Source code for langchain_core.tracers.base

"""Base interfaces for tracing runs."""

from __future__ import annotations

import logging
import sys
import traceback
from abc import ABC, abstractmethod
from datetime import datetime, timezone
from typing import (
    TYPE_CHECKING,
    Any,
    Dict,
    List,
    Literal,
    Optional,
    Sequence,
    Set,
    Tuple,
    Union,
    cast,
)
from uuid import UUID

from tenacity import RetryCallState

from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.exceptions import TracerException
from langchain_core.load import dumpd
from langchain_core.messages import BaseMessage
from langchain_core.outputs import (
    ChatGeneration,
    ChatGenerationChunk,
    GenerationChunk,
    LLMResult,
)
from langchain_core.tracers.schemas import Run

if TYPE_CHECKING:
    from langchain_core.documents import Document

logger = logging.getLogger(__name__)


[docs]class BaseTracer(BaseCallbackHandler, ABC): """Base interface for tracers."""
[docs] def __init__( self, *, _schema_format: Literal["original", "streaming_events"] = "original", **kwargs: Any, ) -> None: """Initialize the tracer. Args: _schema_format: Primarily changes how the inputs and outputs are handled. For internal use only. This API will change. - 'original' is the format used by all current tracers. This format is slightly inconsistent with respect to inputs and outputs. - 'streaming_events' is used for supporting streaming events, for internal usage. It will likely change in the future, or be deprecated entirely in favor of a dedicated async tracer for streaming events. kwargs: Additional keyword arguments that will be passed to the super class. """ super().__init__(**kwargs) self._schema_format = _schema_format # For internal use only API will change. self.run_map: Dict[str, Run] = {} """Map of run ID to run. Cleared on run end.""" self.order_map: Dict[UUID, Tuple[UUID, str]] = {} """Map of run ID to (trace_id, dotted_order). Cleared when tracer GCed."""
@staticmethod def _add_child_run( parent_run: Run, child_run: Run, ) -> None: """Add child run to a chain run or tool run.""" parent_run.child_runs.append(child_run) @abstractmethod def _persist_run(self, run: Run) -> None: """Persist a run.""" @staticmethod def _get_stacktrace(error: BaseException) -> str: """Get the stacktrace of the parent error.""" msg = repr(error) try: if sys.version_info < (3, 10): tb = traceback.format_exception( error.__class__, error, error.__traceback__ ) else: tb = traceback.format_exception(error) return (msg + "\n\n".join(tb)).strip() except: # noqa: E722 return msg def _start_trace(self, run: Run) -> None: """Start a trace for a run.""" current_dotted_order = run.start_time.strftime("%Y%m%dT%H%M%S%fZ") + str(run.id) if run.parent_run_id: if parent := self.order_map.get(run.parent_run_id): run.trace_id, run.dotted_order = parent run.dotted_order += "." + current_dotted_order if parent_run := self.run_map.get(str(run.parent_run_id)): self._add_child_run(parent_run, run) else: logger.debug( f"Parent run {run.parent_run_id} not found for run {run.id}." " Treating as a root run." ) run.parent_run_id = None run.trace_id = run.id run.dotted_order = current_dotted_order else: run.trace_id = run.id run.dotted_order = current_dotted_order self.order_map[run.id] = (run.trace_id, run.dotted_order) self.run_map[str(run.id)] = run self._on_run_create(run) def _end_trace(self, run: Run) -> None: """End a trace for a run.""" if not run.parent_run_id: self._persist_run(run) self.run_map.pop(str(run.id)) self._on_run_update(run) def _get_run( self, run_id: UUID, run_type: Union[str, Set[str], None] = None ) -> Run: try: run = self.run_map[str(run_id)] except KeyError as exc: raise TracerException(f"No indexed run ID {run_id}.") from exc if isinstance(run_type, str): run_types: Union[Set[str], None] = {run_type} else: run_types = run_type if run_types is not None and run.run_type not in run_types: raise TracerException( f"Found {run.run_type} run at ID {run_id}, " f"but expected {run_types} run." ) return run
[docs] def on_chat_model_start( self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any, ) -> Run: """Start a trace for an LLM run.""" if self._schema_format != "streaming_events": # Please keep this un-implemented for backwards compatibility. # When it's unimplemented old tracers that use the "original" format # fallback on the on_llm_start method implementation if they # find that the on_chat_model_start method is not implemented. # This can eventually be cleaned up by writing a "modern" tracer # that has all the updated schema changes corresponding to # the "streaming_events" format. raise NotImplementedError( f"Chat model tracing is not supported in " f"for {self._schema_format} format." ) start_time = datetime.now(timezone.utc) if metadata: kwargs.update({"metadata": metadata}) chat_model_run = Run( id=run_id, parent_run_id=parent_run_id, serialized=serialized, inputs={"messages": [[dumpd(msg) for msg in batch] for batch in messages]}, extra=kwargs, events=[{"name": "start", "time": start_time}], start_time=start_time, # WARNING: This is valid ONLY for streaming_events. # run_type="llm" is what's used by virtually all tracers. # Changing this to "chat_model" may break triggering on_llm_start run_type="chat_model", tags=tags, name=name, # type: ignore[arg-type] ) self._start_trace(chat_model_run) self._on_chat_model_start(chat_model_run) return chat_model_run
[docs] def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any, ) -> Run: """Start a trace for an LLM run.""" start_time = datetime.now(timezone.utc) if metadata: kwargs.update({"metadata": metadata}) llm_run = Run( id=run_id, parent_run_id=parent_run_id, serialized=serialized, # TODO: Figure out how to expose kwargs here inputs={"prompts": prompts}, extra=kwargs, events=[{"name": "start", "time": start_time}], start_time=start_time, run_type="llm", tags=tags or [], name=name, # type: ignore[arg-type] ) self._start_trace(llm_run) self._on_llm_start(llm_run) return llm_run
[docs] def on_llm_new_token( self, token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any, ) -> Run: """Run on new LLM token. Only available when streaming is enabled.""" # "chat_model" is only used for the experimental new streaming_events format. # This change should not affect any existing tracers. llm_run = self._get_run(run_id, run_type={"llm", "chat_model"}) event_kwargs: Dict[str, Any] = {"token": token} if chunk: event_kwargs["chunk"] = chunk llm_run.events.append( { "name": "new_token", "time": datetime.now(timezone.utc), "kwargs": event_kwargs, }, ) self._on_llm_new_token(llm_run, token, chunk) return llm_run
[docs] def on_retry( self, retry_state: RetryCallState, *, run_id: UUID, **kwargs: Any, ) -> Run: llm_run = self._get_run(run_id) retry_d: Dict[str, Any] = { "slept": retry_state.idle_for, "attempt": retry_state.attempt_number, } if retry_state.outcome is None: retry_d["outcome"] = "N/A" elif retry_state.outcome.failed: retry_d["outcome"] = "failed" exception = retry_state.outcome.exception() retry_d["exception"] = str(exception) retry_d["exception_type"] = exception.__class__.__name__ else: retry_d["outcome"] = "success" retry_d["result"] = str(retry_state.outcome.result()) llm_run.events.append( { "name": "retry", "time": datetime.now(timezone.utc), "kwargs": retry_d, }, ) return llm_run
[docs] def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any) -> Run: """End a trace for an LLM run.""" # "chat_model" is only used for the experimental new streaming_events format. # This change should not affect any existing tracers. llm_run = self._get_run(run_id, run_type={"llm", "chat_model"}) llm_run.outputs = response.dict() for i, generations in enumerate(response.generations): for j, generation in enumerate(generations): output_generation = llm_run.outputs["generations"][i][j] if "message" in output_generation: output_generation["message"] = dumpd( cast(ChatGeneration, generation).message ) llm_run.end_time = datetime.now(timezone.utc) llm_run.events.append({"name": "end", "time": llm_run.end_time}) self._end_trace(llm_run) self._on_llm_end(llm_run) return llm_run
[docs] def on_llm_error( self, error: BaseException, *, run_id: UUID, **kwargs: Any, ) -> Run: """Handle an error for an LLM run.""" # "chat_model" is only used for the experimental new streaming_events format. # This change should not affect any existing tracers. llm_run = self._get_run(run_id, run_type={"llm", "chat_model"}) llm_run.error = self._get_stacktrace(error) llm_run.end_time = datetime.now(timezone.utc) llm_run.events.append({"name": "error", "time": llm_run.end_time}) self._end_trace(llm_run) self._on_llm_error(llm_run) return llm_run
[docs] def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, name: Optional[str] = None, **kwargs: Any, ) -> Run: """Start a trace for a chain run.""" start_time = datetime.now(timezone.utc) if metadata: kwargs.update({"metadata": metadata}) chain_run = Run( id=run_id, parent_run_id=parent_run_id, serialized=serialized, inputs=self._get_chain_inputs(inputs), extra=kwargs, events=[{"name": "start", "time": start_time}], start_time=start_time, child_runs=[], run_type=run_type or "chain", name=name, # type: ignore[arg-type] tags=tags or [], ) self._start_trace(chain_run) self._on_chain_start(chain_run) return chain_run
def _get_chain_inputs(self, inputs: Any) -> Any: """Get the inputs for a chain run.""" if self._schema_format == "original": return inputs if isinstance(inputs, dict) else {"input": inputs} elif self._schema_format == "streaming_events": return { "input": inputs, } else: raise ValueError(f"Invalid format: {self._schema_format}") def _get_chain_outputs(self, outputs: Any) -> Any: """Get the outputs for a chain run.""" if self._schema_format == "original": return outputs if isinstance(outputs, dict) else {"output": outputs} elif self._schema_format == "streaming_events": return { "output": outputs, } else: raise ValueError(f"Invalid format: {self._schema_format}")
[docs] def on_chain_end( self, outputs: Dict[str, Any], *, run_id: UUID, inputs: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> Run: """End a trace for a chain run.""" chain_run = self._get_run(run_id) chain_run.outputs = self._get_chain_outputs(outputs) chain_run.end_time = datetime.now(timezone.utc) chain_run.events.append({"name": "end", "time": chain_run.end_time}) if inputs is not None: chain_run.inputs = self._get_chain_inputs(inputs) self._end_trace(chain_run) self._on_chain_end(chain_run) return chain_run
[docs] def on_chain_error( self, error: BaseException, *, inputs: Optional[Dict[str, Any]] = None, run_id: UUID, **kwargs: Any, ) -> Run: """Handle an error for a chain run.""" chain_run = self._get_run(run_id) chain_run.error = self._get_stacktrace(error) chain_run.end_time = datetime.now(timezone.utc) chain_run.events.append({"name": "error", "time": chain_run.end_time}) if inputs is not None: chain_run.inputs = self._get_chain_inputs(inputs) self._end_trace(chain_run) self._on_chain_error(chain_run) return chain_run
[docs] def on_tool_start( self, serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, inputs: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> Run: """Start a trace for a tool run.""" start_time = datetime.now(timezone.utc) if metadata: kwargs.update({"metadata": metadata}) if self._schema_format == "original": inputs = {"input": input_str} elif self._schema_format == "streaming_events": inputs = {"input": inputs} else: raise AssertionError(f"Invalid format: {self._schema_format}") tool_run = Run( id=run_id, parent_run_id=parent_run_id, serialized=serialized, # Wrapping in dict since Run requires a dict object. inputs=inputs, extra=kwargs, events=[{"name": "start", "time": start_time}], start_time=start_time, child_runs=[], run_type="tool", tags=tags or [], name=name, # type: ignore[arg-type] ) self._start_trace(tool_run) self._on_tool_start(tool_run) return tool_run
[docs] def on_tool_end(self, output: Any, *, run_id: UUID, **kwargs: Any) -> Run: """End a trace for a tool run.""" tool_run = self._get_run(run_id, run_type="tool") tool_run.outputs = {"output": output} tool_run.end_time = datetime.now(timezone.utc) tool_run.events.append({"name": "end", "time": tool_run.end_time}) self._end_trace(tool_run) self._on_tool_end(tool_run) return tool_run
[docs] def on_tool_error( self, error: BaseException, *, run_id: UUID, **kwargs: Any, ) -> Run: """Handle an error for a tool run.""" tool_run = self._get_run(run_id, run_type="tool") tool_run.error = self._get_stacktrace(error) tool_run.end_time = datetime.now(timezone.utc) tool_run.events.append({"name": "error", "time": tool_run.end_time}) self._end_trace(tool_run) self._on_tool_error(tool_run) return tool_run
[docs] def on_retriever_start( self, serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any, ) -> Run: """Run when Retriever starts running.""" start_time = datetime.now(timezone.utc) if metadata: kwargs.update({"metadata": metadata}) retrieval_run = Run( id=run_id, name=name or "Retriever", parent_run_id=parent_run_id, serialized=serialized, inputs={"query": query}, extra=kwargs, events=[{"name": "start", "time": start_time}], start_time=start_time, tags=tags, child_runs=[], run_type="retriever", ) self._start_trace(retrieval_run) self._on_retriever_start(retrieval_run) return retrieval_run
[docs] def on_retriever_error( self, error: BaseException, *, run_id: UUID, **kwargs: Any, ) -> Run: """Run when Retriever errors.""" retrieval_run = self._get_run(run_id, run_type="retriever") retrieval_run.error = self._get_stacktrace(error) retrieval_run.end_time = datetime.now(timezone.utc) retrieval_run.events.append({"name": "error", "time": retrieval_run.end_time}) self._end_trace(retrieval_run) self._on_retriever_error(retrieval_run) return retrieval_run
[docs] def on_retriever_end( self, documents: Sequence[Document], *, run_id: UUID, **kwargs: Any ) -> Run: """Run when Retriever ends running.""" retrieval_run = self._get_run(run_id, run_type="retriever") retrieval_run.outputs = {"documents": documents} retrieval_run.end_time = datetime.now(timezone.utc) retrieval_run.events.append({"name": "end", "time": retrieval_run.end_time}) self._end_trace(retrieval_run) self._on_retriever_end(retrieval_run) return retrieval_run
def __deepcopy__(self, memo: dict) -> BaseTracer: """Deepcopy the tracer.""" return self def __copy__(self) -> BaseTracer: """Copy the tracer.""" return self def _on_run_create(self, run: Run) -> None: """Process a run upon creation.""" def _on_run_update(self, run: Run) -> None: """Process a run upon update.""" def _on_llm_start(self, run: Run) -> None: """Process the LLM Run upon start.""" def _on_llm_new_token( self, run: Run, token: str, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]], ) -> None: """Process new LLM token.""" def _on_llm_end(self, run: Run) -> None: """Process the LLM Run.""" def _on_llm_error(self, run: Run) -> None: """Process the LLM Run upon error.""" def _on_chain_start(self, run: Run) -> None: """Process the Chain Run upon start.""" def _on_chain_end(self, run: Run) -> None: """Process the Chain Run.""" def _on_chain_error(self, run: Run) -> None: """Process the Chain Run upon error.""" def _on_tool_start(self, run: Run) -> None: """Process the Tool Run upon start.""" def _on_tool_end(self, run: Run) -> None: """Process the Tool Run.""" def _on_tool_error(self, run: Run) -> None: """Process the Tool Run upon error.""" def _on_chat_model_start(self, run: Run) -> None: """Process the Chat Model Run upon start.""" def _on_retriever_start(self, run: Run) -> None: """Process the Retriever Run upon start.""" def _on_retriever_end(self, run: Run) -> None: """Process the Retriever Run.""" def _on_retriever_error(self, run: Run) -> None: """Process the Retriever Run upon error."""