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Deeplake multimodal retrieval

DeepLakeMultimodalRetrieverPack #

Bases: BaseLlamaPack

深湖多模态检索包。

Source code in llama_index/packs/deeplake_multimodal_retrieval/base.py 
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class DeepLakeMultimodalRetrieverPack(BaseLlamaPack):
    """深湖多模态检索包。"""

    def __init__(
        self,
        dataset_path: str = "llama_index",
        token: Optional[str] = None,
        read_only: Optional[bool] = False,
        overwrite: bool = False,
        verbose: bool = True,
        nodes: Optional[List[BaseNode]] = None,
        top_k: int = 4,
        **kwargs: Any,
    ):
        # text vector store
        self._text_vectorstore = DeepLakeVectorStore(
            dataset_path=dataset_path + "_text",
            token=token,
            read_only=read_only,
            overwrite=overwrite,
            verbose=verbose,
        )

        # image vector store
        self._image_vectorstore = DeepLakeVectorStore(
            dataset_path=dataset_path + "_image",
            token=token,
            read_only=read_only,
            overwrite=overwrite,
            verbose=verbose,
        )

        if nodes is not None:
            self._storage_context = StorageContext.from_defaults(
                vector_store=self._text_vectorstore
            )
            self._index = MultiModalVectorStoreIndex(
                nodes,
                storage_context=self._storage_context,
                image_vector_store=self._image_vectorstore,
            )
        else:
            self._storage_context = StorageContext.from_defaults(
                vector_store=self._text_vectorstore
            )
            self._index = MultiModalVectorStoreIndex.from_vector_store(
                self._text_vectorstore,
                image_vector_store=self._image_vectorstore,
            )
        self.retriever = self._index.as_retriever(
            similarity_top_k=top_k, vector_store_kwargs={"deep_memory": True}
        )
        self.query_engine = SimpleMultiModalQueryEngine(self.retriever)

    def get_modules(self) -> Dict[str, Any]:
        """获取模块。"""
        return {
            "text_vectorstore": self._text_vectorstore,
            "image_vectorstore": self._image_vectorstore,
            "storage_context": self._storage_context,
            "index": self._index,
            "retriever": self.retriever,
            "query_engine": self.query_engine,
        }

    def retrieve(self, query_str: str) -> Any:
        """检索。"""
        return self.query_engine.retrieve(query_str)

    def run(self, *args: Any, **kwargs: Any) -> Any:
        """运行流水线。"""
        return self.query_engine.query(*args, **kwargs)

get_modules #

get_modules() -> Dict[str, Any]

获取模块。

Source code in llama_index/packs/deeplake_multimodal_retrieval/base.py 
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def get_modules(self) -> Dict[str, Any]:
    """获取模块。"""
    return {
        "text_vectorstore": self._text_vectorstore,
        "image_vectorstore": self._image_vectorstore,
        "storage_context": self._storage_context,
        "index": self._index,
        "retriever": self.retriever,
        "query_engine": self.query_engine,
    }

retrieve #

retrieve(query_str: str) -> Any

检索。

Source code in llama_index/packs/deeplake_multimodal_retrieval/base.py 
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def retrieve(self, query_str: str) -> Any:
    """检索。"""
    return self.query_engine.retrieve(query_str)

run #

run(*args: Any, **kwargs: Any) -> Any

运行流水线。

Source code in llama_index/packs/deeplake_multimodal_retrieval/base.py 
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def run(self, *args: Any, **kwargs: Any) -> Any:
    """运行流水线。"""
    return self.query_engine.query(*args, **kwargs)