion_codestral import CodestralTextCompletion
 from .llms.triton import TritonChatCompletion
@@ -1114,6 +1115,73 @@ def completion(
                         "api_base": api_base,
                     },
                 )
+        elif custom_llm_provider == "azure_ai":
+            api_base = (
+                api_base  # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
+                or litellm.api_base
+                or get_secret("AZURE_AI_API_BASE")
+            )
+            # set API KEY
+            api_key = (
+                api_key
+                or litellm.api_key  # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
+                or litellm.openai_key
+                or get_secret("AZURE_AI_API_KEY")
+            )
+
+            headers = headers or litellm.headers
+
+            ## LOAD CONFIG - if set
+            config = litellm.OpenAIConfig.get_config()
+            for k, v in config.items():
+                if (
+                    k not in optional_params
+                ):  # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in
+                    optional_params[k] = v
+
+            ## FOR COHERE
+            if "command-r" in model:  # make sure tool call in messages are str
+                messages = stringify_json_tool_call_content(messages=messages)
+
+            ## COMPLETION CALL
+            try:
+                response = openai_chat_completions.completion(
+                    model=model,
+                    messages=messages,
+                    headers=headers,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    api_key=api_key,
+                    api_base=api_base,
+                    acompletion=acompletion,
+                    logging_obj=logging,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    timeout=timeout,  # type: ignore
+                    custom_prompt_dict=custom_prompt_dict,
+                    client=client,  # pass AsyncOpenAI, OpenAI client
+                    organization=organization,
+                    custom_llm_provider=custom_llm_provider,
+                )
+            except Exception as e:
+                ## LOGGING - log the original exception returned
+                logging.post_call(
+                    input=messages,
+                    api_key=api_key,
+                    original_response=str(e),
+                    additional_args={"headers": headers},
+                )
+                raise e
+
+            if optional_params.get("stream", False):
+                ## LOGGING
+                logging.post_call(
+                    input=messages,
+                    api_key=api_key,
+                    original_response=response,
+                    additional_args={"headers": headers},
+                )
         elif (
             custom_llm_provider == "text-completion-openai"
             or "ft:babbage-002" in model

litellm/tests/test_completion.py

@@ -410,17 +410,21 @@ def test_completion_claude_3_function_call(model):
 
 @pytest.mark.parametrize("sync_mode", [True])
 @pytest.mark.parametrize(
-    "model",
+    "model, api_key, api_base",
     [
-        "gpt-3.5-turbo",
+        ("gpt-3.5-turbo", None, None),
-        "claude-3-opus-20240229",
+        ("claude-3-opus-20240229", None, None),
-        "command-r",
+        ("command-r", None, None),
-        "anthropic.claude-3-sonnet-20240229-v1:0",
+        ("anthropic.claude-3-sonnet-20240229-v1:0", None, None),
-        # "azure_ai/command-r-plus"
+        (
+            "azure_ai/command-r-plus",
+            os.getenv("AZURE_COHERE_API_KEY"),
+            os.getenv("AZURE_COHERE_API_BASE"),
+        ),
     ],
 )
 @pytest.mark.asyncio
-async def test_model_function_invoke(model, sync_mode):
+async def test_model_function_invoke(model, sync_mode, api_key, api_base):
     try:
         litellm.set_verbose = True
 
@@ -483,6 +487,8 @@ async def test_model_function_invoke(model, sync_mode):
             "model": model,
             "messages": messages,
             "tools": tools,
+            "api_key": api_key,
+            "api_base": api_base,
         }
         if sync_mode:
             response = litellm.completion(**data)