#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import re
from copy import deepcopy
from elasticsearch_dsl import Q, Search
from typing import List, Optional, Dict, Union
from dataclasses import dataclass
from rag.settings import es_logger
from rag.utils import rmSpace
from rag.nlp import rag_tokenizer, query, is_english
import numpy as np
def index_name(uid): return f"ragflow_{uid}"
class Dealer:
def __init__(self, es):
self.qryr = query.EsQueryer(es)
self.qryr.flds = [
"title_tks^10",
"title_sm_tks^5",
"important_kwd^30",
"important_tks^20",
"content_ltks^2",
"content_sm_ltks"]
self.es = es
@dataclass
class SearchResult:
total: int
ids: List[str]
query_vector: List[float] = None
field: Optional[Dict] = None
highlight: Optional[Dict] = None
aggregation: Union[List, Dict, None] = None
keywords: Optional[List[str]] = None
group_docs: List[List] = None
def _vector(self, txt, emb_mdl, sim=0.8, topk=10):
qv, c = emb_mdl.encode_queries(txt)
return {
"field": "q_%d_vec" % len(qv),
"k": topk,
"similarity": sim,
"num_candidates": topk * 2,
"query_vector": [float(v) for v in qv]
}
def _add_filters(self, bqry, req):
if req.get("kb_ids"):
bqry.filter.append(Q("terms", kb_id=req["kb_ids"]))
if req.get("doc_ids"):
bqry.filter.append(Q("terms", doc_id=req["doc_ids"]))
if req.get("knowledge_graph_kwd"):
bqry.filter.append(Q("terms", knowledge_graph_kwd=req["knowledge_graph_kwd"]))
if "available_int" in req:
if req["available_int"] == 0:
bqry.filter.append(Q("range", available_int={"lt": 1}))
else:
bqry.filter.append(
Q("bool", must_not=Q("range", available_int={"lt": 1})))
return bqry
def search(self, req, idxnm, emb_mdl=None, highlight=False):
qst = req.get("question", "")
bqry, keywords = self.qryr.question(qst, min_match="30%")
bqry = self._add_filters(bqry, req)
bqry.boost = 0.05
s = Search()
pg = int(req.get("page", 1)) - 1
topk = int(req.get("topk", 1024))
ps = int(req.get("size", topk))
src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd",
"image_id", "doc_id", "q_512_vec", "q_768_vec", "position_int", "knowledge_graph_kwd",
"q_1024_vec", "q_1536_vec", "available_int", "content_with_weight"])
s = s.query(bqry)[pg * ps:(pg + 1) * ps]
s = s.highlight("content_ltks")
s = s.highlight("title_ltks")
if not qst:
if not req.get("sort"):
s = s.sort(
#{"create_time": {"order": "desc", "unmapped_type": "date"}},
{"create_timestamp_flt": {
"order": "desc", "unmapped_type": "float"}}
)
else:
s = s.sort(
{"page_num_int": {"order": "asc", "unmapped_type": "float",
"mode": "avg", "numeric_type": "double"}},
{"top_int": {"order": "asc", "unmapped_type": "float",
"mode": "avg", "numeric_type": "double"}},
#{"create_time": {"order": "desc", "unmapped_type": "date"}},
{"create_timestamp_flt": {
"order": "desc", "unmapped_type": "float"}}
)
if qst:
s = s.highlight_options(
fragment_size=120,
number_of_fragments=5,
boundary_scanner_locale="zh-CN",
boundary_scanner="SENTENCE",
boundary_chars=",./;:\\!()，。？：！……（）——、"
)
s = s.to_dict()
q_vec = []
if req.get("vector"):
assert emb_mdl, "No embedding model selected"
s["knn"] = self._vector(
qst, emb_mdl, req.get(
"similarity", 0.1), topk)
s["knn"]["filter"] = bqry.to_dict()
if not highlight and "highlight" in s:
del s["highlight"]
q_vec = s["knn"]["query_vector"]
es_logger.info("【Q】: {}".format(json.dumps(s)))
res = self.es.search(deepcopy(s), idxnm=idxnm, timeout="600s", src=src)
es_logger.info("TOTAL: {}".format(self.es.getTotal(res)))
if self.es.getTotal(res) == 0 and "knn" in s:
bqry, _ = self.qryr.question(qst, min_match="10%")
if req.get("doc_ids"):
bqry = Q("bool", must=[])
bqry = self._add_filters(bqry, req)
s["query"] = bqry.to_dict()
s["knn"]["filter"] = bqry.to_dict()
s["knn"]["similarity"] = 0.17
res = self.es.search(s, idxnm=idxnm, timeout="600s", src=src)
es_logger.info("【Q】: {}".format(json.dumps(s)))
kwds = set([])
for k in keywords:
kwds.add(k)
for kk in rag_tokenizer.fine_grained_tokenize(k).split(" "):
if len(kk) < 2:
continue
if kk in kwds:
continue
kwds.add(kk)
aggs = self.getAggregation(res, "docnm_kwd")
return self.SearchResult(
total=self.es.getTotal(res),
ids=self.es.getDocIds(res),
query_vector=q_vec,
aggregation=aggs,
highlight=self.getHighlight(res, keywords, "content_with_weight"),
field=self.getFields(res, src),
keywords=list(kwds)
)
def getAggregation(self, res, g):
if not "aggregations" in res or "aggs_" + g not in res["aggregations"]:
return
bkts = res["aggregations"]["aggs_" + g]["buckets"]
return [(b["key"], b["doc_count"]) for b in bkts]
def getHighlight(self, res, keywords, fieldnm):
ans = {}
for d in res["hits"]["hits"]:
hlts = d.get("highlight")
if not hlts:
continue
txt = "...".join([a for a in list(hlts.items())[0][1]])
if not is_english(txt.split(" ")):
ans[d["_id"]] = txt
continue
txt = d["_source"][fieldnm]
txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE|re.MULTILINE)
txts = []
for t in re.split(r"[.?!;\n]", txt):
for w in keywords:
t = re.sub(r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])"%re.escape(w), r"\1<em>\2</em>\3", t, flags=re.IGNORECASE|re.MULTILINE)
if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE|re.MULTILINE): continue
txts.append(t)
ans[d["_id"]] = "...".join(txts) if txts else "...".join([a for a in list(hlts.items())[0][1]])
return ans
def getFields(self, sres, flds):
res = {}
if not flds:
return {}
for d in self.es.getSource(sres):
m = {n: d.get(n) for n in flds if d.get(n) is not None}
for n, v in m.items():
if isinstance(v, type([])):
m[n] = "\t".join([str(vv) if not isinstance(
vv, list) else "\t".join([str(vvv) for vvv in vv]) for vv in v])
continue
if not isinstance(v, type("")):
m[n] = str(m[n])
if n.find("tks") > 0:
m[n] = rmSpace(m[n])
if m:
res[d["id"]] = m
return res
@staticmethod
def trans2floats(txt):
return [float(t) for t in txt.split("\t")]
def insert_citations(self, answer, chunks, chunk_v,
embd_mdl, tkweight=0.1, vtweight=0.9):
assert len(chunks) == len(chunk_v)
if not chunks:
return answer, set([])
pieces = re.split(r"(```)", answer)
if len(pieces) >= 3:
i = 0
pieces_ = []
while i < len(pieces):
if pieces[i] == "```":
st = i
i += 1
while i < len(pieces) and pieces[i] != "```":
i += 1
if i < len(pieces):
i += 1
pieces_.append("".join(pieces[st: i]) + "\n")
else:
pieces_.extend(
re.split(
r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])",
pieces[i]))
i += 1
pieces = pieces_
else:
pieces = re.split(r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", answer)
for i in range(1, len(pieces)):
if re.match(r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", pieces[i]):
pieces[i - 1] += pieces[i][0]
pieces[i] = pieces[i][1:]
idx = []
pieces_ = []
for i, t in enumerate(pieces):
if len(t) < 5:
continue
idx.append(i)
pieces_.append(t)
es_logger.info("{} => {}".format(answer, pieces_))
if not pieces_:
return answer, set([])
ans_v, _ = embd_mdl.encode(pieces_)
assert len(ans_v[0]) == len(chunk_v[0]), "The dimension of query and chunk do not match: {} vs. {}".format(
len(ans_v[0]), len(chunk_v[0]))
chunks_tks = [rag_tokenizer.tokenize(self.qryr.rmWWW(ck)).split(" ")
for ck in chunks]
cites = {}
thr = 0.63
while thr>0.3 and len(cites.keys()) == 0 and pieces_ and chunks_tks:
for i, a in enumerate(pieces_):
sim, tksim, vtsim = self.qryr.hybrid_similarity(ans_v[i],
chunk_v,
rag_tokenizer.tokenize(
self.qryr.rmWWW(pieces_[i])).split(" "),
chunks_tks,
tkweight, vtweight)
mx = np.max(sim) * 0.99
es_logger.info("{} SIM: {}".format(pieces_[i], mx))
if mx < thr:
continue
cites[idx[i]] = list(
set([str(ii) for ii in range(len(chunk_v)) if sim[ii] > mx]))[:4]
thr *= 0.8
res = ""
seted = set([])
for i, p in enumerate(pieces):
res += p
if i not in idx:
continue
if i not in cites:
continue
for c in cites[i]:
assert int(c) < len(chunk_v)
for c in cites[i]:
if c in seted:
continue
res += f" ##{c}$$"
seted.add(c)
return res, seted
def rerank(self, sres, query, tkweight=0.3,
vtweight=0.7, cfield="content_ltks"):
_, keywords = self.qryr.question(query)
ins_embd = [
Dealer.trans2floats(
sres.field[i].get("q_%d_vec" % len(sres.query_vector), "\t".join(["0"] * len(sres.query_vector)))) for i in sres.ids]
if not ins_embd:
return [], [], []
for i in sres.ids:
if isinstance(sres.field[i].get("important_kwd", []), str):
sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
ins_tw = []
for i in sres.ids:
content_ltks = sres.field[i][cfield].split(" ")
title_tks = [t for t in sres.field[i].get("title_tks", "").split(" ") if t]
important_kwd = sres.field[i].get("important_kwd", [])
tks = content_ltks + title_tks + important_kwd
ins_tw.append(tks)
sim, tksim, vtsim = self.qryr.hybrid_similarity(sres.query_vector,
ins_embd,
keywords,
ins_tw, tkweight, vtweight)
return sim, tksim, vtsim
def rerank_by_model(self, rerank_mdl, sres, query, tkweight=0.3,
vtweight=0.7, cfield="content_ltks"):
_, keywords = self.qryr.question(query)
for i in sres.ids:
if isinstance(sres.field[i].get("important_kwd", []), str):
sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
ins_tw = []
for i in sres.ids:
content_ltks = sres.field[i][cfield].split(" ")
title_tks = [t for t in sres.field[i].get("title_tks", "").split(" ") if t]
important_kwd = sres.field[i].get("important_kwd", [])
tks = content_ltks + title_tks + important_kwd
ins_tw.append(tks)
tksim = self.qryr.token_similarity(keywords, ins_tw)
vtsim,_ = rerank_mdl.similarity(query, [rmSpace(" ".join(tks)) for tks in ins_tw])
return tkweight*np.array(tksim) + vtweight*vtsim, tksim, vtsim
def hybrid_similarity(self, ans_embd, ins_embd, ans, inst):
return self.qryr.hybrid_similarity(ans_embd,
ins_embd,
rag_tokenizer.tokenize(ans).split(" "),
rag_tokenizer.tokenize(inst).split(" "))
def retrieval(self, question, embd_mdl, tenant_id, kb_ids, page, page_size, similarity_threshold=0.2,
vector_similarity_weight=0.3, top=1024, doc_ids=None, aggs=True, rerank_mdl=None, highlight=False):
ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
if not question:
return ranks
RERANK_PAGE_LIMIT = 3
req = {"kb_ids": kb_ids, "doc_ids": doc_ids, "size": max(page_size*RERANK_PAGE_LIMIT, 128),
"question": question, "vector": True, "topk": top,
"similarity": similarity_threshold,
"available_int": 1}
if page > RERANK_PAGE_LIMIT:
req["page"] = page
req["size"] = page_size
sres = self.search(req, index_name(tenant_id), embd_mdl, highlight)
ranks["total"] = sres.total
if page <= RERANK_PAGE_LIMIT:
if rerank_mdl:
sim, tsim, vsim = self.rerank_by_model(rerank_mdl,
sres, question, 1 - vector_similarity_weight, vector_similarity_weight)
else:
sim, tsim, vsim = self.rerank(
sres, question, 1 - vector_similarity_weight, vector_similarity_weight)
idx = np.argsort(sim * -1)[(page-1)*page_size:page*page_size]
else:
sim = tsim = vsim = [1]*len(sres.ids)
idx = list(range(len(sres.ids)))
dim = len(sres.query_vector)
for i in idx:
if sim[i] < similarity_threshold:
break
if len(ranks["chunks"]) >= page_size:
if aggs:
continue
break
id = sres.ids[i]
dnm = sres.field[id]["docnm_kwd"]
did = sres.field[id]["doc_id"]
d = {
"chunk_id": id,
"content_ltks": sres.field[id]["content_ltks"],
"content_with_weight": sres.field[id]["content_with_weight"],
"doc_id": sres.field[id]["doc_id"],
"docnm_kwd": dnm,
"kb_id": sres.field[id]["kb_id"],
"important_kwd": sres.field[id].get("important_kwd", []),
"img_id": sres.field[id].get("img_id", ""),
"similarity": sim[i],
"vector_similarity": vsim[i],
"term_similarity": tsim[i],
"vector": self.trans2floats(sres.field[id].get("q_%d_vec" % dim, "\t".join(["0"] * dim))),
"positions": sres.field[id].get("position_int", "").split("\t")
}
if highlight:
if id in sres.highlight:
d["highlight"] = rmSpace(sres.highlight[id])
else:
d["highlight"] = d["content_with_weight"]
if len(d["positions"]) % 5 == 0:
poss = []
for i in range(0, len(d["positions"]), 5):
poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
float(d["positions"][i + 3]), float(d["positions"][i + 4])])
d["positions"] = poss
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)]
return ranks
def sql_retrieval(self, sql, fetch_size=128, format="json"):
from api.settings import chat_logger
sql = re.sub(r"[ `]+", " ", sql)
sql = sql.replace("%", "")
es_logger.info(f"Get es sql: {sql}")
replaces = []
for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql):
fld, v = r.group(1), r.group(3)
match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format(
fld, rag_tokenizer.fine_grained_tokenize(rag_tokenizer.tokenize(v)))
replaces.append(
("{}{}'{}'".format(
r.group(1),
r.group(2),
r.group(3)),
match))
for p, r in replaces:
sql = sql.replace(p, r, 1)
chat_logger.info(f"To es: {sql}")
try:
tbl = self.es.sql(sql, fetch_size, format)
return tbl
except Exception as e:
chat_logger.error(f"SQL failure: {sql} =>" + str(e))
return {"error": str(e)}
def chunk_list(self, doc_id, tenant_id, max_count=1024, fields=["docnm_kwd", "content_with_weight", "img_id"]):
s = Search()
s = s.query(Q("match", doc_id=doc_id))[0:max_count]
s = s.to_dict()
es_res = self.es.search(s, idxnm=index_name(tenant_id), timeout="600s", src=fields)
res = []
for index, chunk in enumerate(es_res['hits']['hits']):
res.append({fld: chunk['_source'].get(fld) for fld in fields})
return res