feat(llm): add embedding adapter with cache and similarity utils (S1.3)

Add OpenAI-compatible embedding support (works with both OpenAI and
OpenRouter), file-based embedding cache with content-digest invalidation,
and pure-Python cosine similarity utilities for downstream redundancy
detection.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

markitect/llm/__init__.py

@@ -26,6 +26,15 @@ from markitect.llm.exceptions import (
     LLMTimeoutError,
     LLMSubprocessError,
 )
+from markitect.llm.embedding_adapter import EmbeddingAdapter
+from markitect.llm.embedding_openai import OpenAICompatibleEmbeddingAdapter
+from markitect.llm.embedding_cache import EmbeddingCache
+from markitect.llm.embedding_factory import create_embedding_adapter
+from markitect.llm.similarity import (
+    cosine_similarity,
+    similarity_matrix,
+    find_similar_pairs,
+)
 
 __all__ = [
     "create_adapter",
@@ -41,4 +50,11 @@ __all__ = [
     "LLMRateLimitError",
     "LLMTimeoutError",
     "LLMSubprocessError",
+    "EmbeddingAdapter",
+    "OpenAICompatibleEmbeddingAdapter",
+    "EmbeddingCache",
+    "create_embedding_adapter",
+    "cosine_similarity",
+    "similarity_matrix",
+    "find_similar_pairs",
 ]

markitect/llm/embedding_adapter.py

@@ -0,0 +1,34 @@
+"""
+Abstract base class for embedding adapters.
+
+Embedding adapters convert text into float vectors. This is a separate
+hierarchy from :class:`LLMAdapter` (text generation) because the API
+contract is fundamentally different: text in, float vectors out.
+"""
+
+from abc import ABC, abstractmethod
+
+
+class EmbeddingAdapter(ABC):
+    """Base class for all embedding adapters."""
+
+    @abstractmethod
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        """Embed a batch of texts into vectors.
+
+        Args:
+            texts: One or more strings to embed.
+
+        Returns:
+            A list of embedding vectors, one per input text,
+            in the same order as *texts*.
+        """
+
+    @abstractmethod
+    def validate(self) -> bool:
+        """Check that the adapter is configured correctly.
+
+        Returns:
+            ``True`` if the adapter has a valid configuration
+            (e.g. API key present), ``False`` otherwise.
+        """

markitect/llm/embedding_cache.py

@@ -0,0 +1,64 @@
+"""
+File-based embedding cache.
+
+Stores embedding vectors in a single JSON file keyed by entity slug.
+Each entry includes a content digest so stale embeddings are
+automatically invalidated when entity content changes.
+"""
+
+import json
+from pathlib import Path
+from typing import Optional
+
+
+class EmbeddingCache:
+    """Persistent cache for embedding vectors.
+
+    Structure on disk (``embeddings.json``)::
+
+        {
+            "division-of-labour": {"digest": "abc123", "vector": [0.1, ...]},
+            ...
+        }
+    """
+
+    def __init__(self, cache_dir: Path):
+        self._path = cache_dir / "embeddings.json"
+        self._data: dict[str, dict] = {}
+        self._hits = 0
+        self._misses = 0
+        self._load()
+
+    def get(self, slug: str, content_digest: str) -> Optional[list[float]]:
+        """Return the cached vector if *content_digest* matches, else ``None``."""
+        entry = self._data.get(slug)
+        if entry is not None and entry.get("digest") == content_digest:
+            self._hits += 1
+            return entry["vector"]
+        self._misses += 1
+        return None
+
+    def put(self, slug: str, content_digest: str, vector: list[float]) -> None:
+        """Store or overwrite the embedding for *slug*."""
+        self._data[slug] = {"digest": content_digest, "vector": vector}
+
+    def save(self) -> None:
+        """Write cache to disk."""
+        self._path.parent.mkdir(parents=True, exist_ok=True)
+        self._path.write_text(json.dumps(self._data, separators=(",", ":")))
+
+    def stats(self) -> dict:
+        """Return cache statistics."""
+        return {
+            "entries": len(self._data),
+            "hits": self._hits,
+            "misses": self._misses,
+        }
+
+    def _load(self) -> None:
+        """Read cache from disk if it exists."""
+        if self._path.is_file():
+            try:
+                self._data = json.loads(self._path.read_text())
+            except (json.JSONDecodeError, OSError):
+                self._data = {}

markitect/llm/embedding_factory.py

@@ -0,0 +1,50 @@
+"""
+Factory for creating embedding adapters by provider name.
+"""
+
+from typing import Optional, Any
+
+from markitect.llm.embedding_adapter import EmbeddingAdapter
+from markitect.llm.exceptions import LLMConfigurationError
+
+_EMBEDDING_PROVIDERS = {
+    "openai": "markitect.llm.embedding_openai.OpenAICompatibleEmbeddingAdapter",
+    "openrouter": "markitect.llm.embedding_openai.OpenAICompatibleEmbeddingAdapter",
+}
+
+
+def create_embedding_adapter(
+    provider: str = "openai",
+    model: Optional[str] = None,
+    api_key: Optional[str] = None,
+    **kwargs: Any,
+) -> EmbeddingAdapter:
+    """Instantiate an :class:`EmbeddingAdapter` for the given *provider*.
+
+    Args:
+        provider: ``"openai"`` or ``"openrouter"``.
+        model: Embedding model name (e.g. ``"text-embedding-3-small"``).
+        api_key: Explicit API key.
+        **kwargs: Extra keyword arguments forwarded to the adapter.
+
+    Returns:
+        A ready-to-use :class:`EmbeddingAdapter` instance.
+
+    Raises:
+        LLMConfigurationError: If *provider* is not recognised.
+    """
+    if provider not in _EMBEDDING_PROVIDERS:
+        known = ", ".join(sorted(_EMBEDDING_PROVIDERS))
+        raise LLMConfigurationError(
+            f"Unknown embedding provider {provider!r}. Choose from: {known}",
+            context={"provider": provider},
+        )
+
+    # Lazy import
+    fqn = _EMBEDDING_PROVIDERS[provider]
+    module_path, class_name = fqn.rsplit(".", 1)
+    import importlib
+    mod = importlib.import_module(module_path)
+    cls = getattr(mod, class_name)
+
+    return cls(model=model, api_key=api_key, provider=provider, **kwargs)

markitect/llm/embedding_openai.py

@@ -0,0 +1,125 @@
+"""
+OpenAI-compatible embedding adapter.
+
+Works with both OpenAI (``/v1/embeddings``) and OpenRouter
+(``/api/v1/embeddings``) since they share the same API format.
+The *provider* parameter determines the default base URL and
+API key environment variable.
+"""
+
+import time
+from typing import Optional, Dict, Any
+
+from markitect.llm.embedding_adapter import EmbeddingAdapter
+from markitect.llm.config import resolve_api_key, find_project_root
+from markitect.llm._http import post_json
+from markitect.llm.exceptions import (
+    LLMConfigurationError,
+    LLMAPIError,
+    LLMRateLimitError,
+)
+
+_DEFAULT_MODEL = "text-embedding-3-small"
+
+_PROVIDER_DEFAULTS: Dict[str, Dict[str, str]] = {
+    "openai": {
+        "api_base": "https://api.openai.com/v1",
+        "env_var": "OPENAI_API_KEY",
+    },
+    "openrouter": {
+        "api_base": "https://openrouter.ai/api/v1",
+        "env_var": "OPENROUTER_API_KEY",
+    },
+}
+
+
+class OpenAICompatibleEmbeddingAdapter(EmbeddingAdapter):
+    """Embedding adapter for OpenAI-compatible endpoints.
+
+    A single class handles both OpenAI and OpenRouter because they
+    expose the same ``/embeddings`` endpoint format.
+    """
+
+    def __init__(
+        self,
+        model: Optional[str] = None,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        provider: str = "openai",
+        max_retries: int = 3,
+    ):
+        if provider not in _PROVIDER_DEFAULTS:
+            known = ", ".join(sorted(_PROVIDER_DEFAULTS))
+            raise LLMConfigurationError(
+                f"Unknown embedding provider {provider!r}. Choose from: {known}",
+                context={"provider": provider},
+            )
+
+        defaults = _PROVIDER_DEFAULTS[provider]
+        self._model = model or _DEFAULT_MODEL
+        self._api_base = (api_base or defaults["api_base"]).rstrip("/")
+        self._max_retries = max_retries
+        self._provider = provider
+
+        # Resolve API key
+        env_var = defaults["env_var"]
+        root = find_project_root()
+        key_file_paths = [root / f"apikey-{provider}.txt"] if root else []
+        self._api_key = resolve_api_key(
+            explicit=api_key,
+            env_var=env_var,
+            key_file_paths=key_file_paths,
+        )
+
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        """Embed texts via the OpenAI-compatible ``/embeddings`` endpoint.
+
+        Raises:
+            LLMConfigurationError: If no API key is configured.
+            LLMAPIError: On HTTP errors after retries are exhausted.
+        """
+        if not self._api_key:
+            raise LLMConfigurationError(
+                "No API key configured for embedding adapter",
+                context={"provider": self._provider},
+            )
+
+        url = f"{self._api_base}/embeddings"
+        payload: Dict[str, Any] = {
+            "model": self._model,
+            "input": texts,
+        }
+        headers = {"Authorization": f"Bearer {self._api_key}"}
+
+        data = self._post_with_retries(url, payload, headers)
+
+        # Response: {"data": [{"embedding": [...], "index": 0}, ...]}
+        # Sort by index to guarantee input order.
+        items = sorted(data["data"], key=lambda d: d["index"])
+        return [item["embedding"] for item in items]
+
+    def validate(self) -> bool:
+        """Return ``True`` if an API key is available."""
+        return self._api_key is not None
+
+    def _post_with_retries(
+        self,
+        url: str,
+        payload: Dict[str, Any],
+        headers: Dict[str, str],
+    ) -> Dict[str, Any]:
+        last_exc: Optional[Exception] = None
+        for attempt in range(self._max_retries + 1):
+            try:
+                return post_json(url, payload, headers)
+            except LLMRateLimitError as exc:
+                last_exc = exc
+                if attempt < self._max_retries:
+                    time.sleep(2 ** attempt)
+            except LLMAPIError as exc:
+                if exc.status_code >= 500 and attempt < self._max_retries:
+                    last_exc = exc
+                    time.sleep(2 ** attempt)
+                else:
+                    raise
+        raise last_exc  # type: ignore[misc]

markitect/llm/similarity.py

@@ -0,0 +1,64 @@
+"""
+Pure-Python vector similarity utilities.
+
+No external dependencies — uses :mod:`math` only.  Sufficient for the
+current entity scale (~100s).  numpy can be substituted later if needed.
+"""
+
+import math
+
+
+def cosine_similarity(a: list[float], b: list[float]) -> float:
+    """Cosine similarity between two vectors.
+
+    Returns a float in [-1, 1].  Returns 0.0 if either vector has
+    zero magnitude (to avoid division by zero).
+    """
+    dot = sum(x * y for x, y in zip(a, b))
+    mag_a = math.sqrt(sum(x * x for x in a))
+    mag_b = math.sqrt(sum(x * x for x in b))
+    if mag_a == 0.0 or mag_b == 0.0:
+        return 0.0
+    return dot / (mag_a * mag_b)
+
+
+def similarity_matrix(embeddings: list[list[float]]) -> list[list[float]]:
+    """Build an NxN cosine similarity matrix.
+
+    ``matrix[i][j]`` is the cosine similarity between
+    ``embeddings[i]`` and ``embeddings[j]``.
+    """
+    n = len(embeddings)
+    mat: list[list[float]] = [[0.0] * n for _ in range(n)]
+    for i in range(n):
+        mat[i][i] = 1.0
+        for j in range(i + 1, n):
+            sim = cosine_similarity(embeddings[i], embeddings[j])
+            mat[i][j] = sim
+            mat[j][i] = sim
+    return mat
+
+
+def find_similar_pairs(
+    embeddings: dict[str, list[float]],
+    threshold: float = 0.80,
+) -> list[tuple[str, str, float]]:
+    """Find all pairs with cosine similarity >= *threshold*.
+
+    Args:
+        embeddings: Mapping of slug → embedding vector.
+        threshold: Minimum similarity to include (default 0.80).
+
+    Returns:
+        List of ``(slug_a, slug_b, similarity)`` tuples sorted by
+        similarity descending.
+    """
+    slugs = sorted(embeddings)
+    pairs: list[tuple[str, str, float]] = []
+    for i, slug_a in enumerate(slugs):
+        for slug_b in slugs[i + 1:]:
+            sim = cosine_similarity(embeddings[slug_a], embeddings[slug_b])
+            if sim >= threshold:
+                pairs.append((slug_a, slug_b, sim))
+    pairs.sort(key=lambda t: t[2], reverse=True)
+    return pairs