Log LLM tool call for streamed response

logfire/_internal/integrations/llm_providers/anthropic.py

@@ -5,7 +5,7 @@
 import anthropic
 from anthropic.types import Message, TextBlock, TextDelta
 
-from .types import EndpointConfig
+from .types import EndpointConfig, StreamState
 
 if TYPE_CHECKING:
     from anthropic._models import FinalRequestOptions
@@ -32,13 +32,12 @@ def get_endpoint_config(options: FinalRequestOptions) -> EndpointConfig:
         return EndpointConfig(
             message_template='Message with {request_data[model]!r}',
             span_data={'request_data': json_data},
-            content_from_stream=content_from_messages,
+            stream_state_cls=AnthropicMessageStreamState,
         )
     else:
         return EndpointConfig(
             message_template='Anthropic API call to {url!r}',
             span_data={'request_data': json_data, 'url': url},
-            content_from_stream=None,
         )
 
 
@@ -50,6 +49,19 @@ def content_from_messages(chunk: anthropic.types.MessageStreamEvent) -> str | No
     return None
 
 
+class AnthropicMessageStreamState(StreamState):
+    def __init__(self):
+        self._content: list[str] = []
+
+    def record_chunk(self, chunk: anthropic.types.MessageStreamEvent) -> None:
+        content = content_from_messages(chunk)
+        if content:
+            self._content.append(content)
+
+    def get_response_data(self) -> Any:
+        return {'combined_chunk_content': ''.join(self._content), 'chunk_count': len(self._content)}
+
+
 def on_response(response: ResponseT, span: LogfireSpan) -> ResponseT:
     """Updates the span based on the type of response."""
     if isinstance(response, Message):  # pragma: no branch

logfire/_internal/integrations/llm_providers/llm_provider.py

@@ -9,7 +9,7 @@
 
 if TYPE_CHECKING:
     from ...main import Logfire, LogfireSpan
-    from .types import EndpointConfig
+    from .types import EndpointConfig, StreamState
 
 
 __all__ = ('instrument_llm_provider',)
@@ -76,21 +76,21 @@ def _instrumentation_setup(**kwargs: Any) -> Any:
         if is_instrumentation_suppressed():
             return None, None, kwargs
 
-        message_template, span_data, content_from_stream = get_endpoint_config_fn(kwargs['options'])
+        message_template, span_data, stream_state_cls = get_endpoint_config_fn(kwargs['options'])
 
         span_data['async'] = is_async
 
         stream = kwargs['stream']
 
-        if stream and content_from_stream:
+        if stream and stream_state_cls:
             stream_cls = kwargs['stream_cls']
             assert stream_cls is not None, 'Expected `stream_cls` when streaming'
 
             if is_async:
 
                 class LogfireInstrumentedAsyncStream(stream_cls):
                     async def __stream__(self) -> AsyncIterator[Any]:
-                        with record_streaming(logfire_llm, span_data, content_from_stream) as record_chunk:
+                        with record_streaming(logfire_llm, span_data, stream_state_cls) as record_chunk:
                             async for chunk in super().__stream__():  # type: ignore
                                 record_chunk(chunk)
                                 yield chunk
@@ -100,7 +100,7 @@ async def __stream__(self) -> AsyncIterator[Any]:
 
                 class LogfireInstrumentedStream(stream_cls):
                     def __stream__(self) -> Iterator[Any]:
-                        with record_streaming(logfire_llm, span_data, content_from_stream) as record_chunk:
+                        with record_streaming(logfire_llm, span_data, stream_state_cls) as record_chunk:
                             for chunk in super().__stream__():  # type: ignore
                                 record_chunk(chunk)
                                 yield chunk
@@ -174,14 +174,13 @@ def maybe_suppress_instrumentation(suppress: bool) -> Iterator[None]:
 def record_streaming(
     logire_llm: Logfire,
     span_data: dict[str, Any],
-    content_from_stream: Callable[[Any], str | None],
+    stream_state_cls: type[StreamState],
 ):
-    content: list[str] = []
+    stream_state = stream_state_cls()
 
-    def record_chunk(chunk: Any) -> Any:
-        chunk_content = content_from_stream(chunk)
-        if chunk_content:
-            content.append(chunk_content)
+    def record_chunk(chunk: Any) -> None:
+        if chunk:
+            stream_state.record_chunk(chunk)
 
     timer = logire_llm._config.advanced.ns_timestamp_generator  # type: ignore
     start = timer()
@@ -193,5 +192,5 @@ def record_chunk(chunk: Any) -> Any:
             'streaming response from {request_data[model]!r} took {duration:.2f}s',
             **span_data,
             duration=duration,
-            response_data={'combined_chunk_content': ''.join(content), 'chunk_count': len(content)},
+            response_data=stream_state.get_response_data(),
         )

logfire/_internal/integrations/llm_providers/openai.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, cast
+from typing import TYPE_CHECKING, Any, cast
 
 import openai
 from openai._legacy_response import LegacyAPIResponse
@@ -10,7 +10,7 @@
 from openai.types.create_embedding_response import CreateEmbeddingResponse
 from openai.types.images_response import ImagesResponse
 
-from .types import EndpointConfig
+from .types import EndpointConfig, StreamState
 
 if TYPE_CHECKING:
     from openai._models import FinalRequestOptions
@@ -38,31 +38,28 @@ def get_endpoint_config(options: FinalRequestOptions) -> EndpointConfig:
         return EndpointConfig(
             message_template='Chat Completion with {request_data[model]!r}',
             span_data={'request_data': json_data},
-            content_from_stream=content_from_chat_completions,
+            stream_state_cls=OpenaiChatCompletionStreamState,
         )
     elif url == '/completions':
         return EndpointConfig(
             message_template='Completion with {request_data[model]!r}',
             span_data={'request_data': json_data},
-            content_from_stream=content_from_completions,
+            stream_state_cls=OpenaiCompletionStreamState,
         )
     elif url == '/embeddings':
         return EndpointConfig(
             message_template='Embedding Creation with {request_data[model]!r}',
             span_data={'request_data': json_data},
-            content_from_stream=None,
         )
     elif url == '/images/generations':
         return EndpointConfig(
             message_template='Image Generation with {request_data[model]!r}',
             span_data={'request_data': json_data},
-            content_from_stream=None,
         )
     else:
         return EndpointConfig(
             message_template='OpenAI API call to {url!r}',
             span_data={'request_data': json_data, 'url': url},
-            content_from_stream=None,
         )
 
 
@@ -72,10 +69,48 @@ def content_from_completions(chunk: Completion | None) -> str | None:
     return None  # pragma: no cover
 
 
-def content_from_chat_completions(chunk: ChatCompletionChunk | None) -> str | None:
-    if chunk and chunk.choices:
-        return chunk.choices[0].delta.content
-    return None
+class OpenaiCompletionStreamState(StreamState):
+    def __init__(self):
+        self._content: list[str] = []
+
+    def record_chunk(self, chunk: Completion) -> None:
+        content = content_from_completions(chunk)
+        if content:
+            self._content.append(content)
+
+    def get_response_data(self) -> Any:
+        return {'combined_chunk_content': ''.join(self._content), 'chunk_count': len(self._content)}
+
+
+try:
+    # ChatCompletionStreamState only exists in openai>=1.40.0
+    from openai.lib.streaming.chat._completions import ChatCompletionStreamState
+
+    class OpenaiChatCompletionStreamState(StreamState):
+        def __init__(self):
+            self._stream_state = ChatCompletionStreamState(
+                # We do not need the response to be parsed into Python objects so can skip
+                # providing the `response_format` and `input_tools` arguments.
+                input_tools=openai.NOT_GIVEN,
+                response_format=openai.NOT_GIVEN,
+            )
+
+        def record_chunk(self, chunk: ChatCompletionChunk) -> None:
+            self._stream_state.handle_chunk(chunk)
+
+        def get_response_data(self) -> Any:
+            try:
+                final_completion = self._stream_state.current_completion_snapshot
+            except AssertionError:
+                # AssertionError is raised when there is no completion snapshot
+                # Return empty content to show an empty Assistant response in the UI
+                return {'combined_chunk_content': '', 'chunk_count': 0}
+            return {
+                'message': final_completion.choices[0].message if final_completion.choices else None,
+                'usage': final_completion.usage,
+            }
+except ImportError:  # pragma: no cover
+    OpenaiChatCompletionStreamState = OpenaiCompletionStreamState  # type: ignore
 
 
 def on_response(response: ResponseT, span: LogfireSpan) -> ResponseT:

logfire/_internal/integrations/llm_providers/types.py

@@ -1,13 +1,26 @@
 from __future__ import annotations
 
-from typing import Any, Callable, NamedTuple
+from abc import ABC, abstractmethod
+from typing import Any, NamedTuple
 
 from typing_extensions import LiteralString
 
 
+class StreamState(ABC):
+    """Keeps track of the state of a streamed response."""
+
+    @abstractmethod
+    def record_chunk(self, chunk: Any) -> None:
+        """Update the state based on a chunk from the streamed response."""
+
+    @abstractmethod
+    def get_response_data(self) -> Any:
+        """Returns the response data for including in the log."""
+
+
 class EndpointConfig(NamedTuple):
     """The configuration for the endpoint of a provider based on request url."""
 
     message_template: LiteralString
     span_data: dict[str, Any]
-    content_from_stream: Callable[[Any], str | None] | None
+    stream_state_cls: type[StreamState] | None = None

pyproject.toml

@@ -231,7 +231,8 @@ exclude = [
     "out",
     "logfire-api",
 ]
-venvPath = ".venv"
+venvPath = "."
+venv = ".venv"
 
 [tool.pytest.ini_options]
 xfail_strict = true