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Adjustments to context sizing

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This commit is contained in:
Nicolas Mowen 2025-10-30 07:09:03 -06:00
parent 31aed17ca2
commit 09bf71b4f9
2 changed files with 22 additions and 16 deletions
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36
frigate/data_processing/post/review_descriptions.py
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@ -53,32 +53,38 @@ class ReviewDescriptionProcessor(PostProcessorApi):
) -> int: ) -> int:
"""Calculate optimal number of frames based on context size and image source. """Calculate optimal number of frames based on context size and image source.
Recordings (480p): ~250 tokens/image, capped at 20 frames Recordings (480p): ~500 tokens/image, capped at 20 frames
Previews (180p): ~100 tokens/image, capped at 20 frames Previews (180p): ~170 tokens/image, capped at 20 frames
Targets 70-80% context utilization while keeping inference time reasonable. Targets 75% context utilization while keeping inference time reasonable.
""" """
context_size = self.genai_client.get_context_size() context_size = self.genai_client.get_context_size()
if image_source == ImageSourceEnum.recordings: if image_source == ImageSourceEnum.recordings:
if context_size > 16000:
return 20
elif context_size > 14000:
return 18
elif context_size > 12000:
return 14
elif context_size > 10000:
return 10
elif context_size > 8000:
return 8
elif context_size > 6000:
return 6
else:
return 4
else:
if context_size > 12000: if context_size > 12000:
return 20 return 20
elif context_size > 10000: elif context_size > 8000:
return 18 return 16
elif context_size > 6000: elif context_size > 6000:
return 14 return 12
elif context_size > 4000: elif context_size > 4000:
return 10 return 10
else: else:
return 6 return 6
else:
if context_size > 10000:
return 20
elif context_size > 6000:
return 16
elif context_size > 4000:
return 12
else:
return 8
def process_data(self, data, data_type): def process_data(self, data, data_type):
self.metrics.review_desc_dps.value = self.review_descs_dps.eps() self.metrics.review_desc_dps.value = self.review_descs_dps.eps()

2
frigate/genai/ollama.py
View File

@ -68,7 +68,7 @@ class OllamaClient(GenAIClient):
f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}" f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"
) )
return result["response"].strip() return result["response"].strip()
except (TimeoutException, ResponseError) as e: except (TimeoutException, ResponseError, ConnectionError) as e:
logger.warning("Ollama returned an error: %s", str(e)) logger.warning("Ollama returned an error: %s", str(e))
return None return None