Add relik graph transformer (#24982)

Relik is a new library for graph extraction that offers smaller and
cheaper models for graph construction

libs/experimental/langchain_experimental/graph_transformers/__init__.py

@@ -2,8 +2,6 @@
 
 from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformer
 from langchain_experimental.graph_transformers.llm import LLMGraphTransformer
+from langchain_experimental.graph_transformers.relik import RelikGraphTransformer
 
-__all__ = [
+__all__ = ["DiffbotGraphTransformer", "LLMGraphTransformer", "RelikGraphTransformer"]
-    "DiffbotGraphTransformer",
-    "LLMGraphTransformer",
-]

libs/experimental/langchain_experimental/graph_transformers/relik.py

@@ -0,0 +1,101 @@
+from typing import List, Sequence
+
+from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
+from langchain_core.documents import Document
+
+DEFAULT_NODE_TYPE = "Node"
+
+
+class RelikGraphTransformer:
+    """
+    A transformer class for converting documents into graph structures
+    using the Relik library and models.
+
+    This class leverages relik models for extracting relationships
+    and nodes from text documents and converting them into a graph format.
+    The relationships are filtered based on a specified confidence threshold.
+
+    For more details on the Relik library, visit their GitHub repository:
+      https://github.com/SapienzaNLP/relik
+
+    Args:
+        model (str): The name of the pretrained Relik model to use.
+          Default is "relik-ie/relik-relation-extraction-small-wikipedia".
+        relationship_confidence_threshold (float): The confidence threshold for
+          filtering relationships. Default is 0.0.
+    """
+
+    def __init__(
+        self,
+        model: str = "relik-ie/relik-relation-extraction-small-wikipedia",
+        relationship_confidence_threshold: float = 0.0,
+    ) -> None:
+        try:
+            import relik  # type: ignore
+        except ImportError:
+            raise ImportError(
+                "Could not import relik python package. "
+                "Please install it with `pip install relik`."
+            )
+        self.relik_model = relik.Relik.from_pretrained(model)
+        self.relationship_confidence_threshold = relationship_confidence_threshold
+
+    def process_document(self, document: Document) -> GraphDocument:
+        relik_out = self.relik_model(document.page_content)
+        nodes = []
+        # Extract nodes
+        for node in relik_out.spans:
+            nodes.append(
+                Node(
+                    id=node.text,
+                    type=DEFAULT_NODE_TYPE
+                    if node.label.strip() == "--NME--"
+                    else node.label.strip(),
+                )
+            )
+
+        relationships = []
+        # Extract relationships
+        for triple in relik_out.triplets:
+            # Ignore relationship if below confidence threshold
+            if triple.confidence < self.relationship_confidence_threshold:
+                continue
+            source_node = Node(
+                id=triple.subject.text,
+                type=DEFAULT_NODE_TYPE
+                if triple.subject.label.strip() == "--NME--"
+                else triple.subject.label.strip(),
+            )
+            target_node = Node(
+                id=triple.object.text,
+                type=DEFAULT_NODE_TYPE
+                if triple.object.label.strip() == "--NME--"
+                else triple.object.label.strip(),
+            )
+
+            relationship = Relationship(
+                source=source_node,
+                target=target_node,
+                type=triple.label.replace(" ", "_").upper(),
+            )
+            relationships.append(relationship)
+
+        return GraphDocument(nodes=nodes, relationships=relationships, source=document)
+
+    def convert_to_graph_documents(
+        self, documents: Sequence[Document]
+    ) -> List[GraphDocument]:
+        """Convert a sequence of documents into graph documents.
+
+        Args:
+            documents (Sequence[Document]): The original documents.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Sequence[GraphDocument]: The transformed documents as graphs.
+        """
+        results = []
+        for document in documents:
+            graph_document = self.process_document(document)
+            results.append(graph_document)
+        return results