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17b8bb62b6
### What problem does this PR solve? Message CRUD. Issue #4213 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
185 lines
7.0 KiB
Python
185 lines
7.0 KiB
Python
#
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# Copyright 2025 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 re
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import logging
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import json
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import numpy as np
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from common.query_base import QueryBase
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from common.doc_store.doc_store_base import MatchDenseExpr, MatchTextExpr
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from common.float_utils import get_float
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from rag.nlp import rag_tokenizer, term_weight, synonym
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def get_vector(txt, emb_mdl, topk=10, similarity=0.1):
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if isinstance(similarity, str) and len(similarity) > 0:
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try:
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similarity = float(similarity)
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except Exception as e:
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logging.warning(f"Convert similarity '{similarity}' to float failed: {e}. Using default 0.1")
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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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class MsgTextQuery(QueryBase):
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def __init__(self):
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self.tw = term_weight.Dealer()
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self.syn = synonym.Dealer()
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self.query_fields = [
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"content"
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]
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def question(self, txt, tbl="messages", min_match: float=0.6):
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original_query = txt
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txt = MsgTextQuery.add_space_between_eng_zh(txt)
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txt = re.sub(
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r"[ :|\r\n\t,，。？?/`!！&^%%()\[\]{}<>]+",
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" ",
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rag_tokenizer.tradi2simp(rag_tokenizer.strQ2B(txt.lower())),
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).strip()
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otxt = txt
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txt = MsgTextQuery.rmWWW(txt)
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if not self.is_chinese(txt):
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txt = self.rmWWW(txt)
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tks = rag_tokenizer.tokenize(txt).split()
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keywords = [t for t in tks if t]
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tks_w = self.tw.weights(tks, preprocess=False)
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tks_w = [(re.sub(r"[ \\\"'^]", "", tk), w) for tk, w in tks_w]
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tks_w = [(re.sub(r"^[a-z0-9]$", "", tk), w) for tk, w in tks_w if tk]
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tks_w = [(re.sub(r"^[\+-]", "", tk), w) for tk, w in tks_w if tk]
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tks_w = [(tk.strip(), w) for tk, w in tks_w if tk.strip()]
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syns = []
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for tk, w in tks_w[:256]:
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syn = self.syn.lookup(tk)
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syn = rag_tokenizer.tokenize(" ".join(syn)).split()
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keywords.extend(syn)
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syn = ["\"{}\"^{:.4f}".format(s, w / 4.) for s in syn if s.strip()]
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syns.append(" ".join(syn))
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q = ["({}^{:.4f}".format(tk, w) + " {})".format(syn) for (tk, w), syn in zip(tks_w, syns) if
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tk and not re.match(r"[.^+\(\)-]", tk)]
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for i in range(1, len(tks_w)):
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left, right = tks_w[i - 1][0].strip(), tks_w[i][0].strip()
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if not left or not right:
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continue
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q.append(
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'"%s %s"^%.4f'
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% (
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tks_w[i - 1][0],
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tks_w[i][0],
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max(tks_w[i - 1][1], tks_w[i][1]) * 2,
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)
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)
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if not q:
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q.append(txt)
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query = " ".join(q)
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return MatchTextExpr(
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self.query_fields, query, 100, {"original_query": original_query}
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), keywords
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def need_fine_grained_tokenize(tk):
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if len(tk) < 3:
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return False
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if re.match(r"[0-9a-z\.\+#_\*-]+$", tk):
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return False
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return True
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txt = self.rmWWW(txt)
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qs, keywords = [], []
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for tt in self.tw.split(txt)[:256]: # .split():
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if not tt:
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continue
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keywords.append(tt)
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twts = self.tw.weights([tt])
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syns = self.syn.lookup(tt)
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if syns and len(keywords) < 32:
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keywords.extend(syns)
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logging.debug(json.dumps(twts, ensure_ascii=False))
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tms = []
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for tk, w in sorted(twts, key=lambda x: x[1] * -1):
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sm = (
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rag_tokenizer.fine_grained_tokenize(tk).split()
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if need_fine_grained_tokenize(tk)
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else []
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)
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sm = [
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re.sub(
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r"[ ,\./;'\[\]\\`~!@#$%\^&\*\(\)=\+_<>\?:\"\{\}\|，。；‘’【】、！￥……（）——《》？：“”-]+",
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"",
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m,
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)
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for m in sm
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]
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sm = [self.sub_special_char(m) for m in sm if len(m) > 1]
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sm = [m for m in sm if len(m) > 1]
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if len(keywords) < 32:
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keywords.append(re.sub(r"[ \\\"']+", "", tk))
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keywords.extend(sm)
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tk_syns = self.syn.lookup(tk)
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tk_syns = [self.sub_special_char(s) for s in tk_syns]
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if len(keywords) < 32:
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keywords.extend([s for s in tk_syns if s])
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tk_syns = [rag_tokenizer.fine_grained_tokenize(s) for s in tk_syns if s]
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tk_syns = [f"\"{s}\"" if s.find(" ") > 0 else s for s in tk_syns]
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if len(keywords) >= 32:
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break
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tk = self.sub_special_char(tk)
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if tk.find(" ") > 0:
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tk = '"%s"' % tk
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if tk_syns:
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tk = f"({tk} OR (%s)^0.2)" % " ".join(tk_syns)
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if sm:
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tk = f'{tk} OR "%s" OR ("%s"~2)^0.5' % (" ".join(sm), " ".join(sm))
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if tk.strip():
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tms.append((tk, w))
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tms = " ".join([f"({t})^{w}" for t, w in tms])
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if len(twts) > 1:
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tms += ' ("%s"~2)^1.5' % rag_tokenizer.tokenize(tt)
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syns = " OR ".join(
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[
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'"%s"'
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% rag_tokenizer.tokenize(self.sub_special_char(s))
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for s in syns
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]
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)
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if syns and tms:
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tms = f"({tms})^5 OR ({syns})^0.7"
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qs.append(tms)
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if qs:
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query = " OR ".join([f"({t})" for t in qs if t])
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if not query:
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query = otxt
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return MatchTextExpr(
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self.query_fields, query, 100, {"minimum_should_match": min_match, "original_query": original_query}
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), keywords
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return None, keywords |