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Feat: refine dataflow and initialize dataflow app (#9952)
### What problem does this PR solve? Refine dataflow and initialize dataflow app. ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Yongteng Lei
14
rag/flow/parser/__init__.py
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rag/flow/parser/__init__.py
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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154
rag/flow/parser/parser.py
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rag/flow/parser/parser.py
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import trio
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from api.db import LLMType
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from api.db.services.llm_service import LLMBundle
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from deepdoc.parser import ExcelParser
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from deepdoc.parser.pdf_parser import PlainParser, RAGFlowPdfParser, VisionParser
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from rag.flow.base import ProcessBase, ProcessParamBase
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from rag.flow.parser.schema import ParserFromUpstream
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from rag.llm.cv_model import Base as VLM
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class ParserParam(ProcessParamBase):
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def __init__(self):
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super().__init__()
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self.allowed_output_format = {
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"pdf": ["json", "markdown"],
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"excel": ["json", "markdown", "html"],
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"ppt": [],
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"image": [],
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"email": [],
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"text": [],
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"audio": [],
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"video": [],
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}
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self.setups = {
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"pdf": {
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"parse_method": "deepdoc", # deepdoc/plain_text/vlm
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"vlm_name": "",
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"lang": "Chinese",
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"suffix": ["pdf"],
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"output_format": "json",
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},
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"excel": {
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"output_format": "html",
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"suffix": ["xls", "xlsx", "csv"],
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},
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"ppt": {},
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"image": {
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"parse_method": "ocr",
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},
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"email": {},
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"text": {},
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"audio": {},
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"video": {},
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}
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def check(self):
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pdf_config = self.setups.get("pdf", {})
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if pdf_config:
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pdf_parse_method = pdf_config.get("parse_method", "")
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self.check_valid_value(pdf_parse_method.lower(), "Parse method abnormal.", ["deepdoc", "plain_text", "vlm"])
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if pdf_parse_method not in ["deepdoc", "plain_text"]:
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self.check_empty(pdf_config.get("vlm_name"), "VLM")
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pdf_language = pdf_config.get("lang", "")
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self.check_empty(pdf_language, "Language")
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pdf_output_format = pdf_config.get("output_format", "")
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self.check_valid_value(pdf_output_format, "PDF output format abnormal.", self.allowed_output_format["pdf"])
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excel_config = self.setups.get("excel", "")
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if excel_config:
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excel_output_format = excel_config.get("output_format", "")
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self.check_valid_value(excel_output_format, "Excel output format abnormal.", self.allowed_output_format["excel"])
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image_config = self.setups.get("image", "")
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if image_config:
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image_parse_method = image_config.get("parse_method", "")
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self.check_valid_value(image_parse_method.lower(), "Parse method abnormal.", ["ocr"])
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def get_input_form(self) -> dict[str, dict]:
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return {}
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class Parser(ProcessBase):
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component_name = "Parser"
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def _pdf(self, blob):
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a PDF.")
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conf = self._param.setups["pdf"]
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self.set_output("output_format", conf["output_format"])
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if conf.get("parse_method") == "deepdoc":
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bboxes = RAGFlowPdfParser().parse_into_bboxes(blob, callback=self.callback)
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elif conf.get("parse_method") == "plain_text":
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lines, _ = PlainParser()(blob)
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bboxes = [{"text": t} for t, _ in lines]
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else:
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assert conf.get("vlm_name")
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vision_model = LLMBundle(self._canvas._tenant_id, LLMType.IMAGE2TEXT, llm_name=conf.get("vlm_name"), lang=self._param.setups["pdf"].get("lang"))
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lines, _ = VisionParser(vision_model=vision_model)(blob, callback=self.callback)
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bboxes = []
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for t, poss in lines:
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pn, x0, x1, top, bott = poss.split(" ")
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bboxes.append({"page_number": int(pn), "x0": float(x0), "x1": float(x1), "top": float(top), "bottom": float(bott), "text": t})
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if conf.get("output_format") == "json":
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self.set_output("json", bboxes)
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if conf.get("output_format") == "markdown":
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mkdn = ""
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for b in bboxes:
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if b.get("layout_type", "") == "title":
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mkdn += "\n## "
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if b.get("layout_type", "") == "figure":
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mkdn += "\n".format(VLM.image2base64(b["image"]))
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continue
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mkdn += b.get("text", "") + "\n"
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self.set_output("markdown", mkdn)
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def _excel(self, blob):
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a Excel.")
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conf = self._param.setups["excel"]
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self.set_output("output_format", conf["output_format"])
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excel_parser = ExcelParser()
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if conf.get("output_format") == "html":
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html = excel_parser.html(blob, 1000000000)
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self.set_output("html", html)
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elif conf.get("output_format") == "json":
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self.set_output("json", [{"text": txt} for txt in excel_parser(blob) if txt])
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elif conf.get("output_format") == "markdown":
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self.set_output("markdown", excel_parser.markdown(blob))
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async def _invoke(self, **kwargs):
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function_map = {
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"pdf": self._pdf,
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"excel": self._excel,
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}
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try:
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from_upstream = ParserFromUpstream.model_validate(kwargs)
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except Exception as e:
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self.set_output("_ERROR", f"Input error: {str(e)}")
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return
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for p_type, conf in self._param.setups.items():
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if from_upstream.name.split(".")[-1].lower() not in conf.get("suffix", []):
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continue
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await trio.to_thread.run_sync(function_map[p_type], from_upstream.blob)
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break
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25
rag/flow/parser/schema.py
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25
rag/flow/parser/schema.py
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@ -0,0 +1,25 @@
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pydantic import BaseModel, ConfigDict, Field
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class ParserFromUpstream(BaseModel):
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created_time: float | None = Field(default=None, alias="_created_time")
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elapsed_time: float | None = Field(default=None, alias="_elapsed_time")
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name: str
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blob: bytes
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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