Changed code to support older Python versions
This commit is contained in:
Malasaur
parent
eb92d2d36f
commit
582458cdd0
5027 changed files with 794942 additions and 4 deletions
|
|
@ -0,0 +1,337 @@
|
|||
from functools import wraps
|
||||
from typing import Any, Callable, List, Optional
|
||||
|
||||
import sentry_sdk
|
||||
from sentry_sdk.ai.utils import (
|
||||
set_data_normalized,
|
||||
normalize_message_roles,
|
||||
truncate_and_annotate_messages,
|
||||
)
|
||||
from sentry_sdk.consts import OP, SPANDATA
|
||||
from sentry_sdk.integrations import DidNotEnable, Integration
|
||||
from sentry_sdk.scope import should_send_default_pii
|
||||
from sentry_sdk.utils import safe_serialize
|
||||
|
||||
|
||||
try:
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.pregel import Pregel
|
||||
except ImportError:
|
||||
raise DidNotEnable("langgraph not installed")
|
||||
|
||||
|
||||
class LanggraphIntegration(Integration):
|
||||
identifier = "langgraph"
|
||||
origin = f"auto.ai.{identifier}"
|
||||
|
||||
def __init__(self, include_prompts=True):
|
||||
# type: (LanggraphIntegration, bool) -> None
|
||||
self.include_prompts = include_prompts
|
||||
|
||||
@staticmethod
|
||||
def setup_once():
|
||||
# type: () -> None
|
||||
# LangGraph lets users create agents using a StateGraph or the Functional API.
|
||||
# StateGraphs are then compiled to a CompiledStateGraph. Both CompiledStateGraph and
|
||||
# the functional API execute on a Pregel instance. Pregel is the runtime for the graph
|
||||
# and the invocation happens on Pregel, so patching the invoke methods takes care of both.
|
||||
# The streaming methods are not patched, because due to some internal reasons, LangGraph
|
||||
# will automatically patch the streaming methods to run through invoke, and by doing this
|
||||
# we prevent duplicate spans for invocations.
|
||||
StateGraph.compile = _wrap_state_graph_compile(StateGraph.compile)
|
||||
if hasattr(Pregel, "invoke"):
|
||||
Pregel.invoke = _wrap_pregel_invoke(Pregel.invoke)
|
||||
if hasattr(Pregel, "ainvoke"):
|
||||
Pregel.ainvoke = _wrap_pregel_ainvoke(Pregel.ainvoke)
|
||||
|
||||
|
||||
def _get_graph_name(graph_obj):
|
||||
# type: (Any) -> Optional[str]
|
||||
for attr in ["name", "graph_name", "__name__", "_name"]:
|
||||
if hasattr(graph_obj, attr):
|
||||
name = getattr(graph_obj, attr)
|
||||
if name and isinstance(name, str):
|
||||
return name
|
||||
return None
|
||||
|
||||
|
||||
def _normalize_langgraph_message(message):
|
||||
# type: (Any) -> Any
|
||||
if not hasattr(message, "content"):
|
||||
return None
|
||||
|
||||
parsed = {"role": getattr(message, "type", None), "content": message.content}
|
||||
|
||||
for attr in ["name", "tool_calls", "function_call", "tool_call_id"]:
|
||||
if hasattr(message, attr):
|
||||
value = getattr(message, attr)
|
||||
if value is not None:
|
||||
parsed[attr] = value
|
||||
|
||||
return parsed
|
||||
|
||||
|
||||
def _parse_langgraph_messages(state):
|
||||
# type: (Any) -> Optional[List[Any]]
|
||||
if not state:
|
||||
return None
|
||||
|
||||
messages = None
|
||||
|
||||
if isinstance(state, dict):
|
||||
messages = state.get("messages")
|
||||
elif hasattr(state, "messages"):
|
||||
messages = state.messages
|
||||
elif hasattr(state, "get") and callable(state.get):
|
||||
try:
|
||||
messages = state.get("messages")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if not messages or not isinstance(messages, (list, tuple)):
|
||||
return None
|
||||
|
||||
normalized_messages = []
|
||||
for message in messages:
|
||||
try:
|
||||
normalized = _normalize_langgraph_message(message)
|
||||
if normalized:
|
||||
normalized_messages.append(normalized)
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
return normalized_messages if normalized_messages else None
|
||||
|
||||
|
||||
def _wrap_state_graph_compile(f):
|
||||
# type: (Callable[..., Any]) -> Callable[..., Any]
|
||||
@wraps(f)
|
||||
def new_compile(self, *args, **kwargs):
|
||||
# type: (Any, Any, Any) -> Any
|
||||
integration = sentry_sdk.get_client().get_integration(LanggraphIntegration)
|
||||
if integration is None:
|
||||
return f(self, *args, **kwargs)
|
||||
with sentry_sdk.start_span(
|
||||
op=OP.GEN_AI_CREATE_AGENT,
|
||||
origin=LanggraphIntegration.origin,
|
||||
) as span:
|
||||
compiled_graph = f(self, *args, **kwargs)
|
||||
|
||||
compiled_graph_name = getattr(compiled_graph, "name", None)
|
||||
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "create_agent")
|
||||
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, compiled_graph_name)
|
||||
|
||||
if compiled_graph_name:
|
||||
span.description = f"create_agent {compiled_graph_name}"
|
||||
else:
|
||||
span.description = "create_agent"
|
||||
|
||||
if kwargs.get("model", None) is not None:
|
||||
span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, kwargs.get("model"))
|
||||
|
||||
tools = None
|
||||
get_graph = getattr(compiled_graph, "get_graph", None)
|
||||
if get_graph and callable(get_graph):
|
||||
graph_obj = compiled_graph.get_graph()
|
||||
nodes = getattr(graph_obj, "nodes", None)
|
||||
if nodes and isinstance(nodes, dict):
|
||||
tools_node = nodes.get("tools")
|
||||
if tools_node:
|
||||
data = getattr(tools_node, "data", None)
|
||||
if data and hasattr(data, "tools_by_name"):
|
||||
tools = list(data.tools_by_name.keys())
|
||||
|
||||
if tools is not None:
|
||||
span.set_data(SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, tools)
|
||||
|
||||
return compiled_graph
|
||||
|
||||
return new_compile
|
||||
|
||||
|
||||
def _wrap_pregel_invoke(f):
|
||||
# type: (Callable[..., Any]) -> Callable[..., Any]
|
||||
|
||||
@wraps(f)
|
||||
def new_invoke(self, *args, **kwargs):
|
||||
# type: (Any, Any, Any) -> Any
|
||||
integration = sentry_sdk.get_client().get_integration(LanggraphIntegration)
|
||||
if integration is None:
|
||||
return f(self, *args, **kwargs)
|
||||
|
||||
graph_name = _get_graph_name(self)
|
||||
span_name = (
|
||||
f"invoke_agent {graph_name}".strip() if graph_name else "invoke_agent"
|
||||
)
|
||||
|
||||
with sentry_sdk.start_span(
|
||||
op=OP.GEN_AI_INVOKE_AGENT,
|
||||
name=span_name,
|
||||
origin=LanggraphIntegration.origin,
|
||||
) as span:
|
||||
if graph_name:
|
||||
span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
|
||||
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
|
||||
|
||||
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
|
||||
|
||||
# Store input messages to later compare with output
|
||||
input_messages = None
|
||||
if (
|
||||
len(args) > 0
|
||||
and should_send_default_pii()
|
||||
and integration.include_prompts
|
||||
):
|
||||
input_messages = _parse_langgraph_messages(args[0])
|
||||
if input_messages:
|
||||
normalized_input_messages = normalize_message_roles(input_messages)
|
||||
scope = sentry_sdk.get_current_scope()
|
||||
messages_data = truncate_and_annotate_messages(
|
||||
normalized_input_messages, span, scope
|
||||
)
|
||||
if messages_data is not None:
|
||||
set_data_normalized(
|
||||
span,
|
||||
SPANDATA.GEN_AI_REQUEST_MESSAGES,
|
||||
messages_data,
|
||||
unpack=False,
|
||||
)
|
||||
|
||||
result = f(self, *args, **kwargs)
|
||||
|
||||
_set_response_attributes(span, input_messages, result, integration)
|
||||
|
||||
return result
|
||||
|
||||
return new_invoke
|
||||
|
||||
|
||||
def _wrap_pregel_ainvoke(f):
|
||||
# type: (Callable[..., Any]) -> Callable[..., Any]
|
||||
|
||||
@wraps(f)
|
||||
async def new_ainvoke(self, *args, **kwargs):
|
||||
# type: (Any, Any, Any) -> Any
|
||||
integration = sentry_sdk.get_client().get_integration(LanggraphIntegration)
|
||||
if integration is None:
|
||||
return await f(self, *args, **kwargs)
|
||||
|
||||
graph_name = _get_graph_name(self)
|
||||
span_name = (
|
||||
f"invoke_agent {graph_name}".strip() if graph_name else "invoke_agent"
|
||||
)
|
||||
|
||||
with sentry_sdk.start_span(
|
||||
op=OP.GEN_AI_INVOKE_AGENT,
|
||||
name=span_name,
|
||||
origin=LanggraphIntegration.origin,
|
||||
) as span:
|
||||
if graph_name:
|
||||
span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
|
||||
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
|
||||
|
||||
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
|
||||
|
||||
input_messages = None
|
||||
if (
|
||||
len(args) > 0
|
||||
and should_send_default_pii()
|
||||
and integration.include_prompts
|
||||
):
|
||||
input_messages = _parse_langgraph_messages(args[0])
|
||||
if input_messages:
|
||||
normalized_input_messages = normalize_message_roles(input_messages)
|
||||
scope = sentry_sdk.get_current_scope()
|
||||
messages_data = truncate_and_annotate_messages(
|
||||
normalized_input_messages, span, scope
|
||||
)
|
||||
if messages_data is not None:
|
||||
set_data_normalized(
|
||||
span,
|
||||
SPANDATA.GEN_AI_REQUEST_MESSAGES,
|
||||
messages_data,
|
||||
unpack=False,
|
||||
)
|
||||
|
||||
result = await f(self, *args, **kwargs)
|
||||
|
||||
_set_response_attributes(span, input_messages, result, integration)
|
||||
|
||||
return result
|
||||
|
||||
return new_ainvoke
|
||||
|
||||
|
||||
def _get_new_messages(input_messages, output_messages):
|
||||
# type: (Optional[List[Any]], Optional[List[Any]]) -> Optional[List[Any]]
|
||||
"""Extract only the new messages added during this invocation."""
|
||||
if not output_messages:
|
||||
return None
|
||||
|
||||
if not input_messages:
|
||||
return output_messages
|
||||
|
||||
# only return the new messages, aka the output messages that are not in the input messages
|
||||
input_count = len(input_messages)
|
||||
new_messages = (
|
||||
output_messages[input_count:] if len(output_messages) > input_count else []
|
||||
)
|
||||
|
||||
return new_messages if new_messages else None
|
||||
|
||||
|
||||
def _extract_llm_response_text(messages):
|
||||
# type: (Optional[List[Any]]) -> Optional[str]
|
||||
if not messages:
|
||||
return None
|
||||
|
||||
for message in reversed(messages):
|
||||
if isinstance(message, dict):
|
||||
role = message.get("role")
|
||||
if role in ["assistant", "ai"]:
|
||||
content = message.get("content")
|
||||
if content and isinstance(content, str):
|
||||
return content
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def _extract_tool_calls(messages):
|
||||
# type: (Optional[List[Any]]) -> Optional[List[Any]]
|
||||
if not messages:
|
||||
return None
|
||||
|
||||
tool_calls = []
|
||||
for message in messages:
|
||||
if isinstance(message, dict):
|
||||
msg_tool_calls = message.get("tool_calls")
|
||||
if msg_tool_calls and isinstance(msg_tool_calls, list):
|
||||
tool_calls.extend(msg_tool_calls)
|
||||
|
||||
return tool_calls if tool_calls else None
|
||||
|
||||
|
||||
def _set_response_attributes(span, input_messages, result, integration):
|
||||
# type: (Any, Optional[List[Any]], Any, LanggraphIntegration) -> None
|
||||
if not (should_send_default_pii() and integration.include_prompts):
|
||||
return
|
||||
|
||||
parsed_response_messages = _parse_langgraph_messages(result)
|
||||
new_messages = _get_new_messages(input_messages, parsed_response_messages)
|
||||
|
||||
llm_response_text = _extract_llm_response_text(new_messages)
|
||||
if llm_response_text:
|
||||
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, llm_response_text)
|
||||
elif new_messages:
|
||||
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, new_messages)
|
||||
else:
|
||||
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, result)
|
||||
|
||||
tool_calls = _extract_tool_calls(new_messages)
|
||||
if tool_calls:
|
||||
set_data_normalized(
|
||||
span,
|
||||
SPANDATA.GEN_AI_RESPONSE_TOOL_CALLS,
|
||||
safe_serialize(tool_calls),
|
||||
unpack=False,
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue