Add Structured Output

CHANGELOG.md

@@ -10,6 +10,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 - `BaseVectorStoreDriver.query_vector` for querying vector stores with vectors.
+- Structured Output support for all Prompt Drivers.
+- `PromptTask.output_schema` for setting an output schema to be used with Structured Output.
+- `Agent.output_schema` for setting an output schema to be used on the Agent's Prompt Task.
+- `BasePromptDriver.structured_output_strategy` for changing the Structured Output strategy between `native`, `tool`, and `rule`.
 
 ## [1.1.1] - 2025-01-03
 

docs/griptape-framework/drivers/prompt-drivers.md

@@ -25,6 +25,29 @@ You can pass images to the Driver if the model supports it:
 --8<-- "docs/griptape-framework/drivers/src/prompt_drivers_images.py"
 ```
 
+## Structured Output
+
+Some LLMs provide functionality often referred to as "Structured Output".
+This means instructing the LLM to output data in a particular format, usually JSON.
+This can be useful for forcing the LLM to output in a parsable format that can be used by downstream systems.
+
+!!! warning
+    Each Driver may have a different default setting depending on the LLM provider's capabilities.
+
+### Prompt Task
+
+The easiest way to get started with structured output is by using a [PromptTask](../structures/tasks.md#prompt)'s [output_schema](../../reference/griptape/tasks/prompt_task.md#griptape.tasks.PromptTask.output_schema) parameter.
+
+You can change _how_ the output is structured by setting the Driver's [structured_output_strategy](../../reference/griptape/drivers/prompt/base_prompt_driver.md#griptape.drivers.prompt.base_prompt_driver.BasePromptDriver.structured_output_strategy) to one of:
+
+- `native`: The Driver will use the LLM's structured output functionality provided by the API.
+- `tool`: The Driver will add a special tool, [StructuredOutputTool](../../reference/griptape/tools/structured_output/tool.md), and will try to force the LLM to use the Tool.
+- `rule`: The Driver will add a [JsonSchemaRule](../structures/rulesets.md#json-schema-rule) to the Task's system prompt. This strategy does not guarantee that the LLM will output JSON and should only be used as a last resort.
+
+```python
+--8<-- "docs/griptape-framework/drivers/src/prompt_drivers_structured_output.py"
+```
+
 ## Prompt Drivers
 
 Griptape offers the following Prompt Drivers for interacting with LLMs.

docs/griptape-framework/drivers/src/prompt_drivers_3.py

@@ -1,7 +1,5 @@
 import os
 
-import schema
-
 from griptape.drivers import OpenAiChatPromptDriver
 from griptape.structures import Agent
 
@@ -11,14 +9,6 @@
         model="gpt-4o-2024-08-06",
         temperature=0.1,
         seed=42,
-        response_format={
-            "type": "json_schema",
-            "json_schema": {
-                "strict": True,
-                "name": "Output",
-                "schema": schema.Schema({"css_code": str, "relevant_emojies": [str]}).json_schema("Output Schema"),
-            },
-        },
     ),
     input="You will be provided with a description of a mood, and your task is to generate the CSS color code for a color that matches it. Description: {{ args[0] }}",
 )

docs/griptape-framework/drivers/src/prompt_drivers_structured_output.py

@@ -0,0 +1,32 @@
+import schema
+from rich.pretty import pprint
+
+from griptape.drivers import OpenAiChatPromptDriver
+from griptape.rules import Rule
+from griptape.structures import Pipeline
+from griptape.tasks import PromptTask
+
+pipeline = Pipeline(
+    tasks=[
+        PromptTask(
+            prompt_driver=OpenAiChatPromptDriver(
+                model="gpt-4o",
+                structured_output_strategy="native",  # optional
+            ),
+            output_schema=schema.Schema(
+                {
+                    "steps": [schema.Schema({"explanation": str, "output": str})],
+                    "final_answer": str,
+                }
+            ),
+            rules=[
+                Rule("You are a helpful math tutor. Guide the user through the solution step by step."),
+            ],
+        )
+    ]
+)
+
+output = pipeline.run("How can I solve 8x + 7 = -23").output.value
+
+
+pprint(output)

docs/griptape-framework/structures/rulesets.md

@@ -26,6 +26,9 @@ A [Ruleset](../../reference/griptape/rules/ruleset.md) can be used to define [Ru
 
 ### Json Schema
 
+!!! tip
+    [Structured Output](../drivers/prompt-drivers.md#structured-output) provides a more robust solution for having the LLM generate structured output.
+
 [JsonSchemaRule](../../reference/griptape/rules/json_schema_rule.md)s defines a structured format for the LLM's output by providing a JSON schema.
 This is particularly useful when you need the LLM to return well-formed data, such as JSON objects, with specific fields and data types.
 

griptape/common/prompt_stack/prompt_stack.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 from attrs import define, field
 
@@ -24,13 +24,16 @@
 from griptape.mixins.serializable_mixin import SerializableMixin
 
 if TYPE_CHECKING:
+    from schema import Schema
+
     from griptape.tools import BaseTool
 
 
 @define
 class PromptStack(SerializableMixin):
     messages: list[Message] = field(factory=list, kw_only=True, metadata={"serializable": True})
     tools: list[BaseTool] = field(factory=list, kw_only=True)
+    output_schema: Optional[Schema] = field(default=None, kw_only=True)
 
     @property
     def system_messages(self) -> list[Message]:

griptape/drivers/prompt/amazon_bedrock_prompt_driver.py

@@ -3,7 +3,7 @@
 import logging
 from typing import TYPE_CHECKING, Any
 
-from attrs import Factory, define, field
+from attrs import Attribute, Factory, define, field
 from schema import Schema
 
 from griptape.artifacts import (
@@ -41,6 +41,7 @@
     import boto3
 
     from griptape.common import PromptStack
+    from griptape.drivers.prompt.base_prompt_driver import StructuredOutputStrategy
     from griptape.tools import BaseTool
 
 logger = logging.getLogger(Defaults.logging_config.logger_name)
@@ -55,9 +56,19 @@ class AmazonBedrockPromptDriver(BasePromptDriver):
         kw_only=True,
     )
     use_native_tools: bool = field(default=True, kw_only=True, metadata={"serializable": True})
+    structured_output_strategy: StructuredOutputStrategy = field(
+        default="tool", kw_only=True, metadata={"serializable": True}
+    )
     tool_choice: dict = field(default=Factory(lambda: {"auto": {}}), kw_only=True, metadata={"serializable": True})
     _client: Any = field(default=None, kw_only=True, alias="client", metadata={"serializable": False})
 
+    @structured_output_strategy.validator  # pyright: ignore[reportAttributeAccessIssue, reportOptionalMemberAccess]
+    def validate_structured_output_strategy(self, _: Attribute, value: str) -> str:
+        if value == "native":
+            raise ValueError(f"{__class__.__name__} does not support `{value}` structured output strategy.")
+
+        return value
+
     @lazy_property()
     def client(self) -> Any:
         return self.session.client("bedrock-runtime")
@@ -103,10 +114,9 @@ def try_stream(self, prompt_stack: PromptStack) -> Iterator[DeltaMessage]:
 
     def _base_params(self, prompt_stack: PromptStack) -> dict:
         system_messages = [{"text": message.to_text()} for message in prompt_stack.system_messages]
-
         messages = self.__to_bedrock_messages([message for message in prompt_stack.messages if not message.is_system()])
 
-        return {
+        params = {
             "modelId": self.model,
             "messages": messages,
             "system": system_messages,
@@ -115,14 +125,22 @@ def _base_params(self, prompt_stack: PromptStack) -> dict:
                 **({"maxTokens": self.max_tokens} if self.max_tokens is not None else {}),
             },
             "additionalModelRequestFields": self.additional_model_request_fields,
-            **(
-                {"toolConfig": {"tools": self.__to_bedrock_tools(prompt_stack.tools), "toolChoice": self.tool_choice}}
-                if prompt_stack.tools and self.use_native_tools
-                else {}
-            ),
             **self.extra_params,
         }
 
+        if prompt_stack.tools and self.use_native_tools:
+            params["toolConfig"] = {
+                "tools": [],
+                "toolChoice": self.tool_choice,
+            }
+
+            if prompt_stack.output_schema is not None and self.structured_output_strategy == "tool":
+                params["toolConfig"]["toolChoice"] = {"any": {}}
+
+            params["toolConfig"]["tools"] = self.__to_bedrock_tools(prompt_stack.tools)
+
+        return params
+
     def __to_bedrock_messages(self, messages: list[Message]) -> list[dict]:
         return [
             {

griptape/drivers/prompt/amazon_sagemaker_jumpstart_prompt_driver.py

@@ -20,6 +20,7 @@
     import boto3
 
     from griptape.common import PromptStack
+    from griptape.drivers.prompt.base_prompt_driver import StructuredOutputStrategy
 
 logger = logging.getLogger(Defaults.logging_config.logger_name)
 
@@ -39,8 +40,18 @@ class AmazonSageMakerJumpstartPromptDriver(BasePromptDriver):
         ),
         kw_only=True,
     )
+    structured_output_strategy: StructuredOutputStrategy = field(
+        default="rule", kw_only=True, metadata={"serializable": True}
+    )
     _client: Any = field(default=None, kw_only=True, alias="client", metadata={"serializable": False})
 
+    @structured_output_strategy.validator  # pyright: ignore[reportAttributeAccessIssue, reportOptionalMemberAccess]
+    def validate_structured_output_strategy(self, _: Attribute, value: str) -> str:
+        if value != "rule":
+            raise ValueError(f"{__class__.__name__} does not support `{value}` structured output strategy.")
+
+        return value
+
     @lazy_property()
     def client(self) -> Any:
         return self.session.client("sagemaker-runtime")

griptape/drivers/prompt/anthropic_prompt_driver.py

@@ -3,7 +3,7 @@
 import logging
 from typing import TYPE_CHECKING, Optional
 
-from attrs import Factory, define, field
+from attrs import Attribute, Factory, define, field
 from schema import Schema
 
 from griptape.artifacts import (
@@ -42,6 +42,7 @@
     from anthropic import Client
     from anthropic.types import ContentBlock, ContentBlockDeltaEvent, ContentBlockStartEvent
 
+    from griptape.drivers.prompt.base_prompt_driver import StructuredOutputStrategy
     from griptape.tools.base_tool import BaseTool
 
 
@@ -68,13 +69,23 @@ class AnthropicPromptDriver(BasePromptDriver):
     top_k: int = field(default=250, kw_only=True, metadata={"serializable": True})
     tool_choice: dict = field(default=Factory(lambda: {"type": "auto"}), kw_only=True, metadata={"serializable": False})
     use_native_tools: bool = field(default=True, kw_only=True, metadata={"serializable": True})
+    structured_output_strategy: StructuredOutputStrategy = field(
+        default="tool", kw_only=True, metadata={"serializable": True}
+    )
     max_tokens: int = field(default=1000, kw_only=True, metadata={"serializable": True})
     _client: Client = field(default=None, kw_only=True, alias="client", metadata={"serializable": False})
 
     @lazy_property()
     def client(self) -> Client:
         return import_optional_dependency("anthropic").Anthropic(api_key=self.api_key)
 
+    @structured_output_strategy.validator  # pyright: ignore[reportAttributeAccessIssue, reportOptionalMemberAccess]
+    def validate_structured_output_strategy(self, _: Attribute, value: str) -> str:
+        if value == "native":
+            raise ValueError(f"{__class__.__name__} does not support `{value}` structured output strategy.")
+
+        return value
+
     @observable
     def try_run(self, prompt_stack: PromptStack) -> Message:
         params = self._base_params(prompt_stack)
@@ -110,23 +121,28 @@ def _base_params(self, prompt_stack: PromptStack) -> dict:
         system_messages = prompt_stack.system_messages
         system_message = system_messages[0].to_text() if system_messages else None
 
-        return {
+        params = {
             "model": self.model,
             "temperature": self.temperature,
             "stop_sequences": self.tokenizer.stop_sequences,
             "top_p": self.top_p,
             "top_k": self.top_k,
             "max_tokens": self.max_tokens,
             "messages": messages,
-            **(
-                {"tools": self.__to_anthropic_tools(prompt_stack.tools), "tool_choice": self.tool_choice}
-                if prompt_stack.tools and self.use_native_tools
-                else {}
-            ),
             **({"system": system_message} if system_message else {}),
             **self.extra_params,
         }
 
+        if prompt_stack.tools and self.use_native_tools:
+            params["tool_choice"] = self.tool_choice
+
+            if prompt_stack.output_schema is not None and self.structured_output_strategy == "tool":
+                params["tool_choice"] = {"type": "any"}
+
+            params["tools"] = self.__to_anthropic_tools(prompt_stack.tools)
+
+        return params
+
     def __to_anthropic_messages(self, messages: list[Message]) -> list[dict]:
         return [
             {"role": self.__to_anthropic_role(message), "content": self.__to_anthropic_content(message)}

griptape/drivers/prompt/base_prompt_driver.py

@@ -1,11 +1,11 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Literal, Optional
 
 from attrs import Factory, define, field
 
-from griptape.artifacts.base_artifact import BaseArtifact
+from griptape.artifacts import BaseArtifact, TextArtifact
 from griptape.common import (
     ActionCallDeltaMessageContent,
     ActionCallMessageContent,
@@ -26,12 +26,15 @@
 )
 from griptape.mixins.exponential_backoff_mixin import ExponentialBackoffMixin
 from griptape.mixins.serializable_mixin import SerializableMixin
+from griptape.rules.json_schema_rule import JsonSchemaRule
 
 if TYPE_CHECKING:
     from collections.abc import Iterator
 
     from griptape.tokenizers import BaseTokenizer
 
+StructuredOutputStrategy = Literal["native", "tool", "rule"]
+
 
 @define(kw_only=True)
 class BasePromptDriver(SerializableMixin, ExponentialBackoffMixin, ABC):
@@ -56,9 +59,13 @@ class BasePromptDriver(SerializableMixin, ExponentialBackoffMixin, ABC):
     tokenizer: BaseTokenizer
     stream: bool = field(default=False, kw_only=True, metadata={"serializable": True})
     use_native_tools: bool = field(default=False, kw_only=True, metadata={"serializable": True})
+    structured_output_strategy: StructuredOutputStrategy = field(
+        default="rule", kw_only=True, metadata={"serializable": True}
+    )
     extra_params: dict = field(factory=dict, kw_only=True, metadata={"serializable": True})
 
     def before_run(self, prompt_stack: PromptStack) -> None:
+        self._init_structured_output(prompt_stack)
         EventBus.publish_event(StartPromptEvent(model=self.model, prompt_stack=prompt_stack))
 
     def after_run(self, result: Message) -> None:
@@ -122,6 +129,34 @@ def try_run(self, prompt_stack: PromptStack) -> Message: ...
     @abstractmethod
     def try_stream(self, prompt_stack: PromptStack) -> Iterator[DeltaMessage]: ...
 
+    def _init_structured_output(self, prompt_stack: PromptStack) -> None:
+        from griptape.tools import StructuredOutputTool
+
+        if (output_schema := prompt_stack.output_schema) is not None:
+            if self.structured_output_strategy == "tool":
+                structured_output_tool = StructuredOutputTool(output_schema=output_schema)
+                if structured_output_tool not in prompt_stack.tools:
+                    prompt_stack.tools.append(structured_output_tool)
+            elif self.structured_output_strategy == "rule":
+                output_artifact = TextArtifact(JsonSchemaRule(output_schema.json_schema("Output Schema")).to_text())
+                system_messages = prompt_stack.system_messages
+                if system_messages:
+                    last_system_message = prompt_stack.system_messages[-1]
+                    last_system_message.content.extend(
+                        [
+                            TextMessageContent(TextArtifact("\n\n")),
+                            TextMessageContent(output_artifact),
+                        ]
+                    )
+                else:
+                    prompt_stack.messages.insert(
+                        0,
+                        Message(
+                            content=[TextMessageContent(output_artifact)],
+                            role=Message.SYSTEM_ROLE,
+                        ),
+                    )
+
     def __process_run(self, prompt_stack: PromptStack) -> Message:
         return self.try_run(prompt_stack)