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185 | class AlibabaCloudAISearchNodeParser(NodeParser):
"""
For further details, please visit `https://help.aliyun.com/zh/open-search/search-platform/developer-reference/document-split-api-details`.
"""
_client: Client = PrivateAttr()
_split_strategy: GetDocumentSplitRequestStrategy = PrivateAttr()
image_reader: Optional[BaseReader] = Field(default=None, exclude=True)
aisearch_api_key: str = Field(default=None, exclude=True)
endpoint: str = None
service_id: str = "ops-document-split-001"
workspace_name: str = "default"
chunk_size: int = 300
need_sentence: bool = False
default_content_encoding: str = "utf8"
default_content_type: str = "text/plain"
num_workers: int = 4
def __init__(
self, endpoint: str = None, aisearch_api_key: str = None, **kwargs: Any
) -> None:
super().__init__(**kwargs)
self.aisearch_api_key = get_from_param_or_env(
"aisearch_api_key", aisearch_api_key, "AISEARCH_API_KEY"
)
self.endpoint = get_from_param_or_env("endpoint", endpoint, "AISEARCH_ENDPOINT")
config = AISearchConfig(
bearer_token=self.aisearch_api_key,
endpoint=self.endpoint,
protocol="http",
)
self._client = Client(config=config)
self._split_strategy = GetDocumentSplitRequestStrategy(
max_chunk_size=self.chunk_size, need_sentence=self.need_sentence
)
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "AlibabaCloudAISearchNodeParser"
def _parse_nodes(
self,
documents: Sequence[BaseNode],
show_progress: bool = False,
**kwargs: Any,
) -> List[BaseNode]:
return asyncio.get_event_loop().run_until_complete(
self._aparse_nodes(documents, show_progress, **kwargs)
)
async def _aparse_nodes(
self,
documents: Sequence[BaseNode],
show_progress: bool = False,
**kwargs: Any,
) -> List[BaseNode]:
"""Parse document into nodes.
Args:
nodes (Sequence[BaseNode]): nodes to parse
"""
jobs = [self._aparse_node(d) for d in documents]
results = await run_jobs(
jobs,
workers=self.num_workers,
desc="Parsing documents into nodes",
show_progress=show_progress,
)
# return flattened results
return [item for sublist in results for item in sublist]
@aretry_decorator
async def _aparse_node(
self,
node: BaseNode,
) -> List[BaseNode]:
content_type = getattr(node, "mimetype", self.default_content_type)
main_type, sub_type = content_type.split("/")
if main_type != "text":
raise ValueError(f"Unsupported content type: {content_type}")
content_encoding = node.metadata.get("encoding", self.default_content_encoding)
document = GetDocumentSplitRequestDocument(
content=node.get_content(),
content_encoding=content_encoding,
content_type=sub_type,
)
request = GetDocumentSplitRequest(
document=document, strategy=self._split_strategy
)
response: GetDocumentSplitResponse = (
await self._client.get_document_split_async(
workspace_name=self.workspace_name,
service_id=self.service_id,
request=request,
)
)
return await self._handle_response(response, node)
async def _handle_response(
self, response: GetDocumentSplitResponse, node: BaseNode
) -> List[TextNode]:
chunks = list(response.body.result.chunks)
if response.body.result.sentences:
chunks.extend(response.body.result.sentences)
chunks.extend(await self._handle_rich_texts(response.body.result.rich_texts))
return build_nodes_from_splits(
[chunk.content for chunk in chunks], node, id_func=self.id_func
)
async def _handle_rich_texts(self, rich_texts) -> List[str]:
chunks = []
if not rich_texts:
return chunks
chunks = list(rich_texts)
for chunk in chunks:
if chunk.meta.get("type") == "image":
chunk.content = await self._handle_image(chunk.content)
return chunks
async def _handle_image(self, url: str) -> str:
content = url
if not self.image_reader:
return content
try:
docs = await self.image_reader.aload_data([url])
content = docs[0].get_content()
except Exception as e:
logger.error(f"Read image {url} error: {e}")
return content
|