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ebec74a
1
Parent(s):
9f566ef
bug fixes
Browse files- app/api_helpers.py +27 -0
- app/openai_handler.py +31 -8
app/api_helpers.py
CHANGED
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@@ -91,6 +91,33 @@ class StreamingReasoningProcessor:
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self.inside_tag = False
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return processed_content, current_reasoning
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def process_streaming_content_with_reasoning_tags(
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self.inside_tag = False
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return processed_content, current_reasoning
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def flush_remaining(self) -> tuple[str, str]:
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"""
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Flush any remaining content in the buffer when the stream ends.
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Returns:
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A tuple of:
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- remaining_content: Any content that was buffered but not yet output
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- remaining_reasoning: Any incomplete reasoning if we were inside a tag
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"""
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remaining_content = ""
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remaining_reasoning = ""
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if self.tag_buffer and not self.inside_tag:
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# If we have buffered content and we're not inside a tag,
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# it's safe to output all of it
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remaining_content = self.tag_buffer
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self.tag_buffer = ""
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elif self.inside_tag:
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# If we're inside a tag when the stream ends, we have an unclosed tag
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# Return the partial content as regular content (including the opening tag)
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remaining_content = f"<{self.tag_name}>{self.reasoning_buffer}{self.tag_buffer}"
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self.reasoning_buffer = ""
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self.tag_buffer = ""
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self.inside_tag = False
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return remaining_content, remaining_reasoning
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def process_streaming_content_with_reasoning_tags(
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app/openai_handler.py
CHANGED
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@@ -120,8 +120,10 @@ class OpenAIDirectHandler:
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# Create processor for tag-based extraction across chunks
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reasoning_processor = StreamingReasoningProcessor(VERTEX_REASONING_TAG)
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async for chunk in stream_response:
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try:
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chunk_as_dict = chunk.model_dump(exclude_unset=True, exclude_none=True)
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@@ -134,11 +136,15 @@ class OpenAIDirectHandler:
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del delta['extra_content']
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content = delta.get('content', '')
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print(content)
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if content:
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# Use the processor to extract reasoning
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processed_content, current_reasoning = reasoning_processor.process_chunk(content)
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# Update delta with processed content
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if current_reasoning:
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delta['reasoning_content'] = current_reasoning
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@@ -159,20 +165,37 @@ class OpenAIDirectHandler:
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yield "data: [DONE]\n\n"
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return
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#
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final_chunk = {
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"id": f"chatcmpl-{int(time.time())}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": request.model,
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"choices": [{"index": 0, "delta": {"content":
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}
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yield f"data: {json.dumps(final_chunk)}\n\n"
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#
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yield "data: [DONE]\n\n"
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# Create processor for tag-based extraction across chunks
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reasoning_processor = StreamingReasoningProcessor(VERTEX_REASONING_TAG)
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chunk_count = 0
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async for chunk in stream_response:
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chunk_count += 1
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try:
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chunk_as_dict = chunk.model_dump(exclude_unset=True, exclude_none=True)
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del delta['extra_content']
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content = delta.get('content', '')
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if content:
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print(f"DEBUG: Chunk {chunk_count} - Raw content: '{content}'")
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# Use the processor to extract reasoning
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processed_content, current_reasoning = reasoning_processor.process_chunk(content)
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# Debug logging for processing results
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if processed_content or current_reasoning:
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print(f"DEBUG: Chunk {chunk_count} - Processed content: '{processed_content}', Reasoning: '{current_reasoning[:50]}...' if len(current_reasoning) > 50 else '{current_reasoning}'")
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# Update delta with processed content
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if current_reasoning:
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delta['reasoning_content'] = current_reasoning
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yield "data: [DONE]\n\n"
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return
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# Debug logging for buffer state and chunk count
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print(f"DEBUG: Stream ended after {chunk_count} chunks. Buffer state - tag_buffer: '{reasoning_processor.tag_buffer}', "
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f"inside_tag: {reasoning_processor.inside_tag}, "
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f"reasoning_buffer: '{reasoning_processor.reasoning_buffer[:50]}...' if reasoning_processor.reasoning_buffer else ''")
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# Flush any remaining buffered content
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remaining_content, remaining_reasoning = reasoning_processor.flush_remaining()
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if remaining_content:
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print(f"DEBUG: Flushing remaining content: '{remaining_content}'")
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final_chunk = {
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"id": f"chatcmpl-{int(time.time())}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": request.model,
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"choices": [{"index": 0, "delta": {"content": remaining_content}, "finish_reason": None}]
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}
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yield f"data: {json.dumps(final_chunk)}\n\n"
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# Send a proper finish reason chunk
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finish_chunk = {
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"id": f"chatcmpl-{int(time.time())}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": request.model,
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"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]
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}
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yield f"data: {json.dumps(finish_chunk)}\n\n"
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# Note: remaining_reasoning is not used here since incomplete reasoning
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# is treated as regular content when tags are unclosed
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yield "data: [DONE]\n\n"
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