diff --git a/codex/embed.py b/codex/embed.py
new file mode 100644
index 0000000..fcfe67e
--- /dev/null
+++ b/codex/embed.py
@@ -0,0 +1,155 @@
+"""Hybrid dense + sparse embeddings via BGE-M3 (ADR-0002).
+
+Uses :class:`FlagEmbedding.BGEM3FlagModel` for encoding because the
+``return_dense`` / ``return_sparse`` kwargs are part of the FlagEmbedding
+API — not ``sentence-transformers``.  The dense output is identical to a
+vanilla ``SentenceTransformer`` load of ``BAAI/bge-m3`` (same weights,
+same model); sparse output is a list of ``{token_id: weight}`` dicts per
+text.
+
+Notes for callers
+-----------------
+* Empty input is handled explicitly — no model call is issued.
+* Dense vectors are L2-normalised row-wise so cosine similarity reduces
+  to a dot product.
+* :func:`get_embedder` returns a process-wide singleton so the model is
+  loaded at most once per Python process.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import torch
+from FlagEmbedding import BGEM3FlagModel
+
+
+class Embedder:
+    """Thin wrapper over :class:`BGEM3FlagModel` with a stable API.
+
+    The wrapper hides the FlagEmbedding-specific encode-dict and exposes
+    three operations relevant to the ingestion pipeline:
+
+    * :meth:`encode_dense` — dense float32 matrix, L2-normalised.
+    * :meth:`encode_sparse` — list of ``{token_id: weight}`` dicts.
+    * :meth:`encode` — both in a single forward pass.
+    """
+
+    def __init__(
+        self,
+        model_name: str = "BAAI/bge-m3",
+        dim: int = 1024,
+        device: str | None = None,
+        batch_size: int = 32,
+    ) -> None:
+        resolved_device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self._model = BGEM3FlagModel(
+            model_name,
+            use_fp16=False,
+            devices=[resolved_device],
+        )
+        self.dim = dim
+        self.batch_size = batch_size
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _l2_normalise(matrix: np.ndarray) -> np.ndarray:
+        """Return ``matrix`` with every row scaled to unit L2 norm.
+
+        Zero-rows are left untouched (we divide by 1 rather than 0 to
+        avoid ``nan``s — the BGE-M3 encoder never emits zero vectors in
+        practice, but the guard keeps the function total).
+        """
+        norms = np.linalg.norm(matrix, axis=1, keepdims=True)
+        norms = np.where(norms == 0.0, 1.0, norms)
+        return (matrix / norms).astype(np.float32)
+
+    @staticmethod
+    def _coerce_sparse(weights: list[dict[int | str, float]]) -> list[dict[int, float]]:
+        """Cast token ids to ``int`` and weights to ``float``.
+
+        FlagEmbedding returns ids as strings in some versions and as
+        ints in others; we normalise to ``int`` so downstream code can
+        rely on a stable key type.
+        """
+        return [
+            {int(token_id): float(weight) for token_id, weight in row.items()} for row in weights
+        ]
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+    def encode_dense(self, texts: list[str]) -> np.ndarray:
+        """Encode ``texts`` to a dense, L2-normalised float32 matrix.
+
+        Returns a ``(0, dim)`` zero-matrix for an empty input.
+        """
+        if not texts:
+            return np.zeros((0, self.dim), dtype=np.float32)
+
+        result = self._model.encode(
+            texts,
+            batch_size=self.batch_size,
+            return_dense=True,
+            return_sparse=False,
+        )
+        dense = np.asarray(result["dense_vecs"], dtype=np.float32)
+        return self._l2_normalise(dense)
+
+    def encode_sparse(self, texts: list[str]) -> list[dict[int, float]]:
+        """Encode ``texts`` to a list of sparse ``{token_id: weight}`` dicts.
+
+        Returns ``[]`` for an empty input.
+        """
+        if not texts:
+            return []
+
+        result = self._model.encode(
+            texts,
+            batch_size=self.batch_size,
+            return_dense=False,
+            return_sparse=True,
+        )
+        return self._coerce_sparse(result["lexical_weights"])
+
+    def encode(self, texts: list[str]) -> tuple[np.ndarray, list[dict[int, float]]]:
+        """Encode ``texts`` to dense **and** sparse in one forward pass.
+
+        Returns ``(zeros((0, dim)), [])`` for an empty input.
+        """
+        if not texts:
+            return np.zeros((0, self.dim), dtype=np.float32), []
+
+        result = self._model.encode(
+            texts,
+            batch_size=self.batch_size,
+            return_dense=True,
+            return_sparse=True,
+        )
+        dense = self._l2_normalise(np.asarray(result["dense_vecs"], dtype=np.float32))
+        sparse = self._coerce_sparse(result["lexical_weights"])
+        return dense, sparse
+
+
+# ---------------------------------------------------------------------------
+# Process-wide singleton
+# ---------------------------------------------------------------------------
+_embedder: Embedder | None = None
+
+
+def get_embedder() -> Embedder:
+    """Return the process-wide :class:`Embedder` singleton.
+
+    The first call constructs the embedder using values from
+    :class:`codex.config.Settings`; subsequent calls return the cached
+    instance.  Tests can reset the cache by setting
+    ``codex.embed._embedder`` back to ``None``.
+    """
+    global _embedder
+    if _embedder is None:
+        from codex.config import Settings
+
+        s = Settings()
+        _embedder = Embedder(model_name=s.embedding_model, dim=s.embedding_dim)
+    return _embedder
diff --git a/tests/embed/__init__.py b/tests/embed/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/embed/test_embedder.py b/tests/embed/test_embedder.py
new file mode 100644
index 0000000..16a62c7
--- /dev/null
+++ b/tests/embed/test_embedder.py
@@ -0,0 +1,186 @@
+"""Tests for codex.embed.
+
+The real :class:`FlagEmbedding.BGEM3FlagModel` would download 2 GB of
+weights and load them onto the device.  We mock it out so the suite
+runs offline in milliseconds.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pytest
+
+import codex.embed as embed_module
+from codex.embed import Embedder, get_embedder
+
+# ---------------------------------------------------------------------------
+# Fakes / fixtures
+# ---------------------------------------------------------------------------
+
+
+class _FakeBGEModel:
+    """Stand-in for :class:`BGEM3FlagModel` that records calls.
+
+    ``encode()`` returns deterministic fake vectors so the L2-norm
+    assertions are exact.  The ``call_count`` attribute lets tests
+    verify the single-forward-pass invariant of :meth:`Embedder.encode`.
+    """
+
+    DIM = 1024
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        self.init_args = args
+        self.init_kwargs = kwargs
+        self.call_count = 0
+        self.last_kwargs: dict[str, Any] | None = None
+
+    def encode(
+        self,
+        sentences: list[str],
+        **kwargs: Any,
+    ) -> dict[str, Any]:
+        self.call_count += 1
+        self.last_kwargs = kwargs
+        n = len(sentences)
+        rng = np.random.default_rng(seed=42)
+        return {
+            "dense_vecs": rng.random((n, self.DIM)).astype(np.float32),
+            "lexical_weights": [{0: 0.5, 7: 0.25} for _ in range(n)],
+        }
+
+
+@pytest.fixture
+def fake_model(monkeypatch: pytest.MonkeyPatch) -> type[_FakeBGEModel]:
+    """Replace ``BGEM3FlagModel`` in ``codex.embed`` with the fake class."""
+    monkeypatch.setattr(embed_module, "BGEM3FlagModel", _FakeBGEModel)
+    return _FakeBGEModel
+
+
+@pytest.fixture(autouse=True)
+def reset_singleton() -> None:
+    """Reset the module-level singleton between tests."""
+    embed_module._embedder = None
+
+
+# ---------------------------------------------------------------------------
+# encode_dense
+# ---------------------------------------------------------------------------
+
+
+def test_encode_dense_shape_dtype_and_norm(fake_model: type[_FakeBGEModel]) -> None:
+    """Dense output is (N, dim) float32 with unit L2 rows."""
+    e = Embedder()
+    out = e.encode_dense(["a", "b"])
+
+    assert out.shape == (2, 1024)
+    assert out.dtype == np.float32
+    norms = np.linalg.norm(out, axis=1)
+    np.testing.assert_allclose(norms, [1.0, 1.0], atol=1e-5)
+
+
+def test_encode_dense_empty_input_returns_empty_matrix(
+    fake_model: type[_FakeBGEModel],
+) -> None:
+    """Empty input -> (0, dim) without invoking the model."""
+    e = Embedder()
+    # Reach into the wrapped model to verify it is not called.
+    fake = e._model
+    assert isinstance(fake, _FakeBGEModel)
+
+    out = e.encode_dense([])
+
+    assert out.shape == (0, 1024)
+    assert out.dtype == np.float32
+    assert fake.call_count == 0
+
+
+# ---------------------------------------------------------------------------
+# encode_sparse
+# ---------------------------------------------------------------------------
+
+
+def test_encode_sparse_returns_list_of_dicts(fake_model: type[_FakeBGEModel]) -> None:
+    """Sparse output has one dict per input string."""
+    e = Embedder()
+    out = e.encode_sparse(["a", "b"])
+
+    assert len(out) == 2
+    for row in out:
+        assert isinstance(row, dict)
+        for key, value in row.items():
+            assert isinstance(key, int)
+            assert isinstance(value, float)
+
+
+def test_encode_sparse_empty_input_returns_empty_list(
+    fake_model: type[_FakeBGEModel],
+) -> None:
+    """Empty input -> [] without invoking the model."""
+    e = Embedder()
+    fake = e._model
+    assert isinstance(fake, _FakeBGEModel)
+
+    out = e.encode_sparse([])
+
+    assert out == []
+    assert fake.call_count == 0
+
+
+# ---------------------------------------------------------------------------
+# encode (combined)
+# ---------------------------------------------------------------------------
+
+
+def test_encode_returns_tuple_and_uses_single_forward_pass(
+    fake_model: type[_FakeBGEModel],
+) -> None:
+    """encode() must issue exactly one model.encode() call for efficiency."""
+    e = Embedder()
+    fake = e._model
+    assert isinstance(fake, _FakeBGEModel)
+
+    dense, sparse = e.encode(["a"])
+
+    assert isinstance(dense, np.ndarray)
+    assert dense.shape == (1, 1024)
+    assert dense.dtype == np.float32
+    np.testing.assert_allclose(np.linalg.norm(dense, axis=1), [1.0], atol=1e-5)
+
+    assert isinstance(sparse, list)
+    assert len(sparse) == 1
+    assert isinstance(sparse[0], dict)
+
+    assert fake.call_count == 1
+    assert fake.last_kwargs is not None
+    assert fake.last_kwargs.get("return_dense") is True
+    assert fake.last_kwargs.get("return_sparse") is True
+
+
+def test_encode_empty_input(fake_model: type[_FakeBGEModel]) -> None:
+    """Empty input -> ((0, dim), [])."""
+    e = Embedder()
+    fake = e._model
+    assert isinstance(fake, _FakeBGEModel)
+
+    dense, sparse = e.encode([])
+
+    assert dense.shape == (0, 1024)
+    assert dense.dtype == np.float32
+    assert sparse == []
+    assert fake.call_count == 0
+
+
+# ---------------------------------------------------------------------------
+# Singleton
+# ---------------------------------------------------------------------------
+
+
+def test_get_embedder_returns_singleton(fake_model: type[_FakeBGEModel]) -> None:
+    """Two calls return the same object."""
+    first = get_embedder()
+    second = get_embedder()
+
+    assert first is second
+    assert isinstance(first, Embedder)