diff --git a/alphatrion/agents/claude.py b/alphatrion/agents/claude.py index 9be757af..989e1e7d 100644 --- a/alphatrion/agents/claude.py +++ b/alphatrion/agents/claude.py @@ -12,7 +12,13 @@ from alphatrion.storage import runtime from alphatrion.storage.sql_models import Status -from alphatrion.tracing.clickhouse_exporter import determine_semantic_kind +from alphatrion.tracing.span_processor import ( + SEMANTIC_KIND_CHAT, + SEMANTIC_KIND_REASONING, + SEMANTIC_KIND_TOOL, + SEMANTIC_KIND_UNKNOWN, +) +from alphatrion.utils.pricing import calculate_cost def handle_hook(hook_type: str): @@ -548,15 +554,6 @@ def process_transcript_incremental( # Turn is complete, create run and multiple LLM spans user_message = current_user_msg.get("message", {}) - # Calculate total tokens and duration from all messages - total_input_tokens = 0 - total_output_tokens = 0 - for msg in current_assistant_messages: - msg_usage = msg.get("message", {}).get("usage", {}) - total_input_tokens += msg_usage.get("input_tokens", 0) - total_output_tokens += msg_usage.get("output_tokens", 0) - total_tokens = total_input_tokens + total_output_tokens - # Calculate duration from timestamps # (first user message to last assistant message) duration = calculate_duration( @@ -598,11 +595,6 @@ def process_transcript_incremental( user_id=session.user_id, status=run_status, duration=duration, - usage={ - "input_tokens": total_input_tokens, - "output_tokens": total_output_tokens, - "total_tokens": total_tokens, - }, ) # Prepare user content (original user message only) @@ -1009,8 +1001,12 @@ def create_clickhouse_spans_for_turn( msg_content = [] msg_usage = message_data.get("usage", {}) - msg_input_tokens = msg_usage.get("input_tokens", 0) - msg_output_tokens = msg_usage.get("output_tokens", 0) + input_tokens = msg_usage.get("input_tokens", 0) + output_tokens = msg_usage.get("output_tokens", 0) + cache_creation_input_tokens = msg_usage.get( + "cache_creation_input_tokens", 0 + ) + cache_read_input_tokens = msg_usage.get("cache_read_input_tokens", 0) # Determine content type tool_use_blocks = [ @@ -1083,13 +1079,17 @@ def create_clickhouse_spans_for_turn( span_id = str(uuid.uuid4()).replace("-", "")[:16] # Determine semantic kind - semantic_kind = determine_semantic_kind({}, msg_content) + semantic_kind = determine_semantic_kind(msg_content) - # Token assignment: - # Each message has actual token usage from Claude API - # Assign actual tokens to each span for accurate aggregation - input_tokens = msg_input_tokens - output_tokens = msg_output_tokens + # Calculate cost for this span + span_costs = calculate_cost( + provider="anthropic", + model=model, + input_tokens=input_tokens, + output_tokens=output_tokens, + cache_creation_input_tokens=cache_creation_input_tokens, + cache_read_input_tokens=cache_read_input_tokens, + ) # Build span attributes span_attributes = { @@ -1099,7 +1099,25 @@ def create_clickhouse_spans_for_turn( "gen_ai.response.model": model, "gen_ai.usage.input_tokens": str(input_tokens), "gen_ai.usage.output_tokens": str(output_tokens), - "llm.usage.total_tokens": str(input_tokens + output_tokens), + "gen_ai.usage.cache_creation_input_tokens": str( + cache_creation_input_tokens + ), + "gen_ai.usage.cache_read_input_tokens": str(cache_read_input_tokens), + "llm.usage.total_tokens": str( + input_tokens + + output_tokens + + cache_creation_input_tokens + + cache_read_input_tokens + ), + "alphatrion.cost.total_tokens": str(span_costs["total_cost"]), + "alphatrion.cost.input_tokens": str(span_costs["input_cost"]), + "alphatrion.cost.output_tokens": str(span_costs["output_cost"]), + "alphatrion.cost.cache_creation_input_tokens": str( + span_costs["cache_creation_input_cost"] + ), + "alphatrion.cost.cache_read_input_tokens": str( + span_costs["cache_read_input_cost"] + ), } # Add prompt to first span only (where the user input is sent) @@ -1222,3 +1240,37 @@ def create_clickhouse_spans_for_turn( import logging logging.error(f"Failed to create ClickHouse spans: {e}", exc_info=True) + + +def determine_semantic_kind(content_blocks: list) -> str: + """Determine semantic kind of a message based on content blocks. + + - If contains tool_use block → "tool" + - Else if contains thinking block → "thinking" + - Else → "text" + + Args: + content_blocks: List of content blocks in the message + + Returns: + Semantic kind string + """ + has_tool_use = any( + isinstance(b, dict) and b.get("type") == "tool_use" for b in content_blocks + ) + if has_tool_use: + return SEMANTIC_KIND_TOOL + + has_thinking = any( + isinstance(b, dict) and b.get("type") == "thinking" for b in content_blocks + ) + if has_thinking: + return SEMANTIC_KIND_REASONING + + has_text = any( + isinstance(b, dict) and b.get("type") == "text" for b in content_blocks + ) + if has_text: + return SEMANTIC_KIND_CHAT + + return SEMANTIC_KIND_UNKNOWN diff --git a/alphatrion/experiment/base.py b/alphatrion/experiment/base.py index 311d33ca..11a77cfa 100644 --- a/alphatrion/experiment/base.py +++ b/alphatrion/experiment/base.py @@ -220,16 +220,9 @@ def _start( # to avoid confusion. if exp_obj and exp_obj.status != Status.COMPLETED: self._id = exp_obj.uuid - usage = exp_obj.usage - - # reset to running status, also need to reset the tokens. - if usage and "total_tokens" in usage: - # delete the tokens in the usage - set to None instead of empty dict - usage = None self._runtime._metadb.update_experiment( experiment_id=self._id, status=Status.RUNNING, - usage=usage, ) elif exp_obj and exp_obj.status == Status.COMPLETED: raise RuntimeError( diff --git a/alphatrion/runtime/runtime.py b/alphatrion/runtime/runtime.py index 726e4e6a..777ac5dc 100644 --- a/alphatrion/runtime/runtime.py +++ b/alphatrion/runtime/runtime.py @@ -1,10 +1,15 @@ # ruff: noqa: PLW0603 +import logging import os +import sys import uuid from alphatrion import envs from alphatrion.storage import runtime as storage_runtime from alphatrion.storage.sqlstore import SQLStore +from alphatrion.utils.pricing import load_pricing_config + +logger = logging.getLogger(__name__) __RUNTIME__ = None @@ -57,6 +62,15 @@ def __init__( team_id: uuid.UUID | None = None, org_id: uuid.UUID | None = None, ): + # Load pricing config at startup - exit if it fails + try: + load_pricing_config() + logger.info("Successfully loaded model pricing configuration") + except Exception as e: + logger.error(f"Failed to load pricing configuration: {e}") + logger.error("Application cannot start without valid pricing configuration") + sys.exit(1) + storage_runtime.init() self._metadb = storage_runtime.storage_runtime().metadb self._tracestore = storage_runtime.storage_runtime().tracestore diff --git a/alphatrion/server/cmd/app.py b/alphatrion/server/cmd/app.py index b8cccc5a..19c8ac10 100644 --- a/alphatrion/server/cmd/app.py +++ b/alphatrion/server/cmd/app.py @@ -109,8 +109,26 @@ async def receive(): return response +# Wrapper to convert context auth errors to HTTP exceptions +async def get_context_with_error_handling(request: Request): + """Wrap get_context to convert auth errors to proper HTTP status codes.""" + try: + return await get_context(request) + except ValueError as e: + # Authentication/authorization errors from get_context + error_msg = str(e).lower() + if ( + "authorization" in error_msg + or "token" in error_msg + or "missing" in error_msg + ): + raise HTTPException(status_code=401, detail=str(e)) + # Other validation errors + raise HTTPException(status_code=400, detail=str(e)) + + # Create GraphQL router with context -graphql_app = GraphQLRouter(schema, context_getter=get_context) +graphql_app = GraphQLRouter(schema, context_getter=get_context_with_error_handling) # Mount /graphql endpoint app.include_router(graphql_app, prefix="/graphql") diff --git a/alphatrion/server/cmd/main.py b/alphatrion/server/cmd/main.py index 0d49ae69..1ef894a8 100644 --- a/alphatrion/server/cmd/main.py +++ b/alphatrion/server/cmd/main.py @@ -204,7 +204,10 @@ def init_command(args): if len(password) < character_length_at_least: console.print( - Text(f"❌ Error: Password must be at least {character_length_at_least} characters", style="bold red") + Text( + f"❌ Error: Password must be at least {character_length_at_least} characters", + style="bold red", + ) ) return diff --git a/alphatrion/server/graphql/resolvers.py b/alphatrion/server/graphql/resolvers.py index 58a57228..4bfee0d9 100644 --- a/alphatrion/server/graphql/resolvers.py +++ b/alphatrion/server/graphql/resolvers.py @@ -12,7 +12,6 @@ from alphatrion.server.graphql.types import ArtifactFile from alphatrion.storage import runtime from alphatrion.storage.sql_models import ( - FINISHED_STATUS, AgentType, Status, ) @@ -25,7 +24,7 @@ ArtifactTag, CreateTeamInput, CreateUserInput, - DailyTokenUsage, + DailyCostUsage, Dataset, Experiment, GraphQLAgentTypeEnum, @@ -235,7 +234,6 @@ def list_experiments( duration=e.duration, status=GraphQLStatusEnum[Status(e.status).name], kind=GraphQLExperimentTypeEnum[GraphQLExperimentType(e.kind).name], - cost=e.cost, created_at=e.created_at, updated_at=e.updated_at, ) @@ -269,7 +267,6 @@ def get_experiment( duration=exp.duration, status=GraphQLStatusEnum[Status(exp.status).name], kind=GraphQLExperimentTypeEnum[GraphQLExperimentType(exp.kind).name], - cost=exp.cost, created_at=exp.created_at, updated_at=exp.updated_at, ) @@ -311,7 +308,6 @@ def list_runs( meta=r.meta, status=GraphQLStatusEnum[Status(r.status).name], duration=r.duration, - cost=r.cost, created_at=r.created_at, ) for r in runs @@ -336,7 +332,6 @@ def get_run(info: Info[GraphQLContext, None], id: strawberry.ID) -> Run | None: meta=run.meta, status=GraphQLStatusEnum[Status(run.status).name], duration=run.duration, - cost=run.cost, created_at=run.created_at, ) return None @@ -510,7 +505,6 @@ def list_runs_by_session_id( meta=r.meta, status=GraphQLStatusEnum[Status(r.status).name], duration=r.duration, - cost=r.cost, created_at=r.created_at, ) for r in runs @@ -635,11 +629,18 @@ def total_datasets(info: Info[GraphQLContext, None], team_id: strawberry.ID) -> return metadb.count_datasets(team_id=team_id) @staticmethod - def aggregate_team_tokens( + def aggregate_team_usage( info: Info[GraphQLContext, None], team_id: strawberry.ID - ) -> dict[str, int]: + ) -> dict[str, int | float]: if os.getenv(envs.ENABLE_TRACING, "false").lower() != "true": - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } org_id = uuid.UUID(info.context.org_id) user_id = uuid.UUID(info.context.user_id) @@ -652,19 +653,32 @@ def aggregate_team_tokens( ) trace_store = runtime.storage_runtime().tracestore - result = trace_store.get_llm_tokens_by_team_id(org_id=org_id, team_id=team_id) - # get_llm_tokens_by_team_id returns a list with one dict + result = trace_store.get_llm_usage_by_team_id(org_id=org_id, team_id=team_id) if result and len(result) > 0: return result[0] - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } @staticmethod - def aggregate_agent_tokens( + def aggregate_agent_usage( info: Info[GraphQLContext, None], agent_id: strawberry.ID - ) -> dict[str, int]: + ) -> dict[str, int | float]: """Aggregate token usage from all spans for an agent.""" if os.getenv(envs.ENABLE_TRACING, "false").lower() != "true": - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } ctx = info.context org_id = uuid.UUID(ctx.org_id) @@ -677,20 +691,34 @@ def aggregate_agent_tokens( ) trace_store = runtime.storage_runtime().tracestore - result = trace_store.get_llm_tokens_by_agent_id( + result = trace_store.get_llm_usage_by_agent_id( org_id=org_id, team_id=agent.team_id, agent_id=agent_id ) if result and len(result) > 0: return result[0] - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } @staticmethod - def aggregate_session_tokens( + def aggregate_session_usage( info: Info[GraphQLContext, None], session_id: strawberry.ID - ) -> dict[str, int]: + ) -> dict[str, int | float]: """Aggregate token usage from all spans for a session.""" if os.getenv(envs.ENABLE_TRACING, "false").lower() != "true": - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } ctx = info.context org_id = uuid.UUID(ctx.org_id) @@ -705,12 +733,19 @@ def aggregate_session_tokens( ) trace_store = runtime.storage_runtime().tracestore - result = trace_store.get_llm_tokens_by_session_id( + result = trace_store.get_llm_usage_by_session_id( org_id=org_id, team_id=session.team_id, session_id=session.uuid ) if result and len(result) > 0: return result[0] - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } @staticmethod def aggregate_model_distributions( @@ -775,7 +810,6 @@ def list_exps_by_timeframe( duration=e.duration, status=GraphQLStatusEnum[Status(e.status).name], kind=GraphQLExperimentTypeEnum[GraphQLExperimentType(e.kind).name], - cost=e.cost, created_at=e.created_at, updated_at=e.updated_at, ) @@ -939,48 +973,32 @@ async def get_artifact_content( raise RuntimeError(f"Failed to get artifact content: {e}") from e @staticmethod - def aggregate_run_tokens( + def aggregate_run_usage( info: Info[GraphQLContext, None], run_id: strawberry.ID - ) -> dict[str, int]: + ) -> dict[str, int | float]: """Aggregate token usage from all traces for a run.""" if os.getenv(envs.ENABLE_TRACING, "false").lower() != "true": - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } user_id = info.context.user_id metadb = runtime.storage_runtime().metadb if not metadb.run_is_accessible_to_user(run_id=run_id, user_id=user_id): raise RuntimeError("Not allowed to access run that user does not belong to") - try: - run = runtime.storage_runtime().metadb.get_run(run_id=run_id) - if run.status in FINISHED_STATUS: - if run.usage and "total_tokens" in run.usage: - return { - "total_tokens": run.usage.get("total_tokens", 0), - "input_tokens": run.usage.get("input_tokens", 0), - "output_tokens": run.usage.get("output_tokens", 0), - } - else: - usage = GraphQLResolvers.get_run_usage(info, run_id) - runtime.storage_runtime().metadb.update_run( - run_id=run_id, usage=usage - ) - return usage - else: - return GraphQLResolvers.get_run_usage(info, run_id) - except Exception as e: - import logging - - logging.error( - f"Failed to aggregate tokens for run {run_id}: {e}", exc_info=True - ) - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return GraphQLResolvers.get_run_usage(info, run_id) @staticmethod def get_run_usage( info: Info[GraphQLContext, None], run_id: strawberry.ID - ) -> dict[str, int]: + ) -> dict[str, float]: ctx = info.context org_id = uuid.UUID(ctx.org_id) run = runtime.storage_runtime().metadb.get_run(run_id=run_id) @@ -994,6 +1012,9 @@ def get_run_usage( total_tokens = 0 input_tokens = 0 output_tokens = 0 + cache_read_input_tokens = 0 + cache_creation_input_tokens = 0 + total_cost = 0.0 for span in spans: span_attrs = span.get("SpanAttributes", {}) @@ -1005,17 +1026,30 @@ def get_run_usage( input_tokens += int(span_attrs["gen_ai.usage.input_tokens"]) if "gen_ai.usage.output_tokens" in span_attrs: output_tokens += int(span_attrs["gen_ai.usage.output_tokens"]) + if "gen_ai.usage.cache_read_input_tokens" in span_attrs: + cache_read_input_tokens += int( + span_attrs["gen_ai.usage.cache_read_input_tokens"] + ) + if "gen_ai.usage.cache_creation_input_tokens" in span_attrs: + cache_creation_input_tokens += int( + span_attrs["gen_ai.usage.cache_creation_input_tokens"] + ) + if "alphatrion.cost.total_tokens" in span_attrs: + total_cost += float(span_attrs["alphatrion.cost.total_tokens"]) return { "total_tokens": total_tokens, "input_tokens": input_tokens, "output_tokens": output_tokens, + "cache_read_input_tokens": cache_read_input_tokens, + "cache_creation_input_tokens": cache_creation_input_tokens, + "total_cost": total_cost, } @staticmethod - def aggregate_experiment_tokens( + def aggregate_experiment_usage( info: Info[GraphQLContext, None], experiment_id: strawberry.ID - ) -> dict[str, int]: + ) -> dict[str, int | float]: """Aggregate token usage from all spans in an experiment.""" org_id = info.context.org_id user_id = info.context.user_id @@ -1029,41 +1063,24 @@ def aggregate_experiment_tokens( ) if os.getenv(envs.ENABLE_TRACING, "false").lower() != "true": - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} - - try: - exp = runtime.storage_runtime().metadb.get_experiment( - experiment_id=experiment_id - ) - if exp.status in FINISHED_STATUS: - if exp.usage and "total_tokens" in exp.usage: - return { - "total_tokens": exp.usage.get("total_tokens", 0), - "input_tokens": exp.usage.get("input_tokens", 0), - "output_tokens": exp.usage.get("output_tokens", 0), - } - else: - usage = GraphQLResolvers.get_experiment_usage( - org_id, exp.team_id, experiment_id - ) - metadb.update_experiment(experiment_id=experiment_id, usage=usage) - return usage - else: - return GraphQLResolvers.get_experiment_usage( - org_id, exp.team_id, experiment_id - ) - except Exception as e: - import logging - - logging.error( - f"Failed to aggregate tokens for experiment {experiment_id}: {e}" - ) - return {"total_tokens": 0, "input_tokens": 0, "output_tokens": 0} + return { + "total_tokens": 0, + "input_tokens": 0, + "output_tokens": 0, + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "total_cost": 0.0, + } + + exp = runtime.storage_runtime().metadb.get_experiment( + experiment_id=experiment_id + ) + return GraphQLResolvers.get_experiment_usage(org_id, exp.team_id, experiment_id) @staticmethod def get_experiment_usage( org_id: strawberry.ID, team_id: strawberry.ID, experiment_id: strawberry.ID - ): + ) -> tuple[dict[str, int], dict[str, float]]: trace_store = runtime.storage_runtime().tracestore # Get all LLM spans for this experiment in a single query spans = trace_store.get_llm_spans_by_exp_id( @@ -1074,6 +1091,9 @@ def get_experiment_usage( total_tokens = 0 input_tokens = 0 output_tokens = 0 + cache_read_input_tokens = 0 + cache_creation_input_tokens = 0 + total_cost = 0.0 for span in spans: span_attrs = span.get("SpanAttributes", {}) @@ -1085,11 +1105,24 @@ def get_experiment_usage( input_tokens += int(span_attrs["gen_ai.usage.input_tokens"]) if "gen_ai.usage.output_tokens" in span_attrs: output_tokens += int(span_attrs["gen_ai.usage.output_tokens"]) + if "gen_ai.usage.cache_read_input_tokens" in span_attrs: + cache_read_input_tokens += int( + span_attrs["gen_ai.usage.cache_read_input_tokens"] + ) + if "gen_ai.usage.cache_creation_input_tokens" in span_attrs: + cache_creation_input_tokens += int( + span_attrs["gen_ai.usage.cache_creation_input_tokens"] + ) + if "alphatrion.cost.total_tokens" in span_attrs: + total_cost += float(span_attrs["alphatrion.cost.total_tokens"]) return { "total_tokens": total_tokens, "input_tokens": input_tokens, "output_tokens": output_tokens, + "cache_read_input_tokens": cache_read_input_tokens, + "cache_creation_input_tokens": cache_creation_input_tokens, + "total_cost": total_cost, } @staticmethod @@ -1265,10 +1298,10 @@ def list_spans_by_session_id( return [] @staticmethod - def get_daily_token_usage( + def get_daily_cost_usage( info: Info[GraphQLContext, None], team_id: strawberry.ID, days: int = 7 - ) -> list[DailyTokenUsage]: - """Get daily token usage from LLM calls for a team.""" + ) -> list[DailyCostUsage]: + """Get daily cost usage from LLM calls for a team.""" # Check if tracing is enabled if os.getenv(envs.ENABLE_TRACING, "false").lower() != "true": @@ -1285,24 +1318,29 @@ def get_daily_token_usage( try: trace_store = runtime.storage_runtime().tracestore - daily_usage = trace_store.get_daily_token_usage( + daily_cost = trace_store.get_daily_cost_usage( org_id=org_id, team_id=team_id, days=days ) # Don't close - it's a shared singleton connection - # Convert to GraphQL DailyTokenUsage objects + # Convert to GraphQL DailyCostUsage objects return [ - DailyTokenUsage( + DailyCostUsage( date=item["date"], - total_tokens=item["total_tokens"], - input_tokens=item["input_tokens"], - output_tokens=item["output_tokens"], + total_cost=item["total_cost"], + total_tokens=item.get("total_tokens", 0), + input_tokens=item.get("input_tokens", 0), + output_tokens=item.get("output_tokens", 0), + cache_read_input_tokens=item.get("cache_read_input_tokens", 0), + cache_creation_input_tokens=item.get( + "cache_creation_input_tokens", 0 + ), ) - for item in daily_usage + for item in daily_cost ] except Exception as e: # Log error and return empty list - don't fail the GraphQL query - print(f"Failed to fetch daily token usage: {e}") + print(f"Failed to fetch daily cost usage: {e}") return [] @staticmethod diff --git a/alphatrion/server/graphql/schema.py b/alphatrion/server/graphql/schema.py index 312f9515..43cf702b 100644 --- a/alphatrion/server/graphql/schema.py +++ b/alphatrion/server/graphql/schema.py @@ -12,7 +12,7 @@ ArtifactTag, CreateTeamInput, CreateUserInput, - DailyTokenUsage, + DailyCostUsage, Dataset, Experiment, Organization, @@ -125,13 +125,13 @@ def spans_by_session_id( ) @strawberry.field - def daily_token_usage( + def daily_cost_usage( self, team_id: strawberry.ID, days: int = 7, info: Info[GraphQLContext, None] = None, - ) -> list[DailyTokenUsage]: - return GraphQLResolvers.get_daily_token_usage( + ) -> list[DailyCostUsage]: + return GraphQLResolvers.get_daily_cost_usage( team_id=team_id, days=days, info=info ) diff --git a/alphatrion/server/graphql/types.py b/alphatrion/server/graphql/types.py index 0226f712..7827f924 100644 --- a/alphatrion/server/graphql/types.py +++ b/alphatrion/server/graphql/types.py @@ -8,10 +8,15 @@ @strawberry.type -class TokenStats: +class AggregatedUsage: + """Aggregated usage and cost information for tokens.""" + total_tokens: int input_tokens: int output_tokens: int + cache_read_input_tokens: int + cache_creation_input_tokens: int + total_cost: float @strawberry.type @@ -21,11 +26,14 @@ class ModelDistribution: @strawberry.type -class DailyTokenUsage: +class DailyCostUsage: date: str + total_cost: float total_tokens: int input_tokens: int output_tokens: int + cache_read_input_tokens: int + cache_creation_input_tokens: int @strawberry.type @@ -86,14 +94,17 @@ def total_sessions(self, info: Info) -> int: return GraphQLResolvers.total_sessions(info=info, team_id=self.id) @strawberry.field - def aggregated_tokens(self, info: Info) -> TokenStats: + def aggregated_usage(self, info: Info) -> AggregatedUsage: from .resolvers import GraphQLResolvers - token_data = GraphQLResolvers.aggregate_team_tokens(info=info, team_id=self.id) - return TokenStats( - total_tokens=token_data["total_tokens"], - input_tokens=token_data["input_tokens"], - output_tokens=token_data["output_tokens"], + usage = GraphQLResolvers.aggregate_team_usage(info=info, team_id=self.id) + return AggregatedUsage( + total_tokens=usage["total_tokens"], + input_tokens=usage["input_tokens"], + output_tokens=usage["output_tokens"], + cache_read_input_tokens=usage["cache_read_input_tokens"], + cache_creation_input_tokens=usage["cache_creation_input_tokens"], + total_cost=usage["total_cost"], ) @strawberry.field @@ -182,7 +193,6 @@ class Experiment: params: JSON | None duration: float status: GraphQLStatusEnum - cost: JSON | None created_at: datetime updated_at: datetime @@ -205,16 +215,19 @@ def metrics(self, info: Info) -> list["Metric"]: return GraphQLResolvers.list_exp_metrics(info=info, experiment_id=self.id) @strawberry.field - def aggregated_tokens(self, info: Info) -> TokenStats: + def aggregated_usage(self, info: Info) -> AggregatedUsage: from .resolvers import GraphQLResolvers - tokens = GraphQLResolvers.aggregate_experiment_tokens( + usage = GraphQLResolvers.aggregate_experiment_usage( info=info, experiment_id=self.id ) - return TokenStats( - total_tokens=tokens["total_tokens"], - input_tokens=tokens["input_tokens"], - output_tokens=tokens["output_tokens"], + return AggregatedUsage( + total_tokens=usage["total_tokens"], + input_tokens=usage["input_tokens"], + output_tokens=usage["output_tokens"], + cache_read_input_tokens=usage["cache_read_input_tokens"], + cache_creation_input_tokens=usage["cache_creation_input_tokens"], + total_cost=usage["total_cost"], ) @strawberry.field @@ -255,16 +268,17 @@ def sessions( ) @strawberry.field - def aggregated_tokens(self, info: Info) -> TokenStats: + def aggregated_usage(self, info: Info) -> AggregatedUsage: from .resolvers import GraphQLResolvers - token_data = GraphQLResolvers.aggregate_agent_tokens( - info=info, agent_id=self.id - ) - return TokenStats( - total_tokens=token_data["total_tokens"], - input_tokens=token_data["input_tokens"], - output_tokens=token_data["output_tokens"], + usage = GraphQLResolvers.aggregate_agent_usage(info=info, agent_id=self.id) + return AggregatedUsage( + total_tokens=usage["total_tokens"], + input_tokens=usage["input_tokens"], + output_tokens=usage["output_tokens"], + cache_read_input_tokens=usage["cache_read_input_tokens"], + cache_creation_input_tokens=usage["cache_creation_input_tokens"], + total_cost=usage["total_cost"], ) @@ -288,16 +302,17 @@ def runs(self, info: Info, page: int = 0, page_size: int = 10) -> list["Run"]: ) @strawberry.field - def aggregated_tokens(self, info: Info) -> TokenStats: + def aggregated_usage(self, info: Info) -> AggregatedUsage: from .resolvers import GraphQLResolvers - token_data = GraphQLResolvers.aggregate_session_tokens( - info=info, session_id=self.id - ) - return TokenStats( - total_tokens=token_data["total_tokens"], - input_tokens=token_data["input_tokens"], - output_tokens=token_data["output_tokens"], + usage = GraphQLResolvers.aggregate_session_usage(info=info, session_id=self.id) + return AggregatedUsage( + total_tokens=usage["total_tokens"], + input_tokens=usage["input_tokens"], + output_tokens=usage["output_tokens"], + cache_read_input_tokens=usage["cache_read_input_tokens"], + cache_creation_input_tokens=usage["cache_creation_input_tokens"], + total_cost=usage["total_cost"], ) @@ -312,7 +327,6 @@ class Run: meta: JSON | None duration: float status: GraphQLStatusEnum - cost: JSON | None created_at: datetime @strawberry.field @@ -330,15 +344,18 @@ def spans(self, info: Info) -> list["Span"]: return GraphQLResolvers.list_spans_by_run_id(info=info, run_id=self.id) @strawberry.field - def aggregated_tokens(self, info: Info) -> TokenStats: + def aggregated_usage(self, info: Info) -> AggregatedUsage: """Get aggregated token usage for this run.""" from .resolvers import GraphQLResolvers - token_data = GraphQLResolvers.aggregate_run_tokens(info=info, run_id=self.id) - return TokenStats( - total_tokens=token_data["total_tokens"], - input_tokens=token_data["input_tokens"], - output_tokens=token_data["output_tokens"], + usage = GraphQLResolvers.aggregate_run_usage(info=info, run_id=self.id) + return AggregatedUsage( + total_tokens=usage["total_tokens"], + input_tokens=usage["input_tokens"], + output_tokens=usage["output_tokens"], + cache_read_input_tokens=usage["cache_read_input_tokens"], + cache_creation_input_tokens=usage["cache_creation_input_tokens"], + total_cost=usage["total_cost"], ) diff --git a/alphatrion/storage/runtime.py b/alphatrion/storage/runtime.py index 94f58858..3135706e 100644 --- a/alphatrion/storage/runtime.py +++ b/alphatrion/storage/runtime.py @@ -4,6 +4,7 @@ from opentelemetry import trace from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import BatchSpanProcessor from traceloop.sdk import Traceloop from alphatrion import envs @@ -11,7 +12,8 @@ from alphatrion.storage.sqlstore import SQLStore from alphatrion.storage.tracestore import TraceStore from alphatrion.tracing.clickhouse_exporter import ClickHouseSpanExporter -from alphatrion.tracing.prometheus_span_processor import PrometheusSpanProcessor +from alphatrion.tracing.cost_enrichment_processor import CostEnrichmentProcessor +from alphatrion.tracing.prometheus_exporter import PrometheusExporter from alphatrion.tracing.span_processor import ContextAttributesSpanProcessor __STORAGE_RUNTIME__ = None @@ -62,23 +64,31 @@ def __init__(self): telemetry_enabled=False, ) - # Add custom span processor to inject context attributes (run_id, etc.) - # into all spans, including child spans created by instrumented libraries + # Add custom span processors tracer_provider = trace.get_tracer_provider() + + # 1. Context attributes processor - injects context (run_id, etc.) into all spans tracer_provider.add_span_processor(ContextAttributesSpanProcessor()) - # Add Prometheus span processor if enabled + # 2. Cost enrichment processor - calculates costs from tokens and adds to span attributes + # This runs early so downstream processors/exporters can access cost data + tracer_provider.add_span_processor(CostEnrichmentProcessor()) + + # 3. Add Prometheus exporter if enabled if os.getenv(envs.ENABLE_PROMETHEUS, "false").lower() == "true": pushgateway_url = os.getenv( envs.PROMETHEUS_PUSHGATEWAY_URL, "localhost:9091" ) job_name = os.getenv(envs.PROMETHEUS_JOB_NAME, "alphatrion") - prometheus_processor = PrometheusSpanProcessor( + prometheus_exporter = PrometheusExporter( pushgateway_url=pushgateway_url, job_name=job_name, ) - tracer_provider.add_span_processor(prometheus_processor) + # Use BatchSpanProcessor for better performance + tracer_provider.add_span_processor( + BatchSpanProcessor(prometheus_exporter) + ) artifact_insecure = os.getenv(envs.ARTIFACT_INSECURE, "false").lower() == "true" if artifact_storage_enabled(): diff --git a/alphatrion/storage/sql_models.py b/alphatrion/storage/sql_models.py index f5cac099..09b57934 100644 --- a/alphatrion/storage/sql_models.py +++ b/alphatrion/storage/sql_models.py @@ -188,17 +188,6 @@ class Experiment(Base): 0: UNKNOWN, 1: PENDING, 2: RUNNING, 9: COMPLETED, \ 10: CANCELLED, 11: FAILED", ) - usage = Column( - MutableDict.as_mutable(JSON), - nullable=True, - comment="The usage information, e.g. for LLM calls: \ - {total_tokens: int, input_tokens: int, output_tokens: int}", - ) - cost = Column( - MutableDict.as_mutable(JSON), - nullable=True, - comment="Cost of the experiment in dollars", - ) created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(UTC)) updated_at = Column( @@ -308,17 +297,6 @@ class Run(Base): 0: UNKNOWN, 1: PENDING, 2: RUNNING, 9: COMPLETED, \ 10: CANCELLED, 11: FAILED", ) - usage = Column( - MutableDict.as_mutable(JSON), - nullable=True, - comment="The usage information, e.g. for LLM calls: \ - {total_tokens: int, input_tokens: int, output_tokens: int}", - ) - cost = Column( - MutableDict.as_mutable(JSON), - nullable=True, - comment="Cost of the run in dollars", - ) created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(UTC)) updated_at = Column( diff --git a/alphatrion/storage/sqlstore.py b/alphatrion/storage/sqlstore.py index e0acd7fa..c860dc1b 100644 --- a/alphatrion/storage/sqlstore.py +++ b/alphatrion/storage/sqlstore.py @@ -958,7 +958,6 @@ def create_run( meta: dict | None = None, status: Status = Status.PENDING, duration: float | None = None, - usage: dict | None = None, ) -> uuid.UUID: session = self._session() @@ -971,7 +970,6 @@ def create_run( meta=meta, status=status, duration=duration, - usage=usage, ) session.add(new_run) session.commit() @@ -1241,6 +1239,7 @@ def team_is_accessible_to_user( ) -> bool: session = self._session() + # Check if user is super admin if self.user_is_super_admin_in_org(user_id, org_id): session.close() return True diff --git a/alphatrion/storage/tracestore.py b/alphatrion/storage/tracestore.py index f0baa1e1..ec108be7 100644 --- a/alphatrion/storage/tracestore.py +++ b/alphatrion/storage/tracestore.py @@ -357,16 +357,16 @@ def get_llm_spans_by_exp_id( logger.error(f"Failed to get spans by exp_id: {e}") return [] - def get_llm_tokens_by_team_id( + def get_llm_usage_by_team_id( self, org_id: uuid.UUID, team_id: uuid.UUID ) -> list[dict[str, Any]]: - """Get all LLM spans for a specific team_id. + """Get aggregated tokens and cost for a team. Args: org_id: The organization ID to filter by team_id: The team ID to filter by Returns: - List of LLM span dictionaries + List with one dict containing tokens and cost """ with self._lock: try: @@ -374,29 +374,36 @@ def get_llm_tokens_by_team_id( SELECT SUM(toInt64OrZero(SpanAttributes['llm.usage.total_tokens'])) as total_tokens, SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.input_tokens'])) as input_tokens, - SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_read_input_tokens'])) as cache_read_input_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_creation_input_tokens'])) as cache_creation_input_tokens, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.total_tokens'])) as total_cost FROM {self.database}.otel_spans WHERE OrgId = '{org_id}' AND TeamId = '{team_id}' """ result = self.client.query(query) - # Convert date to string format and ensure integers return [ { "total_tokens": int(row["total_tokens"]), "input_tokens": int(row["input_tokens"]), "output_tokens": int(row["output_tokens"]), + "cache_read_input_tokens": int(row["cache_read_input_tokens"]), + "cache_creation_input_tokens": int( + row["cache_creation_input_tokens"] + ), + "total_cost": float(row["total_cost"]), } for row in result.named_results() ] except Exception as e: - logger.error(f"Failed to get daily token usage: {e}") + logger.error(f"Failed to get tokens and cost by team: {e}") return [] - def get_llm_tokens_by_agent_id( + def get_llm_usage_by_agent_id( self, org_id: uuid.UUID, team_id: uuid.UUID, agent_id: uuid.UUID ) -> list[dict[str, Any]]: - """Get aggregated LLM token usage for a specific agent. + """Get aggregated LLM token usage and cost for a specific agent. Args: org_id: The organization ID to filter by @@ -411,7 +418,10 @@ def get_llm_tokens_by_agent_id( SELECT SUM(toInt64OrZero(SpanAttributes['llm.usage.total_tokens'])) as total_tokens, SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.input_tokens'])) as input_tokens, - SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_read_input_tokens'])) as cache_read_input_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_creation_input_tokens'])) as cache_creation_input_tokens, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.total_tokens'])) as total_cost FROM {self.database}.otel_spans WHERE OrgId = '{org_id}' AND TeamId = '{team_id}' AND AgentId = '{agent_id}' """ @@ -422,6 +432,11 @@ def get_llm_tokens_by_agent_id( "total_tokens": int(row["total_tokens"]), "input_tokens": int(row["input_tokens"]), "output_tokens": int(row["output_tokens"]), + "cache_read_input_tokens": int(row["cache_read_input_tokens"]), + "cache_creation_input_tokens": int( + row["cache_creation_input_tokens"] + ), + "total_cost": float(row["total_cost"]), } for row in result.named_results() ] @@ -429,10 +444,10 @@ def get_llm_tokens_by_agent_id( logger.error(f"Failed to get agent token usage: {e}") return [] - def get_llm_tokens_by_session_id( + def get_llm_usage_by_session_id( self, org_id: uuid.UUID, team_id: uuid.UUID, session_id: uuid.UUID ) -> list[dict[str, Any]]: - """Get aggregated LLM token usage for a specific session. + """Get aggregated LLM token usage and cost for a specific session. Args: org_id: The organization ID to filter by @@ -447,7 +462,10 @@ def get_llm_tokens_by_session_id( SELECT SUM(toInt64OrZero(SpanAttributes['llm.usage.total_tokens'])) as total_tokens, SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.input_tokens'])) as input_tokens, - SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_read_input_tokens'])) as cache_read_input_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_creation_input_tokens'])) as cache_creation_input_tokens, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.total_tokens'])) as total_cost FROM {self.database}.otel_spans WHERE OrgId = '{org_id}' AND TeamId = '{team_id}' AND SessionId = '{session_id}' """ @@ -458,6 +476,11 @@ def get_llm_tokens_by_session_id( "total_tokens": int(row["total_tokens"]), "input_tokens": int(row["input_tokens"]), "output_tokens": int(row["output_tokens"]), + "cache_read_input_tokens": int(row["cache_read_input_tokens"]), + "cache_creation_input_tokens": int( + row["cache_creation_input_tokens"] + ), + "total_cost": float(row["total_cost"]), } for row in result.named_results() ] @@ -484,7 +507,9 @@ def get_token_distribution_by_semantic_kind( SemanticKind as semantic_kind, SUM(toInt64OrZero(SpanAttributes['llm.usage.total_tokens'])) as total_tokens, SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.input_tokens'])) as input_tokens, - SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_read_input_tokens'])) as cache_read_input_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_creation_input_tokens'])) as cache_creation_input_tokens FROM {self.database}.otel_spans WHERE OrgId = '{org_id}' AND TeamId = '{team_id}' GROUP BY SemanticKind @@ -498,6 +523,10 @@ def get_token_distribution_by_semantic_kind( "total_tokens": int(row["total_tokens"]), "input_tokens": int(row["input_tokens"]), "output_tokens": int(row["output_tokens"]), + "cache_read_input_tokens": int(row["cache_read_input_tokens"]), + "cache_creation_input_tokens": int( + row["cache_creation_input_tokens"] + ), } for row in result.named_results() ] @@ -529,27 +558,28 @@ def get_model_distributions_by_team_id( COUNT(*) as count FROM {self.database}.otel_spans WHERE OrgId = '{org_id}' AND TeamId = '{team_id}' - AND SemanticKind IN ('llm', 'thinking', 'text-generation', 'tool') GROUP BY model ORDER BY count DESC """ result = self.client.query(query) + # ignore empty models return [ { "model": row["model"], "count": int(row["count"]), } for row in result.named_results() + if row["model"] is not None and row["model"] != "" ] except Exception as e: logger.error(f"Failed to get model distributions: {e}") return [] - def get_daily_token_usage( + def get_daily_cost_usage( self, org_id: uuid.UUID, team_id: uuid.UUID, days: int = 30 ) -> list[dict[str, Any]]: - """Get daily token usage from LLM calls for a team. + """Get daily cost and token usage from LLM calls for a team. Args: org_id: The organization ID to filter by @@ -557,16 +587,23 @@ def get_daily_token_usage( days: Number of days to look back (default: 30) Returns: - List of dicts with keys: date, total_tokens, input_tokens, output_tokens + List of dicts with keys: date, total_cost, input_cost, output_cost, total_tokens, input_tokens, output_tokens """ with self._lock: try: query = f""" SELECT toDate(Timestamp) as date, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.total_tokens'])) as total_cost, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.input_tokens'])) as input_cost, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.output_tokens'])) as output_cost, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.cache_creation_input_tokens'])) as cache_creation_input_cost, + SUM(toFloat64OrZero(SpanAttributes['alphatrion.cost.cache_read_input_tokens'])) as cache_read_input_cost, SUM(toInt64OrZero(SpanAttributes['llm.usage.total_tokens'])) as total_tokens, SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.input_tokens'])) as input_tokens, - SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.output_tokens'])) as output_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_read_input_tokens'])) as cache_read_input_tokens, + SUM(toInt64OrZero(SpanAttributes['gen_ai.usage.cache_creation_input_tokens'])) as cache_creation_input_tokens FROM {self.database}.otel_spans WHERE OrgId = '{org_id}' AND TeamId = '{team_id}' AND Timestamp >= now() - INTERVAL {days} DAY @@ -575,18 +612,29 @@ def get_daily_token_usage( """ result = self.client.query(query) - # Convert date to string format and ensure integers + # Convert date to string format return [ { "date": row["date"].strftime("%Y-%m-%d"), + "total_cost": float(row["total_cost"]), + "input_cost": float(row["input_cost"]), + "output_cost": float(row["output_cost"]), + "cache_creation_input_cost": float( + row["cache_creation_input_cost"] + ), + "cache_read_input_cost": float(row["cache_read_input_cost"]), "total_tokens": int(row["total_tokens"]), "input_tokens": int(row["input_tokens"]), "output_tokens": int(row["output_tokens"]), + "cache_read_input_tokens": int(row["cache_read_input_tokens"]), + "cache_creation_input_tokens": int( + row["cache_creation_input_tokens"] + ), } for row in result.named_results() ] except Exception as e: - logger.error(f"Failed to get daily token usage: {e}") + logger.error(f"Failed to get daily cost usage: {e}") return [] def get_trace_stats_by_exp_id( diff --git a/alphatrion/tracing/clickhouse_exporter.py b/alphatrion/tracing/clickhouse_exporter.py index 64653e0c..27afb921 100644 --- a/alphatrion/tracing/clickhouse_exporter.py +++ b/alphatrion/tracing/clickhouse_exporter.py @@ -8,6 +8,11 @@ from opentelemetry.trace import StatusCode from alphatrion.storage.tracestore import TraceStore +from alphatrion.tracing.span_processor import ( + SEMANTIC_KIND_DB, + SEMANTIC_KIND_REASONING, + SEMANTIC_KIND_UNKNOWN, +) logger = logging.getLogger(__name__) @@ -122,6 +127,19 @@ def _convert_span(self, span: ReadableSpan) -> dict[str, Any]: if span.attributes: span_attributes = {k: str(v) for k, v in span.attributes.items()} + # Calculate cost for LLM spans with token usage + # Store cost per span to enable model-level cost analytics + if "alphatrion.cost.total_tokens" in span_attributes: + try: + # Cost attributes are already enriched by CostEnrichmentProcessor + # Just ensure they exist in span_attributes for ClickHouse storage + # (they should already be present from the span) + pass + + except Exception as e: + logger.warning(f"Failed to process LLM span {span.name}: {e}") + # Don't fail span export if processing fails + # Extract core identifiers from span attributes org_id = span_attributes.get("org_id", "") team_id = span_attributes.get("team_id", "") @@ -227,64 +245,33 @@ def force_flush(self, timeout_millis: int = 30000) -> bool: return True -def determine_semantic_kind( - attributes: dict[str, str], content_blocks: list[dict] = None -) -> str: +def determine_semantic_kind(attributes: dict[str, str]) -> str: """Determine the semantic kind of a span. - Priority order: - 1. Content type based (thinking, llm, tool) - if content_blocks provided - 2. Tool calls (has tool attributes) - 3. LLM calls (has token usage) - 4. Database operations (has db attributes) - 5. HTTP requests (has http attributes) - 6. Traceloop semantic kinds (workflow, task, agent) - 7. Unknown - Args: attributes: Span attributes - content_blocks: Optional list of content blocks (for Claude agents) Returns: Semantic kind string """ - # Check content type first (Claude agents) - if content_blocks: - content_types = [b.get("type") for b in content_blocks if isinstance(b, dict)] - if "tool_use" in content_types: - return "tool" - elif "thinking" in content_types: - return "thinking" - elif "text" in content_types: - return "text-generation" - # Has content blocks but unknown type - fallback to llm - elif content_types: - return "llm" - - # Check for tool calls (fallback) - if "tool.name" in attributes: - return "tool" - - # Check for LLM calls (fallback) + if ( - "llm.usage.total_tokens" in attributes - or "gen_ai.usage.output_tokens" in attributes + "gen_ai.usage.reasoning_tokens" in attributes + and int(attributes["gen_ai.usage.reasoning_tokens"]) > 0 ): - return "llm" + return SEMANTIC_KIND_REASONING - # Check for Traceloop semantic kinds - if "traceloop.span.kind" in attributes: - traceloop_kind = attributes["traceloop.span.kind"] - if traceloop_kind in ("workflow", "task", "agent"): - return traceloop_kind + if "llm.request.type" in attributes: + return attributes["llm.request.type"] # Check for database operations if "db.system" in attributes or "db.statement" in attributes: - return "database" + return SEMANTIC_KIND_DB - # Check for HTTP requests - if "http.method" in attributes or "http.url" in attributes: - return "http" + # One of workflow, task, agent, tool + if "traceloop.span.kind" in attributes: + traceloop_kind = attributes["traceloop.span.kind"] + return traceloop_kind # Default to unknown - return "unknown" + return SEMANTIC_KIND_UNKNOWN diff --git a/alphatrion/tracing/cost_enrichment_processor.py b/alphatrion/tracing/cost_enrichment_processor.py new file mode 100644 index 00000000..aa02a869 --- /dev/null +++ b/alphatrion/tracing/cost_enrichment_processor.py @@ -0,0 +1,144 @@ +""" +Cost Enrichment Span Processor. + +This processor enriches spans with cost information by calculating costs from token usage. +It runs early in the processing chain so that downstream processors and exporters can +access pre-calculated costs from span attributes. +""" + +import logging + +from opentelemetry.context import Context +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.sdk.trace.export import SpanProcessor + +from alphatrion.utils.pricing import calculate_cost + +logger = logging.getLogger(__name__) + + +class CostEnrichmentProcessor(SpanProcessor): + """ + Span processor that enriches spans with cost information. + + This processor checks if cost attributes are already present in a span. + If not, it calculates costs from token usage and adds them to the span's + attributes dictionary. This ensures all downstream processors and exporters + have access to consistent cost data. + """ + + def on_start(self, span: ReadableSpan, parent_context: Context | None = None): + """Called when a span is started. No-op for this processor.""" + pass + + def on_end(self, span: ReadableSpan): + """ + Called when a span ends. Calculate and add cost attributes if missing. + + Args: + span: The completed span + """ + try: + # Only process spans with attributes + if not span.attributes: + return + + # Check if costs are already present + if "alphatrion.cost.total_tokens" in span.attributes: + # Costs already calculated (e.g., in claude.py) + return + + # Check if this is an LLM span with token usage + if "gen_ai.usage.input_tokens" not in span.attributes: + # Not an LLM span, skip + return + + # Extract token usage + attributes = span.attributes + provider = determine_provider(str(attributes.get("gen_ai.openai.api_base"))) + model = str( + attributes.get("gen_ai.request.model") + or attributes.get("gen_ai.response.model", "") + ) + input_tokens = int(attributes.get("gen_ai.usage.input_tokens", 0)) + output_tokens = int(attributes.get("gen_ai.usage.output_tokens", 0)) + cache_creation_input_tokens = int( + attributes.get("gen_ai.usage.cache_creation_input_tokens", 0) + ) + cache_read_input_tokens = int( + attributes.get("gen_ai.usage.cache_read_input_tokens", 0) + ) + + # Calculate costs + cost_result = calculate_cost( + provider=provider, + model=model, + input_tokens=input_tokens, + output_tokens=output_tokens, + cache_creation_input_tokens=cache_creation_input_tokens, + cache_read_input_tokens=cache_read_input_tokens, + ) + + # Add cost attributes to span + # Note: We can't modify ReadableSpan.attributes directly after span ends, + # but we can modify the underlying _attributes dict that will be read + # by exporters. This is a bit of a hack but it's the only way to enrich + # spans post-creation without modifying OpenTelemetry internals. + if hasattr(span, "_attributes"): + span._attributes["alphatrion.cost.total_tokens"] = str( + cost_result["total_cost"] + ) + span._attributes["alphatrion.cost.input_tokens"] = str( + cost_result["input_cost"] + ) + span._attributes["alphatrion.cost.output_tokens"] = str( + cost_result["output_cost"] + ) + span._attributes["alphatrion.cost.cache_creation_input_tokens"] = str( + cost_result["cache_creation_input_cost"] + ) + span._attributes["alphatrion.cost.cache_read_input_tokens"] = str( + cost_result["cache_read_input_cost"] + ) + logger.debug( + f"Enriched span {span.name} with cost: ${cost_result['total_cost']:.6f}" + ) + + except Exception as e: + logger.warning(f"Failed to enrich span with cost: {e}", exc_info=True) + + def shutdown(self): + """Shutdown the processor.""" + logger.info("CostEnrichmentProcessor shut down successfully") + + def force_flush(self, timeout_millis: int = 30000) -> bool: + """ + Force flush (no-op for this processor). + + Args: + timeout_millis: Timeout in milliseconds + + Returns: + True always + """ + return True + + +def determine_provider(api_base: str) -> str: + """Determine provider from API base URL. + + Args: + api_base: API base URL (e.g., "https://api.anthropic.com") + + Returns: + Provider name (e.g., "anthropic", "openai", "deepinfra", or "unknown") + """ + api_base = api_base.lower() + if "anthropic" in api_base: + return "anthropic" + elif "deepinfra" in api_base: + return "deepinfra" + elif "openai" in api_base: + return "openai" + else: + return "unknown" diff --git a/alphatrion/tracing/prometheus_exporter.py b/alphatrion/tracing/prometheus_exporter.py new file mode 100644 index 00000000..3f3ee916 --- /dev/null +++ b/alphatrion/tracing/prometheus_exporter.py @@ -0,0 +1,392 @@ +""" +Prometheus Span Exporter. + +Exports OpenTelemetry span metrics to Prometheus push gateway. +""" + +import logging +import socket +import uuid + +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.sdk.trace.export import SpanExporter, SpanExportResult +from prometheus_client import ( + CollectorRegistry, + Counter, + Histogram, + pushadd_to_gateway, +) + +logger = logging.getLogger(__name__) + + +class PrometheusExporter(SpanExporter): + """ + Span exporter that exports metrics to Prometheus push gateway. + """ + + def __init__( + self, + pushgateway_url: str, + job_name: str = "alphatrion", + grouping_key: dict[str, str] | None = None, + ): + """ + Initialize the Prometheus exporter. + + Args: + pushgateway_url: URL of the Prometheus push gateway + job_name: Job name for the metrics in Prometheus + grouping_key: Additional grouping labels + """ + self.pushgateway_url = pushgateway_url + self.job_name = job_name + + if grouping_key is None: + try: + hostname = socket.gethostname() + instance_id = ( + f"{hostname}-{uuid.uuid4().hex}" if hostname else uuid.uuid4().hex + ) + except Exception: + instance_id = uuid.uuid4().hex + + self.grouping_key = {"instance": instance_id} + else: + self.grouping_key = grouping_key + + self.registry = CollectorRegistry() + self._init_metrics() + + logger.info( + f"PrometheusExporter initialized: pushgateway={pushgateway_url}, job={job_name}" + ) + + def _init_metrics(self): + """Initialize Prometheus metrics.""" + # Token metrics + self.llm_tokens_total = Counter( + "llm_tokens_total", + "Total LLM tokens consumed", + ["team_id", "experiment_id", "model", "token_type"], + registry=self.registry, + ) + + self.llm_input_tokens_total = Counter( + "llm_input_tokens_total", + "Total LLM input tokens consumed", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + self.llm_output_tokens_total = Counter( + "llm_output_tokens_total", + "Total LLM output tokens consumed", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + self.llm_cache_read_input_tokens_total = Counter( + "llm_cache_read_input_tokens_total", + "Total LLM cache read input tokens", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + self.llm_cache_creation_input_tokens_total = Counter( + "llm_cache_creation_input_tokens_total", + "Total LLM cache creation input tokens", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + # Cost metrics + self.llm_cost_total = Counter( + "llm_cost_total", + "Total LLM cost in USD", + ["team_id", "experiment_id", "model", "cost_type"], + registry=self.registry, + ) + + self.llm_input_cost_total = Counter( + "llm_input_cost_total", + "Total LLM input cost in USD", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + self.llm_output_cost_total = Counter( + "llm_output_cost_total", + "Total LLM output cost in USD", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + self.llm_cache_read_cost_total = Counter( + "llm_cache_read_cost_total", + "Total LLM cache read cost in USD", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + self.llm_cache_creation_cost_total = Counter( + "llm_cache_creation_cost_total", + "Total LLM cache creation cost in USD", + ["team_id", "experiment_id", "model"], + registry=self.registry, + ) + + # Request metrics + self.llm_requests_total = Counter( + "llm_requests_total", + "Total number of LLM requests", + ["team_id", "experiment_id", "model", "status"], + registry=self.registry, + ) + + # Latency metrics + self.llm_request_duration_seconds = Histogram( + "llm_request_duration_seconds", + "LLM request duration in seconds", + ["team_id", "experiment_id", "model"], + buckets=[0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 30.0, 60.0], + registry=self.registry, + ) + + # Error metrics + self.llm_errors_total = Counter( + "llm_errors_total", + "Total LLM errors by type", + ["error_type"], + registry=self.registry, + ) + + def export(self, spans: list[ReadableSpan]) -> SpanExportResult: + """ + Export spans to Prometheus push gateway. + + Args: + spans: List of spans to export + + Returns: + SpanExportResult indicating success or failure + """ + try: + for span in spans: + self._process_span(span) + + self._push_metrics() + return SpanExportResult.SUCCESS + + except Exception as e: + logger.error(f"Failed to export spans to Prometheus: {e}", exc_info=True) + return SpanExportResult.FAILURE + + def _process_span(self, span: ReadableSpan): + """Process a single span and update metrics.""" + try: + if not span.attributes or "traceloop.workflow.name" not in span.attributes: + return + + attributes = {k: str(v) for k, v in span.attributes.items()} + team_id = attributes.get("team_id", "unknown") + experiment_id = attributes.get("experiment_id", "unknown") + + if "llm.usage.total_tokens" not in attributes: + return + + duration = (span.end_time - span.start_time) / 1_000_000_000 + status_map = {0: "UNSET", 1: "OK", 2: "ERROR"} + status = status_map.get(span.status.status_code.value, "UNSET") + + if status == "ERROR": + error_type = self._classify_error(span, attributes) + self.llm_errors_total.labels(error_type=error_type).inc() + + self._process_llm_span( + span, attributes, team_id, experiment_id, duration, status + ) + + except Exception as e: + logger.error(f"Failed to process span: {e}", exc_info=True) + + def _classify_error(self, span: ReadableSpan, attributes: dict[str, str]) -> str: + """Classify error type from span.""" + status_msg = (span.status.description or "").lower() + + if "timeout" in status_msg or "timed out" in status_msg: + return "timeout" + elif "rate limit" in status_msg or "429" in status_msg: + return "rate_limit" + elif "auth" in status_msg or "401" in status_msg or "403" in status_msg: + return "auth_error" + elif "not found" in status_msg or "404" in status_msg: + return "not_found" + elif "invalid" in status_msg or "400" in status_msg: + return "invalid_request" + elif "connection" in status_msg or "network" in status_msg: + return "connection_error" + elif "500" in status_msg or "502" in status_msg or "503" in status_msg: + return "server_error" + else: + return "unknown" + + def _process_llm_span( + self, + span: ReadableSpan, + attributes: dict[str, str], + team_id: str, + experiment_id: str, + duration: float, + status: str, + ): + """Process LLM-specific metrics from a span.""" + model = attributes.get( + "gen_ai.request.model", attributes.get("gen_ai.response.model", "unknown") + ) + + # Token metrics + total_tokens = int(attributes.get("llm.usage.total_tokens", 0)) + input_tokens = int(attributes.get("gen_ai.usage.input_tokens", 0)) + output_tokens = int(attributes.get("gen_ai.usage.output_tokens", 0)) + cache_read_input_tokens = int( + attributes.get("gen_ai.usage.cache_read_input_tokens", 0) + ) + cache_creation_input_tokens = int( + attributes.get("gen_ai.usage.cache_creation_input_tokens", 0) + ) + + if total_tokens > 0: + self.llm_tokens_total.labels( + team_id=team_id, + experiment_id=experiment_id, + model=model, + token_type="total", + ).inc(total_tokens) + + if input_tokens > 0: + self.llm_input_tokens_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(input_tokens) + self.llm_tokens_total.labels( + team_id=team_id, + experiment_id=experiment_id, + model=model, + token_type="input", + ).inc(input_tokens) + + if output_tokens > 0: + self.llm_output_tokens_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(output_tokens) + self.llm_tokens_total.labels( + team_id=team_id, + experiment_id=experiment_id, + model=model, + token_type="output", + ).inc(output_tokens) + + if cache_read_input_tokens > 0: + self.llm_cache_read_input_tokens_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(cache_read_input_tokens) + self.llm_tokens_total.labels( + team_id=team_id, + experiment_id=experiment_id, + model=model, + token_type="cache_read_input", + ).inc(cache_read_input_tokens) + + if cache_creation_input_tokens > 0: + self.llm_cache_creation_input_tokens_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(cache_creation_input_tokens) + self.llm_tokens_total.labels( + team_id=team_id, + experiment_id=experiment_id, + model=model, + token_type="cache_creation_input", + ).inc(cache_creation_input_tokens) + + # Cost metrics - read from enriched attributes + try: + total_cost = float(attributes.get("alphatrion.cost.total_tokens", 0)) + input_cost = float(attributes.get("alphatrion.cost.input_tokens", 0)) + output_cost = float(attributes.get("alphatrion.cost.output_tokens", 0)) + cache_read_cost = float( + attributes.get("alphatrion.cost.cache_read_input_tokens", 0) + ) + cache_creation_cost = float( + attributes.get("alphatrion.cost.cache_creation_input_tokens", 0) + ) + + if total_cost > 0: + self.llm_cost_total.labels( + team_id=team_id, + experiment_id=experiment_id, + model=model, + cost_type="total", + ).inc(total_cost) + + if input_cost > 0: + self.llm_input_cost_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(input_cost) + + if output_cost > 0: + self.llm_output_cost_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(output_cost) + + if cache_read_cost > 0: + self.llm_cache_read_cost_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(cache_read_cost) + + if cache_creation_cost > 0: + self.llm_cache_creation_cost_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).inc(cache_creation_cost) + + except (ValueError, TypeError) as e: + logger.debug(f"No cost data available for span: {e}") + + # Request count + self.llm_requests_total.labels( + team_id=team_id, experiment_id=experiment_id, model=model, status=status + ).inc() + + # Duration + self.llm_request_duration_seconds.labels( + team_id=team_id, experiment_id=experiment_id, model=model + ).observe(duration) + + def _push_metrics(self): + """Push metrics to Prometheus push gateway.""" + try: + pushadd_to_gateway( + self.pushgateway_url, + job=self.job_name, + registry=self.registry, + grouping_key=self.grouping_key, + ) + logger.debug("Successfully pushed metrics to Prometheus push gateway") + except Exception as e: + logger.warning(f"Failed to push metrics to Prometheus: {e}") + + def shutdown(self): + """Shutdown the exporter and perform final push.""" + try: + self._push_metrics() + logger.info("PrometheusExporter shut down successfully") + except Exception as e: + logger.error(f"Error during PrometheusExporter shutdown: {e}") + + def force_flush(self, timeout_millis: int = 30000) -> bool: + """Force flush metrics to push gateway.""" + try: + self._push_metrics() + return True + except Exception as e: + logger.error(f"Failed to force flush metrics: {e}") + return False diff --git a/alphatrion/tracing/prometheus_span_processor.py b/alphatrion/tracing/prometheus_span_processor.py deleted file mode 100644 index 6cfd75b1..00000000 --- a/alphatrion/tracing/prometheus_span_processor.py +++ /dev/null @@ -1,322 +0,0 @@ -# ruff: noqa: PLR0911 - -import logging -import socket -import uuid - -from opentelemetry.context import Context -from opentelemetry.sdk.trace import ReadableSpan -from opentelemetry.sdk.trace.export import SpanProcessor -from prometheus_client import ( - CollectorRegistry, - Counter, - Histogram, - pushadd_to_gateway, -) - -logger = logging.getLogger(__name__) - - -class PrometheusSpanProcessor(SpanProcessor): - """ - Span processor that exports OpenTelemetry span metrics to Prometheus push gateway. - - This processor extracts metrics from LLM spans (tokens, latency, etc.) and pushes - them to a Prometheus push gateway, making them available for scraping by Prometheus. - """ - - def __init__( - self, - pushgateway_url: str, - job_name: str = "alphatrion", - grouping_key: dict[str, str] | None = None, - ): - """ - Initialize the Prometheus span processor. - - Args: - pushgateway_url: URL of the Prometheus push gateway (e.g., "localhost:9091") - job_name: Job name for the metrics in Prometheus - grouping_key: Additional grouping labels (e.g., {"instance": "app-1"}) - """ - self.pushgateway_url = pushgateway_url - self.job_name = job_name - - # Generate unique instance identifier to prevent metrics from being overwritten - # Combines hostname (for traceability) with UUID (for uniqueness) - if grouping_key is None: - try: - hostname = socket.gethostname() - if hostname: - instance_id = f"{hostname}-{uuid.uuid4().hex}" - else: - instance_id = uuid.uuid4().hex - except Exception: - instance_id = uuid.uuid4().hex - - self.grouping_key = {"instance": instance_id} - else: - self.grouping_key = grouping_key - - # Create a separate registry for push gateway metrics - self.registry = CollectorRegistry() - - # Define metrics - self._init_metrics() - - logger.info( - f"PrometheusSpanProcessor initialized: pushgateway={pushgateway_url}, " - f"job={job_name}" - ) - - def _init_metrics(self): - """Initialize Prometheus metrics.""" - - # LLM Token usage metrics - self.llm_tokens_total = Counter( - "alphatrion_llm_tokens_total", - "Total LLM tokens consumed", - ["team_id", "experiment_id", "model", "token_type"], - registry=self.registry, - ) - - self.llm_input_tokens_total = Counter( - "alphatrion_llm_input_tokens_total", - "Total LLM input tokens consumed", - ["team_id", "experiment_id", "model"], - registry=self.registry, - ) - - self.llm_output_tokens_total = Counter( - "alphatrion_llm_output_tokens_total", - "Total LLM output tokens consumed", - ["team_id", "experiment_id", "model"], - registry=self.registry, - ) - - # LLM Request metrics - self.llm_requests_total = Counter( - "alphatrion_llm_requests_total", - "Total number of LLM requests", - ["team_id", "experiment_id", "model", "status"], - registry=self.registry, - ) - - # LLM Latency metrics - self.llm_duration_seconds = Histogram( - "alphatrion_llm_duration_seconds", - "LLM request duration in seconds", - ["team_id", "experiment_id", "model"], - buckets=[0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 30.0, 60.0], - registry=self.registry, - ) - - # Error tracking - self.llm_errors_total = Counter( - "alphatrion_llm_errors_total", - "Total LLM errors by type", - ["error_type"], - registry=self.registry, - ) - - def on_start(self, span: ReadableSpan, parent_context: Context | None = None): - """Called when a span is started. No-op for this processor.""" - pass - - def on_end(self, span: ReadableSpan): - """ - Called when a span ends. Extract metrics and push to Prometheus. - - Args: - span: The completed span - """ - try: - # Only process spans with traceloop attributes - # (same filter as ClickHouse exporter) - if not span.attributes or "traceloop.workflow.name" not in span.attributes: - return - - # Extract common attributes - attributes = {k: str(v) for k, v in span.attributes.items()} - team_id = attributes.get("team_id", "unknown") - experiment_id = attributes.get("experiment_id", "unknown") - - # Only process LLM spans - if "llm.usage.total_tokens" not in attributes: - return - - # Calculate duration in seconds - duration = (span.end_time - span.start_time) / 1_000_000_000 - - # Status - status_map = {0: "UNSET", 1: "OK", 2: "ERROR"} - status = status_map.get(span.status.status_code.value, "UNSET") - - # Track errors - if status == "ERROR": - error_type = self._classify_error(span, attributes) - self.llm_errors_total.labels( - error_type=error_type, - ).inc() - - # Process LLM-specific metrics - self._process_llm_span( - span, attributes, team_id, experiment_id, duration, status - ) - - # Push to gateway - self._push_metrics() - - except Exception as e: - logger.error(f"Failed to process span metrics: {e}", exc_info=True) - - def _classify_error(self, span: ReadableSpan, attributes: dict[str, str]) -> str: - """ - Classify error type from span. - - Args: - span: The span with error - attributes: Span attributes - - Returns: - Error type string - """ - # Check status message for common error patterns - status_msg = span.status.description or "" - status_msg_lower = status_msg.lower() - - # Common error patterns - if "timeout" in status_msg_lower or "timed out" in status_msg_lower: - return "timeout" - elif "rate limit" in status_msg_lower or "429" in status_msg_lower: - return "rate_limit" - elif ( - "auth" in status_msg_lower - or "401" in status_msg_lower - or "403" in status_msg_lower - ): - return "auth_error" - elif "not found" in status_msg_lower or "404" in status_msg_lower: - return "not_found" - elif "invalid" in status_msg_lower or "400" in status_msg_lower: - return "invalid_request" - elif "connection" in status_msg_lower or "network" in status_msg_lower: - return "connection_error" - elif ( - "500" in status_msg_lower - or "502" in status_msg_lower - or "503" in status_msg_lower - ): - return "server_error" - else: - return "unknown" - - def _process_llm_span( - self, - span: ReadableSpan, - attributes: dict[str, str], - team_id: str, - experiment_id: str, - duration: float, - status: str, - ): - """Process LLM-specific metrics from a span.""" - # Extract model name - model = attributes.get( - "gen_ai.request.model", attributes.get("gen_ai.response.model", "unknown") - ) - - # Token metrics - total_tokens = int(attributes.get("llm.usage.total_tokens", 0)) - input_tokens = int(attributes.get("gen_ai.usage.input_tokens", 0)) - output_tokens = int(attributes.get("gen_ai.usage.output_tokens", 0)) - - if total_tokens > 0: - self.llm_tokens_total.labels( - team_id=team_id, - experiment_id=experiment_id, - model=model, - token_type="total", - ).inc(total_tokens) - - if input_tokens > 0: - self.llm_input_tokens_total.labels( - team_id=team_id, - experiment_id=experiment_id, - model=model, - ).inc(input_tokens) - - self.llm_tokens_total.labels( - team_id=team_id, - experiment_id=experiment_id, - model=model, - token_type="input", - ).inc(input_tokens) - - if output_tokens > 0: - self.llm_output_tokens_total.labels( - team_id=team_id, - experiment_id=experiment_id, - model=model, - ).inc(output_tokens) - - self.llm_tokens_total.labels( - team_id=team_id, - experiment_id=experiment_id, - model=model, - token_type="output", - ).inc(output_tokens) - - # Request count - self.llm_requests_total.labels( - team_id=team_id, - experiment_id=experiment_id, - model=model, - status=status, - ).inc() - - # Duration - self.llm_duration_seconds.labels( - team_id=team_id, - experiment_id=experiment_id, - model=model, - ).observe(duration) - - def _push_metrics(self): - """Push metrics to Prometheus push gateway.""" - try: - # Use pushadd_to_gateway to accumulate counters instead of replacing them - pushadd_to_gateway( - self.pushgateway_url, - job=self.job_name, - registry=self.registry, - grouping_key=self.grouping_key, - ) - logger.debug("Successfully pushed metrics to Prometheus push gateway") - except Exception as e: - logger.warning(f"Failed to push metrics to Prometheus: {e}") - - def shutdown(self): - """Shutdown the processor and perform final push.""" - try: - self._push_metrics() - logger.info("PrometheusSpanProcessor shut down successfully") - except Exception as e: - logger.error(f"Error during PrometheusSpanProcessor shutdown: {e}") - - def force_flush(self, timeout_millis: int = 30000) -> bool: - """ - Force flush metrics to push gateway. - - Args: - timeout_millis: Timeout in milliseconds (not used) - - Returns: - True if successful, False otherwise - """ - try: - self._push_metrics() - return True - except Exception as e: - logger.error(f"Failed to force flush metrics: {e}") - return False diff --git a/alphatrion/tracing/span_processor.py b/alphatrion/tracing/span_processor.py index 4d5015a9..48e59660 100644 --- a/alphatrion/tracing/span_processor.py +++ b/alphatrion/tracing/span_processor.py @@ -10,6 +10,13 @@ logger = logging.getLogger(__name__) +# Semantic kind enums: +SEMANTIC_KIND_TOOL = "tool" +SEMANTIC_KIND_REASONING = "reasoning" +SEMANTIC_KIND_CHAT = "chat" +SEMANTIC_KIND_DB = "db" +SEMANTIC_KIND_UNKNOWN = "unknown" + class ContextAttributesSpanProcessor(SpanProcessor): """SpanProcessor that adds run_id, org_id, team_id, user_id, experiment_id to all spans. @@ -34,6 +41,9 @@ def on_start(self, span: Span, parent_context: Context | None = None) -> None: if run_id is None: # No run context, skip setting attributes + logger.debug( + "Span started with no run_id in context, skipping attribute injection" + ) return if exp_id is None: @@ -50,9 +60,7 @@ def on_start(self, span: Span, parent_context: Context | None = None) -> None: span.set_attribute("org_id", str(runtime.org_id)) span.set_attribute("team_id", str(runtime.team_id)) span.set_attribute("user_id", str(runtime.user_id)) - - if exp_id: - span.set_attribute("experiment_id", str(exp_id)) + span.set_attribute("experiment_id", str(exp_id)) except (RuntimeError, AttributeError) as e: logger.debug(f"Could not set span attributes in processor: {e}") diff --git a/alphatrion/utils/pricing.py b/alphatrion/utils/pricing.py new file mode 100644 index 00000000..d9fbf840 --- /dev/null +++ b/alphatrion/utils/pricing.py @@ -0,0 +1,121 @@ +"""LLM pricing utilities for cost calculation.""" + +import logging +from pathlib import Path +from typing import Any + +import yaml + +logger = logging.getLogger(__name__) + +# Cache pricing config in memory +_PRICING_CACHE: dict[str, Any] | None = None + + +def load_pricing_config() -> dict[str, Any]: + """Load pricing config from YAML file. + + Returns: + Dict with model pricing information + """ + global _PRICING_CACHE + + if _PRICING_CACHE is not None: + return _PRICING_CACHE + + config_path = Path(__file__).parent.parent.parent / "config" / "modelspec.yaml" + + try: + with open(config_path) as f: + config = yaml.safe_load(f) + _PRICING_CACHE = config + logger.info(f"Loaded pricing config from {config_path}") + return config + except Exception as e: + logger.error(f"Failed to load pricing config: {e}") + raise + + +def get_model_pricing(provider: str, model: str) -> dict[str, float]: + """Get pricing for a specific model. + + Args: + provider: Provider name (e.g., "anthropic", "zai-org") + model: Model name (e.g., "claude-4-sonnet", "GLM-4.7-Flash") + + Returns: + Dict with keys: input, output, cache_creation, cache_read (all in USD per MTok) + """ + config = load_pricing_config() + + # Search through all providers + provider_config = config.get(provider, {}) + models = provider_config.get("models", {}) + if model in models: + model_config = models[model] + # Map new field names to expected names + return { + "input_tokens_price": model_config["input_tokens_price"], + "output_tokens_price": model_config["output_tokens_price"], + "cache_creation_input_tokens_price": model_config[ + "cache_creation_input_tokens_price" + ], + "cache_read_input_tokens_price": model_config[ + "cache_read_input_tokens_price" + ], + } + + # Fall back to default + logger.warning(f"No pricing found for model '{model}', using default") + # default to anthropic/claude-4-sonnet. + return { + "input_tokens_price": 3.3, + "output_tokens_price": 16.5, + "cache_creation_input_tokens_price": 3.3, + "cache_read_input_tokens_price": 3.3, + } + + +def calculate_cost( + provider: str, + model: str, + input_tokens: int = 0, + output_tokens: int = 0, + cache_creation_input_tokens: int = 0, + cache_read_input_tokens: int = 0, +) -> dict[str, float]: + """Calculate cost from token usage. + + Args: + provider: Provider name (e.g., "anthropic", "deepinfra") + model: Model name (e.g., "claude-4-sonnet", "GLM-4.7-Flash") + input_tokens: Regular input tokens + output_tokens: Output tokens + cache_creation_input_tokens: Tokens written to cache + cache_read_input_tokens: Tokens read from cache + + Returns: + Dict with keys: input_cost, output_cost, cache_creation_cost, + cache_read_cost, total_cost (all in USD) + """ + pricing = get_model_pricing(provider, model) + + # Convert tokens to millions and multiply by price per MTok + input_cost = (input_tokens / 1_000_000) * pricing["input_tokens_price"] + output_cost = (output_tokens / 1_000_000) * pricing["output_tokens_price"] + cache_creation_cost = (cache_creation_input_tokens / 1_000_000) * pricing[ + "cache_creation_input_tokens_price" + ] + cache_read_cost = (cache_read_input_tokens / 1_000_000) * pricing[ + "cache_read_input_tokens_price" + ] + + total_cost = input_cost + output_cost + cache_creation_cost + cache_read_cost + + return { + "total_cost": round(total_cost, 8), + "input_cost": round(input_cost, 8), + "output_cost": round(output_cost, 8), + "cache_creation_input_cost": round(cache_creation_cost, 8), + "cache_read_input_cost": round(cache_read_cost, 8), + } diff --git a/config/grafana/dashboards/alphatrion-platform.json b/config/grafana/dashboards/alphatrion-platform.json index e977ff3e..8313a08b 100644 --- a/config/grafana/dashboards/alphatrion-platform.json +++ b/config/grafana/dashboards/alphatrion-platform.json @@ -12,67 +12,220 @@ "title": "Total Platform Tokens", "type": "stat", "datasource": "Prometheus", - "gridPos": {"h": 6, "w": 6, "x": 0, "y": 0}, - "targets": [{"expr": "sum(alphatrion_llm_tokens_total{token_type=\"total\"})", "refId": "A"}], + "gridPos": { + "h": 5, + "w": 6, + "x": 0, + "y": 0 + }, + "targets": [ + { + "expr": "sum(llm_tokens_total{token_type=\"total\"})", + "refId": "A" + } + ], "fieldConfig": { "defaults": { "unit": "short", - "color": {"mode": "thresholds"}, - "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}]} + "color": { + "mode": "thresholds" + }, + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + } } }, - "options": {"colorMode": "value", "graphMode": "area", "orientation": "auto"} + "options": { + "colorMode": "value", + "graphMode": "area", + "orientation": "auto" + } }, { "title": "Total Platform Requests", "type": "stat", "datasource": "Prometheus", - "gridPos": {"h": 6, "w": 6, "x": 6, "y": 0}, - "targets": [{"expr": "sum(alphatrion_llm_requests_total)", "refId": "A"}], + "gridPos": { + "h": 5, + "w": 6, + "x": 6, + "y": 0 + }, + "targets": [ + { + "expr": "sum(llm_requests_total)", + "refId": "A" + } + ], "fieldConfig": { "defaults": { "unit": "short", - "color": {"mode": "thresholds"}, - "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}]} + "color": { + "mode": "thresholds" + }, + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + } } }, - "options": {"colorMode": "value", "graphMode": "area", "orientation": "auto"} + "options": { + "colorMode": "value", + "graphMode": "area", + "orientation": "auto" + } }, { "title": "Error Rate", "type": "stat", "datasource": "Prometheus", - "gridPos": {"h": 6, "w": 6, "x": 12, "y": 0}, - "targets": [{"expr": "sum(rate(alphatrion_llm_requests_total{status=\"ERROR\"}[5m])) / sum(rate(alphatrion_llm_requests_total[5m]))", "refId": "A"}], + "gridPos": { + "h": 5, + "w": 6, + "x": 18, + "y": 0 + }, + "targets": [ + { + "expr": "sum(rate(llm_requests_total{status=\"ERROR\"}[5m])) / sum(rate(llm_requests_total[5m]))", + "refId": "A" + } + ], "fieldConfig": { "defaults": { "unit": "percentunit", - "color": {"mode": "thresholds"}, - "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}, {"color": "red", "value": 0.05}]} + "color": { + "mode": "thresholds" + }, + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + }, + { + "color": "red", + "value": 0.05 + } + ] + } } }, - "options": {"colorMode": "value", "graphMode": "area", "orientation": "auto"} + "options": { + "colorMode": "value", + "graphMode": "area", + "orientation": "auto" + } + }, + { + "title": "Total Platform Cost", + "type": "stat", + "datasource": "Prometheus", + "gridPos": { + "h": 5, + "w": 6, + "x": 12, + "y": 0 + }, + "targets": [ + { + "expr": "sum(llm_cost_total{cost_type=\"total\"})", + "refId": "A" + } + ], + "fieldConfig": { + "defaults": { + "unit": "currencyUSD", + "decimals": 4, + "color": { + "mode": "thresholds" + }, + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + }, + { + "color": "yellow", + "value": 10 + }, + { + "color": "red", + "value": 100 + } + ] + } + } + }, + "options": { + "colorMode": "value", + "graphMode": "area", + "orientation": "auto" + } }, { "title": "Errors by Type", "type": "piechart", "datasource": "Prometheus", - "gridPos": {"h": 6, "w": 6, "x": 18, "y": 0}, - "targets": [{"expr": "sum by (error_type) (alphatrion_llm_errors_total)", "legendFormat": "{{error_type}}", "refId": "A"}], - "options": {"legend": {"displayMode": "list", "placement": "bottom"}, "pieType": "pie"} + "gridPos": { + "h": 6, + "w": 8, + "x": 16, + "y": 5 + }, + "targets": [ + { + "expr": "sum by (error_type) (llm_errors_total)", + "legendFormat": "{{error_type}}", + "refId": "A" + } + ], + "options": { + "legend": { + "displayMode": "list", + "placement": "bottom" + }, + "pieType": "pie" + } }, { "title": "Active Experiments (Time Window)", "type": "timeseries", "datasource": "Prometheus", - "gridPos": {"h": 6, "w": 12, "x": 0, "y": 6}, + "gridPos": { + "h": 6, + "w": 8, + "x": 0, + "y": 5 + }, "targets": [ - {"expr": "count(sum by (experiment_id) (increase(alphatrion_llm_requests_total[5m])) > 0)", "legendFormat": "Active Experiments", "refId": "A"} + { + "expr": "count(sum by (experiment_id) (increase(llm_requests_total[5m])) > 0)", + "legendFormat": "Active Experiments", + "refId": "A" + } ], "fieldConfig": { "defaults": { "unit": "short", - "color": {"mode": "fixed", "fixedColor": "blue"}, + "color": { + "mode": "fixed", + "fixedColor": "blue" + }, "custom": { "lineWidth": 2, "fillOpacity": 10, @@ -81,7 +234,10 @@ } }, "options": { - "legend": {"displayMode": "list", "placement": "bottom"} + "legend": { + "displayMode": "list", + "placement": "bottom" + } }, "description": "Number of unique experiments with activity in the last 5 minutes. Drops to 0 when experiments finish." }, @@ -89,14 +245,26 @@ "title": "Active Teams (Time Window)", "type": "timeseries", "datasource": "Prometheus", - "gridPos": {"h": 6, "w": 12, "x": 12, "y": 6}, + "gridPos": { + "h": 6, + "w": 8, + "x": 8, + "y": 5 + }, "targets": [ - {"expr": "count(sum by (team_id) (increase(alphatrion_llm_requests_total[5m])) > 0)", "legendFormat": "Active Teams", "refId": "A"} + { + "expr": "count(sum by (team_id) (increase(llm_requests_total[5m])) > 0)", + "legendFormat": "Active Teams", + "refId": "A" + } ], "fieldConfig": { "defaults": { "unit": "short", - "color": {"mode": "fixed", "fixedColor": "green"}, + "color": { + "mode": "fixed", + "fixedColor": "green" + }, "custom": { "lineWidth": 2, "fillOpacity": 10, @@ -105,7 +273,10 @@ } }, "options": { - "legend": {"displayMode": "list", "placement": "bottom"} + "legend": { + "displayMode": "list", + "placement": "bottom" + } }, "description": "Number of unique teams with activity in the last 5 minutes. Drops when teams stop running experiments." }, @@ -113,11 +284,38 @@ "title": "Token Usage (Platform-wide, Cumulative)", "type": "timeseries", "datasource": "Prometheus", - "gridPos": {"h": 8, "w": 24, "x": 0, "y": 12}, + "gridPos": { + "h": 8, + "w": 14, + "x": 0, + "y": 11 + }, "targets": [ - {"expr": "sum(alphatrion_llm_tokens_total{token_type=\"total\"})", "legendFormat": "Total Tokens", "refId": "A"}, - {"expr": "sum(alphatrion_llm_input_tokens_total)", "legendFormat": "Input Tokens", "refId": "B"}, - {"expr": "sum(alphatrion_llm_output_tokens_total)", "legendFormat": "Output Tokens", "refId": "C"} + { + "expr": "sum(llm_tokens_total{token_type=\"input\"})", + "legendFormat": "Input Tokens", + "refId": "A" + }, + { + "expr": "sum(llm_tokens_total{token_type=\"output\"})", + "legendFormat": "Output Tokens", + "refId": "B" + }, + { + "expr": "sum(llm_tokens_total{token_type=\"cache_read_input\"})", + "legendFormat": "Cache Read Input Tokens", + "refId": "C" + }, + { + "expr": "sum(llm_tokens_total{token_type=\"cache_creation_input\"})", + "legendFormat": "Cache Creation Input Tokens", + "refId": "D" + }, + { + "expr": "sum(llm_tokens_total{token_type=\"total\"})", + "legendFormat": "Total Tokens", + "refId": "E" + } ], "fieldConfig": { "defaults": { @@ -128,67 +326,468 @@ }, "overrides": [ { - "matcher": {"id": "byName", "options": "Total Tokens"}, + "matcher": { + "id": "byName", + "options": "Input Tokens" + }, + "properties": [ + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "green" + } + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Output Tokens" + }, "properties": [ - {"id": "color", "value": {"mode": "fixed", "fixedColor": "blue"}}, - {"id": "custom.lineWidth", "value": 3} + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "orange" + } + } ] }, { - "matcher": {"id": "byName", "options": "Input Tokens"}, + "matcher": { + "id": "byName", + "options": "Cache Read Input Tokens" + }, "properties": [ - {"id": "color", "value": {"mode": "fixed", "fixedColor": "green"}} + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "purple" + } + } ] }, { - "matcher": {"id": "byName", "options": "Output Tokens"}, + "matcher": { + "id": "byName", + "options": "Cache Creation Input Tokens" + }, "properties": [ - {"id": "color", "value": {"mode": "fixed", "fixedColor": "orange"}} + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "yellow" + } + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Total Tokens" + }, + "properties": [ + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "blue" + } + }, + { + "id": "custom.lineWidth", + "value": 3 + } ] } ] }, - "options": {"legend": {"displayMode": "list", "placement": "bottom"}}, - "description": "Accumulated tokens across ALL teams and experiments. Total = Input + Output. Should keep growing as you run more experiments." + "options": { + "legend": { + "displayMode": "list", + "placement": "bottom" + } + }, + "description": "Accumulated tokens across ALL teams and experiments. Total = Input + Output + Cache tokens. Should keep growing as you run more experiments." + }, + { + "title": "Cost Usage (Platform-wide, Cumulative)", + "type": "timeseries", + "datasource": "Prometheus", + "gridPos": { + "h": 8, + "w": 14, + "x": 0, + "y": 19 + }, + "targets": [ + { + "expr": "sum(llm_cost_total{cost_type=\"total\"})", + "legendFormat": "Total Cost", + "refId": "A" + }, + { + "expr": "sum(llm_input_cost_total)", + "legendFormat": "Input Cost", + "refId": "B" + }, + { + "expr": "sum(llm_output_cost_total)", + "legendFormat": "Output Cost", + "refId": "C" + }, + { + "expr": "sum(llm_cache_read_cost_total)", + "legendFormat": "Cache Read Cost", + "refId": "D" + }, + { + "expr": "sum(llm_cache_creation_cost_total)", + "legendFormat": "Cache Creation Cost", + "refId": "E" + } + ], + "fieldConfig": { + "defaults": { + "unit": "currencyUSD", + "decimals": 4, + "custom": { + "fillOpacity": 10, + "lineWidth": 2 + } + }, + "overrides": [ + { + "matcher": { + "id": "byName", + "options": "Total Cost" + }, + "properties": [ + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "blue" + } + }, + { + "id": "custom.lineWidth", + "value": 3 + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Input Cost" + }, + "properties": [ + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "green" + } + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Output Cost" + }, + "properties": [ + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "orange" + } + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Cache Read Cost" + }, + "properties": [ + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "purple" + } + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Cache Creation Cost" + }, + "properties": [ + { + "id": "color", + "value": { + "mode": "fixed", + "fixedColor": "yellow" + } + } + ] + } + ] + }, + "options": { + "legend": { + "displayMode": "list", + "placement": "bottom" + } + }, + "description": "Accumulated costs in USD across ALL teams and experiments. Tracks input, output, and cache-related costs." + }, + { + "title": "Token Breakdown by Type", + "type": "piechart", + "datasource": "Prometheus", + "gridPos": { + "h": 8, + "w": 10, + "x": 14, + "y": 11 + }, + "targets": [ + { + "expr": "sum(llm_tokens_total{token_type=\"input\"})", + "legendFormat": "Input", + "refId": "A" + }, + { + "expr": "sum(llm_tokens_total{token_type=\"output\"})", + "legendFormat": "Output", + "refId": "B" + }, + { + "expr": "sum(llm_tokens_total{token_type=\"cache_read_input\"})", + "legendFormat": "Cache Read Input", + "refId": "C" + }, + { + "expr": "sum(llm_tokens_total{token_type=\"cache_creation_input\"})", + "legendFormat": "Cache Creation Input", + "refId": "D" + } + ], + "options": { + "legend": { + "displayMode": "table", + "placement": "right", + "values": [ + "value" + ] + }, + "pieType": "pie" + } + }, + { + "title": "Cost Breakdown by Type", + "type": "piechart", + "datasource": "Prometheus", + "gridPos": { + "h": 8, + "w": 10, + "x": 14, + "y": 19 + }, + "targets": [ + { + "expr": "sum(llm_input_cost_total)", + "legendFormat": "Input", + "refId": "A" + }, + { + "expr": "sum(llm_output_cost_total)", + "legendFormat": "Output", + "refId": "B" + }, + { + "expr": "sum(llm_cache_read_cost_total)", + "legendFormat": "Cache Read", + "refId": "C" + }, + { + "expr": "sum(llm_cache_creation_cost_total)", + "legendFormat": "Cache Creation", + "refId": "D" + } + ], + "fieldConfig": { + "defaults": { + "unit": "currencyUSD", + "decimals": 4 + } + }, + "options": { + "legend": { + "displayMode": "table", + "placement": "right", + "values": [ + "value" + ] + }, + "pieType": "pie" + } }, { "title": "Requests by Model", "type": "piechart", "datasource": "Prometheus", - "gridPos": {"h": 8, "w": 12, "x": 0, "y": 20}, - "targets": [{"expr": "sum by (model) (alphatrion_llm_requests_total)", "legendFormat": "{{model}}", "refId": "A"}], - "options": {"legend": {"displayMode": "table", "placement": "right", "values": ["value"]}, "pieType": "pie"} + "gridPos": { + "h": 7, + "w": 8, + "x": 0, + "y": 27 + }, + "targets": [ + { + "expr": "sum by (model) (llm_requests_total)", + "legendFormat": "{{model}}", + "refId": "A" + } + ], + "options": { + "legend": { + "displayMode": "table", + "placement": "right", + "values": [ + "value" + ] + }, + "pieType": "pie" + } }, { "title": "Tokens by Model", "type": "piechart", "datasource": "Prometheus", - "gridPos": {"h": 8, "w": 12, "x": 12, "y": 20}, - "targets": [{"expr": "sum by (model) (alphatrion_llm_tokens_total{token_type=\"total\"})", "legendFormat": "{{model}}", "refId": "A"}], - "options": {"legend": {"displayMode": "table", "placement": "right", "values": ["value"]}, "pieType": "pie"} + "gridPos": { + "h": 7, + "w": 8, + "x": 8, + "y": 27 + }, + "targets": [ + { + "expr": "sum by (model) (llm_tokens_total{token_type=\"total\"})", + "legendFormat": "{{model}}", + "refId": "A" + } + ], + "options": { + "legend": { + "displayMode": "table", + "placement": "right", + "values": [ + "value" + ] + }, + "pieType": "pie" + } + }, + { + "title": "Cost by Model", + "type": "piechart", + "datasource": "Prometheus", + "gridPos": { + "h": 7, + "w": 8, + "x": 16, + "y": 27 + }, + "targets": [ + { + "expr": "sum by (model) (llm_cost_total{cost_type=\"total\"})", + "legendFormat": "{{model}}", + "refId": "A" + } + ], + "fieldConfig": { + "defaults": { + "unit": "currencyUSD", + "decimals": 4 + } + }, + "options": { + "legend": { + "displayMode": "table", + "placement": "right", + "values": [ + "value" + ] + }, + "pieType": "pie" + } }, { "title": "LLM Latency (P50, P95) by Model", "type": "timeseries", "datasource": "Prometheus", - "gridPos": {"h": 8, "w": 24, "x": 0, "y": 28}, + "gridPos": { + "h": 8, + "w": 24, + "x": 0, + "y": 34 + }, "targets": [ - {"expr": "histogram_quantile(0.95, sum by (model, le) (rate(alphatrion_llm_duration_seconds_bucket[5m])))", "legendFormat": "p95 - {{model}}", "refId": "A"}, - {"expr": "histogram_quantile(0.50, sum by (model, le) (rate(alphatrion_llm_duration_seconds_bucket[5m])))", "legendFormat": "p50 - {{model}}", "refId": "B"} + { + "expr": "histogram_quantile(0.95, sum by (model, le) (rate(llm_request_duration_seconds_bucket[5m])))", + "legendFormat": "p95 - {{model}}", + "refId": "A" + }, + { + "expr": "histogram_quantile(0.50, sum by (model, le) (rate(llm_request_duration_seconds_bucket[5m])))", + "legendFormat": "p50 - {{model}}", + "refId": "B" + } ], - "fieldConfig": {"defaults": {"unit": "s"}}, - "options": {"legend": {"displayMode": "table", "placement": "right", "calcs": ["mean", "max"]}} + "fieldConfig": { + "defaults": { + "unit": "s" + } + }, + "options": { + "legend": { + "displayMode": "table", + "placement": "right", + "calcs": [ + "mean", + "max" + ] + } + } } ], "refresh": "10s", "schemaVersion": 38, "style": "dark", - "tags": ["alphatrion", "platform"], - "templating": {"list": []}, - "time": {"from": "now-24h", "to": "now"}, + "tags": [ + "alphatrion", + "platform" + ], + "templating": { + "list": [] + }, + "time": { + "from": "now-24h", + "to": "now" + }, "timepicker": {}, "timezone": "browser", "title": "AlphaTrion - Platform Dashboard", "uid": "alphatrion-platform", "version": 1 -} +} \ No newline at end of file diff --git a/config/modelspec.yaml b/config/modelspec.yaml new file mode 100644 index 00000000..49a0933c --- /dev/null +++ b/config/modelspec.yaml @@ -0,0 +1,34 @@ +# Model specifications and pricing +# All prices are in USD per 1 million tokens (MTok) +# Last updated: 2025-04-01 + +anthropic: + models: [] + +deepinfra: + models: + zai-org/GLM-4.7-Flash: + description: "" + input_tokens_price: 0.06 + output_tokens_price: 0.4 + cache_read_input_tokens_price: 0.01 + cache_creation_input_tokens_price: 0.06 + moonshotai/Kimi-K2.5: + description: "" + input_tokens_price: 0.45 + output_tokens_price: 2.25 + cache_read_input_tokens_price: 0.07 + cache_creation_input_tokens_price: 0.45 + Qwen/Qwen3-Coder-480B-A35B-Instruct: + description: "" + quantization: "fp8" + input_tokens_price: 0.4 + output_tokens_price: 1.6 + cache_read_input_tokens_price: 0.4 + cache_creation_input_tokens_price: 0.4 + openai/gpt-oss-120b: + description: "" + input_tokens_price: 0.039 + output_tokens_price: 0.19 + cache_read_input_tokens_price: 0.039 + cache_creation_input_tokens_price: 0.039 \ No newline at end of file diff --git a/dashboard/src/components/dashboard/daily-cost-usage-chart.tsx b/dashboard/src/components/dashboard/daily-cost-usage-chart.tsx new file mode 100644 index 00000000..8e6e2c43 --- /dev/null +++ b/dashboard/src/components/dashboard/daily-cost-usage-chart.tsx @@ -0,0 +1,294 @@ +import { useMemo } from 'react'; +import { + LineChart, + Line, + XAxis, + YAxis, + CartesianGrid, + Tooltip, + Legend, + ResponsiveContainer, +} from 'recharts'; +import { format, subDays, startOfDay } from 'date-fns'; +import type { DailyCostUsage } from '../../hooks/use-cost-usage'; + +interface DailyCostUsageChartProps { + data: DailyCostUsage[]; + timeRange: '7days' | '1month' | '3months'; +} + +type TimeRange = '7days' | '1month' | '3months'; + +const TIME_RANGE_OPTIONS: { value: TimeRange; label: string; days: number }[] = [ + { value: '7days', label: '7 Days', days: 7 }, + { value: '1month', label: '1 Month', days: 30 }, + { value: '3months', label: '3 Months', days: 90 }, +]; + +export function DailyCostUsageChart({ data, timeRange }: DailyCostUsageChartProps) { + const chartData = useMemo(() => { + const selectedRange = TIME_RANGE_OPTIONS.find((r) => r.value === timeRange); + if (!selectedRange) return []; + + const now = new Date(); + + // Create date map for aggregation - fill all dates with 0 + const dateMap = new Map(); + + // Initialize all dates in range with 0 + for (let i = 0; i < selectedRange.days; i++) { + const date = subDays(now, selectedRange.days - 1 - i); + const dateKey = format(startOfDay(date), 'yyyy-MM-dd'); + dateMap.set(dateKey, { + totalCost: 0, + totalTokens: 0, + inputTokens: 0, + outputTokens: 0, + cacheReadInputTokens: 0, + cacheCreationInputTokens: 0, + }); + } + + // Fill in actual data + data.forEach((item) => { + const dateKey = format(new Date(item.date), 'yyyy-MM-dd'); + if (dateMap.has(dateKey)) { + dateMap.set(dateKey, { + totalCost: item.totalCost, + totalTokens: item.totalTokens, + inputTokens: item.inputTokens, + outputTokens: item.outputTokens, + cacheReadInputTokens: item.cacheReadInputTokens, + cacheCreationInputTokens: item.cacheCreationInputTokens, + }); + } + }); + + // Convert to array and format for chart + return Array.from(dateMap.entries()) + .map(([date, values]) => ({ + date, + displayDate: format(new Date(date), 'MMM dd'), + totalCost: values.totalCost, + totalTokens: values.totalTokens, + inputTokens: values.inputTokens, + outputTokens: values.outputTokens, + cacheReadInputTokens: values.cacheReadInputTokens, + cacheCreationInputTokens: values.cacheCreationInputTokens, + })) + .sort((a, b) => a.date.localeCompare(b.date)); + }, [data, timeRange]); + + // Calculate totals across all days + const totalCost = useMemo(() => { + return chartData.reduce((sum, item) => sum + item.totalCost, 0); + }, [chartData]); + + const totalTokens = useMemo(() => { + return chartData.reduce((sum, item) => sum + item.totalTokens, 0); + }, [chartData]); + + const totalInputTokens = useMemo(() => { + return chartData.reduce((sum, item) => sum + item.inputTokens, 0); + }, [chartData]); + + const totalOutputTokens = useMemo(() => { + return chartData.reduce((sum, item) => sum + item.outputTokens, 0); + }, [chartData]); + + const totalCacheReadInputTokens = useMemo(() => { + return chartData.reduce((sum, item) => sum + item.cacheReadInputTokens, 0); + }, [chartData]); + + const totalCacheCreationInputTokens = useMemo(() => { + return chartData.reduce((sum, item) => sum + item.cacheCreationInputTokens, 0); + }, [chartData]); + + return ( +
+
+

Cost & Token Usage

+
+ ${totalCost.toFixed(4)} • {totalTokens.toLocaleString()} tokens (in:{totalInputTokens.toLocaleString()} out:{totalOutputTokens.toLocaleString()} cache-r:{totalCacheReadInputTokens.toLocaleString()} cache-c:{totalCacheCreationInputTokens.toLocaleString()}) +
+
+ + + + + + {/* Left Y-Axis for Tokens */} + { + if (value >= 1000000) return `${(value / 1000000).toFixed(1)}M`; + if (value >= 1000) return `${(value / 1000).toFixed(1)}K`; + return value.toString(); + }} + label={{ + value: 'Tokens', + angle: -90, + position: 'insideLeft', + offset: 0, + style: { textAnchor: 'middle', fontSize: 9 } + }} + /> + {/* Right Y-Axis for Cost */} + + value >= 1 + ? `$${value.toFixed(2)}` + : value >= 0.01 + ? `$${value.toFixed(3)}` + : `$${value.toFixed(4)}` + } + label={{ + value: 'Cost (USD)', + angle: 90, + position: 'insideRight', + offset: 0, + style: { textAnchor: 'middle', fontSize: 9 } + }} + /> + { + if (!active || !payload || !payload.length) return null; + const data = payload[0].payload; + return ( +
+
{label}
+
+
+
+ Cost: + ${data.totalCost.toFixed(4)} +
+
+
+ Total Tokens: + {data.totalTokens.toLocaleString()} +
+
+
+ Input: + {data.inputTokens.toLocaleString()} +
+
+
+ Output: + {data.outputTokens.toLocaleString()} +
+
+
+ Cache Read: + {data.cacheReadInputTokens.toLocaleString()} +
+
+
+ Cache Creation: + {data.cacheCreationInputTokens.toLocaleString()} +
+
+
+ ); + }} + /> + + {/* Token Lines - Left Y-Axis */} + + + + + + {/* Cost Line - Right Y-Axis */} + + + +
+ ); +} diff --git a/dashboard/src/components/traces/trace-timeline.tsx b/dashboard/src/components/traces/trace-timeline.tsx index f529e087..b08ea212 100644 --- a/dashboard/src/components/traces/trace-timeline.tsx +++ b/dashboard/src/components/traces/trace-timeline.tsx @@ -1,4 +1,4 @@ -import { useState, useMemo } from 'react'; +import { useState, useMemo, useEffect } from 'react'; import { ChevronDown, ChevronRight, Clock, Zap, Database, Globe, Bot, X, GitBranch } from 'lucide-react'; import type { Span } from '../../types'; import { Card, CardContent } from '../ui/card'; @@ -26,28 +26,53 @@ const STATUS_COLORS: Record = { 'UNSET': 'bg-green-500', // Treat UNSET as successful (default behavior) }; -// Span type detection and styling +// Span type styling - uses backend's semanticKind directly as label const getSpanType = (span: Span): { label: string; icon: JSX.Element; badgeColor: string } => { - const name = span.spanName.toLowerCase(); - const kind = span.spanKind; - - if (name.includes('openai') || name.includes('chat') || name.includes('completion')) { - return { label: 'LLM', icon: , badgeColor: 'bg-purple-100 text-purple-700 border-purple-200' }; - } - if (kind === 'CLIENT' || name.includes('http') || name.includes('api')) { - return { label: 'API', icon: , badgeColor: 'bg-blue-100 text-blue-700 border-blue-200' }; - } - if (name.includes('db') || name.includes('database') || name.includes('query')) { - return { label: 'DB', icon: , badgeColor: 'bg-cyan-100 text-cyan-700 border-cyan-200' }; - } - if (span.spanAttributes?.['traceloop.span.kind'] === 'workflow') { - return { label: 'Workflow', icon: , badgeColor: 'bg-indigo-100 text-indigo-700 border-indigo-200' }; - } - if (span.spanAttributes?.['traceloop.span.kind'] === 'task') { - return { label: 'Task', icon: , badgeColor: 'bg-amber-100 text-amber-700 border-amber-200' }; + const semantic = span.semanticKind; + + // Map semantic kinds to icons and colors + let icon = ; + let badgeColor = 'bg-gray-100 text-gray-700 border-gray-200'; + + switch (semantic) { + case 'tool': + icon = ; + badgeColor = 'bg-lime-100 text-lime-700 border-lime-200'; + break; + case 'reasoning': + icon = ; + badgeColor = 'bg-amber-100 text-amber-700 border-amber-200'; + break; + case 'chat': + icon = ; + badgeColor = 'bg-sky-100 text-sky-700 border-sky-200'; + break; + case 'db': + icon = ; + badgeColor = 'bg-cyan-100 text-cyan-700 border-cyan-200'; + break; + case 'workflow': + icon = ; + badgeColor = 'bg-emerald-100 text-emerald-700 border-emerald-200'; + break; + case 'task': + icon = ; + badgeColor = 'bg-indigo-100 text-indigo-700 border-indigo-200'; + break; + case 'agent': + icon = ; + badgeColor = 'bg-violet-100 text-violet-700 border-violet-200'; + break; + case 'unknown': + default: + icon = ; + badgeColor = 'bg-slate-100 text-slate-700 border-slate-200'; } - return { label: 'Span', icon: , badgeColor: 'bg-gray-100 text-gray-700 border-gray-200' }; + // Use semanticKind directly as label (capitalize) + const label = semantic.charAt(0).toUpperCase() + semantic.slice(1); + + return { label, icon, badgeColor }; }; export function TraceTimeline({ spans }: TraceTimelineProps) { @@ -56,6 +81,12 @@ export function TraceTimeline({ spans }: TraceTimelineProps) { return new Set(spans.filter(s => !s.parentSpanId || s.parentSpanId === '').map(s => s.spanId)); }); const [selectedSpan, setSelectedSpan] = useState(null); + const [showAllAttributes, setShowAllAttributes] = useState(false); + + // Reset showAllAttributes when span changes + useEffect(() => { + setShowAllAttributes(false); + }, [selectedSpan]); const expandAll = () => { const allSpanIds = new Set(spans.map(s => s.spanId)); @@ -323,6 +354,7 @@ export function TraceTimeline({ spans }: TraceTimelineProps) { const renderSpanDetails = (span: Span) => { const spanType = getSpanType(span); const attrs = span.spanAttributes || {}; + const semantic = span.semanticKind; // Extract model parameters const model = attrs['gen_ai.request.model'] || attrs['gen_ai.response.model']; @@ -353,8 +385,12 @@ export function TraceTimeline({ spans }: TraceTimelineProps) { i++; } - // Check if this is a simple span (no prompts/completions, just basic info) - const isSimpleSpan = prompts.length === 0 && completions.length === 0; + const hasChatHistory = prompts.length > 0 || completions.length > 0; + + // Extract function input/output for workflow and task spans + const functionInput = attrs['traceloop.entity.input']; + const functionOutput = attrs['traceloop.entity.output']; + const hasFunctionIO = functionInput || functionOutput; return ( @@ -378,42 +414,8 @@ export function TraceTimeline({ spans }: TraceTimelineProps) { - {/* Span Attributes */} - {Object.keys(span.spanAttributes).length > 0 && ( -
-
- Span Attributes -
-
- {Object.entries(span.spanAttributes).map(([key, value]) => ( -
- {key}: - {value} -
- ))} -
-
- )} - - {/* Resource Attributes */} - {Object.keys(span.resourceAttributes).length > 0 && ( -
-
- Resource Attributes -
-
- {Object.entries(span.resourceAttributes).map(([key, value]) => ( -
- {key}: - {value} -
- ))} -
-
- )} - - {/* Model Configuration - only show if model parameters exist */} - {!isSimpleSpan && model && ( + {/* Model Configuration - show for chat/reasoning */} + {(semantic === 'chat' || semantic === 'reasoning') && model && (
@@ -442,11 +444,32 @@ export function TraceTimeline({ spans }: TraceTimelineProps) {
)} + {/* Function Input/Output - show for workflow and task spans */} + {(semantic === 'workflow' || semantic === 'task') && hasFunctionIO && ( +
+ {functionInput && ( +
+
+ Input +
+
{functionInput}
+
+ )} + {functionOutput && ( +
+
+ Output +
+
{functionOutput}
+
+ )} +
+ )} - {/* Content - Prompts and Completions */} - {(prompts.length > 0 || completions.length > 0) && ( -
- {/* Prompts */} + + {/* Chat History - for chat/reasoning spans */} + {(semantic === 'chat' || semantic === 'reasoning') && hasChatHistory && ( +
{prompts.map((prompt, idx) => (
@@ -459,9 +482,7 @@ export function TraceTimeline({ spans }: TraceTimelineProps) {
))} - {/* Completions */} {completions.map((completion, idx) => { - // Determine color based on role let roleColor = 'bg-purple-100 dark:bg-purple-900/30 text-purple-700 dark:text-purple-400'; if (completion.role === 'thinking') { roleColor = 'bg-amber-100 dark:bg-amber-900/30 text-amber-700 dark:text-amber-400'; @@ -487,6 +508,57 @@ export function TraceTimeline({ spans }: TraceTimelineProps) {
)} + {/* Show All Attributes Toggle */} + {(Object.keys(span.spanAttributes).length > 0 || Object.keys(span.resourceAttributes).length > 0) && ( +
+ + + {showAllAttributes && ( +
+ {/* Span Attributes */} + {Object.keys(span.spanAttributes).length > 0 && ( +
+
+ Span Attributes +
+
+ {Object.entries(span.spanAttributes).map(([key, value]) => ( +
+ {key}: + {value} +
+ ))} +
+
+ )} + + {/* Resource Attributes */} + {Object.keys(span.resourceAttributes).length > 0 && ( +
+
+ Resource Attributes +
+
+ {Object.entries(span.resourceAttributes).map(([key, value]) => ( +
+ {key}: + {value} +
+ ))} +
+
+ )} +
+ )} +
+ )} + ); diff --git a/dashboard/src/hooks/use-cost-usage.ts b/dashboard/src/hooks/use-cost-usage.ts new file mode 100644 index 00000000..69095e80 --- /dev/null +++ b/dashboard/src/hooks/use-cost-usage.ts @@ -0,0 +1,35 @@ +import { useQuery } from '@tanstack/react-query'; +import { graphqlQuery, queries } from '../lib/graphql-client'; + +export interface DailyCostUsage { + date: string; + totalCost: number; + totalTokens: number; + inputTokens: number; + outputTokens: number; + cacheReadInputTokens: number; + cacheCreationInputTokens: number; +} + +interface GetDailyCostUsageResponse { + dailyCostUsage: DailyCostUsage[]; +} + +/** + * Hook to fetch daily cost usage for a team + * Only includes LLM calls (spans with alphatrion.cost.total_tokens) + */ +export function useDailyCostUsage(teamId: string, days = 30) { + return useQuery({ + queryKey: ['dailyCostUsage', teamId, days], + queryFn: async () => { + const data = await graphqlQuery( + queries.getDailyCostUsage, + { teamId, days } + ); + return data.dailyCostUsage; + }, + enabled: !!teamId, + staleTime: 5 * 60 * 1000, // 5 minutes + }); +} diff --git a/dashboard/src/lib/graphql-client.ts b/dashboard/src/lib/graphql-client.ts index 1ae016a7..0163e350 100644 --- a/dashboard/src/lib/graphql-client.ts +++ b/dashboard/src/lib/graphql-client.ts @@ -20,6 +20,24 @@ const getApiBaseUrl = () => { const API_BASE_URL = getApiBaseUrl(); const GRAPHQL_ENDPOINT = `${API_BASE_URL}/graphql`; +// Setup axios interceptor to handle auth errors globally +axios.interceptors.response.use( + (response) => response, + (error) => { + // Check for 401 Unauthorized or 403 Forbidden + if (error.response?.status === 401 || error.response?.status === 403) { + // Clear stored auth data + localStorage.removeItem('alphatrion_token'); + localStorage.removeItem('alphatrion_org_id'); + localStorage.removeItem('alphatrion_user_id'); + + // Redirect to login page + window.location.href = '/login'; + } + return Promise.reject(error); + } +); + export interface GraphQLResponse { data?: T; errors?: Array<{ @@ -86,6 +104,7 @@ export async function graphqlQuery( return response.data.data; } catch (error) { + // Auth errors are handled by axios interceptor if (axios.isAxiosError(error)) { throw new Error( `GraphQL request failed: ${error.message}` @@ -152,10 +171,13 @@ export const queries = { totalDatasets totalAgents totalSessions - aggregatedTokens { + aggregatedUsage { totalTokens inputTokens outputTokens + cacheReadInputTokens + cacheCreationInputTokens + totalCost } } } @@ -244,10 +266,13 @@ export const queries = { status createdAt updatedAt - aggregatedTokens { + aggregatedUsage { totalTokens inputTokens outputTokens + cacheReadInputTokens + cacheCreationInputTokens + totalCost } traceStats { totalSpans @@ -293,10 +318,13 @@ export const queries = { duration status createdAt - aggregatedTokens { + aggregatedUsage { totalTokens inputTokens outputTokens + cacheReadInputTokens + cacheCreationInputTokens + totalCost } metrics { id @@ -421,6 +449,20 @@ export const queries = { } `, + getDailyCostUsage: ` + query GetDailyCostUsage($teamId: ID!, $days: Int = 30) { + dailyCostUsage(teamId: $teamId, days: $days) { + date + totalCost + totalTokens + inputTokens + outputTokens + cacheReadInputTokens + cacheCreationInputTokens + } + } + `, + listDatasets: ` query ListDatasets($teamId: ID!, $page: Int, $pageSize: Int) { datasets(teamId: $teamId, page: $page, pageSize: $pageSize) { diff --git a/dashboard/src/pages/dashboard/index.tsx b/dashboard/src/pages/dashboard/index.tsx index b1dcb4d1..c076e85d 100644 --- a/dashboard/src/pages/dashboard/index.tsx +++ b/dashboard/src/pages/dashboard/index.tsx @@ -2,7 +2,7 @@ import { useState, useMemo } from 'react'; import { useTeamContext } from '../../context/team-context'; import { useTeam } from '../../hooks/use-teams'; import { useTeamExperiments } from '../../hooks/use-team-experiments'; -import { useDailyTokenUsage } from '../../hooks/use-token-usage'; +import { useDailyCostUsage } from '../../hooks/use-cost-usage'; import { useModelDistributions } from '../../hooks/use-model-distributions'; import { Card, @@ -12,10 +12,10 @@ import { Button } from '../../components/ui/button'; import { Skeleton } from '../../components/ui/skeleton'; import { ExperimentsTimelineChart } from '../../components/dashboard/experiments-timeline-chart'; import { ExperimentsStatusChart } from '../../components/dashboard/experiments-status-chart'; -import { DailyTokenUsageChart } from '../../components/dashboard/daily-token-usage-chart'; +import { DailyCostUsageChart } from '../../components/dashboard/daily-cost-usage-chart'; import { ModelDistributionChart } from '../../components/dashboard/model-distribution-chart'; import { subDays, subMonths } from 'date-fns'; -import { FlaskConical, Coins, Bot, MessagesSquare } from 'lucide-react'; +import { FlaskConical, Coins, Bot, MessagesSquare, DollarSign } from 'lucide-react'; type TimeRange = '7days' | '1month' | '3months'; @@ -39,7 +39,7 @@ export function DashboardPage() { // Get days for selected time range const days = TIME_RANGE_OPTIONS.find((opt) => opt.value === timeRange)?.days || 30; - const { data: dailyTokenUsage, isLoading: tokenUsageLoading } = useDailyTokenUsage( + const { data: dailyCostUsage, isLoading: costUsageLoading } = useDailyCostUsage( selectedTeamId || '', days ); @@ -144,9 +144,9 @@ export function DashboardPage() {

TOKENS

- {(team?.aggregatedTokens?.totalTokens || 0).toLocaleString()} + {(team?.aggregatedUsage?.totalTokens || 0).toLocaleString()} - ({(team?.aggregatedTokens?.inputTokens || 0).toLocaleString()}↓ {(team?.aggregatedTokens?.outputTokens || 0).toLocaleString()}↑) + ({(team?.aggregatedUsage?.inputTokens || 0).toLocaleString()}↓ {(team?.aggregatedUsage?.outputTokens || 0).toLocaleString()}↑)

@@ -156,6 +156,23 @@ export function DashboardPage() {
+ + {/* Total Cost */} + + +
+
+

COST

+

+ ${(team?.aggregatedUsage?.totalCost || 0).toFixed(4)} +

+
+
+ +
+
+ +
)} @@ -172,8 +189,8 @@ export function DashboardPage() { size="sm" onClick={() => setTimeRange(option.value)} className={`h-7 px-2 text-[11px] transition-colors ${timeRange === option.value - ? 'bg-blue-50 border-blue-300 text-blue-700 hover:bg-blue-100' - : 'bg-white hover:bg-gray-50' + ? 'bg-blue-50 border-blue-300 text-blue-700 hover:bg-blue-100' + : 'bg-white hover:bg-gray-50' }`} > {option.label} @@ -207,7 +224,7 @@ export function DashboardPage() {
- {/* Second Row: Model Distribution and Token Usage */} + {/* Second Row: Model Distribution and Combined Cost/Token Usage */}
{/* Model Distribution Pie Chart */} @@ -220,16 +237,16 @@ export function DashboardPage() { - {/* Token Usage Chart */} + {/* Combined Cost & Token Usage Chart */} - {tokenUsageLoading ? ( + {costUsageLoading ? ( - ) : dailyTokenUsage ? ( - + ) : dailyCostUsage ? ( + ) : (
- No token usage data available for this time range + No usage data available for this time range
)} diff --git a/dashboard/src/pages/experiments/[id].tsx b/dashboard/src/pages/experiments/[id].tsx index e753fbdf..4ce284e6 100644 --- a/dashboard/src/pages/experiments/[id].tsx +++ b/dashboard/src/pages/experiments/[id].tsx @@ -189,12 +189,12 @@ export function ExperimentDetailPage() {
Total Tokens
- {experiment.aggregatedTokens?.totalTokens !== undefined && experiment.aggregatedTokens.totalTokens > 0 ? ( + {experiment.aggregatedUsage?.totalTokens !== undefined && experiment.aggregatedUsage.totalTokens > 0 ? ( <> - {Number(experiment.aggregatedTokens.totalTokens).toLocaleString()} - {experiment.aggregatedTokens.inputTokens !== undefined && experiment.aggregatedTokens.outputTokens !== undefined && ( + {Number(experiment.aggregatedUsage.totalTokens).toLocaleString()} + {experiment.aggregatedUsage.inputTokens !== undefined && experiment.aggregatedUsage.outputTokens !== undefined && ( - ({Number(experiment.aggregatedTokens.inputTokens).toLocaleString()}↓ {Number(experiment.aggregatedTokens.outputTokens).toLocaleString()}↑) + ({Number(experiment.aggregatedUsage.inputTokens).toLocaleString()}↓ {Number(experiment.aggregatedUsage.outputTokens).toLocaleString()}↑) )} @@ -203,6 +203,16 @@ export function ExperimentDetailPage() { )}
+
+
Total Cost
+
+ {experiment.aggregatedUsage?.totalCost !== undefined && experiment.aggregatedUsage.totalCost > 0 ? ( + <>${experiment.aggregatedUsage.totalCost.toFixed(4)} + ) : ( + - + )} +
+
Duration
diff --git a/dashboard/src/pages/runs/[id].tsx b/dashboard/src/pages/runs/[id].tsx index dad847c1..ee9d8332 100644 --- a/dashboard/src/pages/runs/[id].tsx +++ b/dashboard/src/pages/runs/[id].tsx @@ -106,65 +106,75 @@ export function RunDetailPage() { {/* Run Overview */} - -

Overview

-
-
-
Total Tokens
-
- {run.aggregatedTokens?.totalTokens !== undefined && run.aggregatedTokens.totalTokens > 0 ? ( - <> - {Number(run.aggregatedTokens.totalTokens).toLocaleString()} - - ({Number(run.aggregatedTokens.inputTokens || 0).toLocaleString()}↓ {Number(run.aggregatedTokens.outputTokens || 0).toLocaleString()}↑) - - - ) : ( - - - )} -
-
-
-
Duration
-
- {run.duration !== undefined && run.duration > 0 ? ( - formatDuration(run.duration) - ) : ( - - - )} -
-
-
-
Created
-
- {formatDistanceToNow(new Date(run.createdAt), { - addSuffix: true, - })} -
-
-
- - - {/* Metadata */} - {run.meta && Object.keys(run.meta).filter(k => k !== 'execution_result').length > 0 && ( -
-

Metadata

+ +

Overview

- {Object.entries(run.meta) - .filter(([key]) => key !== 'execution_result') - .map(([key, value]) => ( -
-
{key}
-
- {typeof value === 'string' ? value : JSON.stringify(value)} -
-
- ))} +
+
Total Tokens
+
+ {run.aggregatedUsage?.totalTokens !== undefined && run.aggregatedUsage.totalTokens > 0 ? ( + <> + {Number(run.aggregatedUsage.totalTokens).toLocaleString()} + + ({Number(run.aggregatedUsage.inputTokens || 0).toLocaleString()}↓ {Number(run.aggregatedUsage.outputTokens || 0).toLocaleString()}↑) + + + ) : ( + - + )} +
+
+
+
Total Cost
+
+ {run.aggregatedUsage?.totalCost !== undefined && run.aggregatedUsage.totalCost > 0 ? ( + <>${run.aggregatedUsage.totalCost.toFixed(4)} + ) : ( + - + )} +
+
+
+
Duration
+
+ {run.duration !== undefined && run.duration > 0 ? ( + formatDuration(run.duration) + ) : ( + - + )} +
+
+
+
Created
+
+ {formatDistanceToNow(new Date(run.createdAt), { + addSuffix: true, + })} +
+
-
- )} - - + + + {/* Metadata */} + {run.meta && Object.keys(run.meta).filter(k => k !== 'execution_result').length > 0 && ( +
+

Metadata

+
+ {Object.entries(run.meta) + .filter(([key]) => key !== 'execution_result') + .map(([key, value]) => ( +
+
{key}
+
+ {typeof value === 'string' ? value : JSON.stringify(value)} +
+
+ ))} +
+
+ )} + + {/* Metrics */} diff --git a/dashboard/src/pages/runs/index.tsx b/dashboard/src/pages/runs/index.tsx index 2d486979..9845046a 100644 --- a/dashboard/src/pages/runs/index.tsx +++ b/dashboard/src/pages/runs/index.tsx @@ -162,9 +162,9 @@ export function RunsPage() { {filteredRuns.map((run) => { - const totalTokens = run.aggregatedTokens?.totalTokens || 0; - const inputTokens = run.aggregatedTokens?.inputTokens || 0; - const outputTokens = run.aggregatedTokens?.outputTokens || 0; + const totalTokens = run.aggregatedUsage?.totalTokens || 0; + const inputTokens = run.aggregatedUsage?.inputTokens || 0; + const outputTokens = run.aggregatedUsage?.outputTokens || 0; return ( { - const totalTokens = runs.reduce((sum, r) => sum + (r.aggregatedTokens?.totalTokens || 0), 0); - const inputTokens = runs.reduce((sum, r) => sum + (r.aggregatedTokens?.inputTokens || 0), 0); - const outputTokens = runs.reduce((sum, r) => sum + (r.aggregatedTokens?.outputTokens || 0), 0); + const totalTokens = runs.reduce((sum, r) => sum + (r.aggregatedUsage?.totalTokens || 0), 0); + const inputTokens = runs.reduce((sum, r) => sum + (r.aggregatedUsage?.inputTokens || 0), 0); + const outputTokens = runs.reduce((sum, r) => sum + (r.aggregatedUsage?.outputTokens || 0), 0); const totalDuration = runs.reduce((sum, r) => sum + (r.duration || 0), 0); const avgDuration = runs.length > 0 ? totalDuration / runs.length : 0; const completedRuns = runs.filter(r => r.status === 'COMPLETED').length; @@ -309,9 +309,9 @@ export function SessionDetailPage() { return runs.map((run, idx) => ({ index: idx + 1, name: `Run ${idx + 1}`, - inputTokens: run.aggregatedTokens?.inputTokens || 0, - outputTokens: run.aggregatedTokens?.outputTokens || 0, - totalTokens: run.aggregatedTokens?.totalTokens || 0, + inputTokens: run.aggregatedUsage?.inputTokens || 0, + outputTokens: run.aggregatedUsage?.outputTokens || 0, + totalTokens: run.aggregatedUsage?.totalTokens || 0, })); }, [runs]); @@ -646,367 +646,364 @@ export function SessionDetailPage() {
{runSpans.slice(completionsPage * COMPLETIONS_PAGE_SIZE, (completionsPage + 1) * COMPLETIONS_PAGE_SIZE).map((runWithSpans, idx) => { const actualIdx = completionsPage * COMPLETIONS_PAGE_SIZE + idx; - const isExpanded = selectedRunIdx === actualIdx; - - // Extract user input from spans - // Sort spans by timestamp to get the first one - const sortedSpans = [...runWithSpans.spans].sort((a, b) => { - const timeA = a.timestamp ? new Date(a.timestamp).getTime() : 0; - const timeB = b.timestamp ? new Date(b.timestamp).getTime() : 0; - return timeA - timeB; - }); - - // Try to find user input from the first span with content (skip processing spans) - let userInput = ''; - for (const span of sortedSpans) { - const attrs = span.spanAttributes || {}; - - // Try different attribute paths in order of likelihood - const promptContent = attrs['gen_ai.prompt.0.content'] as string; - - if (promptContent && promptContent.trim()) { - userInput = promptContent; - break; + const isExpanded = selectedRunIdx === actualIdx; + + // Extract user input from spans + // Sort spans by timestamp to get the first one + const sortedSpans = [...runWithSpans.spans].sort((a, b) => { + const timeA = a.timestamp ? new Date(a.timestamp).getTime() : 0; + const timeB = b.timestamp ? new Date(b.timestamp).getTime() : 0; + return timeA - timeB; + }); + + // Try to find user input from the first span with content (skip processing spans) + let userInput = ''; + for (const span of sortedSpans) { + const attrs = span.spanAttributes || {}; + + // Try different attribute paths in order of likelihood + const promptContent = attrs['gen_ai.prompt.0.content'] as string; + + if (promptContent && promptContent.trim()) { + userInput = promptContent; + break; + } + + // Fallback to other possible locations + if (!userInput) { + userInput = attrs['llm.prompts.0.content'] as string || + attrs['input.value'] as string || + attrs['input'] as string || ''; + } + + if (userInput && userInput.trim()) break; } - // Fallback to other possible locations - if (!userInput) { - userInput = attrs['llm.prompts.0.content'] as string || - attrs['input.value'] as string || - attrs['input'] as string || ''; - } + const displayInput = userInput.length > 100 ? userInput.substring(0, 100) + '...' : userInput; + + return ( + { + setSelectedRunIdx(isExpanded ? null : actualIdx); + setSelectedTimelineItemIdx(null); // Reset timeline item selection + }} + > + + {/* Completion Header */} +
+
+
+ {/* User Input */} +
+ {userInput ? ( +

+ {displayInput} +

+ ) : ( +

+ [No user input] +

+ )} + + {runWithSpans.run.status} + +
+ + {/* Metadata */} +
+ {formatDistanceToNow(new Date(runWithSpans.run.createdAt), { addSuffix: true })} + {runWithSpans.run.duration && ( + <> + • + + {formatDuration(runWithSpans.run.duration)} + + )} + {runWithSpans.run.aggregatedUsage && runWithSpans.run.aggregatedUsage.totalTokens > 0 && ( + <> + • + + {runWithSpans.run.aggregatedUsage.totalTokens.toLocaleString()} tokens + + )} + {runWithSpans.spans.length > 0 && ( + <> + • + {runWithSpans.spans.length} span{runWithSpans.spans.length !== 1 ? 's' : ''} + + )} +
+
+
- if (userInput && userInput.trim()) break; - } + {/* Expanded Content */} + {isExpanded && ( +
- const displayInput = userInput.length > 100 ? userInput.substring(0, 100) + '...' : userInput; - - return ( - { - setSelectedRunIdx(isExpanded ? null : actualIdx); - setSelectedTimelineItemIdx(null); // Reset timeline item selection - }} - > - - {/* Completion Header */} -
-
-
- {/* User Input */} -
- {userInput ? ( -

- {displayInput} -

- ) : ( -

- [No user input] -

- )} - - {runWithSpans.run.status} - -
+ {/* Timeline */} +
+ {(() => { + // Build timeline from all spans + const timeline = buildTimelineFromSpans(runWithSpans.spans, runWithSpans.run.duration || 0); - {/* Metadata */} -
- {formatDistanceToNow(new Date(runWithSpans.run.createdAt), { addSuffix: true })} - {runWithSpans.run.duration && ( - <> - • - - {formatDuration(runWithSpans.run.duration)} - - )} - {runWithSpans.run.aggregatedTokens && runWithSpans.run.aggregatedTokens.totalTokens > 0 && ( - <> - • - - {runWithSpans.run.aggregatedTokens.totalTokens.toLocaleString()} tokens - - )} - {runWithSpans.spans.length > 0 && ( - <> - • - {runWithSpans.spans.length} span{runWithSpans.spans.length !== 1 ? 's' : ''} - - )} -
-
-
- - {/* Expanded Content */} - {isExpanded && ( -
- - {/* Timeline */} -
- {(() => { - // Build timeline from all spans - const timeline = buildTimelineFromSpans(runWithSpans.spans, runWithSpans.run.duration || 0); - - if (timeline.length === 0) { - return ( -
- No timeline data available -
- ); - } - - // Get actual time range from timeline items - const timestamps = timeline.filter(t => t.timestamp && t.timestamp > 0).map(t => t.timestamp!); - const minTimestamp = Math.min(...timestamps); - const maxTimestamp = Math.max(...timestamps); - - // Calculate actual duration based on timestamps, with padding for last operation - const lastItem = timeline[timeline.length - 1]; - const lastItemEnd = lastItem.timestamp && lastItem.duration - ? lastItem.timestamp + (lastItem.duration * 1000) - : maxTimestamp; - const totalDurationMs = Math.max(lastItemEnd - minTimestamp, 1000); // At least 1 second - - return ( - <> - {/* Timeline Header */} -
-
-

Timeline

- - {timeline.length - 1} operations • {formatDuration(totalDurationMs / 1000)} - -
- {runWithSpans.run.aggregatedTokens && ( -
- - {runWithSpans.run.aggregatedTokens.inputTokens}↓ {runWithSpans.run.aggregatedTokens.outputTokens}↑ -
- )} -
- - {/* Timeline Table */} -
- {/* Column Headers */} -
-
- Operation + if (timeline.length === 0) { + return ( +
+ No timeline data available
-
- Duration -
-
- Timeline + ); + } + + // Get actual time range from timeline items + const timestamps = timeline.filter(t => t.timestamp && t.timestamp > 0).map(t => t.timestamp!); + const minTimestamp = Math.min(...timestamps); + const maxTimestamp = Math.max(...timestamps); + + // Calculate actual duration based on timestamps, with padding for last operation + const lastItem = timeline[timeline.length - 1]; + const lastItemEnd = lastItem.timestamp && lastItem.duration + ? lastItem.timestamp + (lastItem.duration * 1000) + : maxTimestamp; + const totalDurationMs = Math.max(lastItemEnd - minTimestamp, 1000); // At least 1 second + + return ( + <> + {/* Timeline Header */} +
+
+

Timeline

+ + {timeline.length - 1} operations • {formatDuration(totalDurationMs / 1000)} + +
+ {runWithSpans.run.aggregatedUsage && ( +
+ + {runWithSpans.run.aggregatedUsage.inputTokens}↓ {runWithSpans.run.aggregatedUsage.outputTokens}↑ +
+ )}
-
- - {/* Timeline Rows */} - {timeline.map((item, idx) => { - const hasActualDuration = item.duration !== undefined && item.duration > 0; - const displayDuration = item.duration || 0; - - // Position: bar STARTS at timestamp and extends for duration - const itemTimestamp = item.timestamp || minTimestamp; - const startOffsetMs = itemTimestamp - minTimestamp; - const leftPercent = (startOffsetMs / totalDurationMs) * 100; - - // Bar width: how long the operation takes - const durationMs = displayDuration * 1000; - const widthPercent = (durationMs / totalDurationMs) * 100; - - let badgeColor = ''; - let barColor = ''; - let icon = null; - let typeLabel = ''; - - if (item.type === 'user_input') { - badgeColor = 'bg-green-100 text-green-700 border-green-200'; - barColor = 'bg-green-500'; - icon = ; - typeLabel = 'User'; - } else if (item.type === 'thinking') { - badgeColor = 'bg-purple-100 text-purple-700 border-purple-200'; - barColor = 'bg-purple-500'; - icon = ; - typeLabel = 'Think'; - } else if (item.type === 'text') { - badgeColor = 'bg-gray-100 text-gray-700 border-gray-200'; - barColor = 'bg-gray-500'; - icon = ; - typeLabel = 'Text'; - } else if (item.type === 'tool_use') { - if (item.isError) { - badgeColor = 'bg-red-100 text-red-700 border-red-200'; - barColor = 'bg-red-500'; - } else { - badgeColor = 'bg-blue-100 text-blue-700 border-blue-200'; - barColor = 'bg-blue-500'; - } - icon = ; - typeLabel = 'Tool'; - } else if (item.type === 'processing') { - badgeColor = 'bg-slate-100 text-slate-600 border-slate-200'; - barColor = 'bg-slate-400'; - icon = ; - typeLabel = 'Proc'; - } - - return ( -
-
{ - e.stopPropagation(); - setSelectedTimelineItemIdx(selectedTimelineItemIdx === idx ? null : idx); - }} - > - {/* Operation Name */} -
- - {icon} - {typeLabel} - - - {item.name} - -
- {/* Duration */} -
- - {hasActualDuration ? formatDuration(displayDuration) : ~0ms} - -
- - {/* Timeline Bar */} -
- {hasActualDuration && ( -
- {formatDuration(displayDuration)} -
- )} -
+ {/* Timeline Table */} +
+ {/* Column Headers */} +
+
+ Operation
+
+ Duration +
+
+ Timeline +
+
- {/* Details Panel */} - {selectedTimelineItemIdx === idx && item.type !== 'processing' && ( -
- {item.type === 'user_input' && item.content && ( -
-
User Input
-
- 
-                                                    {item.content}
-
-
+ {/* Timeline Rows */} + {timeline.map((item, idx) => { + const hasActualDuration = item.duration !== undefined && item.duration > 0; + const displayDuration = item.duration || 0; + + // Position: bar STARTS at timestamp and extends for duration + const itemTimestamp = item.timestamp || minTimestamp; + const startOffsetMs = itemTimestamp - minTimestamp; + const leftPercent = (startOffsetMs / totalDurationMs) * 100; + + // Bar width: how long the operation takes + const durationMs = displayDuration * 1000; + const widthPercent = (durationMs / totalDurationMs) * 100; + + let badgeColor = ''; + let barColor = ''; + let icon = null; + let typeLabel = ''; + + if (item.type === 'user_input') { + badgeColor = 'bg-green-100 text-green-700 border-green-200'; + barColor = 'bg-green-500'; + icon = ; + typeLabel = 'User'; + } else if (item.type === 'thinking') { + badgeColor = 'bg-purple-100 text-purple-700 border-purple-200'; + barColor = 'bg-purple-500'; + icon = ; + typeLabel = 'Think'; + } else if (item.type === 'text') { + badgeColor = 'bg-gray-100 text-gray-700 border-gray-200'; + barColor = 'bg-gray-500'; + icon = ; + typeLabel = 'Text'; + } else if (item.type === 'tool_use') { + if (item.isError) { + badgeColor = 'bg-red-100 text-red-700 border-red-200'; + barColor = 'bg-red-500'; + } else { + badgeColor = 'bg-blue-100 text-blue-700 border-blue-200'; + barColor = 'bg-blue-500'; + } + icon = ; + typeLabel = 'Tool'; + } else if (item.type === 'processing') { + badgeColor = 'bg-slate-100 text-slate-600 border-slate-200'; + barColor = 'bg-slate-400'; + icon = ; + typeLabel = 'Proc'; + } + + return ( +
+
{ + e.stopPropagation(); + setSelectedTimelineItemIdx(selectedTimelineItemIdx === idx ? null : idx); + }} + > + {/* Operation Name */} +
+ + {icon} + {typeLabel} + + + {item.name} +
- )} - {item.type === 'thinking' && item.content && ( -
-
Thinking
-
- 
-                                                    {item.content}
-
-
+ {/* Duration */} +
+ + {hasActualDuration ? formatDuration(displayDuration) : ~0ms} +
- )} - {item.type === 'text' && item.content && ( -
-
Response
-
- 
-                                                    {item.content}
-
-
+ {/* Timeline Bar */} +
+ {hasActualDuration && ( +
+ {formatDuration(displayDuration)} +
+ )}
- )} +
+ + {/* Details Panel */} + {selectedTimelineItemIdx === idx && item.type !== 'processing' && ( +
+ {item.type === 'user_input' && item.content && ( +
+
User Input
+
+ 
+                                                        {item.content}
+
+
+
+ )} - {item.type === 'tool_use' && ( -
- {item.input && ( + {item.type === 'thinking' && item.content && (
-
Input
+
Thinking
- 
-                                                        {(() => {
-                                                          try {
-                                                            return JSON.stringify(JSON.parse(item.input), null, 2);
-                                                          } catch {
-                                                            return item.input;
-                                                          }
-                                                        })()}
+                                                      
+                                                        {item.content}
)} - {item.output && ( + {item.type === 'text' && item.content && (
-
Output
+
Response
- 
-                                                        {item.output}
+                                                      
+                                                        {item.content}
)} - {item.isError && ( -
-

- Tool execution failed -

+ {item.type === 'tool_use' && ( +
+ {item.input && ( +
+
Input
+
+ 
+                                                            {(() => {
+                                                              try {
+                                                                return JSON.stringify(JSON.parse(item.input), null, 2);
+                                                              } catch {
+                                                                return item.input;
+                                                              }
+                                                            })()}
+
+
+
+ )} + + {item.output && ( +
+
Output
+
+ 
+                                                            {item.output}
+
+
+
+ )} + + {item.isError && ( +
+

+ Tool execution failed +

+
+ )}
)}
)}
- )} -
- ); - })} -
- - ); - })()} + ); + })} +
+ + ); + })()} +
-
- )} - - - ); - })} -
- - {/* Pagination */} - - + )} + + + ); + })} +
+ + {/* Pagination */} + + )} diff --git a/dashboard/src/types/index.ts b/dashboard/src/types/index.ts index 604af9b1..df11c6a8 100644 --- a/dashboard/src/types/index.ts +++ b/dashboard/src/types/index.ts @@ -19,10 +19,13 @@ export enum AgentType { CLAUDE = 1, } -export interface TokenStats { +export interface AggregatedUsage { totalTokens: number; inputTokens: number; outputTokens: number; + cacheReadInputTokens: number; + cacheCreationInputTokens: number; + totalCost: number; } export interface Team { @@ -38,7 +41,7 @@ export interface Team { totalDatasets: number; totalAgents: number; totalSessions: number; - aggregatedTokens: TokenStats; + aggregatedUsage: AggregatedUsage; } export interface Organization { @@ -87,7 +90,7 @@ export interface Experiment { status: Status; createdAt: string; updatedAt: string; - aggregatedTokens: TokenStats; + aggregatedUsage: AggregatedUsage; traceStats?: TraceStats; metrics?: Metric[]; } @@ -102,7 +105,7 @@ export interface Run { duration: number; status: Status; createdAt: string; - aggregatedTokens: TokenStats; + aggregatedUsage: AggregatedUsage; metrics?: Metric[]; spans?: Span[]; } diff --git a/dashboard/static/assets/index-BN5cvIeh.css b/dashboard/static/assets/index-BN5cvIeh.css deleted file mode 100644 index a0b07b19..00000000 --- a/dashboard/static/assets/index-BN5cvIeh.css +++ /dev/null @@ -1 +0,0 @@ -*,:before,:after{--tw-border-spacing-x: 0;--tw-border-spacing-y: 0;--tw-translate-x: 0;--tw-translate-y: 0;--tw-rotate: 0;--tw-skew-x: 0;--tw-skew-y: 0;--tw-scale-x: 1;--tw-scale-y: 1;--tw-pan-x: ;--tw-pan-y: ;--tw-pinch-zoom: ;--tw-scroll-snap-strictness: proximity;--tw-gradient-from-position: ;--tw-gradient-via-position: ;--tw-gradient-to-position: ;--tw-ordinal: ;--tw-slashed-zero: ;--tw-numeric-figure: ;--tw-numeric-spacing: ;--tw-numeric-fraction: ;--tw-ring-inset: ;--tw-ring-offset-width: 0px;--tw-ring-offset-color: #fff;--tw-ring-color: rgb(59 130 246 / .5);--tw-ring-offset-shadow: 0 0 #0000;--tw-ring-shadow: 0 0 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WeakMap,Wa=new WeakMap,Uc=new WeakMap,Os=new WeakMap,Ns=new WeakMap,eo=new WeakMap,to=new WeakMap,Ka=new WeakMap,Ps=new WeakMap,Ee=new WeakSet,fu=function(t){me(this,Ee,dy).call(this);let r=I(this,Se).fetch(this.options,t);return t!=null&&t.throwOnError||(r=r.catch(Xt)),r},oy=function(){me(this,Ee,cy).call(this);const t=li(this.options.staleTime,I(this,Se));if(mo||I(this,Vt).isStale||!ty(t))return;const n=CE(I(this,Vt).dataUpdatedAt,t)+1;Q(this,eo,Ui.setTimeout(()=>{I(this,Vt).isStale||this.updateResult()},n))},sy=function(){return(typeof this.options.refetchInterval=="function"?this.options.refetchInterval(I(this,Se)):this.options.refetchInterval)??!1},ly=function(t){me(this,Ee,fy).call(this),Q(this,Ka,t),!(mo||Br(this.options.enabled,I(this,Se))===!1||!ty(I(this,Ka))||I(this,Ka)===0)&&Q(this,to,Ui.setInterval(()=>{(this.options.refetchIntervalInBackground||lb.isFocused())&&me(this,Ee,fu).call(this)},I(this,Ka)))},uy=function(){me(this,Ee,oy).call(this),me(this,Ee,ly).call(this,me(this,Ee,sy).call(this))},cy=function(){I(this,eo)&&(Ui.clearTimeout(I(this,eo)),Q(this,eo,void 0))},fy=function(){I(this,to)&&(Ui.clearInterval(I(this,to)),Q(this,to,void 0))},dy=function(){const t=I(this,sr).getQueryCache().build(I(this,sr),this.options);if(t===I(this,Se))return;const r=I(this,Se);Q(this,Se,t),Q(this,Bc,t.state),this.hasListeners()&&(r==null||r.removeObserver(this),t.addObserver(this))},zE=function(t){St.batch(()=>{t.listeners&&this.listeners.forEach(r=>{r(I(this,Vt))}),I(this,sr).getQueryCache().notify({query:I(this,Se),type:"observerResultsUpdated"})})},pA);function OR(e,t){return Br(t.enabled,e)!==!1&&e.state.data===void 0&&!(e.state.status==="error"&&t.retryOnMount===!1)}function X1(e,t){return OR(e,t)||e.state.data!==void 0&&hy(e,t,t.refetchOnMount)}function hy(e,t,r){if(Br(t.enabled,e)!==!1&&li(t.staleTime,e)!=="static"){const n=typeof r=="function"?r(e):r;return n==="always"||n!==!1&&ub(e,t)}return!1}function Q1(e,t,r,n){return(e!==t||Br(n.enabled,e)===!1)&&(!r.suspense||e.state.status!=="error")&&ub(e,r)}function ub(e,t){return Br(t.enabled,e)!==!1&&e.isStaleByTime(li(t.staleTime,e))}function NR(e,t){return!Qd(e.getCurrentResult(),t)}function J1(e){return{onFetch:(t,r)=>{var f,d,h,p,g;const n=t.options,a=(h=(d=(f=t.fetchOptions)==null?void 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m,y;return(y=(m=t.options).persister)==null?void 0:y.call(m,u,{client:t.client,queryKey:t.queryKey,meta:t.options.meta,signal:t.signal},r)}:t.fetchFn=u}}}function Z1(e,{pages:t,pageParams:r}){const n=t.length-1;return t.length>0?e.getNextPageParam(t[n],t,r[n],r):void 0}function PR(e,{pages:t,pageParams:r}){var n;return t.length>0?(n=e.getPreviousPageParam)==null?void 0:n.call(e,t[0],t,r[0],r):void 0}var Wc,Nn,Gt,ro,Pn,Ca,mA,AR=(mA=class extends LE{constructor(t){super();re(this,Pn);re(this,Wc);re(this,Nn);re(this,Gt);re(this,ro);Q(this,Wc,t.client),this.mutationId=t.mutationId,Q(this,Gt,t.mutationCache),Q(this,Nn,[]),this.state=t.state||BE(),this.setOptions(t.options),this.scheduleGc()}setOptions(t){this.options=t,this.updateGcTime(this.options.gcTime)}get meta(){return this.options.meta}addObserver(t){I(this,Nn).includes(t)||(I(this,Nn).push(t),this.clearGcTimeout(),I(this,Gt).notify({type:"observerAdded",mutation:this,observer:t}))}removeObserver(t){Q(this,Nn,I(this,Nn).filter(r=>r!==t)),this.scheduleGc(),I(this,Gt).notify({type:"observerRemoved",mutation:this,observer:t})}optionalRemove(){I(this,Nn).length||(this.state.status==="pending"?this.scheduleGc():I(this,Gt).remove(this))}continue(){var t;return((t=I(this,ro))==null?void 0:t.continue())??this.execute(this.state.variables)}async execute(t){var o,s,l,u,f,d,h,p,g,m,y,x,v,b,S,w,O,j,P,A;const r=()=>{me(this,Pn,Ca).call(this,{type:"continue"})},n={client:I(this,Wc),meta:this.options.meta,mutationKey:this.options.mutationKey};Q(this,ro,RE({fn:()=>this.options.mutationFn?this.options.mutationFn(t,n):Promise.reject(new Error("No mutationFn 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0:m.call(g,_,null,this.state.variables,this.state.context,this,n)),await((x=(y=this.options).onSettled)==null?void 0:x.call(y,_,null,t,this.state.context,n)),me(this,Pn,Ca).call(this,{type:"success",data:_}),_}catch(_){try{throw await((b=(v=I(this,Gt).config).onError)==null?void 0:b.call(v,_,t,this.state.context,this,n)),await((w=(S=this.options).onError)==null?void 0:w.call(S,_,t,this.state.context,n)),await((j=(O=I(this,Gt).config).onSettled)==null?void 0:j.call(O,void 0,_,this.state.variables,this.state.context,this,n)),await((A=(P=this.options).onSettled)==null?void 0:A.call(P,void 0,_,t,this.state.context,n)),_}finally{me(this,Pn,Ca).call(this,{type:"error",error:_})}}finally{I(this,Gt).runNext(this)}}},Wc=new WeakMap,Nn=new WeakMap,Gt=new WeakMap,ro=new WeakMap,Pn=new WeakSet,Ca=function(t){const r=n=>{switch(t.type){case"failed":return{...n,failureCount:t.failureCount,failureReason:t.error};case"pause":return{...n,isPaused:!0};case"continue":return{...n,isPaused:!1};case"pending":return{...n,context:t.context,data:void 0,failureCount:0,failureReason:null,error:null,isPaused:t.isPaused,status:"pending",variables:t.variables,submittedAt:Date.now()};case"success":return{...n,data:t.data,failureCount:0,failureReason:null,error:null,status:"success",isPaused:!1};case"error":return{...n,data:void 0,error:t.error,failureCount:n.failureCount+1,failureReason:t.error,isPaused:!1,status:"error"}}};this.state=r(this.state),St.batch(()=>{I(this,Nn).forEach(n=>{n.onMutationUpdate(t)}),I(this,Gt).notify({mutation:this,type:"updated",action:t})})},mA);function BE(){return{context:void 0,data:void 0,error:null,failureCount:0,failureReason:null,isPaused:!1,status:"idle",variables:void 0,submittedAt:0}}var Yn,an,Kc,gA,kR=(gA=class extends ml{constructor(t={}){super();re(this,Yn);re(this,an);re(this,Kc);this.config=t,Q(this,Yn,new Set),Q(this,an,new Map),Q(this,Kc,0)}build(t,r,n){const a=new AR({client:t,mutationCache:this,mutationId:++yf(this,Kc)._,options:t.defaultMutationOptions(r),state:n});return this.add(a),a}add(t){I(this,Yn).add(t);const r=Rf(t);if(typeof r=="string"){const n=I(this,an).get(r);n?n.push(t):I(this,an).set(r,[t])}this.notify({type:"added",mutation:t})}remove(t){if(I(this,Yn).delete(t)){const r=Rf(t);if(typeof r=="string"){const n=I(this,an).get(r);if(n)if(n.length>1){const a=n.indexOf(t);a!==-1&&n.splice(a,1)}else n[0]===t&&I(this,an).delete(r)}}this.notify({type:"removed",mutation:t})}canRun(t){const r=Rf(t);if(typeof r=="string"){const n=I(this,an).get(r),a=n==null?void 0:n.find(i=>i.state.status==="pending");return!a||a===t}else return!0}runNext(t){var n;const r=Rf(t);if(typeof r=="string"){const a=(n=I(this,an).get(r))==null?void 0:n.find(i=>i!==t&&i.state.isPaused);return(a==null?void 0:a.continue())??Promise.resolve()}else return Promise.resolve()}clear(){St.batch(()=>{I(this,Yn).forEach(t=>{this.notify({type:"removed",mutation:t})}),I(this,Yn).clear(),I(this,an).clear()})}getAll(){return Array.from(I(this,Yn))}find(t){const r={exact:!0,...t};return this.getAll().find(n=>H1(r,n))}findAll(t={}){return this.getAll().filter(r=>H1(t,r))}notify(t){St.batch(()=>{this.listeners.forEach(r=>{r(t)})})}resumePausedMutations(){const t=this.getAll().filter(r=>r.state.isPaused);return St.batch(()=>Promise.all(t.map(r=>r.continue().catch(Xt))))}},Yn=new WeakMap,an=new WeakMap,Kc=new WeakMap,gA);function Rf(e){var t;return(t=e.options.scope)==null?void 0:t.id}var Xn,qa,lr,Qn,la,xd,py,vA,ER=(vA=class extends ml{constructor(r,n){super();re(this,la);re(this,Xn);re(this,qa);re(this,lr);re(this,Qn);Q(this,Xn,r),this.setOptions(n),this.bindMethods(),me(this,la,xd).call(this)}bindMethods(){this.mutate=this.mutate.bind(this),this.reset=this.reset.bind(this)}setOptions(r){var a;const n=this.options;this.options=I(this,Xn).defaultMutationOptions(r),Qd(this.options,n)||I(this,Xn).getMutationCache().notify({type:"observerOptionsUpdated",mutation:I(this,lr),observer:this}),n!=null&&n.mutationKey&&this.options.mutationKey&&go(n.mutationKey)!==go(this.options.mutationKey)?this.reset():((a=I(this,lr))==null?void 0:a.state.status)==="pending"&&I(this,lr).setOptions(this.options)}onUnsubscribe(){var r;this.hasListeners()||(r=I(this,lr))==null||r.removeObserver(this)}onMutationUpdate(r){me(this,la,xd).call(this),me(this,la,py).call(this,r)}getCurrentResult(){return I(this,qa)}reset(){var r;(r=I(this,lr))==null||r.removeObserver(this),Q(this,lr,void 0),me(this,la,xd).call(this),me(this,la,py).call(this)}mutate(r,n){var a;return Q(this,Qn,n),(a=I(this,lr))==null||a.removeObserver(this),Q(this,lr,I(this,Xn).getMutationCache().build(I(this,Xn),this.options)),I(this,lr).addObserver(this),I(this,lr).execute(r)}},Xn=new WeakMap,qa=new WeakMap,lr=new WeakMap,Qn=new WeakMap,la=new WeakSet,xd=function(){var n;const r=((n=I(this,lr))==null?void 0:n.state)??BE();Q(this,qa,{...r,isPending:r.status==="pending",isSuccess:r.status==="success",isError:r.status==="error",isIdle:r.status==="idle",mutate:this.mutate,reset:this.reset})},py=function(r){St.batch(()=>{var n,a,i,o,s,l,u,f;if(I(this,Qn)&&this.hasListeners()){const d=I(this,qa).variables,h=I(this,qa).context,p={client:I(this,Xn),meta:this.options.meta,mutationKey:this.options.mutationKey};(r==null?void 0:r.type)==="success"?((a=(n=I(this,Qn)).onSuccess)==null||a.call(n,r.data,d,h,p),(o=(i=I(this,Qn)).onSettled)==null||o.call(i,r.data,null,d,h,p)):(r==null?void 0:r.type)==="error"&&((l=(s=I(this,Qn)).onError)==null||l.call(s,r.error,d,h,p),(f=(u=I(this,Qn)).onSettled)==null||f.call(u,void 0,r.error,d,h,p))}this.listeners.forEach(d=>{d(I(this,qa))})})},vA),An,yA,_R=(yA=class extends ml{constructor(t={}){super();re(this,An);this.config=t,Q(this,An,new Map)}build(t,r,n){const a=r.queryKey,i=r.queryHash??ob(a,r);let o=this.get(i);return o||(o=new SR({client:t,queryKey:a,queryHash:i,options:t.defaultQueryOptions(r),state:n,defaultOptions:t.getQueryDefaults(a)}),this.add(o)),o}add(t){I(this,An).has(t.queryHash)||(I(this,An).set(t.queryHash,t),this.notify({type:"added",query:t}))}remove(t){const r=I(this,An).get(t.queryHash);r&&(t.destroy(),r===t&&I(this,An).delete(t.queryHash),this.notify({type:"removed",query:t}))}clear(){St.batch(()=>{this.getAll().forEach(t=>{this.remove(t)})})}get(t){return I(this,An).get(t)}getAll(){return[...I(this,An).values()]}find(t){const r={exact:!0,...t};return this.getAll().find(n=>q1(r,n))}findAll(t={}){const r=this.getAll();return Object.keys(t).length>0?r.filter(n=>q1(t,n)):r}notify(t){St.batch(()=>{this.listeners.forEach(r=>{r(t)})})}onFocus(){St.batch(()=>{this.getAll().forEach(t=>{t.onFocus()})})}onOnline(){St.batch(()=>{this.getAll().forEach(t=>{t.onOnline()})})}},An=new 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ListTeams { + teams { + id + orgId + name + description + meta + createdAt + updatedAt + } + } + `,getUser:` + query GetUser($id: ID!) { + user(id: $id) { + id + orgId + name + email + avatarUrl + meta + createdAt + updatedAt + } + } + `,getTeam:` + query GetTeam($id: ID!) { + team(id: $id) { + id + orgId + name + description + meta + createdAt + updatedAt + totalExperiments + totalRuns + totalDatasets + totalAgents + totalSessions + aggregatedUsage { + totalTokens + inputTokens + outputTokens + cacheReadInputTokens + cacheCreationInputTokens + totalCost + } + } + } + `,getOrganization:` + query GetOrganization($id: ID!) { + organization(id: $id) { + id + name + description + meta + createdAt + updatedAt + } + } + `,getTeamWithExperiments:` + query GetTeamWithExperiments($id: ID!, $startTime: DateTime!, $endTime: DateTime!) { + team(id: $id) { + id + name + expsByTimeframe(startTime: $startTime, endTime: $endTime) { + id + teamId + userId + name + status + createdAt + } + } + } + `,getTeamWithLabelKeys:` + query GetTeamWithLabelKeys($id: ID!) { + team(id: $id) { + id + labelKeys + } + } + `,listExperiments:` + query ListExperiments($teamId: ID!, $labelName: String, $labelValue: String, $tag: String, $page: Int, $pageSize: Int) { + experiments(teamId: $teamId, labelName: $labelName, labelValue: $labelValue, tag: $tag, page: $page, pageSize: $pageSize) { + id + teamId + userId + name + description + kind + meta + params + labels { + name + value + } + tags + duration + status + createdAt + updatedAt + } + } + `,getExperiment:` + query GetExperiment($id: ID!) { + experiment(id: $id) { + id + teamId + userId + name + description + kind + meta + params + labels { + name + value + } + tags + duration + status + createdAt + updatedAt + aggregatedUsage { + totalTokens + inputTokens + outputTokens + cacheReadInputTokens + cacheCreationInputTokens + totalCost + } + traceStats { + totalSpans + successSpans + errorSpans + } + metrics { + id + key + value + teamId + experimentId + runId + createdAt + } + } + } + `,listRuns:` + query ListRuns($experimentId: ID!, $page: Int, $pageSize: Int) { + runs(experimentId: $experimentId, page: $page, pageSize: $pageSize) { + id + teamId + userId + experimentId + meta + duration + status + createdAt + } + } + `,getRun:` + query GetRun($id: ID!) { + run(id: $id) { + id + teamId + userId + experimentId + meta + duration + status + createdAt + aggregatedUsage { + totalTokens + inputTokens + outputTokens + cacheReadInputTokens + cacheCreationInputTokens + totalCost + } + metrics { + id + key + value + teamId + experimentId + runId + createdAt + } + spans { + timestamp + traceId + spanId + parentSpanId + spanName + spanKind + semanticKind + serviceName + duration + statusCode + statusMessage + teamId + runId + experimentId + spanAttributes + resourceAttributes + events { + timestamp + name + attributes + } + links { + traceId + spanId + attributes + } + } + } + } + `,listArtifactRepositories:` + query ListArtifactRepositories { + artifactRepos { + name + } + } + `,listArtifactTags:` + query ListArtifactTags($team_id: ID!, $repo_name: String!) { + artifactTags(teamId: $team_id, repoName: $repo_name) { + name + } + } + `,listArtifactFiles:` + query ListArtifactFiles($team_id: ID!, $tag: String!, $repo_name: String!) { + artifactFiles(teamId: $team_id, tag: $tag, repoName: $repo_name) { + filename + size + contentType + } + } + `,getArtifactContent:` + query GetArtifactContent($team_id: ID!, $tag: String!, $repo_name: String!, $filename: String) { + artifactContent(teamId: $team_id, tag: $tag, repoName: $repo_name, filename: $filename) { + filename + content + contentType + } + } + `,listTraces:` + query ListSpans($runId: ID!) { + spansByRunId(runId: $runId) { + timestamp + traceId + spanId + parentSpanId + spanName + spanKind + semanticKind + serviceName + duration + statusCode + statusMessage + teamId + runId + experimentId + spanAttributes + resourceAttributes + events { + timestamp + name + attributes + } + links { + traceId + spanId + attributes + } + } + } + `,getDailyTokenUsage:` + query GetDailyTokenUsage($teamId: ID!, $days: Int = 30) { + dailyTokenUsage(teamId: $teamId, days: $days) { + date + totalTokens + inputTokens + outputTokens + } + } + `,getDailyCostUsage:` + query GetDailyCostUsage($teamId: ID!, $days: Int = 30) { + dailyCostUsage(teamId: $teamId, days: $days) { + date + totalCost + totalTokens + inputTokens + outputTokens + cacheReadInputTokens + cacheCreationInputTokens + } + } + `,listDatasets:` + query ListDatasets($teamId: ID!, $page: Int, $pageSize: Int) { + datasets(teamId: $teamId, page: $page, pageSize: $pageSize) { + id + name + description + path + meta + teamId + experimentId + runId + userId + createdAt + updatedAt + } + } + `,getDataset:` + query GetDataset($id: ID!) { + dataset(id: $id) { + id + name + description + path + meta + teamId + experimentId + runId + userId + createdAt + updatedAt + } + } + `,listDatasetsByExperiment:` + query ListDatasetsByExperiment($teamId: ID!, $experimentId: ID!, $page: Int, $pageSize: Int) { + datasetsByExperiment(teamId: $teamId, experimentId: $experimentId, page: $page, pageSize: $pageSize) { + id + name + description + path + meta + teamId + experimentId + runId + userId + createdAt + updatedAt + } + } + `,listAgents:` + query ListAgents($teamId: ID!, $page: Int, $pageSize: Int) { + agents(teamId: $teamId, page: $page, pageSize: $pageSize) { + id + teamId + userId + name + type + description + meta + createdAt + updatedAt + } + } + `,getAgent:` + query GetAgent($id: ID!) { + agent(id: $id) { + id + teamId + userId + name + type + description + meta + createdAt + updatedAt + } + } + `,listAllSessions:` + query ListAllSessions($teamId: ID!, $page: Int, $pageSize: Int) { + team(id: $teamId) { + id + agents(page: 0, pageSize: 1000) { + id + sessions(page: $page, pageSize: $pageSize) { + id + agentId + teamId + userId + meta + createdAt + updatedAt + } + } + } + } + `,listAllAgentRuns:` + query ListAllAgentRuns($teamId: ID!, $page: Int, $pageSize: Int) { + team(id: $teamId) { + id + agents(page: 0, pageSize: 1000) { + id + sessions(page: 0, pageSize: 1000) { + id + runs(page: $page, pageSize: $pageSize) { + id + teamId + userId + sessionId + status + duration + createdAt + } + } + } + } + } + `,getSession:` + query GetSession($sessionId: ID!) { + session(sessionId: $sessionId) { + id + agentId + teamId + userId + meta + createdAt + updatedAt + } + } + `},X_={deleteExperiments:` + mutation DeleteExperiments($experimentIds: [ID!]!) { + deleteExperiments(experimentIds: $experimentIds) + } + `,deleteDatasets:` + mutation DeleteDatasets($datasetIds: [ID!]!) { + deleteDatasets(datasetIds: $datasetIds) + } + `};function $F(e=0,t=100){const r=s_();return Nt({queryKey:["teams",r.id,e,t],queryFn:async()=>(await rt(Ft.listTeams)).teams,staleTime:10*60*1e3})}function rf(e){return Nt({queryKey:["team",e],queryFn:async()=>(await 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n=this.props,a=n.data,i=n.className,o=n.children,s=n.x,l=n.y,u=n.width,f=n.height,d=n.alwaysShowText,h=this.state,p=h.startX,g=h.endX,m=h.isTextActive,y=h.isSlideMoving,x=h.isTravellerMoving,v=h.isTravellerFocused;if(!a||!a.length||!G(s)||!G(l)||!G(u)||!G(f)||u<=0||f<=0)return null;var b=fe("recharts-brush",i),S=$.Children.count(o)===1,w=Ese("userSelect","none");return $.createElement(xe,{className:b,onMouseLeave:this.handleLeaveWrapper,onTouchMove:this.handleTouchMove,style:w},this.renderBackground(),S&&this.renderPanorama(),this.renderSlide(p,g),this.renderTravellerLayer(p,"startX"),this.renderTravellerLayer(g,"endX"),(m||y||x||v||d)&&this.renderText())}}],[{key:"renderDefaultTraveller",value:function(n){var a=n.x,i=n.y,o=n.width,s=n.height,l=n.stroke,u=Math.floor(i+s/2)-1;return 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D=Q(this,Gn,P.promise=ny());_(D)},k=I(this,Gn);switch(k.status){case"pending":t.queryHash===n.queryHash&&_(k);break;case"fulfilled":(P.status==="error"||P.data!==k.value)&&E();break;case"rejected":(P.status!=="error"||P.error!==k.reason)&&E();break}}return P}updateResult(){const t=I(this,Vt),r=this.createResult(I(this,Se),this.options);if(Q(this,Ja,I(this,Se).state),Q(this,js,this.options),I(this,Ja).data!==void 0&&Q(this,Ns,I(this,Se)),Xd(r,t))return;Q(this,Vt,r);const n=()=>{if(!t)return!0;const{notifyOnChangeProps:i}=this.options,a=typeof i=="function"?i():i;if(a==="all"||!a&&!I(this,Ps).size)return!0;const o=new Set(a??I(this,Ps));return this.options.throwOnError&&o.add("error"),Object.keys(I(this,Vt)).some(s=>{const l=s;return I(this,Vt)[l]!==t[l]&&o.has(l)})};ge(this,ke,zk).call(this,{listeners:n()})}onQueryUpdate(){this.updateResult(),this.hasListeners()&&ge(this,ke,ly).call(this)}},sr=new WeakMap,Se=new WeakMap,Fc=new WeakMap,Vt=new WeakMap,Ja=new WeakMap,js=new WeakMap,Gn=new WeakMap,Wi=new WeakMap,zc=new WeakMap,Os=new WeakMap,Ns=new WeakMap,Za=new WeakMap,eo=new WeakMap,Ki=new WeakMap,Ps=new WeakMap,ke=new WeakSet,fu=function(t){ge(this,ke,fy).call(this);let r=I(this,Se).fetch(this.options,t);return t!=null&&t.throwOnError||(r=r.catch(Xt)),r},ay=function(){ge(this,ke,uy).call(this);const t=la(this.options.staleTime,I(this,Se));if(mo||I(this,Vt).isStale||!ey(t))return;const n=Ck(I(this,Vt).dataUpdatedAt,t)+1;Q(this,Za,Ba.setTimeout(()=>{I(this,Vt).isStale||this.updateResult()},n))},oy=function(){return(typeof this.options.refetchInterval=="function"?this.options.refetchInterval(I(this,Se)):this.options.refetchInterval)??!1},sy=function(t){ge(this,ke,cy).call(this),Q(this,Ki,t),!(mo||zr(this.options.enabled,I(this,Se))===!1||!ey(I(this,Ki))||I(this,Ki)===0)&&Q(this,eo,Ba.setInterval(()=>{(this.options.refetchIntervalInBackground||sb.isFocused())&&ge(this,ke,fu).call(this)},I(this,Ki)))},ly=function(){ge(this,ke,ay).call(this),ge(this,ke,sy).call(this,ge(this,ke,oy).call(this))},uy=function(){I(this,Za)&&(Ba.clearTimeout(I(this,Za)),Q(this,Za,void 0))},cy=function(){I(this,eo)&&(Ba.clearInterval(I(this,eo)),Q(this,eo,void 0))},fy=function(){const t=I(this,sr).getQueryCache().build(I(this,sr),this.options);if(t===I(this,Se))return;const r=I(this,Se);Q(this,Se,t),Q(this,Fc,t.state),this.hasListeners()&&(r==null||r.removeObserver(this),t.addObserver(this))},zk=function(t){St.batch(()=>{t.listeners&&this.listeners.forEach(r=>{r(I(this,Vt))}),I(this,sr).getQueryCache().notify({query:I(this,Se),type:"observerResultsUpdated"})})},pA);function jR(e,t){return zr(t.enabled,e)!==!1&&e.state.data===void 0&&!(e.state.status==="error"&&t.retryOnMount===!1)}function X1(e,t){return jR(e,t)||e.state.data!==void 0&&dy(e,t,t.refetchOnMount)}function dy(e,t,r){if(zr(t.enabled,e)!==!1&&la(t.staleTime,e)!=="static"){const n=typeof r=="function"?r(e):r;return n==="always"||n!==!1&&lb(e,t)}return!1}function Q1(e,t,r,n){return(e!==t||zr(n.enabled,e)===!1)&&(!r.suspense||e.state.status!=="error")&&lb(e,r)}function lb(e,t){return zr(t.enabled,e)!==!1&&e.isStaleByTime(la(t.staleTime,e))}function OR(e,t){return!Xd(e.getCurrentResult(),t)}function J1(e){return{onFetch:(t,r)=>{var f,d,h,p,g;const n=t.options,i=(h=(d=(f=t.fetchOptions)==null?void 0:f.meta)==null?void 0:d.fetchMore)==null?void 0:h.direction,a=((p=t.state.data)==null?void 0:p.pages)||[],o=((g=t.state.data)==null?void 0:g.pageParams)||[];let s={pages:[],pageParams:[]},l=0;const u=async()=>{let m=!1;const v=b=>{Object.defineProperty(b,"signal",{enumerable:!0,get:()=>(t.signal.aborted?m=!0:t.signal.addEventListener("abort",()=>{m=!0}),t.signal)})},y=Mk(t.options,t.fetchOptions),x=async(b,S,w)=>{if(m)return Promise.reject();if(S==null&&b.pages.length)return Promise.resolve(b);const j=(()=>{const E={client:t.client,queryKey:t.queryKey,pageParam:S,direction:w?"backward":"forward",meta:t.options.meta};return v(E),E})(),P=await y(j),{maxPages:A}=t.options,_=w?mR:pR;return{pages:_(b.pages,P,A),pageParams:_(b.pageParams,S,A)}};if(i&&a.length){const b=i==="backward",S=b?NR:Z1,w={pages:a,pageParams:o},O=S(n,w);s=await x(w,O,b)}else{const b=e??a.length;do{const S=l===0?o[0]??n.initialPageParam:Z1(n,s);if(l>0&&S==null)break;s=await x(s,S),l++}while(l{var m,v;return(v=(m=t.options).persister)==null?void 0:v.call(m,u,{client:t.client,queryKey:t.queryKey,meta:t.options.meta,signal:t.signal},r)}:t.fetchFn=u}}}function Z1(e,{pages:t,pageParams:r}){const n=t.length-1;return t.length>0?e.getNextPageParam(t[n],t,r[n],r):void 0}function NR(e,{pages:t,pageParams:r}){var n;return t.length>0?(n=e.getPreviousPageParam)==null?void 0:n.call(e,t[0],t,r[0],r):void 0}var Bc,On,Gt,to,Nn,Ci,mA,PR=(mA=class extends Lk{constructor(t){super();re(this,Nn);re(this,Bc);re(this,On);re(this,Gt);re(this,to);Q(this,Bc,t.client),this.mutationId=t.mutationId,Q(this,Gt,t.mutationCache),Q(this,On,[]),this.state=t.state||Bk(),this.setOptions(t.options),this.scheduleGc()}setOptions(t){this.options=t,this.updateGcTime(this.options.gcTime)}get meta(){return this.options.meta}addObserver(t){I(this,On).includes(t)||(I(this,On).push(t),this.clearGcTimeout(),I(this,Gt).notify({type:"observerAdded",mutation:this,observer:t}))}removeObserver(t){Q(this,On,I(this,On).filter(r=>r!==t)),this.scheduleGc(),I(this,Gt).notify({type:"observerRemoved",mutation:this,observer:t})}optionalRemove(){I(this,On).length||(this.state.status==="pending"?this.scheduleGc():I(this,Gt).remove(this))}continue(){var t;return((t=I(this,to))==null?void 0:t.continue())??this.execute(this.state.variables)}async execute(t){var o,s,l,u,f,d,h,p,g,m,v,y,x,b,S,w,O,j,P,A;const r=()=>{ge(this,Nn,Ci).call(this,{type:"continue"})},n={client:I(this,Bc),meta:this.options.meta,mutationKey:this.options.mutationKey};Q(this,to,Rk({fn:()=>this.options.mutationFn?this.options.mutationFn(t,n):Promise.reject(new Error("No mutationFn 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0:m.call(g,_,null,this.state.variables,this.state.context,this,n)),await((y=(v=this.options).onSettled)==null?void 0:y.call(v,_,null,t,this.state.context,n)),ge(this,Nn,Ci).call(this,{type:"success",data:_}),_}catch(_){try{throw await((b=(x=I(this,Gt).config).onError)==null?void 0:b.call(x,_,t,this.state.context,this,n)),await((w=(S=this.options).onError)==null?void 0:w.call(S,_,t,this.state.context,n)),await((j=(O=I(this,Gt).config).onSettled)==null?void 0:j.call(O,void 0,_,this.state.variables,this.state.context,this,n)),await((A=(P=this.options).onSettled)==null?void 0:A.call(P,void 0,_,t,this.state.context,n)),_}finally{ge(this,Nn,Ci).call(this,{type:"error",error:_})}}finally{I(this,Gt).runNext(this)}}},Bc=new WeakMap,On=new WeakMap,Gt=new WeakMap,to=new WeakMap,Nn=new WeakSet,Ci=function(t){const r=n=>{switch(t.type){case"failed":return{...n,failureCount:t.failureCount,failureReason:t.error};case"pause":return{...n,isPaused:!0};case"continue":return{...n,isPaused:!1};case"pending":return{...n,context:t.context,data:void 0,failureCount:0,failureReason:null,error:null,isPaused:t.isPaused,status:"pending",variables:t.variables,submittedAt:Date.now()};case"success":return{...n,data:t.data,failureCount:0,failureReason:null,error:null,status:"success",isPaused:!1};case"error":return{...n,data:void 0,error:t.error,failureCount:n.failureCount+1,failureReason:t.error,isPaused:!1,status:"error"}}};this.state=r(this.state),St.batch(()=>{I(this,On).forEach(n=>{n.onMutationUpdate(t)}),I(this,Gt).notify({mutation:this,type:"updated",action:t})})},mA);function Bk(){return{context:void 0,data:void 0,error:null,failureCount:0,failureReason:null,isPaused:!1,status:"idle",variables:void 0,submittedAt:0}}var Yn,rn,Uc,gA,AR=(gA=class extends ml{constructor(t={}){super();re(this,Yn);re(this,rn);re(this,Uc);this.config=t,Q(this,Yn,new Set),Q(this,rn,new Map),Q(this,Uc,0)}build(t,r,n){const i=new PR({client:t,mutationCache:this,mutationId:++gf(this,Uc)._,options:t.defaultMutationOptions(r),state:n});return this.add(i),i}add(t){I(this,Yn).add(t);const r=If(t);if(typeof r=="string"){const n=I(this,rn).get(r);n?n.push(t):I(this,rn).set(r,[t])}this.notify({type:"added",mutation:t})}remove(t){if(I(this,Yn).delete(t)){const r=If(t);if(typeof r=="string"){const n=I(this,rn).get(r);if(n)if(n.length>1){const i=n.indexOf(t);i!==-1&&n.splice(i,1)}else n[0]===t&&I(this,rn).delete(r)}}this.notify({type:"removed",mutation:t})}canRun(t){const r=If(t);if(typeof r=="string"){const n=I(this,rn).get(r),i=n==null?void 0:n.find(a=>a.state.status==="pending");return!i||i===t}else return!0}runNext(t){var n;const r=If(t);if(typeof r=="string"){const i=(n=I(this,rn).get(r))==null?void 0:n.find(a=>a!==t&&a.state.isPaused);return(i==null?void 0:i.continue())??Promise.resolve()}else return Promise.resolve()}clear(){St.batch(()=>{I(this,Yn).forEach(t=>{this.notify({type:"removed",mutation:t})}),I(this,Yn).clear(),I(this,rn).clear()})}getAll(){return Array.from(I(this,Yn))}find(t){const r={exact:!0,...t};return this.getAll().find(n=>H1(r,n))}findAll(t={}){return this.getAll().filter(r=>H1(t,r))}notify(t){St.batch(()=>{this.listeners.forEach(r=>{r(t)})})}resumePausedMutations(){const t=this.getAll().filter(r=>r.state.isPaused);return St.batch(()=>Promise.all(t.map(r=>r.continue().catch(Xt))))}},Yn=new WeakMap,rn=new WeakMap,Uc=new WeakMap,gA);function If(e){var t;return(t=e.options.scope)==null?void 0:t.id}var Xn,qi,lr,Qn,li,vd,hy,vA,ER=(vA=class extends ml{constructor(r,n){super();re(this,li);re(this,Xn);re(this,qi);re(this,lr);re(this,Qn);Q(this,Xn,r),this.setOptions(n),this.bindMethods(),ge(this,li,vd).call(this)}bindMethods(){this.mutate=this.mutate.bind(this),this.reset=this.reset.bind(this)}setOptions(r){var i;const n=this.options;this.options=I(this,Xn).defaultMutationOptions(r),Xd(this.options,n)||I(this,Xn).getMutationCache().notify({type:"observerOptionsUpdated",mutation:I(this,lr),observer:this}),n!=null&&n.mutationKey&&this.options.mutationKey&&go(n.mutationKey)!==go(this.options.mutationKey)?this.reset():((i=I(this,lr))==null?void 0:i.state.status)==="pending"&&I(this,lr).setOptions(this.options)}onUnsubscribe(){var r;this.hasListeners()||(r=I(this,lr))==null||r.removeObserver(this)}onMutationUpdate(r){ge(this,li,vd).call(this),ge(this,li,hy).call(this,r)}getCurrentResult(){return I(this,qi)}reset(){var r;(r=I(this,lr))==null||r.removeObserver(this),Q(this,lr,void 0),ge(this,li,vd).call(this),ge(this,li,hy).call(this)}mutate(r,n){var i;return Q(this,Qn,n),(i=I(this,lr))==null||i.removeObserver(this),Q(this,lr,I(this,Xn).getMutationCache().build(I(this,Xn),this.options)),I(this,lr).addObserver(this),I(this,lr).execute(r)}},Xn=new WeakMap,qi=new WeakMap,lr=new WeakMap,Qn=new WeakMap,li=new WeakSet,vd=function(){var n;const r=((n=I(this,lr))==null?void 0:n.state)??Bk();Q(this,qi,{...r,isPending:r.status==="pending",isSuccess:r.status==="success",isError:r.status==="error",isIdle:r.status==="idle",mutate:this.mutate,reset:this.reset})},hy=function(r){St.batch(()=>{var n,i,a,o,s,l,u,f;if(I(this,Qn)&&this.hasListeners()){const d=I(this,qi).variables,h=I(this,qi).context,p={client:I(this,Xn),meta:this.options.meta,mutationKey:this.options.mutationKey};(r==null?void 0:r.type)==="success"?((i=(n=I(this,Qn)).onSuccess)==null||i.call(n,r.data,d,h,p),(o=(a=I(this,Qn)).onSettled)==null||o.call(a,r.data,null,d,h,p)):(r==null?void 0:r.type)==="error"&&((l=(s=I(this,Qn)).onError)==null||l.call(s,r.error,d,h,p),(f=(u=I(this,Qn)).onSettled)==null||f.call(u,void 0,r.error,d,h,p))}this.listeners.forEach(d=>{d(I(this,qi))})})},vA),Pn,yA,kR=(yA=class extends ml{constructor(t={}){super();re(this,Pn);this.config=t,Q(this,Pn,new Map)}build(t,r,n){const i=r.queryKey,a=r.queryHash??ab(i,r);let o=this.get(a);return o||(o=new wR({client:t,queryKey:i,queryHash:a,options:t.defaultQueryOptions(r),state:n,defaultOptions:t.getQueryDefaults(i)}),this.add(o)),o}add(t){I(this,Pn).has(t.queryHash)||(I(this,Pn).set(t.queryHash,t),this.notify({type:"added",query:t}))}remove(t){const r=I(this,Pn).get(t.queryHash);r&&(t.destroy(),r===t&&I(this,Pn).delete(t.queryHash),this.notify({type:"removed",query:t}))}clear(){St.batch(()=>{this.getAll().forEach(t=>{this.remove(t)})})}get(t){return I(this,Pn).get(t)}getAll(){return[...I(this,Pn).values()]}find(t){const r={exact:!0,...t};return this.getAll().find(n=>q1(r,n))}findAll(t={}){const r=this.getAll();return Object.keys(t).length>0?r.filter(n=>q1(t,n)):r}notify(t){St.batch(()=>{this.listeners.forEach(r=>{r(t)})})}onFocus(){St.batch(()=>{this.getAll().forEach(t=>{t.onFocus()})})}onOnline(){St.batch(()=>{this.getAll().forEach(t=>{t.onOnline()})})}},Pn=new 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description - meta - createdAt - updatedAt - totalExperiments - totalRuns - totalDatasets - totalAgents - totalSessions - aggregatedTokens { - totalTokens - inputTokens - outputTokens - } - } - } - `,getOrganization:` - query GetOrganization($id: ID!) { - organization(id: $id) { - id - name - description - meta - createdAt - updatedAt - } - } - `,getTeamWithExperiments:` - query GetTeamWithExperiments($id: ID!, $startTime: DateTime!, $endTime: DateTime!) { - team(id: $id) { - id - name - expsByTimeframe(startTime: $startTime, endTime: $endTime) { - id - teamId - userId - name - status - createdAt - } - } - } - `,getTeamWithLabelKeys:` - query GetTeamWithLabelKeys($id: ID!) { - team(id: $id) { - id - labelKeys - } - } - `,listExperiments:` - query ListExperiments($teamId: ID!, $labelName: String, $labelValue: String, $tag: String, $page: Int, $pageSize: Int) { - experiments(teamId: $teamId, labelName: $labelName, labelValue: $labelValue, tag: $tag, page: $page, pageSize: $pageSize) { - id - teamId - userId - name - description - kind - meta - params - labels { - name - value - } - tags - duration - status - createdAt - updatedAt - } - } - `,getExperiment:` - query GetExperiment($id: ID!) { - experiment(id: $id) { - id - teamId - userId - name - description - kind - meta - params - labels { - name - value - } - tags - duration - status - createdAt - updatedAt - aggregatedTokens { - totalTokens - inputTokens - outputTokens - } - traceStats { - totalSpans - successSpans - errorSpans - } - metrics { - id - key - value - teamId - experimentId - runId - createdAt - } - } - } - `,listRuns:` - query ListRuns($experimentId: ID!, $page: Int, $pageSize: Int) { - runs(experimentId: $experimentId, page: $page, pageSize: $pageSize) { - id - teamId - userId - experimentId - meta - duration - status - createdAt - } - } - `,getRun:` - query GetRun($id: ID!) { - run(id: $id) { - id - teamId - userId - experimentId - meta - duration - status - createdAt - aggregatedTokens { - totalTokens - inputTokens - outputTokens - } - metrics { - id - key - value - teamId - experimentId - runId - createdAt - } - spans { - timestamp - traceId - spanId - parentSpanId - spanName - spanKind - semanticKind - serviceName - duration - statusCode - statusMessage - teamId - runId - experimentId - spanAttributes - resourceAttributes - events { - timestamp - name - attributes - } - links { - traceId - spanId - attributes - } - } - } - } - `,listArtifactRepositories:` - query ListArtifactRepositories { - artifactRepos { - name - } - } - `,listArtifactTags:` - query ListArtifactTags($team_id: ID!, $repo_name: String!) { - artifactTags(teamId: $team_id, repoName: $repo_name) { - name - } - } - `,listArtifactFiles:` - query ListArtifactFiles($team_id: ID!, $tag: String!, $repo_name: String!) { - artifactFiles(teamId: $team_id, tag: $tag, repoName: $repo_name) { - filename - size - contentType - } - } - `,getArtifactContent:` - query GetArtifactContent($team_id: ID!, $tag: String!, $repo_name: String!, $filename: String) { - artifactContent(teamId: $team_id, tag: $tag, repoName: $repo_name, filename: $filename) { - filename - content - contentType - } - } - `,listTraces:` - query ListSpans($runId: ID!) { - spansByRunId(runId: $runId) { - timestamp - traceId - spanId - parentSpanId - spanName - spanKind - semanticKind - serviceName - duration - statusCode - statusMessage - teamId - runId - experimentId - spanAttributes - resourceAttributes - events { - timestamp - name - attributes - } - links { - traceId - spanId - attributes - } - } - } - `,getDailyTokenUsage:` - query GetDailyTokenUsage($teamId: ID!, $days: Int = 30) { - dailyTokenUsage(teamId: $teamId, days: $days) { - date - totalTokens - inputTokens - outputTokens - } - } - `,listDatasets:` - query ListDatasets($teamId: ID!, $page: Int, $pageSize: Int) { - datasets(teamId: $teamId, page: $page, pageSize: $pageSize) { - id - name - description - path - meta - teamId - experimentId - runId - userId - createdAt - updatedAt - } - } - `,getDataset:` - query GetDataset($id: ID!) { - dataset(id: $id) { - id - name - description - path - meta - teamId - experimentId - runId - userId - createdAt - updatedAt - } - } - `,listDatasetsByExperiment:` - query ListDatasetsByExperiment($teamId: ID!, $experimentId: ID!, $page: Int, $pageSize: Int) { - datasetsByExperiment(teamId: $teamId, experimentId: $experimentId, page: $page, pageSize: $pageSize) { - id - name - description - path - meta - teamId - experimentId - runId - userId - createdAt - updatedAt - } - } - `,listAgents:` - query ListAgents($teamId: ID!, $page: Int, $pageSize: Int) { - agents(teamId: $teamId, page: $page, pageSize: $pageSize) { - id - teamId - userId - name - type - description - meta - createdAt - updatedAt - } - } - `,getAgent:` - query GetAgent($id: ID!) { - agent(id: $id) { - id - teamId - userId - name - type - description - meta - createdAt - updatedAt - } - } - `,listAllSessions:` - query ListAllSessions($teamId: ID!, $page: Int, $pageSize: Int) { - team(id: $teamId) { - id - agents(page: 0, pageSize: 1000) { - id - sessions(page: $page, pageSize: $pageSize) { - id - agentId - teamId - userId - meta - createdAt - updatedAt - } - } - } - } - `,listAllAgentRuns:` - query ListAllAgentRuns($teamId: ID!, $page: Int, $pageSize: Int) { - team(id: $teamId) { - id - agents(page: 0, pageSize: 1000) { - id - sessions(page: 0, pageSize: 1000) { - id - runs(page: $page, pageSize: $pageSize) { - id - teamId - userId - sessionId - status - duration - createdAt - } - } - } - } - } - `,getSession:` - query GetSession($sessionId: ID!) { - session(sessionId: $sessionId) { - id - agentId - teamId - userId - meta - createdAt - updatedAt - } - } - `},Y_={deleteExperiments:` - mutation DeleteExperiments($experimentIds: [ID!]!) { - deleteExperiments(experimentIds: $experimentIds) - } - `,deleteDatasets:` - mutation DeleteDatasets($datasetIds: [ID!]!) { - deleteDatasets(datasetIds: $datasetIds) - } - `};function 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Prometheus ## Overview When enabled, AlphaTrion automatically: -- Extracts metrics from LLM spans (token counts, duration, model usage) -- Pushes metrics to a Prometheus push gateway -- Labels metrics with team_id, experiment_id, run_id, and model +- Enriches spans with cost information from token usage (via `CostEnrichmentProcessor`) +- Extracts metrics from LLM spans (token counts, cost, duration, model usage) +- Pushes metrics to a Prometheus push gateway (via `PrometheusExporter`) +- Labels metrics with team_id, experiment_id, and model + +## Architecture + +``` +Span Creation (OpenTelemetry) + ↓ +CostEnrichmentProcessor (enriches spans with costs) + ↓ +├─ BatchSpanProcessor → ClickHouseExporter +└─ BatchSpanProcessor → PrometheusExporter +``` + +Costs are calculated once by `CostEnrichmentProcessor` and then read by both exporters, ensuring consistency across ClickHouse and Prometheus. ## Setup @@ -66,27 +80,50 @@ async with experiment.CraftExperiment.start(name="my_experiment") as exp: ### LLM Token Metrics -- **`alphatrion_llm_tokens_total`** - Total LLM tokens consumed - - Labels: `team_id`, `experiment_id`, `model`, `token_type` (input/output/total) +- **`llm_tokens_total`** - Total LLM tokens consumed + - Labels: `team_id`, `experiment_id`, `model`, `token_type` (input/output/cache_read_input/cache_creation_input/total) + +- **`llm_input_tokens_total`** - Total input tokens + - Labels: `team_id`, `experiment_id`, `model` + +- **`llm_output_tokens_total`** - Total output tokens + - Labels: `team_id`, `experiment_id`, `model` + +- **`llm_cache_read_input_tokens_total`** - Total cache read input tokens + - Labels: `team_id`, `experiment_id`, `model` + +- **`llm_cache_creation_input_tokens_total`** - Total cache creation input tokens + - Labels: `team_id`, `experiment_id`, `model` + +### LLM Cost Metrics (USD) + +- **`llm_cost_total`** - Total LLM cost in USD + - Labels: `team_id`, `experiment_id`, `model`, `cost_type` (total) + +- **`llm_input_cost_total`** - Total input token cost in USD + - Labels: `team_id`, `experiment_id`, `model` + +- **`llm_output_cost_total`** - Total output token cost in USD + - Labels: `team_id`, `experiment_id`, `model` -- **`alphatrion_llm_input_tokens_total`** - Total input tokens +- **`llm_cache_read_cost_total`** - Total cache read cost in USD - Labels: `team_id`, `experiment_id`, `model` -- **`alphatrion_llm_output_tokens_total`** - Total output tokens +- **`llm_cache_creation_cost_total`** - Total cache creation cost in USD - Labels: `team_id`, `experiment_id`, `model` ### LLM Request Metrics -- **`alphatrion_llm_requests_total`** - Total number of LLM requests +- **`llm_requests_total`** - Total number of LLM requests - Labels: `team_id`, `experiment_id`, `model`, `status` -- **`alphatrion_llm_duration_seconds`** - LLM request duration histogram +- **`llm_request_duration_seconds`** - LLM request duration histogram - Labels: `team_id`, `experiment_id`, `model` - Buckets: 0.1s, 0.5s, 1s, 2s, 5s, 10s, 30s, 60s ### Error Tracking -- **`alphatrion_llm_errors_total`** - Total LLM errors by type +- **`llm_errors_total`** - Total LLM errors by type - Labels: `team_id`, `error_type` - Error types: `timeout`, `rate_limit`, `auth_error`, `invalid_request`, `connection_error`, `server_error`, `unknown` @@ -115,22 +152,43 @@ curl http://localhost:9091/metrics Total tokens by model: ```promql -sum by (model) (alphatrion_llm_tokens_total{token_type="total"}) +sum by (model) (llm_tokens_total{token_type="total"}) +``` + +Total cost by model: +```promql +sum by (model) (llm_cost_total{cost_type="total"}) +``` + +Input vs output cost: +```promql +sum(llm_input_cost_total) +sum(llm_output_cost_total) ``` Request rate per experiment: ```promql -rate(alphatrion_llm_requests_total[5m]) +rate(llm_requests_total[5m]) ``` Average LLM duration by model: ```promql -rate(alphatrion_llm_duration_seconds_sum[5m]) / rate(alphatrion_llm_duration_seconds_count[5m]) +rate(llm_duration_seconds_sum[5m]) / rate(llm_duration_seconds_count[5m]) ``` P95 LLM latency: ```promql -histogram_quantile(0.95, alphatrion_llm_duration_seconds_bucket) +histogram_quantile(0.95, llm_duration_seconds_bucket) +``` + +Cost per experiment: +```promql +sum by (experiment_id) (llm_cost_total{cost_type="total"}) +``` + +Average cost per request: +```promql +sum(llm_cost_total{cost_type="total"}) / sum(llm_requests_total) ``` ## Using Grafana @@ -171,28 +229,46 @@ Create your own panels with queries like: ```promql # Token usage by experiment -sum by (experiment_id) (alphatrion_llm_tokens_total{token_type="total"}) +sum by (experiment_id) (llm_tokens_total{token_type="total"}) + +# Cost by experiment +sum by (experiment_id) (llm_cost_total{cost_type="total"}) # Request rate per team -rate(alphatrion_llm_requests_total{team_id="YOUR_TEAM_ID"}[5m]) +rate(llm_requests_total{team_id="YOUR_TEAM_ID"}[5m]) # Average latency -rate(alphatrion_llm_duration_seconds_sum[5m]) / rate(alphatrion_llm_duration_seconds_count[5m]) +rate(llm_duration_seconds_sum[5m]) / rate(llm_duration_seconds_count[5m]) # Success rate -sum(rate(alphatrion_llm_requests_total{status="OK"}[5m])) / sum(rate(alphatrion_llm_requests_total[5m])) +sum(rate(llm_requests_total{status="OK"}[5m])) / sum(rate(llm_requests_total[5m])) # Top 5 teams by token usage -topk(5, sum by (team_id) (alphatrion_llm_tokens_total{token_type="total"})) +topk(5, sum by (team_id) (llm_tokens_total{token_type="total"})) + +# Top 5 teams by cost +topk(5, sum by (team_id) (llm_cost_total{cost_type="total"})) + +# Cost efficiency (cost per 1k tokens) +sum(llm_cost_total{cost_type="total"}) / (sum(llm_tokens_total{token_type="total"}) / 1000) # Errors by team -sum by (team_id) (alphatrion_llm_errors_total) +sum by (team_id) (llm_errors_total) # Count unique experiments (derived metric) -count(sum by (experiment_id) (alphatrion_llm_requests_total)) +count(sum by (experiment_id) (llm_requests_total)) # Count unique teams (derived metric) -count(sum by (team_id) (alphatrion_llm_requests_total)) +count(sum by (team_id) (llm_requests_total)) + +# Cache cost +sum(llm_cache_read_cost_total) + sum(llm_cache_creation_cost_total) + +# Percentage of cost from cache +( + sum(llm_cache_read_cost_total) + + sum(llm_cache_creation_cost_total) +) / sum(llm_cost_total{cost_type="total"}) * 100 ``` ## Production Considerations @@ -207,16 +283,23 @@ ALPHATRION_PROMETHEUS_PUSHGATEWAY_URL=pushgateway.prod.example.com:9091 ### Grouping Keys -You can customize grouping keys by modifying `PrometheusSpanProcessor` initialization in your code: +You can customize grouping keys by modifying `PrometheusExporter` initialization in your code: ```python -from alphatrion.tracing.prometheus_span_processor import PrometheusSpanProcessor +from alphatrion.tracing.prometheus_exporter import PrometheusExporter +from opentelemetry.sdk.trace.export import BatchSpanProcessor +from opentelemetry import trace -processor = PrometheusSpanProcessor( +# Create exporter with custom grouping +prometheus_exporter = PrometheusExporter( pushgateway_url="localhost:9091", job_name="my-app", grouping_key={"instance": "app-1", "environment": "production"} ) + +# Add to tracer provider with batching +tracer_provider = trace.get_tracer_provider() +tracer_provider.add_span_processor(BatchSpanProcessor(prometheus_exporter)) ``` ### Label Cardinality @@ -251,7 +334,7 @@ Labels like `run_id`, `span_kind`, and `semantic_kind` are intentionally exclude 4. Check logs for errors: ```bash - # Look for "PrometheusSpanProcessor" in logs + # Look for "PrometheusExporter initialized" or "CostEnrichmentProcessor" in logs ``` ### Push gateway connection errors diff --git a/migrations/versions/0d46a93a8733_init_schema.py b/migrations/versions/3196ef00c0b8_init_schema.py similarity index 95% rename from migrations/versions/0d46a93a8733_init_schema.py rename to migrations/versions/3196ef00c0b8_init_schema.py index 1e32c378..02e948a2 100644 --- a/migrations/versions/0d46a93a8733_init_schema.py +++ b/migrations/versions/3196ef00c0b8_init_schema.py @@ -1,8 +1,8 @@ """init schema -Revision ID: 0d46a93a8733 +Revision ID: 3196ef00c0b8 Revises: -Create Date: 2026-03-31 19:56:38.329974 +Create Date: 2026-04-02 00:22:25.946324 """ from typing import Sequence, Union @@ -12,7 +12,7 @@ # revision identifiers, used by Alembic. -revision: str = '0d46a93a8733' +revision: str = '3196ef00c0b8' down_revision: Union[str, Sequence[str], None] = None branch_labels: Union[str, Sequence[str], None] = None depends_on: Union[str, Sequence[str], None] = None @@ -87,8 +87,6 @@ def upgrade() -> None: sa.Column('kind', sa.Integer(), nullable=False, comment='Type of the experiment'), sa.Column('duration', sa.Float(), nullable=True, comment='Duration of the experiment in seconds'), sa.Column('status', sa.Integer(), nullable=False, comment='Status of the experiment, 0: UNKNOWN, 1: PENDING, 2: RUNNING, 9: COMPLETED, 10: CANCELLED, 11: FAILED'), - sa.Column('usage', sa.JSON(), nullable=True, comment='The usage information, e.g. for LLM calls: {total_tokens: int, input_tokens: int, output_tokens: int}'), - sa.Column('cost', sa.JSON(), nullable=True, comment='Cost of the experiment in dollars'), sa.Column('created_at', sa.DateTime(timezone=True), nullable=True), sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True), sa.Column('is_del', sa.Integer(), nullable=True, comment='0 for not deleted, 1 for deleted'), @@ -130,8 +128,6 @@ def upgrade() -> None: sa.Column('meta', sa.JSON(), nullable=True, comment='Additional metadata for the run'), sa.Column('duration', sa.Float(), nullable=True, comment='Duration of the run in seconds'), sa.Column('status', sa.Integer(), nullable=False, comment='Status of the run, 0: UNKNOWN, 1: PENDING, 2: RUNNING, 9: COMPLETED, 10: CANCELLED, 11: FAILED'), - sa.Column('usage', sa.JSON(), nullable=True, comment='The usage information, e.g. for LLM calls: {total_tokens: int, input_tokens: int, output_tokens: int}'), - sa.Column('cost', sa.JSON(), nullable=True, comment='Cost of the run in dollars'), sa.Column('created_at', sa.DateTime(timezone=True), nullable=True), sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True), sa.Column('is_del', sa.Integer(), nullable=True, comment='0 for not deleted, 1 for deleted'), diff --git a/pyproject.toml b/pyproject.toml index af28c616..0bd874d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -73,6 +73,7 @@ lint.ignore = [ "SIM117", # Use a single `with` statement with multiple contexts instead of nested `with` statements "PLR0915", # Too many statements in function "PLC0415", # `import` should be at the top-level of a file + "PLW0603", # Using the global statement ] exclude = ["venv", "migrations", "__pycache__"] diff --git a/tests/integration/server/test_graphql_query.py b/tests/integration/server/test_graphql_query.py index 95047f55..db4f453e 100644 --- a/tests/integration/server/test_graphql_query.py +++ b/tests/integration/server/test_graphql_query.py @@ -35,7 +35,7 @@ def test_query_single_team(execute_graphql, test_org_id, test_user_id, test_team updatedAt totalExperiments totalRuns - aggregatedTokens {{ + aggregatedUsage {{ totalTokens inputTokens outputTokens @@ -352,7 +352,7 @@ async def test_query_single_run( meta status createdAt - aggregatedTokens {{ + aggregatedUsage {{ totalTokens inputTokens outputTokens @@ -378,9 +378,9 @@ async def test_query_single_run( assert response.data["run"]["status"] == "COMPLETED" assert len(response.data["run"]["spans"]) > 0 # Verify tokens are fetched from ClickHouse via GraphQL fields - assert response.data["run"]["aggregatedTokens"]["totalTokens"] is not None - assert response.data["run"]["aggregatedTokens"]["inputTokens"] is not None - assert response.data["run"]["aggregatedTokens"]["outputTokens"] is not None + assert response.data["run"]["aggregatedUsage"]["totalTokens"] is not None + assert response.data["run"]["aggregatedUsage"]["inputTokens"] is not None + assert response.data["run"]["aggregatedUsage"]["outputTokens"] is not None # Verify tokens are NOT cached in meta anymore metadb = runtime.storage_runtime().metadb @@ -518,10 +518,13 @@ async def test_query_experiment_with_usage( experiment(id: "{exp_id}") {{ id status - aggregatedTokens {{ + aggregatedUsage {{ totalTokens inputTokens outputTokens + cacheCreationInputTokens + cacheReadInputTokens + totalCost }} }} }} @@ -534,23 +537,27 @@ async def test_query_experiment_with_usage( assert response.errors is None assert response.data["experiment"]["status"] == "CANCELLED" - assert response.data["experiment"]["aggregatedTokens"] is not None - assert response.data["experiment"]["aggregatedTokens"]["totalTokens"] is not None - assert response.data["experiment"]["aggregatedTokens"]["inputTokens"] is not None - assert response.data["experiment"]["aggregatedTokens"]["outputTokens"] is not None + assert response.data["experiment"]["aggregatedUsage"] is not None + assert response.data["experiment"]["aggregatedUsage"]["totalTokens"] is not None + assert response.data["experiment"]["aggregatedUsage"]["inputTokens"] is not None + assert response.data["experiment"]["aggregatedUsage"]["outputTokens"] is not None + assert ( + response.data["experiment"]["aggregatedUsage"]["cacheCreationInputTokens"] + is not None + ) + assert ( + response.data["experiment"]["aggregatedUsage"]["cacheReadInputTokens"] + is not None + ) + assert response.data["experiment"]["aggregatedUsage"]["totalCost"] is not None exp_obj = runtime.storage_runtime().metadb.get_experiment(experiment_id=exp.id) assert exp_obj.status == Status.CANCELLED - assert exp_obj.usage is not None - assert "total_tokens" in exp_obj.usage - assert "input_tokens" in exp_obj.usage - assert "output_tokens" in exp_obj.usage # resume the experiment async with CraftExperiment.start(name="integration_test_experiment_resume") as exp: exp_obj = runtime.storage_runtime().metadb.get_experiment(experiment_id=exp.id) assert exp_obj.status == Status.RUNNING - assert exp_obj.usage is None @pytest.mark.asyncio