Gemini Special Handling

Gemini-specific message handling like thinking blocks and function call ID generation

Acontext handles Gemini-specific features including thinking parts for Gemini thinking and automatic function call ID generation.

Thinking Parts

Acontext natively stores Gemini thinking parts. The ThoughtSignature is base64-encoded and preserved for multi-turn continuity.

client.sessions.store_message(
    session_id=session.id,
    blob={
        "role": "model",
        "parts": [
            {
                "text": "Let me reason step by step...",
                "thought": True,
                "thoughtSignature": "Z2VtaW5pLXRob3VnaHQtc2ln..."
            },
            {
                "text": "The answer is 42."
            }
        ]
    },
    format="gemini"
)

# Retrieved in Gemini format: thinking parts round-trip with Thought flag and ThoughtSignature
# Retrieved in Anthropic format: thinking parts round-trip as native thinking blocks
# Retrieved in OpenAI format: thinking content is downgraded to plain text

await client.sessions.storeMessage(session.id, {
    role: "model",
    parts: [
        {
            text: "Let me reason step by step...",
            thought: true,
            thoughtSignature: "Z2VtaW5pLXRob3VnaHQtc2ln..."
        },
        {
            text: "The answer is 42."
        }
    ]
}, { format: "gemini" });

// Retrieved in Gemini format: thinking parts round-trip with Thought flag and ThoughtSignature
// Retrieved in Anthropic format: thinking parts round-trip as native thinking blocks
// Retrieved in OpenAI format: thinking content is downgraded to plain text

Gemini FunctionCall parts may omit the id field. Acontext automatically generates a unique ID (format: call_<8-char-hex>) for each function call without one, and stores the ID-to-name mapping in message metadata so that FunctionResponse parts can be matched back correctly.

# Store a model message with a FunctionCall (no ID needed)
client.sessions.store_message(
    session_id=session.id,
    blob={
        "role": "model",
        "parts": [
            {
                "functionCall": {
                    "name": "get_weather",
                    "args": {"city": "San Francisco"}
                }
            }
        ]
    },
    format="gemini"
)

# Store the FunctionResponse — Acontext resolves the tool_call_id automatically
client.sessions.store_message(
    session_id=session.id,
    blob={
        "role": "user",
        "parts": [
            {
                "functionResponse": {
                    "name": "get_weather",
                    "response": {"temperature": "72°F", "condition": "sunny"}
                }
            }
        ]
    },
    format="gemini"
)

// Store a model message with a FunctionCall (no ID needed)
await client.sessions.storeMessage(session.id, {
    role: "model",
    parts: [
        {
            functionCall: {
                name: "get_weather",
                args: { city: "San Francisco" }
            }
        }
    ]
}, { format: "gemini" });

// Store the FunctionResponse — Acontext resolves the tool_call_id automatically
await client.sessions.storeMessage(session.id, {
    role: "user",
    parts: [
        {
            functionResponse: {
                name: "get_weather",
                response: { temperature: "72°F", condition: "sunny" }
            }
        }
    ]
}, { format: "gemini" });

Thinking parts stored from any provider (Anthropic or Gemini) are cross-compatible. Retrieve in any format and Acontext converts automatically. See Multi-provider Messages for details.

Gemini Special Handling

Thinking Parts

Function Call ID Generation

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