Merge pull request #373 from ggozad/feat/rerank-cross-encoder

Add cross-encoder reranking provider support

.github/workflows/test.yml

@@ -66,6 +66,8 @@ jobs:
           key: huggingface-${{ runner.os }}-qwen-tokenizer-v1
       - name: Pre-download tokenizer
         run: uv run python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-0.6B')"
+      - name: Pre-download cross-encoder test model
+        run: uv run python -c "from sentence_transformers import CrossEncoder; CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')"
       - name: Run tests with coverage
         run: uv run pytest -m "not integration" --cov=haiku --cov-report=xml
       - name: Upload coverage to Codecov

CHANGELOG.md

@@ -1,6 +1,10 @@
 # Changelog
 ## [Unreleased]
 
+### Added
+
+- **`cross-encoder` reranking provider.** Runs any HuggingFace cross-encoder reranker in-process via `sentence_transformers.CrossEncoder` — no separate server. Useful for BGE (`BAAI/bge-reranker-v2-m3`), Qwen3-Reranker, MS-MARCO MiniLM, and other CrossEncoder-compatible models when vLLM is not an option. New `[cross-encoder]` extra pulls `sentence-transformers`.
+
 ### Fixed
 
 - **`rebuild --embed-only` no longer buffers the entire corpus in memory.** The previous implementation accumulated every chunk's id, content, content_fts, metadata, and new embedding vector in a single Python list before flushing. The rebuild now stream-copies non-vector columns into a `chunks_rebuild_staging` table (1000 rows / page), recreates the chunks table fresh to honour vector-dim changes, then streams from staging one document at a time, embedding in batches of `embeddings.batch_size` and flushing to the new chunks table every 50 documents.

docs/configuration/providers.md

@@ -465,3 +465,24 @@ reranking:
 ```
 
 **Note:** The Jina Reranker v3 local model is licensed under CC BY-NC 4.0, which restricts commercial use. For commercial applications, use the API mode instead.
+
+### Cross-Encoder (sentence-transformers)
+
+Run any HuggingFace cross-encoder reranker in-process via `sentence-transformers` — no separate server required. Useful when you want a specific model (BGE, Qwen3-Reranker, MS-MARCO MiniLM, etc.) without running vLLM.
+
+Install the extra:
+
+```bash
+uv pip install haiku.rag-slim[cross-encoder]
+```
+
+Then configure with any HuggingFace model id:
+
+```yaml
+reranking:
+  model:
+    provider: cross-encoder
+    name: BAAI/bge-reranker-v2-m3
+```
+
+Other tested models: `Qwen/Qwen3-Reranker-0.6B`, `cross-encoder/ms-marco-MiniLM-L-6-v2`. Any model exposed as a `sentence_transformers.CrossEncoder` works.

haiku_rag_slim/haiku/rag/client/downloads.py

@@ -52,13 +52,14 @@ async def download_models(
     # Sentence-transformers embedder
     if config.embeddings.model.provider == "sentence-transformers":  # pragma: no cover
         try:
-            from sentence_transformers import (  # type: ignore[import-not-found]  # ty: ignore[unresolved-import]
+            from sentence_transformers import (  # type: ignore[import-not-found]
                 SentenceTransformer,
             )
 
             model_name = config.embeddings.model.name
             yield DownloadProgress(model=model_name, status="start")
-            await asyncio.to_thread(SentenceTransformer, model_name)
+            # Wrap in lambda: ty loses ParamSpec inference on third-party __init__.
+            await asyncio.to_thread(lambda: SentenceTransformer(model_name))
             yield DownloadProgress(model=model_name, status="done")
         except ImportError:
             pass

haiku_rag_slim/haiku/rag/reranking/__init__.py

@@ -67,4 +67,17 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
         except ImportError:  # pragma: no cover
             return None
 
+    if config.reranking.model and config.reranking.model.provider == "cross-encoder":
+        try:
+            from haiku.rag.reranking.cross_encoder import CrossEncoderReranker
+
+            name = config.reranking.model.name
+            if not name:
+                raise ValueError(
+                    "cross-encoder reranker requires name in reranking.model"
+                )
+            return CrossEncoderReranker(name)
+        except ImportError:  # pragma: no cover
+            return None
+
     return None

haiku_rag_slim/haiku/rag/reranking/cross_encoder.py

@@ -0,0 +1,39 @@
+import asyncio
+
+try:
+    from sentence_transformers import (
+        CrossEncoder,  # pyright: ignore[reportMissingImports]
+    )
+except ImportError as e:  # pragma: no cover
+    raise ImportError(
+        "sentence-transformers is not installed. Install it with "
+        "`pip install sentence-transformers` or use the cross-encoder optional dependency."
+    ) from e
+
+from haiku.rag.reranking.base import RerankerBase
+from haiku.rag.store.models.chunk import Chunk
+
+
+class CrossEncoderReranker(RerankerBase):
+    """Reranker for any sentence-transformers CrossEncoder model.
+
+    Loads the model in-process. Pass any HuggingFace cross-encoder reranker
+    as ``model`` (e.g. ``BAAI/bge-reranker-v2-m3``, ``Qwen/Qwen3-Reranker-0.6B``,
+    ``cross-encoder/ms-marco-MiniLM-L-6-v2``).
+    """
+
+    def __init__(self, model: str):
+        self._model = model
+        self._reranker = CrossEncoder(model)
+
+    async def rerank(
+        self, query: str, chunks: list[Chunk], top_n: int = 10
+    ) -> list[tuple[Chunk, float]]:
+        if not chunks:
+            return []
+
+        documents = [chunk.content for chunk in chunks]
+        rankings = await asyncio.to_thread(
+            lambda: self._reranker.rank(query, documents, top_k=top_n)
+        )
+        return [(chunks[r["corpus_id"]], float(r["score"])) for r in rankings]

haiku_rag_slim/haiku/rag/reranking/jina_local.py

@@ -1,8 +1,10 @@
+import asyncio
+
 try:
     from transformers import (
         AutoModel,  # pyright: ignore[reportMissingImports]
     )
-except ImportError as e:
+except ImportError as e:  # pragma: no cover
     raise ImportError(
         "transformers is not installed. Please install it with `pip install transformers torch` "
         "or use the jina optional dependency."
@@ -32,6 +34,8 @@ class JinaLocalReranker(RerankerBase):  # pragma: no cover
 
         documents = [chunk.content for chunk in chunks]
 
-        results = self._reranker.rerank(query, documents, top_n=top_n)
+        results = await asyncio.to_thread(
+            lambda: self._reranker.rerank(query, documents, top_n=top_n)
+        )
 
         return [(chunks[r["index"]], float(r["relevance_score"])) for r in results]

haiku_rag_slim/haiku/rag/reranking/mxbai.py

@@ -1,3 +1,5 @@
+import asyncio
+
 from mxbai_rerank import MxbaiRerankV2  # pyright: ignore[reportMissingImports]
 
 from haiku.rag.config import Config
@@ -22,7 +24,9 @@ class MxBAIReranker(RerankerBase):
 
         documents = [chunk.content for chunk in chunks]
 
-        results = self._client.rank(query=query, documents=documents, top_k=top_n)
+        results = await asyncio.to_thread(
+            lambda: self._client.rank(query=query, documents=documents, top_k=top_n)
+        )
         reranked_chunks = []
         for result in results:
             original_chunk = chunks[result.index]

haiku_rag_slim/pyproject.toml

@@ -52,6 +52,7 @@ mxbai = ["mxbai-rerank>=0.1.6"]
 cohere = ["cohere>=5.21.1"]
 zeroentropy = ["zeroentropy>=0.1.0a11"]
 jina = ["transformers>=4.40.0", "torch>=2.0.0"]
+cross-encoder = ["sentence-transformers>=3.0.0"]
 # TUI (chat and inspect commands)
 tui = ["textual>=8.2.4", "textual-image>=0.8.5"]
 # Model providers (delegated to pydantic-ai-slim)

pyproject.toml

@@ -39,6 +39,7 @@ haiku-rag = "haiku.rag.cli:cli"
 [project.optional-dependencies]
 tui = ["textual>=8.2.4"]
 s3 = ["haiku.rag-slim[s3]==0.46.0"]
+cross-encoder = ["haiku.rag-slim[cross-encoder]==0.46.0"]
 
 [build-system]
 requires = ["hatchling"]

tests/test_reranker.py

@@ -62,6 +62,18 @@ async def test_mxbai_reranker():
         pytest.skip("MxBAI package not installed")
 
 
+@pytest.mark.asyncio
+async def test_mxbai_reranker_empty_chunks():
+    try:
+        from haiku.rag.reranking.mxbai import MxBAIReranker
+
+        reranker = MxBAIReranker()
+        result = await reranker.rerank("query", [], top_n=2)
+        assert result == []
+    except ImportError:
+        pytest.skip("MxBAI package not installed")
+
+
 @pytest.mark.asyncio
 @pytest.mark.vcr()
 async def test_cohere_reranker():
@@ -126,6 +138,15 @@ class TestGetReranker:
         with pytest.raises(ValueError, match="vLLM reranker requires base_url"):
             get_reranker(config)
 
+    def test_cross_encoder_provider_without_name_raises_error(self):
+        config = AppConfig(
+            reranking=RerankingConfig(
+                model=ModelConfig(provider="cross-encoder", name="")
+            )
+        )
+        with pytest.raises(ValueError, match="cross-encoder reranker requires name"):
+            get_reranker(config)
+
     @pytest.mark.parametrize(
         "provider, model_name, class_module, class_name, extra_model_kwargs, expected_attrs, env_vars",
         [
@@ -195,6 +216,15 @@ class TestGetReranker:
                 {"_model": "jinaai/jina-reranker-v3"},
                 {},
             ),
+            (
+                "cross-encoder",
+                "cross-encoder/ms-marco-MiniLM-L-6-v2",
+                "haiku.rag.reranking.cross_encoder",
+                "CrossEncoderReranker",
+                {},
+                {"_model": "cross-encoder/ms-marco-MiniLM-L-6-v2"},
+                {},
+            ),
         ],
         ids=[
             "mxbai",
@@ -204,6 +234,7 @@ class TestGetReranker:
             "zeroentropy-default",
             "jina",
             "jina-local",
+            "cross-encoder",
         ],
     )
     def test_provider(
@@ -298,3 +329,33 @@ async def test_jina_local_reranker():
         assert "0" in top_ids or "2" in top_ids  # These chunks mention the book/author
     except ImportError:
         pytest.skip("Jina local dependencies not installed")
+
+
+@pytest.mark.asyncio
+async def test_cross_encoder_reranker():
+    try:
+        from haiku.rag.reranking.cross_encoder import CrossEncoderReranker
+
+        reranker = CrossEncoderReranker("cross-encoder/ms-marco-MiniLM-L-6-v2")
+
+        reranked = await reranker.rerank(
+            "Who wrote 'To Kill a Mockingbird'?", chunks, top_n=2
+        )
+        assert len(reranked) == 2
+        assert all(isinstance(score, float) for chunk, score in reranked)
+        top_ids = [chunk.document_id for chunk, score in reranked]
+        assert "0" in top_ids or "2" in top_ids
+    except ImportError:
+        pytest.skip("sentence-transformers not installed")
+
+
+@pytest.mark.asyncio
+async def test_cross_encoder_reranker_empty_chunks():
+    try:
+        from haiku.rag.reranking.cross_encoder import CrossEncoderReranker
+
+        reranker = CrossEncoderReranker("cross-encoder/ms-marco-MiniLM-L-6-v2")
+        result = await reranker.rerank("query", [], top_n=2)
+        assert result == []
+    except ImportError:
+        pytest.skip("sentence-transformers not installed")

uv.lock

@@ -1445,6 +1445,9 @@ dependencies = [
 ]
 
 [package.optional-dependencies]
+cross-encoder = [
+    { name = "haiku-rag-slim", extra = ["cross-encoder"] },
+]
 s3 = [
     { name = "haiku-rag-slim", extra = ["s3"] },
 ]
@@ -1472,11 +1475,12 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "haiku-rag-slim", extras = ["cross-encoder"], marker = "extra == 'cross-encoder'", editable = "haiku_rag_slim" },
     { name = "haiku-rag-slim", extras = ["docling", "voyageai", "mxbai", "cohere", "zeroentropy", "tui"], editable = "haiku_rag_slim" },
     { name = "haiku-rag-slim", extras = ["s3"], marker = "extra == 's3'", editable = "haiku_rag_slim" },
     { name = "textual", marker = "extra == 'tui'", specifier = ">=8.2.4" },
 ]
-provides-extras = ["tui", "s3"]
+provides-extras = ["tui", "s3", "cross-encoder"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -1557,6 +1561,9 @@ bedrock = [
 cohere = [
     { name = "cohere" },
 ]
+cross-encoder = [
+    { name = "sentence-transformers" },
+]
 docling = [
     { name = "docling" },
     { name = "opencv-python-headless" },
@@ -1621,6 +1628,7 @@ requires-dist = [
     { name = "python-dotenv", specifier = ">=1.2.2" },
     { name = "pyyaml", specifier = ">=6.0.3" },
     { name = "rich", specifier = ">=14.3.3" },
+    { name = "sentence-transformers", marker = "extra == 'cross-encoder'", specifier = ">=3.0.0" },
     { name = "textual", marker = "extra == 'tui'", specifier = ">=8.2.4" },
     { name = "textual-image", marker = "extra == 'tui'", specifier = ">=0.8.5" },
     { name = "torch", marker = "extra == 'jina'", specifier = ">=2.0.0" },
@@ -1630,7 +1638,7 @@ requires-dist = [
     { name = "zeroentropy", marker = "extra == 'zeroentropy'", specifier = ">=0.1.0a11" },
     { name = "zstandard", marker = "python_full_version < '3.14'", specifier = ">=0.23.0" },
 ]
-provides-extras = ["docling", "s3", "voyageai", "mxbai", "cohere", "zeroentropy", "jina", "tui", "anthropic", "groq", "google", "mistral", "bedrock", "vertexai"]
+provides-extras = ["docling", "s3", "voyageai", "mxbai", "cohere", "zeroentropy", "jina", "cross-encoder", "tui", "anthropic", "groq", "google", "mistral", "bedrock", "vertexai"]
 
 [[package]]
 name = "haiku-skills"
@@ -1907,6 +1915,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "joblib"
+version = "1.5.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" }
+wheels = [
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+]
+
 [[package]]
 name = "jsonlines"
 version = "4.0.0"
@@ -4549,6 +4566,50 @@ torch = [
     { name = "torch" },
 ]
 
+[[package]]
+name = "scikit-learn"
+version = "1.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "joblib" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+]
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