mirror of
https://github.com/hwchase17/langchain
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12f868b292
Technically a duplicate fix to #1619 but with unit tests and a small documentation update - Propagate `filter` arg in Chroma `similarity_search` to delegated call to `similarity_search_with_score` - Add `filter` arg to `similarity_search_by_vector` - Clarify doc strings on FakeEmbeddings
23 lines
797 B
Python
23 lines
797 B
Python
"""Fake Embedding class for testing purposes."""
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from typing import List
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from langchain.embeddings.base import Embeddings
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fake_texts = ["foo", "bar", "baz"]
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class FakeEmbeddings(Embeddings):
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"""Fake embeddings functionality for testing."""
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Return simple embeddings.
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Embeddings encode each text as its index."""
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return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
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def embed_query(self, text: str) -> List[float]:
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"""Return constant query embeddings.
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Embeddings are identical to embed_documents(texts)[0].
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Distance to each text will be that text's index,
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as it was passed to embed_documents."""
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return [float(1.0)] * 9 + [float(0.0)]
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