feat: Restructure Rerankers so both NVIDIA and Cohere work properly (#5933)

* Removing reference to nonexistent method

* Restructuring rerankers to inherit from BaseDocumentCompressor. Adding Voyage AI reranker.

* Removing Voyage AI component and dependency.

* [autofix.ci] apply automated fixes

* feat: Add method to compress documents as DataFrame in LCCompressorComponent

* Changing description of abstract build_compressor method

* [autofix.ci] apply automated fixes

* Adding top_n as an argument to the NVIDIA reranker

* [autofix.ci] apply automated fixes

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

src/backend/base/langflow/base/compressors/model.py

@@ -0,0 +1,64 @@
+from abc import abstractmethod
+
+from langflow.custom import Component
+from langflow.field_typing import BaseDocumentCompressor
+from langflow.io import DataInput, IntInput, MultilineInput, SecretStrInput
+from langflow.schema import Data
+from langflow.schema.dataframe import DataFrame
+from langflow.template.field.base import Output
+
+
+class LCCompressorComponent(Component):
+    inputs = [
+        MultilineInput(
+            name="search_query",
+            display_name="Search Query",
+            tool_mode=True,
+        ),
+        SecretStrInput(
+            name="api_key",
+            display_name="API Key",
+        ),
+        DataInput(
+            name="search_results",
+            display_name="Search Results",
+            info="Search Results from a Vector Store.",
+            is_list=True,
+        ),
+        IntInput(name="top_n", display_name="Top N", value=3, advanced=True),
+    ]
+
+    outputs = [
+        Output(
+            display_name="Data",
+            name="compressed_documents",
+            method="Compressed Documents",
+        ),
+        Output(
+            display_name="DataFrame",
+            name="compressed_documents_as_dataframe",
+            method="Compressed Documents as DataFrame",
+        ),
+    ]
+
+    @abstractmethod
+    def build_compressor(self) -> BaseDocumentCompressor:
+        """Builds the Base Document Compressor object."""
+        msg = "build_compressor method must be implemented."
+        raise NotImplementedError(msg)
+
+    async def compress_documents(self) -> list[Data]:
+        """Compresses the documents retrieved from the vector store."""
+        compressor = self.build_compressor()
+        documents = compressor.compress_documents(
+            query=self.search_query,
+            documents=[passage.to_lc_document() for passage in self.search_results if isinstance(passage, Data)],
+        )
+        data = self.to_data(documents)
+        self.status = data
+        return data
+
+    async def compress_documents_as_dataframe(self) -> DataFrame:
+        """Compresses the documents retrieved from the vector store and returns a pandas DataFrame."""
+        data_objs = await self.compress_documents()
+        return DataFrame(data=data_objs)

src/backend/base/langflow/components/cohere/cohere_rerank.py

@@ -1,37 +1,17 @@
-from typing import cast
+from langflow.base.compressors.model import LCCompressorComponent
-
+from langflow.field_typing import BaseDocumentCompressor
-from langchain.retrievers import ContextualCompressionRetriever
+from langflow.io import DropdownInput
-from langchain_cohere import CohereRerank
-
-from langflow.base.vectorstores.model import (
-    LCVectorStoreComponent,
-    check_cached_vector_store,
-)
-from langflow.field_typing import Retriever, VectorStore
-from langflow.io import (
-    DropdownInput,
-    HandleInput,
-    IntInput,
-    MessageTextInput,
-    MultilineInput,
-    SecretStrInput,
-)
-from langflow.schema import Data
 from langflow.template.field.base import Output
 
 
-class CohereRerankComponent(LCVectorStoreComponent):
+class CohereRerankComponent(LCCompressorComponent):
     display_name = "Cohere Rerank"
-    description = "Rerank documents using the Cohere API and a retriever."
+    description = "Rerank documents using the Cohere API."
     name = "CohereRerank"
     icon = "Cohere"
-    legacy: bool = True
 
     inputs = [
-        MultilineInput(
+        *LCCompressorComponent.inputs,
-            name="search_query",
-            display_name="Search Query",
-        ),
         DropdownInput(
             name="model",
             display_name="Model",
@@ -43,48 +23,24 @@ class CohereRerankComponent(LCVectorStoreComponent):
             ],
             value="rerank-english-v3.0",
         ),
-        SecretStrInput(name="api_key", display_name="API Key"),
-        IntInput(name="top_n", display_name="Top N", value=3),
-        MessageTextInput(
-            name="user_agent",
-            display_name="User Agent",
-            value="langflow",
-            advanced=True,
-        ),
-        HandleInput(name="retriever", display_name="Retriever", input_types=["Retriever"]),
     ]
 
     outputs = [
         Output(
-            display_name="Retriever",
+            display_name="Reranked Documents",
-            name="base_retriever",
+            name="reranked_documents",
-            method="build_base_retriever",
+            method="compress_documents",
-        ),
-        Output(
-            display_name="Search Results",
-            name="search_results",
-            method="search_documents",
         ),
     ]
 
-    def build_base_retriever(self) -> Retriever:  # type: ignore[type-var]
+    def build_compressor(self) -> BaseDocumentCompressor:  # type: ignore[type-var]
-        cohere_reranker = CohereRerank(
+        try:
+            from langchain_cohere import CohereRerank
+        except ImportError as e:
+            msg = "Please install langchain-cohere to use the Cohere model."
+            raise ImportError(msg) from e
+        return CohereRerank(
             cohere_api_key=self.api_key,
             model=self.model,
             top_n=self.top_n,
-            user_agent=self.user_agent,
         )
-        retriever = ContextualCompressionRetriever(base_compressor=cohere_reranker, base_retriever=self.retriever)
-        return cast("Retriever", retriever)
-
-    async def search_documents(self) -> list[Data]:  # type: ignore[override]
-        retriever = self.build_base_retriever()
-        documents = await retriever.ainvoke(self.search_query, config={"callbacks": self.get_langchain_callbacks()})
-        data = self.to_data(documents)
-        self.status = data
-        return data
-
-    @check_cached_vector_store
-    def build_vector_store(self) -> VectorStore:
-        msg = "Cohere Rerank does not support vector stores."
-        raise NotImplementedError(msg)

src/backend/base/langflow/components/nvidia/nvidia_rerank.py

@@ -1,25 +1,19 @@
 from typing import Any
 
-from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
+from langflow.base.compressors.model import LCCompressorComponent
-from langflow.field_typing import VectorStore
+from langflow.field_typing import BaseDocumentCompressor
-from langflow.inputs.inputs import DataInput
+from langflow.io import DropdownInput, StrInput
-from langflow.io import DropdownInput, MultilineInput, SecretStrInput, StrInput
-from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
 from langflow.template.field.base import Output
 
 
-class NvidiaRerankComponent(LCVectorStoreComponent):
+class NvidiaRerankComponent(LCCompressorComponent):
     display_name = "NVIDIA Rerank"
-    description = "Rerank documents using the NVIDIA API and a retriever."
+    description = "Rerank documents using the NVIDIA API."
     icon = "NVIDIA"
 
     inputs = [
-        MultilineInput(
+        *LCCompressorComponent.inputs,
-            name="search_query",
-            display_name="Search Query",
-            tool_mode=True,
-        ),
         StrInput(
             name="base_url",
             display_name="Base URL",
@@ -33,27 +27,20 @@ class NvidiaRerankComponent(LCVectorStoreComponent):
             options=["nv-rerank-qa-mistral-4b:1"],
             value="nv-rerank-qa-mistral-4b:1",
         ),
-        SecretStrInput(name="api_key", display_name="API Key"),
-        DataInput(
-            name="search_results",
-            display_name="Search Results",
-            info="Search Results from a Vector Store.",
-            is_list=True,
-        ),
     ]
 
     outputs = [
         Output(
             display_name="Reranked Documents",
             name="reranked_documents",
-            method="rerank_documents",
+            method="compress_documents",
         ),
     ]
 
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
         if field_name == "base_url" and field_value:
             try:
-                build_model = self.build_reranker()
+                build_model = self.build_compressor()
                 ids = [model.id for model in build_model.available_models]
                 build_config["model"]["options"] = ids
                 build_config["model"]["value"] = ids[0]
@@ -62,25 +49,10 @@ class NvidiaRerankComponent(LCVectorStoreComponent):
                 raise ValueError(msg) from e
         return build_config
 
-    def build_reranker(self):
+    def build_compressor(self) -> BaseDocumentCompressor:
         try:
             from langchain_nvidia_ai_endpoints import NVIDIARerank
         except ImportError as e:
             msg = "Please install langchain-nvidia-ai-endpoints to use the NVIDIA model."
             raise ImportError(msg) from e
-        return NVIDIARerank(api_key=self.api_key, model=self.model, base_url=self.base_url)
+        return NVIDIARerank(api_key=self.api_key, model=self.model, base_url=self.base_url, top_n=self.top_n)
-
-    async def rerank_documents(self) -> list[Data]:  # type: ignore[override]
-        reranker = self.build_reranker()
-        documents = reranker.compress_documents(
-            query=self.search_query,
-            documents=[passage.to_lc_document() for passage in self.search_results if isinstance(passage, Data)],
-        )
-        data = self.to_data(documents)
-        self.status = data
-        return data
-
-    @check_cached_vector_store
-    def build_vector_store(self) -> VectorStore:
-        msg = "NVIDIA Rerank does not support vector stores."
-        raise NotImplementedError(msg)

src/backend/base/langflow/field_typing/__init__.py

@@ -4,6 +4,7 @@ from .constants import (
     AgentExecutor,
     BaseChatMemory,
     BaseChatModel,
+    BaseDocumentCompressor,
     BaseLanguageModel,
     BaseLLM,
     BaseLoader,
@@ -61,6 +62,7 @@ __all__ = [
     "AgentExecutor",
     "BaseChatMemory",
     "BaseChatModel",
+    "BaseDocumentCompressor",
     "BaseLLM",
     "BaseLanguageModel",
     "BaseLoader",

src/backend/base/langflow/field_typing/constants.py

@@ -7,6 +7,7 @@ from langchain.memory.chat_memory import BaseChatMemory
 from langchain_core.chat_history import BaseChatMessageHistory
 from langchain_core.document_loaders import BaseLoader
 from langchain_core.documents import Document
+from langchain_core.documents.compressor import BaseDocumentCompressor
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models import BaseLanguageModel, BaseLLM
 from langchain_core.language_models.chat_models import BaseChatModel
@@ -68,6 +69,7 @@ LANGCHAIN_BASE_TYPES = {
     "BaseChatMemory": BaseChatMemory,
     "BaseChatModel": BaseChatModel,
     "Memory": Memory,
+    "BaseDocumentCompressor": BaseDocumentCompressor,
 }
 # Langchain base types plus Python base types
 CUSTOM_COMPONENT_SUPPORTED_TYPES = {
@@ -96,6 +98,7 @@ from langchain_core.memory import BaseMemory
 from langchain_core.output_parsers import BaseLLMOutputParser, BaseOutputParser
 from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate, PromptTemplate
 from langchain_core.retrievers import BaseRetriever
+from langchain_core.documents.compressor import BaseDocumentCompressor
 from langchain_core.tools import BaseTool, Tool
 from langchain_core.vectorstores import VectorStore, VectorStoreRetriever
 from langchain_text_splitters import TextSplitter