Major: Rate limit logic has a potential concurrency issue. When rate limit is exceeded, the code waits and resets state, but doesn't re-check if the new request would exceed limits. If multiple requests come in concurrently, they could all wait and then all proceed, potentially exceeding the rate limit.

Consider using a mutex (like async-mutex package used in OpenAICompatibleEmbedder) to serialize rate limit checks and updates, or implement a proper request queue.

Minor: Rate limit counters are updated before the request is made. If the embedding request fails, the counters will be incorrect, potentially allowing more requests than the limit.

Consider updating counters after successful completion of the request, or decrementing them if the request fails.

Critical: Missing UI implementation - The PR adds backend support for Nebius but doesn't include the corresponding UI changes in webview-ui/src/components/chat/CodeIndexPopover.tsx. Users have no way to:

1. Select "nebius" as a provider in the dropdown
2. Enter their Nebius API key
3. Select Nebius models
4. See validation errors for Nebius configuration

The UI component needs to be updated to include:

• "nebius" option in provider selection dropdown
• Nebius-specific configuration section (similar to other providers)
• Validation schema case for "nebius"
• Secret handling for codebaseIndexNebiusApiKey
• Translation keys for Nebius-specific labels and messages

Nitpick: The i18n translation keys used here (embeddings:nebius.rateLimitExceeded, embeddings:nebius.waitingForRateLimit) don't appear to exist in the translation files. While the code will work (i18n will return the key as fallback), it would be better to add these translations or use generic rate limit messages.

Nitpick: The error message translation key embeddings:serviceFactory.nebiusConfigMissing doesn't appear to exist in translation files. Consider adding it or using a generic message.

Critical: Rate limit logic error - After waiting for rate limit reset, the code sets tokensUsed: estimatedTokens and requestsCount: 1, but this doesn't account for the current request that triggered the wait. This means the first request after reset won't be counted properly.

The logic should be:

1. Wait for reset
2. Reset counters to 0
3. Let the normal flow update counters (lines 113-114)

Suggested fix:

await new Promise((resolve) => setTimeout(resolve, waitTime))
// Reset the window - the current request will be counted by the normal flow below
this.rateLimitState = {
	tokensUsed: 0,
	requestsCount: 0,
	windowStart: Date.now(),
}

Then remove the early return and let execution continue to line 167 where the actual API call happens, and the counters will be properly updated by the existing logic at lines 113-114.

Critical: Rate limiting logic flaw - After waiting for the rate limit window to reset, the code updates the state but doesn't actually retry the original request that triggered the wait.

The flow is:

1. Line 147: checkAndUpdateRateLimit() returns false (rate limited)
2. Lines 150-156: Wait for window reset
3. Lines 158-162: Reset state with new counters
4. Line 167: Proceed with API call

But the tokens/requests from the ORIGINAL failed check (line 147) are never accounted for. The request proceeds with fresh counters, but the original request that should have been retried is lost.

Consider restructuring to use a retry loop similar to OpenAICompatibleEmbedder._embedBatchWithRetries.

Minor: Simplified rate limiting approach - This implementation uses a basic sliding window that completely resets after 60 seconds. This differs from the more sophisticated global rate limiting with exponential backoff used in OpenAICompatibleEmbedder.

While this may be acceptable for Nebius's specific rate limits, consider whether:

1. Multiple concurrent requests could still exceed limits during the same window
2. The lack of exponential backoff could lead to repeated failures
3. Instance-level rate limiting (vs global) is appropriate for this use case

Nitpick: Token estimation accuracy - The rough estimate of 1 token ≈ 4 characters is a common approximation but may not be accurate for all text types. Consider documenting this limitation or using a more accurate tokenization method if Nebius provides one.

This is consistent with other embedders in the codebase, so it's acceptable as-is, but worth noting for future improvements.

Major: Missing provider option in type union - While you've added the codebaseIndexNebiusApiKey field here, the codebaseIndexEmbedderProvider type union on line 284 doesn't include "nebius". This will cause TypeScript errors when users try to select Nebius as a provider in the UI.

The type union currently only has: "openai" | "ollama" | "openai-compatible" | "gemini" | "mistral" | "vercel-ai-gateway"

It needs to be updated to include | "nebius".

Critical: Race condition in rate limit reset logic. After waiting for the rate limit window to reset, the code unconditionally resets rateLimitState and assumes the request will succeed. However, if another request was processed during the wait (in concurrent scenarios), this could lead to exceeding rate limits.

The current flow:

1. Check fails → wait for window reset
2. After wait, unconditionally reset state and assume success
3. Proceed with request

This bypasses the rate limit check after waiting. The code should re-check rate limits after the wait completes, rather than assuming the request can proceed.

Suggested fix:

if (!this.checkAndUpdateRateLimit(estimatedTokens)) {
    const waitTime = this.getWaitTimeMs()
    if (waitTime > 0) {
        console.log(
            t("embeddings:nebius.waitingForRateLimit", {
                waitTimeMs: waitTime,
            }),
        )
        await new Promise((resolve) => setTimeout(resolve, waitTime))
        // Re-check rate limits after waiting instead of unconditionally resetting
        if (!this.checkAndUpdateRateLimit(estimatedTokens)) {
            throw new Error("Rate limit still exceeded after waiting")
        }
    } else {
        throw new Error("Rate limit exceeded")
    }
}