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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
@ -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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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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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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):
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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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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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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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)
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self.graph = nx.Graph()
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self.callback = callback
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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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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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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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for rel in rels:
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self.graph.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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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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doc_id: str,
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chunks: list[str],
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language,
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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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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, 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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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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subgraph.add_node(en["entity_name"], entity_type=en["entity_type"])
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for rel in rels:
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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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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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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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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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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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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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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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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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tenant_id: str,
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kb_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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):
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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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return
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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,
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callback,
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):
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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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"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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"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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"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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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": 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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await trio.to_thread.run_sync(
|
||||
lambda: settings.docStoreConn.delete(
|
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{
|
||||
"knowledge_graph_kwd": "relation",
|
||||
"kb_id": kb_id,
|
||||
"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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await trio.to_thread.run_sync(
|
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lambda: settings.docStoreConn.delete(
|
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{
|
||||
"knowledge_graph_kwd": "entity",
|
||||
"kb_id": kb_id,
|
||||
"entity_kwd": reso.removed_entities,
|
||||
},
|
||||
search.index_name(tenant_id),
|
||||
kb_id,
|
||||
)
|
||||
)
|
||||
now = trio.current_time()
|
||||
callback(msg=f"Graph resolution done in {now - start:.2f}s.")
|
||||
|
||||
|
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class WithCommunity(Dealer):
|
||||
def __init__(self,
|
||||
tenant_id: str,
|
||||
kb_id: str,
|
||||
llm_bdl,
|
||||
embed_bdl=None,
|
||||
callback=None
|
||||
):
|
||||
async def extract_community(
|
||||
graph,
|
||||
doc_ids,
|
||||
tenant_id: str,
|
||||
kb_id: str,
|
||||
doc_id: str,
|
||||
llm_bdl,
|
||||
embed_bdl,
|
||||
callback,
|
||||
):
|
||||
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
|
||||
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)
|
||||
|
||||
self.tenant_id = tenant_id
|
||||
self.kb_id = kb_id
|
||||
self.community_structure = None
|
||||
self.community_reports = None
|
||||
self.llm_bdl = llm_bdl
|
||||
self.embed_bdl = embed_bdl
|
||||
self.callback = callback
|
||||
async def __call__(self):
|
||||
with RedisDistributedLock(self.kb_id, 60*60):
|
||||
self.graph, doc_ids = get_graph(self.tenant_id, self.kb_id)
|
||||
if not self.graph:
|
||||
logging.error(f"Faild to fetch the graph. tenant_id:{self.kb_id}, kb_id:{self.kb_id}")
|
||||
if self.callback:
|
||||
self.callback(-1, msg="Faild to fetch the graph.")
|
||||
return
|
||||
if self.callback:
|
||||
self.callback(msg="Fetch the existing graph.")
|
||||
|
||||
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({
|
||||
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"]]),
|
||||
}
|
||||
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"]
|
||||
),
|
||||
"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):
|
||||
obj = {
|
||||
"report": rep,
|
||||
"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"]),
|
||||
"knowledge_graph_kwd": "community_report",
|
||||
"weight_flt": stru["weight"],
|
||||
"entities_kwd": stru["entities"],
|
||||
"important_kwd": stru["entities"],
|
||||
"kb_id": self.kb_id,
|
||||
"source_id": doc_ids,
|
||||
"available_int": 0
|
||||
}
|
||||
chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"])
|
||||
#try:
|
||||
# ebd, _ = self.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)))
|
||||
"weight_flt": stru["weight"],
|
||||
"entities_kwd": stru["entities"],
|
||||
"important_kwd": stru["entities"],
|
||||
"kb_id": kb_id,
|
||||
"source_id": doc_ids,
|
||||
"available_int": 0,
|
||||
}
|
||||
chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(
|
||||
chunk["content_ltks"]
|
||||
)
|
||||
# try:
|
||||
# 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(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)
|
||||
|
||||
Reference in New Issue
Block a user