chore: initial snapshot for gitea/github upload

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2026-03-26 16:04:46 +08:00
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"""
Opik Logger that logs LLM events to an Opik server
"""
import asyncio
import traceback
from datetime import datetime
from typing import Any, Dict, Optional
from litellm._logging import verbose_logger
from litellm.integrations.custom_batch_logger import CustomBatchLogger
from litellm.llms.custom_httpx.http_handler import (
_get_httpx_client,
get_async_httpx_client,
httpxSpecialProvider,
)
from . import opik_payload_builder, utils
try:
from opik.api_objects import opik_client
except Exception:
opik_client = None
def _should_skip_event(kwargs: Dict[str, Any]) -> bool:
"""Check if event should be skipped due to missing standard_logging_object."""
if kwargs.get("standard_logging_object") is None:
verbose_logger.debug(
"OpikLogger skipping event; no standard_logging_object found"
)
return True
return False
class OpikLogger(CustomBatchLogger):
"""
Opik Logger for logging events to an Opik Server
"""
def __init__(self, **kwargs: Any) -> None:
self.async_httpx_client = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)
self.sync_httpx_client = _get_httpx_client()
self.opik_project_name: str = (
utils.get_opik_config_variable(
"project_name",
user_value=kwargs.get("project_name", None),
default_value="Default Project",
)
or "Default Project"
)
opik_base_url: str = (
utils.get_opik_config_variable(
"url_override",
user_value=kwargs.get("url", None),
default_value="https://www.comet.com/opik/api",
)
or "https://www.comet.com/opik/api"
)
opik_api_key: Optional[str] = utils.get_opik_config_variable(
"api_key", user_value=kwargs.get("api_key", None), default_value=None
)
opik_workspace: Optional[str] = utils.get_opik_config_variable(
"workspace", user_value=kwargs.get("workspace", None), default_value=None
)
self.trace_url: str = f"{opik_base_url}/v1/private/traces/batch"
self.span_url: str = f"{opik_base_url}/v1/private/spans/batch"
self.headers: Dict[str, str] = {}
if opik_workspace:
self.headers["Comet-Workspace"] = opik_workspace
if opik_api_key:
self.headers["authorization"] = opik_api_key
self.opik_workspace: Optional[str] = opik_workspace
self.opik_api_key: Optional[str] = opik_api_key
try:
asyncio.create_task(self.periodic_flush())
self.flush_lock: Optional[asyncio.Lock] = asyncio.Lock()
except Exception as e:
verbose_logger.exception(
f"OpikLogger - Asynchronous processing not initialized as we are not running in an async context {str(e)}"
)
self.flush_lock = None
# Initialize _opik_client attribute
if opik_client is not None:
self._opik_client = opik_client.get_client_cached()
else:
self._opik_client = None
super().__init__(**kwargs, flush_lock=self.flush_lock)
async def async_log_success_event(
self,
kwargs: Dict[str, Any],
response_obj: Any,
start_time: datetime,
end_time: datetime,
) -> None:
try:
if _should_skip_event(kwargs):
return
# Build payload using the payload builder
trace_payload, span_payload = opik_payload_builder.build_opik_payload(
kwargs=kwargs,
response_obj=response_obj,
start_time=start_time,
end_time=end_time,
project_name=self.opik_project_name,
)
if self._opik_client is not None:
# Opik native client is available, use it to send data
if trace_payload is not None:
self._opik_client.trace(
id=trace_payload.id,
name=trace_payload.name,
start_time=datetime.fromisoformat(trace_payload.start_time),
end_time=datetime.fromisoformat(trace_payload.end_time),
input=trace_payload.input,
output=trace_payload.output,
metadata=trace_payload.metadata,
tags=trace_payload.tags,
thread_id=trace_payload.thread_id,
project_name=trace_payload.project_name,
)
self._opik_client.span(
id=span_payload.id,
trace_id=span_payload.trace_id,
parent_span_id=span_payload.parent_span_id,
name=span_payload.name,
type=span_payload.type,
model=span_payload.model,
start_time=datetime.fromisoformat(span_payload.start_time),
end_time=datetime.fromisoformat(span_payload.end_time),
input=span_payload.input,
output=span_payload.output,
metadata=span_payload.metadata,
tags=span_payload.tags,
usage=span_payload.usage,
project_name=span_payload.project_name,
provider=span_payload.provider,
total_cost=span_payload.total_cost,
)
else:
# Add payloads to LiteLLM queue
if trace_payload is not None:
self.log_queue.append(trace_payload.__dict__)
self.log_queue.append(span_payload.__dict__)
verbose_logger.debug(
f"OpikLogger added event to log_queue - Will flush in {self.flush_interval} seconds..."
)
if len(self.log_queue) >= self.batch_size:
verbose_logger.debug("OpikLogger - Flushing batch")
await self.flush_queue()
except Exception as e:
verbose_logger.exception(
f"OpikLogger failed to log success event - {str(e)}\n{traceback.format_exc()}"
)
def _sync_send(
self, url: str, headers: Dict[str, str], batch: Dict[str, Any]
) -> None:
try:
response = self.sync_httpx_client.post(
url=url, headers=headers, json=batch # type: ignore
)
response.raise_for_status()
if response.status_code != 204:
raise Exception(
f"Response from opik API status_code: {response.status_code}, text: {response.text}"
)
except Exception as e:
verbose_logger.exception(
f"OpikLogger failed to send batch - {str(e)}\n{traceback.format_exc()}"
)
def log_success_event(
self,
kwargs: Dict[str, Any],
response_obj: Any,
start_time: datetime,
end_time: datetime,
) -> None:
try:
if _should_skip_event(kwargs):
return
# Build payload using the payload builder
trace_payload, span_payload = opik_payload_builder.build_opik_payload(
kwargs=kwargs,
response_obj=response_obj,
start_time=start_time,
end_time=end_time,
project_name=self.opik_project_name,
)
if self._opik_client is not None:
# Opik native client is available, use it to send data
if trace_payload is not None:
self._opik_client.trace(
id=trace_payload.id,
name=trace_payload.name,
start_time=datetime.fromisoformat(trace_payload.start_time),
end_time=datetime.fromisoformat(trace_payload.end_time),
input=trace_payload.input,
output=trace_payload.output,
metadata=trace_payload.metadata,
tags=trace_payload.tags,
thread_id=trace_payload.thread_id,
project_name=trace_payload.project_name,
)
self._opik_client.span(
id=span_payload.id,
trace_id=span_payload.trace_id,
parent_span_id=span_payload.parent_span_id,
name=span_payload.name,
type=span_payload.type,
model=span_payload.model,
start_time=datetime.fromisoformat(span_payload.start_time),
end_time=datetime.fromisoformat(span_payload.end_time),
input=span_payload.input,
output=span_payload.output,
metadata=span_payload.metadata,
tags=span_payload.tags,
usage=span_payload.usage,
project_name=span_payload.project_name,
provider=span_payload.provider,
total_cost=span_payload.total_cost,
)
else:
# Opik native client is not available, use LiteLLM queue to send data
if trace_payload is not None:
self._sync_send(
url=self.trace_url,
headers=self.headers,
batch={"traces": [trace_payload.__dict__]},
)
# Always send span
self._sync_send(
url=self.span_url,
headers=self.headers,
batch={"spans": [span_payload.__dict__]},
)
except Exception as e:
verbose_logger.exception(
f"OpikLogger failed to log success event - {str(e)}\n{traceback.format_exc()}"
)
async def _submit_batch(
self, url: str, headers: Dict[str, str], batch: Dict[str, Any]
) -> None:
try:
response = await self.async_httpx_client.post(
url=url, headers=headers, json=batch # type: ignore
)
response.raise_for_status()
if response.status_code >= 300:
verbose_logger.error(
f"OpikLogger - Error: {response.status_code} - {response.text}"
)
else:
verbose_logger.info(
f"OpikLogger - {len(self.log_queue)} Opik events submitted"
)
except Exception as e:
verbose_logger.exception(f"OpikLogger failed to send batch - {str(e)}")
def _create_opik_headers(self) -> Dict[str, str]:
headers: Dict[str, str] = {}
if self.opik_workspace:
headers["Comet-Workspace"] = self.opik_workspace
if self.opik_api_key:
headers["authorization"] = self.opik_api_key
return headers
async def async_send_batch(self) -> None:
verbose_logger.info("Calling async_send_batch")
if not self.log_queue:
return
# Split the log_queue into traces and spans
traces, spans = utils.get_traces_and_spans_from_payload(self.log_queue)
# Send trace batch
if len(traces) > 0:
await self._submit_batch(
url=self.trace_url, headers=self.headers, batch={"traces": traces}
)
verbose_logger.info(f"Sent {len(traces)} traces")
if len(spans) > 0:
await self._submit_batch(
url=self.span_url, headers=self.headers, batch={"spans": spans}
)
verbose_logger.info(f"Sent {len(spans)} spans")

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"""
Opik payload builder namespace.
Public API:
build_opik_payload - Main function to create Opik trace and span payloads
"""
from .api import build_opik_payload
__all__ = ["build_opik_payload"]

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"""Public API for Opik payload building."""
from datetime import datetime
from typing import Any, Dict, Optional, Tuple
from litellm.integrations.opik import utils
from . import extractors, payload_builders, types
def build_opik_payload(
kwargs: Dict[str, Any],
response_obj: Dict[str, Any],
start_time: datetime,
end_time: datetime,
project_name: str,
) -> Tuple[Optional[types.TracePayload], types.SpanPayload]:
"""
Build Opik trace and span payloads from LiteLLM completion data.
This is the main public API for creating Opik payloads. It:
1. Extracts all necessary data from LiteLLM kwargs and response
2. Decides whether to create a new trace or attach to existing
3. Builds trace payload (if new trace)
4. Builds span payload (always)
Args:
kwargs: LiteLLM kwargs containing request metadata and logging data
response_obj: LiteLLM response object containing model response
start_time: Request start time
end_time: Request end time
project_name: Default Opik project name
Returns:
Tuple of (optional trace payload, span payload)
- First element is TracePayload if creating a new trace, None if attaching to existing
- Second element is always SpanPayload
"""
standard_logging_object = kwargs["standard_logging_object"]
# Extract litellm params and metadata
litellm_params = kwargs.get("litellm_params", {}) or {}
litellm_metadata = litellm_params.get("metadata", {}) or {}
standard_logging_metadata = standard_logging_object.get("metadata", {}) or {}
# Extract and merge Opik metadata
opik_metadata = extractors.extract_opik_metadata(
litellm_metadata, standard_logging_metadata
)
# Extract project name
current_project_name = opik_metadata.get("project_name", project_name)
# Extract trace identifiers
current_span_data = opik_metadata.get("current_span_data")
trace_id, parent_span_id = extractors.extract_span_identifiers(current_span_data)
# Extract tags and thread_id
tags = extractors.extract_tags(opik_metadata, kwargs.get("custom_llm_provider"))
thread_id = opik_metadata.get("thread_id")
# Apply proxy header overrides
proxy_request = litellm_params.get("proxy_server_request", {}) or {}
proxy_headers = proxy_request.get("headers", {}) or {}
current_project_name, tags, thread_id = extractors.apply_proxy_header_overrides(
current_project_name, tags, thread_id, proxy_headers
)
# Build shared metadata
metadata = extractors.extract_and_build_metadata(
opik_metadata=opik_metadata,
standard_logging_metadata=standard_logging_metadata,
standard_logging_object=standard_logging_object,
litellm_kwargs=kwargs,
)
# Get input/output data
input_data = standard_logging_object.get("messages", {})
output_data = standard_logging_object.get("response", {})
# Decide whether to create a new trace or attach to existing
trace_payload: Optional[types.TracePayload] = None
if trace_id is None:
trace_id = utils.create_uuid7()
trace_payload = payload_builders.build_trace_payload(
project_name=current_project_name,
trace_id=trace_id,
response_obj=response_obj,
start_time=start_time,
end_time=end_time,
input_data=input_data,
output_data=output_data,
metadata=metadata,
tags=tags,
thread_id=thread_id,
)
# Always create a span
usage = utils.create_usage_object(response_obj["usage"])
# Extract provider and cost
provider = extractors.normalize_provider_name(kwargs.get("custom_llm_provider"))
cost = kwargs.get("response_cost")
span_payload = payload_builders.build_span_payload(
project_name=current_project_name,
trace_id=trace_id,
parent_span_id=parent_span_id,
response_obj=response_obj,
start_time=start_time,
end_time=end_time,
input_data=input_data,
output_data=output_data,
metadata=metadata,
tags=tags,
usage=usage,
provider=provider,
cost=cost,
)
return trace_payload, span_payload

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"""Data extraction functions for Opik payload building."""
import json
from typing import Any, Dict, List, Optional, Tuple
from litellm import _logging
def normalize_provider_name(provider: Optional[str]) -> Optional[str]:
"""
Normalize LiteLLM provider names to standardized string names.
Args:
provider: LiteLLM internal provider name
Returns:
Normalized provider name or the original if no mapping exists
"""
if provider is None:
return None
# Provider mapping to names used in Opik
provider_mapping = {
"openai": "openai",
"vertex_ai-language-models": "google_vertexai",
"gemini": "google_ai",
"anthropic": "anthropic",
"vertex_ai-anthropic_models": "anthropic_vertexai",
"bedrock": "bedrock",
"bedrock_converse": "bedrock",
"groq": "groq",
}
return provider_mapping.get(provider, provider)
def extract_opik_metadata(
litellm_metadata: Dict[str, Any],
standard_logging_metadata: Dict[str, Any],
) -> Dict[str, Any]:
"""
Extract and merge Opik metadata from request and requester.
Args:
litellm_metadata: Metadata from litellm_params
standard_logging_metadata: Metadata from standard_logging_object
Returns:
Merged Opik metadata dictionary
"""
opik_meta = litellm_metadata.get("opik", {}).copy()
requester_metadata = standard_logging_metadata.get("requester_metadata", {}) or {}
requester_opik = requester_metadata.get("opik", {}) or {}
opik_meta.update(requester_opik)
_logging.verbose_logger.debug(
f"litellm_opik_metadata - {json.dumps(opik_meta, default=str)}"
)
return opik_meta
def extract_span_identifiers(
current_span_data: Any,
) -> Tuple[Optional[str], Optional[str]]:
"""
Extract trace_id and parent_span_id from current_span_data.
Args:
current_span_data: Either dict with trace_id/id keys or Opik object
Returns:
Tuple of (trace_id, parent_span_id), both optional
"""
if current_span_data is None:
return None, None
if isinstance(current_span_data, dict):
return (current_span_data.get("trace_id"), current_span_data.get("id"))
try:
return current_span_data.trace_id, current_span_data.id
except AttributeError:
_logging.verbose_logger.warning(
f"Unexpected current_span_data format: {type(current_span_data)}"
)
return None, None
def extract_tags(
opik_metadata: Dict[str, Any],
custom_llm_provider: Optional[str],
) -> List[str]:
"""
Extract and build list of tags.
Args:
opik_metadata: Opik metadata dictionary
custom_llm_provider: LLM provider name to add as tag
Returns:
List of tags
"""
tags = list(opik_metadata.get("tags", []))
if custom_llm_provider:
tags.append(custom_llm_provider)
return tags
def apply_proxy_header_overrides(
project_name: str,
tags: List[str],
thread_id: Optional[str],
proxy_headers: Dict[str, Any],
) -> Tuple[str, List[str], Optional[str]]:
"""
Apply overrides from proxy request headers (opik_* prefix).
Args:
project_name: Current project name
tags: Current tags list
thread_id: Current thread ID
proxy_headers: HTTP headers from proxy request
Returns:
Tuple of (project_name, tags, thread_id) with overrides applied
"""
for key, value in proxy_headers.items():
if not key.startswith("opik_") or not value:
continue
param_key = key.replace("opik_", "", 1)
if param_key == "project_name":
project_name = value
elif param_key == "thread_id":
thread_id = value
elif param_key == "tags":
try:
parsed_tags = json.loads(value)
if isinstance(parsed_tags, list):
tags.extend(parsed_tags)
except (json.JSONDecodeError, TypeError):
_logging.verbose_logger.warning(
f"Failed to parse tags from header: {value}"
)
return project_name, tags, thread_id
def extract_and_build_metadata(
opik_metadata: Dict[str, Any],
standard_logging_metadata: Dict[str, Any],
standard_logging_object: Dict[str, Any],
litellm_kwargs: Dict[str, Any],
) -> Dict[str, Any]:
"""
Build the complete metadata dictionary from all available sources.
This combines:
- Opik-specific metadata (tags, etc.)
- Standard logging metadata
- Fields from standard_logging_object (model info, status, etc.)
- Cost information from litellm_kwargs (calculated after completion)
Args:
opik_metadata: Opik-specific metadata from request
standard_logging_metadata: Standard logging metadata
standard_logging_object: Full standard logging object with call details
litellm_kwargs: Original LiteLLM kwargs (includes response_cost)
Returns:
Complete metadata dictionary for trace/span
"""
# Start with opik metadata (excluding current_span_data which is used for trace linking)
metadata = {k: v for k, v in opik_metadata.items() if k != "current_span_data"}
metadata["created_from"] = "litellm"
# Merge with standard logging metadata
metadata.update(standard_logging_metadata)
# Add fields from standard_logging_object
# These come from the LiteLLM logging infrastructure
field_mappings = {
"call_type": "type",
"status": "status",
"model": "model",
"model_id": "model_id",
"model_group": "model_group",
"api_base": "api_base",
"cache_hit": "cache_hit",
"saved_cache_cost": "saved_cache_cost",
"error_str": "error_str",
"model_parameters": "model_parameters",
"hidden_params": "hidden_params",
"model_map_information": "model_map_information",
}
for source_key, dest_key in field_mappings.items():
if source_key in standard_logging_object:
metadata[dest_key] = standard_logging_object[source_key]
# Add cost information
# response_cost is calculated by LiteLLM after completion and added to kwargs
# See: litellm/litellm_core_utils/llm_response_utils/response_metadata.py
if "response_cost" in litellm_kwargs:
metadata["cost"] = {
"total_tokens": litellm_kwargs["response_cost"],
"currency": "USD",
}
# Add debug info if cost calculation failed
if "response_cost_failure_debug_info" in litellm_kwargs:
metadata["response_cost_failure_debug_info"] = litellm_kwargs[
"response_cost_failure_debug_info"
]
return metadata

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"""Payload builders for Opik traces and spans."""
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional
from litellm import _logging
from litellm.integrations.opik import utils
from . import types
def build_trace_payload(
project_name: str,
trace_id: str,
response_obj: Dict[str, Any],
start_time: datetime,
end_time: datetime,
input_data: Any,
output_data: Any,
metadata: Dict[str, Any],
tags: List[str],
thread_id: Optional[str],
) -> types.TracePayload:
"""Build a complete trace payload."""
trace_name = response_obj.get("object", "unknown type")
return types.TracePayload(
project_name=project_name,
id=trace_id,
name=trace_name,
start_time=(
start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z")
),
end_time=end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"),
input=input_data,
output=output_data,
metadata=metadata,
tags=tags,
thread_id=thread_id,
)
def build_span_payload(
project_name: str,
trace_id: str,
parent_span_id: Optional[str],
response_obj: Dict[str, Any],
start_time: datetime,
end_time: datetime,
input_data: Any,
output_data: Any,
metadata: Dict[str, Any],
tags: List[str],
usage: Dict[str, int],
provider: Optional[str] = None,
cost: Optional[float] = None,
) -> types.SpanPayload:
"""Build a complete span payload."""
span_id = utils.create_uuid7()
model = response_obj.get("model", "unknown-model")
obj_type = response_obj.get("object", "unknown-object")
created = response_obj.get("created", 0)
span_name = f"{model}_{obj_type}_{created}"
_logging.verbose_logger.debug(
f"OpikLogger creating span with id {span_id} for trace {trace_id}"
)
return types.SpanPayload(
id=span_id,
project_name=project_name,
trace_id=trace_id,
parent_span_id=parent_span_id,
name=span_name,
type="llm",
model=model,
start_time=(
start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z")
),
end_time=end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"),
input=input_data,
output=output_data,
metadata=metadata,
tags=tags,
usage=usage,
provider=provider,
total_cost=cost,
)

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"""Type definitions for Opik payload building."""
from dataclasses import dataclass
from typing import Any, Dict, List, Literal, Optional, Tuple, Union
@dataclass
class TracePayload:
"""Opik trace payload structure"""
project_name: str
id: str
name: str
start_time: str
end_time: str
input: Any
output: Any
metadata: Dict[str, Any]
tags: List[str]
thread_id: Optional[str] = None
@dataclass
class SpanPayload:
"""Opik span payload structure"""
id: str
project_name: str
trace_id: str
name: str
type: Literal["llm"]
model: str
start_time: str
end_time: str
input: Any
output: Any
metadata: Dict[str, Any]
tags: List[str]
usage: Dict[str, int]
parent_span_id: Optional[str] = None
provider: Optional[str] = None
total_cost: Optional[float] = None
PayloadItem = Union[TracePayload, SpanPayload]
TraceSpanPayloadTuple = Tuple[Optional[TracePayload], SpanPayload]

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import configparser
import os
import time
from typing import Any, Dict, Final, List, Optional, Tuple
CONFIG_FILE_PATH_DEFAULT: Final[str] = "~/.opik.config"
def create_uuid7():
ns = time.time_ns()
last = [0, 0, 0, 0]
# Simple uuid7 implementation
sixteen_secs = 16_000_000_000
t1, rest1 = divmod(ns, sixteen_secs)
t2, rest2 = divmod(rest1 << 16, sixteen_secs)
t3, _ = divmod(rest2 << 12, sixteen_secs)
t3 |= 7 << 12 # Put uuid version in top 4 bits, which are 0 in t3
# The next two bytes are an int (t4) with two bits for
# the variant 2 and a 14 bit sequence counter which increments
# if the time is unchanged.
if t1 == last[0] and t2 == last[1] and t3 == last[2]:
# Stop the seq counter wrapping past 0x3FFF.
# This won't happen in practice, but if it does,
# uuids after the 16383rd with that same timestamp
# will not longer be correctly ordered but
# are still unique due to the 6 random bytes.
if last[3] < 0x3FFF:
last[3] += 1
else:
last[:] = (t1, t2, t3, 0)
t4 = (2 << 14) | last[3] # Put variant 0b10 in top two bits
# Six random bytes for the lower part of the uuid
rand = os.urandom(6)
return f"{t1:>08x}-{t2:>04x}-{t3:>04x}-{t4:>04x}-{rand.hex()}"
def _read_opik_config_file() -> Dict[str, str]:
config_path = os.path.expanduser(CONFIG_FILE_PATH_DEFAULT)
config = configparser.ConfigParser()
config.read(config_path)
config_values = {
section: dict(config.items(section)) for section in config.sections()
}
if "opik" in config_values:
return config_values["opik"]
return {}
def _get_env_variable(key: str) -> Optional[str]:
env_prefix = "opik_"
return os.getenv((env_prefix + key).upper(), None)
def get_opik_config_variable(
key: str, user_value: Optional[str] = None, default_value: Optional[str] = None
) -> Optional[str]:
"""
Get the configuration value of a variable, order priority is:
1. user provided value
2. environment variable
3. Opik configuration file
4. default value
"""
# Return user provided value if it is not None
if user_value is not None:
return user_value
# Return environment variable if it is not None
env_value = _get_env_variable(key)
if env_value is not None:
return env_value
# Return value from Opik configuration file if it is not None
config_values = _read_opik_config_file()
if key in config_values:
return config_values[key]
# Return default value if it is not None
return default_value
def create_usage_object(usage):
usage_dict = {}
if usage.completion_tokens is not None:
usage_dict["completion_tokens"] = usage.completion_tokens
if usage.prompt_tokens is not None:
usage_dict["prompt_tokens"] = usage.prompt_tokens
if usage.total_tokens is not None:
usage_dict["total_tokens"] = usage.total_tokens
return usage_dict
def _remove_nulls(x: Dict[str, Any]) -> Dict[str, Any]:
"""Remove None values from dict."""
return {k: v for k, v in x.items() if v is not None}
def get_traces_and_spans_from_payload(
payload: List[Dict[str, Any]]
) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
"""
Separate traces and spans from payload.
Traces are identified by not having a "type" field.
Spans are identified by having a "type" field.
Args:
payload: List of dicts containing trace and span data
Returns:
Tuple of (traces, spans) where both are lists of dicts with null values removed
"""
traces = [_remove_nulls(x) for x in payload if "type" not in x]
spans = [_remove_nulls(x) for x in payload if "type" in x]
return traces, spans