Files
livedash-node/lib/batchProcessor.ts
Kaj Kowalski f2a3d87636 fix: resolve TypeScript compilation errors in batch processing
- Fix type assertion for session access in batchProcessor.ts
- Add explicit type annotation for batchRequests array in processingScheduler.ts
- Import AIProcessingRequest type from Prisma client
- All TypeScript compilation errors resolved
2025-07-12 00:27:55 +02:00

547 lines
15 KiB
TypeScript

/**
* OpenAI Batch API Processing Utilities
*
* This module implements Phase 1 of the AI Session Processing Pipeline refactor
* to use OpenAI's Batch API for cost-efficient processing of AI requests.
*
* Key benefits:
* - 50% cost reduction compared to real-time API calls
* - Better rate limiting and throughput management
* - Improved error handling and retry mechanisms
*/
import { prisma } from "./prisma";
import { AIBatchRequestStatus, AIRequestStatus, type AIProcessingRequest } from "@prisma/client";
/**
* Configuration for batch processing
*/
const BATCH_CONFIG = {
// Maximum number of requests per batch (OpenAI limit is 50,000)
MAX_REQUESTS_PER_BATCH: 1000,
// Minimum time to wait before checking batch status (in milliseconds)
MIN_STATUS_CHECK_INTERVAL: 60000, // 1 minute
// Maximum time to wait for a batch to complete (24 hours)
MAX_BATCH_TIMEOUT: 24 * 60 * 60 * 1000,
} as const;
/**
* Represents a single request in an OpenAI batch
*/
interface OpenAIBatchRequest {
custom_id: string;
method: "POST";
url: "/v1/chat/completions";
body: {
model: string;
messages: Array<{
role: string;
content: string;
}>;
temperature?: number;
max_tokens?: number;
};
}
/**
* OpenAI Batch API response format
*/
interface OpenAIBatchResponse {
id: string;
object: "batch";
endpoint: string;
errors: {
object: "list";
data: Array<{
code: string;
message: string;
param?: string;
type: string;
}>;
};
input_file_id: string;
completion_window: string;
status: "validating" | "failed" | "in_progress" | "finalizing" | "completed" | "expired" | "cancelling" | "cancelled";
output_file_id?: string;
error_file_id?: string;
created_at: number;
in_progress_at?: number;
expires_at?: number;
finalizing_at?: number;
completed_at?: number;
failed_at?: number;
expired_at?: number;
cancelling_at?: number;
cancelled_at?: number;
request_counts: {
total: number;
completed: number;
failed: number;
};
metadata?: Record<string, string>;
}
/**
* Get pending AI processing requests that need to be batched
*/
export async function getPendingBatchRequests(
companyId: string,
limit: number = BATCH_CONFIG.MAX_REQUESTS_PER_BATCH
): Promise<AIProcessingRequest[]> {
return prisma.aIProcessingRequest.findMany({
where: {
session: {
companyId,
},
processingStatus: AIRequestStatus.PENDING_BATCHING,
batchId: null,
},
include: {
session: {
include: {
messages: {
orderBy: { order: "asc" },
},
},
},
},
take: limit,
orderBy: {
requestedAt: "asc",
},
}) as Promise<(AIProcessingRequest & {
session: {
id: string;
companyId: string;
messages: Array<{
id: string;
role: string;
content: string;
order: number;
}>;
} | null;
})[]>;
}
/**
* Create a new batch request and upload to OpenAI
*/
export async function createBatchRequest(
companyId: string,
requests: AIProcessingRequest[]
): Promise<string> {
if (requests.length === 0) {
throw new Error("Cannot create batch with no requests");
}
if (requests.length > BATCH_CONFIG.MAX_REQUESTS_PER_BATCH) {
throw new Error(`Batch size ${requests.length} exceeds maximum of ${BATCH_CONFIG.MAX_REQUESTS_PER_BATCH}`);
}
// Create batch requests in OpenAI format
const batchRequests: OpenAIBatchRequest[] = requests.map((request) => ({
custom_id: request.id,
method: "POST",
url: "/v1/chat/completions",
body: {
model: request.model,
messages: [
{
role: "system",
content: getSystemPromptForProcessingType(request.processingType),
},
{
role: "user",
content: formatMessagesForProcessing((request as any).session?.messages || []),
},
],
temperature: 0.1,
max_tokens: 1000,
},
}));
// Convert to JSONL format for OpenAI
const jsonlContent = batchRequests
.map((req) => JSON.stringify(req))
.join("\n");
// Upload file to OpenAI
const fileResponse = await uploadFileToOpenAI(jsonlContent);
// Create batch on OpenAI
const batchResponse = await createOpenAIBatch(fileResponse.id);
// Store batch request in our database
const batchRequest = await prisma.aIBatchRequest.create({
data: {
companyId,
openaiBatchId: batchResponse.id,
inputFileId: fileResponse.id,
status: AIBatchRequestStatus.IN_PROGRESS,
processingRequests: {
connect: requests.map((req) => ({ id: req.id })),
},
},
});
// Update individual requests to mark them as batching
await prisma.aIProcessingRequest.updateMany({
where: {
id: {
in: requests.map((req) => req.id),
},
},
data: {
processingStatus: AIRequestStatus.BATCHING_IN_PROGRESS,
batchId: batchRequest.id,
},
});
return batchRequest.id;
}
/**
* Check the status of all in-progress batches for a company
*/
export async function checkBatchStatuses(companyId: string): Promise<void> {
const inProgressBatches = await prisma.aIBatchRequest.findMany({
where: {
companyId,
status: {
in: [
AIBatchRequestStatus.IN_PROGRESS,
AIBatchRequestStatus.VALIDATING,
AIBatchRequestStatus.FINALIZING,
],
},
},
});
for (const batch of inProgressBatches) {
try {
const status = await getOpenAIBatchStatus(batch.openaiBatchId);
await updateBatchStatus(batch.id, status);
} catch (error) {
console.error(`Failed to check status for batch ${batch.id}:`, error);
}
}
}
/**
* Process completed batches and extract results
*/
export async function processCompletedBatches(companyId: string): Promise<void> {
const completedBatches = await prisma.aIBatchRequest.findMany({
where: {
companyId,
status: AIBatchRequestStatus.COMPLETED,
outputFileId: {
not: null,
},
},
include: {
processingRequests: {
include: {
session: true,
},
},
},
});
for (const batch of completedBatches) {
try {
await processBatchResults(batch);
} catch (error) {
console.error(`Failed to process batch results for ${batch.id}:`, error);
await prisma.aIBatchRequest.update({
where: { id: batch.id },
data: { status: AIBatchRequestStatus.FAILED },
});
}
}
}
/**
* Helper function to upload file content to OpenAI
*/
async function uploadFileToOpenAI(content: string): Promise<{ id: string }> {
const formData = new FormData();
formData.append("file", new Blob([content], { type: "application/jsonl" }), "batch_requests.jsonl");
formData.append("purpose", "batch");
const response = await fetch("https://api.openai.com/v1/files", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.OPENAI_API_KEY}`,
},
body: formData,
});
if (!response.ok) {
throw new Error(`Failed to upload file: ${response.statusText}`);
}
return response.json();
}
/**
* Helper function to create a batch request on OpenAI
*/
async function createOpenAIBatch(inputFileId: string): Promise<OpenAIBatchResponse> {
const response = await fetch("https://api.openai.com/v1/batches", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.OPENAI_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
input_file_id: inputFileId,
endpoint: "/v1/chat/completions",
completion_window: "24h",
}),
});
if (!response.ok) {
throw new Error(`Failed to create batch: ${response.statusText}`);
}
return response.json();
}
/**
* Helper function to get batch status from OpenAI
*/
async function getOpenAIBatchStatus(batchId: string): Promise<OpenAIBatchResponse> {
const response = await fetch(`https://api.openai.com/v1/batches/${batchId}`, {
method: "GET",
headers: {
"Authorization": `Bearer ${process.env.OPENAI_API_KEY}`,
},
});
if (!response.ok) {
throw new Error(`Failed to get batch status: ${response.statusText}`);
}
return response.json();
}
/**
* Update batch status in our database based on OpenAI response
*/
async function updateBatchStatus(batchId: string, openAIResponse: OpenAIBatchResponse): Promise<void> {
const statusMapping: Record<string, AIBatchRequestStatus> = {
validating: AIBatchRequestStatus.VALIDATING,
failed: AIBatchRequestStatus.FAILED,
in_progress: AIBatchRequestStatus.IN_PROGRESS,
finalizing: AIBatchRequestStatus.FINALIZING,
completed: AIBatchRequestStatus.COMPLETED,
expired: AIBatchRequestStatus.FAILED,
cancelled: AIBatchRequestStatus.CANCELLED,
};
const ourStatus = statusMapping[openAIResponse.status] || AIBatchRequestStatus.FAILED;
await prisma.aIBatchRequest.update({
where: { id: batchId },
data: {
status: ourStatus,
outputFileId: openAIResponse.output_file_id,
errorFileId: openAIResponse.error_file_id,
completedAt: openAIResponse.completed_at ? new Date(openAIResponse.completed_at * 1000) : null,
},
});
}
/**
* Process results from a completed batch
*/
async function processBatchResults(batch: {
id: string;
outputFileId: string | null;
processingRequests: Array<{ sessionId: string }>;
}): Promise<void> {
if (!batch.outputFileId) {
throw new Error("No output file available for completed batch");
}
// Download results from OpenAI
const results = await downloadOpenAIFile(batch.outputFileId);
// Parse JSONL results
const resultLines = results.split("\n").filter(line => line.trim());
for (const line of resultLines) {
try {
const result = JSON.parse(line);
const requestId = result.custom_id;
if (result.response?.body?.choices?.[0]?.message?.content) {
// Process successful result
await updateProcessingRequestWithResult(requestId, result.response.body);
} else {
// Handle error result
await markProcessingRequestAsFailed(requestId, result.error?.message || "Unknown error");
}
} catch (error) {
console.error("Failed to process batch result line:", error);
}
}
// Mark batch as processed
await prisma.aIBatchRequest.update({
where: { id: batch.id },
data: {
status: AIBatchRequestStatus.PROCESSED,
processedAt: new Date(),
},
});
}
/**
* Download file content from OpenAI
*/
async function downloadOpenAIFile(fileId: string): Promise<string> {
const response = await fetch(`https://api.openai.com/v1/files/${fileId}/content`, {
method: "GET",
headers: {
"Authorization": `Bearer ${process.env.OPENAI_API_KEY}`,
},
});
if (!response.ok) {
throw new Error(`Failed to download file: ${response.statusText}`);
}
return response.text();
}
/**
* Update processing request with successful AI result
*/
async function updateProcessingRequestWithResult(requestId: string, aiResponse: {
usage: {
prompt_tokens: number;
completion_tokens: number;
total_tokens: number;
};
choices: Array<{
message: {
content: string;
};
}>;
}): Promise<void> {
const usage = aiResponse.usage;
const content = aiResponse.choices[0].message.content;
try {
const parsedResult = JSON.parse(content);
// Update the processing request with usage data
await prisma.aIProcessingRequest.update({
where: { id: requestId },
data: {
processingStatus: AIRequestStatus.PROCESSING_COMPLETE,
success: true,
promptTokens: usage.prompt_tokens,
completionTokens: usage.completion_tokens,
totalTokens: usage.total_tokens,
completedAt: new Date(),
},
});
// Update the session with AI analysis results
const request = await prisma.aIProcessingRequest.findUnique({
where: { id: requestId },
include: { session: true },
});
if (request?.session) {
await prisma.session.update({
where: { id: request.sessionId },
data: {
summary: parsedResult.summary,
sentiment: parsedResult.sentiment,
category: parsedResult.category,
language: parsedResult.language,
},
});
}
} catch (error) {
console.error(`Failed to parse AI result for request ${requestId}:`, error);
await markProcessingRequestAsFailed(requestId, "Failed to parse AI response");
}
}
/**
* Mark processing request as failed
*/
async function markProcessingRequestAsFailed(requestId: string, errorMessage: string): Promise<void> {
await prisma.aIProcessingRequest.update({
where: { id: requestId },
data: {
processingStatus: AIRequestStatus.PROCESSING_FAILED,
success: false,
errorMessage,
completedAt: new Date(),
},
});
}
/**
* Get system prompt based on processing type
*/
function getSystemPromptForProcessingType(processingType: string): string {
const prompts = {
sentiment_analysis: "Analyze the sentiment of this conversation and respond with JSON containing: {\"sentiment\": \"POSITIVE|NEUTRAL|NEGATIVE\"}",
categorization: "Categorize this conversation and respond with JSON containing: {\"category\": \"CATEGORY_NAME\"}",
summary: "Summarize this conversation and respond with JSON containing: {\"summary\": \"Brief summary\"}",
full_analysis: `Analyze this conversation for sentiment, category, and provide a summary. Respond with JSON:
{
"sentiment": "POSITIVE|NEUTRAL|NEGATIVE",
"category": "SCHEDULE_HOURS|LEAVE_VACATION|SICK_LEAVE_RECOVERY|SALARY_COMPENSATION|CONTRACT_HOURS|ONBOARDING|OFFBOARDING|WORKWEAR_STAFF_PASS|TEAM_CONTACTS|PERSONAL_QUESTIONS|ACCESS_LOGIN|SOCIAL_QUESTIONS|UNRECOGNIZED_OTHER",
"summary": "Brief summary of the conversation",
"language": "en|de|fr|es|it|pt|nl|sv|da|no|fi|pl|cs|sk|hu|ro|bg|hr|sl|et|lv|lt|el|mt"
}`,
};
return prompts[processingType as keyof typeof prompts] || prompts.full_analysis;
}
/**
* Format session messages for AI processing
*/
function formatMessagesForProcessing(messages: Array<{
role: string;
content: string;
}>): string {
return messages
.map((msg) => `${msg.role}: ${msg.content}`)
.join("\n");
}
/**
* Get statistics about batch processing
*/
export async function getBatchProcessingStats(companyId: string) {
const stats = await prisma.aIBatchRequest.groupBy({
by: ["status"],
where: { companyId },
_count: true,
});
const pendingRequests = await prisma.aIProcessingRequest.count({
where: {
session: { companyId },
processingStatus: AIRequestStatus.PENDING_BATCHING,
},
});
return {
batchStats: stats.reduce((acc, stat) => {
acc[stat.status] = stat._count;
return acc;
}, {} as Record<string, number>),
pendingRequests,
};
}