mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
synced 2025-04-20 13:10:05 +08:00
224 lines
7.8 KiB
Python
224 lines
7.8 KiB
Python
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 util import setup_logging, rmSpace
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from nlp import huqie, query
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from datetime import datetime
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from sklearn.metrics.pairwise import cosine_similarity as CosineSimilarity
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import numpy as np
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from copy import deepcopy
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def index_name(uid):return f"docgpt_{uid}"
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class Dealer:
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def __init__(self, es, emb_mdl):
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self.qryr = query.EsQueryer(es)
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self.qryr.flds = ["title_tks^10", "title_sm_tks^5", "content_ltks^2", "content_sm_ltks"]
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self.es = es
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self.emb_mdl = emb_mdl
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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, sim=0.8, topk=10):
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return {
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"field": "q_vec",
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"k": topk,
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"similarity": sim,
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"num_candidates": 1000,
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"query_vector": self.emb_mdl.encode_queries(txt)
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}
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def search(self, req, idxnm, tks_num=3):
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keywords = []
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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"): bqry.filter.append(Q("terms", kb_id=req["kb_ids"]))
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bqry.filter.append(Q("exists", field="q_tks"))
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bqry.boost = 0.05
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print(bqry)
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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("field", ["docnm_kwd", "content_ltks", "kb_id",
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"image_id", "doc_id", "q_vec"])
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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: s = s.sort({"create_time":{"order":"desc", "unmapped_type":"date"}})
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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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s["knn"] = self._vector(qst, req.get("similarity", 0.4), ps)
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s["knn"]["filter"] = bqry.to_dict()
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del s["highlight"]
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q_vec = s["knn"]["query_vector"]
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res = self.es.search(s, idxnm=idxnm, timeout="600s",src=src)
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print("TOTAL: ", 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"): 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:continue
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if kk in kwds: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, ["docnm_kwd", "content_ltks",
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"kb_id","image_id", "doc_id", "q_vec"]),
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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"]: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:continue
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if len(r)>0 and len(t)>0 and r[-1][-1] in eng and t[0] in eng: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: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: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 type(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 type(v) != type(""):m[n] = str(m[n])
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m[n] = rmSpace(m[n])
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if m: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, vfield = "q_vec", cfield="content_ltks"):
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ins_embd = [Dealer.trans2floats(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
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if not ins_embd:return
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embd = self.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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print(ans[s: e], sim)
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mx = np.max(sim)*0.99
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if mx < 0.55:return
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cita = list(set([top_idx[i] for i in range(len(ins_embd)) if sim[i] >mx]))[:4]
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for i in cita: 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+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, vfield="q_vec", cfield="content_ltks"):
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ins_embd = [Dealer.trans2floats(sres.field[i]["q_vec"]) for i in sres.ids]
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if not ins_embd: return []
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ins_tw =[sres.field[i][cfield].split(" ") for i in sres.ids]
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#return CosineSimilarity([sres.query_vector], ins_embd)[0]
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sim = 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
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if __name__ == "__main__":
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from util import es_conn
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SE = Dealer(es_conn.HuEs("infiniflow"))
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qs = [
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"胡凯",
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""
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]
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for q in qs:
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print(">>>>>>>>>>>>>>>>>>>>", q)
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print(SE.search({"question": q, "kb_ids": "64f072a75f3b97c865718c4a"}, "infiniflow_*"))
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