rawResponse -> raw_response for snake_case consistency (#657)

# rawResponse -> raw_response for snake_case consistency Just updating the `rawResponse` property name to `raw_response` for snake_case consistency in the schema. All other changes here are just auto-formatted ## Testing `pytest` ``` =========================================================== 88 passed, 38 warnings in 3.28s =========================================================== ``` `yarn test` ``` (aiconfig) ryanholinshead@Ryans-MBP typescript % yarn test yarn run v1.22.19 $ jest --runInBand PASS __tests__/parsers/hf/hf.test.ts HuggingFaceTextGeneration ModelParser ✓ uses HuggingFace API token from environment variable if it exists (9 ms) ✓ serializing params to config prompt (1 ms) ✓ serialize callbacks (1 ms) ✓ deserializing config prompt to params (1 ms) ✓ deserialize callbacks (1 ms) ✓ run prompt, non-streaming (4 ms) ✓ run prompt, streaming (2 ms) ✓ run callbacks (1 ms) PASS __tests__/config.test.ts Loading an AIConfig ✓ loading a basic chatgpt query config (6 ms) ✓ loading a prompt chain (4 ms) ✓ deserialize and re-serialize a prompt chain (2 ms) ✓ serialize a prompt chain with different settings (1 ms) PASS __tests__/parsers/palm-text/palm.test.ts PaLM Text ModelParser ✓ serializing params to config prompt (4 ms) ✓ deserializing params to config (2 ms) ✓ run prompt, non-streaming (87 ms) PASS __tests__/testProgramaticallyCreateConfig.ts test Get Global Settings ✓ Retrieving global setting from AIConfig with 1 model (1 ms) ExtractOverrideSettings function ✓ Should return initial settings when no global settings are defined ✓ Should return an override when initial settings differ from global settings (1 ms) ✓ Should return empty override when global settings match initial settings ✓ Should return empty override when Global settings defined and initial settings are empty PASS __tests__/testSave.ts AIConfigRuntime save() ✓ saves the config and checks if the config json doesn't have key filePath (4 ms) Test Suites: 5 passed, 5 total Tests: 21 passed, 21 total Snapshots: 0 total Time: 2.303 s Ran all test suites. ✨ Done in 3.58s. ``` Ran `npx ts-node function-call-stream.ts` and confirmed correct output metadata with raw_response, e.g.: ``` "outputs": [ { "output_type": "execute_result", "data": { "kind": "tool_calls", "value": [ { "type": "function", "function": { "name": "list", "arguments": "{\n\"genre\": \"historical\"\n}" } } ] }, "execution_count": 0, "metadata": { "finish_reason": "function_call", "raw_response": { "role": "assistant", "content": null, "function_call": { "name": "list", "arguments": "{\n\"genre\": \"historical\"\n}" } } } } ] ```
lastmile-ai · Dec 29, 2023 · 8137a6c · 8137a6c
2 parents 164f0b0 + 32b36ce
commit 8137a6c
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diff --git a/extensions/Gemini/python/src/aiconfig_extension_gemini/Gemini.py b/extensions/Gemini/python/src/aiconfig_extension_gemini/Gemini.py
@@ -71,9 +71,7 @@ def construct_regular_outputs(response: "AsyncGenerateContentResponse") -> list[
     return output_list
 
 
-async def construct_stream_outputs(
-    response: "AsyncGenerateContentResponse", options: InferenceOptions
-) -> list[Output]:
+async def construct_stream_outputs(response: "AsyncGenerateContentResponse", options: InferenceOptions) -> list[Output]:
     """
     Construct Outputs while also streaming the response with stream callback
 
@@ -132,17 +130,17 @@ async def serialize(
 
         The data passed in is the completion params a user would use to call the Gemini API directly.
         If the user wanted to call the Gemini API directly, they might do something like this:
-        
+
         ```
         model = genai.GenerativeModel('gemini-pro')
         completion_params = {"contents": "Hello"}
-        
+
         model.generate_content(**completion_params)
-        # Note: The above line is the same as doing this: 
+        # Note: The above line is the same as doing this:
         model.generate_content(contents="Hello")
         ```
         * Important: The contents field is what contains the input data. In this case, prompt input would be the contents field.
-        
+
 
         Args:
             prompt (str): The prompt to be serialized.
@@ -152,7 +150,7 @@ async def serialize(
             str: Serialized representation of the prompt and inference settings.
 
         Sample Usage:
-            1. 
+            1.
                 completion_params = {"contents": "Hello"}
                 serialized_prompts = await ai_config.serialize("prompt", completion_params, "gemini-pro")
 
@@ -188,7 +186,7 @@ async def serialize(
         data = copy.deepcopy(data)
         contents = data.pop("contents", None)
 
-        model_name = self.model.model_name[len("models/"):]
+        model_name = self.model.model_name[len("models/") :]
         model_metadata = ai_config.get_model_metadata(data, model_name)
 
         prompts = []
@@ -202,7 +200,9 @@ async def serialize(
         #     }
         contents_is_role_dict = isinstance(contents, dict) and "role" in contents and "parts"
         # Multi Turn means that the contents is a list of dicts with alternating role and parts. See for more info: https://ai.google.dev/tutorials/python_quickstart#multi-turn_conversations
-        contents_is_multi_turn = isinstance(contents, list) and all(isinstance(item, dict) and "role" in item and "parts" in item for item in contents)
+        contents_is_multi_turn = isinstance(contents, list) and all(
+            isinstance(item, dict) and "role" in item and "parts" in item for item in contents
+        )
 
         if contents is None:
             raise ValueError("No contents found in data. Gemini api request requires a contents field")
@@ -226,14 +226,21 @@ async def serialize(
                     outputs = [
                         ExecuteResult(
                             **{
-                                "output_type": "execute_result", 
+                                "output_type": "execute_result",
                                 "data": model_message_parts[0],
-                                "metadata": {"rawResponse": model_message},
+                                "metadata": {"raw_response": model_message},
                             }
                         )
                     ]
                     i += 1
-                prompt = Prompt(**{"name": f'{prompt_name}_{len(prompts) + 1}', "input": user_message_parts, "metadata": {"model": model_metadata}, "outputs": outputs})
+                prompt = Prompt(
+                    **{
+                        "name": f"{prompt_name}_{len(prompts) + 1}",
+                        "input": user_message_parts,
+                        "metadata": {"model": model_metadata},
+                        "outputs": outputs,
+                    }
+                )
                 prompts.append(prompt)
                 i += 1
         else:
@@ -255,11 +262,7 @@ async def deserialize(self, prompt: Prompt, aiconfig: "AIConfigRuntime", params:
         Returns:
             dict: Model-specific completion parameters.
         """
-        await aiconfig.callback_manager.run_callbacks(
-            CallbackEvent(
-                "on_deserialize_start", __name__, {"prompt": prompt, "params": params}
-            )
-        )
+        await aiconfig.callback_manager.run_callbacks(CallbackEvent("on_deserialize_start", __name__, {"prompt": prompt, "params": params}))
 
         # Build Completion data
         model_settings = self.get_model_settings(prompt, aiconfig)
@@ -270,9 +273,9 @@ async def deserialize(self, prompt: Prompt, aiconfig: "AIConfigRuntime", params:
             messages = self._construct_chat_history(prompt, aiconfig, params)
 
             resolved_prompt = resolve_prompt(prompt, params, aiconfig)
-            
+
             messages.append({"role": "user", "parts": [{"text": resolved_prompt}]})
-            
+
             completion_data["contents"] = messages
         else:
             # If contents is already set, do not construct chat history. TODO: @Ankush-lastmile
@@ -287,15 +290,13 @@ async def deserialize(self, prompt: Prompt, aiconfig: "AIConfigRuntime", params:
             # This is checking attributes and not a dict like object. in schema.py, PromptInput allows arbitrary attributes/data, and gets serialized as an attribute because it is a pydantic type
             if not hasattr(prompt_input, "contents"):
                 # The source code show cases this more than the docs. This curl request docs similar to python sdk: https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini#request_body
-                raise ValueError("Unable to deserialize input. Prompt input type is not a string, Gemini Model Parser expects prompt input to contain a 'contents' field as expected by Gemini API")
+                raise ValueError(
+                    "Unable to deserialize input. Prompt input type is not a string, Gemini Model Parser expects prompt input to contain a 'contents' field as expected by Gemini API"
+                )
 
-            completion_data['contents'] = parameterize_supported_gemini_input_data(prompt_input.contents, prompt, aiconfig, params)
+            completion_data["contents"] = parameterize_supported_gemini_input_data(prompt_input.contents, prompt, aiconfig, params)
 
-        await aiconfig.callback_manager.run_callbacks(
-            CallbackEvent(
-                "on_deserialize_complete", __name__, {"output": completion_data}
-            )
-        )
+        await aiconfig.callback_manager.run_callbacks(CallbackEvent("on_deserialize_complete", __name__, {"output": completion_data}))
         return completion_data
 
     async def run_inference(
@@ -347,9 +348,7 @@ async def run_inference(
             outputs = construct_regular_outputs(response)
 
         prompt.outputs = outputs
-        await aiconfig.callback_manager.run_callbacks(
-            CallbackEvent("on_run_complete", __name__, {"result": prompt.outputs})
-        )
+        await aiconfig.callback_manager.run_callbacks(CallbackEvent("on_run_complete", __name__, {"result": prompt.outputs}))
         return prompt.outputs
 
     def get_output_text(
@@ -375,27 +374,21 @@ def get_output_text(
         # We use this method in the model parser functionality itself,
         # so we must error to let the user know non-string
         # formats are not supported
-        error_message = \
-"""
+        error_message = """
 We currently only support chats where prior messages from a user are only a 
 single message string instance, please see docstring for more details:
 https://github.com/lastmile-ai/aiconfig/blob/v1.1.8/extensions/Gemini/python/src/aiconfig_extension_gemini/Gemini.py#L31-L33
 """
         raise ValueError(error_message)
 
-    def _construct_chat_history(
-        self, prompt: Prompt, aiconfig: "AIConfigRuntime", params: Dict
-    ) -> List:
+    def _construct_chat_history(self, prompt: Prompt, aiconfig: "AIConfigRuntime", params: Dict) -> List:
         """
         Constructs the chat history for the model
         """
         messages = []
         # Default to always use chat context
-        remember_chat_context = not hasattr(
-            prompt.metadata, "remember_chat_context"
-        ) or (
-            hasattr(prompt.metadata, "remember_chat_context")
-            and prompt.metadata.remember_chat_context != False
+        remember_chat_context = not hasattr(prompt.metadata, "remember_chat_context") or (
+            hasattr(prompt.metadata, "remember_chat_context") and prompt.metadata.remember_chat_context != False
         )
         if remember_chat_context:
             # handle chat history. check previous prompts for the same model. if same model, add prompt and its output to completion data if it has a completed output
@@ -404,29 +397,21 @@ def _construct_chat_history(
                 if previous_prompt.name == prompt.name:
                     break
 
-                previous_prompt_is_same_model = aiconfig.get_model_name(
-                    previous_prompt
-                ) == aiconfig.get_model_name(prompt)
+                previous_prompt_is_same_model = aiconfig.get_model_name(previous_prompt) == aiconfig.get_model_name(prompt)
                 if previous_prompt_is_same_model:
-                    previous_prompt_template = resolve_prompt(
-                        previous_prompt, params, aiconfig
-                    )
+                    previous_prompt_template = resolve_prompt(previous_prompt, params, aiconfig)
                     previous_prompt_output = aiconfig.get_latest_output(previous_prompt)
-                    previous_prompt_output_text = self.get_output_text(
-                        previous_prompt, aiconfig, previous_prompt_output
-                    )
+                    previous_prompt_output_text = self.get_output_text(previous_prompt, aiconfig, previous_prompt_output)
 
-                    messages.append(
-                        {"role": "user", "parts": [{"text": previous_prompt_template}]}
-                    )
+                    messages.append({"role": "user", "parts": [{"text": previous_prompt_template}]})
                     messages.append(
                         {
                             "role": "model",
                             "parts": [{"text": previous_prompt_output_text}],
                         }
                     )
 
-        return messages   
+        return messages
 
     def get_prompt_template(self, prompt: Prompt, aiConfig: "AIConfigRuntime") -> str:
         """
@@ -445,26 +430,18 @@ def get_prompt_template(self, prompt: Prompt, aiConfig: "AIConfigRuntime") -> st
                 elif isinstance(contents, list):
                     return " ".join(contents)
                 elif isinstance(contents, dict):
-                    parts= contents["parts"]
+                    parts = contents["parts"]
                     if isinstance(parts, str):
                         return parts
                     elif isinstance(parts, list):
                         return " ".join(parts)
                     else:
-                        raise Exception(
-                            f"Cannot get prompt template string from prompt input: {prompt.input}"
-                        )
+                        raise Exception(f"Cannot get prompt template string from prompt input: {prompt.input}")
                 else:
-                    raise Exception(
-                        f"Cannot get prompt template string from prompt input: {prompt.input}"
-                    )
+                    raise Exception(f"Cannot get prompt template string from prompt input: {prompt.input}")
 
-
-
         else:
-            raise Exception(
-                f"Cannot get prompt template string from prompt input: {prompt.input}"
-            )
+            raise Exception(f"Cannot get prompt template string from prompt input: {prompt.input}")
 
 
 def refine_chat_completion_params(model_settings):
@@ -522,9 +499,7 @@ def contains_prompt_template(prompt: Prompt):
     """
     Check if a prompt's input is a valid string.
     """
-    return isinstance(prompt.input, str) or (
-        hasattr(prompt.input, "data") and isinstance(prompt.input.data, str)
-    )
+    return isinstance(prompt.input, str) or (hasattr(prompt.input, "data") and isinstance(prompt.input.data, str))
 
 
 AIConfigRuntime.register_model_parser(GeminiModelParser("gemini-pro"), "gemini-pro")
diff --git a/...ace/python/src/aiconfig_extension_hugging_face/remote_inference_client/text_generation.py b/...ace/python/src/aiconfig_extension_hugging_face/remote_inference_client/text_generation.py
@@ -109,7 +109,7 @@ def construct_stream_output(
 
 
 def construct_regular_output(response: TextGenerationResponse, response_includes_details: bool) -> Output:
-    metadata = {"rawResponse": response}
+    metadata = {"raw_response": response}
     if response_includes_details:
         metadata["details"] = response.details
 
@@ -209,15 +209,13 @@ async def serialize(
         prompt = Prompt(
             name=prompt_name,
             input=prompt_input,
-            metadata=PromptMetadata(
-                model=model_metadata, parameters=parameters, **kwargs
-            ),
+            metadata=PromptMetadata(model=model_metadata, parameters=parameters, **kwargs),
         )
 
         prompts.append(prompt)
-        
-        await ai_config.callback_manager.run_callbacks(CallbackEvent("on_serialize_complete", __name__, {"result": prompts }))
-        
+
+        await ai_config.callback_manager.run_callbacks(CallbackEvent("on_serialize_complete", __name__, {"result": prompts}))
+
         return prompts
 
     async def deserialize(
@@ -251,9 +249,7 @@ async def deserialize(
 
         return completion_data
 
-    async def run_inference(
-        self, prompt: Prompt, aiconfig: "AIConfigRuntime", options: InferenceOptions, parameters: dict[Any, Any]
-    ) -> List[Output]:
+    async def run_inference(self, prompt: Prompt, aiconfig: "AIConfigRuntime", options: InferenceOptions, parameters: dict[Any, Any]) -> List[Output]:
         """
         Invoked to run a prompt in the .aiconfig. This method should perform
         the actual model inference based on the provided prompt and inference settings.

diff --git a/extensions/HuggingFace/typescript/hf.ts b/extensions/HuggingFace/typescript/hf.ts
@@ -311,7 +311,7 @@ function constructOutput(response: TextGenerationOutput): Output {
     output_type: "execute_result",
     data: response.generated_text,
     execution_count: 0,
-    metadata: { rawResponse: response },
+    metadata: { raw_response: response },
   } as ExecuteResult;
 
   return output;

diff --git a/python/src/aiconfig/default_parsers/hf.py b/python/src/aiconfig/default_parsers/hf.py
@@ -109,14 +109,14 @@ def construct_stream_output(
 
 
 def construct_regular_output(response: TextGenerationResponse, response_includes_details: bool) -> Output:
-    metadata = {"rawResponse": response}
+    metadata = {"raw_response": response}
     if response_includes_details:
         metadata["details"] = response.details
 
     output = ExecuteResult(
         **{
             "output_type": "execute_result",
-            "data": response.generated_text or '',
+            "data": response.generated_text or "",
             "execution_count": 0,
             "metadata": metadata,
         }
@@ -209,15 +209,13 @@ async def serialize(
         prompt = Prompt(
             name=prompt_name,
             input=prompt_input,
-            metadata=PromptMetadata(
-                model=model_metadata, parameters=parameters, **kwargs
-            ),
+            metadata=PromptMetadata(model=model_metadata, parameters=parameters, **kwargs),
         )
 
         prompts.append(prompt)
-        
-        await ai_config.callback_manager.run_callbacks(CallbackEvent("on_serialize_complete", __name__, {"result": prompts }))
-        
+
+        await ai_config.callback_manager.run_callbacks(CallbackEvent("on_serialize_complete", __name__, {"result": prompts}))
+
         return prompts
 
     async def deserialize(
@@ -251,9 +249,7 @@ async def deserialize(
 
         return completion_data
 
-    async def run_inference(
-        self, prompt: Prompt, aiconfig: "AIConfigRuntime", options: InferenceOptions, parameters: dict[Any, Any]
-    ) -> List[Output]:
+    async def run_inference(self, prompt: Prompt, aiconfig: "AIConfigRuntime", options: InferenceOptions, parameters: dict[Any, Any]) -> List[Output]:
         """
         Invoked to run a prompt in the .aiconfig. This method should perform
         the actual model inference based on the provided prompt and inference settings.