diff --git a/litellm/llms/gemini/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index c92af7de145..79985569c5f 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -115,6 +115,19 @@ def _parse_setup(session_configuration_request: str) -> BidiGenerateContentSetup return envelope.get("setup", empty_setup) +def _grounding_metadata_from_frame(frame: Mapping[str, object]) -> tuple[Mapping[str, object], ...]: + """Read ``serverContent.groundingMetadata`` off the frame that carries the turn's usage. + + Live reports grounding in the server frames rather than in ``usageMetadata``, and it emits both + on the same frame, so the per-query charge is countable at the point usage is built. + """ + server_content: Final = frame.get("serverContent") + if not isinstance(server_content, Mapping): + return () + metadata: Final = server_content.get("groundingMetadata") + return (metadata,) if isinstance(metadata, Mapping) else () + + # Google bills Live transcription at an estimated 25 audio tokens/sec of input and # 175 text tokens/min of output (ai.google.dev/gemini-api/docs/pricing). GEMINI_LIVE_TRANSCRIBE_AUDIO_TOKENS_PER_SECOND: Final = 25 @@ -323,7 +336,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ) elif key == "input_audio_transcription" and value is not None: optional_params["inputAudioTranscription"] = {} - elif key == "turn_detection": + elif key == "turn_detection" and value is not None: value_typed = cast(OpenAIRealtimeTurnDetection, value) if ( isinstance(value_typed, dict) @@ -1049,6 +1062,11 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): {**cast(dict, message), "usageMetadata": resolved_usage_metadata}, ), ) + grounding_metadata: Final = _grounding_metadata_from_frame(message) + if grounding_metadata: + VertexGeminiConfig._set_grounding_usage_counters( # pyright: ignore[reportPrivateUsage] # shared with the chat path; no public alias exists yet + _chat_completion_usage, grounding_metadata + ) else: _chat_completion_usage = get_empty_usage() diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_ai_live_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_ai_live_passthrough_logging_handler.py index e26f5f57532..0ff02b29c58 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_ai_live_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_ai_live_passthrough_logging_handler.py @@ -5,18 +5,105 @@ Handles cost tracking and logging for Vertex AI Live API WebSocket passthrough e Supports different modalities: text, audio, video, and web search. """ +from collections.abc import Mapping, Sequence from datetime import datetime -from typing import Any, Final +from itertools import chain, pairwise +from types import MappingProxyType +from typing import Final, Literal, TypeAlias from litellm._logging import verbose_proxy_logger +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.vertex_ai.gemini.grounding_requests import GroundingRequests, calculate_grounding_requests from litellm.proxy.pass_through_endpoints.llm_provider_handlers.base_passthrough_logging_handler import ( BasePassthroughLoggingHandler, ) from litellm.proxy.pass_through_endpoints.llm_provider_handlers.openai_passthrough_logging_handler import ( PassThroughEndpointLoggingTypedDict, ) -from litellm.types.utils import LlmProviders, ModelResponse, Usage -from litellm.utils import get_model_info +from litellm.types.utils import ( + CompletionTokensDetailsWrapper, + CostBreakdown, + LlmProviders, + ModelResponse, + PromptTokensDetailsWrapper, + Usage, +) + +_NO_GROUNDING: Final = GroundingRequests(web_search_requests=None, google_maps_grounding_requests=None) + +_AGGREGATED_FIELDS: Final = frozenset( + { + "promptTokenCount", + "candidatesTokenCount", + "totalTokenCount", + "toolUsePromptTokenCount", + "promptTokensDetails", + "candidatesTokensDetails", + } +) + + +def _detail_entries(raw: object) -> tuple[Mapping[str, object], ...]: + """Narrow one turn's ``*TokensDetails`` value to the entries that are actually shaped like one.""" + return tuple(entry for entry in raw if isinstance(entry, Mapping)) if isinstance(raw, Sequence) else () + + +def _grounding_metadata(websocket_messages: Sequence[object]) -> tuple[Mapping[str, object], ...]: + """Collect every ``serverContent.groundingMetadata`` a session emitted. + + Live reports grounding in the server frames, never in ``usageMetadata``, so the per-query + charge has to be counted here rather than derived from the token totals. + """ + return tuple( + metadata + for message in websocket_messages + if isinstance(message, Mapping) + for server_content in (message.get("serverContent"),) + if isinstance(server_content, Mapping) + for metadata in (server_content.get("groundingMetadata"),) + if isinstance(metadata, Mapping) + ) + + +def _turns(websocket_messages: Sequence[object]) -> tuple[tuple[object, ...], ...]: + """Split a session at every ``usageMetadata`` frame; frames after the last one never got their usage.""" + closes: Final = tuple( + index + 1 + for index, message in enumerate(websocket_messages) + if isinstance(message, Mapping) and isinstance(message.get("usageMetadata"), dict) + ) + return tuple(tuple(websocket_messages[start:end]) for start, end in pairwise((0, *closes))) + + +def _session_grounding_requests(websocket_messages: Sequence[object]) -> GroundingRequests: + per_turn: Final = tuple( + calculate_grounding_requests(_grounding_metadata(turn)) for turn in _turns(websocket_messages) + ) + web_search_requests: Final = sum(requests.web_search_requests or 0 for requests in per_turn) + google_maps_grounding_requests: Final = sum(requests.google_maps_grounding_requests or 0 for requests in per_turn) + return GroundingRequests( + web_search_requests=web_search_requests or None, + google_maps_grounding_requests=google_maps_grounding_requests or None, + ) + + +_SummedField: TypeAlias = Literal[ + "input_cost", + "output_cost", + "tool_usage_cost", + "cache_read_cost", + "cache_creation_cost", + "reasoning_cost", + "original_cost", + "discount_amount", + "margin_fixed_amount", + "margin_total_amount", +] + + +def _summed(breakdowns: Sequence[CostBreakdown], field: _SummedField) -> float | None: + values: Final = tuple(value for breakdown in breakdowns if (value := breakdown.get(field)) is not None) + return sum(values) if values else None class VertexAILivePassthroughLoggingHandler(BasePassthroughLoggingHandler): @@ -48,186 +135,110 @@ class VertexAILivePassthroughLoggingHandler(BasePassthroughLoggingHandler): """Return the LLM provider name.""" return LlmProviders.VERTEX_AI + @staticmethod + def _resolve_detail_counts( + details: Sequence[Mapping[str, object]], + declared_total: object, + ) -> tuple[tuple[str, int], ...]: + """ + Pair each of one turn's ``*TokensDetails`` entries with its token count. + + Live sometimes names the modality that carries the rest of a turn without a + ``tokenCount``, and reading the absent key as zero drops those tokens from the + breakdown, so real audio ends up priced as text. A lone unpriced entry therefore takes + whatever the turn's declared count leaves over. Two or more cannot be told apart, so + they are left out and the cost calculator charges the remainder as text. + """ + priced: Final = tuple( + (str(detail.get("modality", "TEXT")), count) + for detail in details + if isinstance(count := detail.get("tokenCount"), int) + ) + unpriced: Final = tuple( + str(detail.get("modality", "TEXT")) for detail in details if not isinstance(detail.get("tokenCount"), int) + ) + if len(unpriced) != 1 or not isinstance(declared_total, int): + return priced + residual: Final = declared_total - sum(count for _, count in priced) + return priced if residual <= 0 else (*priced, (unpriced[0], residual)) + + @staticmethod + def _sum_by_modality(counts: Sequence[tuple[str, int]]) -> Mapping[str, int]: + """Total the (modality, tokenCount) pairs of one or more turns per modality.""" + return MappingProxyType({modality: sum(c for m, c in counts if m == modality) for modality, _ in counts}) + + @staticmethod + def _merged_modality_totals( + snapshots: Sequence[Mapping[str, object]], + count_key: str, + details_key: str, + ) -> Mapping[str, int]: + """Total every turn's per-modality counts, so the breakdown adds up the way the totals do.""" + return VertexAILivePassthroughLoggingHandler._sum_by_modality( + tuple( + chain.from_iterable( + VertexAILivePassthroughLoggingHandler._resolve_detail_counts( + _detail_entries(snapshot.get(details_key)), snapshot.get(count_key) + ) + for snapshot in snapshots + ) + ) + ) + @staticmethod def _extract_usage_metadata_from_websocket_messages( - websocket_messages: list[dict], + websocket_messages: Sequence[object], ) -> dict | None: """ Extract and aggregate usage metadata from a list of WebSocket messages. + Live emits one ``usageMetadata`` per turn and Google charges per turn for every token in + the session context window, which is the current turn's tokens plus all accumulated + tokens from previous turns, so the turns add up rather than restating each other. See + the Live API note under https://cloud.google.com/vertex-ai/generative-ai/pricing. + Args: websocket_messages: List of WebSocket messages from the Live API Returns: Dictionary containing aggregated usage metadata, or None if not found """ - all_usage_metadata: Final = [] + snapshots: Final = tuple( + metadata + for message in websocket_messages + if isinstance(message, Mapping) + for metadata in (message.get("usageMetadata"),) + if isinstance(metadata, dict) + ) - # Collect all usage metadata messages - for message in websocket_messages: - if isinstance(message, dict) and "usageMetadata" in message: - all_usage_metadata.append(message["usageMetadata"]) - - if not all_usage_metadata: + if not snapshots: return None - # If only one usage metadata, return it as-is - if len(all_usage_metadata) == 1: - return all_usage_metadata[0] - - # Aggregate multiple usage metadata messages - aggregated: Final[dict[str, Any]] = { - "promptTokenCount": 0, - "candidatesTokenCount": 0, - "totalTokenCount": 0, - "promptTokensDetails": [], - "candidatesTokensDetails": [], + prompt_totals: Final = VertexAILivePassthroughLoggingHandler._merged_modality_totals( + snapshots, "promptTokenCount", "promptTokensDetails" + ) + candidate_totals: Final = VertexAILivePassthroughLoggingHandler._merged_modality_totals( + snapshots, "candidatesTokenCount", "candidatesTokensDetails" + ) + return { + **{key: value for key, value in snapshots[0].items() if key not in _AGGREGATED_FIELDS}, + "promptTokenCount": sum(snapshot.get("promptTokenCount", 0) for snapshot in snapshots), + "candidatesTokenCount": sum(snapshot.get("candidatesTokenCount", 0) for snapshot in snapshots), + "totalTokenCount": sum(snapshot.get("totalTokenCount", 0) for snapshot in snapshots), + "toolUsePromptTokenCount": sum(snapshot.get("toolUsePromptTokenCount", 0) for snapshot in snapshots), + "promptTokensDetails": [ + {"modality": modality, "tokenCount": count} for modality, count in prompt_totals.items() if count > 0 + ], + "candidatesTokensDetails": [ + {"modality": modality, "tokenCount": count} for modality, count in candidate_totals.items() if count > 0 + ], } - # Aggregate token counts - for usage in all_usage_metadata: - aggregated["promptTokenCount"] += usage.get("promptTokenCount", 0) - aggregated["candidatesTokenCount"] += usage.get("candidatesTokenCount", 0) - aggregated["totalTokenCount"] += usage.get("totalTokenCount", 0) - - # Aggregate token details by modality - modality_totals: Final = {} - - for usage in all_usage_metadata: - # Process prompt tokens details - for detail in usage.get("promptTokensDetails", []): - modality = detail.get("modality", "TEXT") - token_count = detail.get("tokenCount", 0) - - if modality not in modality_totals: - modality_totals[modality] = {"prompt": 0, "candidate": 0} - modality_totals[modality]["prompt"] += token_count - - # Process candidate tokens details - for detail in usage.get("candidatesTokensDetails", []): - modality = detail.get("modality", "TEXT") - token_count = detail.get("tokenCount", 0) - - if modality not in modality_totals: - modality_totals[modality] = {"prompt": 0, "candidate": 0} - modality_totals[modality]["candidate"] += token_count - - # Convert aggregated modality totals back to details format - for modality, totals in modality_totals.items(): - if totals["prompt"] > 0: - aggregated["promptTokensDetails"].append({"modality": modality, "tokenCount": totals["prompt"]}) - if totals["candidate"] > 0: - aggregated["candidatesTokensDetails"].append({"modality": modality, "tokenCount": totals["candidate"]}) - - # Add any additional fields from the first usage metadata - first_usage: Final = all_usage_metadata[0] - for key, value in first_usage.items(): - if key not in aggregated: - aggregated[key] = value - - return aggregated - - @staticmethod - def _calculate_live_api_cost( - model: str, - usage_metadata: dict, - custom_llm_provider: str = "vertex_ai", - ) -> float: - """ - Calculate cost for Vertex AI Live API based on usage metadata. - - Args: - model: The model name (e.g., "gemini-2.0-flash-live-preview-04-09") - usage_metadata: Usage metadata from the Live API response - custom_llm_provider: The LLM provider (default: "vertex_ai") - - Returns: - Total cost in USD - """ - try: - # Get model pricing information - model_info: Final = get_model_info(model=model, custom_llm_provider=custom_llm_provider) - - verbose_proxy_logger.debug("Vertex AI Live API model info for '%s': %s", model, model_info) - - # Check if pricing info is available - if not model_info or not model_info.get("input_cost_per_token"): - verbose_proxy_logger.error("No pricing info found for %s in local model pricing database", model) - return 0.0 - - total_cost = 0.0 - - # Extract token counts from usage metadata - prompt_token_count: Final = usage_metadata.get("promptTokenCount", 0) - candidates_token_count: Final = usage_metadata.get("candidatesTokenCount", 0) - - # Calculate base text token costs - input_cost_per_token: Final = model_info.get("input_cost_per_token", 0.0) - output_cost_per_token: Final = model_info.get("output_cost_per_token", 0.0) - - total_cost += prompt_token_count * input_cost_per_token - total_cost += candidates_token_count * output_cost_per_token - - # Handle modality-specific costs if present - prompt_tokens_details: Final = usage_metadata.get("promptTokensDetails", []) - candidates_tokens_details: Final = usage_metadata.get("candidatesTokensDetails", []) - - # Process prompt tokens by modality - for detail in prompt_tokens_details: - modality = detail.get("modality", "TEXT") - token_count = detail.get("tokenCount", 0) - - if modality == "AUDIO": - audio_cost_per_token = model_info.get("input_cost_per_audio_token", 0.0) - total_cost += token_count * audio_cost_per_token - elif modality == "VIDEO": - # Video tokens are typically per second, but we'll treat as per token for now - video_cost_per_token = model_info.get("input_cost_per_video_per_second", 0.0) - total_cost += token_count * video_cost_per_token - # TEXT tokens are already handled above - - # Process candidate tokens by modality - for detail in candidates_tokens_details: - modality = detail.get("modality", "TEXT") - token_count = detail.get("tokenCount", 0) - - if modality == "AUDIO": - audio_cost_per_token = model_info.get("output_cost_per_audio_token", 0.0) - total_cost += token_count * audio_cost_per_token - elif modality == "VIDEO": - # Video tokens are typically per second, but we'll treat as per token for now - video_cost_per_token = model_info.get("output_cost_per_video_per_second", 0.0) - total_cost += token_count * video_cost_per_token - # TEXT tokens are already handled above - - # Handle web search costs if present - tool_use_prompt_token_count: Final = usage_metadata.get("toolUsePromptTokenCount", 0) - if tool_use_prompt_token_count > 0: - # Web search typically has a fixed cost per request - web_search_cost: Final = model_info.get("web_search_cost_per_request", 0.0) - if isinstance(web_search_cost, (int, float)) and web_search_cost > 0: - total_cost += web_search_cost - else: - # Fallback to token-based pricing for tool use - total_cost += tool_use_prompt_token_count * input_cost_per_token - - verbose_proxy_logger.debug( - f"Vertex AI Live API cost calculation - Model: {model}, " - f"Prompt tokens: {prompt_token_count}, " - f"Candidate tokens: {candidates_token_count}, " - f"Total cost: ${total_cost:.6f}" - ) - - return total_cost - - except Exception as e: - verbose_proxy_logger.error("Error calculating Vertex AI Live API cost: %s", e) - return 0.0 - @staticmethod def _create_usage_object_from_metadata( usage_metadata: dict, model: str, + grounding_requests: GroundingRequests = _NO_GROUNDING, ) -> Usage: """ Create a LiteLLM Usage object from Live API usage metadata. @@ -235,48 +246,124 @@ class VertexAILivePassthroughLoggingHandler(BasePassthroughLoggingHandler): Args: usage_metadata: Usage metadata from the Live API response model: The model name + grounding_requests: The Search and Maps grounding requests summed over the session's + turns, matching the per-turn charge Returns: LiteLLM Usage object """ - prompt_tokens: Final = usage_metadata.get("promptTokenCount", 0) - completion_tokens: Final = usage_metadata.get("candidatesTokenCount", 0) - total_tokens: Final = usage_metadata.get("totalTokenCount", 0) + prompt_by_modality: Final = VertexAILivePassthroughLoggingHandler._sum_by_modality( + VertexAILivePassthroughLoggingHandler._resolve_detail_counts( + _detail_entries(usage_metadata.get("promptTokensDetails")), usage_metadata.get("promptTokenCount") + ) + ) + candidates_by_modality: Final = VertexAILivePassthroughLoggingHandler._sum_by_modality( + VertexAILivePassthroughLoggingHandler._resolve_detail_counts( + _detail_entries(usage_metadata.get("candidatesTokensDetails")), + usage_metadata.get("candidatesTokenCount"), + ) + ) - # Create modality-specific token details if available - prompt_tokens_details: Final = usage_metadata.get("promptTokensDetails", []) - candidates_tokens_details: Final = usage_metadata.get("candidatesTokensDetails", []) - - # Extract text tokens from details - text_prompt_tokens = 0 - text_completion_tokens = 0 - - for detail in prompt_tokens_details: - if detail.get("modality") == "TEXT": - text_prompt_tokens = detail.get("tokenCount", 0) - break - - for detail in candidates_tokens_details: - if detail.get("modality") == "TEXT": - text_completion_tokens = detail.get("tokenCount", 0) - break - - # If no text tokens found in details, use total counts - if text_prompt_tokens == 0: - text_prompt_tokens = prompt_tokens - if text_completion_tokens == 0: - text_completion_tokens = completion_tokens + prompt_tokens: Final = usage_metadata.get("promptTokenCount", 0) or sum(prompt_by_modality.values()) + completion_tokens: Final = usage_metadata.get("candidatesTokenCount", 0) or sum(candidates_by_modality.values()) return Usage( - prompt_tokens=text_prompt_tokens, - completion_tokens=text_completion_tokens, - total_tokens=total_tokens, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=usage_metadata.get("totalTokenCount", 0) or (prompt_tokens + completion_tokens), + prompt_tokens_details=PromptTokensDetailsWrapper( + text_tokens=prompt_by_modality.get("TEXT"), + audio_tokens=prompt_by_modality.get("AUDIO"), + image_tokens=prompt_by_modality.get("IMAGE"), + video_tokens=prompt_by_modality.get("VIDEO"), + tool_use_tokens=usage_metadata.get("toolUsePromptTokenCount") or None, + web_search_requests=grounding_requests.web_search_requests, + google_maps_grounding_requests=grounding_requests.google_maps_grounding_requests, + ), + completion_tokens_details=CompletionTokensDetailsWrapper( + text_tokens=candidates_by_modality.get("TEXT"), + audio_tokens=candidates_by_modality.get("AUDIO"), + image_tokens=candidates_by_modality.get("IMAGE"), + video_tokens=candidates_by_modality.get("VIDEO"), + ), ) + def _session_usage(self, websocket_messages: Sequence[object], model: str) -> Usage | None: + usage_metadata: Final = self._extract_usage_metadata_from_websocket_messages(websocket_messages) + if usage_metadata is None: + return None + return self._create_usage_object_from_metadata( + usage_metadata=usage_metadata, + grounding_requests=_session_grounding_requests(websocket_messages), + model=model, + ) + + def _turn_cost( + self, + turn: Sequence[object], + model: str, + logging_obj: LiteLLMLoggingObj, + ) -> tuple[float, CostBreakdown] | None: + usage: Final = self._session_usage(turn, model) + if usage is None: + return None + cost: Final = logging_obj._response_cost_calculator( # pyright: ignore[reportPrivateUsage] # the call's own calculator keeps custom pricing and the deployment's region in step with the spend row + result=ModelResponse(model=model, usage=usage), + litellm_model_name=model, + ) + if cost is None: + return None + breakdown: Final = logging_obj.cost_breakdown + return None if breakdown is None else (cost, breakdown) + + def _session_cost( + self, + websocket_messages: Sequence[object], + model: str, + logging_obj: LiteLLMLoggingObj, + ) -> float | None: + """Price each turn on its own tokens and grounding, so two grounded turns pay the query fee twice. + + The fixed cost margin is a flat per-request fee, so the session's single spend row carries it once + rather than once per turn. + """ + turn_costs: Final = tuple(self._turn_cost(turn, model, logging_obj) for turn in _turns(websocket_messages)) + priced: Final = tuple(turn_cost for turn_cost in turn_costs if turn_cost is not None) + if not priced or len(priced) != len(turn_costs): + return None + breakdowns: Final = tuple(breakdown for _, breakdown in priced) + first: Final = breakdowns[0] + fixed_margin: Final = first.get("margin_fixed_amount") or 0.0 + duplicated_fixed_margin: Final = fixed_margin * (len(priced) - 1) + total_cost: Final = sum(cost for cost, _ in priced) - duplicated_fixed_margin + summed_margin_total: Final = _summed(breakdowns, "margin_total_amount") + margin_total_amount: Final = ( + None if summed_margin_total is None else summed_margin_total - duplicated_fixed_margin + ) + logging_obj.set_cost_breakdown( + input_cost=_summed(breakdowns, "input_cost") or 0.0, + output_cost=_summed(breakdowns, "output_cost") or 0.0, + total_cost=total_cost, + cost_for_built_in_tools_cost_usd_dollar=_summed(breakdowns, "tool_usage_cost") or 0.0, + original_cost=_summed(breakdowns, "original_cost"), + discount_percent=first.get("discount_percent"), + discount_amount=_summed(breakdowns, "discount_amount"), + margin_percent=first.get("margin_percent"), + margin_fixed_amount=first.get("margin_fixed_amount"), + margin_total_amount=margin_total_amount, + cache_read_cost=_summed(breakdowns, "cache_read_cost"), + cache_creation_cost=_summed(breakdowns, "cache_creation_cost"), + reasoning_cost=_summed(breakdowns, "reasoning_cost"), + service_tier=first.get("service_tier"), + data_residency=first.get("data_residency"), + vertex_location=first.get("vertex_location"), + ) + return total_cost + def vertex_ai_live_passthrough_handler( self, - websocket_messages: list[dict], - logging_obj, + websocket_messages: Sequence[object], + logging_obj: LiteLLMLoggingObj, url_route: str, start_time: datetime, end_time: datetime, @@ -300,34 +387,25 @@ class VertexAILivePassthroughLoggingHandler(BasePassthroughLoggingHandler): """ try: # Extract model from request body or kwargs - model: Final = kwargs.get("model", "gemini-2.0-flash-live-preview-04-09") + requested_model: Final = kwargs.get("model") + model: Final = ( + requested_model if isinstance(requested_model, str) else "gemini-2.0-flash-live-preview-04-09" + ) custom_llm_provider: Final = kwargs.get("custom_llm_provider", "vertex_ai") verbose_proxy_logger.debug( "Vertex AI Live API model: %s, custom_llm_provider: %s", model, custom_llm_provider ) - # Extract usage metadata from WebSocket messages - usage_metadata: Final = self._extract_usage_metadata_from_websocket_messages(websocket_messages) + usage: Final = self._session_usage(websocket_messages, model) - if not usage_metadata: + if usage is None: verbose_proxy_logger.warning("No usage metadata found in Vertex AI Live API WebSocket messages") return { "result": None, "kwargs": kwargs, } - # Calculate cost using Live API specific pricing - response_cost: Final = self._calculate_live_api_cost( - model=model, - usage_metadata=usage_metadata, - custom_llm_provider=custom_llm_provider, - ) - - # Create Usage object for standard LiteLLM logging - usage: Final = self._create_usage_object_from_metadata( - usage_metadata=usage_metadata, - model=model, - ) + response_cost: Final = self._session_cost(websocket_messages, model, logging_obj) # Create a mock ModelResponse for standard logging litellm_model_response: Final = ModelResponse( @@ -338,9 +416,9 @@ class VertexAILivePassthroughLoggingHandler(BasePassthroughLoggingHandler): usage=usage, choices=[], ) + if response_cost is not None: + litellm_model_response._hidden_params["response_cost"] = response_cost # pyright: ignore[reportPrivateUsage] # the logger reads the cost off the response's hidden params; the constructor's hidden_params kwarg is reset by pydantic - # Update kwargs with cost information - kwargs["response_cost"] = response_cost kwargs["model"] = model kwargs["custom_llm_provider"] = custom_llm_provider @@ -348,12 +426,15 @@ class VertexAILivePassthroughLoggingHandler(BasePassthroughLoggingHandler): import re allowed_pattern: Final = re.compile(r"^[A-Za-z0-9._\-:]+$") - safe_model: Final = model if isinstance(model, str) and allowed_pattern.match(model) else "[REDACTED]" + safe_model: Final = model if allowed_pattern.match(model) else "[REDACTED]" verbose_proxy_logger.debug( - f"Vertex AI Live API passthrough cost tracking - " - f"Model: {safe_model}, Cost: ${response_cost:.6f}, " - f"Prompt tokens: {usage.prompt_tokens}, " - f"Completion tokens: {usage.completion_tokens}" + "Vertex AI Live API passthrough cost tracking - Model: %s, " + "Prompt tokens: %s %s, Completion tokens: %s %s", + safe_model, + usage.prompt_tokens, + usage.prompt_tokens_details, + usage.completion_tokens, + usage.completion_tokens_details, ) return { diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index ea4ede7e513..b66c295d1aa 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -2090,6 +2090,22 @@ def _rewrite_vertex_live_setup_model(text_data: str, setup_model_rewriter: Calla return json.dumps({**message, "setup": {**setup, "model": rewritten_model}}) # mutable-ok: one-shot json payload +def _resolved_vertex_live_setup( + setup_data: Mapping[str, object], setup_model_rewriter: Callable[[str], str] | None +) -> Mapping[str, object]: + """ + Give the model extractor the same fully qualified path the upstream will receive. + + Clients may name a bare gateway alias, which the rewriter turns into a ``projects/...`` path before + it reaches Vertex. The extractor only reads a path containing ``/models/``, so running it on the raw + frame logs the session as ``unknown`` at no cost, which is precisely the supported client form + """ + setup_model: Final = setup_data.get("model") + if setup_model_rewriter is None or not isinstance(setup_model, str): + return setup_data + return {**setup_data, "model": setup_model_rewriter(setup_model)} + + def _truncated_close_reason(reason: str) -> str: """ Fit a close reason inside the byte budget a WebSocket close frame allows, without splitting a character @@ -2314,7 +2330,9 @@ async def websocket_passthrough_request( setup_data, ) if isinstance(setup_data, dict) and "model" in setup_data: - extracted_model = _extract_model_from_vertex_ai_setup(setup_data) + extracted_model = _extract_model_from_vertex_ai_setup( + _resolved_vertex_live_setup(setup_data, setup_model_rewriter) + ) if extracted_model: kwargs["model"] = extracted_model kwargs["custom_llm_provider"] = "vertex_ai-language-models" diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py index 4415b0cbbbb..01fb6cb483d 100644 --- a/litellm/responses/litellm_completion_transformation/transformation.py +++ b/litellm/responses/litellm_completion_transformation/transformation.py @@ -2831,6 +2831,22 @@ class LiteLLMCompletionResponsesConfig: if cache_write_tokens is not None else MappingProxyType({}) ) + # The cost path reads the grounding counters off the input details, and a realtime + # session's usage is rebuilt from its own response.done, so dropping them here bills + # no per-query grounding fee at all. + grounding_request_counts: Final[Mapping[str, int]] = MappingProxyType( + { + counter: count + for counter, count in ( + ("web_search_requests", getattr(prompt_details, "web_search_requests", None)), + ( + "google_maps_grounding_requests", + getattr(prompt_details, "google_maps_grounding_requests", None), + ), + ) + if count is not None + } + ) response_usage.input_tokens_details = InputTokensDetails( cached_tokens=prompt_details.cached_tokens if prompt_details.cached_tokens is not None else 0, text_tokens=prompt_details.text_tokens, @@ -2839,6 +2855,7 @@ class LiteLLMCompletionResponsesConfig: cached_tokens_details if isinstance(cached_tokens_details, CachedTokensDetails) else None ), **cache_write_extra, + **grounding_request_counts, ) # Translate completion_tokens_details to output_tokens_details diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py index d63e3ddf0aa..41a3ded7022 100644 --- a/litellm/responses/utils.py +++ b/litellm/responses/utils.py @@ -1183,6 +1183,10 @@ class ResponseAPILoggingUtils: response_api_usage.input_tokens_details, "cached_tokens_details", None ), cache_write_tokens=getattr(response_api_usage.input_tokens_details, "cache_write_tokens", None), + web_search_requests=getattr(response_api_usage.input_tokens_details, "web_search_requests", None), + google_maps_grounding_requests=getattr( + response_api_usage.input_tokens_details, "google_maps_grounding_requests", None + ), ) completion_tokens_details: CompletionTokensDetailsWrapper | None = None output_tokens_details: Final[OutputTokensDetails | None] = getattr( diff --git a/tests/pass_through_unit_tests/test_vertex_ai_live_passthrough.py b/tests/pass_through_unit_tests/test_vertex_ai_live_passthrough.py index e2eb6d0b68b..8b3dc436b8f 100644 --- a/tests/pass_through_unit_tests/test_vertex_ai_live_passthrough.py +++ b/tests/pass_through_unit_tests/test_vertex_ai_live_passthrough.py @@ -6,12 +6,15 @@ including the logging handler, cost tracking, and WebSocket message processing. """ import json +from collections.abc import Sequence from datetime import datetime from unittest.mock import AsyncMock, Mock, patch, MagicMock from typing import Dict, List, Any, Optional import pytest import httpx +import litellm +from typing_extensions import NotRequired, ReadOnly, TypedDict # Add the parent directory to the system path @@ -22,10 +25,16 @@ from litellm.proxy.pass_through_endpoints.success_handler import ( PassThroughEndpointLogging, ) from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj -from litellm.types.utils import LlmProviders +from litellm.types.utils import CostBreakdown, LlmProviders, Usage from litellm.proxy._types import UserAPIKeyAuth +class _LiveTurn(TypedDict): + prompt: ReadOnly[tuple[int, int]] + candidates: ReadOnly[tuple[int, int]] + candidate_audio_token_count_missing: NotRequired[ReadOnly[bool]] + + class TestVertexAILivePassthroughLoggingHandler: """Test the Vertex AI Live Passthrough Logging Handler""" @@ -39,6 +48,7 @@ class TestVertexAILivePassthroughLoggingHandler: """Create a mock logging object""" mock = MagicMock(spec=LiteLLMLoggingObj) mock.model_call_details = {} + mock._response_cost_calculator.return_value = None return mock @pytest.fixture @@ -201,88 +211,490 @@ class TestVertexAILivePassthroughLoggingHandler: assert text_prompt["tokenCount"] == 10 assert audio_prompt["tokenCount"] == 10 - @patch( - "litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_ai_live_passthrough_logging_handler.get_model_info" - ) - def test_calculate_cost_basic(self, mock_get_model_info, handler): - """Test basic cost calculation""" - mock_get_model_info.return_value = { - "input_cost_per_token": 0.000001, - "output_cost_per_token": 0.000002, - } + def test_usage_carries_every_modality(self, handler): + """Regression: the Usage object reported only TEXT, so audio and image billed as nothing. + prompt_tokens must be the full count and the details must name each modality, + because the cost calculator prices audio and image from *_tokens_details. + """ usage_metadata = { - "promptTokenCount": 100, - "candidatesTokenCount": 50, - "totalTokenCount": 150, - } - - cost = handler._calculate_live_api_cost("gemini-1.5-pro", usage_metadata) - - # The cost calculation may include additional factors, so we check it's reasonable - expected_min_cost = (100 * 0.000001) + (50 * 0.000002) - assert cost >= expected_min_cost - assert cost > 0 - - @patch( - "litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_ai_live_passthrough_logging_handler.get_model_info" - ) - def test_calculate_cost_with_audio(self, mock_get_model_info, handler): - """Test cost calculation with audio tokens""" - mock_get_model_info.return_value = { - "input_cost_per_token": 0.000001, - "output_cost_per_token": 0.000002, - "input_cost_per_audio_token": 0.0001, - "output_cost_per_audio_token": 0.0002, - } - - usage_metadata = { - "promptTokenCount": 100, - "candidatesTokenCount": 50, - "totalTokenCount": 150, + "promptTokenCount": 1300, + "candidatesTokenCount": 124, + "totalTokenCount": 1424, "promptTokensDetails": [ - {"modality": "TEXT", "tokenCount": 80}, - {"modality": "AUDIO", "tokenCount": 20}, + {"modality": "TEXT", "tokenCount": 13}, + {"modality": "AUDIO", "tokenCount": 127}, + {"modality": "IMAGE", "tokenCount": 1160}, ], "candidatesTokensDetails": [ - {"modality": "TEXT", "tokenCount": 30}, - {"modality": "AUDIO", "tokenCount": 20}, + {"modality": "TEXT", "tokenCount": 29}, + {"modality": "AUDIO", "tokenCount": 95}, ], } - cost = handler._calculate_live_api_cost("gemini-1.5-pro", usage_metadata) + usage = handler._create_usage_object_from_metadata( + usage_metadata=usage_metadata, model="gemini-live-2.5-flash" + ) - # Should include both text and audio costs - assert cost > 0 - assert cost > (100 * 0.000001) + ( - 50 * 0.000002 - ) # Should be higher due to audio + assert usage.prompt_tokens == 1300, "the full prompt count must survive, not just its text share" + assert usage.completion_tokens == 124 + assert usage.prompt_tokens_details.text_tokens == 13 + assert usage.prompt_tokens_details.audio_tokens == 127 + assert usage.prompt_tokens_details.image_tokens == 1160 + assert usage.completion_tokens_details.text_tokens == 29 + assert usage.completion_tokens_details.audio_tokens == 95 - @patch( - "litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_ai_live_passthrough_logging_handler.get_model_info" + def test_usage_sums_repeated_modality_entries(self, handler): + """A modality can appear more than once across aggregated turns; sum, don't overwrite.""" + usage = handler._create_usage_object_from_metadata( + usage_metadata={ + "promptTokenCount": 40, + "candidatesTokenCount": 0, + "promptTokensDetails": [ + {"modality": "IMAGE", "tokenCount": 10}, + {"modality": "IMAGE", "tokenCount": 25}, + {"modality": "TEXT", "tokenCount": 5}, + ], + }, + model="gemini-live-2.5-flash", + ) + assert usage.prompt_tokens_details.image_tokens == 35 + assert usage.prompt_tokens_details.text_tokens == 5 + + NATIVE_AUDIO_MODEL = "gemini-live-2.5-flash-preview-native-audio-09-2025" + + # A four-turn native-audio session. Google charges per turn for the whole session context + # window, so the prompt side repeats the accumulated audio while the candidates side reports + # only that turn's own response. The last turn names AUDIO and omits its tokenCount, which is + # the shape Live really emits at the end of a spoken answer. + AUDIO_SESSION: tuple[_LiveTurn, ...] = ( + {"prompt": (14, 122), "candidates": (8, 20)}, + {"prompt": (21, 182), "candidates": (5, 50)}, + {"prompt": (24, 203), "candidates": (13, 27)}, + {"prompt": (24, 203), "candidates": (0, 3), "candidate_audio_token_count_missing": True}, ) - def test_calculate_cost_with_web_search(self, mock_get_model_info, handler): - """Test cost calculation with web search (tool use)""" - mock_get_model_info.return_value = { - "input_cost_per_token": 0.000001, - "output_cost_per_token": 0.000002, - "web_search_cost_per_request": 0.01, - } - usage_metadata = { - "promptTokenCount": 100, - "candidatesTokenCount": 50, - "totalTokenCount": 150, - "toolUsePromptTokenCount": 10, - } + @staticmethod + def _live_messages(turns: Sequence[_LiveTurn]) -> list[dict[str, object]]: + """Wrap (text, audio) prompt/candidate pairs as the server messages a Live session emits.""" + return [{"type": "session.created", "session": {"id": "s"}}] + [ + { + "type": "response.done", + "usageMetadata": { + "promptTokenCount": sum(turn["prompt"]), + "candidatesTokenCount": sum(turn["candidates"]), + "totalTokenCount": sum(turn["prompt"]) + sum(turn["candidates"]), + "promptTokensDetails": [ + {"modality": "TEXT", "tokenCount": turn["prompt"][0]}, + {"modality": "AUDIO", "tokenCount": turn["prompt"][1]}, + ], + "candidatesTokensDetails": ( + [{"modality": "AUDIO"}] + if turn.get("candidate_audio_token_count_missing") + else [ + {"modality": "TEXT", "tokenCount": turn["candidates"][0]}, + {"modality": "AUDIO", "tokenCount": turn["candidates"][1]}, + ] + ), + }, + } + for turn in turns + ] - cost = handler._calculate_live_api_cost("gemini-1.5-pro", usage_metadata) + @staticmethod + def _session_usage( + handler: VertexAILivePassthroughLoggingHandler, + mock_logging_obj: MagicMock, + messages: list[dict[str, object]], + model: str, + ) -> Usage: + result = handler.vertex_ai_live_passthrough_handler( + websocket_messages=messages, + logging_obj=mock_logging_obj, + url_route="/vertex_ai/live", + start_time=datetime.now(), + end_time=datetime.now(), + request_body={}, + model=model, + ) + assert result["result"] is not None, "the handler must produce a usage-bearing response to bill" + return result["result"].usage - # Should include web search cost - expected_base_cost = (100 * 0.000001) + (50 * 0.000002) - # The web search cost might be handled differently, so just check it's reasonable - assert cost >= expected_base_cost - assert cost > 0 + @classmethod + def _session_cost( + cls, + handler: VertexAILivePassthroughLoggingHandler, + mock_logging_obj: MagicMock, + messages: list[dict[str, object]], + model: str, + ) -> float: + from litellm.cost_calculator import completion_cost + from litellm.types.utils import ModelResponse + + usage = cls._session_usage(handler, mock_logging_obj, messages, model) + return completion_cost( + completion_response=ModelResponse( + id="x", object="chat.completion", created=0, model=model, usage=usage, choices=[] + ), + model=f"vertex_ai/{model}", + custom_llm_provider="vertex_ai", + call_type="acompletion", + ) + + @classmethod + def _expected_session_cost(cls, turns: Sequence[_LiveTurn]) -> float: + from litellm.utils import get_model_info + + info = get_model_info(model=cls.NATIVE_AUDIO_MODEL, custom_llm_provider="vertex_ai") + return ( + sum(turn["prompt"][0] for turn in turns) * info["input_cost_per_token"] + + sum(turn["prompt"][1] for turn in turns) * info["input_cost_per_audio_token"] + + sum(turn["candidates"][0] for turn in turns) * info["output_cost_per_token"] + + sum(turn["candidates"][1] for turn in turns) * info["output_cost_per_audio_token"] + ) + + def test_every_turn_of_a_session_is_billed(self, handler, mock_logging_obj): + """Google charges per turn for the whole context window, so every turn adds to the bill. + + Billing one snapshot instead gives away all the other turns: on this session the + largest single turn is well under the session total, and its share of the audio is + priced 6x the text rate, so the gap is money rather than rounding. + """ + turns = self.AUDIO_SESSION[:3] + cost = self._session_cost(handler, mock_logging_obj, self._live_messages(turns), self.NATIVE_AUDIO_MODEL) + + assert cost == pytest.approx(self._expected_session_cost(turns), rel=1e-9) + widest_single_turn = max(self._expected_session_cost([turn]) for turn in turns) + assert cost > widest_single_turn, "billing one snapshot drops every other turn of the session" + + def test_audio_named_without_a_token_count_bills_at_the_audio_rate(self, handler, mock_logging_obj): + """Live can name the modality carrying the rest of a turn and omit its tokenCount. + + Reading the absent key as zero left those tokens inside candidatesTokenCount but outside + the breakdown, so the calculator charged real speech at the text output rate. At this + entry's rates the last turn's 3 audio tokens are $0.0000360 rather than $0.0000060. + """ + turns = self.AUDIO_SESSION + usage = self._session_usage(handler, mock_logging_obj, self._live_messages(turns), self.NATIVE_AUDIO_MODEL) + + assert usage.completion_tokens_details.audio_tokens == 100, "the unpriced entry takes the turn's residual" + assert usage.completion_tokens_details.text_tokens == 26 + assert usage.completion_tokens == 126 + + cost = self._session_cost(handler, mock_logging_obj, self._live_messages(turns), self.NATIVE_AUDIO_MODEL) + assert cost == pytest.approx(self._expected_session_cost(turns), rel=1e-9) + + TOOL_USE_PER_TURN = (100, 250, 400) + + def _grounded_messages(self): + """The three-turn session again, with each turn's own toolUsePromptTokenCount attached.""" + messages = self._live_messages(self.AUDIO_SESSION[:3]) + head, turns = messages[0], messages[1:] + return [head] + [ + {**message, "usageMetadata": {**message["usageMetadata"], "toolUsePromptTokenCount": tool_use}} + for message, tool_use in zip(turns, self.TOOL_USE_PER_TURN) + ] + + def test_server_side_tool_use_prompt_tokens_are_summed_over_the_session(self, handler, mock_logging_obj): + """toolUsePromptTokenCount rode the unknown-key pass-through, so it took the first turn only. + + Every other total beside it is summed across the session, and the first turn is the + smallest number in the series, so a grounded session logged far fewer tool-use tokens + than it used. This session's turns are deliberately distinct, so 750 can only come from + summing: first-turn selection gives 100, last-turn or max gives 400. + """ + grounded = self._grounded_messages() + + usage = self._session_usage(handler, mock_logging_obj, grounded, self.NATIVE_AUDIO_MODEL) + assert usage.prompt_tokens_details.tool_use_tokens == sum(self.TOOL_USE_PER_TURN) + + @staticmethod + def _grounding_frame(metadata: dict[str, object]) -> dict[str, object]: + """One server frame carrying grounding metadata, the way Live reports it.""" + return {"type": "response.done", "serverContent": {"groundingMetadata": metadata}} + + def test_web_grounding_is_counted_so_it_can_be_billed(self, handler, mock_logging_obj): + """Live reports grounding in the server frames and never in usageMetadata. + + Nothing read those frames, so web_search_requests stayed unset and the cost path's only + trigger for the per-query grounding charge never fired. Google bills a grounded Live + prompt on top of its tokens, so the whole fee was missing from the bill. + """ + messages = [ + self._grounding_frame( + { + "webSearchQueries": ["who won the 2026 world cup final"], + "groundingChunks": [{"web": {"uri": "https://example.com"}}], + } + ), + *self._live_messages(self.AUDIO_SESSION[:1]), + ] + + usage = self._session_usage(handler, mock_logging_obj, messages, self.NATIVE_AUDIO_MODEL) + + assert usage.prompt_tokens_details.web_search_requests == 1, "a grounded turn must report its query" + assert getattr(usage.prompt_tokens_details, "google_maps_grounding_requests", None) is None + + def test_maps_grounding_is_counted_under_its_own_sku(self, handler, mock_logging_obj): + """Maps grounding is a separate SKU from web search, so it needs its own counter. + + A maps-only turn carries grounding chunks but no webSearchQueries, so counting queries + alone would report nothing and bill nothing. + """ + messages = [ + self._grounding_frame({"groundingChunks": [{"maps": {"placeId": "abc123"}}]}), + *self._live_messages(self.AUDIO_SESSION[:1]), + ] + + usage = self._session_usage(handler, mock_logging_obj, messages, self.NATIVE_AUDIO_MODEL) + + assert usage.prompt_tokens_details.google_maps_grounding_requests == 1 + assert getattr(usage.prompt_tokens_details, "web_search_requests", None) is None + + def test_an_ungrounded_session_reports_no_grounding(self, handler, mock_logging_obj): + """The counters must stay absent when no tool ran, or every session pays a grounding fee.""" + usage = self._session_usage( + handler, mock_logging_obj, self._live_messages(self.AUDIO_SESSION[:1]), self.NATIVE_AUDIO_MODEL + ) + + assert getattr(usage.prompt_tokens_details, "web_search_requests", None) is None + assert getattr(usage.prompt_tokens_details, "google_maps_grounding_requests", None) is None + + def test_grounding_adds_its_query_fee_to_the_session_bill(self, handler, mock_logging_obj): + """The counter only matters if it reaches the bill, so assert against the cost, not the field. + + Same tokens either way: the difference between the two sessions is the grounding fee alone. + """ + turns = self.AUDIO_SESSION[:1] + plain = self._session_cost(handler, mock_logging_obj, self._live_messages(turns), self.NATIVE_AUDIO_MODEL) + grounded = self._session_cost( + handler, + mock_logging_obj, + [self._grounding_frame({"webSearchQueries": ["q"]}), *self._live_messages(turns)], + self.NATIVE_AUDIO_MODEL, + ) + + assert grounded > plain, "a grounded session must cost more than the same tokens ungrounded" + + def _priced_logging_obj(self) -> LiteLLMLoggingObj: + """A real logging object, since the session's price is handed to it turn by turn.""" + logging_obj = LiteLLMLoggingObj( + model=self.NATIVE_AUDIO_MODEL, + messages=[], + stream=True, + call_type="pass_through_endpoint", + start_time=datetime.now(), + litellm_call_id="live-session", + function_id="live", + ) + logging_obj.update_environment_variables( + model=self.NATIVE_AUDIO_MODEL, + user="u", + optional_params={}, + litellm_params={}, + call_type="pass_through_endpoint", + ) + logging_obj.model_call_details["custom_llm_provider"] = "vertex_ai" + return logging_obj + + def _billed_session( + self, handler: VertexAILivePassthroughLoggingHandler, messages: list[dict[str, object]] + ) -> tuple[float, CostBreakdown]: + logging_obj = self._priced_logging_obj() + result = handler.vertex_ai_live_passthrough_handler( + websocket_messages=messages, + logging_obj=logging_obj, + url_route="/vertex_ai/live", + start_time=datetime.now(), + end_time=datetime.now(), + request_body={}, + model=self.NATIVE_AUDIO_MODEL, + custom_llm_provider="vertex_ai", + ) + assert result["result"] is not None, "the handler must produce a usage-bearing response to bill" + assert logging_obj.cost_breakdown is not None, "the session's price must reach the logging object" + return result["result"]._hidden_params["response_cost"], logging_obj.cost_breakdown + + def test_each_grounded_turn_pays_its_own_query_fee(self, handler): + """Google charges the grounding fee per grounded prompt, not per session. + + Summing the session into one usage collapsed two grounded turns into one query, so the + second question was answered for free. The bill now grows by one fee per grounded turn. + """ + head, turn = self._live_messages(self.AUDIO_SESSION[:1]) + grounding = self._grounding_frame({"webSearchQueries": ["q"]}) + + plain_cost, _ = self._billed_session(handler, [head, turn, turn]) + one_cost, one_breakdown = self._billed_session(handler, [head, grounding, turn, turn]) + two_cost, two_breakdown = self._billed_session(handler, [head, grounding, turn, grounding, turn]) + + fee = one_cost - plain_cost + assert fee > 0, "a grounded turn must cost more than the same tokens ungrounded" + assert two_cost - plain_cost == pytest.approx(2 * fee), "two grounded turns must pay the fee twice" + assert two_breakdown["total_cost"] == pytest.approx(two_cost) + assert two_breakdown["tool_usage_cost"] == pytest.approx(2 * one_breakdown["tool_usage_cost"]) + + def test_a_query_repeated_across_turns_is_reported_once_per_turn(self, handler): + """The reported query count must agree with the bill, which charges every grounded turn. + + The session usage collapsed duplicate query strings across turns while the price was + per turn, so two turns asking the same question paid two fees yet reported one query. + Duplicates within one turn still collapse, since that turn ran one search. + """ + head, turn = self._live_messages(self.AUDIO_SESSION[:1]) + grounding = self._grounding_frame({"webSearchQueries": ["q"]}) + logging_obj = self._priced_logging_obj() + + result = handler.vertex_ai_live_passthrough_handler( + websocket_messages=[head, grounding, turn, grounding, turn], + logging_obj=logging_obj, + url_route="/vertex_ai/live", + start_time=datetime.now(), + end_time=datetime.now(), + request_body={}, + model=self.NATIVE_AUDIO_MODEL, + custom_llm_provider="vertex_ai", + ) + _, one_breakdown = self._billed_session(handler, [head, grounding, turn]) + repeated_within_turn = handler._session_usage( + [head, self._grounding_frame({"webSearchQueries": ["q", "q"]}), turn], self.NATIVE_AUDIO_MODEL + ) + + assert result["result"].usage.prompt_tokens_details.web_search_requests == 2 + assert logging_obj.cost_breakdown["tool_usage_cost"] == pytest.approx(2 * one_breakdown["tool_usage_cost"]) + assert repeated_within_turn.prompt_tokens_details.web_search_requests == 1 + + def test_the_fixed_cost_margin_is_charged_once_per_session(self, handler): + """A fixed cost margin is a flat per-request fee, and a Live session is one spend row. + + Pricing each turn on its own applied the fixed margin per turn, so a two-turn session paid it + twice. The session now carries the fixed margin once no matter how many turns it billed. + """ + head, turn = self._live_messages(self.AUDIO_SESSION[:1]) + grounding = self._grounding_frame({"webSearchQueries": ["q"]}) + messages = [head, grounding, turn, grounding, turn] + + plain_cost, _ = self._billed_session(handler, messages) + + fixed_amount = 0.01 + with patch.object(litellm, "cost_margin_config", {"vertex_ai": {"fixed_amount": fixed_amount}}): + margined_cost, breakdown = self._billed_session(handler, messages) + + assert margined_cost - plain_cost == pytest.approx( + fixed_amount + ), "a two-turn session must add the fixed margin once, not once per billed turn" + assert breakdown["margin_fixed_amount"] == pytest.approx(fixed_amount) + assert breakdown["margin_total_amount"] == pytest.approx(fixed_amount) + + def test_reporting_tool_use_tokens_does_not_move_the_bill(self, handler, mock_logging_obj): + """Deliberate boundary: these tokens are reported here, and priced nowhere. + + generic_cost_per_token reads the input bill out of prompt_tokens_details, and falls + back to prompt_tokens only when the details carry no text or a cache hit overlaps them, + so adding tool-use tokens to prompt_tokens is worth nothing on an ordinary Live turn and + over-charges against the cache-overlap correction when it is not. Pricing them belongs + in the shared input-cost path, beside the modality terms that already read the details. + """ + turns = self.AUDIO_SESSION[:3] + plain_cost = self._session_cost(handler, mock_logging_obj, self._live_messages(turns), self.NATIVE_AUDIO_MODEL) + grounded_cost = self._session_cost( + handler, mock_logging_obj, self._grounded_messages(), self.NATIVE_AUDIO_MODEL + ) + + assert plain_cost == pytest.approx(self._expected_session_cost(turns), rel=1e-9) + assert grounded_cost == pytest.approx(plain_cost, rel=1e-9), "reporting tool use must not move the bill" + + def test_a_malformed_details_entry_does_not_cost_the_whole_session(self, handler, mock_logging_obj): + """A ``*TokensDetails`` value that is not a list of objects must not take the session down. + + The handler's only error path returns no result at all, so one odd frame used to throw + while reading it and the whole session billed nothing. The good turns still bill. + """ + turns = self.AUDIO_SESSION[:3] + messages = self._live_messages(turns) + mangled = [dict(message) for message in messages] + mangled[1]["usageMetadata"] = {**mangled[1]["usageMetadata"], "promptTokensDetails": "TEXT"} + + usage = self._session_usage(handler, mock_logging_obj, mangled, self.NATIVE_AUDIO_MODEL) + + surviving = turns[1:] + assert usage.prompt_tokens_details.audio_tokens == sum(turn["prompt"][1] for turn in surviving) + assert usage.prompt_tokens_details.text_tokens == sum(turn["prompt"][0] for turn in surviving) + assert usage.prompt_tokens == sum(sum(turn["prompt"]) for turn in turns), "the totals still cover every turn" + + direct = handler._create_usage_object_from_metadata( + usage_metadata={ + "promptTokenCount": 40, + "candidatesTokenCount": 12, + "promptTokensDetails": [{"modality": "AUDIO", "tokenCount": 40}, "AUDIO"], + "candidatesTokensDetails": {"modality": "TEXT", "tokenCount": 12}, + }, + model=self.NATIVE_AUDIO_MODEL, + ) + assert direct.prompt_tokens_details.audio_tokens == 40, "the well-formed entry beside a bad one still counts" + assert direct.completion_tokens == 12 + + @pytest.mark.parametrize( + "label,prompt_details,candidate_details", + [ + ("text only", [("TEXT", 6)], [("TEXT", 2)]), + ("audio in", [("TEXT", 13), ("AUDIO", 127)], [("TEXT", 18)]), + ("image in", [("TEXT", 10), ("IMAGE", 258)], [("TEXT", 24)]), + ("frames in", [("TEXT", 11), ("IMAGE", 1032)], [("TEXT", 26)]), + ("audio both ways", [("TEXT", 13), ("AUDIO", 127)], [("TEXT", 29), ("AUDIO", 95)]), + ], + ) + def test_live_session_bills_each_modality_at_its_own_rate(self, handler, label, prompt_details, candidate_details): + """Every payload here is a real Vertex Live session's usageMetadata. + + Before the fix these billed the text share only, from 1x (text) to 55x under. + The expected amount is derived from the entry's own rates rather than hardcoded, + so this stays correct as prices move, and it is asserted exactly, so dropping a + modality and double-charging one both fail. + """ + from litellm.cost_calculator import completion_cost + from litellm.types.utils import ModelResponse + from litellm.utils import get_model_info + + model = self.NATIVE_AUDIO_MODEL + info = get_model_info(model=model, custom_llm_provider="vertex_ai") + + text_in = info["input_cost_per_token"] + audio_in = info.get("input_cost_per_audio_token") or text_in + image_in = info.get("input_cost_per_image_token") or text_in + text_out = info["output_cost_per_token"] + audio_out = info.get("output_cost_per_audio_token") or text_out + rate_in = {"TEXT": text_in, "AUDIO": audio_in, "IMAGE": image_in} + rate_out = {"TEXT": text_out, "AUDIO": audio_out} + + expected = sum(c * rate_in[m] for m, c in prompt_details) + sum(c * rate_out[m] for m, c in candidate_details) + + usage = handler._create_usage_object_from_metadata( + usage_metadata={ + "promptTokenCount": sum(c for _, c in prompt_details), + "candidatesTokenCount": sum(c for _, c in candidate_details), + "promptTokensDetails": [{"modality": m, "tokenCount": c} for m, c in prompt_details], + "candidatesTokensDetails": [{"modality": m, "tokenCount": c} for m, c in candidate_details], + }, + model=model, + ) + + cost = completion_cost( + completion_response=ModelResponse( + id="x", object="chat.completion", created=0, model=model, usage=usage, choices=[] + ), + model=f"vertex_ai/{model}", + custom_llm_provider="vertex_ai", + call_type="acompletion", + ) + + assert cost == pytest.approx(expected, rel=1e-9), label + + text_only = sum(c for m, c in prompt_details if m == "TEXT") * text_in + sum( + c for m, c in candidate_details if m == "TEXT" + ) * text_out + if any(m != "TEXT" for m, _ in prompt_details + candidate_details) and audio_in != text_in: + assert cost > text_only, f"{label}: non-text modalities must add cost" def test_vertex_ai_live_passthrough_handler_integration( self, handler, mock_logging_obj, sample_websocket_messages @@ -376,6 +788,7 @@ class TestVertexAILivePassthroughIntegration: """Create a mock logging object""" mock = MagicMock(spec=LiteLLMLoggingObj) mock.model_call_details = {} + mock._response_cost_calculator.return_value = None return mock @patch( @@ -509,6 +922,7 @@ class TestVertexAILivePassthroughErrorHandling: """Create a mock logging object""" mock = MagicMock(spec=LiteLLMLoggingObj) mock.model_call_details = {} + mock._response_cost_calculator.return_value = None return mock def test_invalid_websocket_messages_format(self): @@ -540,25 +954,24 @@ class TestVertexAILivePassthroughErrorHandling: result = handler._extract_usage_metadata_from_websocket_messages(messages) assert result is None - @patch( - "litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_ai_live_passthrough_logging_handler.get_model_info" - ) - def test_cost_calculation_with_missing_model_info(self, mock_get_model_info): - """Test cost calculation when model info is missing""" + def test_usage_without_modality_details(self): + """Older payloads carry only the totals; fall back to them rather than reporting zero.""" handler = VertexAILivePassthroughLoggingHandler() - # Mock missing model info - mock_get_model_info.return_value = {} + usage = handler._create_usage_object_from_metadata( + usage_metadata={ + "promptTokenCount": 100, + "candidatesTokenCount": 50, + "totalTokenCount": 150, + }, + model="unknown-model", + ) - usage_metadata = { - "promptTokenCount": 100, - "candidatesTokenCount": 50, - "totalTokenCount": 150, - } - - # Should not raise an exception, should return 0 or handle gracefully - cost = handler._calculate_live_api_cost("unknown-model", usage_metadata) - assert cost == 0.0 + assert usage.prompt_tokens == 100 + assert usage.completion_tokens == 50 + assert usage.total_tokens == 150 + assert usage.prompt_tokens_details.audio_tokens is None + assert usage.prompt_tokens_details.image_tokens is None def test_handler_with_none_websocket_messages(self, mock_logging_obj): """Test handler with None websocket messages""" diff --git a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py index 2b3b6343fad..3eb4a70ee15 100644 --- a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py +++ b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py @@ -1,4 +1,6 @@ import json +from collections.abc import Mapping +from typing import cast from unittest.mock import MagicMock import pytest @@ -6,6 +8,7 @@ import pytest import litellm from litellm.llms.gemini.realtime.transformation import GeminiRealtimeConfig +from litellm.types.llms.gemini import BidiGenerateContentServerMessage def test_gemini_realtime_transformation_session_created(): @@ -2178,3 +2181,71 @@ def test_unbilled_usage_on_session_close_flushes_trailing_audio(patch_gemini_tra } assert usage == expected assert config.unbilled_usage_on_session_close("gemini-3.5-transcribe-live") is None + + +def _grounded_live_frame(grounding_metadata: Mapping[str, object] | None) -> Mapping[str, object]: + """One Live server frame. Grounding metadata and usageMetadata arrive together, as Vertex sends them.""" + from typing import Final + + server_content: Final = { + "turnComplete": True, + **({} if grounding_metadata is None else {"groundingMetadata": grounding_metadata}), + } + return { + "serverContent": server_content, + "usageMetadata": { + "promptTokenCount": 19, + "candidatesTokenCount": 157, + "totalTokenCount": 176, + "promptTokensDetails": ({"modality": "TEXT", "tokenCount": 19},), + "candidatesTokensDetails": ({"modality": "AUDIO", "tokenCount": 157},), + }, + } + + +def _response_done_input_details(message: Mapping[str, object]) -> Mapping[str, object]: + """The ``input_tokens_details`` a ``response.done`` event carries, read off the emitted event.""" + from typing import Final + + config: Final = GeminiRealtimeConfig() + event: Final = config.transform_response_done_event( + message=cast( # cast-ok: a test fixture stands in for the server frame TypedDict + BidiGenerateContentServerMessage, message + ), + current_response_id="resp_grounding", + current_conversation_id="conv_grounding", + output_items=None, + ) + usage: Final = event["response"]["usage"] + assert usage, "response.done must carry a usage object" + return usage.get("input_tokens_details") or {} + + +def test_gemini_realtime_response_done_counts_web_grounding(): + """Regression: Live reports grounding in the server frames and never in usageMetadata. + + Nothing read those frames on the realtime path, so web_search_requests stayed unset and the + cost path's only trigger for Google's per-query grounding charge never fired. + + The counter is read off the emitted event, which is what the cost path is handed, so this covers + the grounding read and the usage bridge that carries it together + """ + input_details = _response_done_input_details( + _grounded_live_frame( + { + "webSearchQueries": ["who won the 2026 world cup final"], + "groundingChunks": [{"web": {"uri": "https://example.com"}}], + } + ) + ) + + assert input_details.get("web_search_requests") == 1, "a grounded turn must report its query" + assert input_details.get("text_tokens") == 19, "the modality breakdown must survive alongside it" + + +def test_gemini_realtime_response_done_reports_no_grounding_when_none_ran(): + """The counter must stay unset on an ordinary turn, or every session pays a grounding fee.""" + input_details = _response_done_input_details(_grounded_live_frame(None)) + + assert input_details.get("web_search_requests") is None + assert input_details.get("google_maps_grounding_requests") is None diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index d57bed430c1..11066d4ed38 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -5030,6 +5030,100 @@ async def test_websocket_passthrough_rewrites_gateway_alias_setup_model(): assert sent_setup["model"] == "projects/proj-db/locations/global/publishers/google/models/gemini-live-2.5-flash" +@pytest.mark.parametrize( + "setup_model", + ["gemini-live-2.5-flash", "models/gemini-live-2.5-flash", "publishers/google/models/gemini-live-2.5-flash"], +) +def test_vertex_live_setup_model_resolves_before_extraction(setup_model): + """A bare gateway alias left the session logged as ``unknown`` at zero cost. + + The model was read off the raw client frame, and the extractor only yields a name when the string + already contains ``/models/``. The rewriter qualifies it a few lines later for the upstream, so a + client that addressed the gateway the documented way, by alias, logged no model and therefore + resolved no cost-map entry. Resolving first is what puts the real name on the logging object. + """ + from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( + _build_vertex_live_setup_model_rewriter, + ) + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( + _extract_model_from_vertex_ai_setup, + _resolved_vertex_live_setup, + ) + + rewriter = _build_vertex_live_setup_model_rewriter( + vertex_project="proj-db", vertex_location="global", llm_router=None + ) + setup_data = {"model": setup_model} + + resolved = _extract_model_from_vertex_ai_setup(_resolved_vertex_live_setup(setup_data, rewriter)) + + assert resolved == "gemini-live-2.5-flash", "an unresolved setup model logs the session as 'unknown'" + + +@pytest.mark.asyncio +async def test_websocket_passthrough_logs_a_bare_alias_setup_model(): + """End to end through the relay: a bare alias must reach the logging object as a real model name. + + This is the call-site half of the fix. The helper tests above pass even if extraction moves back + before the rewrite, so this one drives the real websocket relay and asserts on what got logged, + which is the name the cost map is looked up by. An unbilled session logs ``unknown``. + """ + from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( + _build_vertex_live_setup_model_rewriter, + ) + + upstream_ws = RecordingUpstreamWebSocket() + setup_frame = json.dumps({"setup": {"model": "gemini-live-2.5-flash"}}) + websocket = _client_websocket( + AsyncMock( + side_effect=[ + {"type": "websocket.receive", "text": setup_frame}, + {"type": "websocket.disconnect"}, + ] + ) + ) + built = [] + real_logging = litellm.litellm_core_utils.litellm_logging.Logging + + def _capture(*args, **kwargs): + obj = real_logging(*args, **kwargs) + built.append(obj) + return obj + + with _patched_websocket_passthrough_environment(upstream_ws): + with patch("litellm.litellm_core_utils.litellm_logging.Logging", side_effect=_capture): + await websocket_passthrough_request( + websocket=websocket, + target="wss://aiplatform.googleapis.com/ws/google.cloud.aiplatform.v1.LlmBidiService/BidiGenerateContent", + custom_headers={"Authorization": "Bearer token"}, + user_api_key_dict=UserAPIKeyAuth(), + forward_headers=False, + endpoint="/vertex_ai/live", + accept_websocket=False, + setup_model_rewriter=_build_vertex_live_setup_model_rewriter( + vertex_project="proj-db", vertex_location="global", llm_router=None + ), + ) + + assert built, "the relay should have built a logging object" + assert built[0].model == "gemini-live-2.5-flash", "a bare alias must not log as 'unknown'" + + +def test_vertex_live_setup_resolution_is_inert_without_a_rewriter(): + """Non-Live passthrough routes pass no rewriter, so the frame must be handed over untouched.""" + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( + _extract_model_from_vertex_ai_setup, + _resolved_vertex_live_setup, + ) + + setup_data = {"model": "projects/p/locations/global/publishers/google/models/gemini-live-2.5-flash"} + + assert _resolved_vertex_live_setup(setup_data, None) is setup_data + assert _extract_model_from_vertex_ai_setup(_resolved_vertex_live_setup(setup_data, None)) == ( + "gemini-live-2.5-flash" + ) + + @pytest.mark.asyncio @pytest.mark.parametrize("rcvd_close", [None, "abnormal", "no_status"]) async def test_websocket_passthrough_does_not_relay_unsendable_upstream_close(rcvd_close):