Feat: add foundational support for GraphRAG dataset pipeline logs (#10264)

### What problem does this PR solve?

Add foundational support for GraphRAG dataset pipeline logs

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

api/apps/kb_app.py

@@ -14,17 +14,19 @@
 #  limitations under the License.
 #
 import json
+import logging
 
 from flask import request
 from flask_login import login_required, current_user
 
 from api.db.services import duplicate_name
-from api.db.services.document_service import DocumentService
+from api.db.services.document_service import DocumentService, queue_raptor_o_graphrag_tasks
 from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
 from api.db.services.pipeline_operation_log_service import PipelineOperationLogService
+from api.db.services.task_service import TaskService
 from api.db.services.user_service import TenantService, UserTenantService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request, not_allowed_parameters
+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 StatusEnum, FileSource, VALID_FILE_TYPES
 from api.db.services.knowledgebase_service import KnowledgebaseService
@@ -435,18 +437,60 @@ def list_pipeline_logs():
     suffix = req.get("suffix", [])
 
     try:
-        docs, tol = PipelineOperationLogService.get_by_kb_id(kb_id, page_number, items_per_page, orderby, desc, keywords, operation_status, types, suffix)
+        logs, tol = PipelineOperationLogService.get_file_logs_by_kb_id(kb_id, page_number, items_per_page, orderby, desc, keywords, operation_status, types, suffix)
 
         if create_time_from or create_time_to:
             filtered_docs = []
-            for doc in docs:
+            for doc in logs:
                 doc_create_time = doc.get("create_time", 0)
                 if (create_time_from == 0 or doc_create_time >= create_time_from) and (create_time_to == 0 or doc_create_time <= create_time_to):
                     filtered_docs.append(doc)
-            docs = filtered_docs
+            logs = filtered_docs
 
 
-        return get_json_result(data={"total": tol, "docs": docs})
+        return get_json_result(data={"total": tol, "logs": logs})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/list_pipeline_dataset_logs", methods=["POST"])  # noqa: F821
+@login_required
+def list_pipeline_dataset_logs():
+    kb_id = request.args.get("kb_id")
+    if not kb_id:
+        return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR)
+
+    page_number = int(request.args.get("page", 0))
+    items_per_page = int(request.args.get("page_size", 0))
+    orderby = request.args.get("orderby", "create_time")
+    if request.args.get("desc", "true").lower() == "false":
+        desc = False
+    else:
+        desc = True
+    create_time_from = int(request.args.get("create_time_from", 0))
+    create_time_to = int(request.args.get("create_time_to", 0))
+
+    req = request.get_json()
+
+    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"]}
+        if invalid_status:
+            return get_data_error_result(message=f"Invalid filter operation_status status conditions: {', '.join(invalid_status)}")
+
+    try:
+        logs, tol = PipelineOperationLogService.get_dataset_logs_by_kb_id(kb_id, page_number, items_per_page, orderby, desc, operation_status)
+
+        if create_time_from or create_time_to:
+            filtered_docs = []
+            for doc in logs:
+                doc_create_time = doc.get("create_time", 0)
+                if (create_time_from == 0 or doc_create_time >= create_time_from) and (create_time_to == 0 or doc_create_time <= create_time_to):
+                    filtered_docs.append(doc)
+            logs = filtered_docs
+
+
+        return get_json_result(data={"total": tol, "logs": logs})
     except Exception as e:
         return server_error_response(e)
 
@@ -478,3 +522,68 @@ def pipeline_log_detail():
         return get_data_error_result(message="Invalid pipeline log ID")
 
     return get_json_result(data=log.to_dict())
+
+
+@manager.route("/run_graphrag", methods=["POST"])  # noqa: F821
+@login_required
+def run_graphrag():
+    req = request.json
+
+    kb_id = req.get("kb_id", "")
+    if not kb_id:
+        return get_error_data_result(message='Lack of "KB ID"')
+
+    doc_ids = req.get("doc_ids", [])
+    if not doc_ids:
+        return get_error_data_result(message="Need to specify document IDs to run Graph RAG")
+
+    ok, kb = KnowledgebaseService.get_by_id(kb_id)
+    if not ok:
+        return get_error_data_result(message="Invalid Knowledgebase ID")
+
+    task_id = kb.graphrag_task_id
+    ok, task = TaskService.get_by_id(task_id)
+    if not ok:
+        logging.warning(f"A valid GraphRAG task id is expected for kb {kb_id}")
+
+    if task and task.progress not in [-1, 1]:
+        return get_error_data_result(message=f"Task in progress with status {task.progress}. A Graph Task is already running.")
+
+    document_ids = set()
+    sample_document = {}
+    for doc_id in doc_ids:
+        ok, document = DocumentService.get_by_id(doc_id)
+        if ok:
+            document_ids.add(document.id)
+            if not sample_document:
+                sample_document = document.to_dict()
+
+    task_id = queue_raptor_o_graphrag_tasks(doc=sample_document, ty="graphrag", priority=0, fake_doc_id="x", doc_ids=list(document_ids))
+
+    if not KnowledgebaseService.update_by_id(kb.id, {"graphrag_task_id": task_id}):
+        logging.warning(f"Cannot save graphrag_task_id for kb {kb_id}")
+
+    return get_json_result(data={"graphrag_task_id": task_id})
+
+
+@manager.route("/trace_graphrag", methods=["GET"])  # noqa: F821
+@login_required
+def trace_graphrag():
+    kb_id = request.args.get("kb_id", "")
+    if not kb_id:
+        return get_error_data_result(message='Lack of "KB ID"')
+
+    ok, kb = KnowledgebaseService.get_by_id(kb_id)
+    if not ok:
+        return get_error_data_result(message="Invalid Knowledgebase ID")
+
+
+    task_id = kb.graphrag_task_id
+    if not task_id:
+        return get_error_data_result(message="GraphRAG Task ID Not Found")
+
+    ok, task = TaskService.get_by_id(task_id)
+    if not ok:
+        return get_json_result(data=False, message="GraphRAG Task Not Found or Error Occurred", code=settings.RetCode.ARGUMENT_ERROR)
+
+    return get_json_result(data=task.to_dict())

api/db/__init__.py

@@ -124,10 +124,12 @@ VALID_MCP_SERVER_TYPES = {MCPServerType.SSE, MCPServerType.STREAMABLE_HTTP}
 
 class PipelineTaskType(StrEnum):
     PARSE = "Parse"
-    DOWNLOAD = "DOWNLOAD"
+    DOWNLOAD = "Download"
+    RAPTOR = "RAPTOR"
+    GRAPH_RAG = "GraphRAG"
 
 
-VALID_PIPELINE_TASK_TYPES = {PipelineTaskType.PARSE, PipelineTaskType.DOWNLOAD}
+VALID_PIPELINE_TASK_TYPES = {PipelineTaskType.PARSE, PipelineTaskType.DOWNLOAD, PipelineTaskType.RAPTOR, PipelineTaskType.GRAPH_RAG}
 
 
 KNOWLEDGEBASE_FOLDER_NAME=".knowledgebase"

api/db/db_models.py

@@ -649,6 +649,9 @@ class Knowledgebase(DataBaseModel):
     pipeline_id = CharField(max_length=32, null=True, help_text="Pipeline ID", index=True)
     parser_config = JSONField(null=False, default={"pages": [[1, 1000000]]})
     pagerank = IntegerField(default=0, index=False)
+
+    graphrag_task_id = CharField(max_length=32, null=True, help_text="Graph RAG task ID", index=True)
+
     status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True)
 
     def __str__(self):
@@ -1065,11 +1068,15 @@ def migrate_db():
     except Exception:
         pass
     try:
-        migrate(migrator.add_column("knowledgebase", "pipeline_id", CharField(max_length=32, null=True, help_text="default parser ID", index=True)))
+        migrate(migrator.add_column("knowledgebase", "pipeline_id", CharField(max_length=32, null=True, help_text="Pipeline ID", index=True)))
     except Exception:
         pass
     try:
-        migrate(migrator.add_column("document", "pipeline_id", CharField(max_length=32, null=True, help_text="default parser ID", index=True)))
+        migrate(migrator.add_column("document", "pipeline_id", CharField(max_length=32, null=True, help_text="Pipeline ID", index=True)))
+    except Exception:
+        pass
+    try:
+        migrate(migrator.add_column("knowledgebase", "graphrag_task_id", CharField(max_length=32, null=True, help_text="Gragh RAG task ID", index=True)))
     except Exception:
         pass
     logging.disable(logging.NOTSET)

api/db/services/document_service.py

@@ -507,6 +507,9 @@ class DocumentService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_doc_id_by_doc_name(cls, doc_name):
+        """
+        highly rely on the strict deduplication guarantee from Document
+        """
         fields = [cls.model.id]
         doc_id = cls.model.select(*fields) \
             .where(cls.model.name == doc_name)
@@ -656,6 +659,7 @@ class DocumentService(CommonService):
                         queue_raptor_o_graphrag_tasks(d, "graphrag", priority)
                         prg = 0.98 * len(tsks) / (len(tsks) + 1)
                     else:
+                        prg = 1
                         status = TaskStatus.DONE.value
 
                 msg = "\n".join(sorted(msg))
@@ -741,7 +745,11 @@ class DocumentService(CommonService):
             "cancelled": int(cancelled),
         }
 
-def queue_raptor_o_graphrag_tasks(doc, ty, priority):
+def queue_raptor_o_graphrag_tasks(doc, ty, priority, fake_doc_id="", doc_ids=[]):
+    """
+    You can provide a fake_doc_id to bypass the restriction of tasks at the knowledgebase level.
+    Optionally, specify a list of doc_ids to determine which documents participate in the task.
+    """
     chunking_config = DocumentService.get_chunking_config(doc["id"])
     hasher = xxhash.xxh64()
     for field in sorted(chunking_config.keys()):
@@ -751,7 +759,7 @@ def queue_raptor_o_graphrag_tasks(doc, ty, priority):
         nonlocal doc
         return {
             "id": get_uuid(),
-            "doc_id": doc["id"],
+            "doc_id": fake_doc_id if fake_doc_id else doc["id"],
             "from_page": 100000000,
             "to_page": 100000000,
             "task_type": ty,
@@ -764,7 +772,11 @@ def queue_raptor_o_graphrag_tasks(doc, ty, priority):
     hasher.update(ty.encode("utf-8"))
     task["digest"] = hasher.hexdigest()
     bulk_insert_into_db(Task, [task], True)
+
+    if ty == "graphrag":
+        task["doc_ids"] = doc_ids
     assert REDIS_CONN.queue_product(get_svr_queue_name(priority), message=task), "Can't access Redis. Please check the Redis' status."
+    return task["id"]
 
 
 def get_queue_length(priority):

api/db/services/pipeline_operation_log_service.py

@@ -31,7 +31,7 @@ class PipelineOperationLogService(CommonService):
     model = PipelineOperationLog
 
     @classmethod
-    def get_cls_model_fields(cls):
+    def get_file_logs_fields(cls):
         return [
             cls.model.id,
             cls.model.document_id,
@@ -59,9 +59,29 @@ class PipelineOperationLogService(CommonService):
             cls.model.update_date,
         ]
 
+    @classmethod
+    def get_dataset_logs_fields(cls):
+        return [
+            cls.model.id,
+            cls.model.tenant_id,
+            cls.model.kb_id,
+            cls.model.progress,
+            cls.model.progress_msg,
+            cls.model.process_begin_at,
+            cls.model.process_duration,
+            cls.model.task_type,
+            cls.model.operation_status,
+            cls.model.avatar,
+            cls.model.status,
+            cls.model.create_time,
+            cls.model.create_date,
+            cls.model.update_time,
+            cls.model.update_date,
+        ]
+
     @classmethod
     @DB.connection_context()
-    def create(cls, document_id, pipeline_id, task_type):
+    def create(cls, document_id, pipeline_id, task_type, fake_document_ids=[]):
         from rag.flow.pipeline import Pipeline
 
         tenant_id = ""
@@ -69,14 +89,19 @@ class PipelineOperationLogService(CommonService):
         avatar = ""
         dsl = ""
         operation_status = ""
+        referred_document_id = document_id
 
-        ok, document = DocumentService.get_by_id(document_id)
+        if referred_document_id == "x" and fake_document_ids:
+            referred_document_id = fake_document_ids[0]
+        ok, document = DocumentService.get_by_id(referred_document_id)
         if not ok:
-            raise RuntimeError(f"Document {document_id} not found")
+            raise RuntimeError(f"Document for referred_document_id {referred_document_id} not found")
         DocumentService.update_progress_immediately([document.to_dict()])
-        ok, document = DocumentService.get_by_id(document_id)
+        ok, document = DocumentService.get_by_id(referred_document_id)
         if not ok:
-            raise RuntimeError(f"Document {document_id} not found")
+            raise RuntimeError(f"Document for referred_document_id {referred_document_id} not found")
+        if document.progress not in [1, -1]:
+            return
         operation_status = document.run
 
         if pipeline_id:
@@ -84,7 +109,7 @@ class PipelineOperationLogService(CommonService):
             if not ok:
                 raise RuntimeError(f"Pipeline {pipeline_id} not found")
 
-            pipeline = Pipeline(dsl=json.dumps(user_pipeline.dsl), tenant_id=user_pipeline.user_id, doc_id=document_id, task_id="", flow_id=pipeline_id)
+            pipeline = Pipeline(dsl=json.dumps(user_pipeline.dsl), tenant_id=user_pipeline.user_id, doc_id=referred_document_id, task_id="", flow_id=pipeline_id)
 
             tenant_id = user_pipeline.user_id
             title = user_pipeline.title
@@ -93,7 +118,7 @@ class PipelineOperationLogService(CommonService):
         else:
             ok, kb_info = KnowledgebaseService.get_by_id(document.kb_id)
             if not ok:
-                raise RuntimeError(f"Cannot find knowledge base {document.kb_id} for document {document_id}")
+                raise RuntimeError(f"Cannot find knowledge base {document.kb_id} for referred_document {referred_document_id}")
 
             tenant_id = kb_info.tenant_id
             title = document.name
@@ -104,7 +129,7 @@ class PipelineOperationLogService(CommonService):
 
         log = dict(
             id=get_uuid(),
-            document_id=document_id,
+            document_id=document_id,  # "x" or real document_id
             tenant_id=tenant_id,
             kb_id=document.kb_id,
             pipeline_id=pipeline_id,
@@ -132,18 +157,20 @@ class PipelineOperationLogService(CommonService):
 
     @classmethod
     @DB.connection_context()
-    def record_pipeline_operation(cls, document_id, pipeline_id, task_type):
-        return cls.create(document_id=document_id, pipeline_id=pipeline_id, task_type=task_type)
+    def record_pipeline_operation(cls, document_id, pipeline_id, task_type, fake_document_ids=[]):
+        return cls.create(document_id=document_id, pipeline_id=pipeline_id, task_type=task_type, fake_document_ids=fake_document_ids)
 
     @classmethod
     @DB.connection_context()
-    def get_by_kb_id(cls, kb_id, page_number, items_per_page, orderby, desc, keywords, operation_status, types, suffix):
-        fields = cls.get_cls_model_fields()
+    def get_file_logs_by_kb_id(cls, kb_id, page_number, items_per_page, orderby, desc, keywords, operation_status, types, suffix):
+        fields = cls.get_file_logs_fields()
         if keywords:
             logs = cls.model.select(*fields).where((cls.model.kb_id == kb_id), (fn.LOWER(cls.model.document_name).contains(keywords.lower())))
         else:
             logs = cls.model.select(*fields).where(cls.model.kb_id == kb_id)
 
+        logs = logs.where(cls.model.document_id != "x")
+
         if operation_status:
             logs = logs.where(cls.model.operation_status.in_(operation_status))
         if types:
@@ -161,3 +188,23 @@ class PipelineOperationLogService(CommonService):
             logs = logs.paginate(page_number, items_per_page)
 
         return list(logs.dicts()), count
+
+    @classmethod
+    @DB.connection_context()
+    def get_dataset_logs_by_kb_id(cls, kb_id, page_number, items_per_page, orderby, desc, operation_status):
+        fields = cls.get_dataset_logs_fields()
+        logs = cls.model.select(*fields).where((cls.model.kb_id == kb_id), (cls.model.document_id == "x"))
+
+        if operation_status:
+            logs = logs.where(cls.model.operation_status.in_(operation_status))
+
+        count = logs.count()
+        if desc:
+            logs = logs.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            logs = logs.order_by(cls.model.getter_by(orderby).asc())
+
+        if page_number and items_per_page:
+            logs = logs.paginate(page_number, items_per_page)
+
+        return list(logs.dicts()), count

api/db/services/task_service.py

@@ -70,7 +70,7 @@ class TaskService(CommonService):
 
     @classmethod
     @DB.connection_context()
-    def get_task(cls, task_id):
+    def get_task(cls, task_id, doc_ids=[]):
         """Retrieve detailed task information by task ID.
 
         This method fetches comprehensive task details including associated document,
@@ -84,6 +84,10 @@ class TaskService(CommonService):
             dict: Task details dictionary containing all task information and related metadata.
                  Returns None if task is not found or has exceeded retry limit.
         """
+        doc_id = cls.model.doc_id
+        if doc_id == "x" and doc_ids:
+            doc_id = doc_ids[0]
+
         fields = [
             cls.model.id,
             cls.model.doc_id,
@@ -109,7 +113,7 @@ class TaskService(CommonService):
         ]
         docs = (
             cls.model.select(*fields)
-                .join(Document, on=(cls.model.doc_id == Document.id))
+                .join(Document, on=(doc_id == Document.id))
                 .join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id))
                 .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))
                 .where(cls.model.id == task_id)

graphrag/general/index.py

@@ -21,6 +21,7 @@ import networkx as nx
 import trio
 
 from api import settings
+from api.db.services.document_service import DocumentService
 from api.utils import get_uuid
 from api.utils.api_utils import timeout
 from graphrag.entity_resolution import EntityResolution
@@ -54,7 +55,7 @@ async def run_graphrag(
     start = trio.current_time()
     tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
     chunks = []
-    for d in settings.retrievaler.chunk_list(doc_id, tenant_id, [kb_id], fields=["content_with_weight", "doc_id"]):
+    for d in settings.retrievaler.chunk_list(doc_id, tenant_id, [kb_id], fields=["content_with_weight", "doc_id"], sort_by_position=True):
         chunks.append(d["content_with_weight"])
 
     with trio.fail_after(max(120, len(chunks) * 60 * 10) if enable_timeout_assertion else 10000000000):
@@ -125,6 +126,212 @@ async def run_graphrag(
     return
 
 
+async def run_graphrag_for_kb(
+    row: dict,
+    doc_ids: list[str],
+    language: str,
+    kb_parser_config: dict,
+    chat_model,
+    embedding_model,
+    callback,
+    *,
+    with_resolution: bool = True,
+    with_community: bool = True,
+    max_parallel_docs: int = 4,
+) -> dict:
+    tenant_id, kb_id = row["tenant_id"], row["kb_id"]
+    enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION")
+    start = trio.current_time()
+    fields_for_chunks = ["content_with_weight", "doc_id"]
+
+    if not doc_ids:
+        logging.info(f"Fetching all docs for {kb_id}")
+        docs, _ = DocumentService.get_by_kb_id(
+            kb_id=kb_id,
+            page_number=0,
+            items_per_page=0,
+            orderby="create_time",
+            desc=False,
+            keywords="",
+            run_status=[],
+            types=[],
+            suffix=[],
+        )
+        doc_ids = [doc["id"] for doc in docs]
+
+    doc_ids = list(dict.fromkeys(doc_ids))
+    if not doc_ids:
+        callback(msg=f"[GraphRAG] kb:{kb_id} has no processable doc_id.")
+        return {"ok_docs": [], "failed_docs": [], "total_docs": 0, "total_chunks": 0, "seconds": 0.0}
+
+    def load_doc_chunks(doc_id: str) -> list[str]:
+        from rag.utils import num_tokens_from_string
+
+        chunks = []
+        current_chunk = ""
+
+        for d in settings.retrievaler.chunk_list(
+            doc_id,
+            tenant_id,
+            [kb_id],
+            fields=fields_for_chunks,
+            sort_by_position=True,
+        ):
+            content = d["content_with_weight"]
+            if num_tokens_from_string(current_chunk + content) < 1024:
+                current_chunk += content
+            else:
+                if current_chunk:
+                    chunks.append(current_chunk)
+                current_chunk = content
+
+        if current_chunk:
+            chunks.append(current_chunk)
+
+        return chunks
+
+    all_doc_chunks: dict[str, list[str]] = {}
+    total_chunks = 0
+    for doc_id in doc_ids:
+        chunks = load_doc_chunks(doc_id)
+        all_doc_chunks[doc_id] = chunks
+        total_chunks += len(chunks)
+
+    if total_chunks == 0:
+        callback(msg=f"[GraphRAG] kb:{kb_id} has no available chunks in all documents, skip.")
+        return {"ok_docs": [], "failed_docs": doc_ids, "total_docs": len(doc_ids), "total_chunks": 0, "seconds": 0.0}
+
+    semaphore = trio.Semaphore(max_parallel_docs)
+
+    subgraphs: dict[str, object] = {}
+    failed_docs: list[tuple[str, str]] = []  # (doc_id, error)
+
+    async def build_one(doc_id: str):
+        chunks = all_doc_chunks.get(doc_id, [])
+        if not chunks:
+            callback(msg=f"[GraphRAG] doc:{doc_id} has no available chunks, skip generation.")
+            return
+
+        kg_extractor = LightKGExt if ("method" not in kb_parser_config.get("graphrag", {}) or kb_parser_config["graphrag"]["method"] != "general") else GeneralKGExt
+
+        deadline = max(120, len(chunks) * 60 * 10) if enable_timeout_assertion else 10000000000
+
+        async with semaphore:
+            try:
+                msg = f"[GraphRAG] build_subgraph doc:{doc_id}"
+                callback(msg=f"{msg} start (chunks={len(chunks)}, timeout={deadline}s)")
+                with trio.fail_after(deadline):
+                    sg = await generate_subgraph(
+                        kg_extractor,
+                        tenant_id,
+                        kb_id,
+                        doc_id,
+                        chunks,
+                        language,
+                        kb_parser_config.get("graphrag", {}).get("entity_types", []),
+                        chat_model,
+                        embedding_model,
+                        callback,
+                    )
+                if sg:
+                    subgraphs[doc_id] = sg
+                    callback(msg=f"{msg} done")
+                else:
+                    failed_docs.append((doc_id, "subgraph is empty"))
+                    callback(msg=f"{msg} empty")
+            except Exception as e:
+                failed_docs.append((doc_id, repr(e)))
+                callback(msg=f"[GraphRAG] build_subgraph doc:{doc_id} FAILED: {e!r}")
+
+    async with trio.open_nursery() as nursery:
+        for doc_id in doc_ids:
+            nursery.start_soon(build_one, doc_id)
+
+    ok_docs = [d for d in doc_ids if d in subgraphs]
+    if not ok_docs:
+        callback(msg=f"[GraphRAG] kb:{kb_id} no subgraphs generated successfully, end.")
+        now = trio.current_time()
+        return {"ok_docs": [], "failed_docs": failed_docs, "total_docs": len(doc_ids), "total_chunks": total_chunks, "seconds": now - start}
+
+    kb_lock = RedisDistributedLock(f"graphrag_task_{kb_id}", lock_value="batch_merge", timeout=1200)
+    await kb_lock.spin_acquire()
+    callback(msg=f"[GraphRAG] kb:{kb_id} merge lock acquired")
+
+    try:
+        union_nodes: set = set()
+        final_graph = None
+
+        for doc_id in ok_docs:
+            sg = subgraphs[doc_id]
+            union_nodes.update(set(sg.nodes()))
+
+            new_graph = await merge_subgraph(
+                tenant_id,
+                kb_id,
+                doc_id,
+                sg,
+                embedding_model,
+                callback,
+            )
+            if new_graph is not None:
+                final_graph = new_graph
+
+        if final_graph is None:
+            callback(msg=f"[GraphRAG] kb:{kb_id} merge finished (no in-memory graph returned).")
+        else:
+            callback(msg=f"[GraphRAG] kb:{kb_id} merge finished, graph ready.")
+    finally:
+        kb_lock.release()
+
+    if not with_resolution and not with_community:
+        now = trio.current_time()
+        callback(msg=f"[GraphRAG] KB merge only done in {now - start:.2f}s. ok={len(ok_docs)} / total={len(doc_ids)}")
+        return {"ok_docs": ok_docs, "failed_docs": failed_docs, "total_docs": len(doc_ids), "total_chunks": total_chunks, "seconds": now - start}
+
+    await kb_lock.spin_acquire()
+    callback(msg=f"[GraphRAG] kb:{kb_id} post-merge lock acquired for resolution/community")
+
+    try:
+        subgraph_nodes = set()
+        for sg in subgraphs.values():
+            subgraph_nodes.update(set(sg.nodes()))
+
+        if with_resolution:
+            await resolve_entities(
+                final_graph,
+                subgraph_nodes,
+                tenant_id,
+                kb_id,
+                None,
+                chat_model,
+                embedding_model,
+                callback,
+            )
+
+        if with_community:
+            await extract_community(
+                final_graph,
+                tenant_id,
+                kb_id,
+                None,
+                chat_model,
+                embedding_model,
+                callback,
+            )
+    finally:
+        kb_lock.release()
+
+    now = trio.current_time()
+    callback(msg=f"[GraphRAG] GraphRAG for KB {kb_id} done in {now - start:.2f} seconds. ok={len(ok_docs)} failed={len(failed_docs)} total_docs={len(doc_ids)} total_chunks={total_chunks}")
+    return {
+        "ok_docs": ok_docs,
+        "failed_docs": failed_docs,  # [(doc_id, error), ...]
+        "total_docs": len(doc_ids),
+        "total_chunks": total_chunks,
+        "seconds": now - start,
+    }
+
+
 async def generate_subgraph(
     extractor: Extractor,
     tenant_id: str,

rag/flow/pipeline.py

@@ -34,9 +34,9 @@ class Pipeline(Graph):
         if isinstance(dsl, dict):
             dsl = json.dumps(dsl, ensure_ascii=False)
         super().__init__(dsl, tenant_id, task_id)
+        self._doc_id = doc_id
         if self._doc_id == "x":
             self._doc_id = None
-        self._doc_id = doc_id
         self._flow_id = flow_id
         self._kb_id = None
         if self._doc_id:

rag/nlp/search.py

@@ -444,12 +444,27 @@ class Dealer:
     def chunk_list(self, doc_id: str, tenant_id: str,
                    kb_ids: list[str], max_count=1024,
                    offset=0,
-                   fields=["docnm_kwd", "content_with_weight", "img_id"]):
+                   fields=["docnm_kwd", "content_with_weight", "img_id"],
+                   sort_by_position: bool = False):
         condition = {"doc_id": doc_id}
+
+        fields_set = set(fields or [])
+        if sort_by_position:
+            for need in ("page_num_int", "position_int", "top_int"):
+                if need not in fields_set:
+                    fields_set.add(need)
+        fields = list(fields_set)
+
+        orderBy = OrderByExpr()
+        if sort_by_position:
+            orderBy.asc("page_num_int")
+            orderBy.asc("position_int")
+            orderBy.asc("top_int")
+
         res = []
         bs = 128
         for p in range(offset, max_count, bs):
-            es_res = self.dataStore.search(fields, [], condition, [], OrderByExpr(), p, bs, index_name(tenant_id),
+            es_res = self.dataStore.search(fields, [], condition, [], orderBy, p, bs, index_name(tenant_id),
                                            kb_ids)
             dict_chunks = self.dataStore.getFields(es_res, fields)
             for id, doc in dict_chunks.items():

rag/svr/task_executor.py

@@ -25,7 +25,7 @@ from api.db.services.pipeline_operation_log_service import PipelineOperationLogS
 from api.utils.api_utils import timeout
 from api.utils.base64_image import image2id
 from api.utils.log_utils import init_root_logger, get_project_base_directory
-from graphrag.general.index import run_graphrag
+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.flow.pipeline import Pipeline
 from rag.prompts import keyword_extraction, question_proposal, content_tagging
@@ -85,6 +85,12 @@ FACTORY = {
     ParserType.TAG.value: tag
 }
 
+TASK_TYPE_TO_PIPELINE_TASK_TYPE = {
+    "dataflow" : PipelineTaskType.PARSE,
+    "raptor": PipelineTaskType.RAPTOR,
+    "graphrag": PipelineTaskType.GRAPH_RAG,
+}
+
 UNACKED_ITERATOR = None
 
 CONSUMER_NO = "0" if len(sys.argv) < 2 else sys.argv[1]
@@ -215,6 +221,10 @@ async def collect():
     canceled = False
     if msg.get("doc_id", "") == "x":
         task = msg
+        if task["task_type"] == "graphrag" and msg.get("doc_ids", []):
+            print(f"hack {msg['doc_ids']=}=",flush=True)
+            task = TaskService.get_task(msg["id"], msg["doc_ids"])
+            task["doc_ids"] = msg["doc_ids"]
     else:
         task = TaskService.get_task(msg["id"])
 
@@ -580,7 +590,19 @@ async def do_handle_task(task):
         with_resolution = graphrag_conf.get("resolution", False)
         with_community = graphrag_conf.get("community", False)
         async with kg_limiter:
-            await run_graphrag(task, task_language, with_resolution, with_community, chat_model, embedding_model, progress_callback)
+            # await run_graphrag(task, task_language, with_resolution, with_community, chat_model, embedding_model, progress_callback)
+            result = await run_graphrag_for_kb(
+                row=task,
+                doc_ids=task.get("doc_ids", []),
+                language=task_language,
+                kb_parser_config=task_parser_config,
+                chat_model=chat_model,
+                embedding_model=embedding_model,
+                callback=progress_callback,
+                with_resolution=with_resolution,
+                with_community=with_community,
+            )
+            logging.info(f"GraphRAG task result for task {task}:\n{result}")
         progress_callback(prog=1.0, msg="Knowledge Graph done ({:.2f}s)".format(timer() - start_ts))
         return
     else:
@@ -650,7 +672,6 @@ async def do_handle_task(task):
                                                                                      timer() - start_ts))
 
     DocumentService.increment_chunk_num(task_doc_id, task_dataset_id, token_count, chunk_count, 0)
-    PipelineOperationLogService.record_pipeline_operation(document_id=task_doc_id, pipeline_id="", task_type=PipelineTaskType.PARSE)
 
     time_cost = timer() - start_ts
     task_time_cost = timer() - task_start_ts
@@ -667,6 +688,10 @@ async def handle_task():
     if not task:
         await trio.sleep(5)
         return
+
+    task_type = task["task_type"]
+    pipeline_task_type = TASK_TYPE_TO_PIPELINE_TASK_TYPE.get(task_type, PipelineTaskType.PARSE) or PipelineTaskType.PARSE
+
     try:
         logging.info(f"handle_task begin for task {json.dumps(task)}")
         CURRENT_TASKS[task["id"]] = copy.deepcopy(task)
@@ -686,7 +711,12 @@ async def handle_task():
         except Exception:
             pass
         logging.exception(f"handle_task got exception for task {json.dumps(task)}")
-        PipelineOperationLogService.record_pipeline_operation(document_id=task["doc_id"], pipeline_id=task.get("dataflow_id", "") or "", task_type=PipelineTaskType.PARSE)
+    finally:
+        task_document_ids = []
+        if task_type in ["graphrag"]:
+            task_document_ids = task["doc_ids"]
+        PipelineOperationLogService.record_pipeline_operation(document_id=task["doc_id"], pipeline_id=task.get("dataflow_id", "") or "", task_type=pipeline_task_type, fake_document_ids=task_document_ids)
+
     redis_msg.ack()