Changed code to support older Python versions

venv/lib/python3.11/site-packages/sentry_sdk/ai/__init__.py

@@ -0,0 +1,7 @@
+from .utils import (
+    set_data_normalized,
+    GEN_AI_MESSAGE_ROLE_MAPPING,
+    GEN_AI_MESSAGE_ROLE_REVERSE_MAPPING,
+    normalize_message_role,
+    normalize_message_roles,
+)  # noqa: F401

venv/lib/python3.11/site-packages/sentry_sdk/ai/monitoring.py

@@ -0,0 +1,137 @@
+import inspect
+from functools import wraps
+
+from sentry_sdk.consts import SPANDATA
+import sentry_sdk.utils
+from sentry_sdk import start_span
+from sentry_sdk.tracing import Span
+from sentry_sdk.utils import ContextVar
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Optional, Callable, Awaitable, Any, Union, TypeVar
+
+    F = TypeVar("F", bound=Union[Callable[..., Any], Callable[..., Awaitable[Any]]])
+
+_ai_pipeline_name = ContextVar("ai_pipeline_name", default=None)
+
+
+def set_ai_pipeline_name(name):
+    # type: (Optional[str]) -> None
+    _ai_pipeline_name.set(name)
+
+
+def get_ai_pipeline_name():
+    # type: () -> Optional[str]
+    return _ai_pipeline_name.get()
+
+
+def ai_track(description, **span_kwargs):
+    # type: (str, Any) -> Callable[[F], F]
+    def decorator(f):
+        # type: (F) -> F
+        def sync_wrapped(*args, **kwargs):
+            # type: (Any, Any) -> Any
+            curr_pipeline = _ai_pipeline_name.get()
+            op = span_kwargs.pop("op", "ai.run" if curr_pipeline else "ai.pipeline")
+
+            with start_span(name=description, op=op, **span_kwargs) as span:
+                for k, v in kwargs.pop("sentry_tags", {}).items():
+                    span.set_tag(k, v)
+                for k, v in kwargs.pop("sentry_data", {}).items():
+                    span.set_data(k, v)
+                if curr_pipeline:
+                    span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, curr_pipeline)
+                    return f(*args, **kwargs)
+                else:
+                    _ai_pipeline_name.set(description)
+                    try:
+                        res = f(*args, **kwargs)
+                    except Exception as e:
+                        event, hint = sentry_sdk.utils.event_from_exception(
+                            e,
+                            client_options=sentry_sdk.get_client().options,
+                            mechanism={"type": "ai_monitoring", "handled": False},
+                        )
+                        sentry_sdk.capture_event(event, hint=hint)
+                        raise e from None
+                    finally:
+                        _ai_pipeline_name.set(None)
+                    return res
+
+        async def async_wrapped(*args, **kwargs):
+            # type: (Any, Any) -> Any
+            curr_pipeline = _ai_pipeline_name.get()
+            op = span_kwargs.pop("op", "ai.run" if curr_pipeline else "ai.pipeline")
+
+            with start_span(name=description, op=op, **span_kwargs) as span:
+                for k, v in kwargs.pop("sentry_tags", {}).items():
+                    span.set_tag(k, v)
+                for k, v in kwargs.pop("sentry_data", {}).items():
+                    span.set_data(k, v)
+                if curr_pipeline:
+                    span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, curr_pipeline)
+                    return await f(*args, **kwargs)
+                else:
+                    _ai_pipeline_name.set(description)
+                    try:
+                        res = await f(*args, **kwargs)
+                    except Exception as e:
+                        event, hint = sentry_sdk.utils.event_from_exception(
+                            e,
+                            client_options=sentry_sdk.get_client().options,
+                            mechanism={"type": "ai_monitoring", "handled": False},
+                        )
+                        sentry_sdk.capture_event(event, hint=hint)
+                        raise e from None
+                    finally:
+                        _ai_pipeline_name.set(None)
+                    return res
+
+        if inspect.iscoroutinefunction(f):
+            return wraps(f)(async_wrapped)  # type: ignore
+        else:
+            return wraps(f)(sync_wrapped)  # type: ignore
+
+    return decorator
+
+
+def record_token_usage(
+    span,
+    input_tokens=None,
+    input_tokens_cached=None,
+    output_tokens=None,
+    output_tokens_reasoning=None,
+    total_tokens=None,
+):
+    # type: (Span, Optional[int], Optional[int], Optional[int], Optional[int], Optional[int]) -> None
+
+    # TODO: move pipeline name elsewhere
+    ai_pipeline_name = get_ai_pipeline_name()
+    if ai_pipeline_name:
+        span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, ai_pipeline_name)
+
+    if input_tokens is not None:
+        span.set_data(SPANDATA.GEN_AI_USAGE_INPUT_TOKENS, input_tokens)
+
+    if input_tokens_cached is not None:
+        span.set_data(
+            SPANDATA.GEN_AI_USAGE_INPUT_TOKENS_CACHED,
+            input_tokens_cached,
+        )
+
+    if output_tokens is not None:
+        span.set_data(SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens)
+
+    if output_tokens_reasoning is not None:
+        span.set_data(
+            SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS_REASONING,
+            output_tokens_reasoning,
+        )
+
+    if total_tokens is None and input_tokens is not None and output_tokens is not None:
+        total_tokens = input_tokens + output_tokens
+
+    if total_tokens is not None:
+        span.set_data(SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS, total_tokens)

venv/lib/python3.11/site-packages/sentry_sdk/ai/utils.py

@@ -0,0 +1,144 @@
+import json
+from collections import deque
+from typing import TYPE_CHECKING
+from sys import getsizeof
+
+if TYPE_CHECKING:
+    from typing import Any, Callable, Dict, List, Optional, Tuple
+
+    from sentry_sdk.tracing import Span
+
+import sentry_sdk
+from sentry_sdk.utils import logger
+
+MAX_GEN_AI_MESSAGE_BYTES = 20_000  # 20KB
+
+
+class GEN_AI_ALLOWED_MESSAGE_ROLES:
+    SYSTEM = "system"
+    USER = "user"
+    ASSISTANT = "assistant"
+    TOOL = "tool"
+
+
+GEN_AI_MESSAGE_ROLE_REVERSE_MAPPING = {
+    GEN_AI_ALLOWED_MESSAGE_ROLES.SYSTEM: ["system"],
+    GEN_AI_ALLOWED_MESSAGE_ROLES.USER: ["user", "human"],
+    GEN_AI_ALLOWED_MESSAGE_ROLES.ASSISTANT: ["assistant", "ai"],
+    GEN_AI_ALLOWED_MESSAGE_ROLES.TOOL: ["tool", "tool_call"],
+}
+
+GEN_AI_MESSAGE_ROLE_MAPPING = {}
+for target_role, source_roles in GEN_AI_MESSAGE_ROLE_REVERSE_MAPPING.items():
+    for source_role in source_roles:
+        GEN_AI_MESSAGE_ROLE_MAPPING[source_role] = target_role
+
+
+def _normalize_data(data, unpack=True):
+    # type: (Any, bool) -> Any
+    # convert pydantic data (e.g. OpenAI v1+) to json compatible format
+    if hasattr(data, "model_dump"):
+        try:
+            return _normalize_data(data.model_dump(), unpack=unpack)
+        except Exception as e:
+            logger.warning("Could not convert pydantic data to JSON: %s", e)
+            return data if isinstance(data, (int, float, bool, str)) else str(data)
+
+    if isinstance(data, list):
+        if unpack and len(data) == 1:
+            return _normalize_data(data[0], unpack=unpack)  # remove empty dimensions
+        return list(_normalize_data(x, unpack=unpack) for x in data)
+
+    if isinstance(data, dict):
+        return {k: _normalize_data(v, unpack=unpack) for (k, v) in data.items()}
+
+    return data if isinstance(data, (int, float, bool, str)) else str(data)
+
+
+def set_data_normalized(span, key, value, unpack=True):
+    # type: (Span, str, Any, bool) -> None
+    normalized = _normalize_data(value, unpack=unpack)
+    if isinstance(normalized, (int, float, bool, str)):
+        span.set_data(key, normalized)
+    else:
+        span.set_data(key, json.dumps(normalized))
+
+
+def normalize_message_role(role):
+    # type: (str) -> str
+    """
+    Normalize a message role to one of the 4 allowed gen_ai role values.
+    Maps "ai" -> "assistant" and keeps other standard roles unchanged.
+    """
+    return GEN_AI_MESSAGE_ROLE_MAPPING.get(role, role)
+
+
+def normalize_message_roles(messages):
+    # type: (list[dict[str, Any]]) -> list[dict[str, Any]]
+    """
+    Normalize roles in a list of messages to use standard gen_ai role values.
+    Creates a deep copy to avoid modifying the original messages.
+    """
+    normalized_messages = []
+    for message in messages:
+        if not isinstance(message, dict):
+            normalized_messages.append(message)
+            continue
+        normalized_message = message.copy()
+        if "role" in message:
+            normalized_message["role"] = normalize_message_role(message["role"])
+        normalized_messages.append(normalized_message)
+
+    return normalized_messages
+
+
+def get_start_span_function():
+    # type: () -> Callable[..., Any]
+    current_span = sentry_sdk.get_current_span()
+    transaction_exists = (
+        current_span is not None and current_span.containing_transaction is not None
+    )
+    return sentry_sdk.start_span if transaction_exists else sentry_sdk.start_transaction
+
+
+def _find_truncation_index(messages, max_bytes):
+    # type: (List[Dict[str, Any]], int) -> int
+    """
+    Find the index of the first message that would exceed the max bytes limit.
+    Compute the individual message sizes, and return the index of the first message from the back
+    of the list that would exceed the max bytes limit.
+    """
+    running_sum = 0
+    for idx in range(len(messages) - 1, -1, -1):
+        size = len(json.dumps(messages[idx], separators=(",", ":")).encode("utf-8"))
+        running_sum += size
+        if running_sum > max_bytes:
+            return idx + 1
+
+    return 0
+
+
+def truncate_messages_by_size(messages, max_bytes=MAX_GEN_AI_MESSAGE_BYTES):
+    # type: (List[Dict[str, Any]], int) -> Tuple[List[Dict[str, Any]], int]
+    serialized_json = json.dumps(messages, separators=(",", ":"))
+    current_size = len(serialized_json.encode("utf-8"))
+
+    if current_size <= max_bytes:
+        return messages, 0
+
+    truncation_index = _find_truncation_index(messages, max_bytes)
+    return messages[truncation_index:], truncation_index
+
+
+def truncate_and_annotate_messages(
+    messages, span, scope, max_bytes=MAX_GEN_AI_MESSAGE_BYTES
+):
+    # type: (Optional[List[Dict[str, Any]]], Any, Any, int) -> Optional[List[Dict[str, Any]]]
+    if not messages:
+        return None
+
+    truncated_messages, removed_count = truncate_messages_by_size(messages, max_bytes)
+    if removed_count > 0:
+        scope._gen_ai_original_message_count[span.span_id] = len(messages)
+
+    return truncated_messages