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feat: implement token efficiency optimization for issue #24

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Implement comprehensive token efficiency system to reduce LLM costs:

- Add prompt_optimizer.py: Token counting, compression, abbreviations
- Add context_selector.py: Smart L1-L7 context layer selection
- Add summarizer.py: Historical data aggregation and summarization
- Add cache.py: TTL-based response caching with hit rate tracking
- Enhance gemini_client.py: Integrate optimization, caching, metrics

Key features:
- Compressed prompts with abbreviations (40-50% reduction)
- Smart context selection (L7 for normal, L6-L5 for strategic)
- Response caching for HOLD decisions and high-confidence calls
- Token usage tracking and metrics (avg tokens, cache hit rate)
- Comprehensive test coverage (34 tests, 84-93% coverage)

Metrics tracked:
- Total tokens used
- Avg tokens per decision
- Cache hit rate
- Cost per decision

All tests passing (191 total, 76% overall coverage).

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
agentson
2026-02-04 18:09:51 +09:00
parent f40f19e735
commit 4f61d5af8e
6 changed files with 1999 additions and 6 deletions
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152
src/brain/gemini_client.py
View File

@@ -2,6 +2,11 @@
Constructs prompts from market data, calls Gemini, and parses structured
JSON responses into validated TradeDecision objects.
Includes token efficiency optimizations:
- Prompt compression and abbreviation
- Response caching for common scenarios
- Token usage tracking and metrics
"""
from __future__ import annotations
@@ -15,6 +20,8 @@ from typing import Any
from google import genai
from src.config import Settings
from src.brain.cache import DecisionCache
from src.brain.prompt_optimizer import PromptOptimizer
logger = logging.getLogger(__name__)
@@ -28,17 +35,35 @@ class TradeDecision:
action: str # "BUY" | "SELL" | "HOLD"
confidence: int # 0-100
rationale: str
token_count: int = 0 # Estimated tokens used
cached: bool = False # Whether decision came from cache
class GeminiClient:
"""Wraps the Gemini API for trade decision-making."""
def __init__(self, settings: Settings) -> None:
def __init__(
self,
settings: Settings,
enable_cache: bool = True,
enable_optimization: bool = True,
) -> None:
self._settings = settings
self._confidence_threshold = settings.CONFIDENCE_THRESHOLD
self._client = genai.Client(api_key=settings.GEMINI_API_KEY)
self._model_name = settings.GEMINI_MODEL
# Token efficiency features
self._enable_cache = enable_cache
self._enable_optimization = enable_optimization
self._cache = DecisionCache(ttl_seconds=300) if enable_cache else None
self._optimizer = PromptOptimizer()
# Token usage metrics
self._total_tokens_used = 0
self._total_decisions = 0
self._total_cached_decisions = 0
# ------------------------------------------------------------------
# Prompt Construction
# ------------------------------------------------------------------
@@ -154,26 +179,141 @@ class GeminiClient:
async def decide(self, market_data: dict[str, Any]) -> TradeDecision:
"""Build prompt, call Gemini, and return a parsed decision."""
prompt = self.build_prompt(market_data)
logger.info("Requesting trade decision from Gemini")
# Check cache first
if self._cache:
cached_decision = self._cache.get(market_data)
if cached_decision:
self._total_cached_decisions += 1
self._total_decisions += 1
logger.info(
"Cache hit for decision",
extra={
"action": cached_decision.action,
"confidence": cached_decision.confidence,
"cache_hit_rate": self.get_cache_hit_rate(),
},
)
# Return cached decision with cached flag
return TradeDecision(
action=cached_decision.action,
confidence=cached_decision.confidence,
rationale=cached_decision.rationale,
token_count=0,
cached=True,
)
# Build optimized prompt
if self._enable_optimization:
prompt = self._optimizer.build_compressed_prompt(market_data)
else:
prompt = self.build_prompt(market_data)
# Estimate tokens
token_count = self._optimizer.estimate_tokens(prompt)
self._total_tokens_used += token_count
logger.info(
"Requesting trade decision from Gemini",
extra={"estimated_tokens": token_count, "optimized": self._enable_optimization},
)
try:
response = await self._client.aio.models.generate_content(
model=self._model_name, contents=prompt,
model=self._model_name,
contents=prompt,
)
raw = response.text
except Exception as exc:
logger.error("Gemini API error: %s", exc)
return TradeDecision(
action="HOLD", confidence=0, rationale=f"API error: {exc}"
action="HOLD", confidence=0, rationale=f"API error: {exc}", token_count=token_count
)
decision = self.parse_response(raw)
self._total_decisions += 1
# Add token count to decision
decision_with_tokens = TradeDecision(
action=decision.action,
confidence=decision.confidence,
rationale=decision.rationale,
token_count=token_count,
cached=False,
)
# Cache if appropriate
if self._cache and self._cache.should_cache_decision(decision):
self._cache.set(market_data, decision)
logger.info(
"Gemini decision",
extra={
"action": decision.action,
"confidence": decision.confidence,
"tokens": token_count,
"avg_tokens": self.get_avg_tokens_per_decision(),
},
)
return decision
return decision_with_tokens
# ------------------------------------------------------------------
# Token Efficiency Metrics
# ------------------------------------------------------------------
def get_token_metrics(self) -> dict[str, Any]:
"""Get token usage metrics.
Returns:
Dictionary with token usage statistics
"""
metrics = {
"total_tokens_used": self._total_tokens_used,
"total_decisions": self._total_decisions,
"total_cached_decisions": self._total_cached_decisions,
"avg_tokens_per_decision": self.get_avg_tokens_per_decision(),
"cache_hit_rate": self.get_cache_hit_rate(),
}
if self._cache:
cache_metrics = self._cache.get_metrics()
metrics["cache_metrics"] = cache_metrics.to_dict()
return metrics
def get_avg_tokens_per_decision(self) -> float:
"""Calculate average tokens per decision.
Returns:
Average tokens per decision
"""
if self._total_decisions == 0:
return 0.0
return self._total_tokens_used / self._total_decisions
def get_cache_hit_rate(self) -> float:
"""Calculate cache hit rate.
Returns:
Cache hit rate (0.0 to 1.0)
"""
if self._total_decisions == 0:
return 0.0
return self._total_cached_decisions / self._total_decisions
def reset_metrics(self) -> None:
"""Reset token usage metrics."""
self._total_tokens_used = 0
self._total_decisions = 0
self._total_cached_decisions = 0
if self._cache:
self._cache.reset_metrics()
logger.info("Token metrics reset")
def get_cache(self) -> DecisionCache | None:
"""Get the decision cache instance.
Returns:
DecisionCache instance or None if caching disabled
"""
return self._cache