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57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
import os
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.ollama import ollama_model_complete, ollama_embed
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from lightrag.utils import EmbeddingFunc
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# WorkingDir
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ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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WORKING_DIR = os.path.join(ROOT_DIR, "myKG")
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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print(f"WorkingDir: {WORKING_DIR}")
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# mongo
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os.environ["MONGO_URI"] = "mongodb://root:root@localhost:27017/"
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os.environ["MONGO_DATABASE"] = "LightRAG"
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# neo4j
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BATCH_SIZE_NODES = 500
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BATCH_SIZE_EDGES = 100
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os.environ["NEO4J_URI"] = "bolt://localhost:7687"
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os.environ["NEO4J_USERNAME"] = "neo4j"
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os.environ["NEO4J_PASSWORD"] = "neo4j"
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# milvus
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os.environ["MILVUS_URI"] = "http://localhost:19530"
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os.environ["MILVUS_USER"] = "root"
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os.environ["MILVUS_PASSWORD"] = "root"
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os.environ["MILVUS_DB_NAME"] = "lightrag"
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=ollama_model_complete,
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llm_model_name="qwen2.5:14b",
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llm_model_max_async=4,
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llm_model_max_token_size=32768,
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llm_model_kwargs={"host": "http://127.0.0.1:11434", "options": {"num_ctx": 32768}},
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embedding_func=EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=8192,
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func=lambda texts: ollama_embed(
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texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
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),
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),
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kv_storage="MongoKVStorage",
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graph_storage="Neo4JStorage",
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vector_storage="MilvusVectorDBStorage",
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)
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file = "./book.txt"
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with open(file, "r") as f:
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rag.insert(f.read())
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
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)
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