225 lines
8.1 KiB
Python
225 lines
8.1 KiB
Python
# Copyright The OpenTelemetry Authors
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Any
|
|
from uuid import UUID
|
|
|
|
from langchain_core.callbacks import BaseCallbackHandler # type: ignore
|
|
from langchain_core.messages import BaseMessage # type: ignore
|
|
from langchain_core.outputs import LLMResult # type: ignore
|
|
|
|
from opentelemetry.instrumentation.langchain.span_manager import _SpanManager
|
|
from opentelemetry.semconv._incubating.attributes import (
|
|
gen_ai_attributes as GenAI,
|
|
)
|
|
from opentelemetry.trace import Tracer
|
|
|
|
|
|
class OpenTelemetryLangChainCallbackHandler(BaseCallbackHandler): # type: ignore[misc]
|
|
"""
|
|
A callback handler for LangChain that uses OpenTelemetry to create spans for LLM calls and chains, tools etc,. in future.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
tracer: Tracer,
|
|
) -> None:
|
|
super().__init__() # type: ignore
|
|
|
|
self.span_manager = _SpanManager(
|
|
tracer=tracer,
|
|
)
|
|
|
|
def on_chat_model_start(
|
|
self,
|
|
serialized: dict[str, Any],
|
|
messages: list[list[BaseMessage]], # type: ignore
|
|
*,
|
|
run_id: UUID,
|
|
tags: list[str] | None,
|
|
parent_run_id: UUID | None,
|
|
metadata: dict[str, Any] | None,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
# Other providers/LLMs may be supported in the future and telemetry for them is skipped for now.
|
|
if serialized.get("name") not in ("ChatOpenAI", "ChatBedrock"):
|
|
return
|
|
|
|
if "invocation_params" in kwargs:
|
|
params = (
|
|
kwargs["invocation_params"].get("params")
|
|
or kwargs["invocation_params"]
|
|
)
|
|
else:
|
|
params = kwargs
|
|
|
|
request_model = "unknown"
|
|
for model_tag in (
|
|
"model_name", # ChatOpenAI
|
|
"model_id", # ChatBedrock
|
|
):
|
|
if (model := (params or {}).get(model_tag)) is not None:
|
|
request_model = model
|
|
break
|
|
elif (model := (metadata or {}).get(model_tag)) is not None:
|
|
request_model = model
|
|
break
|
|
|
|
# Skip telemetry for unsupported request models
|
|
if request_model == "unknown":
|
|
return
|
|
|
|
span = self.span_manager.create_chat_span(
|
|
run_id=run_id,
|
|
parent_run_id=parent_run_id,
|
|
request_model=request_model,
|
|
)
|
|
|
|
if params is not None:
|
|
top_p = params.get("top_p")
|
|
if top_p is not None:
|
|
span.set_attribute(GenAI.GEN_AI_REQUEST_TOP_P, top_p)
|
|
frequency_penalty = params.get("frequency_penalty")
|
|
if frequency_penalty is not None:
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_REQUEST_FREQUENCY_PENALTY, frequency_penalty
|
|
)
|
|
presence_penalty = params.get("presence_penalty")
|
|
if presence_penalty is not None:
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_REQUEST_PRESENCE_PENALTY, presence_penalty
|
|
)
|
|
stop_sequences = params.get("stop")
|
|
if stop_sequences is not None:
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_REQUEST_STOP_SEQUENCES, stop_sequences
|
|
)
|
|
seed = params.get("seed")
|
|
if seed is not None:
|
|
span.set_attribute(GenAI.GEN_AI_REQUEST_SEED, seed)
|
|
# ChatOpenAI
|
|
temperature = params.get("temperature")
|
|
if temperature is not None:
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_REQUEST_TEMPERATURE, temperature
|
|
)
|
|
# ChatOpenAI
|
|
max_tokens = params.get("max_completion_tokens")
|
|
if max_tokens is not None:
|
|
span.set_attribute(GenAI.GEN_AI_REQUEST_MAX_TOKENS, max_tokens)
|
|
|
|
if metadata is not None:
|
|
provider = metadata.get("ls_provider")
|
|
if provider is not None:
|
|
span.set_attribute("gen_ai.provider.name", provider)
|
|
# ChatBedrock
|
|
temperature = metadata.get("ls_temperature")
|
|
if temperature is not None:
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_REQUEST_TEMPERATURE, temperature
|
|
)
|
|
# ChatBedrock
|
|
max_tokens = metadata.get("ls_max_tokens")
|
|
if max_tokens is not None:
|
|
span.set_attribute(GenAI.GEN_AI_REQUEST_MAX_TOKENS, max_tokens)
|
|
|
|
def on_llm_end(
|
|
self,
|
|
response: LLMResult, # type: ignore [reportUnknownParameterType]
|
|
*,
|
|
run_id: UUID,
|
|
parent_run_id: UUID | None,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
span = self.span_manager.get_span(run_id)
|
|
|
|
if span is None:
|
|
# If the span does not exist, we cannot set attributes or end it
|
|
return
|
|
|
|
finish_reasons: list[str] = []
|
|
for generation in getattr(response, "generations", []): # type: ignore
|
|
for chat_generation in generation:
|
|
generation_info = getattr(
|
|
chat_generation, "generation_info", None
|
|
)
|
|
if generation_info is not None:
|
|
finish_reason = generation_info.get(
|
|
"finish_reason", "unknown"
|
|
)
|
|
if finish_reason is not None:
|
|
finish_reasons.append(str(finish_reason))
|
|
if chat_generation.message:
|
|
if (
|
|
generation_info is None
|
|
and chat_generation.message.response_metadata
|
|
):
|
|
finish_reason = (
|
|
chat_generation.message.response_metadata.get(
|
|
"stopReason", "unknown"
|
|
)
|
|
)
|
|
if finish_reason is not None:
|
|
finish_reasons.append(str(finish_reason))
|
|
if chat_generation.message.usage_metadata:
|
|
input_tokens = (
|
|
chat_generation.message.usage_metadata.get(
|
|
"input_tokens", 0
|
|
)
|
|
)
|
|
output_tokens = (
|
|
chat_generation.message.usage_metadata.get(
|
|
"output_tokens", 0
|
|
)
|
|
)
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_USAGE_INPUT_TOKENS, input_tokens
|
|
)
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens
|
|
)
|
|
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_RESPONSE_FINISH_REASONS, finish_reasons
|
|
)
|
|
|
|
llm_output = getattr(response, "llm_output", None) # type: ignore
|
|
if llm_output is not None:
|
|
response_model = llm_output.get("model_name") or llm_output.get(
|
|
"model"
|
|
)
|
|
if response_model is not None:
|
|
span.set_attribute(
|
|
GenAI.GEN_AI_RESPONSE_MODEL, str(response_model)
|
|
)
|
|
|
|
response_id = llm_output.get("id")
|
|
if response_id is not None:
|
|
span.set_attribute(GenAI.GEN_AI_RESPONSE_ID, str(response_id))
|
|
|
|
# End the LLM span
|
|
self.span_manager.end_span(run_id)
|
|
|
|
def on_llm_error(
|
|
self,
|
|
error: BaseException,
|
|
*,
|
|
run_id: UUID,
|
|
parent_run_id: UUID | None,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
self.span_manager.handle_error(error, run_id)
|