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fix(llama-index): Suppress unhandled event warnings for SparseEmbeddingEndEvent and Workflow*OutputEvent #2908

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fix(llama-index): Suppress unhandled event warnings for SparseEmbeddingEndEvent and Workflow*OutputEvent #2908

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8db770b
fix(llama-index): handle SparseEmbeddingEndEvent and Workflow output …
claude Mar 23, 2026
aa041d2
fix(llama-index): add direct SparseEmbeddingStartEvent handler; make …
claude Mar 23, 2026
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37 changes: 37 additions & 0 deletions ...nce-instrumentation-llama-index/src/openinference/instrumentation/llama_index/_handler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,6 +97,8 @@
from llama_index.core.instrumentation.events.embedding import (
EmbeddingEndEvent,
EmbeddingStartEvent,
SparseEmbeddingEndEvent,
SparseEmbeddingStartEvent,
)
from llama_index.core.instrumentation.events.llm import (
LLMChatEndEvent,
Expand Down Expand Up @@ -521,6 +523,11 @@ def _(self, event: EmbeddingStartEvent) -> None:
if not self._span_kind:
self._span_kind = EMBEDDING

@_process_event.register
def _(self, event: SparseEmbeddingStartEvent) -> None:
if not self._span_kind:
self._span_kind = EMBEDDING

@_process_event.register
def _(self, event: EmbeddingEndEvent) -> None:
i = self._list_attr_len[EMBEDDING_EMBEDDINGS]
Expand All @@ -530,6 +537,17 @@ def _(self, event: EmbeddingEndEvent) -> None:
i += 1
self._list_attr_len[EMBEDDING_EMBEDDINGS] = i

@_process_event.register
def _(self, event: SparseEmbeddingEndEvent) -> None:
# Sparse embedding vectors use Dict[int, float] (token-id → weight) which
# has no matching EMBEDDING_VECTOR semantic convention (expects List[float]).
# Record the text chunks so the span is not empty; skip the vectors.
i = self._list_attr_len[EMBEDDING_EMBEDDINGS]
for text in event.chunks:
self[f"{EMBEDDING_EMBEDDINGS}.{i}.{EMBEDDING_TEXT}"] = text
i += 1
self._list_attr_len[EMBEDDING_EMBEDDINGS] = i

@_process_event.register
def _(self, event: StreamChatStartEvent) -> None:
if not self._span_kind:
Expand Down Expand Up @@ -685,6 +703,25 @@ def _(self, event: GetResponseEndEvent) -> None:
return
self._process_response_text_type(event.response)

try:
from workflows import (
SpanCancelledEvent as _SpanCancelledEvent,
WorkflowRunOutputEvent as _WorkflowRunOutputEvent,
WorkflowStepOutputEvent as _WorkflowStepOutputEvent,
)

@_process_event.register
def _(self, event: _WorkflowStepOutputEvent) -> None: ... # type: ignore[misc]

@_process_event.register
def _(self, event: _WorkflowRunOutputEvent) -> None: ... # type: ignore[misc]

@_process_event.register
def _(self, event: _SpanCancelledEvent) -> None: ... # type: ignore[misc]

except ImportError:
pass

def _extract_token_counts(self, response: Union[ChatResponse, CompletionResponse]) -> None:
if raw := getattr(response, "raw", None):
usage = raw.get("usage") if isinstance(raw, Mapping) else getattr(raw, "usage", None)
Expand Down
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