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Dashscope

DashScopeCloudIndex #

Bases: BaseManagedIndex

DashScope Cloud Platform Index.

Source code in llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/base.py 
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class DashScopeCloudIndex(BaseManagedIndex):
    """DashScope Cloud Platform Index."""

    def __init__(
        self,
        name: str,
        nodes: Optional[List[BaseNode]] = None,
        transformations: Optional[List[TransformComponent]] = None,
        timeout: int = 60,
        workspace_id: Optional[str] = None,
        api_key: Optional[str] = None,
        base_url: Optional[str] = DASHSCOPE_DEFAULT_BASE_URL,
        show_progress: bool = False,
        callback_manager: Optional[CallbackManager] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize the Platform Index."""
        self.name = name
        self.transformations = transformations or []

        if nodes is not None:
            raise ValueError(
                "DashScopeCloudIndex does not support nodes on initialization"
            )

        self.workspace_id = workspace_id or os.environ.get("DASHSCOPE_WORKSPACE_ID")
        self._api_key = api_key or os.environ.get("DASHSCOPE_API_KEY")
        self._base_url = os.environ.get("DASHSCOPE_BASE_URL", None) or base_url
        self._headers = {
            "Content-Type": "application/json",
            "Accept-Encoding": "utf-8",
            "X-DashScope-WorkSpace": self.workspace_id,
            "Authorization": "Bearer " + self._api_key,
            "X-DashScope-OpenAPISource": "CloudSDK",
        }
        self._timeout = timeout
        self._show_progress = show_progress
        self._service_context = None
        self._callback_manager = callback_manager or Settings.callback_manager

    @classmethod
    def from_documents(  # type: ignore
        cls: Type["DashScopeCloudIndex"],
        documents: List[Document],
        name: str,
        transformations: Optional[List[TransformComponent]] = None,
        workspace_id: Optional[str] = None,
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        timeout: int = 60,
        verbose: bool = True,
        **kwargs: Any,
    ) -> "DashScopeCloudIndex":
        """Build a DashScope index from a sequence of documents."""
        pipeline_create = get_pipeline_create(
            name, transformations or default_transformations(), documents
        )

        workspace_id = workspace_id or os.environ.get("DASHSCOPE_WORKSPACE_ID")
        api_key = api_key or os.environ.get("DASHSCOPE_API_KEY")
        base_url = (
            base_url
            or os.environ.get("DASHSCOPE_BASE_URL", None)
            or DASHSCOPE_DEFAULT_BASE_URL
        )
        headers = {
            "Content-Type": "application/json",
            "Accept-Encoding": "utf-8",
            "X-DashScope-WorkSpace": workspace_id,
            "Authorization": "Bearer " + api_key,
            "X-DashScope-OpenAPISource": "CloudSDK",
        }

        response = requests.put(
            base_url + UPSERT_PIPELINE_ENDPOINT,
            data=json.dumps(pipeline_create),
            headers=headers,
        )
        response_text = response.json()
        pipeline_id = response_text.get("id", None)

        if response_text.get("code", "") != Status.SUCCESS.value or pipeline_id is None:
            raise ValueError(
                f"Failed to create index: {response_text.get('message', '')}\n{response_text}"
            )
        if verbose:
            print(f"Starting creating index {name}, pipeline_id: {pipeline_id}")

        response = requests.post(
            base_url + START_PIPELINE_ENDPOINT.format(pipeline_id=pipeline_id),
            headers=headers,
        )
        response_text = response.json()
        ingestion_id = response_text.get("ingestionId", None)

        if (
            response_text.get("code", "") != Status.SUCCESS.value
            or ingestion_id is None
        ):
            raise ValueError(
                f"Failed to start ingestion: {response_text.get('message', '')}\n{response_text}"
            )
        if verbose:
            print(f"Starting ingestion for index {name}, ingestion_id: {ingestion_id}")

        ingestion_status, failed_docs = run_ingestion(
            base_url
            + CHECK_INGESTION_ENDPOINT.format(
                pipeline_id=pipeline_id, ingestion_id=ingestion_id
            ),
            headers,
            verbose,
        )

        if verbose:
            print(f"ingestion_status {ingestion_status}")
            print(f"failed_docs: {failed_docs}")

        if ingestion_status == "FAILED":
            print("Index {name} created failed!")
            return None

        if verbose:
            print(f"Index {name} created successfully!")

        return cls(
            name,
            transformations=transformations,
            workspace_id=workspace_id,
            api_key=api_key,
            base_url=base_url,
            timeout=timeout,
            **kwargs,
        )

    def as_retriever(self, **kwargs: Any) -> BaseRetriever:
        """Return a Retriever for this managed index."""
        from llama_index.indices.managed.dashscope.retriever import (
            DashScopeCloudRetriever,
        )

        return DashScopeCloudRetriever(
            self.name,
            **kwargs,
        )

    def as_query_engine(self, **kwargs: Any) -> BaseQueryEngine:
        from llama_index.core.query_engine.retriever_query_engine import (
            RetrieverQueryEngine,
        )

        kwargs["retriever"] = self.as_retriever(**kwargs)
        return RetrieverQueryEngine.from_args(**kwargs)

    def _insert(
        self,
        documents: List[Document],
        transformations: Optional[List[TransformComponent]] = None,
        verbose: bool = True,
        **insert_kwargs: Any,
    ) -> None:
        """Insert a set of documents (each a node)."""
        pipeline_id = get_pipeline_id(
            self._base_url + PIPELINE_SIMPLE_ENDPOINT,
            self._headers,
            {"pipeline_name": self.name},
        )
        doc_insert = get_doc_insert(
            transformations or default_transformations(),
            documents,
        )
        response = requests.put(
            self._base_url + INSERT_DOC_ENDPOINT.format(pipeline_id=pipeline_id),
            data=json.dumps(doc_insert),
            headers=self._headers,
        )
        response_text = response.json()
        ingestion_id = response_text.get("ingestionId", None)
        if (
            response_text.get("code", "") != Status.SUCCESS.value
            or ingestion_id is None
        ):
            raise ValueError(
                f"Failed to insert documents: {response_text.get('message', '')}\n{response_text}"
            )

        ingestion_status, failed_docs = run_ingestion(
            self._base_url
            + CHECK_INGESTION_ENDPOINT.format(
                pipeline_id=pipeline_id, ingestion_id=ingestion_id
            ),
            self._headers,
            verbose,
        )

        if verbose:
            print(f"ingestion_status {ingestion_status}")
            print(f"failed_docs: {failed_docs}")

    def delete_ref_doc(
        self,
        ref_doc_ids: Union[str, List[str]],
        verbose: bool = True,
        **delete_kwargs: Any,
    ) -> None:
        """Delete documents in index."""
        if isinstance(ref_doc_ids, str):
            ref_doc_ids = [ref_doc_ids]
        pipeline_id = get_pipeline_id(
            self._base_url + PIPELINE_SIMPLE_ENDPOINT,
            self._headers,
            {"pipeline_name": self.name},
        )
        doc_delete = get_doc_delete(ref_doc_ids)
        response = requests.post(
            self._base_url + DELETE_DOC_ENDPOINT.format(pipeline_id=pipeline_id),
            json=doc_delete,
            headers=self._headers,
        )
        response_text = response.json()
        if response_text.get("code", "") != Status.SUCCESS.value:
            raise ValueError(
                f"Failed to delete documents: {response_text.get('message', '')}\n{response_text}"
            )
        if verbose:
            print(f"Delete documents {ref_doc_ids} successfully!")

    def update_ref_doc(self, document: Document, **update_kwargs: Any) -> None:
        """Update a document and it's corresponding nodes."""
        raise NotImplementedError("update_ref_doc not implemented.")

from_documents classmethod #

from_documents(documents: List[Document], name: str, transformations: Optional[List[TransformComponent]] = None, workspace_id: Optional[str] = None, api_key: Optional[str] = None, base_url: Optional[str] = None, timeout: int = 60, verbose: bool = True, **kwargs: Any) -> DashScopeCloudIndex

Build a DashScope index from a sequence of documents.

Source code in llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/base.py 
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@classmethod
def from_documents(  # type: ignore
    cls: Type["DashScopeCloudIndex"],
    documents: List[Document],
    name: str,
    transformations: Optional[List[TransformComponent]] = None,
    workspace_id: Optional[str] = None,
    api_key: Optional[str] = None,
    base_url: Optional[str] = None,
    timeout: int = 60,
    verbose: bool = True,
    **kwargs: Any,
) -> "DashScopeCloudIndex":
    """Build a DashScope index from a sequence of documents."""
    pipeline_create = get_pipeline_create(
        name, transformations or default_transformations(), documents
    )

    workspace_id = workspace_id or os.environ.get("DASHSCOPE_WORKSPACE_ID")
    api_key = api_key or os.environ.get("DASHSCOPE_API_KEY")
    base_url = (
        base_url
        or os.environ.get("DASHSCOPE_BASE_URL", None)
        or DASHSCOPE_DEFAULT_BASE_URL
    )
    headers = {
        "Content-Type": "application/json",
        "Accept-Encoding": "utf-8",
        "X-DashScope-WorkSpace": workspace_id,
        "Authorization": "Bearer " + api_key,
        "X-DashScope-OpenAPISource": "CloudSDK",
    }

    response = requests.put(
        base_url + UPSERT_PIPELINE_ENDPOINT,
        data=json.dumps(pipeline_create),
        headers=headers,
    )
    response_text = response.json()
    pipeline_id = response_text.get("id", None)

    if response_text.get("code", "") != Status.SUCCESS.value or pipeline_id is None:
        raise ValueError(
            f"Failed to create index: {response_text.get('message', '')}\n{response_text}"
        )
    if verbose:
        print(f"Starting creating index {name}, pipeline_id: {pipeline_id}")

    response = requests.post(
        base_url + START_PIPELINE_ENDPOINT.format(pipeline_id=pipeline_id),
        headers=headers,
    )
    response_text = response.json()
    ingestion_id = response_text.get("ingestionId", None)

    if (
        response_text.get("code", "") != Status.SUCCESS.value
        or ingestion_id is None
    ):
        raise ValueError(
            f"Failed to start ingestion: {response_text.get('message', '')}\n{response_text}"
        )
    if verbose:
        print(f"Starting ingestion for index {name}, ingestion_id: {ingestion_id}")

    ingestion_status, failed_docs = run_ingestion(
        base_url
        + CHECK_INGESTION_ENDPOINT.format(
            pipeline_id=pipeline_id, ingestion_id=ingestion_id
        ),
        headers,
        verbose,
    )

    if verbose:
        print(f"ingestion_status {ingestion_status}")
        print(f"failed_docs: {failed_docs}")

    if ingestion_status == "FAILED":
        print("Index {name} created failed!")
        return None

    if verbose:
        print(f"Index {name} created successfully!")

    return cls(
        name,
        transformations=transformations,
        workspace_id=workspace_id,
        api_key=api_key,
        base_url=base_url,
        timeout=timeout,
        **kwargs,
    )

as_retriever #

as_retriever(**kwargs: Any) -> BaseRetriever

Return a Retriever for this managed index.

Source code in llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/base.py 
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def as_retriever(self, **kwargs: Any) -> BaseRetriever:
    """Return a Retriever for this managed index."""
    from llama_index.indices.managed.dashscope.retriever import (
        DashScopeCloudRetriever,
    )

    return DashScopeCloudRetriever(
        self.name,
        **kwargs,
    )

delete_ref_doc #

delete_ref_doc(ref_doc_ids: Union[str, List[str]], verbose: bool = True, **delete_kwargs: Any) -> None

Delete documents in index.

Source code in llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/base.py 
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def delete_ref_doc(
    self,
    ref_doc_ids: Union[str, List[str]],
    verbose: bool = True,
    **delete_kwargs: Any,
) -> None:
    """Delete documents in index."""
    if isinstance(ref_doc_ids, str):
        ref_doc_ids = [ref_doc_ids]
    pipeline_id = get_pipeline_id(
        self._base_url + PIPELINE_SIMPLE_ENDPOINT,
        self._headers,
        {"pipeline_name": self.name},
    )
    doc_delete = get_doc_delete(ref_doc_ids)
    response = requests.post(
        self._base_url + DELETE_DOC_ENDPOINT.format(pipeline_id=pipeline_id),
        json=doc_delete,
        headers=self._headers,
    )
    response_text = response.json()
    if response_text.get("code", "") != Status.SUCCESS.value:
        raise ValueError(
            f"Failed to delete documents: {response_text.get('message', '')}\n{response_text}"
        )
    if verbose:
        print(f"Delete documents {ref_doc_ids} successfully!")

update_ref_doc #

update_ref_doc(document: Document, **update_kwargs: Any) -> None

Update a document and it's corresponding nodes.

Source code in llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/base.py 
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def update_ref_doc(self, document: Document, **update_kwargs: Any) -> None:
    """Update a document and it's corresponding nodes."""
    raise NotImplementedError("update_ref_doc not implemented.")