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30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
from qdrant_client import QdrantClient
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from qdrant_client.http import models
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# 在下文中提醒如果数据量大, 需要使用专用的向量搜索引擎, 如Pinecone, Weaviate, Qdrant
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# https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb
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"""
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This indexing stage can be executed offline and only runs once to precompute the indexes for the dataset
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so that each piece of content can be retrieved later. Since this is a small example, we will store and search the
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embeddings locally. If you have a larger dataset, consider using a vector search engine like Pinecone,
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Weaviate or Qdrant to power the search.
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"""
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collection_name = "qyt_gpt_collection"
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qdrant_url = "http://localhost:6333"
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client = QdrantClient(qdrant_url)
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dimension = 1536
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if __name__ == "__main__":
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client.recreate_collection(
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collection_name=collection_name,
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vectors_config=models.VectorParams(size=dimension, distance=models.Distance.COSINE),
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)
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# 使用游览器访问 http://localhost:6333/collections
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# {"result":{"collections":[{"name":"example_collection"},{"name":"qyt_gpt_collection"},
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# {"name":"qytang_gpt"}]},"status":"ok","time":0.000035}
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