Add graphrag (#1793)

### What problem does this PR solve?

#1594

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

graphrag/entity_embedding.py

@@ -0,0 +1,78 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+"""
+Reference:
+ - [graphrag](https://github.com/microsoft/graphrag)
+"""
+
+from typing import Any
+
+import numpy as np
+import networkx as nx
+from graphrag.leiden import stable_largest_connected_component
+
+
+@dataclass
+class NodeEmbeddings:
+    """Node embeddings class definition."""
+
+    nodes: list[str]
+    embeddings: np.ndarray
+
+
+def embed_nod2vec(
+    graph: nx.Graph | nx.DiGraph,
+    dimensions: int = 1536,
+    num_walks: int = 10,
+    walk_length: int = 40,
+    window_size: int = 2,
+    iterations: int = 3,
+    random_seed: int = 86,
+) -> NodeEmbeddings:
+    """Generate node embeddings using Node2Vec."""
+    # generate embedding
+    lcc_tensors = gc.embed.node2vec_embed(  # type: ignore
+        graph=graph,
+        dimensions=dimensions,
+        window_size=window_size,
+        iterations=iterations,
+        num_walks=num_walks,
+        walk_length=walk_length,
+        random_seed=random_seed,
+    )
+    return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1])
+
+
+def run(graph: nx.Graph, args: dict[str, Any]) -> NodeEmbeddings:
+    """Run method definition."""
+    if args.get("use_lcc", True):
+        graph = stable_largest_connected_component(graph)
+
+    # create graph embedding using node2vec
+    embeddings = embed_nod2vec(
+        graph=graph,
+        dimensions=args.get("dimensions", 1536),
+        num_walks=args.get("num_walks", 10),
+        walk_length=args.get("walk_length", 40),
+        window_size=args.get("window_size", 2),
+        iterations=args.get("iterations", 3),
+        random_seed=args.get("random_seed", 86),
+    )
+
+    pairs = zip(embeddings.nodes, embeddings.embeddings.tolist(), strict=True)
+    sorted_pairs = sorted(pairs, key=lambda x: x[0])
+
+    return dict(sorted_pairs)