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import {
generateErrorResponse,
generateInvalidProviderResponseError,
} from '@api/utils/ai-provider';
import type {
AIProviderFunctionConfig,
ResponseChunkStreamTransformFunction,
ResponseTransformFunction,
} from '@shared/types/ai-providers/config';
import type { ChatCompletionRequestBody } from '@shared/types/api/routes/chat-completions-api';
import { ChatCompletionMessageRole } from '@shared/types/api/routes/shared/messages';
import { AIProvider } from '@shared/types/constants';
// TODOS: this configuration does not enforce the maximum token limit for the input parameter. If you want to enforce this, you might need to add a custom validation function or a max property to the ParameterConfig interface, and then use it in the input configuration. However, this might be complex because the token count is not a simple length check, but depends on the specific tokenization method used by the model.
// TODOS: this configuration might have to check on the max value of n
export const deepInfraChatCompleteConfig: AIProviderFunctionConfig = {
model: {
param: 'model',
required: true,
default: 'deepinfra/airoboros-70b',
},
messages: {
param: 'messages',
required: true,
default: [],
transform: (saRequestBody: ChatCompletionRequestBody) => {
return saRequestBody.messages?.map((message) => {
if (message.role === ChatCompletionMessageRole.DEVELOPER)
return { ...message, role: ChatCompletionMessageRole.SYSTEM };
return message;
});
},
},
frequency_penalty: {
param: 'frequency_penalty',
default: 0,
min: -2,
max: 2,
},
max_tokens: {
param: 'max_tokens',
default: 100,
min: 1,
},
max_completion_tokens: {
param: 'max_tokens',
default: 100,
min: 1,
},
n: {
param: 'n',
default: 1,
min: 1,
max: 1,
},
presence_penalty: {
param: 'presence_penalty',
min: -2,
max: 2,
default: 0,
},
temperature: {
param: 'temperature',
default: 1,
min: 0,
max: 2,
},
top_p: {
param: 'top_p',
default: 1,
min: 0,
max: 1,
},
stop: {
param: 'stop',
default: null,
},
stream: {
param: 'stream',
default: false,
},
};
interface DeepInfraStreamChunk {
id: string;
object: string;
created: number;
model: string;
choices: {
delta: {
role?: string | null;
content?: string;
};
index: number;
finish_reason: string | null;
}[];
usage?: {
prompt_tokens: number;
completion_tokens: number;
total_tokens: number;
};
}
export const deepInfraChatCompleteResponseTransform: ResponseTransformFunction =
(aiProviderResponseBody, aiProviderResponseStatus) => {
if (
'detail' in aiProviderResponseBody &&
aiProviderResponseStatus !== 200 &&
(aiProviderResponseBody.detail as string | string[]).length
) {
let firstError: Record<string, unknown> | undefined;
let errorField: string | null = null;
let errorMessage: string | undefined;
let errorType: string | null = null;
if (Array.isArray(aiProviderResponseBody.detail)) {
[firstError] = aiProviderResponseBody.detail;
if (firstError) {
errorField = Array.isArray(firstError.loc)
? firstError.loc.join('.')
: '';
errorMessage =
typeof firstError.msg === 'string' ? firstError.msg : undefined;
errorType =
typeof firstError.type === 'string' ? firstError.type : null;
}
} else {
errorMessage = String(aiProviderResponseBody.detail);
}
return generateErrorResponse(
{
message: `${errorField ? `${errorField}: ` : ''}${errorMessage}`,
type: errorType ?? undefined,
param: undefined,
code: undefined,
},
AIProvider.DEEPINFRA,
);
}
if ('choices' in aiProviderResponseBody) {
const response =
aiProviderResponseBody as unknown as DeepInfraChatCompleteResponse;
return {
id: response.id,
object: response.object,
created: response.created,
model: response.model,
provider: AIProvider.DEEPINFRA,
choices: response.choices.map((c) => ({
index: c.index,
message: {
role: c.message.role,
content: c.message.content,
},
finish_reason: c.finish_reason,
})),
usage: {
prompt_tokens: response.usage?.prompt_tokens,
completion_tokens: response.usage?.completion_tokens,
total_tokens: response.usage?.total_tokens,
},
};
}
return generateInvalidProviderResponseError(
aiProviderResponseBody,
AIProvider.DEEPINFRA,
);
};
export const deepInfraChatCompleteStreamChunkTransform: ResponseChunkStreamTransformFunction =
(responseChunk) => {
// Matches a ping chunk `: ping - 2025-04-13 03:55:09.637341+00:00`
if (
responseChunk.match(
/^:\s*ping\s*-\s*\d{4}-\d{2}-\d{2}\s\d{2}:\d{2}:\d{2}\.\d{6}\+\d{2}:\d{2}$/,
)
) {
return '';
}
let chunk = responseChunk.trim();
chunk = chunk.replace(/^data: /, '');
chunk = chunk.trim();
if (chunk === '[DONE]') {
return `data: ${chunk}\n\n`;
}
const parsedChunk: DeepInfraStreamChunk = JSON.parse(chunk);
return `data: ${JSON.stringify({
id: parsedChunk.id,
object: parsedChunk.object,
created: parsedChunk.created,
model: parsedChunk.model,
provider: AIProvider.DEEPINFRA,
choices: [
{
index: parsedChunk.choices[0].index,
delta: parsedChunk.choices[0].delta,
finish_reason: parsedChunk.choices[0].finish_reason,
},
],
usage: parsedChunk.usage
? {
prompt_tokens: parsedChunk.usage.prompt_tokens,
completion_tokens: parsedChunk.usage.completion_tokens,
total_tokens: parsedChunk.usage.total_tokens,
}
: undefined,
})}\n\n`;
};
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