community[minor]: add FlashRank ranker (#16785)

**Description:** This PR adds support for
[flashrank](https://github.com/PrithivirajDamodaran/FlashRank) for
reranking as alternative to Cohere.

I'm not sure `libs/langchain` is the right place for this change. At
first, I wanted to put it under `libs/community`. All the compressors
were under `libs/langchain/retrievers/document_compressors` though. Hope
this makes sense!

libs/langchain/langchain/retrievers/document_compressors/__init__.py

@@ -9,6 +9,7 @@ from langchain.retrievers.document_compressors.cohere_rerank import CohereRerank
 from langchain.retrievers.document_compressors.embeddings_filter import (
     EmbeddingsFilter,
 )
+from langchain.retrievers.document_compressors.flashrank_rerank import FlashrankRerank
 
 __all__ = [
     "DocumentCompressorPipeline",
@@ -16,4 +17,5 @@ __all__ = [
     "LLMChainExtractor",
     "LLMChainFilter",
     "CohereRerank",
+    "FlashrankRerank",
 ]

libs/langchain/langchain/retrievers/document_compressors/flashrank_rerank.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Dict, Optional, Sequence
+
+from langchain_core.documents import Document
+from langchain_core.pydantic_v1 import Extra, root_validator
+
+from langchain.callbacks.manager import Callbacks
+from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
+
+if TYPE_CHECKING:
+    from flashrank import Ranker, RerankRequest
+else:
+    # Avoid pydantic annotation issues when actually instantiating
+    # while keeping this import optional
+    try:
+        from flashrank import Ranker, RerankRequest
+    except ImportError:
+        pass
+
+DEFAULT_MODEL_NAME = "ms-marco-MultiBERT-L-12"
+
+
+class FlashrankRerank(BaseDocumentCompressor):
+    """Document compressor using Flashrank interface."""
+
+    client: Ranker
+    """Flashrank client to use for compressing documents"""
+    top_n: int = 3
+    """Number of documents to return."""
+    model: Optional[str] = None
+    """Model to use for reranking."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        try:
+            from flashrank import Ranker
+        except ImportError:
+            raise ImportError(
+                "Could not import flashrank python package. "
+                "Please install it with `pip install flashrank`."
+            )
+
+        values["model"] = values.get("model", DEFAULT_MODEL_NAME)
+        values["client"] = Ranker(model_name=values["model"])
+        return values
+
+    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.client.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