add classify --local mode

README.md

@@ -204,7 +204,7 @@ jina grep serve stop     # stop when done
 
 ## Local mode
 
-`jina embed`, `jina rerank`, and `jina dedup` support `--local` to run on Apple Silicon via the jina-grep embedding server instead of the Jina API. No API key needed.
+`jina embed`, `jina rerank`, `jina classify`, and `jina dedup` support `--local` to run on Apple Silicon via the jina-grep embedding server instead of the Jina API. No API key needed.
 
 ```bash
 # Start the local server first
@@ -217,6 +217,9 @@ cat texts.txt | jina embed --local --json
 # Local reranking (cosine similarity on local embeddings)
 cat docs.txt | jina rerank --local "machine learning"
 
+# Local classification (cosine similarity on local embeddings)
+jina classify --local "this is great" --labels positive,negative
+
 # Local deduplication
 cat items.txt | jina dedup --local
 ```

jina_cli/api.py

@@ -450,6 +450,38 @@ def local_embed(
     return data.get("data", [])
 
 
+def local_classify(
+    texts: list[str],
+    labels: list[str],
+    model: str = "jina-embeddings-v5-nano",
+    task: str = "text-matching",
+) -> list[dict]:
+    """Classify texts into labels using local embeddings and cosine similarity."""
+    all_texts = texts + labels
+    embeddings_data = local_embed(all_texts, model=model, task=task)
+    embeddings = [item["embedding"] for item in embeddings_data]
+
+    text_embs = embeddings[:len(texts)]
+    label_embs = embeddings[len(texts):]
+
+    results = []
+    for i, text_emb in enumerate(text_embs):
+        best_label = labels[0]
+        best_score = -1.0
+        for j, label_emb in enumerate(label_embs):
+            score = _cosine_similarity(text_emb, label_emb)
+            if score > best_score:
+                best_score = score
+                best_label = labels[j]
+        results.append({
+            "index": i,
+            "prediction": best_label,
+            "score": best_score,
+        })
+
+    return results
+
+
 def local_rerank(
     query: str,
     documents: list[str],

jina_cli/main.py

@@ -410,11 +410,12 @@ def dedup(ctx, k, local, as_json, api_key):
 @click.argument("text", nargs=-1)
 @click.option("--labels", required=True, multiple=True,
               help="Labels for classification (comma-separated or repeated --labels)")
-@click.option("--model", default=None, help="Model name (default: jina-embeddings-v5-text-small)")
+@click.option("--model", default=None, help="Model name (default: jina-embeddings-v5-text-small, or v5-nano with --local)")
+@click.option("--local", is_flag=True, help="Use local MLX server (requires: jina-grep serve start)")
 @click.option("--json", "as_json", is_flag=True, help="Output as JSON")
 @click.option("--api-key", default=None, help="Jina API key")
 @click.pass_context
-def classify(ctx, text, labels, model, as_json, api_key):
+def classify(ctx, text, labels, model, local, as_json, api_key):
     """Classify text into labels.
 
     Input from arguments or stdin (one text per line).
@@ -424,6 +425,7 @@ def classify(ctx, text, labels, model, as_json, api_key):
         jina classify "this is great" --labels positive,negative
         echo "stock price rose" | jina classify --labels business,sports,tech
         jina classify "text1" "text2" --labels cat1 --labels cat2 --labels cat3
+        jina classify --local "this is great" --labels positive,negative
     """
     key = api_key or ctx.obj.get("api_key")
 
@@ -450,10 +452,13 @@ def classify(ctx, text, labels, model, as_json, api_key):
                    "Fix: --labels positive,negative", err=True)
         sys.exit(EXIT_USER_ERROR)
 
-    _model = model or "jina-embeddings-v5-text-small"
-
     try:
-        result = api.classify(texts, parsed_labels, api_key=key, model=_model)
+        if local:
+            _model = model or "jina-embeddings-v5-nano"
+            result = api.local_classify(texts, parsed_labels, model=_model)
+        else:
+            _model = model or "jina-embeddings-v5-text-small"
+            result = api.classify(texts, parsed_labels, api_key=key, model=_model)
         click.echo(utils.format_classify_results(result, as_json=as_json))
     except Exception as e:
         utils.handle_http_error(e)

tests/test_local.py

@@ -0,0 +1,72 @@
+"""Unit tests for local mode functions (no API key or server needed)."""
+
+from unittest.mock import patch
+
+from jina_cli.api import local_classify, _cosine_similarity
+
+
+class TestCosineSimlarity:
+    def test_identical_vectors(self):
+        assert abs(_cosine_similarity([1, 0, 0], [1, 0, 0]) - 1.0) < 1e-6
+
+    def test_orthogonal_vectors(self):
+        assert abs(_cosine_similarity([1, 0], [0, 1])) < 1e-6
+
+    def test_zero_vector(self):
+        assert _cosine_similarity([0, 0], [1, 1]) == 0.0
+
+
+class TestLocalClassify:
+    def test_single_text(self):
+        fake_embeddings = [
+            {"embedding": [0.9, 0.1, 0.0]},   # "I love this" - text
+            {"embedding": [0.8, 0.2, 0.0]},   # "positive" - label (close)
+            {"embedding": [0.0, 0.1, 0.9]},   # "negative" - label (far)
+        ]
+
+        with patch("jina_cli.api.local_embed", return_value=fake_embeddings):
+            result = local_classify(
+                texts=["I love this"],
+                labels=["positive", "negative"],
+            )
+
+        assert len(result) == 1
+        assert result[0]["prediction"] == "positive"
+        assert result[0]["score"] > 0.5
+        assert result[0]["index"] == 0
+
+    def test_multiple_texts(self):
+        fake_embeddings = [
+            {"embedding": [0.9, 0.1]},   # text 1 - closer to label 1
+            {"embedding": [0.1, 0.9]},   # text 2 - closer to label 2
+            {"embedding": [0.8, 0.2]},   # label "sports"
+            {"embedding": [0.2, 0.8]},   # label "politics"
+        ]
+
+        with patch("jina_cli.api.local_embed", return_value=fake_embeddings):
+            result = local_classify(
+                texts=["goal scored", "election results"],
+                labels=["sports", "politics"],
+            )
+
+        assert len(result) == 2
+        assert result[0]["prediction"] == "sports"
+        assert result[1]["prediction"] == "politics"
+
+    def test_result_format(self):
+        """Results should have index, prediction, score keys."""
+        fake_embeddings = [
+            {"embedding": [1.0, 0.0]},
+            {"embedding": [0.9, 0.1]},
+        ]
+
+        with patch("jina_cli.api.local_embed", return_value=fake_embeddings):
+            result = local_classify(
+                texts=["test"],
+                labels=["label1"],
+            )
+
+        assert "index" in result[0]
+        assert "prediction" in result[0]
+        assert "score" in result[0]
+        assert result[0]["prediction"] == "label1"