Source code for langchain_community.callbacks.tracers.comet
from types import ModuleType, SimpleNamespace
from typing import TYPE_CHECKING, Any, Callable, Dict
from langchain_core.tracers import BaseTracer
if TYPE_CHECKING:
from uuid import UUID
from comet_llm import Span
from comet_llm.chains.chain import Chain
from langchain_community.callbacks.tracers.schemas import Run
def _get_run_type(run: "Run") -> str:
if isinstance(run.run_type, str):
return run.run_type
elif hasattr(run.run_type, "value"):
return run.run_type.value
else:
return str(run.run_type)
[docs]def import_comet_llm_api() -> SimpleNamespace:
"""Import comet_llm api and raise an error if it is not installed."""
try:
from comet_llm import (
experiment_info, # noqa: F401
flush, # noqa: F401
)
from comet_llm.chains import api as chain_api # noqa: F401
from comet_llm.chains import (
chain, # noqa: F401
span, # noqa: F401
)
except ImportError:
raise ImportError(
"To use the CometTracer you need to have the "
"`comet_llm>=2.0.0` python package installed. Please install it with"
" `pip install -U comet_llm`"
)
return SimpleNamespace(
chain=chain,
span=span,
chain_api=chain_api,
experiment_info=experiment_info,
flush=flush,
)
[docs]class CometTracer(BaseTracer):
[docs] def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
self._span_map: Dict["UUID", "Span"] = {}
self._chains_map: Dict["UUID", "Chain"] = {}
self._initialize_comet_modules()
def _initialize_comet_modules(self) -> None:
comet_llm_api = import_comet_llm_api()
self._chain: ModuleType = comet_llm_api.chain
self._span: ModuleType = comet_llm_api.span
self._chain_api: ModuleType = comet_llm_api.chain_api
self._experiment_info: ModuleType = comet_llm_api.experiment_info
self._flush: Callable[[], None] = comet_llm_api.flush
def _persist_run(self, run: "Run") -> None:
chain_ = self._chains_map[run.id]
chain_.set_outputs(outputs=run.outputs)
self._chain_api.log_chain(chain_)
def _process_start_trace(self, run: "Run") -> None:
if not run.parent_run_id:
# This is the first run, which maps to a chain
chain_: "Chain" = self._chain.Chain(
inputs=run.inputs,
metadata=None,
experiment_info=self._experiment_info.get(),
)
self._chains_map[run.id] = chain_
else:
span: "Span" = self._span.Span(
inputs=run.inputs,
category=_get_run_type(run),
metadata=run.extra,
name=run.name,
)
span.__api__start__(self._chains_map[run.parent_run_id])
self._chains_map[run.id] = self._chains_map[run.parent_run_id]
self._span_map[run.id] = span
def _process_end_trace(self, run: "Run") -> None:
if not run.parent_run_id:
pass
# Langchain will call _persist_run for us
else:
span = self._span_map[run.id]
span.set_outputs(outputs=run.outputs)
span.__api__end__()
[docs] def flush(self) -> None:
self._flush()
def _on_llm_start(self, run: "Run") -> None:
"""Process the LLM Run upon start."""
self._process_start_trace(run)
def _on_llm_end(self, run: "Run") -> None:
"""Process the LLM Run."""
self._process_end_trace(run)
def _on_llm_error(self, run: "Run") -> None:
"""Process the LLM Run upon error."""
self._process_end_trace(run)
def _on_chain_start(self, run: "Run") -> None:
"""Process the Chain Run upon start."""
self._process_start_trace(run)
def _on_chain_end(self, run: "Run") -> None:
"""Process the Chain Run."""
self._process_end_trace(run)
def _on_chain_error(self, run: "Run") -> None:
"""Process the Chain Run upon error."""
self._process_end_trace(run)
def _on_tool_start(self, run: "Run") -> None:
"""Process the Tool Run upon start."""
self._process_start_trace(run)
def _on_tool_end(self, run: "Run") -> None:
"""Process the Tool Run."""
self._process_end_trace(run)
def _on_tool_error(self, run: "Run") -> None:
"""Process the Tool Run upon error."""
self._process_end_trace(run)