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Integration with Infinity (#2894)
### What problem does this PR solve? Integration with Infinity - Replaced ELASTICSEARCH with dataStoreConn - Renamed deleteByQuery with delete - Renamed bulk to upsertBulk - getHighlight, getAggregation - Fix KGSearch.search - Moved Dealer.sql_retrieval to es_conn.py ### Type of change - [x] Refactoring
This commit is contained in:
Zhichang Yu
@ -14,34 +14,25 @@
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# limitations under the License.
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#
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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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import json
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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.settings import doc_store_logger
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from rag.utils import rmSpace
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from rag.nlp import rag_tokenizer, query, is_english
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from rag.nlp import rag_tokenizer, query
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import numpy as np
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from rag.utils.doc_store_conn import DocStoreConnection, MatchDenseExpr, FusionExpr, OrderByExpr
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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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def __init__(self, dataStore: DocStoreConnection):
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self.qryr = query.FulltextQueryer()
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self.dataStore = dataStore
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@dataclass
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class SearchResult:
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@ -54,170 +45,99 @@ class Dealer:
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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 get_vector(self, txt, emb_mdl, topk=10, similarity=0.1):
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qv, _ = emb_mdl.encode_queries(txt)
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embedding_data = [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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def _add_filters(self, bqry, 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 req.get("knowledge_graph_kwd"):
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bqry.filter.append(Q("terms", knowledge_graph_kwd=req["knowledge_graph_kwd"]))
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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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def get_filters(self, req):
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condition = dict()
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for key, field in {"kb_ids": "kb_id", "doc_ids": "doc_id"}.items():
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if key in req and req[key] is not None:
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condition[field] = req[key]
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# TODO(yzc): `available_int` is nullable however infinity doesn't support nullable columns.
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for key in ["knowledge_graph_kwd"]:
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if key in req and req[key] is not None:
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condition[key] = req[key]
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return condition
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def search(self, req, idxnms, emb_mdl=None, highlight=False):
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qst = req.get("question", "")
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bqry, keywords = self.qryr.question(qst, min_match="30%")
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bqry = self._add_filters(bqry, req)
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bqry.boost = 0.05
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def search(self, req, idx_names: list[str], kb_ids: list[str], emb_mdl=None, highlight = False):
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filters = self.get_filters(req)
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orderBy = OrderByExpr()
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s = Search()
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pg = int(req.get("page", 1)) - 1
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topk = int(req.get("topk", 1024))
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ps = int(req.get("size", topk))
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offset, limit = pg * ps, (pg + 1) * ps
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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", "knowledge_graph_kwd",
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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 not highlight and "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), idxnms=idxnms, 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("doc_ids"):
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bqry = Q("bool", must=[])
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bqry = self._add_filters(bqry, req)
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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, idxnms=idxnms, timeout="600s", src=src)
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es_logger.info("【Q】: {}".format(json.dumps(s)))
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"doc_id", "position_list", "knowledge_graph_kwd",
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"available_int", "content_with_weight"])
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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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qst = req.get("question", "")
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q_vec = []
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if not qst:
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if req.get("sort"):
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orderBy.desc("create_timestamp_flt")
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res = self.dataStore.search(src, [], filters, [], orderBy, offset, limit, idx_names, kb_ids)
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total=self.dataStore.getTotal(res)
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doc_store_logger.info("Dealer.search TOTAL: {}".format(total))
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else:
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highlightFields = ["content_ltks", "title_tks"] if highlight else []
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matchText, keywords = self.qryr.question(qst, min_match=0.3)
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if emb_mdl is None:
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matchExprs = [matchText]
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res = self.dataStore.search(src, highlightFields, filters, matchExprs, orderBy, offset, limit, idx_names, kb_ids)
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total=self.dataStore.getTotal(res)
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doc_store_logger.info("Dealer.search TOTAL: {}".format(total))
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else:
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matchDense = self.get_vector(qst, emb_mdl, topk, req.get("similarity", 0.1))
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q_vec = matchDense.embedding_data
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src.append(f"q_{len(q_vec)}_vec")
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fusionExpr = FusionExpr("weighted_sum", topk, {"weights": "0.05, 0.95"})
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matchExprs = [matchText, matchDense, fusionExpr]
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res = self.dataStore.search(src, highlightFields, filters, matchExprs, orderBy, offset, limit, idx_names, kb_ids)
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total=self.dataStore.getTotal(res)
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doc_store_logger.info("Dealer.search TOTAL: {}".format(total))
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# If result is empty, try again with lower min_match
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if total == 0:
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matchText, _ = self.qryr.question(qst, min_match=0.1)
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if "doc_ids" in filters:
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del filters["doc_ids"]
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matchDense.extra_options["similarity"] = 0.17
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res = self.dataStore.search(src, highlightFields, filters, [matchText, matchDense, fusionExpr], orderBy, offset, limit, idx_names, kb_ids)
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total=self.dataStore.getTotal(res)
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doc_store_logger.info("Dealer.search 2 TOTAL: {}".format(total))
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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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doc_store_logger.info(f"TOTAL: {total}")
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ids=self.dataStore.getChunkIds(res)
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keywords=list(kwds)
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highlight = self.dataStore.getHighlight(res, keywords, "content_with_weight")
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aggs = self.dataStore.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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total=total,
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ids=ids,
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query_vector=q_vec,
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aggregation=aggs,
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highlight=self.getHighlight(res, keywords, "content_with_weight"),
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field=self.getFields(res, src),
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keywords=list(kwds)
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highlight=highlight,
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field=self.dataStore.getFields(res, src),
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keywords=keywords
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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, keywords, fieldnm):
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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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txt = "...".join([a for a in list(hlts.items())[0][1]])
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if not is_english(txt.split(" ")):
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ans[d["_id"]] = txt
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continue
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txt = d["_source"][fieldnm]
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txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE|re.MULTILINE)
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txts = []
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for t in re.split(r"[.?!;\n]", txt):
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for w in keywords:
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t = re.sub(r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])"%re.escape(w), r"\1<em>\2</em>\3", t, flags=re.IGNORECASE|re.MULTILINE)
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if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE|re.MULTILINE): continue
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txts.append(t)
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ans[d["_id"]] = "...".join(txts) if txts else "...".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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@ -260,7 +180,7 @@ class Dealer:
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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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doc_store_logger.info("{} => {}".format(answer, pieces_))
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if not pieces_:
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return answer, set([])
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@ -281,7 +201,7 @@ class Dealer:
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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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doc_store_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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@ -309,9 +229,15 @@ class Dealer:
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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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vector_size = len(sres.query_vector)
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vector_column = f"q_{vector_size}_vec"
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zero_vector = [0.0] * vector_size
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ins_embd = []
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for chunk_id in sres.ids:
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vector = sres.field[chunk_id].get(vector_column, zero_vector)
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if isinstance(vector, str):
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vector = [float(v) for v in vector.split("\t")]
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ins_embd.append(vector)
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if not ins_embd:
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return [], [], []
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@ -377,7 +303,7 @@ class Dealer:
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if isinstance(tenant_ids, str):
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tenant_ids = tenant_ids.split(",")
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sres = self.search(req, [index_name(tid) for tid in tenant_ids], embd_mdl, highlight)
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sres = self.search(req, [index_name(tid) for tid in tenant_ids], kb_ids, embd_mdl, highlight)
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ranks["total"] = sres.total
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if page <= RERANK_PAGE_LIMIT:
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@ -393,6 +319,8 @@ class Dealer:
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idx = list(range(len(sres.ids)))
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dim = len(sres.query_vector)
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vector_column = f"q_{dim}_vec"
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zero_vector = [0.0] * dim
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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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@ -401,34 +329,32 @@ class Dealer:
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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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chunk = sres.field[id]
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dnm = chunk["docnm_kwd"]
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did = chunk["doc_id"]
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position_list = chunk.get("position_list", "[]")
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if not position_list:
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position_list = "[]"
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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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"content_ltks": chunk["content_ltks"],
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"content_with_weight": chunk["content_with_weight"],
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"doc_id": chunk["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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"kb_id": chunk["kb_id"],
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"important_kwd": chunk.get("important_kwd", []),
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"image_id": chunk.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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"vector": chunk.get(vector_column, zero_vector),
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"positions": json.loads(position_list)
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}
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if highlight:
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if id in sres.highlight:
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d["highlight"] = rmSpace(sres.highlight[id])
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else:
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d["highlight"] = d["content_with_weight"]
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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)
|
||||
if dnm not in ranks["doc_aggs"]:
|
||||
ranks["doc_aggs"][dnm] = {"doc_id": did, "count": 0}
|
||||
@ -442,39 +368,11 @@ class Dealer:
|
||||
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))
|
||||
tbl = self.dataStore.sql(sql, fetch_size, format)
|
||||
return tbl
|
||||
|
||||
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, idxnms=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
|
||||
def chunk_list(self, doc_id: str, tenant_id: str, kb_ids: list[str], max_count=1024, fields=["docnm_kwd", "content_with_weight", "img_id"]):
|
||||
condition = {"doc_id": doc_id}
|
||||
res = self.dataStore.search(fields, [], condition, [], OrderByExpr(), 0, max_count, index_name(tenant_id), kb_ids)
|
||||
dict_chunks = self.dataStore.getFields(res, fields)
|
||||
return dict_chunks.values()
|
||||
|
||||
Reference in New Issue
Block a user