mirror of
https://github.com/infiniflow/ragflow.git
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Fix: anthropic llm issue. (#8633)
### What problem does this PR solve? ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Kevin Hu
561
rag/utils/opensearch_conn.py
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561
rag/utils/opensearch_conn.py
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@ -0,0 +1,561 @@
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#
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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 logging
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import re
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import json
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import time
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import os
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import copy
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from opensearchpy import OpenSearch, NotFoundError
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from opensearchpy import UpdateByQuery, Q, Search, Index
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from opensearchpy import ConnectionTimeout
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from rag import settings
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from rag.settings import TAG_FLD, PAGERANK_FLD
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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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from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, \
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FusionExpr
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from rag.nlp import is_english, rag_tokenizer
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ATTEMPT_TIME = 2
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logger = logging.getLogger('ragflow.opensearch_conn')
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@singleton
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class OSConnection(DocStoreConnection):
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def __init__(self):
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self.info = {}
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logger.info(f"Use OpenSearch {settings.OS['hosts']} as the doc engine.")
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for _ in range(ATTEMPT_TIME):
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try:
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self.os = OpenSearch(
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settings.OS["hosts"].split(","),
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http_auth=(settings.OS["username"], settings.OS[
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"password"]) if "username" in settings.OS and "password" in settings.OS else None,
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verify_certs=False,
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timeout=600
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)
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if self.os:
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self.info = self.os.info()
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break
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except Exception as e:
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logger.warning(f"{str(e)}. Waiting OpenSearch {settings.OS['hosts']} to be healthy.")
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time.sleep(5)
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if not self.os.ping():
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msg = f"OpenSearch {settings.OS['hosts']} is unhealthy in 120s."
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logger.error(msg)
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raise Exception(msg)
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v = self.info.get("version", {"number": "2.18.0"})
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v = v["number"].split(".")[0]
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if int(v) < 2:
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msg = f"OpenSearch version must be greater than or equal to 2, current version: {v}"
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logger.error(msg)
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raise Exception(msg)
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fp_mapping = os.path.join(get_project_base_directory(), "conf", "os_mapping.json")
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if not os.path.exists(fp_mapping):
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msg = f"OpenSearch mapping file not found at {fp_mapping}"
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logger.error(msg)
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raise Exception(msg)
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self.mapping = json.load(open(fp_mapping, "r"))
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logger.info(f"OpenSearch {settings.OS['hosts']} is healthy.")
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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 "opensearch"
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def health(self) -> dict:
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health_dict = dict(self.os.cluster.health())
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health_dict["type"] = "opensearch"
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return health_dict
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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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try:
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from opensearchpy.client import IndicesClient
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return IndicesClient(self.os).create(index=indexName,
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body=self.mapping)
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except Exception:
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logger.exception("OSConnection.createIndex error %s" % (indexName))
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def deleteIdx(self, indexName: str, knowledgebaseId: str):
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if len(knowledgebaseId) > 0:
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# The index need to be alive after any kb deletion since all kb under this tenant are in one index.
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return
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try:
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self.os.indices.delete(index=indexName, allow_no_indices=True)
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except NotFoundError:
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pass
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except Exception:
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logger.exception("OSConnection.deleteIdx error %s" % (indexName))
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def indexExist(self, indexName: str, knowledgebaseId: str = None) -> bool:
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s = Index(indexName, self.os)
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for i in range(ATTEMPT_TIME):
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try:
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return s.exists()
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except Exception as e:
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logger.exception("OSConnection.indexExist got exception")
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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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break
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return False
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"""
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CRUD operations
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"""
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def search(
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self, selectFields: list[str],
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highlightFields: list[str],
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condition: dict,
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matchExprs: list[MatchExpr],
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orderBy: OrderByExpr,
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offset: int,
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limit: int,
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indexNames: str | list[str],
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knowledgebaseIds: list[str],
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aggFields: list[str] = [],
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rank_feature: dict | None = None
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):
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"""
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Refers to https://github.com/opensearch-project/opensearch-py/blob/main/guides/dsl.md
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"""
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use_knn = False
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if isinstance(indexNames, str):
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indexNames = indexNames.split(",")
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assert isinstance(indexNames, list) and len(indexNames) > 0
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assert "_id" not in condition
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bqry = Q("bool", must=[])
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condition["kb_id"] = knowledgebaseIds
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for k, v in condition.items():
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if k == "available_int":
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if v == 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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continue
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if not v:
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continue
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if isinstance(v, list):
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bqry.filter.append(Q("terms", **{k: v}))
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elif isinstance(v, str) or isinstance(v, int):
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bqry.filter.append(Q("term", **{k: v}))
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else:
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raise Exception(
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f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.")
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s = Search()
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vector_similarity_weight = 0.5
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for m in matchExprs:
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if isinstance(m, FusionExpr) and m.method == "weighted_sum" and "weights" in m.fusion_params:
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assert len(matchExprs) == 3 and isinstance(matchExprs[0], MatchTextExpr) and isinstance(matchExprs[1],
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MatchDenseExpr) and isinstance(
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matchExprs[2], FusionExpr)
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weights = m.fusion_params["weights"]
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vector_similarity_weight = float(weights.split(",")[1])
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knn_query = {}
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for m in matchExprs:
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if isinstance(m, MatchTextExpr):
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minimum_should_match = m.extra_options.get("minimum_should_match", 0.0)
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if isinstance(minimum_should_match, float):
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minimum_should_match = str(int(minimum_should_match * 100)) + "%"
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bqry.must.append(Q("query_string", fields=m.fields,
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type="best_fields", query=m.matching_text,
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minimum_should_match=minimum_should_match,
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boost=1))
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bqry.boost = 1.0 - vector_similarity_weight
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# Elasticsearch has the encapsulation of KNN_search in python sdk
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# while the Python SDK for OpenSearch does not provide encapsulation for KNN_search,
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# the following codes implement KNN_search in OpenSearch using DSL
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# Besides, Opensearch's DSL for KNN_search query syntax differs from that in Elasticsearch, I also made some adaptions for it
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elif isinstance(m, MatchDenseExpr):
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assert (bqry is not None)
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similarity = 0.0
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if "similarity" in m.extra_options:
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similarity = m.extra_options["similarity"]
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use_knn = True
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vector_column_name = m.vector_column_name
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knn_query[vector_column_name] = {}
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knn_query[vector_column_name]["vector"] = list(m.embedding_data)
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knn_query[vector_column_name]["k"] = m.topn
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knn_query[vector_column_name]["filter"] = bqry.to_dict()
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knn_query[vector_column_name]["boost"] = similarity
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if bqry and rank_feature:
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for fld, sc in rank_feature.items():
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if fld != PAGERANK_FLD:
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fld = f"{TAG_FLD}.{fld}"
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bqry.should.append(Q("rank_feature", field=fld, linear={}, boost=sc))
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if bqry:
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s = s.query(bqry)
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for field in highlightFields:
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s = s.highlight(field,force_source=True,no_match_size=30,require_field_match=False)
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if orderBy:
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orders = list()
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for field, order in orderBy.fields:
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order = "asc" if order == 0 else "desc"
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if field in ["page_num_int", "top_int"]:
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order_info = {"order": order, "unmapped_type": "float",
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"mode": "avg", "numeric_type": "double"}
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elif field.endswith("_int") or field.endswith("_flt"):
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order_info = {"order": order, "unmapped_type": "float"}
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else:
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order_info = {"order": order, "unmapped_type": "text"}
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orders.append({field: order_info})
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s = s.sort(*orders)
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for fld in aggFields:
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s.aggs.bucket(f'aggs_{fld}', 'terms', field=fld, size=1000000)
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if limit > 0:
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s = s[offset:offset + limit]
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q = s.to_dict()
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logger.debug(f"OSConnection.search {str(indexNames)} query: " + json.dumps(q))
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if use_knn:
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del q["query"]
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q["query"] = {"knn" : knn_query}
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for i in range(ATTEMPT_TIME):
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try:
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res = self.os.search(index=indexNames,
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body=q,
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timeout=600,
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# search_type="dfs_query_then_fetch",
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track_total_hits=True,
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_source=True)
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if str(res.get("timed_out", "")).lower() == "true":
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raise Exception("OpenSearch Timeout.")
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logger.debug(f"OSConnection.search {str(indexNames)} res: " + str(res))
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return res
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except Exception as e:
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logger.exception(f"OSConnection.search {str(indexNames)} query: " + 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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logger.error(f"OSConnection.search timeout for {ATTEMPT_TIME} times!")
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raise Exception("OSConnection.search timeout.")
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def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None:
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for i in range(ATTEMPT_TIME):
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try:
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res = self.os.get(index=(indexName),
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id=chunkId, _source=True, )
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if str(res.get("timed_out", "")).lower() == "true":
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raise Exception("Es Timeout.")
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chunk = res["_source"]
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chunk["id"] = chunkId
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return chunk
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except NotFoundError:
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return None
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except Exception as e:
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logger.exception(f"OSConnection.get({chunkId}) got exception")
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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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logger.error(f"OSConnection.get timeout for {ATTEMPT_TIME} times!")
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raise Exception("OSConnection.get timeout.")
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def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]:
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# Refers to https://opensearch.org/docs/latest/api-reference/document-apis/bulk/
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operations = []
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for d in documents:
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assert "_id" not in d
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assert "id" in d
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d_copy = copy.deepcopy(d)
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meta_id = d_copy.pop("id", "")
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operations.append(
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{"index": {"_index": indexName, "_id": meta_id}})
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operations.append(d_copy)
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res = []
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for _ in range(ATTEMPT_TIME):
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try:
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res = []
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r = self.os.bulk(index=(indexName), body=operations,
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refresh=False, timeout=60)
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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 item in r["items"]:
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for action in ["create", "delete", "index", "update"]:
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if action in item and "error" in item[action]:
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res.append(str(item[action]["_id"]) + ":" + str(item[action]["error"]))
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return res
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except Exception as e:
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res.append(str(e))
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logger.warning("OSConnection.insert got exception: " + str(e))
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res = []
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if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
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res.append(str(e))
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time.sleep(3)
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continue
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return res
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def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool:
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doc = copy.deepcopy(newValue)
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doc.pop("id", None)
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if "id" in condition and isinstance(condition["id"], str):
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# update specific single document
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chunkId = condition["id"]
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for i in range(ATTEMPT_TIME):
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try:
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self.os.update(index=indexName, id=chunkId, body=doc)
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return True
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except Exception as e:
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logger.exception(
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f"OSConnection.update(index={indexName}, id={id}, doc={json.dumps(condition, ensure_ascii=False)}) got exception")
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if re.search(r"(timeout|connection)", str(e).lower()):
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continue
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break
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return False
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# update unspecific maybe-multiple documents
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bqry = Q("bool")
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for k, v in condition.items():
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if not isinstance(k, str) or not v:
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continue
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if k == "exists":
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bqry.filter.append(Q("exists", field=v))
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continue
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if isinstance(v, list):
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bqry.filter.append(Q("terms", **{k: v}))
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elif isinstance(v, str) or isinstance(v, int):
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bqry.filter.append(Q("term", **{k: v}))
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else:
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raise Exception(
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f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.")
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scripts = []
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params = {}
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for k, v in newValue.items():
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if k == "remove":
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if isinstance(v, str):
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scripts.append(f"ctx._source.remove('{v}');")
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if isinstance(v, dict):
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for kk, vv in v.items():
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scripts.append(f"int i=ctx._source.{kk}.indexOf(params.p_{kk});ctx._source.{kk}.remove(i);")
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params[f"p_{kk}"] = vv
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continue
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if k == "add":
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if isinstance(v, dict):
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for kk, vv in v.items():
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scripts.append(f"ctx._source.{kk}.add(params.pp_{kk});")
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params[f"pp_{kk}"] = vv.strip()
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continue
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if (not isinstance(k, str) or not v) and k != "available_int":
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continue
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if isinstance(v, str):
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v = re.sub(r"(['\n\r]|\\.)", " ", v)
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params[f"pp_{k}"] = v
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scripts.append(f"ctx._source.{k}=params.pp_{k};")
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elif isinstance(v, int) or isinstance(v, float):
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scripts.append(f"ctx._source.{k}={v};")
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elif isinstance(v, list):
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scripts.append(f"ctx._source.{k}=params.pp_{k};")
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params[f"pp_{k}"] = json.dumps(v, ensure_ascii=False)
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else:
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raise Exception(
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f"newValue `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str.")
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ubq = UpdateByQuery(
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index=indexName).using(
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self.os).query(bqry)
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ubq = ubq.script(source="".join(scripts), params=params)
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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 _ in range(ATTEMPT_TIME):
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try:
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_ = ubq.execute()
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return True
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except Exception as e:
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logger.error("OSConnection.update got exception: " + str(e) + "\n".join(scripts))
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if re.search(r"(timeout|connection|conflict)", str(e).lower()):
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continue
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break
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return False
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def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int:
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qry = None
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assert "_id" not in condition
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if "id" in condition:
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chunk_ids = condition["id"]
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if not isinstance(chunk_ids, list):
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chunk_ids = [chunk_ids]
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if not chunk_ids: # when chunk_ids is empty, delete all
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qry = Q("match_all")
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else:
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qry = Q("ids", values=chunk_ids)
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else:
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qry = Q("bool")
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for k, v in condition.items():
|
||||
if k == "exists":
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qry.filter.append(Q("exists", field=v))
|
||||
|
||||
elif k == "must_not":
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if isinstance(v, dict):
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for kk, vv in v.items():
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if kk == "exists":
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qry.must_not.append(Q("exists", field=vv))
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||||
|
||||
elif isinstance(v, list):
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qry.must.append(Q("terms", **{k: v}))
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||||
elif isinstance(v, str) or isinstance(v, int):
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qry.must.append(Q("term", **{k: v}))
|
||||
else:
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||||
raise Exception("Condition value must be int, str or list.")
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||||
logger.debug("OSConnection.delete query: " + json.dumps(qry.to_dict()))
|
||||
for _ in range(ATTEMPT_TIME):
|
||||
try:
|
||||
#print(Search().query(qry).to_dict(), flush=True)
|
||||
res = self.os.delete_by_query(
|
||||
index=indexName,
|
||||
body=Search().query(qry).to_dict(),
|
||||
refresh=True)
|
||||
return res["deleted"]
|
||||
except Exception as e:
|
||||
logger.warning("OSConnection.delete got exception: " + str(e))
|
||||
if re.search(r"(timeout|connection)", str(e).lower()):
|
||||
time.sleep(3)
|
||||
continue
|
||||
if re.search(r"(not_found)", str(e), re.IGNORECASE):
|
||||
return 0
|
||||
return 0
|
||||
|
||||
"""
|
||||
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 getChunkIds(self, res):
|
||||
return [d["_id"] for d in res["hits"]["hits"]]
|
||||
|
||||
def __getSource(self, res):
|
||||
rr = []
|
||||
for d in res["hits"]["hits"]:
|
||||
d["_source"]["id"] = d["_id"]
|
||||
d["_source"]["_score"] = d["_score"]
|
||||
rr.append(d["_source"])
|
||||
return rr
|
||||
|
||||
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):
|
||||
logger.debug(f"OSConnection.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)
|
||||
logger.debug(f"OSConnection.sql to os: {sql}")
|
||||
|
||||
for i in range(ATTEMPT_TIME):
|
||||
try:
|
||||
res = self.os.sql.query(body={"query": sql, "fetch_size": fetch_size}, format=format,
|
||||
request_timeout="2s")
|
||||
return res
|
||||
except ConnectionTimeout:
|
||||
logger.exception("OSConnection.sql timeout")
|
||||
continue
|
||||
except Exception:
|
||||
logger.exception("OSConnection.sql got exception")
|
||||
return None
|
||||
logger.error(f"OSConnection.sql timeout for {ATTEMPT_TIME} times!")
|
||||
return None
|
||||
Reference in New Issue
Block a user