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https://github.com/hwchase17/langchain
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- Description: Add two new document transformers that translates documents into different languages and converts documents into q&a format to improve vector search results. Uses OpenAI function calling via the [doctran](https://github.com/psychic-api/doctran/tree/main) library. - Issue: N/A - Dependencies: `doctran = "^0.0.5"` - Tag maintainer: @rlancemartin @eyurtsev @hwchase17 - Twitter handle: @psychicapi or @jfan001 Notes - Adheres to the `DocumentTransformer` abstraction set by @dev2049 in #3182 - refactored `EmbeddingsRedundantFilter` to put it in a file under a new `document_transformers` module - Added basic docs for `DocumentInterrogator`, `DocumentTransformer` as well as the existing `EmbeddingsRedundantFilter` --------- Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Bagatur <baskaryan@gmail.com>
72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
"""Integration test for embedding-based redundant doc filtering."""
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from langchain.document_transformers.embeddings_redundant_filter import (
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EmbeddingsClusteringFilter,
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EmbeddingsRedundantFilter,
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_DocumentWithState,
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)
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.schema import Document
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def test_embeddings_redundant_filter() -> None:
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texts = [
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"What happened to all of my cookies?",
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"Where did all of my cookies go?",
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"I wish there were better Italian restaurants in my neighborhood.",
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]
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docs = [Document(page_content=t) for t in texts]
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embeddings = OpenAIEmbeddings()
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redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
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actual = redundant_filter.transform_documents(docs)
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assert len(actual) == 2
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assert set(texts[:2]).intersection([d.page_content for d in actual])
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def test_embeddings_redundant_filter_with_state() -> None:
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texts = ["What happened to all of my cookies?", "foo bar baz"]
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state = {"embedded_doc": [0.5] * 10}
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docs = [_DocumentWithState(page_content=t, state=state) for t in texts]
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embeddings = OpenAIEmbeddings()
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redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
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actual = redundant_filter.transform_documents(docs)
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assert len(actual) == 1
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def test_embeddings_clustering_filter() -> None:
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texts = [
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"What happened to all of my cookies?",
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"A cookie is a small, baked sweet treat and you can find it in the cookie",
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"monsters' jar.",
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"Cookies are good.",
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"I have nightmares about the cookie monster.",
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"The most popular pizza styles are: Neapolitan, New York-style and",
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"Chicago-style. You can find them on iconic restaurants in major cities.",
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"Neapolitan pizza: This is the original pizza style,hailing from Naples,",
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"Italy.",
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"I wish there were better Italian Pizza restaurants in my neighborhood.",
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"New York-style pizza: This is characterized by its large, thin crust, and",
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"generous toppings.",
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"The first movie to feature a robot was 'A Trip to the Moon' (1902).",
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"The first movie to feature a robot that could pass for a human was",
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"'Blade Runner' (1982)",
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"The first movie to feature a robot that could fall in love with a human",
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"was 'Her' (2013)",
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"A robot is a machine capable of carrying out complex actions automatically.",
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"There are certainly hundreds, if not thousands movies about robots like:",
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"'Blade Runner', 'Her' and 'A Trip to the Moon'",
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]
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docs = [Document(page_content=t) for t in texts]
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embeddings = OpenAIEmbeddings()
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redundant_filter = EmbeddingsClusteringFilter(
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embeddings=embeddings,
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num_clusters=3,
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num_closest=1,
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sorted=True,
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)
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actual = redundant_filter.transform_documents(docs)
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assert len(actual) == 3
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assert texts[1] in [d.page_content for d in actual]
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assert texts[4] in [d.page_content for d in actual]
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assert texts[11] in [d.page_content for d in actual]
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