mirror of
https://github.com/infiniflow/ragflow.git
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592 lines
26 KiB
Python
592 lines
26 KiB
Python
#
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# Copyright 2024 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 json
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import logging
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import re
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import math
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import os
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from collections import OrderedDict
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from dataclasses import dataclass
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from rag.prompts.generator import relevant_chunks_with_toc
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from rag.settings import TAG_FLD, PAGERANK_FLD
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from rag.utils import rmSpace, get_float
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from rag.nlp import rag_tokenizer, query
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import numpy as np
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from rag.utils.doc_store_conn import DocStoreConnection, MatchDenseExpr, FusionExpr, OrderByExpr
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def index_name(uid): return f"ragflow_{uid}"
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class Dealer:
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def __init__(self, dataStore: DocStoreConnection):
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self.qryr = query.FulltextQueryer()
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self.dataStore = dataStore
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@dataclass
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class SearchResult:
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total: int
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ids: list[str]
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query_vector: list[float] | None = None
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field: dict | None = None
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highlight: dict | None = None
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aggregation: list | dict | None = None
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keywords: list[str] | None = None
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group_docs: list[list] | None = None
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def get_vector(self, txt, emb_mdl, topk=10, similarity=0.1):
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qv, _ = emb_mdl.encode_queries(txt)
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shape = np.array(qv).shape
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if len(shape) > 1:
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raise Exception(
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f"Dealer.get_vector returned array's shape {shape} doesn't match expectation(exact one dimension).")
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embedding_data = [get_float(v) for v in qv]
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vector_column_name = f"q_{len(embedding_data)}_vec"
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return MatchDenseExpr(vector_column_name, embedding_data, 'float', 'cosine', topk, {"similarity": similarity})
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def get_filters(self, req):
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condition = dict()
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for key, field in {"kb_ids": "kb_id", "doc_ids": "doc_id"}.items():
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if key in req and req[key] is not None:
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condition[field] = req[key]
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# TODO(yzc): `available_int` is nullable however infinity doesn't support nullable columns.
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for key in ["knowledge_graph_kwd", "available_int", "entity_kwd", "from_entity_kwd", "to_entity_kwd", "removed_kwd"]:
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if key in req and req[key] is not None:
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condition[key] = req[key]
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return condition
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def search(self, req, idx_names: str | list[str],
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kb_ids: list[str],
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emb_mdl=None,
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highlight: bool | list = False,
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rank_feature: dict | None = None
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):
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filters = self.get_filters(req)
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orderBy = OrderByExpr()
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pg = int(req.get("page", 1)) - 1
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topk = int(req.get("topk", 1024))
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ps = int(req.get("size", topk))
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offset, limit = pg * ps, ps
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src = req.get("fields",
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["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd", "position_int",
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"doc_id", "page_num_int", "top_int", "create_timestamp_flt", "knowledge_graph_kwd",
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"question_kwd", "question_tks", "doc_type_kwd",
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"available_int", "content_with_weight", PAGERANK_FLD, TAG_FLD])
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kwds = set([])
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qst = req.get("question", "")
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q_vec = []
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if not qst:
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if req.get("sort"):
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orderBy.asc("page_num_int")
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orderBy.asc("top_int")
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orderBy.desc("create_timestamp_flt")
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res = self.dataStore.search(src, [], filters, [], orderBy, offset, limit, idx_names, kb_ids)
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total = self.dataStore.getTotal(res)
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logging.debug("Dealer.search TOTAL: {}".format(total))
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else:
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highlightFields = ["content_ltks", "title_tks"]
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if not highlight:
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highlightFields = []
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elif isinstance(highlight, list):
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highlightFields = highlight
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matchText, keywords = self.qryr.question(qst, min_match=0.3)
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if emb_mdl is None:
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matchExprs = [matchText]
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res = self.dataStore.search(src, highlightFields, filters, matchExprs, orderBy, offset, limit,
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idx_names, kb_ids, rank_feature=rank_feature)
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total = self.dataStore.getTotal(res)
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logging.debug("Dealer.search TOTAL: {}".format(total))
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else:
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matchDense = self.get_vector(qst, emb_mdl, topk, req.get("similarity", 0.1))
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q_vec = matchDense.embedding_data
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src.append(f"q_{len(q_vec)}_vec")
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fusionExpr = FusionExpr("weighted_sum", topk, {"weights": "0.05,0.95"})
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matchExprs = [matchText, matchDense, fusionExpr]
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res = self.dataStore.search(src, highlightFields, filters, matchExprs, orderBy, offset, limit,
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idx_names, kb_ids, rank_feature=rank_feature)
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total = self.dataStore.getTotal(res)
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logging.debug("Dealer.search TOTAL: {}".format(total))
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# If result is empty, try again with lower min_match
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if total == 0:
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if filters.get("doc_id"):
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res = self.dataStore.search(src, [], filters, [], orderBy, offset, limit, idx_names, kb_ids)
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total = self.dataStore.getTotal(res)
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else:
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matchText, _ = self.qryr.question(qst, min_match=0.1)
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matchDense.extra_options["similarity"] = 0.17
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res = self.dataStore.search(src, highlightFields, filters, [matchText, matchDense, fusionExpr],
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orderBy, offset, limit, idx_names, kb_ids, rank_feature=rank_feature)
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total = self.dataStore.getTotal(res)
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logging.debug("Dealer.search 2 TOTAL: {}".format(total))
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for k in keywords:
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kwds.add(k)
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for kk in rag_tokenizer.fine_grained_tokenize(k).split():
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if len(kk) < 2:
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continue
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if kk in kwds:
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continue
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kwds.add(kk)
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logging.debug(f"TOTAL: {total}")
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ids = self.dataStore.getChunkIds(res)
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keywords = list(kwds)
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highlight = self.dataStore.getHighlight(res, keywords, "content_with_weight")
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aggs = self.dataStore.getAggregation(res, "docnm_kwd")
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return self.SearchResult(
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total=total,
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ids=ids,
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query_vector=q_vec,
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aggregation=aggs,
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highlight=highlight,
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field=self.dataStore.getFields(res, src + ["_score"]),
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keywords=keywords
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)
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@staticmethod
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def trans2floats(txt):
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return [get_float(t) for t in txt.split("\t")]
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def insert_citations(self, answer, chunks, chunk_v,
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embd_mdl, tkweight=0.1, vtweight=0.9):
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assert len(chunks) == len(chunk_v)
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if not chunks:
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return answer, set([])
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pieces = re.split(r"(```)", answer)
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if len(pieces) >= 3:
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i = 0
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pieces_ = []
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while i < len(pieces):
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if pieces[i] == "```":
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st = i
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i += 1
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while i < len(pieces) and pieces[i] != "```":
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i += 1
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if i < len(pieces):
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i += 1
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pieces_.append("".join(pieces[st: i]) + "\n")
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else:
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pieces_.extend(
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re.split(
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r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])",
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pieces[i]))
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i += 1
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pieces = pieces_
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else:
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pieces = re.split(r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", answer)
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for i in range(1, len(pieces)):
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if re.match(r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", pieces[i]):
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pieces[i - 1] += pieces[i][0]
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pieces[i] = pieces[i][1:]
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idx = []
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pieces_ = []
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for i, t in enumerate(pieces):
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if len(t) < 5:
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continue
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idx.append(i)
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pieces_.append(t)
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logging.debug("{} => {}".format(answer, pieces_))
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if not pieces_:
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return answer, set([])
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ans_v, _ = embd_mdl.encode(pieces_)
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for i in range(len(chunk_v)):
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if len(ans_v[0]) != len(chunk_v[i]):
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chunk_v[i] = [0.0]*len(ans_v[0])
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logging.warning("The dimension of query and chunk do not match: {} vs. {}".format(len(ans_v[0]), len(chunk_v[i])))
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assert len(ans_v[0]) == len(chunk_v[0]), "The dimension of query and chunk do not match: {} vs. {}".format(
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len(ans_v[0]), len(chunk_v[0]))
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chunks_tks = [rag_tokenizer.tokenize(self.qryr.rmWWW(ck)).split()
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for ck in chunks]
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cites = {}
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thr = 0.63
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while thr > 0.3 and len(cites.keys()) == 0 and pieces_ and chunks_tks:
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for i, a in enumerate(pieces_):
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sim, tksim, vtsim = self.qryr.hybrid_similarity(ans_v[i],
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chunk_v,
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rag_tokenizer.tokenize(
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self.qryr.rmWWW(pieces_[i])).split(),
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chunks_tks,
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tkweight, vtweight)
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mx = np.max(sim) * 0.99
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logging.debug("{} SIM: {}".format(pieces_[i], mx))
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if mx < thr:
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continue
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cites[idx[i]] = list(
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set([str(ii) for ii in range(len(chunk_v)) if sim[ii] > mx]))[:4]
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thr *= 0.8
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res = ""
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seted = set([])
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for i, p in enumerate(pieces):
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res += p
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if i not in idx:
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continue
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if i not in cites:
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continue
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for c in cites[i]:
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assert int(c) < len(chunk_v)
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for c in cites[i]:
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if c in seted:
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continue
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res += f" [ID:{c}]"
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seted.add(c)
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return res, seted
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def _rank_feature_scores(self, query_rfea, search_res):
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## For rank feature(tag_fea) scores.
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rank_fea = []
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pageranks = []
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for chunk_id in search_res.ids:
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pageranks.append(search_res.field[chunk_id].get(PAGERANK_FLD, 0))
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pageranks = np.array(pageranks, dtype=float)
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if not query_rfea:
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return np.array([0 for _ in range(len(search_res.ids))]) + pageranks
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q_denor = np.sqrt(np.sum([s*s for t,s in query_rfea.items() if t != PAGERANK_FLD]))
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for i in search_res.ids:
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nor, denor = 0, 0
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if not search_res.field[i].get(TAG_FLD):
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rank_fea.append(0)
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continue
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for t, sc in eval(search_res.field[i].get(TAG_FLD, "{}")).items():
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if t in query_rfea:
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nor += query_rfea[t] * sc
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denor += sc * sc
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if denor == 0:
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rank_fea.append(0)
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else:
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rank_fea.append(nor/np.sqrt(denor)/q_denor)
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return np.array(rank_fea)*10. + pageranks
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def rerank(self, sres, query, tkweight=0.3,
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vtweight=0.7, cfield="content_ltks",
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rank_feature: dict | None = None
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):
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_, keywords = self.qryr.question(query)
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vector_size = len(sres.query_vector)
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vector_column = f"q_{vector_size}_vec"
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zero_vector = [0.0] * vector_size
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ins_embd = []
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for chunk_id in sres.ids:
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vector = sres.field[chunk_id].get(vector_column, zero_vector)
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if isinstance(vector, str):
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vector = [get_float(v) for v in vector.split("\t")]
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ins_embd.append(vector)
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if not ins_embd:
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return [], [], []
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for i in sres.ids:
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if isinstance(sres.field[i].get("important_kwd", []), str):
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sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
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ins_tw = []
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for i in sres.ids:
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content_ltks = list(OrderedDict.fromkeys(sres.field[i][cfield].split()))
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title_tks = [t for t in sres.field[i].get("title_tks", "").split() if t]
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question_tks = [t for t in sres.field[i].get("question_tks", "").split() if t]
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important_kwd = sres.field[i].get("important_kwd", [])
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tks = content_ltks + title_tks * 2 + important_kwd * 5 + question_tks * 6
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ins_tw.append(tks)
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## For rank feature(tag_fea) scores.
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rank_fea = self._rank_feature_scores(rank_feature, sres)
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sim, tksim, vtsim = self.qryr.hybrid_similarity(sres.query_vector,
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ins_embd,
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keywords,
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ins_tw, tkweight, vtweight)
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return sim + rank_fea, tksim, vtsim
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def rerank_by_model(self, rerank_mdl, sres, query, tkweight=0.3,
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vtweight=0.7, cfield="content_ltks",
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rank_feature: dict | None = None):
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_, keywords = self.qryr.question(query)
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for i in sres.ids:
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if isinstance(sres.field[i].get("important_kwd", []), str):
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sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
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ins_tw = []
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for i in sres.ids:
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content_ltks = sres.field[i][cfield].split()
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title_tks = [t for t in sres.field[i].get("title_tks", "").split() if t]
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important_kwd = sres.field[i].get("important_kwd", [])
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tks = content_ltks + title_tks + important_kwd
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ins_tw.append(tks)
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tksim = self.qryr.token_similarity(keywords, ins_tw)
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vtsim, _ = rerank_mdl.similarity(query, [rmSpace(" ".join(tks)) for tks in ins_tw])
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## For rank feature(tag_fea) scores.
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rank_fea = self._rank_feature_scores(rank_feature, sres)
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return tkweight * (np.array(tksim)+rank_fea) + vtweight * vtsim, tksim, vtsim
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def hybrid_similarity(self, ans_embd, ins_embd, ans, inst):
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return self.qryr.hybrid_similarity(ans_embd,
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ins_embd,
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rag_tokenizer.tokenize(ans).split(),
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rag_tokenizer.tokenize(inst).split())
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def retrieval(self, question, embd_mdl, tenant_ids, kb_ids, page, page_size, similarity_threshold=0.2,
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vector_similarity_weight=0.3, top=1024, doc_ids=None, aggs=True,
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rerank_mdl=None, highlight=False,
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rank_feature: dict | None = {PAGERANK_FLD: 10}):
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ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
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if not question:
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return ranks
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# Ensure RERANK_LIMIT is multiple of page_size
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RERANK_LIMIT = math.ceil(64/page_size) * page_size if page_size>1 else 1
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req = {"kb_ids": kb_ids, "doc_ids": doc_ids, "page": math.ceil(page_size*page/RERANK_LIMIT), "size": RERANK_LIMIT,
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"question": question, "vector": True, "topk": top,
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"similarity": similarity_threshold,
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"available_int": 1}
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if isinstance(tenant_ids, str):
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tenant_ids = tenant_ids.split(",")
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sres = self.search(req, [index_name(tid) for tid in tenant_ids],
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kb_ids, embd_mdl, highlight, rank_feature=rank_feature)
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if rerank_mdl and sres.total > 0:
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sim, tsim, vsim = self.rerank_by_model(rerank_mdl,
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sres, question, 1 - vector_similarity_weight,
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vector_similarity_weight,
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rank_feature=rank_feature)
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else:
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lower_case_doc_engine = os.getenv('DOC_ENGINE', 'elasticsearch')
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if lower_case_doc_engine == "elasticsearch":
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# ElasticSearch doesn't normalize each way score before fusion.
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sim, tsim, vsim = self.rerank(
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sres, question, 1 - vector_similarity_weight, vector_similarity_weight,
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rank_feature=rank_feature)
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else:
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# Don't need rerank here since Infinity normalizes each way score before fusion.
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sim = [sres.field[id].get("_score", 0.0) for id in sres.ids]
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tsim = sim
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vsim = sim
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# Already paginated in search function
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begin = ((page % (RERANK_LIMIT//page_size)) - 1) * page_size
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sim = sim[begin : begin + page_size]
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sim_np = np.array(sim)
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idx = np.argsort(sim_np * -1)
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dim = len(sres.query_vector)
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vector_column = f"q_{dim}_vec"
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zero_vector = [0.0] * dim
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filtered_count = (sim_np >= similarity_threshold).sum()
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ranks["total"] = int(filtered_count) # Convert from np.int64 to Python int otherwise JSON serializable error
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for i in idx:
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if sim[i] < similarity_threshold:
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break
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id = sres.ids[i]
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chunk = sres.field[id]
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dnm = chunk.get("docnm_kwd", "")
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did = chunk.get("doc_id", "")
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if len(ranks["chunks"]) >= page_size:
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if aggs:
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if dnm not in ranks["doc_aggs"]:
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ranks["doc_aggs"][dnm] = {"doc_id": did, "count": 0}
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ranks["doc_aggs"][dnm]["count"] += 1
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continue
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break
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position_int = chunk.get("position_int", [])
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d = {
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"chunk_id": id,
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"content_ltks": chunk["content_ltks"],
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"content_with_weight": chunk["content_with_weight"],
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"doc_id": did,
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"docnm_kwd": dnm,
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"kb_id": chunk["kb_id"],
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"important_kwd": chunk.get("important_kwd", []),
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"image_id": chunk.get("img_id", ""),
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||
"similarity": sim[i],
|
||
"vector_similarity": vsim[i],
|
||
"term_similarity": tsim[i],
|
||
"vector": chunk.get(vector_column, zero_vector),
|
||
"positions": position_int,
|
||
"doc_type_kwd": chunk.get("doc_type_kwd", "")
|
||
}
|
||
if highlight and sres.highlight:
|
||
if id in sres.highlight:
|
||
d["highlight"] = rmSpace(sres.highlight[id])
|
||
else:
|
||
d["highlight"] = d["content_with_weight"]
|
||
ranks["chunks"].append(d)
|
||
if dnm not in ranks["doc_aggs"]:
|
||
ranks["doc_aggs"][dnm] = {"doc_id": did, "count": 0}
|
||
ranks["doc_aggs"][dnm]["count"] += 1
|
||
ranks["doc_aggs"] = [{"doc_name": k,
|
||
"doc_id": v["doc_id"],
|
||
"count": v["count"]} for k,
|
||
v in sorted(ranks["doc_aggs"].items(),
|
||
key=lambda x: x[1]["count"] * -1)]
|
||
ranks["chunks"] = ranks["chunks"][:page_size]
|
||
|
||
return ranks
|
||
|
||
def sql_retrieval(self, sql, fetch_size=128, format="json"):
|
||
tbl = self.dataStore.sql(sql, fetch_size, format)
|
||
return tbl
|
||
|
||
def chunk_list(self, doc_id: str, tenant_id: str,
|
||
kb_ids: list[str], max_count=1024,
|
||
offset=0,
|
||
fields=["docnm_kwd", "content_with_weight", "img_id"],
|
||
sort_by_position: bool = False):
|
||
condition = {"doc_id": doc_id}
|
||
|
||
fields_set = set(fields or [])
|
||
if sort_by_position:
|
||
for need in ("page_num_int", "position_int", "top_int"):
|
||
if need not in fields_set:
|
||
fields_set.add(need)
|
||
fields = list(fields_set)
|
||
|
||
orderBy = OrderByExpr()
|
||
if sort_by_position:
|
||
orderBy.asc("page_num_int")
|
||
orderBy.asc("position_int")
|
||
orderBy.asc("top_int")
|
||
|
||
res = []
|
||
bs = 128
|
||
for p in range(offset, max_count, bs):
|
||
es_res = self.dataStore.search(fields, [], condition, [], orderBy, p, bs, index_name(tenant_id),
|
||
kb_ids)
|
||
dict_chunks = self.dataStore.getFields(es_res, fields)
|
||
for id, doc in dict_chunks.items():
|
||
doc["id"] = id
|
||
if dict_chunks:
|
||
res.extend(dict_chunks.values())
|
||
if len(dict_chunks.values()) < bs:
|
||
break
|
||
return res
|
||
|
||
def all_tags(self, tenant_id: str, kb_ids: list[str], S=1000):
|
||
if not self.dataStore.indexExist(index_name(tenant_id), kb_ids[0]):
|
||
return []
|
||
res = self.dataStore.search([], [], {}, [], OrderByExpr(), 0, 0, index_name(tenant_id), kb_ids, ["tag_kwd"])
|
||
return self.dataStore.getAggregation(res, "tag_kwd")
|
||
|
||
def all_tags_in_portion(self, tenant_id: str, kb_ids: list[str], S=1000):
|
||
res = self.dataStore.search([], [], {}, [], OrderByExpr(), 0, 0, index_name(tenant_id), kb_ids, ["tag_kwd"])
|
||
res = self.dataStore.getAggregation(res, "tag_kwd")
|
||
total = np.sum([c for _, c in res])
|
||
return {t: (c + 1) / (total + S) for t, c in res}
|
||
|
||
def tag_content(self, tenant_id: str, kb_ids: list[str], doc, all_tags, topn_tags=3, keywords_topn=30, S=1000):
|
||
idx_nm = index_name(tenant_id)
|
||
match_txt = self.qryr.paragraph(doc["title_tks"] + " " + doc["content_ltks"], doc.get("important_kwd", []), keywords_topn)
|
||
res = self.dataStore.search([], [], {}, [match_txt], OrderByExpr(), 0, 0, idx_nm, kb_ids, ["tag_kwd"])
|
||
aggs = self.dataStore.getAggregation(res, "tag_kwd")
|
||
if not aggs:
|
||
return False
|
||
cnt = np.sum([c for _, c in aggs])
|
||
tag_fea = sorted([(a, round(0.1*(c + 1) / (cnt + S) / max(1e-6, all_tags.get(a, 0.0001)))) for a, c in aggs],
|
||
key=lambda x: x[1] * -1)[:topn_tags]
|
||
doc[TAG_FLD] = {a.replace(".", "_"): c for a, c in tag_fea if c > 0}
|
||
return True
|
||
|
||
def tag_query(self, question: str, tenant_ids: str | list[str], kb_ids: list[str], all_tags, topn_tags=3, S=1000):
|
||
if isinstance(tenant_ids, str):
|
||
idx_nms = index_name(tenant_ids)
|
||
else:
|
||
idx_nms = [index_name(tid) for tid in tenant_ids]
|
||
match_txt, _ = self.qryr.question(question, min_match=0.0)
|
||
res = self.dataStore.search([], [], {}, [match_txt], OrderByExpr(), 0, 0, idx_nms, kb_ids, ["tag_kwd"])
|
||
aggs = self.dataStore.getAggregation(res, "tag_kwd")
|
||
if not aggs:
|
||
return {}
|
||
cnt = np.sum([c for _, c in aggs])
|
||
tag_fea = sorted([(a, round(0.1*(c + 1) / (cnt + S) / max(1e-6, all_tags.get(a, 0.0001)))) for a, c in aggs],
|
||
key=lambda x: x[1] * -1)[:topn_tags]
|
||
return {a.replace(".", "_"): max(1, c) for a, c in tag_fea}
|
||
|
||
def retrieval_by_toc(self, query:str, chunks:list[dict], tenant_ids:list[str], chat_mdl, topn: int=6):
|
||
if not chunks:
|
||
return []
|
||
idx_nms = [index_name(tid) for tid in tenant_ids]
|
||
ranks, doc_id2kb_id = {}, {}
|
||
for ck in chunks:
|
||
if ck["doc_id"] not in ranks:
|
||
ranks[ck["doc_id"]] = 0
|
||
ranks[ck["doc_id"]] += ck["similarity"]
|
||
doc_id2kb_id[ck["doc_id"]] = ck["kb_id"]
|
||
doc_id = sorted(ranks.items(), key=lambda x: x[1]*-1.)[0][0]
|
||
kb_ids = [doc_id2kb_id[doc_id]]
|
||
es_res = self.dataStore.search(["content_with_weight"], [], {"doc_id": doc_id, "toc_kwd": "toc"}, [], OrderByExpr(), 0, 128, idx_nms,
|
||
kb_ids)
|
||
toc = []
|
||
dict_chunks = self.dataStore.getFields(es_res, ["content_with_weight"])
|
||
for _, doc in dict_chunks.items():
|
||
try:
|
||
toc.extend(json.loads(doc["content_with_weight"]))
|
||
except Exception as e:
|
||
logging.exception(e)
|
||
if not toc:
|
||
return chunks
|
||
|
||
ids = relevant_chunks_with_toc(query, toc, chat_mdl, topn*2)
|
||
if not ids:
|
||
return chunks
|
||
|
||
vector_size = 1024
|
||
id2idx = {ck["chunk_id"]: i for i, ck in enumerate(chunks)}
|
||
for cid, sim in ids:
|
||
if cid in id2idx:
|
||
chunks[id2idx[cid]]["similarity"] += sim
|
||
continue
|
||
chunk = self.dataStore.get(cid, idx_nms, kb_ids)
|
||
d = {
|
||
"chunk_id": cid,
|
||
"content_ltks": chunk["content_ltks"],
|
||
"content_with_weight": chunk["content_with_weight"],
|
||
"doc_id": doc_id,
|
||
"docnm_kwd": chunk.get("docnm_kwd", ""),
|
||
"kb_id": chunk["kb_id"],
|
||
"important_kwd": chunk.get("important_kwd", []),
|
||
"image_id": chunk.get("img_id", ""),
|
||
"similarity": sim,
|
||
"vector_similarity": sim,
|
||
"term_similarity": sim,
|
||
"vector": [0.0] * vector_size,
|
||
"positions": chunk.get("position_int", []),
|
||
"doc_type_kwd": chunk.get("doc_type_kwd", "")
|
||
}
|
||
for k in chunk.keys():
|
||
if k[-4:] == "_vec":
|
||
d["vector"] = chunk[k]
|
||
vector_size = len(chunk[k])
|
||
break
|
||
chunks.append(d)
|
||
|
||
return sorted(chunks, key=lambda x:x["similarity"]*-1)[:topn]
|