heyong.fu
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
# 完整流程
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from chromadb import PersistentClient
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# 创建持久化客户端
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client = PersistentClient(path="./chromadb_store")
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# 获取或者创建集合
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collection = client.get_or_create_collection(name="example")
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# 准备说明文档
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documents = [
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"机器学习包含监督学习和无监督学习",
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"Python 拥有丰富的数据科学生态",
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"数据库可以持久化结构化或非结构化数据",
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]
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# 创建元数据
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metadatas = [
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{"topic": "ml", "level": "intro"},
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{"topic": "python", "level": "beginner"},
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{"topic": "database", "level": "intro"},
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]
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# ids
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ids = ["doc1", "doc2", "dic3"]
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# 写入数据
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collection.add(documents=documents, metadatas=metadatas, ids=ids)
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abc = collection.get(ids=["doc2"])
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print(abc)
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# 查询
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result = collection.query(query_texts=["如何入门机器学习"], n_results=2)
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# print(result)
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# {
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# "ids": [["doc1", "doc2"]],
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# "embeddings": None,
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# "documents": [
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# ["机器学习包含监督学习和无监督学习", "Python 拥有丰富的数据科学生态"]
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# ],
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# "uris": None,
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# "included": ["metadatas", "documents", "distances"],
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# "data": None,
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# "metadatas": [
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# [{"topic": "ml", "level": "intro"}, {"topic": "python", "level": "beginner"}]
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# ],
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# "distances": [[0.24633410573005676, 0.8512163758277893]],
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# }
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# for index, (id, doc, metadata, distance) in enumerate(
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# zip(
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# result["ids"][0],
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# result["documents"][0],
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# result["metadatas"][0],
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# result["distances"][0],
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# ),
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# 1,
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# ):
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# print(f"匹配结果 {index}:")
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# print(f" 文档：{doc}")
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# print(f" 元数据：{metadata}")
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# print(f" 距离：{distance:.4f}")
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# print()
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