community[minor]: Add OpenVINO rerank model support (#19791)

@eaidova @AlexKoff88 Could you help to review, thanks

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/community/langchain_community/document_compressors/__init__.py

@@ -3,6 +3,7 @@ from typing import Any
 
 _module_lookup = {
     "LLMLinguaCompressor": "langchain_community.document_compressors.llmlingua_filter",
+    "OpenVINOReranker": "langchain_community.document_compressors.openvino_rerank",
 }
 
 

libs/community/langchain_community/document_compressors/openvino_rerank.py

@@ -0,0 +1,155 @@
+from pathlib import Path
+from typing import Any, Dict, Optional, Sequence
+
+import numpy as np
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import Document
+from langchain_core.documents.compressor import BaseDocumentCompressor
+from langchain_core.pydantic_v1 import Field
+
+
+class RerankRequest:
+    def __init__(self, query: Any = None, passages: Any = None):
+        self.query = query
+        self.passages = passages if passages is not None else []
+
+
+class OpenVINOReranker(BaseDocumentCompressor):
+    """
+    OpenVINO rerank models.
+    """
+
+    ov_model: Any
+    """OpenVINO model object."""
+    tokenizer: Any
+    """Tokenizer for embedding model."""
+    model_name_or_path: str
+    """HuggingFace model id."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Keyword arguments passed to the model."""
+    top_n: int = 4
+    """return Top n texts."""
+
+    def __init__(self, **kwargs: Any):
+        super().__init__(**kwargs)
+
+        try:
+            from optimum.intel.openvino import OVModelForSequenceClassification
+        except ImportError as e:
+            raise ValueError(
+                "Could not import optimum-intel python package. "
+                "Please install it with: "
+                "pip install -U 'optimum[openvino,nncf]'"
+            ) from e
+
+        try:
+            from huggingface_hub import HfApi
+        except ImportError as e:
+            raise ValueError(
+                "Could not import huggingface_hub python package. "
+                "Please install it with: "
+                "`pip install -U huggingface_hub`."
+            ) from e
+
+        def require_model_export(
+            model_id: str, revision: Any = None, subfolder: Any = None
+        ) -> bool:
+            model_dir = Path(model_id)
+            if subfolder is not None:
+                model_dir = model_dir / subfolder
+            if model_dir.is_dir():
+                return (
+                    not (model_dir / "openvino_model.xml").exists()
+                    or not (model_dir / "openvino_model.bin").exists()
+                )
+            hf_api = HfApi()
+            try:
+                model_info = hf_api.model_info(model_id, revision=revision or "main")
+                normalized_subfolder = (
+                    None if subfolder is None else Path(subfolder).as_posix()
+                )
+                model_files = [
+                    file.rfilename
+                    for file in model_info.siblings
+                    if normalized_subfolder is None
+                    or file.rfilename.startswith(normalized_subfolder)
+                ]
+                ov_model_path = (
+                    "openvino_model.xml"
+                    if subfolder is None
+                    else f"{normalized_subfolder}/openvino_model.xml"
+                )
+                return (
+                    ov_model_path not in model_files
+                    or ov_model_path.replace(".xml", ".bin") not in model_files
+                )
+            except Exception:
+                return True
+
+        if require_model_export(self.model_name_or_path):
+            # use remote model
+            self.ov_model = OVModelForSequenceClassification.from_pretrained(
+                self.model_name_or_path, export=True, **self.model_kwargs
+            )
+        else:
+            # use local model
+            self.ov_model = OVModelForSequenceClassification.from_pretrained(
+                self.model_name_or_path, **self.model_kwargs
+            )
+
+        try:
+            from transformers import AutoTokenizer
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import transformers, please install with "
+                "`pip install -U transformers`."
+            ) from e
+
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
+
+    def rerank(self, request: Any) -> Any:
+        query = request.query
+        passages = request.passages
+
+        query_passage_pairs = [[query, passage["text"]] for passage in passages]
+        input_tensors = self.tokenizer(
+            query_passage_pairs, padding=True, truncation=True, return_tensors="pt"
+        )
+
+        outputs = self.ov_model(**input_tensors, return_dict=True)
+        if outputs[0].shape[1] > 1:
+            scores = outputs[0][:, 1]
+        else:
+            scores = outputs[0].flatten()
+
+        scores = list(1 / (1 + np.exp(-scores)))
+
+        # Combine scores with passages, including metadata
+        for score, passage in zip(scores, passages):
+            passage["score"] = score
+
+        # Sort passages based on scores
+        passages.sort(key=lambda x: x["score"], reverse=True)
+
+        return passages
+
+    def compress_documents(
+        self,
+        documents: Sequence[Document],
+        query: str,
+        callbacks: Optional[Callbacks] = None,
+    ) -> Sequence[Document]:
+        passages = [
+            {"id": i, "text": doc.page_content} for i, doc in enumerate(documents)
+        ]
+
+        rerank_request = RerankRequest(query=query, passages=passages)
+        rerank_response = self.rerank(rerank_request)[: self.top_n]
+        final_results = []
+        for r in rerank_response:
+            doc = Document(
+                page_content=r["text"],
+                metadata={"id": r["id"], "relevance_score": r["score"]},
+            )
+            final_results.append(doc)
+        return final_results

libs/community/langchain_community/embeddings/openvino.py

@@ -16,8 +16,6 @@ DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索
 class OpenVINOEmbeddings(BaseModel, Embeddings):
     """OpenVINO embedding models.
 
-    To use, you should have the ``sentence_transformers`` python package installed.
-
     Example:
         .. code-block:: python