mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
synced 2025-04-22 22:20:07 +08:00
648a2baaa9
### What problem does this PR solve? Fix that disabled doc is still retreivalable ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
410 lines
16 KiB
Python
410 lines
16 KiB
Python
# -*- coding: utf-8 -*-
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import json
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import re
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from copy import deepcopy
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from elasticsearch_dsl import Q, Search
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from typing import List, Optional, 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 rag_tokenizer, 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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"important_kwd^30",
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"important_tks^20",
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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": [float(v) for v in 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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def add_filters(bqry):
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nonlocal req
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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:
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bqry.filter.append(Q("range", available_int={"lt": 1}))
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else:
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bqry.filter.append(
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Q("bool", must_not=Q("range", available_int={"lt": 1})))
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return bqry
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bqry = add_filters(bqry)
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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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topk = int(req.get("topk", 1024))
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src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd",
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"image_id", "doc_id", "q_512_vec", "q_768_vec", "position_int",
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"q_1024_vec", "q_1536_vec", "available_int", "content_with_weight"])
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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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if not req.get("sort"):
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s = s.sort(
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{"create_time": {"order": "desc", "unmapped_type": "date"}},
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{"create_timestamp_flt": {
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"order": "desc", "unmapped_type": "float"}}
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)
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else:
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s = s.sort(
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{"page_num_int": {"order": "asc", "unmapped_type": "float",
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"mode": "avg", "numeric_type": "double"}},
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{"top_int": {"order": "asc", "unmapped_type": "float",
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"mode": "avg", "numeric_type": "double"}},
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{"create_time": {"order": "desc", "unmapped_type": "date"}},
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{"create_timestamp_flt": {
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"order": "desc", "unmapped_type": "float"}}
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)
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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(
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qst, emb_mdl, req.get(
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"similarity", 0.1), topk)
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s["knn"]["filter"] = bqry.to_dict()
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if "highlight" in s:
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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(deepcopy(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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bqry = add_filters(bqry)
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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"] = 0.17
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res = self.es.search(s, idxnm=idxnm, timeout="600s", src=src)
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es_logger.info("【Q】: {}".format(json.dumps(s)))
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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 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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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) if not isinstance(
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vv, list) else "\t".join([str(vvv) for vvv in 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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if n.find("tks") > 0:
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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, 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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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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es_logger.info("{} => {}".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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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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es_logger.info("{} 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" ##{c}$$"
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seted.add(c)
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return res, seted
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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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_, keywords = self.qryr.question(query)
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ins_embd = [
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Dealer.trans2floats(
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sres.field[i].get("q_%d_vec" % len(sres.query_vector), "\t".join(["0"] * 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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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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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, 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_id, 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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ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
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if not question:
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return ranks
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req = {"kb_ids": kb_ids, "doc_ids": doc_ids, "size": page_size,
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"question": question, "vector": True, "topk": top,
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"similarity": similarity_threshold}
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sres = self.search(req, index_name(tenant_id), embd_mdl)
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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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idx = np.argsort(sim * -1)
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dim = len(sres.query_vector)
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start_idx = (page - 1) * page_size
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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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ranks["total"] += 1
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start_idx -= 1
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if start_idx >= 0:
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continue
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if len(ranks["chunks"]) >= page_size:
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if aggs:
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continue
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break
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id = sres.ids[i]
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dnm = sres.field[id]["docnm_kwd"]
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did = sres.field[id]["doc_id"]
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d = {
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"chunk_id": id,
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"content_ltks": sres.field[id]["content_ltks"],
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"content_with_weight": sres.field[id]["content_with_weight"],
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"doc_id": sres.field[id]["doc_id"],
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"docnm_kwd": dnm,
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"kb_id": sres.field[id]["kb_id"],
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"important_kwd": sres.field[id].get("important_kwd", []),
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"img_id": sres.field[id].get("img_id", ""),
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"similarity": sim[i],
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"vector_similarity": vsim[i],
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"term_similarity": tsim[i],
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"vector": self.trans2floats(sres.field[id].get("q_%d_vec" % dim, "\t".join(["0"] * dim))),
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"positions": sres.field[id].get("position_int", "").split("\t")
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}
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if len(d["positions"]) % 5 == 0:
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poss = []
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for i in range(0, len(d["positions"]), 5):
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poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
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float(d["positions"][i + 3]), float(d["positions"][i + 4])])
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d["positions"] = poss
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ranks["chunks"].append(d)
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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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ranks["doc_aggs"] = [{"doc_name": k,
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"doc_id": v["doc_id"],
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"count": v["count"]} for k,
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v in sorted(ranks["doc_aggs"].items(),
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key=lambda x:x[1]["count"] * -1)]
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return ranks
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def sql_retrieval(self, sql, fetch_size=128, format="json"):
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from api.settings import chat_logger
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sql = re.sub(r"[ `]+", " ", sql)
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sql = sql.replace("%", "")
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es_logger.info(f"Get es sql: {sql}")
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replaces = []
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for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql):
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fld, v = r.group(1), r.group(3)
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match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format(
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fld, rag_tokenizer.fine_grained_tokenize(rag_tokenizer.tokenize(v)))
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replaces.append(
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("{}{}'{}'".format(
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r.group(1),
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r.group(2),
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r.group(3)),
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match))
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for p, r in replaces:
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sql = sql.replace(p, r, 1)
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chat_logger.info(f"To es: {sql}")
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try:
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tbl = self.es.sql(sql, fetch_size, format)
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return tbl
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except Exception as e:
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chat_logger.error(f"SQL failure: {sql} =>" + str(e))
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return {"error": str(e)}
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