Fixing Griptape's JSON Serialization Error

Default Value Serialization Issues in Griptape: A Deep Dive

In the realm of AI and natural language processing, Griptape stands out as a powerful framework for building complex applications. However, like any sophisticated tool, it can encounter challenges. One such issue that developers might face is the 'Default value <class 'griptape.utils.json_schema_to_pydantic.model_builder.DynamicModel'> is not JSON serializable' warning. This message, often accompanied by the PydanticJsonSchemaWarning, highlights a problem with how default values are handled when generating JSON schemas within your Griptape applications. Let's break down this issue and explore how to address it.

Understanding the Problem: JSON Serialization and Default Values

At its core, this warning stems from the limitations of JSON serialization. JSON (JavaScript Object Notation) is a widely used format for data interchange. It's a text-based format that's easy for both humans and machines to read and write. However, JSON has constraints. It can only represent a specific set of data types: strings, numbers, booleans, arrays, and objects (and null). When you're working with Python objects, especially complex ones, not all of them can be directly converted into a JSON-compatible format. This is where the warning arises.

The DynamicModel class, found within griptape.utils.json_schema_to_pydantic.model_builder, is likely involved in the creation of dynamic data models based on JSON schemas. When a default value of this DynamicModel type is specified in a Pydantic model (which Griptape often uses for data validation and schema definition), it can't be directly serialized to JSON. The serializer encounters an object it doesn't know how to represent in the JSON format, and hence the warning.

Impact of the Warning

The presence of this warning doesn't necessarily mean your application will crash immediately. However, it signifies a potential problem that could lead to unexpected behavior. For example, when your application generates JSON schemas for APIs or data exchange, the default values might be omitted or misrepresented, potentially leading to errors in data validation or incorrect interpretation of the data. Furthermore, it could indicate a deeper problem with how the default values are intended to be used within your application. The PydanticJsonSchemaWarning is there to alert you to this and prompt you to take action.

Code Analysis and Context

The provided code snippet showcases the scenario where the warning appears. It involves the following key elements:

• Griptape Components: Uses GriptapeCloudPromptDriver, ConversationMemory, and Agent to build an AI agent.
• Schema Definition: Defines an output_schema using create_model from pydantic. This schema is meant to specify the structure of the agent's output.
• Tool Integration: Includes MCPTool, a tool designed to interact with a Model Context Protocol (MCP) server.

The warning occurs during the generation of the JSON schema for the AgentOutputSchema. Because one or more default values in the AgentOutputSchema cannot be serialized into JSON, the warning is triggered. The crucial clue lies in the structure of the output_schema and how the default values are employed. The create_model function and the way the types are defined are crucial points where the issue can be addressed.

Solutions and Best Practices

Addressing this warning involves understanding where the default values are being applied and how they are used within the schema.

1. Identify the Source of the Default Value

The first step is to pinpoint the exact location in your code where the default value is being set. Inspect the output_schema definition and any related components to identify the field or fields causing the issue. The warning message provides a clue by specifying the class that cannot be serialized. Carefully review how the default values are being created or assigned.

2. Adjust Default Value Types

Ensure that the default values are of a type that can be serialized into JSON. This may involve using basic Python data types (strings, numbers, booleans, lists, dictionaries) or creating custom serializers for more complex objects. If the default value is a DynamicModel instance, consider how it's used and whether you can replace it with a JSON-serializable representation.

3. Using Pydantic's Field and default_factory

Pydantic's Field allows for more fine-grained control over schema definitions. You can use default to specify a default value and default_factory if the default value needs to be created dynamically. Using default_factory can be useful when you need to create a new instance of a class as a default value.

from pydantic import BaseModel, Field

class MyModel(BaseModel):
    my_field: str = Field(default="default_value")
    # or
    # my_field: list[str] = Field(default_factory=list)

4. Custom JSON Encoders

If you have a complex object that you need to serialize, you can create a custom JSON encoder to handle its serialization. This involves creating a custom class that inherits from json.JSONEncoder and overrides the default() method. This allows you to define how your custom objects should be converted into JSON-compatible formats.

import json
from datetime import datetime

class CustomEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime):
            return obj.isoformat()
        return super().default(obj)

# Usage
json_data = json.dumps(my_object, cls=CustomEncoder)

5. Review Schema Design and Data Flow

Carefully review the design of your output schema and the data flow within your agent. Make sure that the structure of the output schema aligns with the expected data from your tools and prompt drivers. Consider whether you need a default value at all. In some cases, omitting the default value (i.e., making the field optional) might be a better approach.

6. Simplification of Model Structures

In some cases, the complexity of the model structure might be the root cause of the serialization issue. Consider simplifying the model if possible. This might involve breaking down complex objects into simpler ones or using more basic data types.

7. Upgrading Dependencies

Ensure that you are using the latest versions of Griptape and Pydantic. Newer versions might contain fixes or improvements related to JSON serialization.

Implementation and Practical Example

Let's consider a practical example based on the original code snippet. The output_schema is the place to start. Let's assume the problem is with the default value of generated_image_urls which is a list[str]. This is usually fine, so the issue may not be in this section. However, let us imagine it uses the DynamicModel. The solution might look like this:

from __future__ import annotations

from griptape.drivers.prompt.griptape_cloud import GriptapeCloudPromptDriver
from griptape.memory.structure import ConversationMemory
from griptape.structures import Agent
from griptape.tasks import PromptTask
from griptape.tools import MCPTool
from griptape.utils.json_schema_to_pydantic import (create_model as create_model_from_json_schema)
from pydantic import create_model, Field  # Import Field

# Define a custom default value (if needed) or remove it.
# generated_image_urls_default = []  # If the default is a list, this is a valid approach.

output_schema = create_model(
    "AgentOutputSchema",
    generated_image_urls=(list[str], Field(default=[])),  # Use Field to specify a default.
    conversation_output=(str, ...),
)

from griptape.tools.mcp.sessions import StdioConnection
from griptape.tools.mcp.tool import MCPTool

# Create a Connection to the MCP server.
# This example uses MCP's everything demo server: https://github.com/modelcontextprotocol/servers/tree/main/src/everything
everything_connection: StdioConnection = {  # pyright: ignore[reportAssignmentType]
    "transport": "stdio",
    "command": "npx",
    "args": ["-y", "@modelcontextprotocol/server-everything"],
}

# Create a tool configured to use the MCP server.
mcp_tool = MCPTool(connection=everything_connection)


agent = Agent(
    conversation_memory_strategy="per_task",
    tasks=[
        PromptTask(
            output_schema=output_schema,
            tools=[MCPTool(connection=everything_connection)],
            prompt_driver=GriptapeCloudPromptDriver(model="gpt-4.1"),
            conversation_memory=ConversationMemory(),
        )
    ],
)

agent.run("Hi")
agent.run("I want to iterate on some ideas for Halloween gags, can you help?")

In this example, we import Field from pydantic. The default value for generated_image_urls is now set using Field(default=[]). If you were using a custom object as the default, you would replace [] with an instance of your custom object or utilize the default_factory approach if creating the default value dynamically.

Conclusion

The 'Default value is not JSON serializable' warning in Griptape indicates a mismatch between your Python objects and the requirements of JSON serialization. By understanding the underlying problem, identifying the source of the issue, and employing appropriate solutions such as adjusting default value types, using Field, and creating custom encoders, you can resolve the warning and ensure that your Griptape applications function correctly. Remember to thoroughly review your schema definitions, data flow, and dependencies to prevent this issue from recurring and to create more robust and reliable AI applications. Paying attention to these details will not only resolve the warning but also contribute to a deeper understanding of how Griptape interacts with data serialization and validation processes.

For more detailed information, consider the Pydantic documentation at Pydantic Documentation.