Merge pull request #1037 from parea-ai/PAI-1442-openai-structured-out…

…puts

openai-structured-outputs

cookbook/openai/tracing_with_openai_with_structured_output.py

@@ -0,0 +1,109 @@
+import os
+
+from dotenv import load_dotenv
+from openai import OpenAI
+from pydantic import BaseModel
+
+from parea import Parea
+
+load_dotenv()
+
+client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+p = Parea(api_key=os.getenv("PAREA_API_KEY"))
+p.wrap_openai_client(client)
+
+
+class CalendarEvent(BaseModel):
+    name: str
+    date: str
+    participants: list[str]
+
+
+def with_pydantic():
+    completion = client.beta.chat.completions.parse(
+        model="gpt-4o-2024-08-06",
+        messages=[
+            {"role": "system", "content": "Extract the event information."},
+            {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
+        ],
+        response_format=CalendarEvent,
+    )
+    event = completion.choices[0].message.parsed
+    print(event)
+
+
+def with_json_schema():
+    response = client.chat.completions.create(
+        model="gpt-4o-2024-08-06",
+        messages=[
+            {"role": "system", "content": "You are a helpful math tutor. Guide the user through the solution step by step."},
+            {"role": "user", "content": "how can I solve 8x + 7 = -23"},
+        ],
+        response_format={
+            "type": "json_schema",
+            "json_schema": {
+                "name": "math_response",
+                "schema": {
+                    "type": "object",
+                    "properties": {
+                        "steps": {
+                            "type": "array",
+                            "items": {
+                                "type": "object",
+                                "properties": {"explanation": {"type": "string"}, "output": {"type": "string"}},
+                                "required": ["explanation", "output"],
+                                "additionalProperties": False,
+                            },
+                        },
+                        "final_answer": {"type": "string"},
+                    },
+                    "required": ["steps", "final_answer"],
+                    "additionalProperties": False,
+                },
+                "strict": True,
+            },
+        },
+    )
+    print(response.choices[0].message.content)
+
+
+def with_tools():
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_delivery_date",
+                "description": "Get the delivery date for a customer's order. Call this whenever you need to know the delivery date, for example when a customer asks 'Where is my package'",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "order_id": {
+                            "type": "string",
+                            "description": "The customer's order ID.",
+                        },
+                    },
+                    "required": ["order_id"],
+                    "additionalProperties": False,
+                },
+            },
+            "strict": True,
+        }
+    ]
+
+    messages = [
+        {"role": "system", "content": "You are a helpful customer support assistant. Use the supplied tools to assist the user."},
+        {"role": "user", "content": "Hi, can you tell me the delivery date for my order with id 5?"},
+    ]
+
+    response = client.chat.completions.create(
+        model="gpt-4o-2024-08-06",
+        messages=messages,
+        tools=tools,
+    )
+    print(response.choices[0].message.tool_calls)
+
+
+if __name__ == "__main__":
+    with_pydantic()
+    with_json_schema()
+    with_tools()

parea/constants.py

@@ -146,6 +146,11 @@ def str2bool(v):
         "completion": 15.0,
         "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 128000},
     },
+    "gpt-4o-2024-08-06": {
+        "prompt": 5.0,
+        "completion": 15.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 128000},
+    },
     "gpt-4o-mini": {
         "prompt": 0.15,
         "completion": 0.6,

parea/utils/trace_utils.py

@@ -49,7 +49,9 @@ def log_in_thread(target_func: Callable, data: Dict[str, Any]):
     logging_thread.start()
 
 
-def merge(old, new):
+def merge(old, new, key=None):
+    if key == "error" and old:
+        return json_dumps([old, new])
     if isinstance(old, dict) and isinstance(new, dict):
         return dict(ChainMap(new, old))
     if isinstance(old, list) and isinstance(new, list):
@@ -112,7 +114,7 @@ def trace_insert(data: Dict[str, Any], trace_id: Optional[str] = None):
             return
         for key, new_value in data.items():
             existing_value = current_trace_data.__getattribute__(key)
-            current_trace_data.__setattr__(key, merge(existing_value, new_value) if existing_value else new_value)
+            current_trace_data.__setattr__(key, merge(existing_value, new_value, key) if existing_value else new_value)
     except Exception as e:
         logger.debug(f"Error occurred inserting data into trace log, {e}", exc_info=e)
 

parea/utils/universal_encoder.py

@@ -81,6 +81,16 @@ def handle_dspy_response(self, obj) -> Any:
         else:
             return None
 
+    def handle_openai_not_given(self, obj) -> Any:
+        try:
+            from openai import NotGiven
+        except ImportError:
+            return None
+
+        if isinstance(obj, NotGiven):
+            return {"not_given": None}
+        return None
+
     def default(self, obj: Any):
         if isinstance(obj, str):
             return obj
@@ -116,6 +126,8 @@ def default(self, obj: Any):
             return obj.to_dict(orient="records")
         elif dspy_response := self.handle_dspy_response(obj):
             return dspy_response
+        elif is_openai_not_given := self.handle_openai_not_given(obj):
+            return is_openai_not_given["not_given"]
         elif callable(obj):
             try:
                 return f"<callable {obj.__name__}>"

parea/wrapper/openai/openai.py

@@ -58,6 +58,13 @@ def get_original_methods(self, module_client=openai):
                 original_methods = {"chat.completions.create": module_client.chat.completions.create}
             except openai.OpenAIError:
                 original_methods = {}
+
+            try:
+                latest_methods = {"beta.chat.completions.parse": module_client.beta.chat.completions.parse}
+                original_methods.update(latest_methods)
+            except Exception:
+                pass
+
         return list(original_methods.keys())
 
     def init(self, log: Callable, cache: Cache = None, module_client=openai):
@@ -103,7 +110,7 @@ def resolver(self, trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any],
         trace_data.get()[trace_id].output_tokens = output_tokens
         trace_data.get()[trace_id].total_tokens = total_tokens
         trace_data.get()[trace_id].cost = _compute_cost(input_tokens, output_tokens, model)
-        trace_data.get()[trace_id].output = output
+        trace_data.get()[trace_id].output = json_dumps(output) if not isinstance(output, str) else output
         return response
 
     def gen_resolver(self, trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], response, final_log):
@@ -269,7 +276,13 @@ def _kwargs_to_llm_configuration(kwargs):
 
     @staticmethod
     def _get_output(result: Any, model: Optional[str] = None) -> str:
-        if not isinstance(result, OpenAIObject) and isinstance(result, dict):
+        try:
+            from openai.types.chat import ParsedChatCompletion, ParsedChatCompletionMessage
+        except ImportError:
+            ParsedChatCompletion = None
+            ParsedChatCompletionMessage = None
+
+        if not isinstance(result, (OpenAIObject, ParsedChatCompletion)) and isinstance(result, dict):
             result = convert_to_openai_object(
                 {
                     "choices": [
@@ -282,7 +295,9 @@ def _get_output(result: Any, model: Optional[str] = None) -> str:
                 }
             )
         response_message = result.choices[0].message
-        if not response_message.get("content", None) if is_old_openai else not response_message.content:
+        if isinstance(response_message, ParsedChatCompletionMessage):
+            completion = response_message.parsed.model_dump_json() if response_message.parsed else ""
+        elif not response_message.get("content", None) if is_old_openai else not response_message.content:
             completion = OpenAIWrapper._format_function_call(response_message)
         else:
             completion = response_message.content

parea/wrapper/utils.py

@@ -6,7 +6,9 @@
 from functools import lru_cache, wraps
 
 import tiktoken
+from openai import NotGiven
 from openai import __version__ as openai_version
+from pydantic._internal._model_construction import ModelMetaclass
 
 from parea.constants import ALL_NON_AZURE_MODELS_INFO, AZURE_MODEL_INFO, TURN_OFF_PAREA_EVAL_LOGGING
 from parea.parea_logger import parea_logger
@@ -220,9 +222,12 @@ def clean_json_string(s):
 
 def _resolve_functions(kwargs):
     if "functions" in kwargs:
-        return kwargs.get("functions", [])
+        f = kwargs.get("functions", [])
+        return None if isinstance(f, NotGiven) else f
     elif "tools" in kwargs:
         tools = kwargs["tools"]
+        if isinstance(tools, NotGiven):
+            return None
         if isinstance(tools, list):
             return [d.get("function", {}) for d in tools]
 
@@ -234,19 +239,27 @@ def _resolve_functions(kwargs):
 def _kwargs_to_llm_configuration(kwargs, model=None) -> LLMInputs:
     functions = _resolve_functions(kwargs)
     function_call_default = "auto" if functions else None
+    function_call = kwargs.get("function_call", function_call_default) or kwargs.get("tool_choice", function_call_default)
+    response_format = kwargs.get("response_format", None)
+    response_format = {"type": "json_schema", "json_schema": str(response_format)} if isinstance(response_format, ModelMetaclass) else response_format
+    temp = kwargs.get("temperature", 1.0)
+    max_length = kwargs.get("max_tokens", None)
+    top_p = kwargs.get("top_p", 1.0)
+    frequency_penalty = kwargs.get("frequency_penalty", 0.0)
+    presence_penalty = kwargs.get("presence_penalty", 0.0)
     return LLMInputs(
         model=model or kwargs.get("model", None),
         provider="openai",
         messages=_convert_oai_messages(kwargs.get("messages", None)),
         functions=functions,
-        function_call=kwargs.get("function_call", function_call_default) or kwargs.get("tool_choice", function_call_default),
+        function_call=None if isinstance(function_call, NotGiven) else function_call,
         model_params=ModelParams(
-            temp=kwargs.get("temperature", 1.0),
-            max_length=kwargs.get("max_tokens", None),
-            top_p=kwargs.get("top_p", 1.0),
-            frequency_penalty=kwargs.get("frequency_penalty", 0.0),
-            presence_penalty=kwargs.get("presence_penalty", 0.0),
-            response_format=kwargs.get("response_format", None),
+            temp=None if isinstance(temp, NotGiven) else temp,
+            max_length=None if isinstance(max_length, NotGiven) else max_length,
+            top_p=None if isinstance(top_p, NotGiven) else top_p,
+            frequency_penalty=None if isinstance(frequency_penalty, NotGiven) else frequency_penalty,
+            presence_penalty=None if isinstance(presence_penalty, NotGiven) else presence_penalty,
+            response_format=response_format,
         ),
     )
 
@@ -302,7 +315,11 @@ def _compute_cost(prompt_tokens: int, completion_tokens: int, model: str) -> flo
 
 def _process_response(response, model_inputs, trace_id):
     response_message = response.choices[0].message
-    if response_message.content:
+    if response_message.finish_reason == "content_filter":
+        trace_insert({"error": "Error: The content was filtered due to policy violations."}, trace_id)
+    if hasattr(response_message, "refusal"):
+        completion = response_message.refusal
+    elif response_message.content:
         completion = response_message.content
     else:
         completion = _format_function_call(response_message)