fix(litellm): fix gen_ai.request.messages to be as expected

sentry_sdk/ai/utils.py

@@ -5,6 +5,8 @@
 from sys import getsizeof
 from typing import TYPE_CHECKING
 
+from sentry_sdk._types import SENSITIVE_DATA_SUBSTITUTE
+
 if TYPE_CHECKING:
     from typing import Any, Callable, Dict, List, Optional, Tuple
 
@@ -141,6 +143,57 @@ def _find_truncation_index(messages: "List[Dict[str, Any]]", max_bytes: int) ->
     return 0
 
 
+def redact_blob_message_parts(
+    messages: "List[Dict[str, Any]]",
+) -> "List[Dict[str, Any]]":
+    """
+    Redact blob message parts from the messages, by removing the "content" key.
+    e.g:
+    {
+        "role": "user",
+        "content": [
+            {
+                "text": "How many ponies do you see in the image?",
+                "type": "text"
+            },
+            {
+                "type": "blob",
+                "modality": "image",
+                "mime_type": "image/jpeg",
+                "content": "data:image/jpeg;base64,..."
+            }
+        ]
+    }
+    becomes:
+    {
+        "role": "user",
+        "content": [
+            {
+                "text": "How many ponies do you see in the image?",
+                "type": "text"
+            },
+            {
+                "type": "blob",
+                "modality": "image",
+                "mime_type": "image/jpeg",
+                "content": "[Filtered]"
+            }
+        ]
+    }
+    """
+
+    for message in messages:
+        if not isinstance(message, dict):
+            continue
+
+        content = message.get("content")
+        if isinstance(content, list):
+            for item in content:
+                if isinstance(item, dict) and item.get("type") == "blob":
+                    item["content"] = SENSITIVE_DATA_SUBSTITUTE
+    return messages
+
+
 def truncate_messages_by_size(
     messages: "List[Dict[str, Any]]",
     max_bytes: int = MAX_GEN_AI_MESSAGE_BYTES,
@@ -186,6 +239,8 @@ def truncate_and_annotate_messages(
     if not messages:
         return None
 
+    messages = redact_blob_message_parts(messages)
+
     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)

sentry_sdk/integrations/litellm.py

@@ -14,7 +14,7 @@
 from sentry_sdk.utils import event_from_exception
 
 if TYPE_CHECKING:
-    from typing import Any, Dict
+    from typing import Any, Dict, List
     from datetime import datetime
 
 try:
@@ -35,6 +35,68 @@ def _get_metadata_dict(kwargs: "Dict[str, Any]") -> "Dict[str, Any]":
     return metadata
 
 
+def _convert_message_parts(messages: "List[Dict[str, Any]]") -> "List[Dict[str, Any]]":
+    """
+    Convert the message parts from OpenAI format to the `gen_ai.request.messages` format.
+    e.g:
+    {
+        "role": "user",
+        "content": [
+            {
+                "text": "How many ponies do you see in the image?",
+                "type": "text"
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": "data:image/jpeg;base64,...",
+                    "detail": "high"
+                }
+            }
+        ]
+    }
+    becomes:
+    {
+        "role": "user",
+        "content": [
+            {
+                "text": "How many ponies do you see in the image?",
+                "type": "text"
+            },
+            {
+                "type": "blob",
+                "modality": "image",
+                "mime_type": "image/jpeg",
+                "content": "data:image/jpeg;base64,..."
+            }
+        ]
+    }
+    """
+
+    def _map_item(item: "Dict[str, Any]") -> "Dict[str, Any]":
+        if item.get("type") == "image_url":
+            image_url = item.get("image_url") or {}
+            if image_url.get("url", "").startswith("data:"):
+                return {
+                    "type": "blob",
+                    "modality": "image",
+                    "mime_type": item["image_url"]["url"].split(";base64,")[0],
+                    "content": item["image_url"]["url"].split(";base64,")[1],
+                }
+            else:
+                return {
+                    "type": "uri",
+                    "uri": item["image_url"]["url"],
+                }
+        return item
+
+    for message in messages:
+        content = message.get("content")
+        if isinstance(content, list):
+            message["content"] = [_map_item(item) for item in content]
+    return messages
+
+
 def _input_callback(kwargs: "Dict[str, Any]") -> None:
     """Handle the start of a request."""
     integration = sentry_sdk.get_client().get_integration(LiteLLMIntegration)
@@ -101,6 +163,7 @@ def _input_callback(kwargs: "Dict[str, Any]") -> None:
             messages = kwargs.get("messages", [])
             if messages:
                 scope = sentry_sdk.get_current_scope()
+                messages = _convert_message_parts(messages)
                 messages_data = truncate_and_annotate_messages(messages, span, scope)
                 if messages_data is not None:
                     set_data_normalized(

tests/integrations/litellm/test_litellm.py

@@ -1,3 +1,4 @@
+import base64
 import json
 import pytest
 import time
@@ -23,6 +24,7 @@ async def __call__(self, *args, **kwargs):
 from sentry_sdk.consts import OP, SPANDATA
 from sentry_sdk.integrations.litellm import (
     LiteLLMIntegration,
+    _convert_message_parts,
     _input_callback,
     _success_callback,
     _failure_callback,
@@ -753,3 +755,162 @@ def test_litellm_message_truncation(sentry_init, capture_events):
     assert "small message 4" in str(parsed_messages[0])
     assert "small message 5" in str(parsed_messages[1])
     assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5
+
+
+IMAGE_DATA = b"fake_image_data_12345"
+IMAGE_B64 = base64.b64encode(IMAGE_DATA).decode("utf-8")
+IMAGE_DATA_URI = f"data:image/png;base64,{IMAGE_B64}"
+
+
+def test_binary_content_encoding_image_url(sentry_init, capture_events):
+    sentry_init(
+        integrations=[LiteLLMIntegration(include_prompts=True)],
+        traces_sample_rate=1.0,
+        send_default_pii=True,
+    )
+    events = capture_events()
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Look at this image:"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": IMAGE_DATA_URI, "detail": "high"},
+                },
+            ],
+        }
+    ]
+    mock_response = MockCompletionResponse()
+
+    with start_transaction(name="litellm test"):
+        kwargs = {"model": "gpt-4-vision-preview", "messages": messages}
+        _input_callback(kwargs)
+        _success_callback(kwargs, mock_response, datetime.now(), datetime.now())
+
+    (event,) = events
+    (span,) = event["spans"]
+    messages_data = json.loads(span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])
+
+    blob_item = next(
+        (
+            item
+            for msg in messages_data
+            if "content" in msg
+            for item in msg["content"]
+            if item.get("type") == "blob"
+        ),
+        None,
+    )
+    assert blob_item is not None
+    assert blob_item["modality"] == "image"
+    assert blob_item["mime_type"] == "data:image/png"
+    assert IMAGE_B64 in blob_item["content"] or "[Filtered]" in str(
+        blob_item["content"]
+    )
+
+
+def test_binary_content_encoding_mixed_content(sentry_init, capture_events):
+    sentry_init(
+        integrations=[LiteLLMIntegration(include_prompts=True)],
+        traces_sample_rate=1.0,
+        send_default_pii=True,
+    )
+    events = capture_events()
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Here is an image:"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": IMAGE_DATA_URI},
+                },
+                {"type": "text", "text": "What do you see?"},
+            ],
+        }
+    ]
+    mock_response = MockCompletionResponse()
+
+    with start_transaction(name="litellm test"):
+        kwargs = {"model": "gpt-4-vision-preview", "messages": messages}
+        _input_callback(kwargs)
+        _success_callback(kwargs, mock_response, datetime.now(), datetime.now())
+
+    (event,) = events
+    (span,) = event["spans"]
+    messages_data = json.loads(span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])
+
+    content_items = [
+        item for msg in messages_data if "content" in msg for item in msg["content"]
+    ]
+    assert any(item.get("type") == "text" for item in content_items)
+    assert any(item.get("type") == "blob" for item in content_items)
+
+
+def test_binary_content_encoding_uri_type(sentry_init, capture_events):
+    sentry_init(
+        integrations=[LiteLLMIntegration(include_prompts=True)],
+        traces_sample_rate=1.0,
+        send_default_pii=True,
+    )
+    events = capture_events()
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://example.com/image.jpg"},
+                }
+            ],
+        }
+    ]
+    mock_response = MockCompletionResponse()
+
+    with start_transaction(name="litellm test"):
+        kwargs = {"model": "gpt-4-vision-preview", "messages": messages}
+        _input_callback(kwargs)
+        _success_callback(kwargs, mock_response, datetime.now(), datetime.now())
+
+    (event,) = events
+    (span,) = event["spans"]
+    messages_data = json.loads(span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])
+
+    uri_item = next(
+        (
+            item
+            for msg in messages_data
+            if "content" in msg
+            for item in msg["content"]
+            if item.get("type") == "uri"
+        ),
+        None,
+    )
+    assert uri_item is not None
+    assert uri_item["uri"] == "https://example.com/image.jpg"
+
+
+def test_convert_message_parts_direct():
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Hello"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": IMAGE_DATA_URI},
+                },
+            ],
+        }
+    ]
+    converted = _convert_message_parts(messages)
+    blob_item = next(
+        item for item in converted[0]["content"] if item.get("type") == "blob"
+    )
+    assert blob_item["modality"] == "image"
+    assert blob_item["mime_type"] == "data:image/png"
+    assert IMAGE_B64 in blob_item["content"]