MessageContent

Multi-modal content types for messages and variables in the Python SDK.

Overview

Content types support text, images, PDFs, tool calls/responses, reasoning, errors, and search results. The MessageContent class is a oneOf union wrapper — the actual content is accessed via the actual_instance property.

MessageContent (Union Type)

Polymorphic content type supporting multiple modalities. Uses actual_instance to hold the concrete content variant.

from adaline_api.models.message_content import MessageContent

The actual_instance must be one of:

Type	Modality	Description
`TextContent`	`"text"`	Plain text content
`ImageContent`	`"image"`	Image with detail level
`PdfContent`	`"pdf"`	PDF document with file metadata
`ReasoningContent`	`"reasoning"`	Chain-of-thought reasoning
`ToolCallContent`	`"tool-call"`	Tool/function call request
`ToolResponseContent`	`"tool-response"`	Tool/function execution response
`ErrorContent`	`"error"`	Safety and content filtering errors
`SearchResultContent`	`"search-result"`	Web search grounding results

Accessing content:

from adaline_api.models.message_content import MessageContent
from adaline_api.models.text_content import TextContent
from adaline_api.models.image_content import ImageContent

content = MessageContent(actual_instance=TextContent(
    modality="text",
    value="Hello!"
))

# Access the underlying content
inner = content.actual_instance  # TextContent instance
print(inner.modality)            # 'text'
print(inner.value)               # 'Hello!'

Type checking:

from adaline_api.models.text_content import TextContent
from adaline_api.models.image_content import ImageContent
from adaline_api.models.tool_call_content import ToolCallContent

def process_content(content: MessageContent):
    instance = content.actual_instance

    if isinstance(instance, TextContent):
        print("Text:", instance.value)
    elif isinstance(instance, ImageContent):
        print("Image, detail:", instance.detail)
    elif isinstance(instance, ToolCallContent):
        print("Tool call:", instance.name)

Text Content

TextContent

See the dedicated TextContent page for full documentation. Plain text content for messages.

from adaline_api.models.text_content import TextContent

Fields

str

required

Must be "text".

str

required

The text content string.

Example

from adaline_api.models.text_content import TextContent
from adaline_api.models.message_content import MessageContent

text = TextContent(modality="text", value="Hello, how are you?")

content = MessageContent(actual_instance=text)

JSON:

{
  "modality": "text",
  "value": "Hello, how are you?"
}

Image Content

ImageContent

See the dedicated ImageContent page for full documentation. Image content with detail level specification.

from adaline_api.models.image_content import ImageContent

Fields

str

required

Must be "image".

str

required

Detail level. One of: "low", "medium", "high", "auto".

ImageContentValue

required

Image data — either a URL or base64-encoded content. See ImageContentValue.

ImageContentValue

Union of URL or base64 image content. Use from_dict() to construct.

from adaline_api.models.image_content_value import ImageContentValue

Example

from adaline_api.models.image_content import ImageContent
from adaline_api.models.image_content_value import ImageContentValue
from adaline_api.models.message_content import MessageContent

# URL image
image = ImageContent(
    modality="image",
    detail="high",
    value=ImageContentValue.from_dict({
        "type": "url",
        "url": "https://example.com/chart.png"
    })
)

content = MessageContent(actual_instance=image)

# Base64 image
base64_image = ImageContent(
    modality="image",
    detail="auto",
    value=ImageContentValue.from_dict({
        "type": "base64",
        "base64": "iVBORw0KGgoAAAANSUhEUgA...",
        "mediaType": "jpeg"
    })
)

JSON:

{
  "modality": "image",
  "detail": "high",
  "value": {
    "type": "url",
    "url": "https://example.com/image.jpg"
  }
}

PDF Content

PdfContent

See the dedicated PdfContent page for full documentation. PDF document content with file metadata.

from adaline_api.models.pdf_content import PdfContent

Fields

str

required

Must be "pdf".

PdfContentValue

required

PDF data — either a URL or base64-encoded content.

PdfContentFile

required

File metadata with name, id, and optional size.

Example

from adaline_api.models.pdf_content import PdfContent
from adaline_api.models.pdf_content_value import PdfContentValue
from adaline_api.models.pdf_content_file import PdfContentFile
from adaline_api.models.message_content import MessageContent

pdf = PdfContent(
    modality="pdf",
    value=PdfContentValue.from_dict({
        "type": "url",
        "url": "https://example.com/report.pdf"
    }),
    file=PdfContentFile(
        name="Q4_Report.pdf",
        id="file_abc123",
        size=1024000
    )
)

content = MessageContent(actual_instance=pdf)

JSON:

{
  "modality": "pdf",
  "value": {
    "type": "url",
    "url": "https://example.com/report.pdf"
  },
  "file": {
    "name": "report.pdf",
    "id": "file_123",
    "size": 1024000
  }
}

Tool Content

ToolCallContent

See the dedicated ToolCallContent page for full documentation. Tool/function call request from LLM.

from adaline_api.models.tool_call_content import ToolCallContent

Fields

str

required

Must be "tool-call".

int

required

Zero-based index of the tool call. Minimum: 0.

str

required

Unique identifier for this tool call.

str

required

Name of the function to call.

str

required

JSON-encoded string of function arguments.

str | None

Optional MCP server name. Aliased as serverName in JSON.

Example

import json
from adaline_api.models.tool_call_content import ToolCallContent
from adaline_api.models.message_content import MessageContent

tool_call = ToolCallContent(
    modality="tool-call",
    index=0,
    id="call_abc123",
    name="get_weather",
    arguments=json.dumps({
        "city": "San Francisco",
        "units": "fahrenheit"
    }),
    server_name="weather-api"
)

content = MessageContent(actual_instance=tool_call)

JSON:

{
  "modality": "tool-call",
  "index": 0,
  "id": "call_abc123",
  "name": "get_weather",
  "arguments": "{\"city\":\"San Francisco\"}"
}

ToolResponseContent

See the dedicated ToolResponseContent page for full documentation. Tool/function execution response.

from adaline_api.models.tool_response_content import ToolResponseContent

Fields

str

required

Must be "tool-response".

int

required

Zero-based index matching the tool call. Minimum: 0.

str

required

Identifier matching the tool call id.

str

required

Name of the function that was called.

str

required

JSON-encoded string of the function result.

ToolResponseContentApiResponse | None

Optional API response metadata with status_code. Aliased as apiResponse in JSON.

Example

import json
from adaline_api.models.tool_response_content import ToolResponseContent
from adaline_api.models.message_content import MessageContent

tool_response = ToolResponseContent(
    modality="tool-response",
    index=0,
    id="call_abc123",
    name="get_weather",
    data=json.dumps({
        "temperature": 72,
        "conditions": "sunny",
        "humidity": 65
    })
)

content = MessageContent(actual_instance=tool_response)

JSON:

{
  "modality": "tool-response",
  "index": 0,
  "id": "call_abc123",
  "name": "get_weather",
  "data": "{\"temperature\":72,\"conditions\":\"sunny\"}"
}

Reasoning Content

ReasoningContent

See the dedicated ReasoningContent page for full documentation. Reasoning content for chain-of-thought responses.

from adaline_api.models.reasoning_content import ReasoningContent

Fields

str

required

Must be "reasoning".

ReasoningContentValueUnion

required

The reasoning value — either a thinking type with content and signature, or a redacted type.

Example

from adaline_api.models.reasoning_content import ReasoningContent
from adaline_api.models.reasoning_content_value_union import ReasoningContentValueUnion
from adaline_api.models.message_content import MessageContent

reasoning = ReasoningContent(
    modality="reasoning",
    value=ReasoningContentValueUnion.from_dict({
        "type": "thinking",
        "thinking": "Let me analyze this step by step...",
        "signature": "sig_abc123"
    })
)

content = MessageContent(actual_instance=reasoning)

Error Content

ErrorContent

See the dedicated ErrorContent page for full documentation. Error content type for LLM safety and content filtering errors.

from adaline_api.models.error_content import ErrorContent

Fields

str

required

Must be "error".

SafetyErrorContentValue

required

Safety error value with type and value fields.

Example

from adaline_api.models.error_content import ErrorContent
from adaline_api.models.safety_error_content_value import SafetyErrorContentValue
from adaline_api.models.message_content import MessageContent

error = ErrorContent(
    modality="error",
    value=SafetyErrorContentValue(
        type="safety",
        value="Content filtered due to safety policy."
    )
)

content = MessageContent(actual_instance=error)

Search Result Content

SearchResultContent

See the dedicated SearchResultContent page for full documentation. Search result content for grounding LLM responses with web search data.

from adaline_api.models.search_result_content import SearchResultContent

Fields

str

required

Must be "search-result".

SearchResultGoogleContentValue

required

Google search result value with type, references, and responses.

Example

from adaline_api.models.search_result_content import SearchResultContent
from adaline_api.models.search_result_google_content_value import SearchResultGoogleContentValue
from adaline_api.models.message_content import MessageContent

search_result = SearchResultContent(
    modality="search-result",
    value=SearchResultGoogleContentValue.from_dict({
        "type": "google",
        "references": [{"title": "Example", "url": "https://example.com", "snippet": "..."}],
        "responses": [{"text": "Search grounding result..."}]
    })
)

content = MessageContent(actual_instance=search_result)

Complete Examples

from adaline_api.models.prompt_message import PromptMessage
from adaline_api.models.message_content import MessageContent
from adaline_api.models.text_content import TextContent
from adaline_api.models.image_content import ImageContent
from adaline_api.models.image_content_value import ImageContentValue
from adaline_api.models.pdf_content import PdfContent
from adaline_api.models.pdf_content_value import PdfContentValue
from adaline_api.models.pdf_content_file import PdfContentFile

message = PromptMessage(
    role="user",
    content=[
        MessageContent(actual_instance=TextContent(
            modality="text",
            value="Analyze this image and document"
        )),
        MessageContent(actual_instance=ImageContent(
            modality="image",
            detail="high",
            value=ImageContentValue.from_dict({
                "type": "url",
                "url": "https://example.com/image.jpg"
            })
        )),
        MessageContent(actual_instance=PdfContent(
            modality="pdf",
            value=PdfContentValue.from_dict({
                "type": "url",
                "url": "https://example.com/report.pdf"
            }),
            file=PdfContentFile(name="report.pdf", id="file_123")
        )),
    ]
)

Tool Call Flow

import json
from adaline_api.models.prompt_message import PromptMessage
from adaline_api.models.message_content import MessageContent
from adaline_api.models.text_content import TextContent
from adaline_api.models.tool_call_content import ToolCallContent
from adaline_api.models.tool_response_content import ToolResponseContent

# 1. Assistant makes tool call
assistant_with_tool = PromptMessage(
    role="assistant",
    content=[MessageContent(actual_instance=ToolCallContent(
        modality="tool-call",
        index=0,
        id="call_123",
        name="get_weather",
        arguments=json.dumps({"city": "Paris"})
    ))]
)

# 2. Tool responds
tool_result = PromptMessage(
    role="tool",
    content=[MessageContent(actual_instance=ToolResponseContent(
        modality="tool-response",
        index=0,
        id="call_123",
        name="get_weather",
        data=json.dumps({"temp": 24, "conditions": "sunny"})
    ))]
)

# 3. Assistant uses tool result
final_response = PromptMessage(
    role="assistant",
    content=[MessageContent(actual_instance=TextContent(
        modality="text",
        value="The weather in Paris is sunny with a temperature of 24°C."
    ))]
)

Content Filtering

from adaline_api.models.text_content import TextContent
from adaline_api.models.image_content import ImageContent
from adaline_api.models.tool_call_content import ToolCallContent

message: PromptMessage = ...

# Get all text content
text_items = [
    c.actual_instance for c in message.content
    if isinstance(c.actual_instance, TextContent)
]

# Get all images
images = [
    c.actual_instance for c in message.content
    if isinstance(c.actual_instance, ImageContent)
]

# Get tool calls
tool_calls = [
    c.actual_instance for c in message.content
    if isinstance(c.actual_instance, ToolCallContent)
]

# Extract text values
text_values = " ".join(t.value for t in text_items)

​MessageContent

​Overview

​MessageContent (Union Type)

​Text Content

​TextContent

​Fields

​Example

​Image Content

​ImageContent

​Fields

​ImageContentValue

​Example

​PDF Content

​PdfContent

​Fields

​Example

​Tool Content

​ToolCallContent

​Fields

​Example

​ToolResponseContent

​Fields

​Example

​Reasoning Content

​ReasoningContent

​Fields

​Example

​Error Content

​ErrorContent

​Fields

​Example

​Search Result Content

​SearchResultContent

​Fields

​Example

​Complete Examples

​Multi-Modal Message

​Tool Call Flow

​Content Filtering

MessageContent

Overview

MessageContent (Union Type)

Text Content

TextContent

Fields

Example

Image Content

ImageContent

Fields

ImageContentValue

Example

PDF Content

PdfContent

Fields

Example

Tool Content

ToolCallContent

Fields

Example

ToolResponseContent

Fields

Example

Reasoning Content

ReasoningContent

Fields

Example

Error Content

ErrorContent

Fields

Example

Search Result Content

SearchResultContent

Fields

Example

Complete Examples

Multi-Modal Message

Tool Call Flow

Content Filtering