mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
synced 2025-04-21 05:29:57 +08:00
245 lines
8.2 KiB
Python
245 lines
8.2 KiB
Python
# -*- coding: utf-8 -*-
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import json
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import re
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from elasticsearch_dsl import Q, Search, A
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from typing import List, Optional, Tuple, Dict, Union
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from dataclasses import dataclass
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from rag.settings import es_logger
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from rag.utils import rmSpace
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from rag.nlp import huqie, query
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import numpy as np
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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, es):
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self.qryr = query.EsQueryer(es)
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self.qryr.flds = [
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"title_tks^10",
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"title_sm_tks^5",
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"content_ltks^2",
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"content_sm_ltks"]
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self.es = es
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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
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field: Optional[Dict] = None
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highlight: Optional[Dict] = None
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aggregation: Union[List, Dict, None] = None
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keywords: Optional[List[str]] = None
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group_docs: List[List] = None
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def _vector(self, txt, emb_mdl, sim=0.8, topk=10):
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qv, c = emb_mdl.encode_queries(txt)
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return {
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"field": "q_%d_vec"%len(qv),
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"k": topk,
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"similarity": sim,
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"num_candidates": topk*2,
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"query_vector": qv
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}
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def search(self, req, idxnm, emb_mdl=None):
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qst = req.get("question", "")
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bqry, keywords = self.qryr.question(qst)
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if req.get("kb_ids"):
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bqry.filter.append(Q("terms", kb_id=req["kb_ids"]))
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if req.get("doc_ids"):
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bqry.filter.append(Q("terms", doc_id=req["doc_ids"]))
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if "available_int" in req:
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if req["available_int"] == 0: bqry.filter.append(Q("range", available_int={"lt": 1}))
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else: bqry.filter.append(Q("bool", must_not=Q("range", available_int={"lt": 1})))
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bqry.boost = 0.05
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s = Search()
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pg = int(req.get("page", 1)) - 1
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ps = int(req.get("size", 1000))
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src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id","img_id",
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"image_id", "doc_id", "q_512_vec", "q_768_vec",
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"q_1024_vec", "q_1536_vec", "available_int"])
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s = s.query(bqry)[pg * ps:(pg + 1) * ps]
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s = s.highlight("content_ltks")
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s = s.highlight("title_ltks")
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if not qst:
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s = s.sort(
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{"create_time": {"order": "desc", "unmapped_type": "date"}})
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if qst:
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s = s.highlight_options(
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fragment_size=120,
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number_of_fragments=5,
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boundary_scanner_locale="zh-CN",
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boundary_scanner="SENTENCE",
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boundary_chars=",./;:\\!()，。？：！……（）——、"
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)
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s = s.to_dict()
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q_vec = []
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if req.get("vector"):
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assert emb_mdl, "No embedding model selected"
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s["knn"] = self._vector(qst, emb_mdl, req.get("similarity", 0.4), ps)
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s["knn"]["filter"] = bqry.to_dict()
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if "highlight" in s: del s["highlight"]
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q_vec = s["knn"]["query_vector"]
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es_logger.info("【Q】: {}".format(json.dumps(s)))
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res = self.es.search(s, idxnm=idxnm, timeout="600s", src=src)
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es_logger.info("TOTAL: {}".format(self.es.getTotal(res)))
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if self.es.getTotal(res) == 0 and "knn" in s:
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bqry, _ = self.qryr.question(qst, min_match="10%")
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if req.get("kb_ids"):
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bqry.filter.append(Q("terms", kb_id=req["kb_ids"]))
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s["query"] = bqry.to_dict()
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s["knn"]["filter"] = bqry.to_dict()
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s["knn"]["similarity"] = 0.7
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res = self.es.search(s, idxnm=idxnm, timeout="600s", src=src)
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kwds = set([])
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for k in keywords:
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kwds.add(k)
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for kk in huqie.qieqie(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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aggs = self.getAggregation(res, "docnm_kwd")
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return self.SearchResult(
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total=self.es.getTotal(res),
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ids=self.es.getDocIds(res),
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query_vector=q_vec,
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aggregation=aggs,
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highlight=self.getHighlight(res),
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field=self.getFields(res, src),
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keywords=list(kwds)
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)
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def getAggregation(self, res, g):
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if not "aggregations" in res or "aggs_" + g not in res["aggregations"]:
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return
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bkts = res["aggregations"]["aggs_" + g]["buckets"]
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return [(b["key"], b["doc_count"]) for b in bkts]
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def getHighlight(self, res):
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def rmspace(line):
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eng = set(list("qwertyuioplkjhgfdsazxcvbnm"))
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r = []
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for t in line.split(" "):
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if not t:
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continue
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if len(r) > 0 and len(
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t) > 0 and r[-1][-1] in eng and t[0] in eng:
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r.append(" ")
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r.append(t)
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r = "".join(r)
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return r
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ans = {}
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for d in res["hits"]["hits"]:
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hlts = d.get("highlight")
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if not hlts:
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continue
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ans[d["_id"]] = "".join([a for a in list(hlts.items())[0][1]])
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return ans
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def getFields(self, sres, flds):
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res = {}
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if not flds:
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return {}
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for d in self.es.getSource(sres):
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m = {n: d.get(n) for n in flds if d.get(n) is not None}
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for n, v in m.items():
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if isinstance(v, type([])):
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m[n] = "\t".join([str(vv) for vv in v])
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continue
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if not isinstance(v, type("")):
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m[n] = str(m[n])
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m[n] = rmSpace(m[n])
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if m:
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res[d["id"]] = m
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return res
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@staticmethod
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def trans2floats(txt):
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return [float(t) for t in txt.split("\t")]
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def insert_citations(self, ans, top_idx, sres, emb_mdl,
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vfield="q_vec", cfield="content_ltks"):
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ins_embd = [Dealer.trans2floats(
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sres.field[sres.ids[i]][vfield]) for i in top_idx]
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ins_tw = [sres.field[sres.ids[i]][cfield].split(" ") for i in top_idx]
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s = 0
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e = 0
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res = ""
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def citeit():
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nonlocal s, e, ans, res, emb_mdl
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if not ins_embd:
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return
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embd = emb_mdl.encode(ans[s: e])
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sim = self.qryr.hybrid_similarity(embd,
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ins_embd,
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huqie.qie(ans[s:e]).split(" "),
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ins_tw)
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mx = np.max(sim) * 0.99
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if mx < 0.55:
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return
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cita = list(set([top_idx[i]
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for i in range(len(ins_embd)) if sim[i] > mx]))[:4]
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for i in cita:
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res += f"@?{i}?@"
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return cita
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punct = set("；。？!！")
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if not self.qryr.isChinese(ans):
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punct.add("?")
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punct.add(".")
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while e < len(ans):
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if e - s < 12 or ans[e] not in punct:
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e += 1
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continue
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if ans[e] == "." and e + \
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1 < len(ans) and re.match(r"[0-9]", ans[e + 1]):
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e += 1
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continue
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if ans[e] == "." and e - 2 >= 0 and ans[e - 2] == "\n":
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e += 1
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continue
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res += ans[s: e]
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citeit()
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res += ans[e]
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e += 1
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s = e
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if s < len(ans):
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res += ans[s:]
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citeit()
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return res
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def rerank(self, sres, query, tkweight=0.3, vtweight=0.7, cfield="content_ltks"):
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ins_embd = [
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Dealer.trans2floats(
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sres.field[i]["q_%d_vec"%len(sres.query_vector)]) for i in sres.ids]
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if not ins_embd:
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return []
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ins_tw = [sres.field[i][cfield].split(" ") for i in sres.ids]
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sim, tksim, vtsim = self.qryr.hybrid_similarity(sres.query_vector,
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ins_embd,
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huqie.qie(query).split(" "),
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ins_tw, tkweight, vtweight)
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return sim, tksim, vtsim
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