feat: 增加redis KV存储，增加openai+neo4j+milvus+redis的demo测试，新增lightrag.py: RedisKVStorage，新增requirements.txt:aioredis依赖

examples/lightrag_openai_neo4j_milvus_redis_demo.py

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+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import ollama_embed, openai_complete_if_cache
+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}")
+
+# redis
+os.environ["REDIS_URI"] = "redis://localhost:6379"
+
+# neo4j
+BATCH_SIZE_NODES = 500
+BATCH_SIZE_EDGES = 100
+os.environ["NEO4J_URI"] = "bolt://117.50.173.35:7687"
+os.environ["NEO4J_USERNAME"] = "neo4j"
+os.environ["NEO4J_PASSWORD"] = "12345678"
+
+# milvus
+os.environ["MILVUS_URI"] = "http://117.50.173.35:19530"
+os.environ["MILVUS_USER"] = "root"
+os.environ["MILVUS_PASSWORD"] = "Milvus"
+os.environ["MILVUS_DB_NAME"] = "lightrag"
+
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+) -> str:
+    return await openai_complete_if_cache(
+        "deepseek-chat",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key="sk-91d0b59f25554251aa813ed756d79a6d",
+        base_url="https://api.deepseek.com",
+        **kwargs,
+    )
+
+
+embedding_func = EmbeddingFunc(
+    embedding_dim=768,
+    max_token_size=512,
+    func=lambda texts: ollama_embed(
+        texts, embed_model="shaw/dmeta-embedding-zh", host="http://117.50.173.35:11434"
+    ),
+)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=llm_model_func,
+    llm_model_max_token_size=32768,
+    embedding_func=embedding_func,
+    chunk_token_size=512,
+    chunk_overlap_token_size=256,
+    kv_storage="RedisKVStorage",
+    graph_storage="Neo4JStorage",
+    vector_storage="MilvusVectorDBStorge",
+    doc_status_storage="RedisKVStorage",
+)
+
+file = "../book.txt"
+with open(file, "r", encoding="utf-8") as f:
+    rag.insert(f.read())
+
+print(rag.query("谁会3D建模 ？", param=QueryParam(mode="mix")))