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.
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Celes Renata
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"""Replaceable compact embedding backend for novelty retrieval.
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Provides:
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- EmbeddingBackend protocol for pluggable embedding models
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- MockEmbeddingBackend for deterministic testing
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- SentenceTransformerBackend stub for production (all-MiniLM-L6-v2)
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- cosine_similarity utility function
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"""
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from __future__ import annotations
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import hashlib
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import math
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import struct
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from typing import Protocol, runtime_checkable
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@runtime_checkable
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class EmbeddingBackend(Protocol):
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"""Protocol for embedding backends.
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Implementations must produce fixed-dimension vectors for a batch of texts.
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The backend is designed to be replaceable: swap between mock, local model,
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and remote API backends without changing scoring logic.
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"""
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@property
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def dimension(self) -> int:
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"""Return the embedding dimension produced by this backend."""
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...
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def embed(self, texts: list[str]) -> list[list[float]]:
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"""Embed a batch of texts into dense vectors.
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Args:
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texts: List of text strings to embed.
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Returns:
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List of embedding vectors, one per input text.
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Each vector has length == self.dimension.
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"""
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...
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class MockEmbeddingBackend:
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"""Deterministic hash-based embedding backend for testing.
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Produces consistent embeddings using text hashing. Useful for
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unit tests and integration tests that need repeatable results
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without loading a real model.
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"""
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def __init__(self, dimension: int = 384) -> None:
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self._dimension = dimension
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@property
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def dimension(self) -> int:
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return self._dimension
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def embed(self, texts: list[str]) -> list[list[float]]:
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"""Generate deterministic embeddings from text hashes.
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Uses SHA-256 expanded to fill the dimension. Normalizes to unit length
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for compatibility with cosine similarity.
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"""
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results = []
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for text in texts:
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raw = self._hash_to_vector(text)
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norm = math.sqrt(sum(x * x for x in raw))
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if norm > 0:
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normalized = [x / norm for x in raw]
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else:
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normalized = raw
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results.append(normalized)
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return results
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def _hash_to_vector(self, text: str) -> list[float]:
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"""Expand text hash into a vector of the target dimension."""
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vector = []
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# Generate enough hash bytes to fill dimension
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chunk_idx = 0
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while len(vector) < self._dimension:
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data = f"{text}:{chunk_idx}".encode("utf-8")
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digest = hashlib.sha256(data).digest()
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# Convert 32 bytes to 8 floats (4 bytes each)
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for i in range(0, 32, 4):
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if len(vector) >= self._dimension:
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break
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# Unpack as float in [-1, 1] range
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raw_int = struct.unpack("<I", digest[i : i + 4])[0]
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value = (raw_int / (2**32 - 1)) * 2.0 - 1.0
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vector.append(value)
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chunk_idx += 1
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return vector[: self._dimension]
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class SentenceTransformerBackend:
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"""Production embedding backend using sentence-transformers.
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Wraps all-MiniLM-L6-v2 (384-dimensional) for compact, fast embeddings.
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The model loads lazily on first call to avoid startup cost when not needed.
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Note: Requires `sentence-transformers` package to be installed.
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This is a stub—actual model loading is deferred to production deployment.
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"""
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MODEL_NAME = "all-MiniLM-L6-v2"
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def __init__(self) -> None:
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self._model = None
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self._dimension = 384
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@property
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def dimension(self) -> int:
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return self._dimension
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def embed(self, texts: list[str]) -> list[list[float]]:
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"""Embed texts using the sentence-transformers model.
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Lazily loads the model on first invocation.
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Raises:
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ImportError: If sentence-transformers is not installed.
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"""
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if self._model is None:
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self._load_model()
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embeddings = self._model.encode(texts, normalize_embeddings=True)
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return [emb.tolist() for emb in embeddings]
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def _load_model(self) -> None:
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"""Load the sentence-transformers model."""
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try:
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from sentence_transformers import SentenceTransformer
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except ImportError as e:
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raise ImportError(
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"sentence-transformers package is required for SentenceTransformerBackend. "
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"Install with: pip install sentence-transformers"
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) from e
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self._model = SentenceTransformer(self.MODEL_NAME)
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def cosine_similarity(a: list[float], b: list[float]) -> float:
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"""Compute cosine similarity between two embedding vectors.
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Args:
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a: First embedding vector.
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b: Second embedding vector (must be same dimension as a).
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Returns:
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Cosine similarity in range [-1, 1]. Returns 0.0 for zero vectors.
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Raises:
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ValueError: If vectors have different dimensions.
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"""
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if len(a) != len(b):
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raise ValueError(f"Vectors must have same dimension: {len(a)} != {len(b)}")
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dot = sum(x * y for x, y in zip(a, b))
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norm_a = math.sqrt(sum(x * x for x in a))
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norm_b = math.sqrt(sum(x * x for x in b))
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if norm_a == 0.0 or norm_b == 0.0:
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return 0.0
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return dot / (norm_a * norm_b)
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