feat: Add GLiNERLinkExtractorComponent

src/backend/base/langflow/components/link_extractors/GliNERLinkExtractor.py

@@ -0,0 +1,40 @@
+from typing import Any
+
+from langchain_community.graph_vectorstores.extractors import LinkExtractorTransformer, GLiNERLinkExtractor
+from langchain_core.documents import BaseDocumentTransformer
+
+from langflow.base.document_transformers.model import LCDocumentTransformerComponent
+from langflow.inputs import DataInput, StrInput, DictInput
+
+
+class GLiNERLinkExtractorComponent(LCDocumentTransformerComponent):
+    display_name = "GliNER Link Extractor"
+    description = "Extract named entities links from documents using GLiNER"
+    documentation = "https://python.langchain.com/api_reference/community/graph_vectorstores/langchain_community.graph_vectorstores.extractors.gliner_link_extractor.GLiNERLinkExtractor.html"
+    name = "GLiNERLinkExtractor"
+
+    inputs = [
+        StrInput(name="labels", display_name="List of kinds of entities to extract", required=True, is_list=True),
+        StrInput(name="kind", display_name="Kind of edge", value="entity"),
+        StrInput(name="model", display_name="GLiNER model to use", value="urchade/gliner_mediumv2.1"),
+        DictInput(
+            name="extract_kwargs",
+            display_name="Arguments to pass to GLiNER.",
+            is_list=True,
+            advanced=True,
+        ),
+        DataInput(
+            name="data_input",
+            display_name="Input",
+            info="The texts from which to extract links.",
+            input_types=["Document", "Data"],
+        ),
+    ]
+
+    def get_data_input(self) -> Any:
+        return self.data_input
+
+    def build_document_transformer(self) -> BaseDocumentTransformer:
+        return LinkExtractorTransformer(
+            [GLiNERLinkExtractor(self.labels, kind=self.kind, model=self.model, extract_kwargs=self.extract_kwargs)]
+        )