feat: Intelligence Pipeline v3 — full implementation
Multi-stage evidence-grounded inference architecture replacing the monolithic 9B model extraction pipeline. CPU-first specialist services handle routine extraction while the 9B vLLM model is preserved for semantic adjudication of ambiguous cases. Key components: - Capability-aware inference gateway (OpenAI-compatible + Ollama) - Endpoint registry with DB migrations and REST API - Sentence-aware document segmenter (property tests) - Deterministic financial parsing with offset integrity - Symbol resolution with ambiguity detection - Specialist service (GLiNER2, dynamic batching, K8s deployment) - Company-specific sentiment (FinBERT, calibration) - Retrieval-based novelty and duplicate detection - Confidence calibration pipeline - Deterministic routing engine (property tests) - 9B adjudication layer with VRAM gating - Stock-specific impact model (features, labels, baseline, trained) - Pipeline orchestrator (state machine, queues, leases, feature flags) - Bounded parallelism (async workers, semaphore, load shedding) - Observability (tracing, metrics, alerts) - Compatibility adapter (v3→v2 golden mapping tests) - Shadow/canary promotion framework - Active learning and fine-tuning pipeline Test results: 1,161 tests pass, ruff lint clean. All 282 spec tasks completed.
This commit is contained in:
Celes Renata
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"""Pydantic request/response models for the inference registry API.
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All response models EXCLUDE actual auth_secret_ref values.
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Instead they show a status string: "configured" or "not_configured".
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Requirements: 3.6, 3.7
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"""
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from __future__ import annotations
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from datetime import datetime
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from typing import Any, Literal
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from uuid import UUID
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from pydantic import BaseModel, Field, field_validator
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VALID_PROTOCOLS = ("ollama_native", "openai_chat", "specialist_http")
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# ---------------------------------------------------------------------------
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# Endpoint schemas
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# ---------------------------------------------------------------------------
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class EndpointCreate(BaseModel):
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"""Request body for creating an inference endpoint."""
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name: str = Field(..., min_length=1, max_length=255)
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protocol: Literal["ollama_native", "openai_chat", "specialist_http"]
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base_url: str = Field(..., min_length=1)
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auth_secret_ref: str | None = None
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auth_scheme: str = "bearer"
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default_headers: dict[str, str] = Field(default_factory=dict)
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health_path: str | None = None
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enabled: bool = True
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@field_validator("base_url")
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@classmethod
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def validate_url(cls, v: str) -> str:
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"""Validate that base_url looks like a valid URL."""
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if not v.startswith(("http://", "https://")):
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raise ValueError("base_url must start with http:// or https://")
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return v.rstrip("/")
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@field_validator("protocol")
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@classmethod
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def validate_protocol(cls, v: str) -> str:
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if v not in VALID_PROTOCOLS:
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raise ValueError(f"protocol must be one of {VALID_PROTOCOLS}")
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return v
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class EndpointUpdate(BaseModel):
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"""Request body for updating an inference endpoint."""
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name: str | None = None
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protocol: Literal["ollama_native", "openai_chat", "specialist_http"] | None = None
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base_url: str | None = None
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auth_secret_ref: str | None = Field(default=None)
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auth_scheme: str | None = None
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default_headers: dict[str, str] | None = None
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health_path: str | None = None
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enabled: bool | None = None
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@field_validator("base_url")
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@classmethod
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def validate_url(cls, v: str | None) -> str | None:
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if v is not None:
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if not v.startswith(("http://", "https://")):
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raise ValueError("base_url must start with http:// or https://")
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return v.rstrip("/")
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return v
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@field_validator("protocol")
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@classmethod
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def validate_protocol(cls, v: str | None) -> str | None:
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if v is not None and v not in VALID_PROTOCOLS:
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raise ValueError(f"protocol must be one of {VALID_PROTOCOLS}")
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return v
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class EndpointResponse(BaseModel):
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"""Response model for an inference endpoint. NEVER includes auth_secret_ref value."""
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id: UUID
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name: str
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protocol: str
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base_url: str
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auth_secret_status: str = "not_configured" # "configured" or "not_configured"
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auth_scheme: str = "bearer"
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default_headers: dict[str, str] = Field(default_factory=dict)
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health_path: str | None = None
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enabled: bool = True
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revision: int = 1
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created_at: datetime | None = None
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updated_at: datetime | None = None
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last_probe: ProbeResponse | None = None
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capabilities: dict[str, Any] | None = None
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active_bindings: list[BindingResponse] | None = None
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class EndpointListResponse(BaseModel):
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"""Response model for listing endpoints."""
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id: UUID
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name: str
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protocol: str
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base_url: str
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auth_secret_status: str = "not_configured"
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enabled: bool = True
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revision: int = 1
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created_at: datetime | None = None
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updated_at: datetime | None = None
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# ---------------------------------------------------------------------------
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# Deployment schemas
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# ---------------------------------------------------------------------------
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class DeploymentCreate(BaseModel):
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"""Request body for creating a model deployment."""
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endpoint_id: UUID
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served_model_name: str = Field(..., min_length=1)
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display_name: str = Field(..., min_length=1)
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capabilities: dict[str, Any] = Field(default_factory=dict)
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context_window: int | None = None
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max_output_tokens: int | None = None
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quantization: str | None = None
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runtime_metadata: dict[str, Any] = Field(default_factory=dict)
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enabled: bool = True
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class DeploymentResponse(BaseModel):
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"""Response model for a model deployment."""
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id: UUID
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endpoint_id: UUID
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served_model_name: str
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display_name: str
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capabilities: dict[str, Any] = Field(default_factory=dict)
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context_window: int | None = None
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max_output_tokens: int | None = None
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quantization: str | None = None
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runtime_metadata: dict[str, Any] = Field(default_factory=dict)
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enabled: bool = True
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revision: int = 1
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# ---------------------------------------------------------------------------
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# Binding schemas
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# ---------------------------------------------------------------------------
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class BindingCreate(BaseModel):
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"""Request body for creating an agent stage binding."""
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agent_id: UUID
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stage: str = Field(..., min_length=1)
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model_deployment_id: UUID | None = None
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route_order: int = 0
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routing_config: dict[str, Any] = Field(default_factory=dict)
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is_active: bool = True
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class BindingResponse(BaseModel):
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"""Response model for an agent stage binding."""
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id: UUID
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agent_id: UUID
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stage: str
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model_deployment_id: UUID | None = None
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route_order: int = 0
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routing_config: dict[str, Any] = Field(default_factory=dict)
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is_active: bool = True
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revision: int = 1
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# ---------------------------------------------------------------------------
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# Probe schemas
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# ---------------------------------------------------------------------------
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class ProbeResponse(BaseModel):
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"""Response model for probe results."""
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endpoint_id: UUID
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timestamp: datetime | None = None
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software_version: str | None = None
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probe_duration_ms: int = 0
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health_success: bool = False
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health_detail: str = ""
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model_listing_success: bool | None = None
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json_schema_success: bool | None = None
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usage_success: bool | None = None
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seed_success: bool | None = None
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output_token_field_success: bool | None = None
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# ---------------------------------------------------------------------------
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# Egress confirmation schema
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# ---------------------------------------------------------------------------
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class EgressConfirmation(BaseModel):
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"""Request body for confirming external endpoint egress enablement.
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Requires explicit confirmed=true flag.
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"""
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confirmed: bool = Field(
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...,
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description="Must be explicitly set to true to confirm external egress enablement",
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)
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@field_validator("confirmed")
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@classmethod
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def must_be_true(cls, v: bool) -> bool:
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if not v:
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raise ValueError("confirmed must be true to enable external egress")
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return v
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# ---------------------------------------------------------------------------
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# Structured output test schemas
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# ---------------------------------------------------------------------------
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class StructuredOutputTestRequest(BaseModel):
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"""Request body for testing structured output on an endpoint."""
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json_schema: dict[str, Any] = Field(
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default_factory=lambda: {
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"type": "object",
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"properties": {"status": {"type": "string"}},
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"required": ["status"],
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}
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)
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prompt: str = 'Respond with JSON: {"status": "ok"}'
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class StructuredOutputTestResponse(BaseModel):
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"""Response for structured output test."""
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success: bool
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structured_mode: str = ""
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content: str = ""
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parsed: dict[str, Any] | None = None
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schema_valid: bool = False
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latency_ms: int = 0
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error: str | None = None
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