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feat(js/plugins/compat-oai): Add reasoning_content and json_schema support to OpenAI Compatible API plugin #3679

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feat(js/plugins/compat-oai): Add reasoning_content and json_schema support to OpenAI Compatible API plugin #3679

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83 changes: 62 additions & 21 deletions js/plugins/compat-oai/src/model.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -277,19 +277,36 @@ export function fromOpenAIChoice(
const toolRequestParts = choice.message.tool_calls?.map((toolCall) =>
fromOpenAIToolCall(toolCall, choice)
);

// Build content array based on what's present in the message
let content: Part[] = [];

if (toolRequestParts) {
content = toolRequestParts as ToolRequestPart[];
} else {
// Handle reasoning_content if present
if (
'reasoning_content' in choice.message &&
choice.message.reasoning_content
) {
content.push({ reasoning: choice.message.reasoning_content as string });
}

// Handle regular content if present
if (choice.message.content) {
content.push(
jsonMode
? { data: JSON.parse(choice.message.content!) }
: { text: choice.message.content! }
);
}
}

return {
finishReason: finishReasonMap[choice.finish_reason] || 'other',
message: {
role: 'model',
content: toolRequestParts
? // Note: Not sure why I have to cast here exactly.
// Otherwise it thinks toolRequest must be 'undefined' if provided
(toolRequestParts as ToolRequestPart[])
: [
jsonMode
? { data: JSON.parse(choice.message.content!) }
: { text: choice.message.content! },
],
content,
},
};
}
Expand All @@ -308,21 +325,35 @@ export function fromOpenAIChunkChoice(
const toolRequestParts = choice.delta.tool_calls?.map((toolCall) =>
fromOpenAIToolCall(toolCall, choice)
);

// Build content array based on what's present in the delta
let content: Part[] = [];

if (toolRequestParts) {
content = toolRequestParts as ToolRequestPart[];
} else {
// Handle reasoning_content if present
if ('reasoning_content' in choice.delta && choice.delta.reasoning_content) {
content.push({ reasoning: choice.delta.reasoning_content as string });
}

// Handle regular content if present
if (choice.delta.content) {
content.push(
jsonMode
? { data: JSON.parse(choice.delta.content!) }
: { text: choice.delta.content! }
);
}
}

return {
finishReason: choice.finish_reason
? finishReasonMap[choice.finish_reason] || 'other'
: 'unknown',
message: {
role: 'model',
content: toolRequestParts
? // Note: Not sure why I have to cast here exactly.
// Otherwise it thinks toolRequest must be 'undefined' if provided
(toolRequestParts as ToolRequestPart[])
: [
jsonMode
? { data: JSON.parse(choice.delta.content!) }
: { text: choice.delta.content! },
],
content,
},
};
}
Expand Down Expand Up @@ -383,9 +414,19 @@ export function toOpenAIRequestBody(
}
const response_format = request.output?.format;
if (response_format === 'json') {
body.response_format = {
type: 'json_object',
};
if (request.output?.schema) {
body.response_format = {
type: 'json_schema',
json_schema: {
name: 'output',
schema: request.output!.schema,
},
};
Comment on lines +417 to +424
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I could not find API reference for json_schema for DeepSeek. Are you sure this works?

http://api-docs.deepseek.com/api/create-chat-completion#request

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@neokn neokn Oct 17, 2025 •
edited
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hi @ssbushi thanks for your review, yes, DeepSeek didn't support json_schema
so there still has fallback for json_object
when integrate to DeepSeek, user can set the output.format without JsonSchema
e.g. ai.generate({output: {format: 'json'}});

then it will fallback to

body.response_format = {
    type: 'json_object',
};

I'm follow these two document for implement json_schema part
LiteLLM
OpenAI
xAI

} else {
body.response_format = {
type: 'json_object',
};
}
} else if (response_format === 'text') {
body.response_format = {
type: 'text',
Expand Down
106 changes: 106 additions & 0 deletions js/plugins/compat-oai/tests/compat_oai_test.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -412,6 +412,48 @@ describe('fromOpenAiChoice', () => {
finishReason: 'stop',
},
},
{
should: 'should work with reasoning_content',
choice: {
index: 0,
message: {
role: 'assistant',
content: null,
reasoning_content: 'Let me think about this step by step...',
refusal: null,
} as any,
finish_reason: 'stop',
logprobs: null,
},
expectedOutput: {
finishReason: 'stop',
message: {
role: 'model',
content: [{ reasoning: 'Let me think about this step by step...' }],
},
},
},
{
should: 'should work with both reasoning_content and content',
choice: {
index: 0,
message: {
role: 'assistant',
content: 'Final answer',
reasoning_content: 'Let me think...',
refusal: null,
} as any,
finish_reason: 'stop',
logprobs: null,
},
expectedOutput: {
finishReason: 'stop',
message: {
role: 'model',
content: [{ reasoning: 'Let me think...' }, { text: 'Final answer' }],
},
},
},
];

for (const test of testCases) {
Expand Down Expand Up @@ -503,6 +545,43 @@ describe('fromOpenAiChunkChoice', () => {
finishReason: 'stop',
},
},
{
should: 'should work with reasoning_content',
chunkChoice: {
index: 0,
delta: {
role: 'assistant',
reasoning_content: 'Let me think about this step by step...',
} as any,
finish_reason: null,
},
expectedOutput: {
finishReason: 'unknown',
message: {
role: 'model',
content: [{ reasoning: 'Let me think about this step by step...' }],
},
},
},
{
should: 'should work with both reasoning_content and content',
chunkChoice: {
index: 0,
delta: {
role: 'assistant',
reasoning_content: 'Let me think...',
content: 'Final answer',
} as any,
finish_reason: 'stop',
},
expectedOutput: {
finishReason: 'stop',
message: {
role: 'model',
content: [{ reasoning: 'Let me think...' }, { text: 'Final answer' }],
},
},
},
];

for (const test of testCases) {
Expand Down Expand Up @@ -1284,6 +1363,33 @@ describe('toOpenAiRequestBody', () => {
},
});
});
it('sets json_schema response_format when an output schema is provided', () => {
const schema = {
type: 'object',
properties: { foo: { type: 'string' } },
required: ['foo'],
additionalProperties: false,
};
const request = {
messages: [{ role: 'user', content: [{ text: 'hello' }] }],
output: { format: 'json', schema },
} as unknown as GenerateRequest;

const actualOutput = toOpenAIRequestBody('gpt-4o', request) as unknown as {
response_format?: {
type: string;
json_schema?: { name: string; schema: unknown };
};
};

expect(actualOutput.response_format).toStrictEqual({
type: 'json_schema',
json_schema: {
name: 'output',
schema,
},
});
});
});

describe('openAIModelRunner', () => {
Expand Down