Add Milvus as vector storage

examples/lightrag_ollama_neo4j_milvus_demo.py

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+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import ollama_model_complete, ollama_embed
+from lightrag.utils import EmbeddingFunc
+
+# WorkingDir
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+WORKING_DIR = os.path.join(ROOT_DIR, "myKG")
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+print(f"WorkingDir: {WORKING_DIR}")
+
+# neo4j
+BATCH_SIZE_NODES = 500
+BATCH_SIZE_EDGES = 100
+os.environ["NEO4J_URI"] = "bolt://localhost:7687"
+os.environ["NEO4J_USERNAME"] = "neo4j"
+os.environ["NEO4J_PASSWORD"] = "neo4j"
+
+# milvus
+os.environ["MILVUS_URI"] = "http://localhost:19530"
+os.environ["MILVUS_USER"] = "root"
+os.environ["MILVUS_PASSWORD"] = "root"
+os.environ["MILVUS_DB_NAME"] = "lightrag"
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=ollama_model_complete,
+    llm_model_name="qwen2.5:14b",
+    llm_model_max_async=4,
+    llm_model_max_token_size=32768,
+    llm_model_kwargs={"host": "http://127.0.0.1:11434", "options": {"num_ctx": 32768}},
+    embedding_func=EmbeddingFunc(
+        embedding_dim=1024,
+        max_token_size=8192,
+        func=lambda texts: ollama_embed(
+            texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
+        ),
+    ),
+    graph_storage="Neo4JStorage",
+    vector_storage="MilvusVectorDBStorge",
+)
+
+file = "./book.txt"
+with open(file, "r") as f:
+    rag.insert(f.read())
+
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)