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https://github.com/hwchase17/langchain
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## Description This PR intends to add support for Qdrant's new [sparse vector retrieval](https://qdrant.tech/articles/sparse-vectors/) by introducing a new retriever class, `QdrantSparseVectorRetriever`. Necessary usage docs and integration tests have been added for the retriever. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
from langchain_community.retrievers import __all__
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EXPECTED_ALL = [
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"AmazonKendraRetriever",
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"AmazonKnowledgeBasesRetriever",
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"ArceeRetriever",
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"ArxivRetriever",
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"AzureCognitiveSearchRetriever",
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"ChatGPTPluginRetriever",
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"ChaindeskRetriever",
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"CohereRagRetriever",
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"ElasticSearchBM25Retriever",
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"EmbedchainRetriever",
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"GoogleDocumentAIWarehouseRetriever",
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"GoogleCloudEnterpriseSearchRetriever",
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"GoogleVertexAIMultiTurnSearchRetriever",
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"GoogleVertexAISearchRetriever",
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"KayAiRetriever",
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"KNNRetriever",
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"LlamaIndexGraphRetriever",
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"LlamaIndexRetriever",
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"MetalRetriever",
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"MilvusRetriever",
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"OutlineRetriever",
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"PineconeHybridSearchRetriever",
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"PubMedRetriever",
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"QdrantSparseVectorRetriever",
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"RemoteLangChainRetriever",
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"SVMRetriever",
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"TavilySearchAPIRetriever",
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"TFIDFRetriever",
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"BM25Retriever",
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"VespaRetriever",
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"WeaviateHybridSearchRetriever",
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"WikipediaRetriever",
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"ZepRetriever",
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"ZillizRetriever",
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"DocArrayRetriever",
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]
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def test_all_imports() -> None:
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assert set(__all__) == set(EXPECTED_ALL)
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