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Refactor graphrag to remove redis lock (#5828)
### What problem does this PR solve? Refactor graphrag to remove redis lock ### Type of change - [x] Refactoring
This commit is contained in:
Zhichang Yu
GitHub
parent
1163e9e409
commit
6ec6ca6971
@ -42,16 +42,22 @@ from api.db.init_data import init_web_data
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from api.versions import get_ragflow_version
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from api.utils import show_configs
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from rag.settings import print_rag_settings
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from rag.utils.redis_conn import RedisDistributedLock
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stop_event = threading.Event()
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def update_progress():
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redis_lock = RedisDistributedLock("update_progress", timeout=60)
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while not stop_event.is_set():
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try:
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if not redis_lock.acquire():
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continue
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DocumentService.update_progress()
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stop_event.wait(6)
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except Exception:
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logging.exception("update_progress exception")
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finally:
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redis_lock.release()
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def signal_handler(sig, frame):
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logging.info("Received interrupt signal, shutting down...")
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@ -93,7 +93,7 @@ class Extractor:
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return dict(maybe_nodes), dict(maybe_edges)
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async def __call__(
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self, chunks: list[tuple[str, str]],
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self, doc_id: str, chunks: list[str],
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callback: Callable | None = None
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):
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@ -101,9 +101,9 @@ class Extractor:
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start_ts = trio.current_time()
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out_results = []
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async with trio.open_nursery() as nursery:
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for i, (cid, ck) in enumerate(chunks):
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for i, ck in enumerate(chunks):
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ck = truncate(ck, int(self._llm.max_length*0.8))
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nursery.start_soon(lambda: self._process_single_content((cid, ck), i, len(chunks), out_results))
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nursery.start_soon(lambda: self._process_single_content((doc_id, ck), i, len(chunks), out_results))
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maybe_nodes = defaultdict(list)
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maybe_edges = defaultdict(list)
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@ -241,10 +241,13 @@ class Extractor:
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) -> str:
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summary_max_tokens = 512
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use_description = truncate(description, summary_max_tokens)
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description_list=use_description.split(GRAPH_FIELD_SEP),
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if len(description_list) <= 12:
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return use_description
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prompt_template = SUMMARIZE_DESCRIPTIONS_PROMPT
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context_base = dict(
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entity_name=entity_or_relation_name,
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description_list=use_description.split(GRAPH_FIELD_SEP),
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description_list=description_list,
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language=self._language,
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)
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use_prompt = prompt_template.format(**context_base)
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@ -15,196 +15,353 @@
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#
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import json
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import logging
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from functools import reduce, partial
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from functools import partial
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import networkx as nx
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import trio
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from api import settings
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from graphrag.light.graph_extractor import GraphExtractor as LightKGExt
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from graphrag.general.graph_extractor import GraphExtractor as GeneralKGExt
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from graphrag.general.community_reports_extractor import CommunityReportsExtractor
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from graphrag.entity_resolution import EntityResolution
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from graphrag.general.extractor import Extractor
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from graphrag.general.graph_extractor import DEFAULT_ENTITY_TYPES
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from graphrag.utils import graph_merge, set_entity, get_relation, set_relation, get_entity, get_graph, set_graph, \
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chunk_id, update_nodes_pagerank_nhop_neighbour
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from graphrag.utils import (
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graph_merge,
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set_entity,
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get_relation,
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set_relation,
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get_entity,
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get_graph,
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set_graph,
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chunk_id,
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update_nodes_pagerank_nhop_neighbour,
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does_graph_contains,
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get_graph_doc_ids,
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)
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from rag.nlp import rag_tokenizer, search
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from rag.utils.redis_conn import RedisDistributedLock
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from rag.utils.redis_conn import REDIS_CONN
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class Dealer:
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def __init__(self,
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def graphrag_task_set(tenant_id, kb_id, doc_id) -> bool:
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key = f"graphrag:{tenant_id}:{kb_id}"
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ok = REDIS_CONN.set(key, doc_id, exp=3600 * 24)
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if not ok:
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raise Exception(f"Faild to set the {key} to {doc_id}")
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def graphrag_task_get(tenant_id, kb_id) -> str | None:
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key = f"graphrag:{tenant_id}:{kb_id}"
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doc_id = REDIS_CONN.get(key)
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return doc_id
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async def run_graphrag(
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row: dict,
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language,
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with_resolution: bool,
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with_community: bool,
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chat_model,
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embedding_model,
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callback,
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):
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start = trio.current_time()
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tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
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chunks = []
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for d in settings.retrievaler.chunk_list(
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doc_id, tenant_id, [kb_id], fields=["content_with_weight", "doc_id"]
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):
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chunks.append(d["content_with_weight"])
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graph, doc_ids = await update_graph(
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LightKGExt
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if row["parser_config"]["graphrag"]["method"] != "general"
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else GeneralKGExt,
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tenant_id,
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kb_id,
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doc_id,
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chunks,
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language,
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row["parser_config"]["graphrag"]["entity_types"],
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chat_model,
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embedding_model,
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callback,
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)
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if not graph:
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return
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if with_resolution or with_community:
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graphrag_task_set(tenant_id, kb_id, doc_id)
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if with_resolution:
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await resolve_entities(
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graph,
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doc_ids,
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tenant_id,
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kb_id,
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doc_id,
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chat_model,
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embedding_model,
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callback,
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)
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if with_community:
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await extract_community(
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graph,
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doc_ids,
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tenant_id,
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kb_id,
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doc_id,
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chat_model,
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embedding_model,
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callback,
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)
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now = trio.current_time()
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callback(msg=f"GraphRAG for doc {doc_id} done in {now - start:.2f} seconds.")
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return
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async def update_graph(
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extractor: Extractor,
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tenant_id: str,
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kb_id: str,
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llm_bdl,
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chunks: list[tuple[str, str]],
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doc_id: str,
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chunks: list[str],
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language,
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entity_types=DEFAULT_ENTITY_TYPES,
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embed_bdl=None,
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callback=None
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entity_types,
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llm_bdl,
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embed_bdl,
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callback,
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):
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self.tenant_id = tenant_id
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self.kb_id = kb_id
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self.chunks = chunks
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self.llm_bdl = llm_bdl
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self.embed_bdl = embed_bdl
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self.ext = extractor(self.llm_bdl, language=language,
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contains = await does_graph_contains(tenant_id, kb_id, doc_id)
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if contains:
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callback(msg=f"Graph already contains {doc_id}, cancel myself")
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return None, None
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start = trio.current_time()
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ext = extractor(
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llm_bdl,
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language=language,
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entity_types=entity_types,
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get_entity=partial(get_entity, tenant_id, kb_id),
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set_entity=partial(set_entity, tenant_id, kb_id, self.embed_bdl),
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set_entity=partial(set_entity, tenant_id, kb_id, embed_bdl),
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get_relation=partial(get_relation, tenant_id, kb_id),
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set_relation=partial(set_relation, tenant_id, kb_id, self.embed_bdl)
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set_relation=partial(set_relation, tenant_id, kb_id, embed_bdl),
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)
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self.graph = nx.Graph()
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self.callback = callback
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async def __call__(self):
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docids = list(set([docid for docid, _ in self.chunks]))
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ents, rels = await self.ext(self.chunks, self.callback)
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ents, rels = await ext(doc_id, chunks, callback)
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subgraph = nx.Graph()
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for en in ents:
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self.graph.add_node(en["entity_name"], entity_type=en["entity_type"])#, description=en["description"])
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subgraph.add_node(en["entity_name"], entity_type=en["entity_type"])
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for rel in rels:
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self.graph.add_edge(
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subgraph.add_edge(
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rel["src_id"],
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rel["tgt_id"],
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weight=rel["weight"],
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# description=rel["description"]
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)
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# TODO: infinity doesn't support array search
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chunk = {
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"content_with_weight": json.dumps(
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nx.node_link_data(subgraph, edges="edges"), ensure_ascii=False, indent=2
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),
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"knowledge_graph_kwd": "subgraph",
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"kb_id": kb_id,
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"source_id": [doc_id],
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"available_int": 0,
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"removed_kwd": "N",
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}
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cid = chunk_id(chunk)
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await trio.to_thread.run_sync(
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lambda: settings.docStoreConn.insert(
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[{"id": cid, **chunk}], search.index_name(tenant_id), kb_id
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)
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)
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now = trio.current_time()
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callback(msg=f"generated subgraph for doc {doc_id} in {now - start:.2f} seconds.")
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start = now
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with RedisDistributedLock(self.kb_id, 60*60):
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old_graph, old_doc_ids = get_graph(self.tenant_id, self.kb_id)
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while True:
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new_graph = subgraph
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now_docids = set([doc_id])
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old_graph, old_doc_ids = await get_graph(tenant_id, kb_id)
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if old_graph is not None:
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logging.info("Merge with an exiting graph...................")
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self.graph = reduce(graph_merge, [old_graph, self.graph])
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update_nodes_pagerank_nhop_neighbour(self.tenant_id, self.kb_id, self.graph, 2)
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new_graph = graph_merge(old_graph, subgraph)
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await update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, new_graph, 2)
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if old_doc_ids:
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docids.extend(old_doc_ids)
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docids = list(set(docids))
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set_graph(self.tenant_id, self.kb_id, self.graph, docids)
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for old_doc_id in old_doc_ids:
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now_docids.add(old_doc_id)
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old_doc_ids2 = await get_graph_doc_ids(tenant_id, kb_id)
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delta_doc_ids = set(old_doc_ids2) - set(old_doc_ids)
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if delta_doc_ids:
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callback(
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msg="The global graph has changed during merging, try again"
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)
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await trio.sleep(1)
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continue
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break
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await set_graph(tenant_id, kb_id, new_graph, list(now_docids))
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now = trio.current_time()
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callback(
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msg=f"merging subgraph for doc {doc_id} into the global graph done in {now - start:.2f} seconds."
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)
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return new_graph, now_docids
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class WithResolution(Dealer):
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def __init__(self,
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async def resolve_entities(
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graph,
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doc_ids,
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tenant_id: str,
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kb_id: str,
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doc_id: str,
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llm_bdl,
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embed_bdl=None,
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callback=None
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embed_bdl,
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callback,
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):
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self.tenant_id = tenant_id
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self.kb_id = kb_id
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self.llm_bdl = llm_bdl
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self.embed_bdl = embed_bdl
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self.callback = callback
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async def __call__(self):
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with RedisDistributedLock(self.kb_id, 60*60):
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self.graph, doc_ids = await trio.to_thread.run_sync(lambda: get_graph(self.tenant_id, self.kb_id))
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if not self.graph:
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logging.error(f"Faild to fetch the graph. tenant_id:{self.kb_id}, kb_id:{self.kb_id}")
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if self.callback:
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self.callback(-1, msg="Faild to fetch the graph.")
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working_doc_id = graphrag_task_get(tenant_id, kb_id)
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if doc_id != working_doc_id:
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callback(
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msg=f"Another graphrag task of doc_id {working_doc_id} is working on this kb, cancel myself"
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)
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return
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start = trio.current_time()
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er = EntityResolution(
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llm_bdl,
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get_entity=partial(get_entity, tenant_id, kb_id),
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set_entity=partial(set_entity, tenant_id, kb_id, embed_bdl),
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get_relation=partial(get_relation, tenant_id, kb_id),
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set_relation=partial(set_relation, tenant_id, kb_id, embed_bdl),
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)
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reso = await er(graph)
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graph = reso.graph
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callback(msg=f"Graph resolution removed {len(reso.removed_entities)} nodes.")
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await update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, graph, 2)
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callback(msg="Graph resolution updated pagerank.")
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if self.callback:
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self.callback(msg="Fetch the existing graph.")
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er = EntityResolution(self.llm_bdl,
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get_entity=partial(get_entity, self.tenant_id, self.kb_id),
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set_entity=partial(set_entity, self.tenant_id, self.kb_id, self.embed_bdl),
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get_relation=partial(get_relation, self.tenant_id, self.kb_id),
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set_relation=partial(set_relation, self.tenant_id, self.kb_id, self.embed_bdl))
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reso = await er(self.graph)
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self.graph = reso.graph
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logging.info("Graph resolution is done. Remove {} nodes.".format(len(reso.removed_entities)))
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if self.callback:
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self.callback(msg="Graph resolution is done. Remove {} nodes.".format(len(reso.removed_entities)))
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await trio.to_thread.run_sync(lambda: update_nodes_pagerank_nhop_neighbour(self.tenant_id, self.kb_id, self.graph, 2))
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await trio.to_thread.run_sync(lambda: set_graph(self.tenant_id, self.kb_id, self.graph, doc_ids))
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working_doc_id = graphrag_task_get(tenant_id, kb_id)
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if doc_id != working_doc_id:
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callback(
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msg=f"Another graphrag task of doc_id {working_doc_id} is working on this kb, cancel myself"
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)
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return
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await set_graph(tenant_id, kb_id, graph, doc_ids)
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await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({
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await trio.to_thread.run_sync(
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lambda: settings.docStoreConn.delete(
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{
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"knowledge_graph_kwd": "relation",
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"kb_id": self.kb_id,
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"from_entity_kwd": reso.removed_entities
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}, search.index_name(self.tenant_id), self.kb_id))
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await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({
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"kb_id": kb_id,
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"from_entity_kwd": reso.removed_entities,
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},
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search.index_name(tenant_id),
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kb_id,
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)
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)
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await trio.to_thread.run_sync(
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lambda: settings.docStoreConn.delete(
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{
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"knowledge_graph_kwd": "relation",
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"kb_id": self.kb_id,
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"to_entity_kwd": reso.removed_entities
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}, search.index_name(self.tenant_id), self.kb_id))
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await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({
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"kb_id": kb_id,
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"to_entity_kwd": reso.removed_entities,
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},
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search.index_name(tenant_id),
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kb_id,
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)
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)
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await trio.to_thread.run_sync(
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lambda: settings.docStoreConn.delete(
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{
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"knowledge_graph_kwd": "entity",
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"kb_id": self.kb_id,
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"entity_kwd": reso.removed_entities
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}, search.index_name(self.tenant_id), self.kb_id))
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"kb_id": kb_id,
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"entity_kwd": reso.removed_entities,
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},
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search.index_name(tenant_id),
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kb_id,
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)
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)
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now = trio.current_time()
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callback(msg=f"Graph resolution done in {now - start:.2f}s.")
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class WithCommunity(Dealer):
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def __init__(self,
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async def extract_community(
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graph,
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doc_ids,
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tenant_id: str,
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kb_id: str,
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doc_id: str,
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llm_bdl,
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embed_bdl=None,
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callback=None
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embed_bdl,
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callback,
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):
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self.tenant_id = tenant_id
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self.kb_id = kb_id
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self.community_structure = None
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self.community_reports = None
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self.llm_bdl = llm_bdl
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self.embed_bdl = embed_bdl
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self.callback = callback
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async def __call__(self):
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with RedisDistributedLock(self.kb_id, 60*60):
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self.graph, doc_ids = get_graph(self.tenant_id, self.kb_id)
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if not self.graph:
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logging.error(f"Faild to fetch the graph. tenant_id:{self.kb_id}, kb_id:{self.kb_id}")
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if self.callback:
|
||||
self.callback(-1, msg="Faild to fetch the graph.")
|
||||
working_doc_id = graphrag_task_get(tenant_id, kb_id)
|
||||
if doc_id != working_doc_id:
|
||||
callback(
|
||||
msg=f"Another graphrag task of doc_id {working_doc_id} is working on this kb, cancel myself"
|
||||
)
|
||||
return
|
||||
if self.callback:
|
||||
self.callback(msg="Fetch the existing graph.")
|
||||
start = trio.current_time()
|
||||
ext = CommunityReportsExtractor(
|
||||
llm_bdl,
|
||||
get_entity=partial(get_entity, tenant_id, kb_id),
|
||||
set_entity=partial(set_entity, tenant_id, kb_id, embed_bdl),
|
||||
get_relation=partial(get_relation, tenant_id, kb_id),
|
||||
set_relation=partial(set_relation, tenant_id, kb_id, embed_bdl),
|
||||
)
|
||||
cr = await ext(graph, callback=callback)
|
||||
community_structure = cr.structured_output
|
||||
community_reports = cr.output
|
||||
working_doc_id = graphrag_task_get(tenant_id, kb_id)
|
||||
if doc_id != working_doc_id:
|
||||
callback(
|
||||
msg=f"Another graphrag task of doc_id {working_doc_id} is working on this kb, cancel myself"
|
||||
)
|
||||
return
|
||||
await set_graph(tenant_id, kb_id, graph, doc_ids)
|
||||
|
||||
cr = CommunityReportsExtractor(self.llm_bdl,
|
||||
get_entity=partial(get_entity, self.tenant_id, self.kb_id),
|
||||
set_entity=partial(set_entity, self.tenant_id, self.kb_id, self.embed_bdl),
|
||||
get_relation=partial(get_relation, self.tenant_id, self.kb_id),
|
||||
set_relation=partial(set_relation, self.tenant_id, self.kb_id, self.embed_bdl))
|
||||
cr = await cr(self.graph, callback=self.callback)
|
||||
self.community_structure = cr.structured_output
|
||||
self.community_reports = cr.output
|
||||
await trio.to_thread.run_sync(lambda: set_graph(self.tenant_id, self.kb_id, self.graph, doc_ids))
|
||||
|
||||
if self.callback:
|
||||
self.callback(msg="Graph community extraction is done. Indexing {} reports.".format(len(cr.structured_output)))
|
||||
|
||||
await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({
|
||||
"knowledge_graph_kwd": "community_report",
|
||||
"kb_id": self.kb_id
|
||||
}, search.index_name(self.tenant_id), self.kb_id))
|
||||
|
||||
for stru, rep in zip(self.community_structure, self.community_reports):
|
||||
now = trio.current_time()
|
||||
callback(
|
||||
msg=f"Graph extracted {len(cr.structured_output)} communities in {now - start:.2f}s."
|
||||
)
|
||||
start = now
|
||||
await trio.to_thread.run_sync(
|
||||
lambda: settings.docStoreConn.delete(
|
||||
{"knowledge_graph_kwd": "community_report", "kb_id": kb_id},
|
||||
search.index_name(tenant_id),
|
||||
kb_id,
|
||||
)
|
||||
)
|
||||
for stru, rep in zip(community_structure, community_reports):
|
||||
obj = {
|
||||
"report": rep,
|
||||
"evidences": "\n".join([f["explanation"] for f in stru["findings"]])
|
||||
"evidences": "\n".join([f["explanation"] for f in stru["findings"]]),
|
||||
}
|
||||
chunk = {
|
||||
"docnm_kwd": stru["title"],
|
||||
"title_tks": rag_tokenizer.tokenize(stru["title"]),
|
||||
"content_with_weight": json.dumps(obj, ensure_ascii=False),
|
||||
"content_ltks": rag_tokenizer.tokenize(obj["report"] +" "+ obj["evidences"]),
|
||||
"content_ltks": rag_tokenizer.tokenize(
|
||||
obj["report"] + " " + obj["evidences"]
|
||||
),
|
||||
"knowledge_graph_kwd": "community_report",
|
||||
"weight_flt": stru["weight"],
|
||||
"entities_kwd": stru["entities"],
|
||||
"important_kwd": stru["entities"],
|
||||
"kb_id": self.kb_id,
|
||||
"kb_id": kb_id,
|
||||
"source_id": doc_ids,
|
||||
"available_int": 0
|
||||
"available_int": 0,
|
||||
}
|
||||
chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"])
|
||||
chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(
|
||||
chunk["content_ltks"]
|
||||
)
|
||||
# try:
|
||||
# ebd, _ = self.embed_bdl.encode([", ".join(community["entities"])])
|
||||
# ebd, _ = embed_bdl.encode([", ".join(community["entities"])])
|
||||
# chunk["q_%d_vec" % len(ebd[0])] = ebd[0]
|
||||
# except Exception as e:
|
||||
# logging.exception(f"Fail to embed entity relation: {e}")
|
||||
await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert([{"id": chunk_id(chunk), **chunk}], search.index_name(self.tenant_id)))
|
||||
await trio.to_thread.run_sync(
|
||||
lambda: settings.docStoreConn.insert(
|
||||
[{"id": chunk_id(chunk), **chunk}], search.index_name(tenant_id)
|
||||
)
|
||||
)
|
||||
|
||||
now = trio.current_time()
|
||||
callback(
|
||||
msg=f"Graph indexed {len(cr.structured_output)} communities in {now - start:.2f}s."
|
||||
)
|
||||
return community_structure, community_reports
|
||||
|
||||
@ -16,7 +16,7 @@
|
||||
|
||||
import argparse
|
||||
import json
|
||||
|
||||
import logging
|
||||
import networkx as nx
|
||||
import trio
|
||||
|
||||
@ -26,42 +26,85 @@ from api.db.services.document_service import DocumentService
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from api.db.services.llm_service import LLMBundle
|
||||
from api.db.services.user_service import TenantService
|
||||
from graphrag.general.index import WithCommunity, Dealer, WithResolution
|
||||
from graphrag.light.graph_extractor import GraphExtractor
|
||||
from rag.utils.redis_conn import RedisDistributedLock
|
||||
from graphrag.general.graph_extractor import GraphExtractor
|
||||
from graphrag.general.index import update_graph, with_resolution, with_community
|
||||
|
||||
settings.init_settings()
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
def callback(prog=None, msg="Processing..."):
|
||||
logging.info(msg)
|
||||
|
||||
|
||||
async def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-t', '--tenant_id', default=False, help="Tenant ID", action='store', required=True)
|
||||
parser.add_argument('-d', '--doc_id', default=False, help="Document ID", action='store', required=True)
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--tenant_id",
|
||||
default=False,
|
||||
help="Tenant ID",
|
||||
action="store",
|
||||
required=True,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--doc_id",
|
||||
default=False,
|
||||
help="Document ID",
|
||||
action="store",
|
||||
required=True,
|
||||
)
|
||||
args = parser.parse_args()
|
||||
e, doc = DocumentService.get_by_id(args.doc_id)
|
||||
if not e:
|
||||
raise LookupError("Document not found.")
|
||||
kb_id = doc.kb_id
|
||||
|
||||
chunks = [d["content_with_weight"] for d in
|
||||
settings.retrievaler.chunk_list(args.doc_id, args.tenant_id, [kb_id], max_count=6,
|
||||
fields=["content_with_weight"])]
|
||||
chunks = [("x", c) for c in chunks]
|
||||
|
||||
RedisDistributedLock.clean_lock(kb_id)
|
||||
chunks = [
|
||||
d["content_with_weight"]
|
||||
for d in settings.retrievaler.chunk_list(
|
||||
args.doc_id,
|
||||
args.tenant_id,
|
||||
[kb_id],
|
||||
max_count=6,
|
||||
fields=["content_with_weight"],
|
||||
)
|
||||
]
|
||||
|
||||
_, tenant = TenantService.get_by_id(args.tenant_id)
|
||||
llm_bdl = LLMBundle(args.tenant_id, LLMType.CHAT, tenant.llm_id)
|
||||
_, kb = KnowledgebaseService.get_by_id(kb_id)
|
||||
embed_bdl = LLMBundle(args.tenant_id, LLMType.EMBEDDING, kb.embd_id)
|
||||
|
||||
dealer = Dealer(GraphExtractor, args.tenant_id, kb_id, llm_bdl, chunks, "English", embed_bdl=embed_bdl)
|
||||
trio.run(dealer())
|
||||
print(json.dumps(nx.node_link_data(dealer.graph), ensure_ascii=False, indent=2))
|
||||
graph, doc_ids = await update_graph(
|
||||
GraphExtractor,
|
||||
args.tenant_id,
|
||||
kb_id,
|
||||
args.doc_id,
|
||||
chunks,
|
||||
"English",
|
||||
llm_bdl,
|
||||
embed_bdl,
|
||||
callback,
|
||||
)
|
||||
print(json.dumps(nx.node_link_data(graph), ensure_ascii=False, indent=2))
|
||||
|
||||
dealer = WithResolution(args.tenant_id, kb_id, llm_bdl, embed_bdl)
|
||||
trio.run(dealer())
|
||||
dealer = WithCommunity(args.tenant_id, kb_id, llm_bdl, embed_bdl)
|
||||
trio.run(dealer())
|
||||
await with_resolution(
|
||||
args.tenant_id, kb_id, args.doc_id, llm_bdl, embed_bdl, callback
|
||||
)
|
||||
community_structure, community_reports = await with_community(
|
||||
args.tenant_id, kb_id, args.doc_id, llm_bdl, embed_bdl, callback
|
||||
)
|
||||
|
||||
print("------------------ COMMUNITY REPORT ----------------------\n", dealer.community_reports)
|
||||
print(json.dumps(dealer.community_structure, ensure_ascii=False, indent=2))
|
||||
print(
|
||||
"------------------ COMMUNITY STRUCTURE--------------------\n",
|
||||
json.dumps(community_structure, ensure_ascii=False, indent=2),
|
||||
)
|
||||
print(
|
||||
"------------------ COMMUNITY REPORTS----------------------\n",
|
||||
community_reports,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
trio.run(main)
|
||||
|
||||
@ -18,22 +18,42 @@ import argparse
|
||||
import json
|
||||
from api import settings
|
||||
import networkx as nx
|
||||
import logging
|
||||
import trio
|
||||
|
||||
from api.db import LLMType
|
||||
from api.db.services.document_service import DocumentService
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from api.db.services.llm_service import LLMBundle
|
||||
from api.db.services.user_service import TenantService
|
||||
from graphrag.general.index import Dealer
|
||||
from graphrag.general.index import update_graph
|
||||
from graphrag.light.graph_extractor import GraphExtractor
|
||||
from rag.utils.redis_conn import RedisDistributedLock
|
||||
|
||||
settings.init_settings()
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
def callback(prog=None, msg="Processing..."):
|
||||
logging.info(msg)
|
||||
|
||||
|
||||
async def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-t', '--tenant_id', default=False, help="Tenant ID", action='store', required=True)
|
||||
parser.add_argument('-d', '--doc_id', default=False, help="Document ID", action='store', required=True)
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--tenant_id",
|
||||
default=False,
|
||||
help="Tenant ID",
|
||||
action="store",
|
||||
required=True,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--doc_id",
|
||||
default=False,
|
||||
help="Document ID",
|
||||
action="store",
|
||||
required=True,
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
e, doc = DocumentService.get_by_id(args.doc_id)
|
||||
@ -41,18 +61,36 @@ if __name__ == "__main__":
|
||||
raise LookupError("Document not found.")
|
||||
kb_id = doc.kb_id
|
||||
|
||||
chunks = [d["content_with_weight"] for d in
|
||||
settings.retrievaler.chunk_list(args.doc_id, args.tenant_id, [kb_id], max_count=6,
|
||||
fields=["content_with_weight"])]
|
||||
chunks = [("x", c) for c in chunks]
|
||||
|
||||
RedisDistributedLock.clean_lock(kb_id)
|
||||
chunks = [
|
||||
d["content_with_weight"]
|
||||
for d in settings.retrievaler.chunk_list(
|
||||
args.doc_id,
|
||||
args.tenant_id,
|
||||
[kb_id],
|
||||
max_count=6,
|
||||
fields=["content_with_weight"],
|
||||
)
|
||||
]
|
||||
|
||||
_, tenant = TenantService.get_by_id(args.tenant_id)
|
||||
llm_bdl = LLMBundle(args.tenant_id, LLMType.CHAT, tenant.llm_id)
|
||||
_, kb = KnowledgebaseService.get_by_id(kb_id)
|
||||
embed_bdl = LLMBundle(args.tenant_id, LLMType.EMBEDDING, kb.embd_id)
|
||||
|
||||
dealer = Dealer(GraphExtractor, args.tenant_id, kb_id, llm_bdl, chunks, "English", embed_bdl=embed_bdl)
|
||||
graph, doc_ids = await update_graph(
|
||||
GraphExtractor,
|
||||
args.tenant_id,
|
||||
kb_id,
|
||||
args.doc_id,
|
||||
chunks,
|
||||
"English",
|
||||
llm_bdl,
|
||||
embed_bdl,
|
||||
callback,
|
||||
)
|
||||
|
||||
print(json.dumps(nx.node_link_data(dealer.graph), ensure_ascii=False, indent=2))
|
||||
print(json.dumps(nx.node_link_data(graph), ensure_ascii=False, indent=2))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
trio.run(main)
|
||||
|
||||
@ -352,25 +352,57 @@ def set_relation(tenant_id, kb_id, embd_mdl, from_ent_name, to_ent_name, meta):
|
||||
chunk["q_%d_vec" % len(ebd)] = ebd
|
||||
settings.docStoreConn.insert([{"id": chunk_id(chunk), **chunk}], search.index_name(tenant_id), kb_id)
|
||||
|
||||
async def does_graph_contains(tenant_id, kb_id, doc_id):
|
||||
# Get doc_ids of graph
|
||||
fields = ["source_id"]
|
||||
condition = {
|
||||
"knowledge_graph_kwd": ["graph"],
|
||||
"removed_kwd": "N",
|
||||
}
|
||||
res = await trio.to_thread.run_sync(lambda: settings.docStoreConn.search(fields, [], condition, [], OrderByExpr(), 0, 1, search.index_name(tenant_id), [kb_id]))
|
||||
fields2 = settings.docStoreConn.getFields(res, fields)
|
||||
graph_doc_ids = set()
|
||||
for chunk_id in fields2.keys():
|
||||
graph_doc_ids = set(fields2[chunk_id]["source_id"])
|
||||
return doc_id in graph_doc_ids
|
||||
|
||||
def get_graph(tenant_id, kb_id):
|
||||
async def get_graph_doc_ids(tenant_id, kb_id) -> list[str]:
|
||||
conds = {
|
||||
"fields": ["source_id"],
|
||||
"removed_kwd": "N",
|
||||
"size": 1,
|
||||
"knowledge_graph_kwd": ["graph"]
|
||||
}
|
||||
res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search(conds, search.index_name(tenant_id), [kb_id]))
|
||||
doc_ids = []
|
||||
if res.total == 0:
|
||||
return doc_ids
|
||||
for id in res.ids:
|
||||
doc_ids = res.field[id]["source_id"]
|
||||
return doc_ids
|
||||
|
||||
|
||||
async def get_graph(tenant_id, kb_id):
|
||||
conds = {
|
||||
"fields": ["content_with_weight", "source_id"],
|
||||
"removed_kwd": "N",
|
||||
"size": 1,
|
||||
"knowledge_graph_kwd": ["graph"]
|
||||
}
|
||||
res = settings.retrievaler.search(conds, search.index_name(tenant_id), [kb_id])
|
||||
res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search(conds, search.index_name(tenant_id), [kb_id]))
|
||||
if res.total == 0:
|
||||
return None, []
|
||||
for id in res.ids:
|
||||
try:
|
||||
return json_graph.node_link_graph(json.loads(res.field[id]["content_with_weight"]), edges="edges"), \
|
||||
res.field[id]["source_id"]
|
||||
except Exception:
|
||||
continue
|
||||
return rebuild_graph(tenant_id, kb_id)
|
||||
result = await rebuild_graph(tenant_id, kb_id)
|
||||
return result
|
||||
|
||||
|
||||
def set_graph(tenant_id, kb_id, graph, docids):
|
||||
async def set_graph(tenant_id, kb_id, graph, docids):
|
||||
chunk = {
|
||||
"content_with_weight": json.dumps(nx.node_link_data(graph, edges="edges"), ensure_ascii=False,
|
||||
indent=2),
|
||||
@ -380,12 +412,12 @@ def set_graph(tenant_id, kb_id, graph, docids):
|
||||
"available_int": 0,
|
||||
"removed_kwd": "N"
|
||||
}
|
||||
res = settings.retrievaler.search({"knowledge_graph_kwd": "graph", "size": 1, "fields": []}, search.index_name(tenant_id), [kb_id])
|
||||
res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search({"knowledge_graph_kwd": "graph", "size": 1, "fields": []}, search.index_name(tenant_id), [kb_id]))
|
||||
if res.ids:
|
||||
settings.docStoreConn.update({"knowledge_graph_kwd": "graph"}, chunk,
|
||||
search.index_name(tenant_id), kb_id)
|
||||
await trio.to_thread.run_sync(lambda: settings.docStoreConn.update({"knowledge_graph_kwd": "graph"}, chunk,
|
||||
search.index_name(tenant_id), kb_id))
|
||||
else:
|
||||
settings.docStoreConn.insert([{"id": chunk_id(chunk), **chunk}], search.index_name(tenant_id), kb_id)
|
||||
await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert([{"id": chunk_id(chunk), **chunk}], search.index_name(tenant_id), kb_id))
|
||||
|
||||
|
||||
def is_continuous_subsequence(subseq, seq):
|
||||
@ -430,7 +462,7 @@ def merge_tuples(list1, list2):
|
||||
return result
|
||||
|
||||
|
||||
def update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, graph, n_hop):
|
||||
async def update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, graph, n_hop):
|
||||
def n_neighbor(id):
|
||||
nonlocal graph, n_hop
|
||||
count = 0
|
||||
@ -460,10 +492,10 @@ def update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, graph, n_hop):
|
||||
for n, p in pr.items():
|
||||
graph.nodes[n]["pagerank"] = p
|
||||
try:
|
||||
settings.docStoreConn.update({"entity_kwd": n, "kb_id": kb_id},
|
||||
await trio.to_thread.run_sync(lambda: settings.docStoreConn.update({"entity_kwd": n, "kb_id": kb_id},
|
||||
{"rank_flt": p,
|
||||
"n_hop_with_weight": json.dumps(n_neighbor(n), ensure_ascii=False)},
|
||||
search.index_name(tenant_id), kb_id)
|
||||
"n_hop_with_weight": json.dumps( (n), ensure_ascii=False)},
|
||||
search.index_name(tenant_id), kb_id))
|
||||
except Exception as e:
|
||||
logging.exception(e)
|
||||
|
||||
@ -480,21 +512,21 @@ def update_nodes_pagerank_nhop_neighbour(tenant_id, kb_id, graph, n_hop):
|
||||
"knowledge_graph_kwd": "ty2ents",
|
||||
"available_int": 0
|
||||
}
|
||||
res = settings.retrievaler.search({"knowledge_graph_kwd": "ty2ents", "size": 1, "fields": []},
|
||||
search.index_name(tenant_id), [kb_id])
|
||||
res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search({"knowledge_graph_kwd": "ty2ents", "size": 1, "fields": []},
|
||||
search.index_name(tenant_id), [kb_id]))
|
||||
if res.ids:
|
||||
settings.docStoreConn.update({"knowledge_graph_kwd": "ty2ents"},
|
||||
await trio.to_thread.run_sync(lambda: settings.docStoreConn.update({"knowledge_graph_kwd": "ty2ents"},
|
||||
chunk,
|
||||
search.index_name(tenant_id), kb_id)
|
||||
search.index_name(tenant_id), kb_id))
|
||||
else:
|
||||
settings.docStoreConn.insert([{"id": chunk_id(chunk), **chunk}], search.index_name(tenant_id), kb_id)
|
||||
await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert([{"id": chunk_id(chunk), **chunk}], search.index_name(tenant_id), kb_id))
|
||||
|
||||
|
||||
def get_entity_type2sampels(idxnms, kb_ids: list):
|
||||
es_res = settings.retrievaler.search({"knowledge_graph_kwd": "ty2ents", "kb_id": kb_ids,
|
||||
async def get_entity_type2sampels(idxnms, kb_ids: list):
|
||||
es_res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search({"knowledge_graph_kwd": "ty2ents", "kb_id": kb_ids,
|
||||
"size": 10000,
|
||||
"fields": ["content_with_weight"]},
|
||||
idxnms, kb_ids)
|
||||
idxnms, kb_ids))
|
||||
|
||||
res = defaultdict(list)
|
||||
for id in es_res.ids:
|
||||
@ -522,18 +554,18 @@ def flat_uniq_list(arr, key):
|
||||
return list(set(res))
|
||||
|
||||
|
||||
def rebuild_graph(tenant_id, kb_id):
|
||||
async def rebuild_graph(tenant_id, kb_id):
|
||||
graph = nx.Graph()
|
||||
src_ids = []
|
||||
flds = ["entity_kwd", "entity_type_kwd", "from_entity_kwd", "to_entity_kwd", "weight_int", "knowledge_graph_kwd", "source_id"]
|
||||
bs = 256
|
||||
for i in range(0, 39*bs, bs):
|
||||
es_res = settings.docStoreConn.search(flds, [],
|
||||
es_res = await trio.to_thread.run_sync(lambda: settings.docStoreConn.search(flds, [],
|
||||
{"kb_id": kb_id, "knowledge_graph_kwd": ["entity", "relation"]},
|
||||
[],
|
||||
OrderByExpr(),
|
||||
i, bs, search.index_name(tenant_id), [kb_id]
|
||||
)
|
||||
))
|
||||
tot = settings.docStoreConn.getTotal(es_res)
|
||||
if tot == 0:
|
||||
return None, None
|
||||
|
||||
@ -15,18 +15,25 @@
|
||||
#
|
||||
import logging
|
||||
import re
|
||||
from threading import Lock
|
||||
import umap
|
||||
import numpy as np
|
||||
from sklearn.mixture import GaussianMixture
|
||||
import trio
|
||||
|
||||
from graphrag.utils import get_llm_cache, get_embed_cache, set_embed_cache, set_llm_cache, chat_limiter
|
||||
from graphrag.utils import (
|
||||
get_llm_cache,
|
||||
get_embed_cache,
|
||||
set_embed_cache,
|
||||
set_llm_cache,
|
||||
chat_limiter,
|
||||
)
|
||||
from rag.utils import truncate
|
||||
|
||||
|
||||
class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
|
||||
def __init__(self, max_cluster, llm_model, embd_model, prompt, max_token=512, threshold=0.1):
|
||||
def __init__(
|
||||
self, max_cluster, llm_model, embd_model, prompt, max_token=512, threshold=0.1
|
||||
):
|
||||
self._max_cluster = max_cluster
|
||||
self._llm_model = llm_model
|
||||
self._embd_model = embd_model
|
||||
@ -34,22 +41,24 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
|
||||
self._prompt = prompt
|
||||
self._max_token = max_token
|
||||
|
||||
def _chat(self, system, history, gen_conf):
|
||||
async def _chat(self, system, history, gen_conf):
|
||||
response = get_llm_cache(self._llm_model.llm_name, system, history, gen_conf)
|
||||
if response:
|
||||
return response
|
||||
response = self._llm_model.chat(system, history, gen_conf)
|
||||
response = await trio.to_thread.run_sync(
|
||||
lambda: self._llm_model.chat(system, history, gen_conf)
|
||||
)
|
||||
response = re.sub(r"<think>.*</think>", "", response, flags=re.DOTALL)
|
||||
if response.find("**ERROR**") >= 0:
|
||||
raise Exception(response)
|
||||
set_llm_cache(self._llm_model.llm_name, system, response, history, gen_conf)
|
||||
return response
|
||||
|
||||
def _embedding_encode(self, txt):
|
||||
async def _embedding_encode(self, txt):
|
||||
response = get_embed_cache(self._embd_model.llm_name, txt)
|
||||
if response is not None:
|
||||
return response
|
||||
embds, _ = self._embd_model.encode([txt])
|
||||
embds, _ = await trio.to_thread.run_sync(lambda: self._embd_model.encode([txt]))
|
||||
if len(embds) < 1 or len(embds[0]) < 1:
|
||||
raise Exception("Embedding error: ")
|
||||
embds = embds[0]
|
||||
@ -74,36 +83,48 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
|
||||
return []
|
||||
chunks = [(s, a) for s, a in chunks if s and len(a) > 0]
|
||||
|
||||
async def summarize(ck_idx, lock):
|
||||
async def summarize(ck_idx: list[int]):
|
||||
nonlocal chunks
|
||||
try:
|
||||
texts = [chunks[i][0] for i in ck_idx]
|
||||
len_per_chunk = int((self._llm_model.max_length - self._max_token) / len(texts))
|
||||
cluster_content = "\n".join([truncate(t, max(1, len_per_chunk)) for t in texts])
|
||||
len_per_chunk = int(
|
||||
(self._llm_model.max_length - self._max_token) / len(texts)
|
||||
)
|
||||
cluster_content = "\n".join(
|
||||
[truncate(t, max(1, len_per_chunk)) for t in texts]
|
||||
)
|
||||
async with chat_limiter:
|
||||
cnt = await trio.to_thread.run_sync(lambda: self._chat("You're a helpful assistant.",
|
||||
[{"role": "user",
|
||||
"content": self._prompt.format(cluster_content=cluster_content)}],
|
||||
{"temperature": 0.3, "max_tokens": self._max_token}
|
||||
))
|
||||
cnt = re.sub("(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)", "",
|
||||
cnt)
|
||||
cnt = await self._chat(
|
||||
"You're a helpful assistant.",
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
"content": self._prompt.format(
|
||||
cluster_content=cluster_content
|
||||
),
|
||||
}
|
||||
],
|
||||
{"temperature": 0.3, "max_tokens": self._max_token},
|
||||
)
|
||||
cnt = re.sub(
|
||||
"(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)",
|
||||
"",
|
||||
cnt,
|
||||
)
|
||||
logging.debug(f"SUM: {cnt}")
|
||||
embds, _ = self._embd_model.encode([cnt])
|
||||
with lock:
|
||||
chunks.append((cnt, self._embedding_encode(cnt)))
|
||||
except Exception as e:
|
||||
logging.exception("summarize got exception")
|
||||
return e
|
||||
embds = await self._embedding_encode(cnt)
|
||||
chunks.append((cnt, embds))
|
||||
|
||||
labels = []
|
||||
lock = Lock()
|
||||
while end - start > 1:
|
||||
embeddings = [embd for _, embd in chunks[start:end]]
|
||||
if len(embeddings) == 2:
|
||||
await summarize([start, start + 1], lock)
|
||||
await summarize([start, start + 1])
|
||||
if callback:
|
||||
callback(msg="Cluster one layer: {} -> {}".format(end - start, len(chunks) - end))
|
||||
callback(
|
||||
msg="Cluster one layer: {} -> {}".format(
|
||||
end - start, len(chunks) - end
|
||||
)
|
||||
)
|
||||
labels.extend([0, 0])
|
||||
layers.append((end, len(chunks)))
|
||||
start = end
|
||||
@ -112,7 +133,9 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
|
||||
|
||||
n_neighbors = int((len(embeddings) - 1) ** 0.8)
|
||||
reduced_embeddings = umap.UMAP(
|
||||
n_neighbors=max(2, n_neighbors), n_components=min(12, len(embeddings) - 2), metric="cosine"
|
||||
n_neighbors=max(2, n_neighbors),
|
||||
n_components=min(12, len(embeddings) - 2),
|
||||
metric="cosine",
|
||||
).fit_transform(embeddings)
|
||||
n_clusters = self._get_optimal_clusters(reduced_embeddings, random_state)
|
||||
if n_clusters == 1:
|
||||
@ -127,18 +150,22 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
|
||||
async with trio.open_nursery() as nursery:
|
||||
for c in range(n_clusters):
|
||||
ck_idx = [i + start for i in range(len(lbls)) if lbls[i] == c]
|
||||
if not ck_idx:
|
||||
continue
|
||||
assert len(ck_idx) > 0
|
||||
async with chat_limiter:
|
||||
nursery.start_soon(lambda: summarize(ck_idx, lock))
|
||||
nursery.start_soon(lambda: summarize(ck_idx))
|
||||
|
||||
assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters)
|
||||
assert len(chunks) - end == n_clusters, "{} vs. {}".format(
|
||||
len(chunks) - end, n_clusters
|
||||
)
|
||||
labels.extend(lbls)
|
||||
layers.append((end, len(chunks)))
|
||||
if callback:
|
||||
callback(msg="Cluster one layer: {} -> {}".format(end - start, len(chunks) - end))
|
||||
callback(
|
||||
msg="Cluster one layer: {} -> {}".format(
|
||||
end - start, len(chunks) - end
|
||||
)
|
||||
)
|
||||
start = end
|
||||
end = len(chunks)
|
||||
|
||||
return chunks
|
||||
|
||||
|
||||
@ -20,9 +20,7 @@ import random
|
||||
import sys
|
||||
|
||||
from api.utils.log_utils import initRootLogger, get_project_base_directory
|
||||
from graphrag.general.index import WithCommunity, WithResolution, Dealer
|
||||
from graphrag.light.graph_extractor import GraphExtractor as LightKGExt
|
||||
from graphrag.general.graph_extractor import GraphExtractor as GeneralKGExt
|
||||
from graphrag.general.index import run_graphrag
|
||||
from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
|
||||
from rag.prompts import keyword_extraction, question_proposal, content_tagging
|
||||
|
||||
@ -45,6 +43,7 @@ import tracemalloc
|
||||
import resource
|
||||
import signal
|
||||
import trio
|
||||
import exceptiongroup
|
||||
|
||||
import numpy as np
|
||||
from peewee import DoesNotExist
|
||||
@ -453,24 +452,6 @@ async def run_raptor(row, chat_mdl, embd_mdl, vector_size, callback=None):
|
||||
return res, tk_count
|
||||
|
||||
|
||||
async def run_graphrag(row, chat_model, language, embedding_model, callback=None):
|
||||
chunks = []
|
||||
for d in settings.retrievaler.chunk_list(row["doc_id"], row["tenant_id"], [str(row["kb_id"])],
|
||||
fields=["content_with_weight", "doc_id"]):
|
||||
chunks.append((d["doc_id"], d["content_with_weight"]))
|
||||
|
||||
dealer = Dealer(LightKGExt if row["parser_config"]["graphrag"]["method"] != 'general' else GeneralKGExt,
|
||||
row["tenant_id"],
|
||||
str(row["kb_id"]),
|
||||
chat_model,
|
||||
chunks=chunks,
|
||||
language=language,
|
||||
entity_types=row["parser_config"]["graphrag"]["entity_types"],
|
||||
embed_bdl=embedding_model,
|
||||
callback=callback)
|
||||
await dealer()
|
||||
|
||||
|
||||
async def do_handle_task(task):
|
||||
task_id = task["id"]
|
||||
task_from_page = task["from_page"]
|
||||
@ -526,24 +507,10 @@ async def do_handle_task(task):
|
||||
return
|
||||
start_ts = timer()
|
||||
chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
|
||||
await run_graphrag(task, chat_model, task_language, embedding_model, progress_callback)
|
||||
progress_callback(prog=1.0, msg="Knowledge Graph basic is done ({:.2f}s)".format(timer() - start_ts))
|
||||
if graphrag_conf.get("resolution", False):
|
||||
start_ts = timer()
|
||||
with_res = WithResolution(
|
||||
task["tenant_id"], str(task["kb_id"]), chat_model, embedding_model,
|
||||
progress_callback
|
||||
)
|
||||
await with_res()
|
||||
progress_callback(prog=1.0, msg="Knowledge Graph resolution is done ({:.2f}s)".format(timer() - start_ts))
|
||||
if graphrag_conf.get("community", False):
|
||||
start_ts = timer()
|
||||
with_comm = WithCommunity(
|
||||
task["tenant_id"], str(task["kb_id"]), chat_model, embedding_model,
|
||||
progress_callback
|
||||
)
|
||||
await with_comm()
|
||||
progress_callback(prog=1.0, msg="Knowledge Graph community is done ({:.2f}s)".format(timer() - start_ts))
|
||||
with_resolution = graphrag_conf.get("resolution", False)
|
||||
with_community = graphrag_conf.get("community", False)
|
||||
await run_graphrag(task, task_language, with_resolution, with_community, chat_model, embedding_model, progress_callback)
|
||||
progress_callback(prog=1.0, msg="Knowledge Graph done ({:.2f}s)".format(timer() - start_ts))
|
||||
return
|
||||
else:
|
||||
# Standard chunking methods
|
||||
@ -622,7 +589,11 @@ async def handle_task():
|
||||
FAILED_TASKS += 1
|
||||
CURRENT_TASKS.pop(task["id"], None)
|
||||
try:
|
||||
set_progress(task["id"], prog=-1, msg=f"[Exception]: {e}")
|
||||
err_msg = str(e)
|
||||
while isinstance(e, exceptiongroup.ExceptionGroup):
|
||||
e = e.exceptions[0]
|
||||
err_msg += ' -- ' + str(e)
|
||||
set_progress(task["id"], prog=-1, msg=f"[Exception]: {err_msg}")
|
||||
except Exception:
|
||||
pass
|
||||
logging.exception(f"handle_task got exception for task {json.dumps(task)}")
|
||||
|
||||
@ -16,13 +16,12 @@
|
||||
|
||||
import logging
|
||||
import json
|
||||
import time
|
||||
import uuid
|
||||
|
||||
import valkey as redis
|
||||
from rag import settings
|
||||
from rag.utils import singleton
|
||||
|
||||
from valkey.lock import Lock
|
||||
|
||||
class RedisMsg:
|
||||
def __init__(self, consumer, queue_name, group_name, msg_id, message):
|
||||
@ -281,29 +280,23 @@ REDIS_CONN = RedisDB()
|
||||
|
||||
|
||||
class RedisDistributedLock:
|
||||
def __init__(self, lock_key, timeout=10):
|
||||
def __init__(self, lock_key, lock_value=None, timeout=10, blocking_timeout=1):
|
||||
self.lock_key = lock_key
|
||||
if lock_value:
|
||||
self.lock_value = lock_value
|
||||
else:
|
||||
self.lock_value = str(uuid.uuid4())
|
||||
self.timeout = timeout
|
||||
self.lock = Lock(REDIS_CONN.REDIS, lock_key, timeout=timeout, blocking_timeout=blocking_timeout)
|
||||
|
||||
@staticmethod
|
||||
def clean_lock(lock_key):
|
||||
REDIS_CONN.REDIS.delete(lock_key)
|
||||
def acquire(self):
|
||||
return self.lock.acquire()
|
||||
|
||||
def acquire_lock(self):
|
||||
end_time = time.time() + self.timeout
|
||||
while time.time() < end_time:
|
||||
if REDIS_CONN.REDIS.setnx(self.lock_key, self.lock_value):
|
||||
return True
|
||||
time.sleep(1)
|
||||
return False
|
||||
|
||||
def release_lock(self):
|
||||
if REDIS_CONN.REDIS.get(self.lock_key) == self.lock_value:
|
||||
REDIS_CONN.REDIS.delete(self.lock_key)
|
||||
def release(self):
|
||||
return self.lock.release()
|
||||
|
||||
def __enter__(self):
|
||||
self.acquire_lock()
|
||||
self.acquire()
|
||||
|
||||
def __exit__(self, exception_type, exception_value, exception_traceback):
|
||||
self.release_lock()
|
||||
self.release()
|
||||
Loading…
x
Reference in New Issue
Block a user