feat(openai): Make tiktoken encoding name configurable + tiktoken usage opt-in

sentry_sdk/integrations/langchain.py

@@ -27,28 +27,6 @@
     raise DidNotEnable("langchain not installed")
 
 
-try:
-    import tiktoken  # type: ignore
-
-    enc = tiktoken.get_encoding("cl100k_base")
-
-    def count_tokens(s):
-        # type: (str) -> int
-        return len(enc.encode_ordinary(s))
-
-    logger.debug("[langchain] using tiktoken to count tokens")
-except ImportError:
-    logger.info(
-        "The Sentry Python SDK requires 'tiktoken' in order to measure token usage from streaming langchain calls."
-        "Please install 'tiktoken' if you aren't receiving accurate token usage in Sentry."
-        "See https://docs.sentry.io/platforms/python/integrations/langchain/ for more information."
-    )
-
-    def count_tokens(s):
-        # type: (str) -> int
-        return 1
-
-
 DATA_FIELDS = {
     "temperature": SPANDATA.AI_TEMPERATURE,
     "top_p": SPANDATA.AI_TOP_P,
@@ -78,10 +56,13 @@ class LangchainIntegration(Integration):
     # 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):
-        # type: (LangchainIntegration, bool, int) -> None
+    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():
@@ -109,11 +90,23 @@ class SentryLangchainCallback(BaseCallbackHandler):  # type: ignore[misc]
 
     max_span_map_size = 0
 
-    def __init__(self, max_span_map_size, include_prompts):
-        # type: (int, bool) -> None
+    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
 
@@ -244,9 +237,9 @@ def on_chat_model_start(self, serialized, messages, *, run_id, **kwargs):
             if not watched_span.no_collect_tokens:
                 for list_ in messages:
                     for message in list_:
-                        self.span_map[run_id].num_prompt_tokens += count_tokens(
+                        self.span_map[run_id].num_prompt_tokens += self.count_tokens(
                             message.content
-                        ) + count_tokens(message.type)
+                        ) + self.count_tokens(message.type)
 
     def on_llm_new_token(self, token, *, run_id, **kwargs):
         # type: (SentryLangchainCallback, str, UUID, Any) -> Any
@@ -257,7 +250,7 @@ def on_llm_new_token(self, token, *, run_id, **kwargs):
             span_data = self.span_map[run_id]
             if not span_data or span_data.no_collect_tokens:
                 return
-            span_data.num_completion_tokens += count_tokens(token)
+            span_data.num_completion_tokens += self.count_tokens(token)
 
     def on_llm_end(self, response, *, run_id, **kwargs):
         # type: (SentryLangchainCallback, LLMResult, UUID, Any) -> Any
@@ -461,7 +454,9 @@ def new_configure(*args, **kwargs):
             if not already_added:
                 new_callbacks.append(
                     SentryLangchainCallback(
-                        integration.max_spans, integration.include_prompts
+                        integration.max_spans,
+                        integration.include_prompts,
+                        integration.tiktoken_encoding_name,
                     )
                 )
         return f(*args, **kwargs)

sentry_sdk/integrations/openai.py

@@ -14,7 +14,6 @@
 from sentry_sdk.scope import should_send_default_pii
 from sentry_sdk.integrations import DidNotEnable, Integration
 from sentry_sdk.utils import (
-    logger,
     capture_internal_exceptions,
     event_from_exception,
     ensure_integration_enabled,
@@ -29,45 +28,33 @@
 except ImportError:
     raise DidNotEnable("OpenAI not installed")
 
-try:
-    import tiktoken  # type: ignore
-
-    enc = None  # lazy initialize
-
-    def count_tokens(s):
-        # type: (str) -> int
-        global enc
-        if enc is None:
-            enc = tiktoken.get_encoding("cl100k_base")
-        return len(enc.encode_ordinary(s))
-
-    logger.debug("[OpenAI] using tiktoken to count tokens")
-except ImportError:
-    logger.info(
-        "The Sentry Python SDK requires 'tiktoken' in order to measure token usage from some OpenAI APIs"
-        "Please install 'tiktoken' if you aren't receiving token usage in Sentry."
-        "See https://docs.sentry.io/platforms/python/integrations/openai/ for more information."
-    )
-
-    def count_tokens(s):
-        # type: (str) -> int
-        return 0
-
 
 class OpenAIIntegration(Integration):
     identifier = "openai"
     origin = f"auto.ai.{identifier}"
 
-    def __init__(self, include_prompts=True):
-        # type: (OpenAIIntegration, bool) -> None
+    def __init__(self, include_prompts=True, tiktoken_encoding_name=None):
+        # type: (OpenAIIntegration, bool, Optional[str]) -> None
         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)
+
     @staticmethod
     def setup_once():
         # type: () -> None
         Completions.create = _wrap_chat_completion_create(Completions.create)
         Embeddings.create = _wrap_embeddings_create(Embeddings.create)
 
+    def count_tokens(self, s):
+        # type: (OpenAIIntegration, str) -> int
+        if self.tiktoken_encoding is not None:
+            return len(self.tiktoken_encoding.encode_ordinary(s))
+        return 0
+
 
 def _capture_exception(exc):
     # type: (Any) -> None
@@ -80,9 +67,9 @@ def _capture_exception(exc):
 
 
 def _calculate_chat_completion_usage(
-    messages, response, span, streaming_message_responses=None
+    messages, response, span, streaming_message_responses, count_tokens
 ):
-    # type: (Iterable[ChatCompletionMessageParam], Any, Span, Optional[List[str]]) -> None
+    # type: (Iterable[ChatCompletionMessageParam], Any, Span, Optional[List[str]], Callable[..., Any]) -> None
     completion_tokens = 0  # type: Optional[int]
     prompt_tokens = 0  # type: Optional[int]
     total_tokens = 0  # type: Optional[int]
@@ -173,7 +160,9 @@ def new_chat_completion(*args, **kwargs):
                         "ai.responses",
                         list(map(lambda x: x.message, res.choices)),
                     )
-                _calculate_chat_completion_usage(messages, res, span)
+                _calculate_chat_completion_usage(
+                    messages, res, span, None, integration.count_tokens
+                )
                 span.__exit__(None, None, None)
             elif hasattr(res, "_iterator"):
                 data_buf: list[list[str]] = []  # one for each choice
@@ -208,7 +197,11 @@ def new_iterator():
                                     span, SPANDATA.AI_RESPONSES, all_responses
                                 )
                             _calculate_chat_completion_usage(
-                                messages, res, span, all_responses
+                                messages,
+                                res,
+                                span,
+                                all_responses,
+                                integration.count_tokens,
                             )
                     span.__exit__(None, None, None)
 
@@ -266,7 +259,7 @@ def new_embeddings_create(*args, **kwargs):
                     total_tokens = response.usage.total_tokens
 
             if prompt_tokens == 0:
-                prompt_tokens = count_tokens(kwargs["input"] or "")
+                prompt_tokens = integration.count_tokens(kwargs["input"] or "")
 
             record_token_usage(span, prompt_tokens, None, total_tokens or prompt_tokens)
 

tests/integrations/langchain/test_langchain.py

@@ -46,6 +46,15 @@ def _llm_type(self) -> str:
         return llm_type
 
 
+def tiktoken_encoding_if_installed():
+    try:
+        import tiktoken  # type: ignore # noqa # pylint: disable=unused-import
+
+        return "cl100k_base"
+    except ImportError:
+        return None
+
+
 @pytest.mark.parametrize(
     "send_default_pii, include_prompts, use_unknown_llm_type",
     [
@@ -62,7 +71,12 @@ def test_langchain_agent(
     llm_type = "acme-llm" if use_unknown_llm_type else "openai-chat"
 
     sentry_init(
-        integrations=[LangchainIntegration(include_prompts=include_prompts)],
+        integrations=[
+            LangchainIntegration(
+                include_prompts=include_prompts,
+                tiktoken_encoding_name=tiktoken_encoding_if_installed(),
+            )
+        ],
         traces_sample_rate=1.0,
         send_default_pii=send_default_pii,
     )

tests/integrations/openai/test_openai.py

@@ -78,6 +78,15 @@ def test_nonstreaming_chat_completion(
     assert span["measurements"]["ai_total_tokens_used"]["value"] == 30
 
 
+def tiktoken_encoding_if_installed():
+    try:
+        import tiktoken  # type: ignore # noqa # pylint: disable=unused-import
+
+        return "cl100k_base"
+    except ImportError:
+        return None
+
+
 # noinspection PyTypeChecker
 @pytest.mark.parametrize(
     "send_default_pii, include_prompts",
@@ -87,7 +96,12 @@ def test_streaming_chat_completion(
     sentry_init, capture_events, send_default_pii, include_prompts
 ):
     sentry_init(
-        integrations=[OpenAIIntegration(include_prompts=include_prompts)],
+        integrations=[
+            OpenAIIntegration(
+                include_prompts=include_prompts,
+                tiktoken_encoding_name=tiktoken_encoding_if_installed(),
+            )
+        ],
         traces_sample_rate=1.0,
         send_default_pii=send_default_pii,
     )