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import os |
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import asyncio |
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from lightrag.kg.postgres_impl import PGGraphStorage |
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from lightrag.llm.ollama import ollama_embedding |
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from lightrag.utils import EmbeddingFunc |
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WORKING_DIR = "./local_neo4jWorkDir" |
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if not os.path.exists(WORKING_DIR): |
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os.mkdir(WORKING_DIR) |
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os.environ["AGE_GRAPH_NAME"] = "dickens" |
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os.environ["POSTGRES_HOST"] = "localhost" |
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os.environ["POSTGRES_PORT"] = "15432" |
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os.environ["POSTGRES_USER"] = "rag" |
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os.environ["POSTGRES_PASSWORD"] = "rag" |
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os.environ["POSTGRES_DATABASE"] = "rag" |
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async def main(): |
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graph_db = PGGraphStorage( |
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namespace="dickens", |
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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_embedding( |
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texts, embed_model="bge-m3", host="http://localhost:11434" |
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), |
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), |
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global_config={}, |
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) |
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await graph_db.initialize() |
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labels = await graph_db.get_all_labels() |
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print("all labels", labels) |
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res = await graph_db.get_knowledge_graph("FEZZIWIG") |
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print("knowledge graphs", res) |
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await graph_db.finalize() |
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if __name__ == "__main__": |
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asyncio.run(main()) |
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