mirror of
https://github.com/hwchase17/langchain
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44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
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import lancedb
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from langchain.vectorstores import LanceDB
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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def test_lancedb() -> None:
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embeddings = FakeEmbeddings()
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db = lancedb.connect("/tmp/lancedb")
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texts = ["text 1", "text 2", "item 3"]
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vectors = embeddings.embed_documents(texts)
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table = db.create_table(
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"my_table",
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data=[
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{"vector": vectors[idx], "id": text, "text": text}
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for idx, text in enumerate(texts)
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],
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mode="overwrite",
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)
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store = LanceDB(table, embeddings)
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result = store.similarity_search("text 1")
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result_texts = [doc.page_content for doc in result]
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assert "text 1" in result_texts
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def test_lancedb_add_texts() -> None:
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embeddings = FakeEmbeddings()
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db = lancedb.connect("/tmp/lancedb")
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texts = ["text 1"]
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vectors = embeddings.embed_documents(texts)
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table = db.create_table(
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"my_table",
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data=[
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{"vector": vectors[idx], "id": text, "text": text}
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for idx, text in enumerate(texts)
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],
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mode="overwrite",
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)
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store = LanceDB(table, embeddings)
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store.add_texts(["text 2"])
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result = store.similarity_search("text 2")
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result_texts = [doc.page_content for doc in result]
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assert "text 2" in result_texts
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