Hacked By AnonymousFox
from collections import OrderedDict
from functools import wraps
import sentry_sdk
from sentry_sdk._types import TYPE_CHECKING
from sentry_sdk.ai.monitoring import set_ai_pipeline_name, record_token_usage
from sentry_sdk.consts import OP, SPANDATA
from sentry_sdk.ai.utils import set_data_normalized
from sentry_sdk.scope import should_send_default_pii
from sentry_sdk.tracing import Span
if TYPE_CHECKING:
from typing import Any, List, Callable, Dict, Union, Optional
from uuid import UUID
from sentry_sdk.integrations import DidNotEnable, Integration
from sentry_sdk.utils import logger, capture_internal_exceptions
try:
from langchain_core.messages import BaseMessage
from langchain_core.outputs import LLMResult
from langchain_core.callbacks import (
manager,
BaseCallbackHandler,
)
from langchain_core.agents import AgentAction, AgentFinish
except ImportError:
raise DidNotEnable("langchain not installed")
DATA_FIELDS = {
"temperature": SPANDATA.AI_TEMPERATURE,
"top_p": SPANDATA.AI_TOP_P,
"top_k": SPANDATA.AI_TOP_K,
"function_call": SPANDATA.AI_FUNCTION_CALL,
"tool_calls": SPANDATA.AI_TOOL_CALLS,
"tools": SPANDATA.AI_TOOLS,
"response_format": SPANDATA.AI_RESPONSE_FORMAT,
"logit_bias": SPANDATA.AI_LOGIT_BIAS,
"tags": SPANDATA.AI_TAGS,
}
# To avoid double collecting tokens, we do *not* measure
# token counts for models for which we have an explicit integration
NO_COLLECT_TOKEN_MODELS = [
"openai-chat",
"anthropic-chat",
"cohere-chat",
"huggingface_endpoint",
]
class LangchainIntegration(Integration):
identifier = "langchain"
origin = f"auto.ai.{identifier}"
# The most number of spans (e.g., LLM calls) that can be processed at the same time.
max_spans = 1024
def __init__(
self, include_prompts=True, max_spans=1024, tiktoken_encoding_name=None
):
# type: (LangchainIntegration, bool, int, Optional[str]) -> None
self.include_prompts = include_prompts
self.max_spans = max_spans
self.tiktoken_encoding_name = tiktoken_encoding_name
@staticmethod
def setup_once():
# type: () -> None
manager._configure = _wrap_configure(manager._configure)
class WatchedSpan:
span = None # type: Span
num_completion_tokens = 0 # type: int
num_prompt_tokens = 0 # type: int
no_collect_tokens = False # type: bool
children = [] # type: List[WatchedSpan]
is_pipeline = False # type: bool
def __init__(self, span):
# type: (Span) -> None
self.span = span
class SentryLangchainCallback(BaseCallbackHandler): # type: ignore[misc]
"""Base callback handler that can be used to handle callbacks from langchain."""
span_map = OrderedDict() # type: OrderedDict[UUID, WatchedSpan]
max_span_map_size = 0
def __init__(self, max_span_map_size, include_prompts, tiktoken_encoding_name=None):
# type: (int, bool, Optional[str]) -> None
self.max_span_map_size = max_span_map_size
self.include_prompts = include_prompts
self.tiktoken_encoding = None
if tiktoken_encoding_name is not None:
import tiktoken # type: ignore
self.tiktoken_encoding = tiktoken.get_encoding(tiktoken_encoding_name)
def count_tokens(self, s):
# type: (str) -> int
if self.tiktoken_encoding is not None:
return len(self.tiktoken_encoding.encode_ordinary(s))
return 0
def gc_span_map(self):
# type: () -> None
while len(self.span_map) > self.max_span_map_size:
run_id, watched_span = self.span_map.popitem(last=False)
self._exit_span(watched_span, run_id)
def _handle_error(self, run_id, error):
# type: (UUID, Any) -> None
if not run_id or run_id not in self.span_map:
return
span_data = self.span_map[run_id]
if not span_data:
return
sentry_sdk.capture_exception(error, span_data.span.scope)
span_data.span.__exit__(None, None, None)
del self.span_map[run_id]
def _normalize_langchain_message(self, message):
# type: (BaseMessage) -> Any
parsed = {"content": message.content, "role": message.type}
parsed.update(message.additional_kwargs)
return parsed
def _create_span(self, run_id, parent_id, **kwargs):
# type: (SentryLangchainCallback, UUID, Optional[Any], Any) -> WatchedSpan
watched_span = None # type: Optional[WatchedSpan]
if parent_id:
parent_span = self.span_map[parent_id] # type: Optional[WatchedSpan]
if parent_span:
watched_span = WatchedSpan(parent_span.span.start_child(**kwargs))
parent_span.children.append(watched_span)
if watched_span is None:
watched_span = WatchedSpan(sentry_sdk.start_span(**kwargs))
if kwargs.get("op", "").startswith("ai.pipeline."):
if kwargs.get("description"):
set_ai_pipeline_name(kwargs.get("description"))
watched_span.is_pipeline = True
watched_span.span.__enter__()
self.span_map[run_id] = watched_span
self.gc_span_map()
return watched_span
def _exit_span(self, span_data, run_id):
# type: (SentryLangchainCallback, WatchedSpan, UUID) -> None
if span_data.is_pipeline:
set_ai_pipeline_name(None)
span_data.span.__exit__(None, None, None)
del self.span_map[run_id]
def on_llm_start(
self,
serialized,
prompts,
*,
run_id,
tags=None,
parent_run_id=None,
metadata=None,
**kwargs,
):
# type: (SentryLangchainCallback, Dict[str, Any], List[str], UUID, Optional[List[str]], Optional[UUID], Optional[Dict[str, Any]], Any) -> Any
"""Run when LLM starts running."""
with capture_internal_exceptions():
if not run_id:
return
all_params = kwargs.get("invocation_params", {})
all_params.update(serialized.get("kwargs", {}))
watched_span = self._create_span(
run_id,
kwargs.get("parent_run_id"),
op=OP.LANGCHAIN_RUN,
description=kwargs.get("name") or "Langchain LLM call",
origin=LangchainIntegration.origin,
)
span = watched_span.span
if should_send_default_pii() and self.include_prompts:
set_data_normalized(span, SPANDATA.AI_INPUT_MESSAGES, prompts)
for k, v in DATA_FIELDS.items():
if k in all_params:
set_data_normalized(span, v, all_params[k])
def on_chat_model_start(self, serialized, messages, *, run_id, **kwargs):
# type: (SentryLangchainCallback, Dict[str, Any], List[List[BaseMessage]], UUID, Any) -> Any
"""Run when Chat Model starts running."""
with capture_internal_exceptions():
if not run_id:
return
all_params = kwargs.get("invocation_params", {})
all_params.update(serialized.get("kwargs", {}))
watched_span = self._create_span(
run_id,
kwargs.get("parent_run_id"),
op=OP.LANGCHAIN_CHAT_COMPLETIONS_CREATE,
description=kwargs.get("name") or "Langchain Chat Model",
origin=LangchainIntegration.origin,
)
span = watched_span.span
model = all_params.get(
"model", all_params.get("model_name", all_params.get("model_id"))
)
watched_span.no_collect_tokens = any(
x in all_params.get("_type", "") for x in NO_COLLECT_TOKEN_MODELS
)
if not model and "anthropic" in all_params.get("_type"):
model = "claude-2"
if model:
span.set_data(SPANDATA.AI_MODEL_ID, model)
if should_send_default_pii() and self.include_prompts:
set_data_normalized(
span,
SPANDATA.AI_INPUT_MESSAGES,
[
[self._normalize_langchain_message(x) for x in list_]
for list_ in messages
],
)
for k, v in DATA_FIELDS.items():
if k in all_params:
set_data_normalized(span, v, all_params[k])
if not watched_span.no_collect_tokens:
for list_ in messages:
for message in list_:
self.span_map[run_id].num_prompt_tokens += self.count_tokens(
message.content
) + self.count_tokens(message.type)
def on_llm_new_token(self, token, *, run_id, **kwargs):
# type: (SentryLangchainCallback, str, UUID, Any) -> Any
"""Run on new LLM token. Only available when streaming is enabled."""
with capture_internal_exceptions():
if not run_id or run_id not in self.span_map:
return
span_data = self.span_map[run_id]
if not span_data or span_data.no_collect_tokens:
return
span_data.num_completion_tokens += self.count_tokens(token)
def on_llm_end(self, response, *, run_id, **kwargs):
# type: (SentryLangchainCallback, LLMResult, UUID, Any) -> Any
"""Run when LLM ends running."""
with capture_internal_exceptions():
if not run_id:
return
token_usage = (
response.llm_output.get("token_usage") if response.llm_output else None
)
span_data = self.span_map[run_id]
if not span_data:
return
if should_send_default_pii() and self.include_prompts:
set_data_normalized(
span_data.span,
SPANDATA.AI_RESPONSES,
[[x.text for x in list_] for list_ in response.generations],
)
if not span_data.no_collect_tokens:
if token_usage:
record_token_usage(
span_data.span,
token_usage.get("prompt_tokens"),
token_usage.get("completion_tokens"),
token_usage.get("total_tokens"),
)
else:
record_token_usage(
span_data.span,
span_data.num_prompt_tokens,
span_data.num_completion_tokens,
)
self._exit_span(span_data, run_id)
def on_llm_error(self, error, *, run_id, **kwargs):
# type: (SentryLangchainCallback, Union[Exception, KeyboardInterrupt], UUID, Any) -> Any
"""Run when LLM errors."""
with capture_internal_exceptions():
self._handle_error(run_id, error)
def on_chain_start(self, serialized, inputs, *, run_id, **kwargs):
# type: (SentryLangchainCallback, Dict[str, Any], Dict[str, Any], UUID, Any) -> Any
"""Run when chain starts running."""
with capture_internal_exceptions():
if not run_id:
return
watched_span = self._create_span(
run_id,
kwargs.get("parent_run_id"),
op=(
OP.LANGCHAIN_RUN
if kwargs.get("parent_run_id") is not None
else OP.LANGCHAIN_PIPELINE
),
description=kwargs.get("name") or "Chain execution",
origin=LangchainIntegration.origin,
)
metadata = kwargs.get("metadata")
if metadata:
set_data_normalized(watched_span.span, SPANDATA.AI_METADATA, metadata)
def on_chain_end(self, outputs, *, run_id, **kwargs):
# type: (SentryLangchainCallback, Dict[str, Any], UUID, Any) -> Any
"""Run when chain ends running."""
with capture_internal_exceptions():
if not run_id or run_id not in self.span_map:
return
span_data = self.span_map[run_id]
if not span_data:
return
self._exit_span(span_data, run_id)
def on_chain_error(self, error, *, run_id, **kwargs):
# type: (SentryLangchainCallback, Union[Exception, KeyboardInterrupt], UUID, Any) -> Any
"""Run when chain errors."""
self._handle_error(run_id, error)
def on_agent_action(self, action, *, run_id, **kwargs):
# type: (SentryLangchainCallback, AgentAction, UUID, Any) -> Any
with capture_internal_exceptions():
if not run_id:
return
watched_span = self._create_span(
run_id,
kwargs.get("parent_run_id"),
op=OP.LANGCHAIN_AGENT,
description=action.tool or "AI tool usage",
origin=LangchainIntegration.origin,
)
if action.tool_input and should_send_default_pii() and self.include_prompts:
set_data_normalized(
watched_span.span, SPANDATA.AI_INPUT_MESSAGES, action.tool_input
)
def on_agent_finish(self, finish, *, run_id, **kwargs):
# type: (SentryLangchainCallback, AgentFinish, UUID, Any) -> Any
with capture_internal_exceptions():
if not run_id:
return
span_data = self.span_map[run_id]
if not span_data:
return
if should_send_default_pii() and self.include_prompts:
set_data_normalized(
span_data.span, SPANDATA.AI_RESPONSES, finish.return_values.items()
)
self._exit_span(span_data, run_id)
def on_tool_start(self, serialized, input_str, *, run_id, **kwargs):
# type: (SentryLangchainCallback, Dict[str, Any], str, UUID, Any) -> Any
"""Run when tool starts running."""
with capture_internal_exceptions():
if not run_id:
return
watched_span = self._create_span(
run_id,
kwargs.get("parent_run_id"),
op=OP.LANGCHAIN_TOOL,
description=serialized.get("name")
or kwargs.get("name")
or "AI tool usage",
origin=LangchainIntegration.origin,
)
if should_send_default_pii() and self.include_prompts:
set_data_normalized(
watched_span.span,
SPANDATA.AI_INPUT_MESSAGES,
kwargs.get("inputs", [input_str]),
)
if kwargs.get("metadata"):
set_data_normalized(
watched_span.span, SPANDATA.AI_METADATA, kwargs.get("metadata")
)
def on_tool_end(self, output, *, run_id, **kwargs):
# type: (SentryLangchainCallback, str, UUID, Any) -> Any
"""Run when tool ends running."""
with capture_internal_exceptions():
if not run_id or run_id not in self.span_map:
return
span_data = self.span_map[run_id]
if not span_data:
return
if should_send_default_pii() and self.include_prompts:
set_data_normalized(span_data.span, SPANDATA.AI_RESPONSES, output)
self._exit_span(span_data, run_id)
def on_tool_error(self, error, *args, run_id, **kwargs):
# type: (SentryLangchainCallback, Union[Exception, KeyboardInterrupt], UUID, Any) -> Any
"""Run when tool errors."""
self._handle_error(run_id, error)
def _wrap_configure(f):
# type: (Callable[..., Any]) -> Callable[..., Any]
@wraps(f)
def new_configure(*args, **kwargs):
# type: (Any, Any) -> Any
integration = sentry_sdk.get_client().get_integration(LangchainIntegration)
with capture_internal_exceptions():
new_callbacks = [] # type: List[BaseCallbackHandler]
if "local_callbacks" in kwargs:
existing_callbacks = kwargs["local_callbacks"]
kwargs["local_callbacks"] = new_callbacks
elif len(args) > 2:
existing_callbacks = args[2]
args = (
args[0],
args[1],
new_callbacks,
) + args[3:]
else:
existing_callbacks = []
if existing_callbacks:
if isinstance(existing_callbacks, list):
for cb in existing_callbacks:
new_callbacks.append(cb)
elif isinstance(existing_callbacks, BaseCallbackHandler):
new_callbacks.append(existing_callbacks)
else:
logger.warn("Unknown callback type: %s", existing_callbacks)
already_added = False
for callback in new_callbacks:
if isinstance(callback, SentryLangchainCallback):
already_added = True
if not already_added:
new_callbacks.append(
SentryLangchainCallback(
integration.max_spans,
integration.include_prompts,
integration.tiktoken_encoding_name,
)
)
return f(*args, **kwargs)
return new_configure
Hacked By AnonymousFox1.0, Coded By AnonymousFox