Fix: debug PDF positions.. (#10365)

### What problem does this PR solve?

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

api/apps/canvas_app.py

@@ -440,9 +440,7 @@ def list_canvas():
         desc = False
     else:
         desc = True
-    owner_ids = request.args.get("owner_ids", [])
-    if owner_ids and isinstance(owner_ids, str):
-        owner_ids = [owner_ids]
+    owner_ids = [id for id in request.args.get("owner_ids", "").strip().split(",") if id]
     if not owner_ids:
         tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
         tenants = [m["tenant_id"] for m in tenants]

api/apps/kb_app.py

@@ -28,7 +28,7 @@ from api.db.services.task_service import TaskService, GRAPH_RAPTOR_FAKE_DOC_ID
 from api.db.services.user_service import TenantService, UserTenantService
 from api.utils.api_utils import get_error_data_result, server_error_response, get_data_error_result, validate_request, not_allowed_parameters
 from api.utils import get_uuid
-from api.db import PipelineTaskType, StatusEnum, FileSource, VALID_FILE_TYPES
+from api.db import PipelineTaskType, StatusEnum, FileSource, VALID_FILE_TYPES, VALID_TASK_STATUS
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.db_models import File
 from api.utils.api_utils import get_json_result
@@ -453,7 +453,7 @@ def list_pipeline_logs():
 
     operation_status = req.get("operation_status", [])
     if operation_status:
-        invalid_status = {s for s in operation_status if s not in ["success", "failed", "running", "pending"]}
+        invalid_status = {s for s in operation_status if s not in VALID_TASK_STATUS}
         if invalid_status:
             return get_data_error_result(message=f"Invalid filter operation_status status conditions: {', '.join(invalid_status)}")
 
@@ -495,7 +495,7 @@ def list_pipeline_dataset_logs():
 
     operation_status = req.get("operation_status", [])
     if operation_status:
-        invalid_status = {s for s in operation_status if s not in ["success", "failed", "running", "pending"]}
+        invalid_status = {s for s in operation_status if s not in VALID_TASK_STATUS}
         if invalid_status:
             return get_data_error_result(message=f"Invalid filter operation_status status conditions: {', '.join(invalid_status)}")
 

api/db/services/pipeline_operation_log_service.py

@@ -176,7 +176,7 @@ class PipelineOperationLogService(CommonService):
         with DB.atomic():
             obj = cls.save(**log)
 
-            limit = int(os.getenv("PIPELINE_OPERATION_LOG_LIMIT", 1))
+            limit = int(os.getenv("PIPELINE_OPERATION_LOG_LIMIT", 1000))
             total = cls.model.select().where(cls.model.kb_id == document.kb_id).count()
 
             if total > limit:

deepdoc/parser/pdf_parser.py

@@ -1104,12 +1104,14 @@ class RAGFlowPdfParser:
                 if self.boxes[min_i]["bottom"] < top+self.page_cum_height[pn]:
                     min_i += 1
                 self.boxes.insert(min_i, {
-                    "page_number": pn+1, "x0": left, "x1": right, "top": top+self.page_cum_height[pn], "bottom": bott+self.page_cum_height[pn], "layout_type": layout_type, "text": txt, "image": img
+                    "page_number": pn+1, "x0": left, "x1": right, "top": top+self.page_cum_height[pn], "bottom": bott+self.page_cum_height[pn], "layout_type": layout_type, "text": txt, "image": img,
+                    "positions": [[pn+1, int(left), int(right), int(top), int(bott)]]
                 })
 
         for b in self.boxes:
             b["position_tag"] = self._line_tag(b, zoomin)
             b["image"] = self.crop(b["position_tag"], zoomin)
+            b["positions"] = [[pos[0][-1]+1, *pos[1:]] for pos in RAGFlowPdfParser.extract_positions(b["position_tag"])]
 
         insert_table_figures(tbls, "table")
         insert_table_figures(figs, "figure")

rag/flow/extractor/extractor.py

@@ -13,6 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 import random
+from copy import deepcopy
 from agent.component.llm import LLMParam, LLM
 from rag.flow.base import ProcessBase, ProcessParamBase
 
@@ -40,7 +41,7 @@ class Extractor(ProcessBase, LLM):
         for k, v in inputs.items():
             args[k] = v["value"]
             if isinstance(args[k], list):
-                chunks = args[k]
+                chunks = deepcopy(args[k])
                 chunks_key = k
 
         if chunks:

rag/flow/parser/parser.py

@@ -14,7 +14,6 @@
 #  limitations under the License.
 import io
 import json
-import logging
 import os
 import random
 from functools import partial
@@ -31,6 +30,7 @@ from api.utils import get_uuid
 from api.utils.base64_image import image2id
 from deepdoc.parser import ExcelParser
 from deepdoc.parser.pdf_parser import PlainParser, RAGFlowPdfParser, VisionParser
+from rag.app.naive import Docx
 from rag.flow.base import ProcessBase, ProcessParamBase
 from rag.flow.parser.schema import ParserFromUpstream
 from rag.llm.cv_model import Base as VLM
@@ -243,19 +243,13 @@ class Parser(ProcessBase):
             self.set_output("markdown", spreadsheet_parser.markdown(blob))
 
     def _word(self, name, blob):
-        from tika import parser as word_parser
-
         self.callback(random.randint(1, 5) / 100.0, "Start to work on a Word Processor Document")
         conf = self._param.setups["word"]
         self.set_output("output_format", conf["output_format"])
-        doc_parsed = word_parser.from_buffer(blob)
-        sections = []
-        if doc_parsed.get("content"):
-            sections = doc_parsed["content"].split("\n")
-            sections = [{"text": section} for section in sections if section]
-        else:
-            logging.warning(f"tika.parser got empty content from {name}.")
-
+        docx_parser = Docx()
+        sections, tbls = docx_parser(name, binary=blob)
+        sections = [{"text": section[0], "image": section[1]} for section in sections if section]
+        sections.extend([{"text": tb, "image": None} for ((_,tb), _) in tbls])
         # json
         assert conf.get("output_format") == "json", "have to be json for doc"
         if conf.get("output_format") == "json":

rag/flow/splitter/splitter.py

@@ -100,7 +100,7 @@ class Splitter(ProcessBase):
             {
                 "text": RAGFlowPdfParser.remove_tag(c),
                 "image": img,
-                "positions": [[pos[0][-1], *pos[1:]] for pos in RAGFlowPdfParser.extract_positions(c)],
+                "positions": [[pos[0][-1]+1, *pos[1:]] for pos in RAGFlowPdfParser.extract_positions(c)],
             }
             for c, img in zip(chunks, images) if c.strip()
         ]

rag/llm/embedding_model.py

@@ -756,7 +756,7 @@ class SILICONFLOWEmbed(Base):
             texts_batch = texts[i : i + batch_size]
             if self.model_name in ["BAAI/bge-large-zh-v1.5", "BAAI/bge-large-en-v1.5"]:
                 # limit 512, 340 is almost safe
-                texts_batch = [" " if not text.strip() else truncate(text, 340) for text in texts_batch]
+                texts_batch = [" " if not text.strip() else truncate(text, 256) for text in texts_batch]
             else:
                 texts_batch = [" " if not text.strip() else text for text in texts_batch]
 

rag/svr/task_executor.py

@@ -503,9 +503,9 @@ async def run_dataflow(task: dict):
 
     embedding_token_consumption = chunks.get("embedding_token_consumption", 0)
     if chunks.get("chunks"):
-        chunks = chunks["chunks"]
+        chunks = copy.deepcopy(chunks["chunks"])
     elif chunks.get("json"):
-        chunks = chunks["json"]
+        chunks = copy.deepcopy(chunks["json"])
     elif chunks.get("markdown"):
         chunks = [{"text": [chunks["markdown"]]}]
     elif chunks.get("text"):
@@ -515,34 +515,40 @@ async def run_dataflow(task: dict):
 
     keys = [k for o in chunks for k in list(o.keys())]
     if not any([re.match(r"q_[0-9]+_vec", k) for k in keys]):
-        set_progress(task_id, prog=0.82, msg="\n-------------------------------------\nStart to embedding...")
-        e, kb = KnowledgebaseService.get_by_id(task["kb_id"])
-        embedding_id = kb.embd_id
-        embedding_model = LLMBundle(task["tenant_id"], LLMType.EMBEDDING, llm_name=embedding_id)
-        @timeout(60)
-        def batch_encode(txts):
-            nonlocal embedding_model
-            return embedding_model.encode([truncate(c, embedding_model.max_length - 10) for c in txts])
-        vects = np.array([])
-        texts = [o.get("questions", o.get("summary", o["text"])) for o in chunks]
-        delta = 0.20/(len(texts)//EMBEDDING_BATCH_SIZE+1)
-        prog = 0.8
-        for i in range(0, len(texts), EMBEDDING_BATCH_SIZE):
-            async with embed_limiter:
-                vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + EMBEDDING_BATCH_SIZE]))
-            if len(vects) == 0:
-                vects = vts
-            else:
-                vects = np.concatenate((vects, vts), axis=0)
-            embedding_token_consumption += c
-            prog += delta
-            if i % (len(texts)//EMBEDDING_BATCH_SIZE/100+1) == 1:
-                set_progress(task_id, prog=prog, msg=f"{i+1} / {len(texts)//EMBEDDING_BATCH_SIZE}")
+        try:
+            set_progress(task_id, prog=0.82, msg="\n-------------------------------------\nStart to embedding...")
+            e, kb = KnowledgebaseService.get_by_id(task["kb_id"])
+            embedding_id = kb.embd_id
+            embedding_model = LLMBundle(task["tenant_id"], LLMType.EMBEDDING, llm_name=embedding_id)
+            @timeout(60)
+            def batch_encode(txts):
+                nonlocal embedding_model
+                return embedding_model.encode([truncate(c, embedding_model.max_length - 10) for c in txts])
+            vects = np.array([])
+            texts = [o.get("questions", o.get("summary", o["text"])) for o in chunks]
+            delta = 0.20/(len(texts)//EMBEDDING_BATCH_SIZE+1)
+            prog = 0.8
+            for i in range(0, len(texts), EMBEDDING_BATCH_SIZE):
+                async with embed_limiter:
+                    vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + EMBEDDING_BATCH_SIZE]))
+                if len(vects) == 0:
+                    vects = vts
+                else:
+                    vects = np.concatenate((vects, vts), axis=0)
+                embedding_token_consumption += c
+                prog += delta
+                if i % (len(texts)//EMBEDDING_BATCH_SIZE/100+1) == 1:
+                    set_progress(task_id, prog=prog, msg=f"{i+1} / {len(texts)//EMBEDDING_BATCH_SIZE}")
+
+            assert len(vects) == len(chunks)
+            for i, ck in enumerate(chunks):
+                v = vects[i].tolist()
+                ck["q_%d_vec" % len(v)] = v
+        except Exception as e:
+            set_progress(task_id, prog=-1, msg=f"[ERROR]: {e}")
+            PipelineOperationLogService.create(document_id=doc_id, pipeline_id=dataflow_id, task_type=PipelineTaskType.PARSE, dsl=str(pipeline))
+            return
 
-        assert len(vects) == len(chunks)
-        for i, ck in enumerate(chunks):
-            v = vects[i].tolist()
-            ck["q_%d_vec" % len(v)] = v
 
     metadata = {}
     def dict_update(meta):