Remove the mxbai reranking provider

CHANGELOG.md

@@ -3,7 +3,11 @@
 
 ### Changed
 
-- `mxbai` extra allows `transformers` 5.x (`<6.0.0`); `MxBAIReranker` supplies the `tokenizer.prepare_for_model` variant `mxbai-rerank` needs when the tokenizer lacks it.
+- Unknown `reranking.model.provider` raises `ValueError` instead of silently disabling reranking.
+
+### Removed
+
+- `mxbai` reranking provider and extra; the `transformers<5.0.0` cap goes with it. Migrate to `provider: cross-encoder` with the same model name (`mixedbread-ai/mxbai-rerank-base-v2`), installed via the `cross-encoder` extra.
 
 ### Fixed
 

README.md

@@ -13,7 +13,7 @@ Agentic RAG built on [LanceDB](https://lancedb.com/), [Pydantic AI](https://ai.p
 - **Multimodal & cross-modal search** — Multimodal embedders (vLLM, VoyageAI, Cohere) put picture vectors in the same space as text; supports text-as-query → figure hits and image-as-query
 - **Question answering** — RAG skill with citations (page numbers, section headings)
 - **Vision QA** — Vision-capable models receive figure bytes alongside chunk text
-- **Reranking** — MxBAI, Cohere, Zero Entropy, or vLLM
+- **Reranking** — local cross-encoders, Cohere, Zero Entropy, or vLLM
 - **Analysis skill** — Complex analytical tasks via sandboxed Python code execution (aggregation, computation, multi-document analysis)
 - **Conversational RAG** — Chat TUI and web application for multi-turn conversations with session memory
 - **Document structure** — Stores full [DoclingDocument](https://docling-project.github.io/docling/concepts/docling_document/), enabling structure-aware context expansion

docker/README.md

@@ -1,6 +1,6 @@
 # haiku.rag Docker Image
 
-The full haiku.rag Docker image includes all features and extras (docling, voyageai, mxbai). You can build it locally using the provided Dockerfile.
+The full haiku.rag Docker image includes all features and extras (docling, voyageai, cross-encoder). You can build it locally using the provided Dockerfile.
 
 ## Building the Image
 

docs/cli.md

@@ -322,7 +322,7 @@ It also probes the external endpoints the config uses and reports them under a P
 - docling-serve is reachable when used as the converter or chunker (`{base_url}/health`)
 - custom OpenAI-compatible and vLLM endpoints respond (`{base_url}/models`)
 
-SaaS providers (OpenAI, Anthropic, Cohere, Jina, ZeroEntropy, Voyage) are covered by the API-key check rather than a network probe. In-process local models (sentence-transformers, cross-encoder, mxbai, jina-local) have no endpoint and are reported as such.
+SaaS providers (OpenAI, Anthropic, Cohere, Jina, ZeroEntropy, Voyage) are covered by the API-key check rather than a network probe. In-process local models (sentence-transformers, cross-encoder, jina-local) have no endpoint and are reported as such.
 
 Each failure prints the command that fixes it (`rebuild`, `create-index`, `migrate`, `rebuild --set-embedder`). `doctor` makes no changes. It exits with status 1 when any check fails, so it can gate CI or monitoring.
 

docs/configuration/index.md

@@ -97,7 +97,7 @@ embeddings:
 
 reranking:
   model:
-    provider: ""  # Empty to disable, or mxbai, cohere, zeroentropy, vllm
+    provider: ""  # Empty to disable, or cross-encoder, cohere, zeroentropy, vllm
     name: ""
 
 qa:

docs/configuration/providers.md

@@ -390,23 +390,6 @@ Reranking improves search quality by re-ordering the initial search results usin
 
 Reranking is **disabled by default** (`provider: ""`) for faster searches. You can enable it by configuring one of the providers below.
 
-### MixedBread AI
-
-If you installed `haiku.rag` (full package), MxBAI is already included. If you installed `haiku.rag-slim`, add the mxbai extra:
-
-```bash
-uv pip install haiku.rag-slim[mxbai]
-```
-
-Then configure:
-
-```yaml
-reranking:
-  model:
-    provider: mxbai
-    name: mixedbread-ai/mxbai-rerank-base-v2
-```
-
 ### Cohere
 
 If you installed `haiku.rag` (full package), Cohere is already included. If you installed `haiku.rag-slim`, add the cohere extra:
@@ -523,7 +506,7 @@ Then configure with any HuggingFace model id:
 reranking:
   model:
     provider: cross-encoder
-    name: BAAI/bge-reranker-v2-m3
+    name: mixedbread-ai/mxbai-rerank-base-v2
 ```
 
-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.
+Other tested models: `BAAI/bge-reranker-v2-m3`, `Qwen/Qwen3-Reranker-0.6B`, `cross-encoder/ms-marco-MiniLM-L-6-v2`. Any model exposed as a `sentence_transformers.CrossEncoder` works.

docs/installation.md

@@ -25,14 +25,14 @@ uv pip install haiku.rag-slim
 uv pip install haiku.rag-slim[docling]
 
 # With specific providers
-uv pip install haiku.rag-slim[docling,voyageai,mxbai]
+uv pip install haiku.rag-slim[docling,voyageai,cross-encoder]
 ```
 
 The slim package has minimal dependencies and lets you install only what you need:
 
 - `docling` - PDF, DOCX, PPTX, images, and other document formats
 - `voyageai` - VoyageAI embeddings
-- `mxbai` - MixedBread AI reranking
+- `cross-encoder` - Local reranking via sentence-transformers
 - `cohere` - Cohere reranking
 - `zeroentropy` - Zero Entropy reranking
 - `tui` - Terminal UI for `chat` and `inspect` commands

evaluations/configs/orb_text.yaml

@@ -17,7 +17,7 @@ embeddings:
 
 reranking:
   model:
-    provider: mxbai
+    provider: cross-encoder
     name: mixedbread-ai/mxbai-rerank-base-v2
 
 qa:

evaluations/configs/t2_finqa.yaml

@@ -22,7 +22,7 @@ processing:
 
 reranking:
   model:
-    provider: mxbai
+    provider: cross-encoder
     name: mixedbread-ai/mxbai-rerank-base-v2
 
 qa:

evaluations/configs/wix.yaml

@@ -16,7 +16,7 @@ embeddings:
 
 reranking:
   model:
-    provider: mxbai
+    provider: cross-encoder
     name: mixedbread-ai/mxbai-rerank-base-v2
 
 qa:

evaluations/tests/test_benchmark.py

@@ -63,12 +63,12 @@ class TestBuildExperimentMetadata:
     def test_with_reranker(self) -> None:
         config = AppConfig()
         config.reranking.model = ModelConfig(
-            provider="mxbai", name="mixedbread-ai/mxbai-rerank-base-v2"
+            provider="cross-encoder", name="mixedbread-ai/mxbai-rerank-base-v2"
         )
         result = build_experiment_metadata(
             dataset_key="test", test_cases=1, config=config
         )
-        assert result["rerank_provider"] == "mxbai"
+        assert result["rerank_provider"] == "cross-encoder"
         assert result["rerank_model"] == "mixedbread-ai/mxbai-rerank-base-v2"
 
 

haiku_rag_slim/README.md

@@ -35,7 +35,7 @@ Adds support for 40+ file formats including PDF, DOCX, HTML, and more.
 - `voyageai` - VoyageAI embeddings
 
 **Rerankers:**
-- `mxbai` - MixedBread AI
+- `cross-encoder` - Local reranking via sentence-transformers
 - `cohere` - Cohere
 - `zeroentropy` - Zero Entropy
 
@@ -51,7 +51,7 @@ Adds support for 40+ file formats including PDF, DOCX, HTML, and more.
 
 ```bash
 # Common combinations
-uv pip install haiku.rag-slim[docling,anthropic,mxbai]
+uv pip install haiku.rag-slim[docling,anthropic,cross-encoder]
 uv pip install haiku.rag-slim[docling,groq]
 ```
 

haiku_rag_slim/haiku/rag/client/downloads.py

@@ -28,7 +28,7 @@ async def download_models(
     - Docling models
     - HuggingFace tokenizer
     - Sentence-transformers embedder (if configured)
-    - HuggingFace reranker models (mxbai, jina-local)
+    - HuggingFace reranker models (cross-encoder, jina-local)
     - Ollama models
     """
     # Docling models
@@ -69,14 +69,14 @@ async def download_models(
         provider = config.reranking.model.provider
         model_name = config.reranking.model.name
 
-        if provider == "mxbai":
+        if provider == "cross-encoder":
             try:
-                from mxbai_rerank import MxbaiRerankV2
+                from sentence_transformers import (  # type: ignore[import-not-found]
+                    CrossEncoder,
+                )
 
                 yield DownloadProgress(model=model_name, status="start")
-                await asyncio.to_thread(
+                await asyncio.to_thread(lambda: CrossEncoder(model_name))
-                    MxbaiRerankV2, model_name, disable_transformers_warnings=True
-                )
                 yield DownloadProgress(model=model_name, status="done")
             except ImportError:
                 pass

haiku_rag_slim/haiku/rag/doctor.py

@@ -34,7 +34,7 @@ _PROVIDER_ENV_VARS: dict[str, str] = {
 }
 
 # Providers backed by in-process local models — no endpoint to probe.
-_LOCAL_PROVIDERS = {"sentence-transformers", "mxbai", "cross-encoder", "jina-local"}
+_LOCAL_PROVIDERS = {"sentence-transformers", "cross-encoder", "jina-local"}
 
 # Item labels that never yield a standalone chunk: pictures (handled via the
 # image path), headings (folded into chunk context, not embedded alone), and

haiku_rag_slim/haiku/rag/reranking/__init__.py

@@ -1,5 +1,3 @@
-import os
-
 from haiku.rag.config import AppConfig, Config
 from haiku.rag.reranking.base import RerankerBase
 
@@ -12,12 +10,6 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
         return None
 
     try:
-        if model.provider == "mxbai":
-            from haiku.rag.reranking.mxbai import MxBAIReranker
-
-            os.environ["TOKENIZERS_PARALLELISM"] = "true"
-            return MxBAIReranker()
-
         if model.provider == "cohere":
             from haiku.rag.reranking.cohere import CohereReranker
 
@@ -56,4 +48,4 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
     except ImportError:  # pragma: no cover
         return None
 
-    return None
+    raise ValueError(f"Unknown reranking provider: {model.provider}")

haiku_rag_slim/haiku/rag/reranking/mxbai.py

@@ -1,58 +0,0 @@
-import asyncio
-import threading
-
-import tqdm
-from mxbai_rerank import MxbaiRerankV2  # pyright: ignore[reportMissingImports]
-
-from haiku.rag.config import Config
-from haiku.rag.reranking.base import RerankerBase
-from haiku.rag.store.models.chunk import Chunk
-
-# tqdm's default class lock is a multiprocessing.RLock; constructing it spawns
-# resource_tracker, which inherits sys.stderr's fileno. Inside Textual's chat
-# TUI, sys.stderr.fileno() returns -1, landing in fds_to_keep and failing the
-# fork_exec validation. A threading lock is sufficient since we never share
-# tqdm progress bars across processes.
-tqdm.tqdm.set_lock(threading.RLock())
-
-
-def _prepare_for_model(
-    ids: list[int],
-    pair_ids: list[int] | None = None,
-    max_length: int | None = None,
-    **_,
-) -> dict[str, list[int]]:
-    # transformers 5.x removed tokenizer.prepare_for_model, which mxbai-rerank
-    # calls with add_special_tokens=False and truncation="only_second"; for that
-    # call pattern it reduces to truncating the pair and concatenating.
-    pair_ids = pair_ids or []
-    if max_length is not None:
-        pair_ids = pair_ids[: max(0, max_length - len(ids))]
-    return {"input_ids": ids + pair_ids}
-
-
-class MxBAIReranker(RerankerBase):
-    def __init__(self):
-        model_name = (
-            Config.reranking.model.name
-            if Config.reranking.model
-            else "mixedbread-ai/mxbai-rerank-base-v2"
-        )
-        self._client = MxbaiRerankV2(model_name, disable_transformers_warnings=True)
-        if not hasattr(self._client.tokenizer, "prepare_for_model"):
-            self._client.tokenizer.prepare_for_model = _prepare_for_model
-
-    async def _rerank(
-        self, query: str, chunks: list[Chunk], top_n: int = 10
-    ) -> list[tuple[Chunk, float]]:
-        documents = [chunk.content for chunk in chunks]
-
-        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]
-            reranked_chunks.append((original_chunk, result.score))
-
-        return reranked_chunks

haiku_rag_slim/pyproject.toml

@@ -51,7 +51,6 @@ s3 = ["obstore>=0.9,<0.10"]
 # Embedding providers
 voyageai = ["pydantic-ai-slim[voyageai]"]
 # Rerankers
-mxbai = ["mxbai-rerank>=0.1.6", "transformers>=4.49.0,<6.0.0"]
 cohere = ["cohere>=5.21.1"]
 zeroentropy = ["zeroentropy>=0.1.0a11"]
 jina = ["transformers>=4.40.0", "torch>=2.0.0"]

pyproject.toml

@@ -30,7 +30,7 @@ classifiers = [
 ]
 
 dependencies = [
-    "haiku.rag-slim[docling,voyageai,mxbai,cohere,zeroentropy,tui,cross-encoder]==0.65.1",
+    "haiku.rag-slim[docling,voyageai,cohere,zeroentropy,tui,cross-encoder]==0.65.1",
 ]
 
 [project.scripts]

tests/test_reranker.py

@@ -11,7 +11,6 @@ from haiku.rag.store.models.chunk import Chunk
 # Providers whose constructor loads a model in-process. Factory-routing tests
 # patch the loader so they assert dispatch without paying the model load.
 HEAVY_LOADERS = {
-    "mxbai": "MxbaiRerankV2",
     "jina-local": "AutoModel",
     "cross-encoder": "CrossEncoder",
 }
@@ -53,40 +52,6 @@ async def test_reranker_base():
         await reranker.rerank("query", chunks)
 
 
-@pytest.mark.asyncio
-@pytest.mark.integration
-async def test_mxbai_reranker():
-    try:
-        from haiku.rag.config import Config
-        from haiku.rag.config.models import ModelConfig
-        from haiku.rag.reranking.mxbai import MxBAIReranker
-
-        Config.reranking.model = ModelConfig(
-            provider="mxbai", name="mixedbread-ai/mxbai-rerank-base-v2"
-        )
-        reranker = MxBAIReranker()
-        reranked = await reranker.rerank(
-            "Who wrote 'To Kill a Mockingbird'?", chunks, top_n=2
-        )
-        assert [chunk.document_id for chunk, score in reranked] == ["0", "2"]
-        assert all(isinstance(score, float) for chunk, score in reranked)
-        Config.reranking.model = None
-
-    except ImportError:
-        pytest.skip("MxBAI package not installed")
-
-
-def test_mxbai_prepare_for_model_shim():
-    pytest.importorskip("mxbai_rerank")
-    from haiku.rag.reranking.mxbai import _prepare_for_model
-
-    assert _prepare_for_model([1, 2], [3, 4]) == {"input_ids": [1, 2, 3, 4]}
-    assert _prepare_for_model([1, 2], [3, 4, 5], max_length=4) == {
-        "input_ids": [1, 2, 3, 4]
-    }
-    assert _prepare_for_model([1, 2], [3, 4], max_length=2) == {"input_ids": [1, 2]}
-
-
 @pytest.mark.asyncio
 @pytest.mark.vcr()
 async def test_cohere_reranker():
@@ -133,14 +98,14 @@ class TestGetReranker:
         result = get_reranker(config)
         assert result is None
 
-    def test_unknown_provider_returns_none(self):
+    def test_unknown_provider_raises_error(self):
         config = AppConfig(
             reranking=RerankingConfig(
                 model=ModelConfig(provider="unknown_provider", name="some-model")
             )
         )
-        result = get_reranker(config)
+        with pytest.raises(ValueError, match="Unknown reranking provider"):
-        assert result is None
+            get_reranker(config)
 
     def test_vllm_provider_without_base_url_raises_error(self):
         config = AppConfig(
@@ -163,15 +128,6 @@ class TestGetReranker:
     @pytest.mark.parametrize(
         "provider, model_name, class_module, class_name, extra_model_kwargs, expected_attrs, env_vars",
         [
-            (
-                "mxbai",
-                "mixedbread-ai/mxbai-rerank-base-v2",
-                "haiku.rag.reranking.mxbai",
-                "MxBAIReranker",
-                {},
-                {},
-                {},
-            ),
             (
                 "cohere",
                 "rerank-v3.5",
@@ -240,7 +196,6 @@ class TestGetReranker:
             ),
         ],
         ids=[
-            "mxbai",
             "cohere",
             "vllm",
             "zeroentropy",

uv.lock

@@ -323,15 +323,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/95/adcb68e20c34162e9135f370d6e31737719c2b6f94bc953fe7ed1f10fe21/authlib-1.7.2-py2.py3-none-any.whl", hash = "sha256:3e1faedc9d87e7d56a164eca3ccb6ace0d61b94abe83e92242f8dc8bba9b4a9f", size = 259548, upload-time = "2026-05-06T08:10:21.436Z" },
 ]
 
-[[package]]
-name = "batched"
-version = "0.1.5"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/c9/40/8d9a8ed9b95cb95acf599698557b7074b462df652823a61e7e43899aa519/batched-0.1.5.tar.gz", hash = "sha256:58b8a41d3f8d4d39a0edba79c6238ed204938cfc2c8908224919d70af07c610d", size = 23940, upload-time = "2025-07-14T09:58:31.862Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/fb/c8/16a977fd90cdc974ef7781e237b8a0e0008a6204768ededbef2b2ff1bb43/batched-0.1.5-py3-none-any.whl", hash = "sha256:356dae99f15c906629992e4bd3481a857114790b5316268fa38fe8ad0d0b9480", size = 29367, upload-time = "2025-07-14T09:58:30.968Z" },
-]
-
 [[package]]
 name = "beartype"
 version = "0.22.9"
@@ -1583,7 +1574,7 @@ name = "haiku-rag"
 version = "0.65.1"
 source = { editable = "." }
 dependencies = [
-    { name = "haiku-rag-slim", extra = ["cohere", "cross-encoder", "docling", "mxbai", "tui", "voyageai", "zeroentropy"] },
+    { name = "haiku-rag-slim", extra = ["cohere", "cross-encoder", "docling", "tui", "voyageai", "zeroentropy"] },
 ]
 
 [package.optional-dependencies]
@@ -1619,7 +1610,7 @@ 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", "cross-encoder"], editable = "haiku_rag_slim" },
+    { name = "haiku-rag-slim", extras = ["docling", "voyageai", "cohere", "zeroentropy", "tui", "cross-encoder"], editable = "haiku_rag_slim" },
     { name = "haiku-rag-slim", extras = ["ingester"], marker = "extra == 'ingester'", 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" },
@@ -1732,10 +1723,6 @@ jina = [
 mistral = [
     { name = "pydantic-ai-slim", extra = ["mistral"] },
 ]
-mxbai = [
-    { name = "mxbai-rerank" },
-    { name = "transformers" },
-]
 s3 = [
     { name = "obstore" },
 ]
@@ -1769,7 +1756,6 @@ requires-dist = [
     { name = "jinja2", specifier = ">=3.1.0" },
     { name = "jsonpatch", specifier = ">=1.33" },
     { name = "lancedb", specifier = "==0.30.2" },
-    { name = "mxbai-rerank", marker = "extra == 'mxbai'", specifier = ">=0.1.6" },
     { name = "obstore", marker = "extra == 's3'", specifier = ">=0.9,<0.10" },
     { name = "opencv-python-headless", marker = "extra == 'docling'", specifier = ">=4.6.0.66,<5.0.0.0" },
     { name = "pathspec", specifier = ">=1.0.4" },
@@ -1793,7 +1779,6 @@ requires-dist = [
     { name = "textual-image", specifier = ">=0.8.5" },
     { name = "torch", marker = "extra == 'jina'", specifier = ">=2.0.0" },
     { name = "transformers", marker = "extra == 'jina'", specifier = ">=4.40.0" },
-    { name = "transformers", marker = "extra == 'mxbai'", specifier = ">=4.49.0,<6.0.0" },
     { name = "tree-sitter", marker = "extra == 'tui'", specifier = ">=0.25.2" },
     { name = "tree-sitter-json", marker = "extra == 'tui'", specifier = ">=0.24.8" },
     { name = "typer", specifier = ">=0.21.0,<0.22.0" },
@@ -1802,7 +1787,7 @@ 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", "cross-encoder", "ingester", "tui", "anthropic", "groq", "google", "mistral", "bedrock", "vertexai"]
+provides-extras = ["docling", "s3", "voyageai", "cohere", "zeroentropy", "jina", "cross-encoder", "ingester", "tui", "anthropic", "groq", "google", "mistral", "bedrock", "vertexai"]
 
 [[package]]
 name = "haiku-skills"
@@ -2814,23 +2799,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7e/82/69e539c4c2027f1e1697e09aaa2449243085a0edf81ae2c6341e84d769b6/multiprocess-0.70.19-py39-none-any.whl", hash = "sha256:0d4b4397ed669d371c81dcd1ef33fd384a44d6c3de1bd0ca7ac06d837720d3c5", size = 133477, upload-time = "2026-01-19T06:47:38.619Z" },
 ]
 
-[[package]]
-name = "mxbai-rerank"
-version = "0.1.6"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "accelerate" },
-    { name = "batched" },
-    { name = "numpy" },
-    { name = "torch" },
-    { name = "tqdm" },
-    { name = "transformers" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/0f/76/a19c864a1025222d3304a888ed4ed9217bfdf55dbaf4ed37500ee03935e0/mxbai_rerank-0.1.6.tar.gz", hash = "sha256:8d08e8464796429a7415314ce6de682bf9b538eb4ee5a7ddcd1a07839ee02879", size = 21449, upload-time = "2025-06-02T14:59:42.45Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/a6/2a/503622b3a80272c662dabef421c9635168e5cbf6d51f0aa1883998561292/mxbai_rerank-0.1.6-py3-none-any.whl", hash = "sha256:aee94e7a14d5fba6520052ff2098f0f03db6cd9cc39553b7d2e82389deec9e05", size = 18458, upload-time = "2025-06-02T14:59:41.003Z" },
-]
-
 [[package]]
 name = "networkx"
 version = "3.6.1"