5f447dfd53
Architecture: - director_v2: always-on brain, produces DirectorPlan with tool_sequence - thinker_v2: pure executor, runs tools from DirectorPlan - interpreter_v1: factual result summarizer, no hallucination - v2_director_drives graph: Input -> Director -> Thinker -> Output Infrastructure: - Split into 3 pods: cog-frontend (nginx), cog-runtime (FastAPI), cog-mcp (SSE proxy) - MCP survives runtime restarts (separate pod, proxies via HTTP) - Async send pipeline: /api/send/check -> /api/send -> /api/result with progress - Zero-downtime rolling updates (maxUnavailable: 0) - Dynamic graph visualization (fetched from API, not hardcoded) Tests: 22 new mocked unit tests (director_v2: 7, thinker_v2: 8, interpreter_v1: 7) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
"""Message types flowing between nodes."""
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from dataclasses import dataclass, field, asdict
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@dataclass
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class Envelope:
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"""What flows between nodes."""
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text: str
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user_id: str = "anon"
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session_id: str = ""
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timestamp: str = ""
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@dataclass
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class InputAnalysis:
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"""Structured classification from Input node."""
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who: str = "unknown"
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language: str = "en"
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intent: str = "request" # question | request | social | action | feedback
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topic: str = ""
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tone: str = "casual" # casual | frustrated | playful | urgent
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complexity: str = "simple" # trivial | simple | complex
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context: str = ""
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@dataclass
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class Command:
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"""Input node's structured perception of what was heard."""
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analysis: InputAnalysis
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source_text: str
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metadata: dict = field(default_factory=dict)
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@property
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def instruction(self) -> str:
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"""Backward-compatible summary string for logging/thinker."""
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a = self.analysis
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return f"{a.who} ({a.intent}, {a.tone}): {a.topic}"
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@dataclass
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class DirectorPlan:
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"""Director v2's output — tells Thinker exactly what to execute."""
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goal: str = ""
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steps: list = field(default_factory=list) # ["query_db('SHOW TABLES')", ...]
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present_as: str = "summary" # table | summary | machine
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tool_sequence: list = field(default_factory=list) # [{"tool": "query_db", "args": {...}}, ...]
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reasoning: str = "" # Director's internal reasoning (for audit)
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response_hint: str = "" # How to phrase the response if no tools needed
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@property
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def has_tools(self) -> bool:
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return bool(self.tool_sequence)
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@property
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def is_direct_response(self) -> bool:
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return not self.tool_sequence and bool(self.response_hint)
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@dataclass
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class InterpretedResult:
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"""Interpreter's factual summary of tool output."""
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summary: str # Factual text summary
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row_count: int = 0 # Number of data rows (for DB)
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key_facts: list = field(default_factory=list) # ["693 customers", "avg 5.2 devices"]
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confidence: str = "high" # high | medium | low
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@dataclass
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class ThoughtResult:
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"""Thinker node's output — either a direct answer or tool results."""
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response: str
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tool_used: str = ""
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tool_output: str = ""
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actions: list = field(default_factory=list) # [{label, action, payload?}]
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state_updates: dict = field(default_factory=dict) # {key: value} from set_state
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display_items: list = field(default_factory=list) # [{type, label, value?, style?}] from emit_display
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machine_ops: list = field(default_factory=list) # [{op, id, ...}] from machine tools
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