feat(api): add POST /project UMAP projection for Galaxy VIEW

Backend dimensionality reduction seam (umap-learn) so Galaxy can place tokens from embeddings without re-encoding; pca/tsne return 501 until later epics.

Co-authored-by: Cursor <cursoragent@cursor.com>

architecture_spec.md

@@ -17,6 +17,7 @@ VHectorLab 3D is a 3D semantic vector visualizer and vector arithmetic explorer.
 - `POST /tokenize`: Returns tokenization details.
 - `POST /arithmetic`: Computes $V_{res} = V_A - V_B + V_C$ and returns top-$K$ nearest vocabulary words and component vectors.
 - `POST /compare`: Batch-encodes 1–1024 texts, L2-normalizes embeddings, and returns per-item cosine vs the first token (anchor).
+- `POST /project`: Projects precomputed embedding vectors to 2D/3D (does **not** re-encode text). v1 method = **`umap` only**; `pca` / `tsne` → **501**; other methods → **400**. Default seed `42`. When `dim > 50`, internal PCA→50 before UMAP. Positions are zero-mean and RMS-scaled server-side.
 
 ### Data Contracts
 
@@ -62,3 +63,32 @@ Returns:
 }
 ```
 `cosine_vs_first` is $\text{dot}(\hat{e}_i, \hat{e}_0)$ on L2-normalized embeddings. Frontend reorders may recompute scores in memory without re-calling `/compare`.
+
+#### Project (Galaxy / UMAP)
+```json
+{
+  "vectors": [[0.01, "..."], ["..."]],
+  "method": "umap",
+  "n_components": 3,
+  "seed": 42,
+  "params": { "n_neighbors": 15, "min_dist": 0.1, "metric": "cosine" }
+}
+```
+Constraints: `len(vectors)` ∈ 1..1024; uniform row dim; `n_components` ∈ {2, 3}; `method` = `umap` (green path).
+
+Returns:
+```json
+{
+  "method": "umap",
+  "n_components": 3,
+  "positions": [[x, y, z], "..."],
+  "meta": {
+    "seed": 42,
+    "n_neighbors": 15,
+    "min_dist": 0.1,
+    "metric": "cosine",
+    "pre_pca_dims": 50
+  }
+}
+```
+`meta.pre_pca_dims` is present only when the internal PCA pre-step ran. Encoding stays on `/compare` (or SAE encode); `/project` is additive.

backend/projection.py

@@ -0,0 +1,169 @@
+"""
+Dimensionality reduction for Galaxy VIEW.
+
+Deep seam: project_embeddings(vectors, ...) → positions + meta.
+UMAP only in v1; PCA/t-SNE raise ProjectError(501).
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+PRE_PCA_DIMS = 50
+MAX_VECTORS = 1024
+DEFAULT_N_NEIGHBORS = 15
+DEFAULT_MIN_DIST = 0.1
+DEFAULT_METRIC = "cosine"
+
+
+class ProjectError(Exception):
+    """Validation / capability error with HTTP-ish status code."""
+
+    def __init__(self, message: str, status_code: int = 400) -> None:
+        super().__init__(message)
+        self.message = message
+        self.status_code = status_code
+
+
+def _as_matrix(vectors: list[list[float]] | np.ndarray) -> np.ndarray:
+    if isinstance(vectors, np.ndarray):
+        mat = np.asarray(vectors, dtype=np.float64)
+    else:
+        if not vectors:
+            raise ProjectError("vectors list cannot be empty (need 1..1024)")
+        lengths = {len(row) for row in vectors}
+        if len(lengths) != 1:
+            raise ProjectError("all vectors must have the same dimension")
+        if next(iter(lengths)) < 1:
+            raise ProjectError("vector dimension must be >= 1")
+        mat = np.asarray(vectors, dtype=np.float64)
+
+    if mat.ndim != 2:
+        raise ProjectError("vectors must be a 2D matrix [n, dim]")
+    n, dim = mat.shape
+    if n < 1 or n > MAX_VECTORS:
+        raise ProjectError(f"len(vectors) must be in 1..{MAX_VECTORS}, got {n}")
+    if dim < 1:
+        raise ProjectError("vector dimension must be >= 1")
+    if not np.isfinite(mat).all():
+        raise ProjectError("vectors must contain only finite values")
+    return mat
+
+
+def _normalize_positions(pos: np.ndarray) -> np.ndarray:
+    """Zero-mean; scale so RMS distance from origin is 1 (stable camera defaults)."""
+    centered = pos - pos.mean(axis=0, keepdims=True)
+    rms = float(np.sqrt(np.mean(np.sum(centered**2, axis=1))))
+    if rms < 1e-12:
+        return centered
+    return centered / rms
+
+
+def _resolve_umap_params(
+    n_samples: int, params: dict[str, Any] | None
+) -> dict[str, Any]:
+    raw = params or {}
+    n_neighbors = int(raw.get("n_neighbors", DEFAULT_N_NEIGHBORS))
+    min_dist = float(raw.get("min_dist", DEFAULT_MIN_DIST))
+    metric = str(raw.get("metric", DEFAULT_METRIC))
+
+    # UMAP requires n_neighbors < n_samples
+    max_nn = max(2, n_samples - 1)
+    n_neighbors = max(n_neighbors, 2)
+    n_neighbors = min(n_neighbors, max_nn)
+
+    if min_dist < 0.0:
+        raise ProjectError("params.min_dist must be >= 0")
+
+    return {
+        "n_neighbors": n_neighbors,
+        "min_dist": min_dist,
+        "metric": metric,
+    }
+
+
+def project_embeddings(
+    vectors: list[list[float]] | np.ndarray,
+    *,
+    method: str = "umap",
+    n_components: int = 3,
+    seed: int = 42,
+    params: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """
+    Project embedding rows to 2D/3D.
+
+    Returns:
+        { method, n_components, positions, meta }
+    """
+    method_l = (method or "").strip().lower()
+    if method_l in ("pca", "tsne"):
+        raise ProjectError(
+            f"method '{method_l}' is not implemented yet; use 'umap'",
+            status_code=501,
+        )
+    if method_l != "umap":
+        raise ProjectError(
+            f"unsupported method '{method}'; v1 accepts 'umap' only "
+            "(pca/tsne coming later)",
+            status_code=400,
+        )
+
+    if n_components not in (2, 3):
+        raise ProjectError("n_components must be 2 or 3")
+
+    mat = _as_matrix(vectors)
+    n_samples, dim = mat.shape
+
+    # Need at least n_components + 1 points for a meaningful UMAP; allow smaller
+    # with clamped neighbors (smoke / edge cases).
+    if n_samples < 3:
+        raise ProjectError("UMAP requires at least 3 vectors")
+
+    umap_params = _resolve_umap_params(n_samples, params)
+    pre_pca_dims: int | None = None
+    work = mat
+
+    if dim > PRE_PCA_DIMS:
+        from sklearn.decomposition import PCA
+
+        pca_dims = min(PRE_PCA_DIMS, n_samples - 1, dim)
+        if pca_dims < n_components:
+            raise ProjectError(
+                f"cannot pre-reduce: need at least {n_components} PCA dims "
+                f"(n={n_samples}, dim={dim})"
+            )
+        pca = PCA(n_components=pca_dims, random_state=seed)
+        work = pca.fit_transform(mat)
+        pre_pca_dims = pca_dims
+
+    import umap
+
+    reducer = umap.UMAP(
+        n_components=n_components,
+        n_neighbors=umap_params["n_neighbors"],
+        min_dist=umap_params["min_dist"],
+        metric=umap_params["metric"],
+        random_state=seed,
+        n_jobs=1,
+    )
+    embedded = reducer.fit_transform(work)
+    positions = _normalize_positions(np.asarray(embedded, dtype=np.float64))
+
+    meta: dict[str, Any] = {
+        "seed": seed,
+        "n_neighbors": umap_params["n_neighbors"],
+        "min_dist": umap_params["min_dist"],
+        "metric": umap_params["metric"],
+    }
+    if pre_pca_dims is not None:
+        meta["pre_pca_dims"] = pre_pca_dims
+
+    return {
+        "method": "umap",
+        "n_components": n_components,
+        "positions": positions.tolist(),
+        "meta": meta,
+    }

backend/pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "httpx>=0.27.0",
     "huggingface-hub>=0.21.0",
     "orjson>=3.11.9",
+    "umap-learn==0.5.12",
 ]
 
 [project.optional-dependencies]

backend/routers/core.py

@@ -1,10 +1,11 @@
 """
 Core API Router for VHectorLab 3D.
-Provides /health, /embed, /tokenize, and /arithmetic endpoints.
+Provides /health, /embed, /tokenize, /arithmetic, /compare, and /project endpoints.
 """
 
-from typing import Any
+from typing import Any, Literal
 
+from backend.projection import ProjectError, project_embeddings
 from backend.state import state
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field
@@ -42,6 +43,32 @@ class CompareRequest(BaseModel):
     )
 
 
+class ProjectParams(BaseModel):
+    n_neighbors: int = Field(default=15, ge=2, le=200)
+    min_dist: float = Field(default=0.1, ge=0.0)
+    metric: str = Field(default="cosine")
+
+
+class ProjectRequest(BaseModel):
+    vectors: list[list[float]] = Field(
+        ...,
+        description="Precomputed embedding rows (1..1024); does not re-encode text",
+    )
+    method: str = Field(
+        default="umap",
+        description="Projection method; v1 accepts 'umap' only",
+    )
+    n_components: Literal[2, 3] = Field(
+        default=3,
+        description="Output dimensionality (Galaxy uses 3; 2 reserved for future 2D VIEW)",
+    )
+    seed: int = Field(default=42, description="RNG seed for reproducibility")
+    params: ProjectParams | None = Field(
+        default=None,
+        description="UMAP hyperparameters (n_neighbors, min_dist, metric)",
+    )
+
+
 @router.get("/health")
 def health_check() -> dict[str, Any]:
     return {
@@ -117,3 +144,21 @@ def perform_compare(req: CompareRequest) -> dict[str, Any]:
     except Exception as e:  # noqa: BLE001
         raise HTTPException(status_code=500, detail=str(e))
 
+
+@router.post("/project")
+def perform_project(req: ProjectRequest) -> dict[str, Any]:
+    """Project embedding vectors to 2D/3D (UMAP). Does not encode text."""
+    params = req.params.model_dump() if req.params is not None else None
+    try:
+        return project_embeddings(
+            req.vectors,
+            method=req.method,
+            n_components=req.n_components,
+            seed=req.seed,
+            params=params,
+        )
+    except ProjectError as e:
+        raise HTTPException(status_code=e.status_code, detail=e.message)
+    except Exception as e:  # noqa: BLE001
+        raise HTTPException(status_code=500, detail=str(e))
+

backend/tests/test_project.py

@@ -0,0 +1,198 @@
+"""Tests for POST /project (UMAP dimensionality reduction)."""
+
+from __future__ import annotations
+
+from contextlib import asynccontextmanager
+
+import numpy as np
+import pytest
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.testclient import TestClient
+
+
+@pytest.fixture()
+def client():
+    """Lightweight app with core router only (no model load)."""
+    from backend.routers.core import router as core_router
+
+    @asynccontextmanager
+    async def _noop_lifespan(_app):
+        yield
+
+    app = FastAPI(lifespan=_noop_lifespan)
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=False,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+    app.include_router(core_router)
+    app.include_router(core_router, prefix="/api")
+
+    with TestClient(app) as c:
+        yield c
+
+
+def _random_vectors(n: int = 24, dim: int = 32, seed: int = 0) -> list[list[float]]:
+    rng = np.random.default_rng(seed)
+    mat = rng.standard_normal((n, dim)).astype(np.float64)
+    norms = np.linalg.norm(mat, axis=1, keepdims=True)
+    norms[norms == 0] = 1e-9
+    return (mat / norms).tolist()
+
+
+def test_project_rejects_empty_vectors(client: TestClient):
+    res = client.post(
+        "/project",
+        json={"vectors": [], "method": "umap", "n_components": 3, "seed": 42},
+    )
+    assert res.status_code == 400
+    assert "empty" in res.json()["detail"].lower() or "1" in res.json()["detail"]
+
+
+def test_project_rejects_too_many_vectors(client: TestClient):
+    vecs = _random_vectors(n=2, dim=4)
+    bloated = vecs * 513  # 1026
+    res = client.post(
+        "/project",
+        json={"vectors": bloated, "method": "umap", "n_components": 3},
+    )
+    assert res.status_code == 400
+    assert "1024" in res.json()["detail"]
+
+
+def test_project_rejects_inconsistent_dims(client: TestClient):
+    res = client.post(
+        "/project",
+        json={
+            "vectors": [[1.0, 0.0], [0.0, 1.0, 0.0]],
+            "method": "umap",
+            "n_components": 2,
+        },
+    )
+    assert res.status_code == 400
+    assert "dim" in res.json()["detail"].lower()
+
+
+def test_project_rejects_invalid_n_components(client: TestClient):
+    res = client.post(
+        "/project",
+        json={"vectors": _random_vectors(8, 8), "method": "umap", "n_components": 4},
+    )
+    assert res.status_code == 422  # pydantic Field constraint
+
+
+@pytest.mark.parametrize("method", ["pca", "tsne"])
+def test_project_pca_tsne_not_implemented(client: TestClient, method: str):
+    res = client.post(
+        "/project",
+        json={
+            "vectors": _random_vectors(12, 8),
+            "method": method,
+            "n_components": 3,
+            "seed": 42,
+        },
+    )
+    assert res.status_code == 501
+    detail = res.json()["detail"].lower()
+    assert method in detail
+    assert "not implemented" in detail or "umap" in detail
+
+
+def test_project_rejects_unknown_method(client: TestClient):
+    res = client.post(
+        "/project",
+        json={
+            "vectors": _random_vectors(12, 8),
+            "method": "mds",
+            "n_components": 3,
+        },
+    )
+    assert res.status_code == 400
+    assert "umap" in res.json()["detail"].lower()
+
+
+def test_project_umap_smoke_3d_seeded(client: TestClient):
+    vectors = _random_vectors(n=20, dim=16, seed=7)
+    payload = {
+        "vectors": vectors,
+        "method": "umap",
+        "n_components": 3,
+        "seed": 42,
+        "params": {"n_neighbors": 5, "min_dist": 0.1, "metric": "cosine"},
+    }
+    res = client.post("/project", json=payload)
+    assert res.status_code == 200, res.text
+    data = res.json()
+    assert data["method"] == "umap"
+    assert data["n_components"] == 3
+    assert len(data["positions"]) == 20
+    assert all(len(p) == 3 for p in data["positions"])
+
+    meta = data["meta"]
+    assert meta["seed"] == 42
+    assert meta["n_neighbors"] == 5
+    assert meta["min_dist"] == pytest.approx(0.1)
+    assert meta["metric"] == "cosine"
+
+    # Server-side normalize: approximately zero-mean
+    pos = np.asarray(data["positions"], dtype=np.float64)
+    means = pos.mean(axis=0)
+    assert np.allclose(means, 0.0, atol=1e-5)
+
+    # Same inputs → same positions (seeded)
+    res2 = client.post("/project", json=payload)
+    assert res2.status_code == 200
+    pos2 = np.asarray(res2.json()["positions"], dtype=np.float64)
+    assert np.allclose(pos, pos2, atol=1e-5)
+
+
+def test_project_umap_n_components_2(client: TestClient):
+    vectors = _random_vectors(n=16, dim=12, seed=3)
+    res = client.post(
+        "/project",
+        json={
+            "vectors": vectors,
+            "method": "umap",
+            "n_components": 2,
+            "seed": 42,
+            "params": {"n_neighbors": 5},
+        },
+    )
+    assert res.status_code == 200
+    data = res.json()
+    assert data["n_components"] == 2
+    assert all(len(p) == 2 for p in data["positions"])
+
+
+def test_project_umap_pre_pca_when_high_dim(client: TestClient):
+    """dim > 50 triggers internal PCA→50 before UMAP; meta reports pre_pca_dims."""
+    vectors = _random_vectors(n=60, dim=128, seed=1)
+    res = client.post(
+        "/project",
+        json={
+            "vectors": vectors,
+            "method": "umap",
+            "n_components": 3,
+            "seed": 42,
+            "params": {"n_neighbors": 8},
+        },
+    )
+    assert res.status_code == 200, res.text
+    meta = res.json()["meta"]
+    assert meta["pre_pca_dims"] == 50
+
+
+def test_project_module_unit_rejects_method():
+    from backend.projection import ProjectError, project_embeddings
+
+    with pytest.raises(ProjectError) as ei:
+        project_embeddings(
+            _random_vectors(10, 8),
+            method="pca",
+            n_components=3,
+            seed=42,
+        )
+    assert ei.value.status_code == 501

backend/uv.lock

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+version = "0.48.0"
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+
 [[package]]
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 version = "9.1.1"
@@ -1378,8 +1413,7 @@ resolution-markers = [
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-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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     { name = "threadpoolctl", marker = "python_full_version >= '3.11'" },
@@ -1559,7 +1593,7 @@ resolution-markers = [
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-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
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@@ -1612,8 +1646,7 @@ source = { registry = "https://pypi.org/simple" }
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-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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@@ -1832,8 +1865,7 @@ source = { registry = "https://pypi.org/simple" }
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-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
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     { name = "packaging" },
     { name = "pyyaml" },
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+[[package]]
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+version = "0.5.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "pynndescent" },
+    { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "scikit-learn", version = "1.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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+    { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "tqdm" },
+]
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+wheels = [
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+]
+
 [[package]]
 name = "uvicorn"
 version = "0.52.1"
@@ -1925,11 +1978,11 @@ dependencies = [
     { name = "httpx" },
     { name = "huggingface-hub" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.11.*'" },
-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "orjson" },
     { name = "sentence-transformers" },
     { name = "torch" },
+    { name = "umap-learn" },
     { name = "uvicorn" },
 ]
 
@@ -1952,6 +2005,7 @@ requires-dist = [
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     { name = "sentence-transformers", specifier = ">=2.5.0" },
     { name = "torch", specifier = ">=2.2.0" },
+    { name = "umap-learn", specifier = "==0.5.12" },
     { name = "uvicorn", specifier = ">=0.28.0" },
 ]
 provides-extras = ["dev"]