mirror of
https://github.com/hwchase17/langchain
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73 lines
3.7 KiB
Python
73 lines
3.7 KiB
Python
"""**Retriever** class returns Documents given a text **query**.
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It is more general than a vector store. A retriever does not need to be able to
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store documents, only to return (or retrieve) it. Vector stores can be used as
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the backbone of a retriever, but there are other types of retrievers as well.
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**Class hierarchy:**
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.. code-block::
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BaseRetriever --> <name>Retriever # Examples: ArxivRetriever, MergerRetriever
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**Main helpers:**
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.. code-block::
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Document, Serializable, Callbacks,
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CallbackManagerForRetrieverRun, AsyncCallbackManagerForRetrieverRun
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"""
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import importlib
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from typing import Any
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_module_lookup = {
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"AmazonKendraRetriever": "langchain_community.retrievers.kendra",
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"AmazonKnowledgeBasesRetriever": "langchain_community.retrievers.bedrock",
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"ArceeRetriever": "langchain_community.retrievers.arcee",
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"ArxivRetriever": "langchain_community.retrievers.arxiv",
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"AzureCognitiveSearchRetriever": "langchain_community.retrievers.azure_cognitive_search", # noqa: E501
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"BM25Retriever": "langchain_community.retrievers.bm25",
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"BreebsRetriever": "langchain_community.retrievers.breebs",
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"ChaindeskRetriever": "langchain_community.retrievers.chaindesk",
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"ChatGPTPluginRetriever": "langchain_community.retrievers.chatgpt_plugin_retriever",
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"CohereRagRetriever": "langchain_community.retrievers.cohere_rag_retriever",
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"DocArrayRetriever": "langchain_community.retrievers.docarray",
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"ElasticSearchBM25Retriever": "langchain_community.retrievers.elastic_search_bm25",
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"EmbedchainRetriever": "langchain_community.retrievers.embedchain",
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"GoogleCloudEnterpriseSearchRetriever": "langchain_community.retrievers.google_vertex_ai_search", # noqa: E501
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"GoogleDocumentAIWarehouseRetriever": "langchain_community.retrievers.google_cloud_documentai_warehouse", # noqa: E501
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"GoogleVertexAIMultiTurnSearchRetriever": "langchain_community.retrievers.google_vertex_ai_search", # noqa: E501
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"GoogleVertexAISearchRetriever": "langchain_community.retrievers.google_vertex_ai_search", # noqa: E501
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"KNNRetriever": "langchain_community.retrievers.knn",
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"KayAiRetriever": "langchain_community.retrievers.kay",
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"LlamaIndexGraphRetriever": "langchain_community.retrievers.llama_index",
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"LlamaIndexRetriever": "langchain_community.retrievers.llama_index",
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"MetalRetriever": "langchain_community.retrievers.metal",
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"MilvusRetriever": "langchain_community.retrievers.milvus",
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"OutlineRetriever": "langchain_community.retrievers.outline",
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"PineconeHybridSearchRetriever": "langchain_community.retrievers.pinecone_hybrid_search", # noqa: E501
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"PubMedRetriever": "langchain_community.retrievers.pubmed",
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"QdrantSparseVectorRetriever": "langchain_community.retrievers.qdrant_sparse_vector_retriever", # noqa: E501
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"RemoteLangChainRetriever": "langchain_community.retrievers.remote_retriever",
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"SVMRetriever": "langchain_community.retrievers.svm",
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"TFIDFRetriever": "langchain_community.retrievers.tfidf",
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"TavilySearchAPIRetriever": "langchain_community.retrievers.tavily_search_api",
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"VespaRetriever": "langchain_community.retrievers.vespa_retriever",
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"WeaviateHybridSearchRetriever": "langchain_community.retrievers.weaviate_hybrid_search", # noqa: E501
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"WikipediaRetriever": "langchain_community.retrievers.wikipedia",
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"YouRetriever": "langchain_community.retrievers.you",
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"ZepRetriever": "langchain_community.retrievers.zep",
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"ZillizRetriever": "langchain_community.retrievers.zilliz",
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}
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def __getattr__(name: str) -> Any:
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if name in _module_lookup:
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module = importlib.import_module(_module_lookup[name])
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return getattr(module, name)
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raise AttributeError(f"module {__name__} has no attribute {name}")
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__all__ = list(_module_lookup.keys())
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