@juspay/neurolink
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Universal AI Development Platform with working MCP integration, multi-provider support, voice (TTS/STT/realtime), and professional CLI. 58+ external MCP servers discoverable, multimodal file processing, RAG pipelines. Build, test, and deploy AI applicatio
100 lines • 4.52 kB
JavaScript
/**
* Context Budget Checker
*
* Pre-generation validation that estimates total input token cost
* and compares against the model's available input space.
*
* This runs BEFORE every LLM call to prevent context overflow.
*/
import { getAvailableInputTokens } from "../constants/contextWindows.js";
import { estimateMessagesTokens, estimateTokens, TOKENS_PER_MESSAGE, } from "../utils/tokenEstimation.js";
import { SpanSerializer, SpanType, SpanStatus, getMetricsAggregator, } from "../observability/index.js";
import { getActiveTraceContext } from "../telemetry/traceContext.js";
/** Default compaction threshold (80% of available input) */
const DEFAULT_COMPACTION_THRESHOLD = 0.8;
/** Estimated tokens per tool definition */
const TOKENS_PER_TOOL_DEFINITION = 200;
/**
* Check whether a request fits within the model's context budget.
*
* Estimates total input tokens from: system prompt + tool definitions +
* conversation history + current prompt + file attachments, and compares
* against available input space.
*/
export function checkContextBudget(params) {
const { traceId, parentSpanId } = getActiveTraceContext();
const span = SpanSerializer.createSpan(SpanType.CONTEXT_COMPACTION, "context.budgetCheck", {
"context.operation": "budgetCheck",
}, parentSpanId, traceId);
const startTime = Date.now();
try {
const { provider, model, maxTokens, systemPrompt, conversationMessages, currentPrompt, toolDefinitions, fileAttachments, compactionThreshold = DEFAULT_COMPACTION_THRESHOLD, } = params;
const availableInputTokens = getAvailableInputTokens(provider, model, maxTokens);
// Estimate each category
const systemPromptTokens = systemPrompt
? estimateTokens(systemPrompt, provider) + TOKENS_PER_MESSAGE
: 0;
const conversationHistoryTokens = conversationMessages?.length
? estimateMessagesTokens(conversationMessages, provider)
: 0;
const currentPromptTokens = currentPrompt
? estimateTokens(currentPrompt, provider) + TOKENS_PER_MESSAGE
: 0;
const toolDefinitionTokens = toolDefinitions?.length
? toolDefinitions.reduce((sum, tool) => {
try {
const serialized = JSON.stringify(tool);
return sum + estimateTokens(serialized, provider);
}
catch {
return sum + TOKENS_PER_TOOL_DEFINITION;
}
}, 0)
: 0;
const fileAttachmentTokens = fileAttachments?.length
? fileAttachments.reduce((sum, file) => sum + estimateTokens(file.content, provider), 0)
: 0;
const estimatedInputTokens = systemPromptTokens +
conversationHistoryTokens +
currentPromptTokens +
toolDefinitionTokens +
fileAttachmentTokens;
const usageRatio = availableInputTokens > 0
? estimatedInputTokens / availableInputTokens
: 1;
const withinBudget = estimatedInputTokens <= availableInputTokens;
const shouldCompact = usageRatio >= compactionThreshold;
const result = {
withinBudget,
estimatedInputTokens,
availableInputTokens,
usageRatio,
shouldCompact,
breakdown: {
systemPrompt: systemPromptTokens,
conversationHistory: conversationHistoryTokens,
currentPrompt: currentPromptTokens,
toolDefinitions: toolDefinitionTokens,
fileAttachments: fileAttachmentTokens,
},
};
span.durationMs = Date.now() - startTime;
const endedSpan = SpanSerializer.endSpan(SpanSerializer.updateAttributes(span, {
"context.budgetUsage": usageRatio,
"context.triggered": shouldCompact,
"context.estimatedTokens": estimatedInputTokens,
"context.availableTokens": availableInputTokens,
}), SpanStatus.OK);
getMetricsAggregator().recordSpan(endedSpan);
return result;
}
catch (error) {
span.durationMs = Date.now() - startTime;
const endedSpan = SpanSerializer.endSpan(span, SpanStatus.ERROR);
endedSpan.statusMessage =
error instanceof Error ? error.message : String(error);
getMetricsAggregator().recordSpan(endedSpan);
throw error;
}
}
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