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56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
import os
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import logging
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.zhipu import zhipu_complete, zhipu_embedding
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from lightrag.utils import EmbeddingFunc
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WORKING_DIR = "./dickens"
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logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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api_key = os.environ.get("ZHIPUAI_API_KEY")
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if api_key is None:
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raise Exception("Please set ZHIPU_API_KEY in your environment")
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=zhipu_complete,
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llm_model_name="glm-4-flashx", # Using the most cost/performance balance model, but you can change it here.
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llm_model_max_async=4,
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llm_model_max_token_size=32768,
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embedding_func=EmbeddingFunc(
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embedding_dim=2048, # Zhipu embedding-3 dimension
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max_token_size=8192,
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func=lambda texts: zhipu_embedding(texts),
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),
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)
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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# Perform naive search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
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)
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# Perform local search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
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)
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# Perform global search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
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)
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# Perform hybrid search
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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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