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flow.py
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304 lines (268 loc) · 9.77 KB
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import logging
import typing
from functools import wraps
from typing import Any, Awaitable, Callable, Literal, Optional, TypeVar, Union, overload
from opentelemetry.trace import Span, Tracer
from typing_extensions import ParamSpec
from humanloop.base_client import BaseHumanloop
from humanloop.context import (
DecoratorContext,
get_trace_id,
set_decorator_context,
set_trace_id,
)
from humanloop.decorators.helpers import bind_args
from humanloop.evals.run import HumanloopRuntimeError
from humanloop.evals.types import FileEvalConfig
from humanloop.otel.constants import (
HUMANLOOP_FILE_PATH_KEY,
HUMANLOOP_FILE_TYPE_KEY,
HUMANLOOP_FLOW_SPAN_NAME,
HUMANLOOP_LOG_KEY,
)
from humanloop.otel.helpers import process_output, write_to_opentelemetry_span
from humanloop.requests import FlowKernelRequestParams as FlowDict
from humanloop.types.chat_message import ChatMessage
from humanloop.types.flow_log_response import FlowLogResponse
logger = logging.getLogger("humanloop.sdk")
P = ParamSpec("P")
R = TypeVar("R")
def flow_decorator_factory(
client: "BaseHumanloop",
opentelemetry_tracer: Tracer,
path: str,
attributes: Optional[dict[str, Any]] = None,
):
def decorator(func: Callable[P, R]) -> Callable[P, Optional[R]]:
decorator_path = path or func.__name__
file_type = "flow"
flow_kernel = {"attributes": attributes or {}}
wrapper = _wrapper_factory(
client=client,
opentelemetry_tracer=opentelemetry_tracer,
func=func,
path=path,
flow_kernel=flow_kernel,
is_awaitable=False,
)
wrapper.file = FileEvalConfig( # type: ignore
path=decorator_path,
type=file_type, # type: ignore [arg-type, typeddict-item]
version=FlowDict(**flow_kernel), # type: ignore
callable=wrapper,
)
return wrapper
return decorator
def a_flow_decorator_factory(
client: "BaseHumanloop",
opentelemetry_tracer: Tracer,
path: str,
attributes: Optional[dict[str, Any]] = None,
):
def decorator(func: Callable[P, Awaitable[R]]):
decorator_path = path or func.__name__
file_type = "flow"
flow_kernel = {"attributes": attributes or {}}
wrapper = _wrapper_factory(
client=client,
opentelemetry_tracer=opentelemetry_tracer,
func=func,
path=path,
flow_kernel=flow_kernel,
is_awaitable=True,
)
wrapper.file = FileEvalConfig( # type: ignore
path=decorator_path,
type=file_type, # type: ignore [arg-type, typeddict-item]
version=FlowDict(**flow_kernel), # type: ignore
callable=wrapper,
)
return wrapper
return decorator
@overload
def _wrapper_factory(
client: "BaseHumanloop",
opentelemetry_tracer: Tracer,
func: Callable[P, Awaitable[R]],
path: str,
flow_kernel: dict[str, Any],
is_awaitable: Literal[True],
) -> Callable[P, Awaitable[Optional[R]]]: ...
@overload
def _wrapper_factory(
client: "BaseHumanloop",
opentelemetry_tracer: Tracer,
func: Callable[P, R],
path: str,
flow_kernel: dict[str, Any],
is_awaitable: Literal[False],
) -> Callable[P, Optional[R]]: ...
def _wrapper_factory( # type: ignore [misc]
client: "BaseHumanloop",
opentelemetry_tracer: Tracer,
func: Union[Callable[P, Awaitable[R]], Callable[P, R]],
path: str,
flow_kernel: dict[str, Any],
is_awaitable: bool,
):
if is_awaitable:
func = typing.cast(Callable[P, Awaitable[R]], func)
@wraps(func)
async def wrapper(*args: P.args, **kwargs: P.kwargs) -> Optional[R]:
span: Span
with set_decorator_context(
DecoratorContext(
path=path,
type="flow",
version=flow_kernel,
)
) as decorator_context:
with opentelemetry_tracer.start_as_current_span(HUMANLOOP_FLOW_SPAN_NAME) as span: # type: ignore
span, flow_log = _process_inputs(
client=client,
span=span,
decorator_context=decorator_context,
decorator_path=path,
file_type="flow",
func=func,
args=args,
kwargs=kwargs,
)
with set_trace_id(flow_log.id):
func_output: Optional[R]
try:
func_output = await func(*args, **kwargs) # type: ignore [misc]
error = None
except HumanloopRuntimeError as e:
# Critical error, re-raise
client.logs.delete(id=flow_log.id)
span.record_exception(e)
raise e
except Exception as e:
logger.error(f"Error calling {func.__name__}: {e}")
error = e
func_output = None
_process_output(
func=func,
span=span,
func_output=func_output,
error=error,
flow_log=flow_log,
)
# Return the output of the decorated function
return func_output
else:
func = typing.cast(Callable[P, R], func)
@wraps(func)
def wrapper(*args: P.args, **kwargs: P.kwargs) -> Optional[R]:
span: Span
with set_decorator_context(
DecoratorContext(
path=path,
type="flow",
version=flow_kernel,
)
) as decorator_context:
with opentelemetry_tracer.start_as_current_span(HUMANLOOP_FLOW_SPAN_NAME) as span: # type: ignore
span, flow_log = _process_inputs(
client=client,
span=span,
decorator_context=decorator_context,
decorator_path=path,
file_type="flow",
func=func,
args=args,
kwargs=kwargs,
)
with set_trace_id(flow_log.id):
func_output: Optional[R]
try:
func_output = func(*args, **kwargs)
error = None
except HumanloopRuntimeError as e:
# Critical error, re-raise
client.logs.delete(id=flow_log.id)
span.record_exception(e)
raise e
except Exception as e:
logger.error(f"Error calling {func.__name__}: {e}")
error = e
func_output = None
_process_output(
func=func,
span=span,
func_output=func_output,
error=error,
flow_log=flow_log,
)
# Return the output of the decorated function
return func_output
return wrapper
def _process_inputs(
client: "BaseHumanloop",
span: Span,
decorator_context: DecoratorContext,
decorator_path: str,
file_type: str,
func: Callable[P, R],
args: tuple[Any, ...],
kwargs: dict[str, Any],
):
span.set_attribute(HUMANLOOP_FILE_PATH_KEY, decorator_path)
span.set_attribute(HUMANLOOP_FILE_TYPE_KEY, file_type)
trace_id = get_trace_id()
func_args = bind_args(func, args, kwargs)
# Create the trace ahead so we have a parent ID to reference
init_log_inputs = {
"inputs": {k: v for k, v in func_args.items() if k != "messages"},
"messages": func_args.get("messages"),
"trace_parent_id": trace_id,
}
flow_log: FlowLogResponse = client.flows._log( # type: ignore [attr-defined]
path=decorator_context.path,
flow=decorator_context.version,
log_status="incomplete",
**init_log_inputs,
)
return span, flow_log
def _process_output(
func: Union[Callable[P, R], Callable[P, Awaitable[R]]],
span: Span,
func_output: Optional[R],
error: Optional[Exception],
flow_log: FlowLogResponse,
):
log_output: Optional[str]
log_error: Optional[str]
log_output_message: Optional[ChatMessage]
if not error:
if (
isinstance(func_output, dict)
and len(func_output.keys()) == 2
and "role" in func_output
and "content" in func_output
):
log_output_message = func_output # type: ignore [assignment]
log_output = None
else:
log_output = process_output(func=func, output=func_output)
log_output_message = None
log_error = None
else:
log_output = None
log_output_message = None
log_error = str(error)
func_output = None
updated_flow_log = {
"log_status": "complete",
"output": log_output,
"error": log_error,
"output_message": log_output_message,
"id": flow_log.id,
}
# Write the Flow Log to the Span on HL_LOG_OT_KEY
write_to_opentelemetry_span(
span=span, # type: ignore [arg-type]
key=HUMANLOOP_LOG_KEY,
value=updated_flow_log, # type: ignore
)