Fix: parent-children chunking method.

api/apps/canvas_app.py

@@ -147,7 +147,7 @@ async def run():
     if cvs.canvas_category == CanvasCategory.DataFlow:
         task_id = get_uuid()
         Pipeline(cvs.dsl, tenant_id=current_user.id, doc_id=CANVAS_DEBUG_DOC_ID, task_id=task_id, flow_id=req["id"])
-        ok, error_message = await asyncio.to_thread(queue_dataflow, user_id, req["id"], task_id, files[0], 0)
+        ok, error_message = await asyncio.to_thread(queue_dataflow, user_id, req["id"], task_id, CANVAS_DEBUG_DOC_ID, files[0], 0)
         if not ok:
             return get_data_error_result(message=error_message)
         return get_json_result(data={"message_id": task_id})

api/apps/chunk_app.py

@@ -386,6 +386,7 @@ async def _retrieval():
                                                    LLMBundle(kb.tenant_id, LLMType.CHAT))
             if ck["content_with_weight"]:
                 ranks["chunks"].insert(0, ck)
+        ranks["chunks"] = settings.retriever.retrieval_by_children(ranks["chunks"], tenant_ids)
 
         for c in ranks["chunks"]:
             c.pop("vector", None)

common/metadata_utils.py

@@ -13,7 +13,10 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from typing import Any, Callable
+import logging
+from typing import Any, Callable, Dict
+
+import json_repair
 
 from rag.prompts.generator import gen_meta_filter
 
@@ -140,3 +143,63 @@ async def apply_meta_data_filter(
             doc_ids = ["-999"]
 
     return doc_ids
+
+
+def update_metadata_to(metadata, meta):
+    if not meta:
+        return metadata
+    if isinstance(meta, str):
+        try:
+            meta = json_repair.loads(meta)
+        except Exception:
+            logging.error("Meta data format error.")
+            return metadata
+    if not isinstance(meta, dict):
+        return metadata
+    for k, v in meta.items():
+        if isinstance(v, list):
+            v = [vv for vv in v if isinstance(vv, str)]
+            if not v:
+                continue
+        if not isinstance(v, list) and not isinstance(v, str):
+            continue
+        if k not in metadata:
+            metadata[k] = v
+            continue
+        if isinstance(metadata[k], list):
+            if isinstance(v, list):
+                metadata[k].extend(v)
+            else:
+                metadata[k].append(v)
+        else:
+            metadata[k] = v
+
+    return metadata
+
+
+def metadata_schema(metadata: list|None) -> Dict[str, Any]:
+    if not metadata:
+        return {}
+    properties = {}
+
+    for item in metadata:
+        key = item.get("key")
+        if not key:
+            continue
+
+        prop_schema = {
+            "description": item.get("description", "")
+        }
+        if "enum" in item and item["enum"]:
+            prop_schema["enum"] = item["enum"]
+            prop_schema["type"] = "string"
+
+        properties[key] = prop_schema
+
+    json_schema = {
+        "type": "object",
+        "properties": properties,
+    }
+
+    json_schema["additionalProperties"] = False
+    return json_schema

graphrag/general/extractor.py

@@ -78,7 +78,7 @@ def _chat(self, system, history, gen_conf={}, task_id=""):
                     raise TaskCanceledException(f"Task {task_id} was cancelled")
 
             try:
-                response = self._llm.chat(system_msg[0]["content"], hist, conf)
+                response = asyncio.run(self._llm.async_chat(system_msg[0]["content"], hist, conf))
                 response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
                 if response.find("**ERROR**") >= 0:
                     raise Exception(response)

rag/app/naive.py

@@ -635,9 +635,14 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
         "parser_config", {
             "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC", "analyze_hyperlink": True})
 
-    child_deli = re.findall(r"`([^`]+)`", parser_config.get("children_delimiter", ""))
-    child_deli = sorted(set(child_deli), key=lambda x: -len(x))
-    child_deli = "|".join(re.escape(t) for t in child_deli if t)
+    child_deli = parser_config.get("children_delimiter", "").encode('utf-8').decode('unicode_escape').encode('latin1').decode('utf-8')
+    cust_child_deli = re.findall(r"`([^`]+)`", child_deli)
+    child_deli = "|".join(re.sub(r"`([^`]+)`", "", child_deli))
+    if cust_child_deli:
+        cust_child_deli = sorted(set(cust_child_deli), key=lambda x: -len(x))
+        cust_child_deli = "|".join(re.escape(t) for t in cust_child_deli if t)
+        child_deli += cust_child_deli
+
     is_markdown = False
     table_context_size = max(0, int(parser_config.get("table_context_size", 0) or 0))
     image_context_size = max(0, int(parser_config.get("image_context_size", 0) or 0))

rag/flow/splitter/splitter.py

@@ -60,14 +60,7 @@ async def _invoke(self, **kwargs):
                 deli += f"`{d}`"
             else:
                 deli += d
-        child_deli = ""
-        for d in self._param.children_delimiters:
-            if len(d) > 1:
-                child_deli += f"`{d}`"
-            else:
-                child_deli += d
-        child_deli = [m.group(1) for m in re.finditer(r"`([^`]+)`", child_deli)]
-        custom_pattern = "|".join(re.escape(t) for t in sorted(set(child_deli), key=len, reverse=True))
+        custom_pattern = "|".join(re.escape(t) for t in sorted(set(self._param.children_delimiters), key=len, reverse=True))
 
         self.set_output("output_format", "chunks")
         self.callback(random.randint(1, 5) / 100.0, "Start to split into chunks.")

rag/nlp/__init__.py

@@ -273,6 +273,21 @@ def tokenize(d, txt, eng):
     d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
 
 
+def split_with_pattern(d, pattern:str, content:str, eng) -> list:
+    docs = []
+    txts = [txt for txt in re.split(r"(%s)" % pattern, content, flags=re.DOTALL)]
+    for j in range(0, len(txts), 2):
+        txt = txts[j]
+        if not txt:
+            continue
+        if j + 1 < len(txts):
+            txt += txts[j+1]
+        dd = copy.deepcopy(d)
+        tokenize(dd, txt, eng)
+        docs.append(dd)
+    return docs
+
+
 def tokenize_chunks(chunks, doc, eng, pdf_parser=None, child_delimiters_pattern=None):
     res = []
     # wrap up as es documents
@@ -293,10 +308,7 @@ def tokenize_chunks(chunks, doc, eng, pdf_parser=None, child_delimiters_pattern=
 
         if child_delimiters_pattern:
             d["mom_with_weight"] = ck
-            for txt in re.split(r"(%s)" % child_delimiters_pattern, ck, flags=re.DOTALL):
-                dd = copy.deepcopy(d)
-                tokenize(dd, txt, eng)
-                res.append(dd)
+            res.extend(split_with_pattern(d, child_delimiters_pattern, ck, eng))
             continue
 
         tokenize(d, ck, eng)
@@ -316,10 +328,7 @@ def tokenize_chunks_with_images(chunks, doc, eng, images, child_delimiters_patte
         add_positions(d, [[ii]*5])
         if child_delimiters_pattern:
             d["mom_with_weight"] = ck
-            for txt in re.split(r"(%s)" % child_delimiters_pattern, ck, flags=re.DOTALL):
-                dd = copy.deepcopy(d)
-                tokenize(dd, txt, eng)
-                res.append(dd)
+            res.extend(split_with_pattern(d, child_delimiters_pattern, ck, eng))
             continue
         tokenize(d, ck, eng)
         res.append(d)

rag/prompts/generator.py

@@ -821,3 +821,13 @@ async def relevant_chunks_with_toc(query: str, toc:list[dict], chat_mdl, topn: i
     except Exception as e:
         logging.exception(e)
     return []
+
+
+META_DATA = load_prompt("meta_data")
+async def gen_metadata(chat_mdl, schema:dict, content:str):
+    template = PROMPT_JINJA_ENV.from_string(META_DATA)
+    system_prompt = template.render(content=content, schema=schema)
+    user_prompt = "Output: "
+    _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length)
+    ans = await chat_mdl.async_chat(msg[0]["content"], msg[1:])
+    return re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)

rag/prompts/meta_data.md

@@ -0,0 +1,13 @@
+Extract important structured information from the given content. 
+Output ONLY a valid JSON string with no additional text. 
+If no important structured information is found, output an empty JSON object: {}.
+
+Important structured information structure as following: 
+
+{{ schema }}
+
+---------------------------
+The given content as following:
+
+{{ content }}
+

rag/svr/task_executor.py

@@ -23,19 +23,19 @@
 import threading
 import time
 
-import json_repair
-
 from api.db import PIPELINE_SPECIAL_PROGRESS_FREEZE_TASK_TYPES
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.pipeline_operation_log_service import PipelineOperationLogService
 from common.connection_utils import timeout
+from common.metadata_utils import update_metadata_to, metadata_schema
 from rag.utils.base64_image import image2id
 from rag.utils.raptor_utils import should_skip_raptor, get_skip_reason
 from common.log_utils import init_root_logger
 from common.config_utils import show_configs
 from graphrag.general.index import run_graphrag_for_kb
 from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
-from rag.prompts.generator import keyword_extraction, question_proposal, content_tagging, run_toc_from_text
+from rag.prompts.generator import keyword_extraction, question_proposal, content_tagging, run_toc_from_text, \
+    gen_metadata
 import logging
 import os
 from datetime import datetime
@@ -369,6 +369,45 @@ async def doc_question_proposal(chat_mdl, d, topn):
             raise
         progress_callback(msg="Question generation {} chunks completed in {:.2f}s".format(len(docs), timer() - st))
 
+    if task["parser_config"].get("enable_metadata", False) and task["parser_config"].get("metadata"):
+        st = timer()
+        progress_callback(msg="Start to generate meta-data for every chunk ...")
+        chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"])
+
+        async def gen_metadata_task(chat_mdl, d):
+            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "metadata")
+            if not cached:
+                async with chat_limiter:
+                    cached = await gen_metadata(chat_mdl,
+                                                metadata_schema(task["parser_config"]["metadata"]),
+                                                d["content_with_weight"])
+                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "metadata")
+            if cached:
+                d["metadata_obj"] = cached
+        tasks = []
+        for d in docs:
+            tasks.append(asyncio.create_task(gen_metadata_task(chat_mdl, d)))
+        try:
+            await asyncio.gather(*tasks, return_exceptions=False)
+        except Exception as e:
+            logging.error("Error in doc_question_proposal", exc_info=e)
+            for t in tasks:
+                t.cancel()
+            await asyncio.gather(*tasks, return_exceptions=True)
+            raise
+        metadata = {}
+        for ck in cks:
+            metadata = update_metadata_to(metadata, ck["metadata_obj"])
+            del ck["metadata_obj"]
+        if metadata:
+            e, doc = DocumentService.get_by_id(task["doc_id"])
+            if e:
+                if isinstance(doc.meta_fields, str):
+                    doc.meta_fields = json.loads(doc.meta_fields)
+                metadata = update_metadata_to(metadata, doc.meta_fields)
+                DocumentService.update_by_id(task["doc_id"], {"meta_fields": metadata})
+        progress_callback(msg="Question generation {} chunks completed in {:.2f}s".format(len(docs), timer() - st))
+
     if task["kb_parser_config"].get("tag_kb_ids", []):
         progress_callback(msg="Start to tag for every chunk ...")
         kb_ids = task["kb_parser_config"]["tag_kb_ids"]
@@ -603,36 +642,6 @@ def batch_encode(txts):
 
 
     metadata = {}
-    def dict_update(meta):
-        nonlocal metadata
-        if not meta:
-            return
-        if isinstance(meta, str):
-            try:
-                meta = json_repair.loads(meta)
-            except Exception:
-                logging.error("Meta data format error.")
-                return
-        if not isinstance(meta, dict):
-            return
-        for k, v in meta.items():
-            if isinstance(v, list):
-                v = [vv for vv in v if isinstance(vv, str)]
-                if not v:
-                    continue
-            if not isinstance(v, list) and not isinstance(v, str):
-                continue
-            if k not in metadata:
-                metadata[k] = v
-                continue
-            if isinstance(metadata[k], list):
-                if isinstance(v, list):
-                    metadata[k].extend(v)
-                else:
-                    metadata[k].append(v)
-            else:
-                metadata[k] = v
-
     for ck in chunks:
         ck["doc_id"] = doc_id
         ck["kb_id"] = [str(task["kb_id"])]
@@ -657,7 +666,7 @@ def dict_update(meta):
                 ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
             del ck["summary"]
         if "metadata" in ck:
-            dict_update(ck["metadata"])
+            metadata = update_metadata_to(metadata, ck["metadata"])
             del ck["metadata"]
         if "content_with_weight" not in ck:
             ck["content_with_weight"] = ck["text"]
@@ -671,7 +680,7 @@ def dict_update(meta):
         if e:
             if isinstance(doc.meta_fields, str):
                 doc.meta_fields = json.loads(doc.meta_fields)
-            dict_update(doc.meta_fields)
+            metadata = update_metadata_to(metadata, doc.meta_fields)
             DocumentService.update_by_id(doc_id, {"meta_fields": metadata})
 
     start_ts = timer()