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fix: separate thinking from text parts in streaming mode

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Copybara import of the project:

--
79962881ca1c17eb6d7bd9dcf31a44df93c9badd by Almas Akchabayev <almas.akchabayev@gmail.com>:

fix: separate thinking from text parts in streaming mode
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/777 from almeynman:separate-thinking-and-text-parts-in-stream-mode b63dcc7fd0fc3973888dcbb9d4cc7e7e0a66e7f7
PiperOrigin-RevId: 764561932
This commit is contained in:
Almas Akchabayev
2025-05-28 22:10:39 -07:00
committed by Copybara-Service
parent 60ceea72bd
commit 795605a37e
2 changed files with 96 additions and 10 deletions
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32
src/google/adk/models/google_llm.py
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@@ -97,6 +97,7 @@ class Gemini(BaseLlm):
config=llm_request.config,
)
response = None
thought_text = ''
text = ''
usage_metadata = None
# for sse, similar as bidi (see receive method in gemini_llm_connecton.py),
@@ -113,32 +114,43 @@ class Gemini(BaseLlm):
and llm_response.content.parts
and llm_response.content.parts[0].text
):
text += llm_response.content.parts[0].text
part0 = llm_response.content.parts[0]
if part0.thought:
thought_text += part0.text
else:
text += part0.text
llm_response.partial = True
elif text and (
elif (thought_text or text) and (
not llm_response.content
or not llm_response.content.parts
# don't yield the merged text event when receiving audio data
or not llm_response.content.parts[0].inline_data
):
parts = []
if thought_text:
parts.append(types.Part(text=thought_text, thought=True))
if text:
parts.append(types.Part.from_text(text=text))
yield LlmResponse(
content=types.ModelContent(
parts=[types.Part.from_text(text=text)],
),
usage_metadata=usage_metadata,
content=types.ModelContent(parts=parts),
usage_metadata=llm_response.usage_metadata,
)
thought_text = ''
text = ''
yield llm_response
if (
text
(text or thought_text)
and response
and response.candidates
and response.candidates[0].finish_reason == types.FinishReason.STOP
):
parts = []
if thought_text:
parts.append(types.Part(text=thought_text, thought=True))
if text:
parts.append(types.Part.from_text(text=text))
yield LlmResponse(
content=types.ModelContent(
parts=[types.Part.from_text(text=text)],
),
content=types.ModelContent(parts=parts),
usage_metadata=usage_metadata,
)