feat: Add support for struct. output

README.md

@@ -153,6 +153,35 @@ which would lead to this in your prompt:
 - C
 ```
 
+### Using structured output (function calling and structured output)
+
+*Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don't need to worry about the model omitting a required key, or hallucinating an invalid enum value.* - OpenAI website
+
+#### How to get it to work
+
+1. Check with the model developer/provider whether the model supports some kind of structured output.
+2. Toggle structured output switch
+3. Select one of the supported structured output methods (a model might support all of them but also none of them):
+   - `None` - no structured output is used (equals to toggle being in off state)
+   - `Function calling` - hacky way of implementing structured outputs before `Response format` was implemented into API 
+   - `Response format` - new way of implementing structured outputs 
+4. Provide JSON schema in `json schema` text input (can be generated from `pydantic` model or `zod` if you use `nodejs`) where `title` must satisfy `'^[a-zA-Z0-9_-]+$'`:
+```json
+{
+    "title": "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'",
+    "type": "object",
+    "properties": {
+      "order_id": {
+        "type": "string"
+      }
+    },
+    "required": ["order_id"],
+    "additionalProperties": false
+}
+```
+example from OpenAI website (slightly modified). For more examples see [https://json-schema.org/learn/miscellaneous-examples](https://json-schema.org/learn/miscellaneous-examples).
+
 ### Postprocessing the model outputs
 
 When working with LLMs, you would often postprocess the raw generated text. Prompterator

prompterator/constants.py

@@ -1,4 +1,6 @@
+import dataclasses
 import os
+from enum import Enum
 from typing import Any, Dict, Generic, Optional, TypeVar
 
 from pydantic import BaseModel
@@ -16,6 +18,25 @@ class ConfigurableModelParameter(GenericModel, Generic[DataT]):
     step: DataT
 
 
+class StructuredOutputImplementation(Enum):
+    NONE = "None"
+    FUNCTION_CALLING = "Function calling"
+    RESPONSE_FORMAT = "Response format"
+
+
+@dataclasses.dataclass
+class StructuredOutputData:
+    enabled: bool
+    schema: str
+    method: StructuredOutputImplementation
+
+
+@dataclasses.dataclass
+class ModelInputs:
+    inputs: Dict[str, Any]
+    structured_output_data: StructuredOutputData
+
+
 class ModelProperties(BaseModel):
     name: str
     is_chat_model: bool = False
@@ -26,7 +47,9 @@ class ModelProperties(BaseModel):
 
     configurable_params: Dict[str, ConfigurableModelParameter] = {}
     non_configurable_params: Dict[str, Any] = {}
-
+    supports_structured_output: Optional[list[StructuredOutputImplementation]] = [
+        StructuredOutputImplementation.NONE
+    ]
     # By default, models are sorted by their position index, which is used to order them in the UI.
     # The 1e6 default value is used to ensure that models without a position index are sorted last.
     position_index: int = int(1e6)
@@ -115,3 +138,25 @@ def call(self, input, **kwargs):
 PROMPT_PREVIEW_TEXT_AREA_HEIGHT = 200
 
 DATAFILE_FILTER_ALL = "all"
+
+DEFAULT_JSON_SCHEMA = """{
+  "title": "translate_json_schema",
+  "description": "Translation schema",
+  "type": "object",
+  "properties": {
+    "originalText": {
+      "type": "string",
+      "additionalProperties": false
+    },
+    "translatedText": {
+      "type": "string", 
+      "additionalProperties": false
+    }
+  },
+  "required": [
+    "originalText",
+    "translatedText" 
+  ],
+  "additionalProperties": false,
+  "$schema": "http://json-schema.org/draft-07/schema#"
+}"""

prompterator/main.py

@@ -1,3 +1,4 @@
+import json
 import logging
 import os
 import traceback as tb
@@ -113,10 +114,22 @@ def set_up_dynamic_session_state_vars():
 
 def run_prompt(progress_ui_area):
     progress_bar = progress_ui_area.progress(0, text="generating texts")
-
     system_prompt_template = st.session_state.system_prompt
     user_prompt_template = st.session_state.user_prompt
 
+    structured_output_enabled = st.session_state.structured_output_enabled
+    prompt_json_schema = None
+    selected_structured_output_method = None
+    if structured_output_enabled:
+        prompt_json_schema = st.session_state.prompt_json_schema
+        selected_structured_output_method = c.StructuredOutputImplementation(
+            st.session_state.selected_structured_output_method
+        )
+
+    structured_output_params = c.StructuredOutputData(
+        structured_output_enabled, prompt_json_schema, selected_structured_output_method
+    )
+
     if not st.session_state.system_prompt.strip() and not st.session_state.user_prompt.strip():
         st.error("Both prompts are empty, not running the text generation any further.")
         return
@@ -127,12 +140,15 @@ def run_prompt(progress_ui_area):
     df_old = st.session_state.df.copy()
 
     try:
-        model_inputs = {
+        inputs = {
             i: u.create_model_input(
                 model, model_instance, user_prompt_template, system_prompt_template, row
             )
             for i, row in df_old.iterrows()
         }
+        model_inputs = c.ModelInputs(
+            inputs=inputs, structured_output_data=structured_output_params
+        )
     except Exception as e:
         traceback = u.format_traceback_for_markdown(tb.format_exc())
         st.error(
@@ -141,7 +157,7 @@ def run_prompt(progress_ui_area):
         )
         return
 
-    if len(model_inputs) == 0:
+    if len(model_inputs.inputs) == 0:
         st.error("No input data to generate texts from!")
         return
 
@@ -398,7 +414,7 @@ def set_up_ui_saved_datafiles():
 
 
 def set_up_ui_generation():
-    col1, col2 = st.columns([1, 2])
+    col1, col2, col3 = st.columns([3, 3, 1])
     col1.text_input(
         placeholder="name your prompt version",
         label="Prompt name",
@@ -411,6 +427,23 @@ def set_up_ui_generation():
         label_visibility="collapsed",
         key=c.PROMPT_COMMENT_KEY,
     )
+    selected_model: c.ModelProperties = st.session_state.model
+    available_structured_output_settings = selected_model.supports_structured_output
+
+    # Allow structured outputs only if the model allows other implementation
+    # than NONE, other implementations currently include FUNCTION_CALLING
+    # and RESPONSE_FORMAT. Models by default do not require this parameter to be set.
+    structured_output_available = (
+        len(set(available_structured_output_settings) - {c.StructuredOutputImplementation.NONE})
+        > 0
+    )
+
+    structured_output_enabled = col3.toggle(
+        label="Structured output",
+        value=False,
+        key="structured_output_enabled",
+        disabled=(not structured_output_available),
+    )
 
     progress_ui_area = st.empty()
 
@@ -434,6 +467,27 @@ def set_up_ui_generation():
         disabled=not model_supports_user_prompt,
     )
 
+    if structured_output_available and structured_output_enabled:
+        json_input = st.container()
+        json_input.text_area(
+            label="JSON Schema",
+            placeholder="Your JSON schema goes here",
+            value=c.DEFAULT_JSON_SCHEMA,
+            key="prompt_json_schema",
+            height=c.PROMPT_TEXT_AREA_HEIGHT,
+        )
+        struct_options_1, struct_options_2 = st.columns([3, 2])
+        struct_options_1.select_slider(
+            "Select which method to use for structured output",
+            options=[setting.value for setting in available_structured_output_settings],
+            key="selected_structured_output_method",
+        )
+
+        if u.validate_json(st.session_state.prompt_json_schema):
+            struct_options_2.success("JSON is valid", icon="🟢")
+        else:
+            struct_options_2.error("JSON is invalid", icon="🔴")
+
     if "df" in st.session_state:
         prompt_parsing_error_message_area = st.empty()
         col1, col2 = st.columns([3, 1])

prompterator/models/openai_models.py

@@ -5,12 +5,16 @@
 import openai
 from openai import AzureOpenAI, OpenAI
 
+from prompterator.utils import build_function_calling_tooling, build_response_format
+
 logger = logging.getLogger(__name__)
 
 from prompterator.constants import (  # isort:skip
     CONFIGURABLE_MODEL_PARAMETER_PROPERTIES,
     ModelProperties,
     PrompteratorLLM,
+    StructuredOutputImplementation as soi,
+    StructuredOutputData,
 )
 
 
@@ -86,12 +90,39 @@ def __init__(self):
         super().__init__()
 
     def call(self, idx, input, **kwargs):
+        structured_output_data: StructuredOutputData = kwargs["structured_output"]
         model_params = kwargs["model_params"]
         try:
+            if structured_output_data.enabled:
+                if structured_output_data.method == soi.FUNCTION_CALLING:
+                    model_params["tools"], function_name = build_function_calling_tooling(
+                        structured_output_data.schema
+                    )
+                    model_params["tool_choice"] = {
+                        "type": "function",
+                        "function": {"name": function_name},
+                    }
+                if structured_output_data.method == soi.RESPONSE_FORMAT:
+                    model_params["response_format"] = build_response_format(
+                        structured_output_data.schema
+                    )
+
             response_data = self.client.chat.completions.create(
                 model=self.specific_model_name or self.name, messages=input, **model_params
             )
-            response_text = response_data.choices[0].message.content
+
+            response_text = None
+            if structured_output_data.enabled:
+                if structured_output_data.method == soi.FUNCTION_CALLING:
+                    response_text = (
+                        response_data.choices[0].message.tool_calls[0].function.arguments
+                    )
+                elif structured_output_data.method == soi.RESPONSE_FORMAT:
+                    response_text = response_data.choices[0].message.content
+                else:
+                    response_text = response_data.choices[0].message.content
+            else:
+                response_text = response_data.choices[0].message.content
 
             return {"response": response_text, "data": response_data, "idx": idx}
         except openai.RateLimitError as e:
@@ -129,6 +160,11 @@ class GPT4o(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=1,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+            soi.RESPONSE_FORMAT,
+        ],
     )
 
 
@@ -140,6 +176,11 @@ class GPT4oAzure(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=6,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+            soi.RESPONSE_FORMAT,
+        ],
     )
     openai_variant = "azure"
     specific_model_name = "gpt-4o"
@@ -153,6 +194,11 @@ class GPT4oMini(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=2,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+            soi.RESPONSE_FORMAT,
+        ],
     )
 
 
@@ -164,6 +210,11 @@ class GPT4oMiniAzure(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=7,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+            soi.RESPONSE_FORMAT,
+        ],
     )
     openai_variant = "azure"
     specific_model_name = "gpt-4o-mini"
@@ -177,6 +228,10 @@ class GPT35Turbo(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=3,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+        ],
     )
 
 
@@ -188,6 +243,10 @@ class GPT35TurboAzure(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=8,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+        ],
     )
     openai_variant = "azure"
     specific_model_name = "gpt-35-turbo"
@@ -201,6 +260,10 @@ class GPT4(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=4,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+        ],
     )
 
 
@@ -212,6 +275,10 @@ class GPT4Azure(ChatGPTMixin):
         handles_batches_of_inputs=False,
         configurable_params=CONFIGURABLE_MODEL_PARAMETER_PROPERTIES.copy(),
         position_index=9,
+        supports_structured_output=[
+            soi.NONE,
+            soi.FUNCTION_CALLING,
+        ],
     )
     openai_variant = "azure"
     specific_model_name = "gpt-4"