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feat(performance): Optimize system resources and token estimation

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- Improve JSON history file processing
- Add memory and disk space validation
- Enhance parallel request handling
- Refine token counting heuristics
This commit is contained in:
SMKRV
2024-12-06 02:51:06 +03:00
parent 0fb9acfa8f
commit c13ef1921d
6 changed files with 674 additions and 332 deletions
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custom_components/ha_text_ai/sensor.py
+68 -47
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@@ -67,6 +67,7 @@ from .const import (
ENTITY_ICON_PROCESSING,
DEFAULT_NAME_PREFIX,
CONF_MAX_HISTORY_SIZE,
MAX_ATTRIBUTE_SIZE,
)
from .coordinator import HATextAICoordinator
@@ -178,12 +179,29 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
def _sanitize_attributes(self, attributes: Dict[str, Any]) -> Dict[str, Any]:
"""Sanitize all attributes for JSON serialization."""
return {
sanitized = {
key: self._sanitize_value(value)
for key, value in attributes.items()
if value is not None
}
# Log metrics for debugging
metrics_keys = [
METRIC_TOTAL_TOKENS,
METRIC_PROMPT_TOKENS,
METRIC_COMPLETION_TOKENS,
METRIC_SUCCESSFUL_REQUESTS,
METRIC_FAILED_REQUESTS,
METRIC_AVERAGE_LATENCY,
METRIC_MAX_LATENCY,
METRIC_MIN_LATENCY,
]
metrics_values = {k: sanitized.get(k) for k in metrics_keys if k in sanitized}
_LOGGER.debug(f"Metrics for {self.entity_id}: {metrics_values}")
return sanitized
@property
def native_value(self) -> StateType:
"""Return the native value of the sensor."""
@@ -212,68 +230,65 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
try:
data = self.coordinator.data
metrics = data.get("metrics", {})
# Base attributes
attributes = {
ATTR_MODEL: self._config_entry.data.get(CONF_MODEL, "Unknown"),
ATTR_API_PROVIDER: self._config_entry.data.get(
CONF_API_PROVIDER, "Unknown"
),
ATTR_API_PROVIDER: self._config_entry.data.get(CONF_API_PROVIDER, "Unknown"),
ATTR_API_STATUS: self._current_state,
ATTR_TOTAL_ERRORS: self._error_count,
ATTR_LAST_ERROR: self._last_error,
ATTR_TOTAL_ERRORS: metrics.get("total_errors", 0),
"instance_name": self._instance_name,
"normalized_name": self._normalized_name,
ATTR_SYSTEM_PROMPT: data.get("system_prompt"),
ATTR_SYSTEM_PROMPT: (data.get("system_prompt", "")[:MAX_ATTRIBUTE_SIZE]
if data.get("system_prompt") else None),
ATTR_IS_PROCESSING: data.get("is_processing", False),
ATTR_IS_RATE_LIMITED: data.get("is_rate_limited", False),
ATTR_IS_MAINTENANCE: data.get("is_maintenance", False),
ATTR_ENDPOINT_STATUS: data.get("endpoint_status", "unknown"),
ATTR_UPTIME: data.get("uptime", 0),
ATTR_UPTIME: round(data.get("uptime", 0), 2),
ATTR_HISTORY_SIZE: data.get("history_size", 0),
ATTR_CONVERSATION_HISTORY: data.get("conversation_history", []),
}
# Add metrics
metrics = data.get("metrics", {})
if isinstance(metrics, dict):
self._metrics = metrics
attributes.update(
{
METRIC_TOTAL_TOKENS: metrics.get("total_tokens", 0),
METRIC_PROMPT_TOKENS: metrics.get("prompt_tokens", 0),
METRIC_COMPLETION_TOKENS: metrics.get("completion_tokens", 0),
METRIC_SUCCESSFUL_REQUESTS: metrics.get(
"successful_requests", 0
),
METRIC_FAILED_REQUESTS: metrics.get("failed_requests", 0),
METRIC_AVERAGE_LATENCY: metrics.get("average_latency", 0),
METRIC_MAX_LATENCY: metrics.get("max_latency", 0),
METRIC_MIN_LATENCY: metrics.get("min_latency", float("inf")),
# History limit
conversation_history = data.get("conversation_history", [])
if conversation_history:
limited_history = []
for entry in conversation_history:
limited_entry = {
"timestamp": entry["timestamp"],
"question": entry["question"][:MAX_ATTRIBUTE_SIZE],
"response": entry["response"][:MAX_ATTRIBUTE_SIZE]
}
)
limited_history.append(limited_entry)
attributes[ATTR_CONVERSATION_HISTORY] = limited_history
# Add last response
# Metrics
if isinstance(metrics, dict):
attributes.update({
METRIC_TOTAL_TOKENS: metrics.get("total_tokens", 0),
METRIC_PROMPT_TOKENS: metrics.get("prompt_tokens", 0),
METRIC_COMPLETION_TOKENS: metrics.get("completion_tokens", 0),
METRIC_SUCCESSFUL_REQUESTS: metrics.get("successful_requests", 0),
METRIC_FAILED_REQUESTS: metrics.get("failed_requests", 0),
METRIC_AVERAGE_LATENCY: round(metrics.get("average_latency", 0), 2),
METRIC_MAX_LATENCY: round(metrics.get("max_latency", 0), 2),
METRIC_MIN_LATENCY: (metrics.get("min_latency")
if metrics.get("min_latency") != float("inf")
else None),
})
# Last response handling
last_response = data.get("last_response", {})
if isinstance(last_response, dict):
self._last_response = last_response
attributes.update(
{
ATTR_RESPONSE: last_response.get("response", ""),
ATTR_QUESTION: last_response.get("question", ""),
"last_model": last_response.get("model", ""),
"last_timestamp": last_response.get("timestamp", ""),
"last_error": last_response.get("error"),
}
)
# Add performance metrics if available
if ATTR_PERFORMANCE_METRICS in data:
attributes[ATTR_PERFORMANCE_METRICS] = data[
ATTR_PERFORMANCE_METRICS
]
# Add API version if available
if ATTR_API_VERSION in data:
attributes[ATTR_API_VERSION] = data[ATTR_API_VERSION]
attributes.update({
ATTR_RESPONSE: last_response.get("response", "")[:MAX_ATTRIBUTE_SIZE],
ATTR_QUESTION: last_response.get("question", "")[:MAX_ATTRIBUTE_SIZE],
"last_model": last_response.get("model", ""),
"last_timestamp": last_response.get("timestamp", ""),
"last_error": (last_response.get("error", "")[:MAX_ATTRIBUTE_SIZE]
if last_response.get("error") else None),
})
return self._sanitize_attributes(attributes)
@@ -299,6 +314,12 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
self._is_processing = data.get("is_processing", False)
# Update metrics
metrics = data.get("metrics", {})
if isinstance(metrics, dict):
self._metrics.update(metrics)
_LOGGER.debug(f"Updated metrics for {self.entity_id}: {self._metrics}")
# Update conversation history and system prompt
self._conversation_history = data.get("conversation_history", [])
self._system_prompt = data.get("system_prompt")