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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
@ -1,29 +1,29 @@
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import re
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import json
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import time
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import copy
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import os
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from typing import List, Dict
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import elasticsearch
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from elastic_transport import ConnectionTimeout
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import copy
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from elasticsearch import Elasticsearch
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from elasticsearch_dsl import UpdateByQuery, Search, Index
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from rag.settings import es_logger
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from elasticsearch_dsl import UpdateByQuery, Q, Search, Index
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from elastic_transport import ConnectionTimeout
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from rag.settings import doc_store_logger
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from rag import settings
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from rag.utils import singleton
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from api.utils.file_utils import get_project_base_directory
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import polars as pl
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from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, FusionExpr
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from rag.nlp import is_english, rag_tokenizer
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es_logger.info("Elasticsearch version: "+str(elasticsearch.__version__))
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doc_store_logger.info("Elasticsearch sdk version: "+str(elasticsearch.__version__))
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@singleton
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class ESConnection:
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class ESConnection(DocStoreConnection):
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def __init__(self):
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self.info = {}
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self.conn()
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self.idxnm = settings.ES.get("index_name", "")
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if not self.es.ping():
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raise Exception("Can't connect to ES cluster")
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def conn(self):
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for _ in range(10):
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try:
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self.es = Elasticsearch(
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@ -34,390 +34,317 @@ class ESConnection:
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)
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if self.es:
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self.info = self.es.info()
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es_logger.info("Connect to es.")
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doc_store_logger.info("Connect to es.")
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break
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except Exception as e:
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es_logger.error("Fail to connect to es: " + str(e))
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doc_store_logger.error("Fail to connect to es: " + str(e))
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time.sleep(1)
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def version(self):
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if not self.es.ping():
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raise Exception("Can't connect to ES cluster")
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v = self.info.get("version", {"number": "5.6"})
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v = v["number"].split(".")[0]
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return int(v) >= 7
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if int(v) < 8:
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raise Exception(f"ES version must be greater than or equal to 8, current version: {v}")
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fp_mapping = os.path.join(get_project_base_directory(), "conf", "mapping.json")
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if not os.path.exists(fp_mapping):
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raise Exception(f"Mapping file not found at {fp_mapping}")
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self.mapping = json.load(open(fp_mapping, "r"))
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def health(self):
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return dict(self.es.cluster.health())
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"""
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Database operations
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"""
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def dbType(self) -> str:
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return "elasticsearch"
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def upsert(self, df, idxnm=""):
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res = []
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for d in df:
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id = d["id"]
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del d["id"]
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d = {"doc": d, "doc_as_upsert": "true"}
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T = False
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for _ in range(10):
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try:
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if not self.version():
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r = self.es.update(
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index=(
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self.idxnm if not idxnm else idxnm),
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body=d,
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id=id,
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doc_type="doc",
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refresh=True,
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retry_on_conflict=100)
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else:
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r = self.es.update(
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index=(
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self.idxnm if not idxnm else idxnm),
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body=d,
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id=id,
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refresh=True,
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retry_on_conflict=100)
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es_logger.info("Successfully upsert: %s" % id)
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T = True
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break
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except Exception as e:
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es_logger.warning("Fail to index: " +
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json.dumps(d, ensure_ascii=False) + str(e))
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if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
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time.sleep(3)
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continue
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self.conn()
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T = False
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def health(self) -> dict:
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return dict(self.es.cluster.health()) + {"type": "elasticsearch"}
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if not T:
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res.append(d)
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es_logger.error(
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"Fail to index: " +
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re.sub(
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"[\r\n]",
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"",
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json.dumps(
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d,
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ensure_ascii=False)))
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d["id"] = id
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d["_index"] = self.idxnm
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if not res:
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"""
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Table operations
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"""
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def createIdx(self, indexName: str, knowledgebaseId: str, vectorSize: int):
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if self.indexExist(indexName, knowledgebaseId):
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return True
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return False
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try:
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from elasticsearch.client import IndicesClient
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return IndicesClient(self.es).create(index=indexName,
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settings=self.mapping["settings"],
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mappings=self.mapping["mappings"])
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except Exception as e:
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doc_store_logger.error("ES create index error %s ----%s" % (indexName, str(e)))
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def bulk(self, df, idx_nm=None):
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ids, acts = {}, []
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for d in df:
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id = d["id"] if "id" in d else d["_id"]
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ids[id] = copy.deepcopy(d)
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ids[id]["_index"] = self.idxnm if not idx_nm else idx_nm
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if "id" in d:
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del d["id"]
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if "_id" in d:
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del d["_id"]
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acts.append(
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{"update": {"_id": id, "_index": ids[id]["_index"]}, "retry_on_conflict": 100})
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acts.append({"doc": d, "doc_as_upsert": "true"})
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def deleteIdx(self, indexName: str, knowledgebaseId: str):
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try:
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return self.es.indices.delete(indexName, allow_no_indices=True)
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except Exception as e:
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doc_store_logger.error("ES delete index error %s ----%s" % (indexName, str(e)))
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res = []
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for _ in range(100):
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try:
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if elasticsearch.__version__[0] < 8:
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r = self.es.bulk(
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index=(
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self.idxnm if not idx_nm else idx_nm),
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body=acts,
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refresh=False,
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timeout="600s")
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else:
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r = self.es.bulk(index=(self.idxnm if not idx_nm else
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idx_nm), operations=acts,
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refresh=False, timeout="600s")
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if re.search(r"False", str(r["errors"]), re.IGNORECASE):
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return res
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for it in r["items"]:
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if "error" in it["update"]:
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res.append(str(it["update"]["_id"]) +
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":" + str(it["update"]["error"]))
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return res
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except Exception as e:
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es_logger.warn("Fail to bulk: " + str(e))
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if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
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time.sleep(3)
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continue
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self.conn()
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return res
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def bulk4script(self, df):
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ids, acts = {}, []
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for d in df:
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id = d["id"]
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ids[id] = copy.deepcopy(d["raw"])
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acts.append({"update": {"_id": id, "_index": self.idxnm}})
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acts.append(d["script"])
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es_logger.info("bulk upsert: %s" % id)
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res = []
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for _ in range(10):
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try:
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if not self.version():
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r = self.es.bulk(
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index=self.idxnm,
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body=acts,
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refresh=False,
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timeout="600s",
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doc_type="doc")
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else:
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r = self.es.bulk(
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index=self.idxnm,
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body=acts,
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refresh=False,
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timeout="600s")
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if re.search(r"False", str(r["errors"]), re.IGNORECASE):
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return res
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for it in r["items"]:
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if "error" in it["update"]:
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res.append(str(it["update"]["_id"]))
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return res
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except Exception as e:
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es_logger.warning("Fail to bulk: " + str(e))
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if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
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time.sleep(3)
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continue
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self.conn()
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return res
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def rm(self, d):
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for _ in range(10):
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try:
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if not self.version():
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r = self.es.delete(
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index=self.idxnm,
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id=d["id"],
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doc_type="doc",
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refresh=True)
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else:
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r = self.es.delete(
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index=self.idxnm,
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id=d["id"],
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refresh=True,
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doc_type="_doc")
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es_logger.info("Remove %s" % d["id"])
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return True
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except Exception as e:
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es_logger.warn("Fail to delete: " + str(d) + str(e))
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if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
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time.sleep(3)
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continue
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if re.search(r"(not_found)", str(e), re.IGNORECASE):
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return True
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self.conn()
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es_logger.error("Fail to delete: " + str(d))
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return False
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def search(self, q, idxnms=None, src=False, timeout="2s"):
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if not isinstance(q, dict):
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q = Search().query(q).to_dict()
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if isinstance(idxnms, str):
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idxnms = idxnms.split(",")
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for i in range(3):
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try:
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res = self.es.search(index=(self.idxnm if not idxnms else idxnms),
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body=q,
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timeout=timeout,
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# search_type="dfs_query_then_fetch",
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track_total_hits=True,
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_source=src)
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if str(res.get("timed_out", "")).lower() == "true":
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raise Exception("Es Timeout.")
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return res
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except Exception as e:
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es_logger.error(
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"ES search exception: " +
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str(e) +
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"【Q】：" +
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str(q))
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if str(e).find("Timeout") > 0:
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continue
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raise e
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es_logger.error("ES search timeout for 3 times!")
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raise Exception("ES search timeout.")
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def sql(self, sql, fetch_size=128, format="json", timeout="2s"):
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for i in range(3):
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try:
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res = self.es.sql.query(body={"query": sql, "fetch_size": fetch_size}, format=format, request_timeout=timeout)
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return res
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except ConnectionTimeout as e:
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es_logger.error("Timeout【Q】：" + sql)
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continue
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except Exception as e:
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raise e
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es_logger.error("ES search timeout for 3 times!")
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raise ConnectionTimeout()
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def get(self, doc_id, idxnm=None):
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for i in range(3):
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try:
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res = self.es.get(index=(self.idxnm if not idxnm else idxnm),
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id=doc_id)
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if str(res.get("timed_out", "")).lower() == "true":
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raise Exception("Es Timeout.")
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return res
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except Exception as e:
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es_logger.error(
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"ES get exception: " +
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str(e) +
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"【Q】：" +
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doc_id)
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if str(e).find("Timeout") > 0:
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continue
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raise e
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es_logger.error("ES search timeout for 3 times!")
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raise Exception("ES search timeout.")
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def updateByQuery(self, q, d):
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ubq = UpdateByQuery(index=self.idxnm).using(self.es).query(q)
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scripts = ""
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for k, v in d.items():
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scripts += "ctx._source.%s = params.%s;" % (str(k), str(k))
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ubq = ubq.script(source=scripts, params=d)
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ubq = ubq.params(refresh=False)
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ubq = ubq.params(slices=5)
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ubq = ubq.params(conflicts="proceed")
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for i in range(3):
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try:
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r = ubq.execute()
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return True
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except Exception as e:
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es_logger.error("ES updateByQuery exception: " +
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str(e) + "【Q】：" + str(q.to_dict()))
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if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
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continue
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self.conn()
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return False
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def updateScriptByQuery(self, q, scripts, idxnm=None):
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ubq = UpdateByQuery(
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index=self.idxnm if not idxnm else idxnm).using(
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self.es).query(q)
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ubq = ubq.script(source=scripts)
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ubq = ubq.params(refresh=True)
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ubq = ubq.params(slices=5)
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ubq = ubq.params(conflicts="proceed")
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for i in range(3):
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try:
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r = ubq.execute()
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return True
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except Exception as e:
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es_logger.error("ES updateByQuery exception: " +
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str(e) + "【Q】：" + str(q.to_dict()))
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if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
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continue
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self.conn()
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return False
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def deleteByQuery(self, query, idxnm=""):
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for i in range(3):
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||||
try:
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r = self.es.delete_by_query(
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index=idxnm if idxnm else self.idxnm,
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refresh = True,
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body=Search().query(query).to_dict())
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return True
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except Exception as e:
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es_logger.error("ES updateByQuery deleteByQuery: " +
|
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str(e) + "【Q】：" + str(query.to_dict()))
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if str(e).find("NotFoundError") > 0: return True
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||||
if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
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||||
continue
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return False
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def update(self, id, script, routing=None):
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for i in range(3):
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try:
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if not self.version():
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||||
r = self.es.update(
|
||||
index=self.idxnm,
|
||||
id=id,
|
||||
body=json.dumps(
|
||||
script,
|
||||
ensure_ascii=False),
|
||||
doc_type="doc",
|
||||
routing=routing,
|
||||
refresh=False)
|
||||
else:
|
||||
r = self.es.update(index=self.idxnm, id=id, body=json.dumps(script, ensure_ascii=False),
|
||||
routing=routing, refresh=False) # , doc_type="_doc")
|
||||
return True
|
||||
except Exception as e:
|
||||
es_logger.error(
|
||||
"ES update exception: " + str(e) + " id：" + str(id) + ", version:" + str(self.version()) +
|
||||
json.dumps(script, ensure_ascii=False))
|
||||
if str(e).find("Timeout") > 0:
|
||||
continue
|
||||
|
||||
return False
|
||||
|
||||
def indexExist(self, idxnm):
|
||||
s = Index(idxnm if idxnm else self.idxnm, self.es)
|
||||
def indexExist(self, indexName: str, knowledgebaseId: str) -> bool:
|
||||
s = Index(indexName, self.es)
|
||||
for i in range(3):
|
||||
try:
|
||||
return s.exists()
|
||||
except Exception as e:
|
||||
es_logger.error("ES updateByQuery indexExist: " + str(e))
|
||||
doc_store_logger.error("ES indexExist: " + str(e))
|
||||
if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
|
||||
continue
|
||||
|
||||
return False
|
||||
|
||||
def docExist(self, docid, idxnm=None):
|
||||
"""
|
||||
CRUD operations
|
||||
"""
|
||||
def search(self, selectFields: list[str], highlightFields: list[str], condition: dict, matchExprs: list[MatchExpr], orderBy: OrderByExpr, offset: int, limit: int, indexNames: str|list[str], knowledgebaseIds: list[str]) -> list[dict] | pl.DataFrame:
|
||||
"""
|
||||
Refers to https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html
|
||||
"""
|
||||
if isinstance(indexNames, str):
|
||||
indexNames = indexNames.split(",")
|
||||
assert isinstance(indexNames, list) and len(indexNames) > 0
|
||||
assert "_id" not in condition
|
||||
s = Search()
|
||||
bqry = None
|
||||
vector_similarity_weight = 0.5
|
||||
for m in matchExprs:
|
||||
if isinstance(m, FusionExpr) and m.method=="weighted_sum" and "weights" in m.fusion_params:
|
||||
assert len(matchExprs)==3 and isinstance(matchExprs[0], MatchTextExpr) and isinstance(matchExprs[1], MatchDenseExpr) and isinstance(matchExprs[2], FusionExpr)
|
||||
weights = m.fusion_params["weights"]
|
||||
vector_similarity_weight = float(weights.split(",")[1])
|
||||
for m in matchExprs:
|
||||
if isinstance(m, MatchTextExpr):
|
||||
minimum_should_match = "0%"
|
||||
if "minimum_should_match" in m.extra_options:
|
||||
minimum_should_match = str(int(m.extra_options["minimum_should_match"] * 100)) + "%"
|
||||
bqry = Q("bool",
|
||||
must=Q("query_string", fields=m.fields,
|
||||
type="best_fields", query=m.matching_text,
|
||||
minimum_should_match = minimum_should_match,
|
||||
boost=1),
|
||||
boost = 1.0 - vector_similarity_weight,
|
||||
)
|
||||
if condition:
|
||||
for k, v in condition.items():
|
||||
if not isinstance(k, str) or not v:
|
||||
continue
|
||||
if isinstance(v, list):
|
||||
bqry.filter.append(Q("terms", **{k: v}))
|
||||
elif isinstance(v, str) or isinstance(v, int):
|
||||
bqry.filter.append(Q("term", **{k: v}))
|
||||
else:
|
||||
raise Exception(f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.")
|
||||
elif isinstance(m, MatchDenseExpr):
|
||||
assert(bqry is not None)
|
||||
similarity = 0.0
|
||||
if "similarity" in m.extra_options:
|
||||
similarity = m.extra_options["similarity"]
|
||||
s = s.knn(m.vector_column_name,
|
||||
m.topn,
|
||||
m.topn * 2,
|
||||
query_vector = list(m.embedding_data),
|
||||
filter = bqry.to_dict(),
|
||||
similarity = similarity,
|
||||
)
|
||||
if matchExprs:
|
||||
s.query = bqry
|
||||
for field in highlightFields:
|
||||
s = s.highlight(field)
|
||||
|
||||
if orderBy:
|
||||
orders = list()
|
||||
for field, order in orderBy.fields:
|
||||
order = "asc" if order == 0 else "desc"
|
||||
orders.append({field: {"order": order, "unmapped_type": "float",
|
||||
"mode": "avg", "numeric_type": "double"}})
|
||||
s = s.sort(*orders)
|
||||
|
||||
if limit > 0:
|
||||
s = s[offset:limit]
|
||||
q = s.to_dict()
|
||||
doc_store_logger.info("ESConnection.search [Q]: " + json.dumps(q))
|
||||
|
||||
for i in range(3):
|
||||
try:
|
||||
return self.es.exists(index=(idxnm if idxnm else self.idxnm),
|
||||
id=docid)
|
||||
res = self.es.search(index=indexNames,
|
||||
body=q,
|
||||
timeout="600s",
|
||||
# search_type="dfs_query_then_fetch",
|
||||
track_total_hits=True,
|
||||
_source=True)
|
||||
if str(res.get("timed_out", "")).lower() == "true":
|
||||
raise Exception("Es Timeout.")
|
||||
doc_store_logger.info("ESConnection.search res: " + str(res))
|
||||
return res
|
||||
except Exception as e:
|
||||
es_logger.error("ES Doc Exist: " + str(e))
|
||||
if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
|
||||
doc_store_logger.error(
|
||||
"ES search exception: " +
|
||||
str(e) +
|
||||
"\n[Q]: " +
|
||||
str(q))
|
||||
if str(e).find("Timeout") > 0:
|
||||
continue
|
||||
raise e
|
||||
doc_store_logger.error("ES search timeout for 3 times!")
|
||||
raise Exception("ES search timeout.")
|
||||
|
||||
def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None:
|
||||
for i in range(3):
|
||||
try:
|
||||
res = self.es.get(index=(indexName),
|
||||
id=chunkId, source=True,)
|
||||
if str(res.get("timed_out", "")).lower() == "true":
|
||||
raise Exception("Es Timeout.")
|
||||
if not res.get("found"):
|
||||
return None
|
||||
chunk = res["_source"]
|
||||
chunk["id"] = chunkId
|
||||
return chunk
|
||||
except Exception as e:
|
||||
doc_store_logger.error(
|
||||
"ES get exception: " +
|
||||
str(e) +
|
||||
"[Q]: " +
|
||||
chunkId)
|
||||
if str(e).find("Timeout") > 0:
|
||||
continue
|
||||
raise e
|
||||
doc_store_logger.error("ES search timeout for 3 times!")
|
||||
raise Exception("ES search timeout.")
|
||||
|
||||
def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str) -> list[str]:
|
||||
# Refers to https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-bulk.html
|
||||
operations = []
|
||||
for d in documents:
|
||||
assert "_id" not in d
|
||||
assert "id" in d
|
||||
d_copy = copy.deepcopy(d)
|
||||
meta_id = d_copy["id"]
|
||||
del d_copy["id"]
|
||||
operations.append(
|
||||
{"index": {"_index": indexName, "_id": meta_id}})
|
||||
operations.append(d_copy)
|
||||
|
||||
res = []
|
||||
for _ in range(100):
|
||||
try:
|
||||
r = self.es.bulk(index=(indexName), operations=operations,
|
||||
refresh=False, timeout="600s")
|
||||
if re.search(r"False", str(r["errors"]), re.IGNORECASE):
|
||||
return res
|
||||
|
||||
for item in r["items"]:
|
||||
for action in ["create", "delete", "index", "update"]:
|
||||
if action in item and "error" in item[action]:
|
||||
res.append(str(item[action]["_id"]) + ":" + str(item[action]["error"]))
|
||||
return res
|
||||
except Exception as e:
|
||||
doc_store_logger.warning("Fail to bulk: " + str(e))
|
||||
if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
|
||||
time.sleep(3)
|
||||
continue
|
||||
return res
|
||||
|
||||
def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool:
|
||||
doc = copy.deepcopy(newValue)
|
||||
del doc['id']
|
||||
if "id" in condition and isinstance(condition["id"], str):
|
||||
# update specific single document
|
||||
chunkId = condition["id"]
|
||||
for i in range(3):
|
||||
try:
|
||||
self.es.update(index=indexName, id=chunkId, doc=doc)
|
||||
return True
|
||||
except Exception as e:
|
||||
doc_store_logger.error(
|
||||
"ES update exception: " + str(e) + " id:" + str(id) +
|
||||
json.dumps(newValue, ensure_ascii=False))
|
||||
if str(e).find("Timeout") > 0:
|
||||
continue
|
||||
else:
|
||||
# update unspecific maybe-multiple documents
|
||||
bqry = Q("bool")
|
||||
for k, v in condition.items():
|
||||
if not isinstance(k, str) or not v:
|
||||
continue
|
||||
if isinstance(v, list):
|
||||
bqry.filter.append(Q("terms", **{k: v}))
|
||||
elif isinstance(v, str) or isinstance(v, int):
|
||||
bqry.filter.append(Q("term", **{k: v}))
|
||||
else:
|
||||
raise Exception(f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.")
|
||||
scripts = []
|
||||
for k, v in newValue.items():
|
||||
if not isinstance(k, str) or not v:
|
||||
continue
|
||||
if isinstance(v, str):
|
||||
scripts.append(f"ctx._source.{k} = '{v}'")
|
||||
elif isinstance(v, int):
|
||||
scripts.append(f"ctx._source.{k} = {v}")
|
||||
else:
|
||||
raise Exception(f"newValue `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str.")
|
||||
ubq = UpdateByQuery(
|
||||
index=indexName).using(
|
||||
self.es).query(bqry)
|
||||
ubq = ubq.script(source="; ".join(scripts))
|
||||
ubq = ubq.params(refresh=True)
|
||||
ubq = ubq.params(slices=5)
|
||||
ubq = ubq.params(conflicts="proceed")
|
||||
for i in range(3):
|
||||
try:
|
||||
_ = ubq.execute()
|
||||
return True
|
||||
except Exception as e:
|
||||
doc_store_logger.error("ES update exception: " +
|
||||
str(e) + "[Q]:" + str(bqry.to_dict()))
|
||||
if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
|
||||
continue
|
||||
return False
|
||||
|
||||
def createIdx(self, idxnm, mapping):
|
||||
try:
|
||||
if elasticsearch.__version__[0] < 8:
|
||||
return self.es.indices.create(idxnm, body=mapping)
|
||||
from elasticsearch.client import IndicesClient
|
||||
return IndicesClient(self.es).create(index=idxnm,
|
||||
settings=mapping["settings"],
|
||||
mappings=mapping["mappings"])
|
||||
except Exception as e:
|
||||
es_logger.error("ES create index error %s ----%s" % (idxnm, str(e)))
|
||||
def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int:
|
||||
qry = None
|
||||
assert "_id" not in condition
|
||||
if "id" in condition:
|
||||
chunk_ids = condition["id"]
|
||||
if not isinstance(chunk_ids, list):
|
||||
chunk_ids = [chunk_ids]
|
||||
qry = Q("ids", values=chunk_ids)
|
||||
else:
|
||||
qry = Q("bool")
|
||||
for k, v in condition.items():
|
||||
if isinstance(v, list):
|
||||
qry.must.append(Q("terms", **{k: v}))
|
||||
elif isinstance(v, str) or isinstance(v, int):
|
||||
qry.must.append(Q("term", **{k: v}))
|
||||
else:
|
||||
raise Exception("Condition value must be int, str or list.")
|
||||
doc_store_logger.info("ESConnection.delete [Q]: " + json.dumps(qry.to_dict()))
|
||||
for _ in range(10):
|
||||
try:
|
||||
res = self.es.delete_by_query(
|
||||
index=indexName,
|
||||
body = Search().query(qry).to_dict(),
|
||||
refresh=True)
|
||||
return res["deleted"]
|
||||
except Exception as e:
|
||||
doc_store_logger.warning("Fail to delete: " + str(filter) + str(e))
|
||||
if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
|
||||
time.sleep(3)
|
||||
continue
|
||||
if re.search(r"(not_found)", str(e), re.IGNORECASE):
|
||||
return 0
|
||||
return 0
|
||||
|
||||
def deleteIdx(self, idxnm):
|
||||
try:
|
||||
return self.es.indices.delete(idxnm, allow_no_indices=True)
|
||||
except Exception as e:
|
||||
es_logger.error("ES delete index error %s ----%s" % (idxnm, str(e)))
|
||||
|
||||
"""
|
||||
Helper functions for search result
|
||||
"""
|
||||
def getTotal(self, res):
|
||||
if isinstance(res["hits"]["total"], type({})):
|
||||
return res["hits"]["total"]["value"]
|
||||
return res["hits"]["total"]
|
||||
|
||||
def getDocIds(self, res):
|
||||
def getChunkIds(self, res):
|
||||
return [d["_id"] for d in res["hits"]["hits"]]
|
||||
|
||||
def getSource(self, res):
|
||||
def __getSource(self, res):
|
||||
rr = []
|
||||
for d in res["hits"]["hits"]:
|
||||
d["_source"]["id"] = d["_id"]
|
||||
@ -425,40 +352,89 @@ class ESConnection:
|
||||
rr.append(d["_source"])
|
||||
return rr
|
||||
|
||||
def scrollIter(self, pagesize=100, scroll_time='2m', q={
|
||||
"query": {"match_all": {}}, "sort": [{"updated_at": {"order": "desc"}}]}):
|
||||
for _ in range(100):
|
||||
def getFields(self, res, fields: List[str]) -> Dict[str, dict]:
|
||||
res_fields = {}
|
||||
if not fields:
|
||||
return {}
|
||||
for d in self.__getSource(res):
|
||||
m = {n: d.get(n) for n in fields if d.get(n) is not None}
|
||||
for n, v in m.items():
|
||||
if isinstance(v, list):
|
||||
m[n] = v
|
||||
continue
|
||||
if not isinstance(v, str):
|
||||
m[n] = str(m[n])
|
||||
# if n.find("tks") > 0:
|
||||
# m[n] = rmSpace(m[n])
|
||||
|
||||
if m:
|
||||
res_fields[d["id"]] = m
|
||||
return res_fields
|
||||
|
||||
def getHighlight(self, res, keywords: List[str], fieldnm: str):
|
||||
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 getAggregation(self, res, fieldnm: str):
|
||||
agg_field = "aggs_" + fieldnm
|
||||
if "aggregations" not in res or agg_field not in res["aggregations"]:
|
||||
return list()
|
||||
bkts = res["aggregations"][agg_field]["buckets"]
|
||||
return [(b["key"], b["doc_count"]) for b in bkts]
|
||||
|
||||
|
||||
"""
|
||||
SQL
|
||||
"""
|
||||
def sql(self, sql: str, fetch_size: int, format: str):
|
||||
doc_store_logger.info(f"ESConnection.sql get sql: {sql}")
|
||||
sql = re.sub(r"[ `]+", " ", sql)
|
||||
sql = sql.replace("%", "")
|
||||
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)
|
||||
doc_store_logger.info(f"ESConnection.sql to es: {sql}")
|
||||
|
||||
for i in range(3):
|
||||
try:
|
||||
page = self.es.search(
|
||||
index=self.idxnm,
|
||||
scroll=scroll_time,
|
||||
size=pagesize,
|
||||
body=q,
|
||||
_source=None
|
||||
)
|
||||
break
|
||||
res = self.es.sql.query(body={"query": sql, "fetch_size": fetch_size}, format=format, request_timeout="2s")
|
||||
return res
|
||||
except ConnectionTimeout:
|
||||
doc_store_logger.error("ESConnection.sql timeout [Q]: " + sql)
|
||||
continue
|
||||
except Exception as e:
|
||||
es_logger.error("ES scrolling fail. " + str(e))
|
||||
time.sleep(3)
|
||||
|
||||
sid = page['_scroll_id']
|
||||
scroll_size = page['hits']['total']["value"]
|
||||
es_logger.info("[TOTAL]%d" % scroll_size)
|
||||
# Start scrolling
|
||||
while scroll_size > 0:
|
||||
yield page["hits"]["hits"]
|
||||
for _ in range(100):
|
||||
try:
|
||||
page = self.es.scroll(scroll_id=sid, scroll=scroll_time)
|
||||
break
|
||||
except Exception as e:
|
||||
es_logger.error("ES scrolling fail. " + str(e))
|
||||
time.sleep(3)
|
||||
|
||||
# Update the scroll ID
|
||||
sid = page['_scroll_id']
|
||||
# Get the number of results that we returned in the last scroll
|
||||
scroll_size = len(page['hits']['hits'])
|
||||
|
||||
|
||||
ELASTICSEARCH = ESConnection()
|
||||
doc_store_logger.error(f"ESConnection.sql failure: {sql} => " + str(e))
|
||||
return None
|
||||
doc_store_logger.error("ESConnection.sql timeout for 3 times!")
|
||||
return None
|
||||
|
||||
Reference in New Issue
Block a user