Add e2e tests for embedding raw flag

.github/workflows/embeddings.yml

@@ -0,0 +1,59 @@
+# Embedding CLI build and tests
+name: Embedding CLI
+
+on:
+    workflow_dispatch:
+    push:
+        branches:
+            - feature/*
+            - master
+        paths:
+            - '.github/workflows/embeddings.yml'
+            - 'examples/embedding/**'
+            - 'examples/tests/**'
+    pull_request:
+        types: [opened, synchronize, reopened]
+        paths:
+            - '.github/workflows/embeddings.yml'
+            - 'examples/embedding/**'
+            - 'examples/tests/**'
+
+jobs:
+    embedding-cli-tests:
+        runs-on: ubuntu-latest
+
+        steps:
+            - name: Install system deps
+              run: |
+                  sudo apt-get update
+                  sudo apt-get -y install \
+                    build-essential \
+                    cmake \
+                    curl \
+                    libcurl4-openssl-dev \
+                    python3-pip
+
+            - name: Checkout repository
+              uses: actions/checkout@v4
+              with:
+                  fetch-depth: 0
+
+            - name: Set up Python
+              uses: actions/setup-python@v5
+              with:
+                  python-version: '3.11'
+
+            - name: Install Python deps
+              run: |
+                  pip install -r requirements.txt || echo "No extra requirements found"
+                  pip install pytest
+
+            - name: Build llama-embedding
+              run: |
+                  cmake -B build \
+                    -DCMAKE_BUILD_TYPE=Release
+                  cmake --build build --target llama-embedding -j $(nproc)
+
+            - name: Run embedding tests
+              run: |
+                  pytest -v examples/tests

examples/tests/test_embedding.py

@@ -0,0 +1,184 @@
+import os, json, subprocess, hashlib
+from pathlib import Path
+import numpy as np
+import pytest
+
+# ---------------------------------------------------------------------------
+# Configuration constants
+# ---------------------------------------------------------------------------
+
+EPS = 1e-3
+REPO_ROOT = Path(__file__).resolve().parents[2]
+EXE = REPO_ROOT / ("build/bin/llama-embedding.exe" if os.name == "nt" else "build/bin/llama-embedding")
+DEFAULT_ENV = {**os.environ, "LLAMA_CACHE": os.environ.get("LLAMA_CACHE", "tmp")}
+SEED = "42"
+
+
+# ---------------------------------------------------------------------------
+# Model setup helpers
+# ---------------------------------------------------------------------------
+
+def get_model_hf_params():
+    """Default lightweight embedding model."""
+    return {
+        "hf_repo": "ggml-org/embeddinggemma-300M-qat-q4_0-GGUF",
+        "hf_file": "embeddinggemma-300M-qat-Q4_0.gguf",
+    }
+
+
+@pytest.fixture(scope="session")
+def embedding_model():
+    """Download/cache model once per session."""
+    exe_path = EXE
+    if not exe_path.exists():
+        alt = REPO_ROOT / "build/bin/Release/llama-embedding.exe"
+        if alt.exists():
+            exe_path = alt
+        else:
+            raise FileNotFoundError(f"llama-embedding binary not found under {REPO_ROOT}/build/bin")
+
+    params = get_model_hf_params()
+    cmd = [
+        str(exe_path),
+        "-hfr", params["hf_repo"],
+        "-hff", params["hf_file"],
+        "--ctx-size", "16",
+        "--embd-output-format", "json",
+        "--no-warmup",
+        "--threads", "1",
+        "--seed", SEED,
+    ]
+    res = subprocess.run(cmd, input="ok", capture_output=True, text=True, env=DEFAULT_ENV)
+    assert res.returncode == 0, f"model download failed: {res.stderr}"
+    return params
+
+
+# ---------------------------------------------------------------------------
+# Utility functions
+# ---------------------------------------------------------------------------
+
+def run_embedding(text: str, fmt: str = "raw", params=None) -> str:
+    """Runs llama-embedding and returns stdout (string)."""
+    exe_path = EXE
+    if not exe_path.exists():
+        raise FileNotFoundError(f"Missing binary: {exe_path}")
+    params = params or get_model_hf_params()
+    cmd = [
+        str(exe_path),
+        "-hfr", params["hf_repo"],
+        "-hff", params["hf_file"],
+        "--ctx-size", "2048",
+        "--embd-output-format", fmt,
+        "--threads", "1",
+        "--seed", SEED,
+    ]
+    result = subprocess.run(cmd, input=text, capture_output=True, text=True, env=DEFAULT_ENV)
+    if result.returncode:
+        raise AssertionError(f"embedding failed ({result.returncode}):\n{result.stderr[:400]}")
+    out = result.stdout.strip()
+    assert out, f"empty output for text={text!r}, fmt={fmt}"
+    return out
+
+
+def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
+
+
+def embedding_hash(vec: np.ndarray) -> str:
+    """Return short deterministic signature for regression tracking."""
+    return hashlib.sha256(vec[:8].tobytes()).hexdigest()[:16]
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+# Register custom mark so pytest doesn't warn about it
+pytestmark = pytest.mark.filterwarnings("ignore::pytest.PytestUnknownMarkWarning")
+
+@pytest.mark.slow
+@pytest.mark.parametrize("fmt", ["raw", "json"])
+@pytest.mark.parametrize("text", ["hello world", "hi 🌎", "line1\nline2\nline3"])
+def test_embedding_runs_and_finite(fmt, text, embedding_model):
+    """Ensure embeddings run end-to-end and produce finite floats."""
+    out = run_embedding(text, fmt, embedding_model)
+    floats = (
+        np.array(out.split(), float)
+        if fmt == "raw"
+        else np.array(json.loads(out)["data"][0]["embedding"], float)
+    )
+    assert len(floats) > 100
+    assert np.all(np.isfinite(floats)), f"non-finite values in {fmt} output"
+    assert 0.1 < np.linalg.norm(floats) < 10
+
+
+def test_raw_vs_json_consistency(embedding_model):
+    """Compare raw vs JSON embedding output for same text."""
+    text = "hello world"
+    raw = np.array(run_embedding(text, "raw", embedding_model).split(), float)
+    jsn = np.array(json.loads(run_embedding(text, "json", embedding_model))["data"][0]["embedding"], float)
+
+    assert raw.shape == jsn.shape
+    cos = cosine_similarity(raw, jsn)
+    assert cos > 0.999, f"divergence: cos={cos:.4f}"
+    assert embedding_hash(raw) == embedding_hash(jsn), "hash mismatch → possible nondeterminism"
+
+
+def test_empty_input_deterministic(embedding_model):
+    """Empty input should yield finite, deterministic vector."""
+    v1 = np.array(run_embedding("", "raw", embedding_model).split(), float)
+    v2 = np.array(run_embedding("", "raw", embedding_model).split(), float)
+    assert np.all(np.isfinite(v1))
+    cos = cosine_similarity(v1, v2)
+    assert cos > 0.9999, f"Empty input not deterministic (cos={cos:.5f})"
+    assert 0.1 < np.linalg.norm(v1) < 10
+
+
+@pytest.mark.slow
+def test_very_long_input_stress(embedding_model):
+    """Stress test: large input near context window."""
+    text = "lorem " * 2000
+    vec = np.array(run_embedding(text, "raw", embedding_model).split(), float)
+    assert len(vec) > 100
+    assert np.isfinite(np.linalg.norm(vec))
+
+
+@pytest.mark.parametrize(
+    "text",
+    ["   ", "\n\n\n", "123 456 789"],
+)
+def test_low_information_inputs_stable(text, embedding_model):
+    """Whitespace/numeric inputs should yield stable embeddings."""
+    v1 = np.array(run_embedding(text, "raw", embedding_model).split(), float)
+    v2 = np.array(run_embedding(text, "raw", embedding_model).split(), float)
+    cos = cosine_similarity(v1, v2)
+    assert cos > 0.999, f"unstable embedding for {text!r}"
+
+
+@pytest.mark.parametrize("flag", ["--no-such-flag", "--help"])
+def test_invalid_or_help_flag(flag):
+    """Invalid flags should fail; help should succeed."""
+    res = subprocess.run([str(EXE), flag], capture_output=True, text=True)
+    if flag == "--no-such-flag":
+        assert res.returncode != 0
+        assert any(k in res.stderr.lower() for k in ("error", "invalid", "unknown"))
+    else:
+        assert res.returncode == 0
+        assert "usage" in (res.stdout.lower() + res.stderr.lower())
+
+
+@pytest.mark.parametrize("fmt", ["raw", "json"])
+@pytest.mark.parametrize("text", ["deterministic test", "deterministic test again"])
+def test_repeated_call_consistent(fmt, text, embedding_model):
+    """Same input → same hash across repeated runs."""
+    out1 = run_embedding(text, fmt, embedding_model)
+    out2 = run_embedding(text, fmt, embedding_model)
+
+    if fmt == "json":
+        v1 = np.array(json.loads(out1)["data"][0]["embedding"], float)
+        v2 = np.array(json.loads(out2)["data"][0]["embedding"], float)
+    else:
+        v1 = np.array(out1.split(), float)
+        v2 = np.array(out2.split(), float)
+
+    assert embedding_hash(v1) == embedding_hash(v2)