mirror of
https://github.com/infiniflow/ragflow.git
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fb1bedbd3c
### What problem does this PR solve? Since `description_list` was a tuple containing a single element (which was the actual list of descriptions), `len(description_list)` was always **1**. The subsequent check: `if len(description_list) <= 12:` always evaluated to `True` (since $1 \le 12$), even if the inner list contained more than 12 descriptions. This prevented the necessary summarization logic from running for long lists. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
244 lines
11 KiB
Python
244 lines
11 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import os
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import re
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from collections import Counter, defaultdict
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from copy import deepcopy
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from typing import Callable
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import networkx as nx
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import trio
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from api.utils.api_utils import timeout
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from graphrag.general.graph_prompt import SUMMARIZE_DESCRIPTIONS_PROMPT
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from graphrag.utils import (
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GraphChange,
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chat_limiter,
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flat_uniq_list,
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get_from_to,
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get_llm_cache,
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handle_single_entity_extraction,
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handle_single_relationship_extraction,
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set_llm_cache,
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split_string_by_multi_markers,
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)
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from rag.llm.chat_model import Base as CompletionLLM
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from rag.prompts.generator import message_fit_in
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from rag.utils import truncate
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GRAPH_FIELD_SEP = "<SEP>"
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DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event", "category"]
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ENTITY_EXTRACTION_MAX_GLEANINGS = 2
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MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK = int(os.environ.get("MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK", 10))
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class Extractor:
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_llm: CompletionLLM
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def __init__(
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self,
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llm_invoker: CompletionLLM,
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language: str | None = "English",
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entity_types: list[str] | None = None,
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):
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self._llm = llm_invoker
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self._language = language
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self._entity_types = entity_types or DEFAULT_ENTITY_TYPES
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@timeout(60 * 20)
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def _chat(self, system, history, gen_conf={}):
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hist = deepcopy(history)
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conf = deepcopy(gen_conf)
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response = get_llm_cache(self._llm.llm_name, system, hist, conf)
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if response:
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return response
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_, system_msg = message_fit_in([{"role": "system", "content": system}], int(self._llm.max_length * 0.92))
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response = ""
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for attempt in range(3):
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try:
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response = self._llm.chat(system_msg[0]["content"], hist, conf)
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response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
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if response.find("**ERROR**") >= 0:
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raise Exception(response)
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set_llm_cache(self._llm.llm_name, system, response, history, gen_conf)
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except Exception as e:
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logging.exception(e)
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if attempt == 2:
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raise
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return response
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def _entities_and_relations(self, chunk_key: str, records: list, tuple_delimiter: str):
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maybe_nodes = defaultdict(list)
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maybe_edges = defaultdict(list)
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ent_types = [t.lower() for t in self._entity_types]
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for record in records:
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record_attributes = split_string_by_multi_markers(record, [tuple_delimiter])
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if_entities = handle_single_entity_extraction(record_attributes, chunk_key)
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if if_entities is not None and if_entities.get("entity_type", "unknown").lower() in ent_types:
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maybe_nodes[if_entities["entity_name"]].append(if_entities)
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continue
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if_relation = handle_single_relationship_extraction(record_attributes, chunk_key)
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if if_relation is not None:
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maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append(if_relation)
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return dict(maybe_nodes), dict(maybe_edges)
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async def __call__(self, doc_id: str, chunks: list[str], callback: Callable | None = None):
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self.callback = callback
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start_ts = trio.current_time()
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async def extract_all(doc_id, chunks, max_concurrency=MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK):
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out_results = []
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limiter = trio.Semaphore(max_concurrency)
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async def worker(chunk_key_dp: tuple[str, str], idx: int, total: int):
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async with limiter:
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await self._process_single_content(chunk_key_dp, idx, total, out_results)
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async with trio.open_nursery() as nursery:
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for i, ck in enumerate(chunks):
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nursery.start_soon(worker, (doc_id, ck), i, len(chunks))
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return out_results
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out_results = await extract_all(doc_id, chunks, max_concurrency=MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK)
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maybe_nodes = defaultdict(list)
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maybe_edges = defaultdict(list)
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sum_token_count = 0
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for m_nodes, m_edges, token_count in out_results:
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for k, v in m_nodes.items():
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maybe_nodes[k].extend(v)
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for k, v in m_edges.items():
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maybe_edges[tuple(sorted(k))].extend(v)
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sum_token_count += token_count
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now = trio.current_time()
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if callback:
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callback(msg=f"Entities and relationships extraction done, {len(maybe_nodes)} nodes, {len(maybe_edges)} edges, {sum_token_count} tokens, {now - start_ts:.2f}s.")
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start_ts = now
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logging.info("Entities merging...")
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all_entities_data = []
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async with trio.open_nursery() as nursery:
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for en_nm, ents in maybe_nodes.items():
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nursery.start_soon(self._merge_nodes, en_nm, ents, all_entities_data)
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now = trio.current_time()
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if callback:
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callback(msg=f"Entities merging done, {now - start_ts:.2f}s.")
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start_ts = now
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logging.info("Relationships merging...")
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all_relationships_data = []
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async with trio.open_nursery() as nursery:
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for (src, tgt), rels in maybe_edges.items():
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nursery.start_soon(self._merge_edges, src, tgt, rels, all_relationships_data)
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now = trio.current_time()
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if callback:
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callback(msg=f"Relationships merging done, {now - start_ts:.2f}s.")
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if not len(all_entities_data) and not len(all_relationships_data):
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logging.warning("Didn't extract any entities and relationships, maybe your LLM is not working")
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if not len(all_entities_data):
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logging.warning("Didn't extract any entities")
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if not len(all_relationships_data):
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logging.warning("Didn't extract any relationships")
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return all_entities_data, all_relationships_data
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async def _merge_nodes(self, entity_name: str, entities: list[dict], all_relationships_data):
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if not entities:
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return
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entity_type = sorted(
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Counter([dp["entity_type"] for dp in entities]).items(),
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key=lambda x: x[1],
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reverse=True,
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)[0][0]
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description = GRAPH_FIELD_SEP.join(sorted(set([dp["description"] for dp in entities])))
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already_source_ids = flat_uniq_list(entities, "source_id")
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description = await self._handle_entity_relation_summary(entity_name, description)
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node_data = dict(
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entity_type=entity_type,
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description=description,
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source_id=already_source_ids,
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)
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node_data["entity_name"] = entity_name
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all_relationships_data.append(node_data)
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async def _merge_edges(self, src_id: str, tgt_id: str, edges_data: list[dict], all_relationships_data=None):
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if not edges_data:
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return
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weight = sum([edge["weight"] for edge in edges_data])
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description = GRAPH_FIELD_SEP.join(sorted(set([edge["description"] for edge in edges_data])))
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description = await self._handle_entity_relation_summary(f"{src_id} -> {tgt_id}", description)
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keywords = flat_uniq_list(edges_data, "keywords")
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source_id = flat_uniq_list(edges_data, "source_id")
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edge_data = dict(src_id=src_id, tgt_id=tgt_id, description=description, keywords=keywords, weight=weight, source_id=source_id)
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all_relationships_data.append(edge_data)
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async def _merge_graph_nodes(self, graph: nx.Graph, nodes: list[str], change: GraphChange):
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if len(nodes) <= 1:
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return
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change.added_updated_nodes.add(nodes[0])
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change.removed_nodes.update(nodes[1:])
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nodes_set = set(nodes)
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node0_attrs = graph.nodes[nodes[0]]
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node0_neighbors = set(graph.neighbors(nodes[0]))
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for node1 in nodes[1:]:
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# Merge two nodes, keep "entity_name", "entity_type", "page_rank" unchanged.
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node1_attrs = graph.nodes[node1]
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node0_attrs["description"] += f"{GRAPH_FIELD_SEP}{node1_attrs['description']}"
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node0_attrs["source_id"] = sorted(set(node0_attrs["source_id"] + node1_attrs["source_id"]))
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for neighbor in graph.neighbors(node1):
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change.removed_edges.add(get_from_to(node1, neighbor))
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if neighbor not in nodes_set:
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edge1_attrs = graph.get_edge_data(node1, neighbor)
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if neighbor in node0_neighbors:
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# Merge two edges
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change.added_updated_edges.add(get_from_to(nodes[0], neighbor))
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edge0_attrs = graph.get_edge_data(nodes[0], neighbor)
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edge0_attrs["weight"] += edge1_attrs["weight"]
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edge0_attrs["description"] += f"{GRAPH_FIELD_SEP}{edge1_attrs['description']}"
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for attr in ["keywords", "source_id"]:
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edge0_attrs[attr] = sorted(set(edge0_attrs[attr] + edge1_attrs[attr]))
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edge0_attrs["description"] = await self._handle_entity_relation_summary(f"({nodes[0]}, {neighbor})", edge0_attrs["description"])
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graph.add_edge(nodes[0], neighbor, **edge0_attrs)
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else:
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graph.add_edge(nodes[0], neighbor, **edge1_attrs)
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graph.remove_node(node1)
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node0_attrs["description"] = await self._handle_entity_relation_summary(nodes[0], node0_attrs["description"])
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graph.nodes[nodes[0]].update(node0_attrs)
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async def _handle_entity_relation_summary(self, entity_or_relation_name: str, description: str) -> 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=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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logging.info(f"Trigger summary: {entity_or_relation_name}")
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async with chat_limiter:
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summary = await trio.to_thread.run_sync(self._chat, "", [{"role": "user", "content": use_prompt}])
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return summary
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