headroom/proxy/models.py

"""Data models for the Headroom proxy.

Contains configuration and data classes used across the proxy modules.
Extracted from server.py to keep the codebase maintainable.
"""

from __future__ import annotations

from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Literal

# =============================================================================
# Data Models
# =============================================================================


@dataclass
class RequestLog:
    """Complete log of a single request."""

    request_id: str
    timestamp: str
    provider: str
    model: str

    # Tokens
    input_tokens_original: int
    input_tokens_optimized: int
    output_tokens: int | None
    tokens_saved: int
    savings_percent: float

    # Performance
    optimization_latency_ms: float
    total_latency_ms: float | None

    # Metadata
    tags: dict[str, str]
    cache_hit: bool
    transforms_applied: list[str]

    # Waste signals detected in original messages
    waste_signals: dict[str, int] | None = None

    # Request/Response (optional, for debugging)
    request_messages: list[dict] | None = None
    response_content: str | None = None
    error: str | None = None


@dataclass
class CacheEntry:
    """Cached response entry."""

    response_body: bytes
    response_headers: dict[str, str]
    created_at: datetime
    ttl_seconds: int
    hit_count: int = 0
    tokens_saved_per_hit: int = 0


@dataclass
class RateLimitState:
    """Token bucket rate limiter state."""

    tokens: float
    last_update: float


@dataclass
class ProxyConfig:
    """Proxy configuration."""

    # Server
    host: str = "127.0.0.1"
    port: int = 8787
    anthropic_api_url: str | None = None  # Custom Anthropic API URL override
    openai_api_url: str | None = None  # Custom OpenAI API URL override
    gemini_api_url: str | None = None  # Custom Gemini API URL override

    # Backend: "anthropic" (direct API), "litellm-*" (via LiteLLM), or "anyllm" (via any-llm)
    backend: str = "anthropic"
    bedrock_region: str = "us-west-2"
    bedrock_profile: str | None = None
    anyllm_provider: str = "openai"

    # Optimization mode
    mode: str = "token_headroom"

    # Optimization
    optimize: bool = True
    image_optimize: bool = True
    min_tokens_to_crush: int = 500
    max_items_after_crush: int = 50
    keep_last_turns: int = 4

    # CCR Tool Injection
    ccr_inject_tool: bool = True
    ccr_inject_system_instructions: bool = False

    # CCR Response Handling
    ccr_handle_responses: bool = True
    ccr_max_retrieval_rounds: int = 3

    # CCR Context Tracking
    ccr_context_tracking: bool = True
    ccr_proactive_expansion: bool = True
    ccr_max_proactive_expansions: int = 2

    # Code-aware compression
    code_aware_enabled: bool = True

    # Per-tool compression profiles
    tool_profiles: dict[str, Any] | None = None

    # Read lifecycle management
    read_lifecycle: bool = True

    # Smart content routing
    smart_routing: bool = True

    # Intelligent context management
    intelligent_context: bool = True
    intelligent_context_scoring: bool = True
    intelligent_context_compress_first: bool = True

    # Caching
    cache_enabled: bool = True
    cache_ttl_seconds: int = 3600
    cache_max_entries: int = 1000

    # Rate limiting
    rate_limit_enabled: bool = True
    rate_limit_requests_per_minute: int = 60
    rate_limit_tokens_per_minute: int = 100000

    # Retry
    retry_enabled: bool = True
    retry_max_attempts: int = 3
    retry_base_delay_ms: int = 1000
    retry_max_delay_ms: int = 30000

    # Prefix freeze
    prefix_freeze_enabled: bool = True
    prefix_freeze_session_ttl: int = 600

    # Cost tracking
    cost_tracking_enabled: bool = True
    budget_limit_usd: float | None = None
    budget_period: Literal["hourly", "daily", "monthly"] = "daily"

    # Logging
    log_requests: bool = True
    log_file: str | None = None
    log_full_messages: bool = False

    # Fallback
    fallback_enabled: bool = False
    fallback_provider: str | None = None

    # Timeouts
    request_timeout_seconds: int = 300
    connect_timeout_seconds: int = 10

    # Connection pool
    max_connections: int = 500
    max_keepalive_connections: int = 100
    http2: bool = True

    # Memory System
    memory_enabled: bool = False
    memory_backend: Literal["local", "qdrant-neo4j"] = "local"
    memory_db_path: str = "headroom_memory.db"
    memory_inject_tools: bool = True
    traffic_learning_enabled: bool = False
    memory_use_native_tool: bool = False
    memory_inject_context: bool = True
    memory_top_k: int = 10
    memory_min_similarity: float = 0.3
    memory_qdrant_host: str = "localhost"
    memory_qdrant_port: int = 6333
    memory_neo4j_uri: str = "neo4j://localhost:7687"
    memory_neo4j_user: str = "neo4j"
    memory_neo4j_password: str = "password"
    memory_bridge_enabled: bool = False
    memory_bridge_md_paths: list[str] = field(default_factory=list)
    memory_bridge_md_format: str = "auto"
    memory_bridge_auto_import: bool = False
    memory_bridge_export_path: str = ""

    # License / Usage Reporting
    license_key: str | None = None
    license_cloud_url: str = "https://app.headroomlabs.ai"
    license_report_interval: int = 300

    # Compression Hooks
    hooks: Any = None