Added support for Amazon Bedrock models

examples/lightrag_bedrock_demo.py

@@ -0,0 +1,48 @@
+"""
+LightRAG meets Amazon Bedrock ⛰️
+"""
+
+import os
+
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import bedrock_complete, bedrock_embedding
+from lightrag.utils import EmbeddingFunc
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=bedrock_complete,
+    llm_model_name="anthropic.claude-3-haiku-20240307-v1:0",
+    node2vec_params = {
+        'dimensions': 1024,
+        'num_walks': 10,
+        'walk_length': 40,
+        'window_size': 2,
+        'iterations': 3,
+        'random_seed': 3
+    },
+    embedding_func=EmbeddingFunc(
+        embedding_dim=1024,
+        max_token_size=8192,
+        func=lambda texts: bedrock_embedding(texts)
+    )
+)
+
+with open("./book.txt") as f:
+    rag.insert(f.read())
+
+# Naive search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+
+# Local search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
+
+# Global search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
+
+# Hybrid search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))