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41 lines
1002 B
Python
41 lines
1002 B
Python
from qdrant_client import QdrantClient
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# Initialize the client
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client = QdrantClient(":memory:") # or QdrantClient(path="path/to/db")
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# Prepare your documents, metadata, and IDs
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docs = [
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"Qdrant has Langchain integrations",
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"Qdrant also has Llama Index integrations",
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# ...more documents...
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]
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metadatas = [
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{"source": "notion"},
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{"source": "google-docs"},
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# ...more metadata...
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]
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ids = [
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1,
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2,
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3,
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50,
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63,
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] # unique for each doc, if not mentioned, we'll generate random IDs, can lead to duplicates
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# Use the new upsert_docs method
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client.upsert_docs(
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collection_name="demo_collection",
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docs={"documents": docs, "metadatas": metadatas, "ids": ids},
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batch_size=512, # Adjust as needed
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wait=True, # Wait for the operation to complete
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)
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search_result = client.query(
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collection_name="demo_collection",
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query_texts=["This is a query document"],
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n_results=2,
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with_vectors=True,
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with_payload=True,
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)
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print(search_result)
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