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move the save to dataset to telemetry

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This commit is contained in:
Dinesh Yeduguru 2024-12-05 13:36:46 -08:00
parent 4c78432bc8
commit f5d427c178
9 changed files with 79 additions and 64 deletions
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5
llama_stack/apis/eval/eval.py
View file

@ -90,11 +90,6 @@ class Eval(Protocol):
task_config: EvalTaskConfig,
) -> EvaluateResponse: ...
@webmethod(route="/eval/create-annotation-dataset", method="POST")
async def create_annotation_dataset(
self, session_id: str, dataset_id: str
) -> None: ...
@webmethod(route="/eval/job/status", method="GET")
async def job_status(self, task_id: str, job_id: str) -> Optional[JobStatus]: ...

65
llama_stack/apis/telemetry/telemetry.py
View file

@ -21,6 +21,8 @@ from llama_models.schema_utils import json_schema_type, webmethod
from pydantic import BaseModel, Field
from typing_extensions import Annotated
from llama_stack.apis.datasetio import DatasetIO
# Add this constant near the top of the file, after the imports
DEFAULT_TTL_DAYS = 7
@ -165,6 +167,8 @@ class QueryCondition(BaseModel):
@runtime_checkable
class Telemetry(Protocol):
datasetio_api: DatasetIO
@webmethod(route="/telemetry/log-event")
async def log_event(
self, event: Event, ttl_seconds: int = DEFAULT_TTL_DAYS * 86400
@ -186,3 +190,64 @@ class Telemetry(Protocol):
attributes_to_return: Optional[List[str]] = None,
max_depth: Optional[int] = None,
) -> SpanWithChildren: ...
@webmethod(route="/telemetry/query-spans", method="POST")
async def query_spans(
self,
attribute_filters: List[QueryCondition],
attributes_to_return: List[str],
max_depth: Optional[int] = None,
) -> List[Dict[str, Any]]:
traces = await self.query_traces(attribute_filters=attribute_filters)
rows = []
for trace in traces:
span_tree = await self.get_span_tree(
span_id=trace.root_span_id,
attributes_to_return=attributes_to_return,
max_depth=max_depth,
)
def extract_spans(span: SpanWithChildren) -> List[Dict[str, Any]]:
rows = []
if span.attributes and all(
attr in span.attributes and span.attributes[attr] is not None
for attr in attributes_to_return
):
row = {
"trace_id": trace.root_span_id,
"span_id": span.span_id,
"step_name": span.name,
}
for attr in attributes_to_return:
row[attr] = str(span.attributes[attr])
rows.append(row)
for child in span.children:
rows.extend(extract_spans(child))
return rows
rows.extend(extract_spans(span_tree))
return rows
@webmethod(route="/telemetry/save-traces-to-dataset", method="POST")
async def save_traces_to_dataset(
self,
attribute_filters: List[QueryCondition],
attributes_to_save: List[str],
dataset_id: str,
max_depth: Optional[int] = None,
) -> None:
annotation_rows = await self.query_spans(
attribute_filters=attribute_filters,
attributes_to_return=attributes_to_save,
max_depth=max_depth,
)
if annotation_rows:
await self.datasetio_api.append_rows(
dataset_id=dataset_id, rows=annotation_rows
)

11
llama_stack/distribution/resolver.py
View file

@ -349,10 +349,13 @@ def check_protocol_compliance(obj: Any, protocol: Any) -> None:
method_owner = next(
(cls for cls in mro if name in cls.__dict__), None
)
if (
method_owner is None
or method_owner.__name__ == protocol.__name__
):
proto_method = getattr(protocol, name)
if method_owner is None:
missing_methods.append((name, "not_actually_implemented"))
elif method_owner.__name__ == protocol.__name__:
# Check if it's just a stub (...) or has real implementation
proto_source = inspect.getsource(proto_method)
if "..." in proto_source:
missing_methods.append((name, "not_actually_implemented"))
if missing_methods:

1
llama_stack/providers/inline/eval/meta_reference/__init__.py
View file

@ -23,7 +23,6 @@ async def get_provider_impl(
deps[Api.scoring],
deps[Api.inference],
deps[Api.agents],
deps[Api.telemetry],
)
await impl.initialize()
return impl

50
llama_stack/providers/inline/eval/meta_reference/eval.py
View file

@ -16,7 +16,6 @@ from llama_stack.apis.datasets import Datasets
from llama_stack.apis.eval_tasks import EvalTask
from llama_stack.apis.inference import Inference
from llama_stack.apis.scoring import Scoring
from llama_stack.apis.telemetry import QueryCondition, SpanWithChildren, Telemetry
from llama_stack.providers.datatypes import EvalTasksProtocolPrivate
from llama_stack.providers.utils.kvstore import kvstore_impl
from tqdm import tqdm
@ -43,7 +42,6 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
scoring_api: Scoring,
inference_api: Inference,
agents_api: Agents,
telemetry_api: Telemetry,
) -> None:
self.config = config
self.datasetio_api = datasetio_api
@ -51,7 +49,6 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
self.scoring_api = scoring_api
self.inference_api = inference_api
self.agents_api = agents_api
self.telemetry_api = telemetry_api
# TODO: assume sync job, will need jobs API for async scheduling
self.jobs = {}
@ -272,50 +269,3 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
raise ValueError(f"Job is not completed, Status: {status.value}")
return self.jobs[job_id]
async def create_annotation_dataset(self, session_id: str, dataset_id: str) -> None:
traces = await self.telemetry_api.query_traces(
attribute_filters=[
QueryCondition(key="session_id", op="eq", value=session_id),
]
)
annotation_rows = []
for trace in traces:
span_tree = await self.telemetry_api.get_span_tree(
span_id=trace.root_span_id,
attributes_to_return=[
"input",
"output",
"name",
],
)
def extract_spans(span: SpanWithChildren) -> List[Dict[str, Any]]:
rows = []
if (
span.attributes
and "input" in span.attributes
and "output" in span.attributes
):
row = {
"input_query": span.attributes.get("input", ""),
"generated_answer": span.attributes.get("output", ""),
"trace_id": trace.root_span_id,
"span_id": span.span_id,
"step_name": span.name,
}
rows.append(row)
for child in span.children:
rows.extend(extract_spans(child))
return rows
annotation_rows.extend(extract_spans(span_tree))
if annotation_rows:
await self.datasetio_api.append_rows(
dataset_id=dataset_id, rows=annotation_rows
)

2
llama_stack/providers/inline/telemetry/meta_reference/__init__.py
View file

@ -13,6 +13,6 @@ __all__ = ["TelemetryConfig", "TelemetryAdapter", "TelemetrySink"]
async def get_provider_impl(config: TelemetryConfig, deps: Dict[str, Any]):
impl = TelemetryAdapter(config)
impl = TelemetryAdapter(config, deps)
await impl.initialize()
return impl

7
llama_stack/providers/inline/telemetry/meta_reference/telemetry.py
View file

@ -5,7 +5,7 @@
# the root directory of this source tree.
import threading
from typing import List, Optional
from typing import Any, Dict, List, Optional
from opentelemetry import metrics, trace
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
@ -28,6 +28,8 @@ from llama_stack.providers.utils.telemetry.sqlite_trace_store import SQLiteTrace
from llama_stack.apis.telemetry import * # noqa: F403
from llama_stack.distribution.datatypes import Api
from .config import TelemetryConfig, TelemetrySink
_GLOBAL_STORAGE = {
@ -55,8 +57,9 @@ def is_tracing_enabled(tracer):
class TelemetryAdapter(Telemetry):
def __init__(self, config: TelemetryConfig) -> None:
def __init__(self, config: TelemetryConfig, deps: Dict[str, Any]) -> None:
self.config = config
self.datasetio_api = deps[Api.datasetio]
resource = Resource.create(
{

1
llama_stack/providers/registry/eval.py
View file

@ -23,7 +23,6 @@ def available_providers() -> List[ProviderSpec]:
Api.scoring,
Api.inference,
Api.agents,
Api.telemetry,
],
),
]

1
llama_stack/providers/registry/telemetry.py
View file

@ -18,6 +18,7 @@ def available_providers() -> List[ProviderSpec]:
"opentelemetry-sdk",
"opentelemetry-exporter-otlp-proto-http",
],
api_dependencies=[Api.datasetio],
module="llama_stack.providers.inline.telemetry.meta_reference",
config_class="llama_stack.providers.inline.telemetry.meta_reference.config.TelemetryConfig",
),