ragflow/rag/utils/opensearch_conn.py

#
#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#

import logging
import re
import json
import time
import os

import copy
from opensearchpy import OpenSearch, NotFoundError
from opensearchpy import UpdateByQuery, Q, Search, Index
from opensearchpy import ConnectionTimeout
from common.decorator import singleton
from common.file_utils import get_project_base_directory
from common.doc_store.doc_store_base import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, \
    FusionExpr
from rag.nlp import is_english, rag_tokenizer
from common.constants import PAGERANK_FLD, TAG_FLD
from common import settings

ATTEMPT_TIME = 2

_PAGERANK_FEA_ADJUST_SCRIPT = """
double cur = 0.0;
if (ctx._source.containsKey(params.pf)) {
  Object v = ctx._source[params.pf];
  if (v != null) {
    if (v instanceof Number) {
      cur = ((Number)v).doubleValue();
    } else {
      try { cur = Double.parseDouble(v.toString()); } catch (Exception e) { cur = 0.0; }
    }
  }
}
double nw = cur + params.delta;
if (nw < params.min_w) { nw = params.min_w; }
if (nw > params.max_w) { nw = params.max_w; }
if (nw <= 0.0) {
  if (ctx._source.containsKey(params.pf)) {
    ctx._source.remove(params.pf);
  }
} else {
  ctx._source[params.pf] = nw;
}
"""

logger = logging.getLogger('ragflow.opensearch_conn')


@singleton
class OSConnection(DocStoreConnection):
    def __init__(self):
        self.info = {}
        logger.info(f"Use OpenSearch {settings.OS['hosts']} as the doc engine.")
        for _ in range(ATTEMPT_TIME):
            try:
                self.os = OpenSearch(
                    settings.OS["hosts"].split(","),
                    http_auth=(settings.OS["username"], settings.OS[
                        "password"]) if "username" in settings.OS and "password" in settings.OS else None,
                    verify_certs=False,
                    timeout=600
                )
                if self.os:
                    self.info = self.os.info()
                    break
            except Exception as e:
                logger.warning(f"{str(e)}. Waiting OpenSearch {settings.OS['hosts']} to be healthy.")
                time.sleep(5)
        if not self.os.ping():
            msg = f"OpenSearch {settings.OS['hosts']} is unhealthy in 120s."
            logger.error(msg)
            raise Exception(msg)
        v = self.info.get("version", {"number": "2.18.0"})
        v = v["number"].split(".")[0]
        if int(v) < 2:
            msg = f"OpenSearch version must be greater than or equal to 2, current version: {v}"
            logger.error(msg)
            raise Exception(msg)
        fp_mapping = os.path.join(get_project_base_directory(), "conf", "os_mapping.json")
        if not os.path.exists(fp_mapping):
            msg = f"OpenSearch mapping file not found at {fp_mapping}"
            logger.error(msg)
            raise Exception(msg)
        with open(fp_mapping, "r") as f:
            self.mapping = json.load(f)
        logger.info(f"OpenSearch {settings.OS['hosts']} is healthy.")
        self._init_hybrid_search()

    # normalization-processor (needed to merge the BM25 and KNN scores) only
    # exists on OpenSearch 2.10+.
    HYBRID_MIN_VERSION = (2, 10)

    def _init_hybrid_search(self):
        """Create the hybrid-search pipeline if it isn't there yet.

        A {"hybrid": {...}} query is scored by a normalization-processor that has
        to live on a search pipeline, otherwise OpenSearch rejects the query. We
        create it once at startup (PUT _search/pipeline is idempotent) so there's
        no extra setup step to run.

        Sets self.hybrid_search_enabled. If the pipeline can't be created
        (OpenSearch < 2.10, or no permission to manage pipelines) we log a
        warning, leave it off, and search() keeps doing vector-only.
        """
        self.hybrid_search_enabled = False
        self._hybrid_pipeline = os.environ.get("OS_HYBRID_PIPELINE") \
            or settings.OS.get("hybrid_search_pipeline") or "ragflow_hybrid_pipeline"

        version_number = self.info.get("version", {}).get("number", "")
        try:
            version = tuple(int(p) for p in version_number.split(".")[:2])
        except (ValueError, AttributeError):
            version = (0, 0)
        if version < self.HYBRID_MIN_VERSION:
            logger.warning(f"OpenSearch {version_number or 'unknown'} does not support the "
                           f"normalization-processor (requires >= {self.HYBRID_MIN_VERSION[0]}."
                           f"{self.HYBRID_MIN_VERSION[1]}); hybrid search is disabled and "
                           f"queries fall back to vector-only.")
            return

        weights = settings.OS.get("hybrid_search_weights", [0.5, 0.5])
        pipeline_body = {
            "description": "RAGFlow hybrid search normalization pipeline (BM25 + KNN).",
            "phase_results_processors": [
                {"normalization-processor": {
                    "normalization": {"technique": "min_max"},
                    "combination": {"technique": "arithmetic_mean",
                                    "parameters": {"weights": weights}}}}
            ],
        }
        try:
            self.os.transport.perform_request(
                "PUT", f"/_search/pipeline/{self._hybrid_pipeline}", body=pipeline_body)
            self.hybrid_search_enabled = True
            logger.info(f"OpenSearch hybrid search enabled via pipeline "
                        f"'{self._hybrid_pipeline}' (weights {weights}).")
        except Exception:
            logger.warning(f"Could not create OpenSearch search pipeline '{self._hybrid_pipeline}'; "
                           f"hybrid search is disabled and queries fall back to vector-only. "
                           f"Creating a search pipeline needs the "
                           f"'cluster:admin/search/pipeline/put' privilege (relevant on "
                           f"locked-down or managed OpenSearch).", exc_info=True)

    """
    Database operations
    """

    def db_type(self) -> str:
        return "opensearch"

    def health(self) -> dict:
        health_dict = dict(self.os.cluster.health())
        health_dict["type"] = "opensearch"
        return health_dict

    """
    Table operations
    """

    def create_idx(self, indexName: str, knowledgebaseId: str, vectorSize: int, parser_id: str = None):
        if self.index_exist(indexName, knowledgebaseId):
            return True
        try:
            from opensearchpy.client import IndicesClient
            return IndicesClient(self.os).create(index=indexName,
                                                 body=self.mapping)
        except Exception:
            logger.exception("OSConnection.createIndex error %s" % (indexName))

    def create_doc_meta_idx(self, index_name: str):
        """
        Create a per-tenant document metadata index on OpenSearch.

        Mirrors ESConnectionBase.create_doc_meta_idx so that the
        DocMetadataService dispatches uniformly across ES and OS backends.
        Index name pattern: ragflow_doc_meta_{tenant_id}
        """
        if self.index_exist(index_name, ""):
            return True
        try:
            fp_mapping = os.path.join(get_project_base_directory(), "conf", "doc_meta_es_mapping.json")
            if not os.path.exists(fp_mapping):
                logger.error(f"Document metadata mapping file not found at {fp_mapping}")
                return False

            with open(fp_mapping, "r") as f:
                doc_meta_mapping = json.load(f)

            mappings = doc_meta_mapping["mappings"]
            # `conf/doc_meta_es_mapping.json` declares a top-level
            # `"dynamic": "runtime"`. Runtime fields are an Elasticsearch-only
            # feature; OpenSearch cannot parse the value and rejects index
            # creation with `mapper_parsing_exception: Could not convert
            # [dynamic.dynamic] to boolean`. Fall back to standard dynamic
            # mapping (`true`) on OpenSearch so dynamic field discovery is kept
            # without the ES-specific runtime semantics. The shared mapping file
            # is left untouched so the Elasticsearch backend still gets runtime
            # fields.
            if mappings.get("dynamic") == "runtime":
                mappings = {**mappings, "dynamic": True}

            from opensearchpy.client import IndicesClient
            body = {
                "settings": doc_meta_mapping["settings"],
                "mappings": mappings,
            }
            return IndicesClient(self.os).create(index=index_name, body=body)
        except Exception as e:
            logger.exception(f"OSConnection.create_doc_meta_idx error creating {index_name}: {e}")
            return False

    def refresh_idx(self, index_name: str) -> bool:
        """
        Refresh an index so that recently inserted documents become searchable.

        DocMetadataService used to call ``settings.docStoreConn.es.indices.refresh``
        directly, which raised AttributeError on the OpenSearch backend because
        OSConnection exposes ``self.os`` rather than ``self.es``. This wrapper
        gives both backends a uniform abstract entry point.
        """
        try:
            self.os.indices.refresh(index=index_name)
            return True
        except NotFoundError:
            return False
        except Exception as e:
            logger.warning(f"OSConnection.refresh_idx({index_name}) failed: {e}")
            return False

    def count_idx(self, index_name: str) -> int:
        """
        Return the document count for an index, or -1 if the call fails.

        Used by DocMetadataService._drop_empty_metadata_table to decide whether
        a per-tenant metadata index is empty without paying a full search.
        """
        try:
            response = self.os.count(index=index_name)
            return int(response.get("count", 0))
        except NotFoundError:
            return 0
        except Exception as e:
            logger.warning(f"OSConnection.count_idx({index_name}) failed: {e}")
            return -1

    def replace_meta_fields(self, index_name: str, doc_id: str, meta_fields: dict) -> bool:
        """
        Replace the ``meta_fields`` object on a single document.

        ES.update with a ``doc`` body deep-merges object fields, which retains
        old keys that should be removed. The fix in ESConnection is a script
        that fully assigns the new meta_fields. We provide the same primitive
        on OpenSearch so the service layer never reaches into ``self.es`` or
        ``self.os`` directly.
        """
        body = {
            "script": {
                "source": "ctx._source.meta_fields = params.meta_fields",
                "params": {"meta_fields": meta_fields},
            }
        }
        for _ in range(ATTEMPT_TIME):
            try:
                self.os.update(index=index_name, id=doc_id, body=body, refresh=True)
                return True
            except NotFoundError:
                return False
            except Exception as e:
                logger.warning(f"OSConnection.replace_meta_fields({index_name}, {doc_id}) failed: {e}")
                if re.search(r"(timeout|connection)", str(e).lower()):
                    time.sleep(1)
                    continue
                return False
        return False

    def delete_idx(self, indexName: str, knowledgebaseId: str):
        if len(knowledgebaseId) > 0:
            # The index need to be alive after any kb deletion since all kb under this tenant are in one index.
            return
        try:
            self.os.indices.delete(index=indexName, allow_no_indices=True)
        except NotFoundError:
            pass
        except Exception:
            logger.exception("OSConnection.deleteIdx error %s" % (indexName))

    def index_exist(self, indexName: str, knowledgebaseId: str = None) -> bool:
        s = Index(indexName, self.os)
        for i in range(ATTEMPT_TIME):
            try:
                return s.exists()
            except Exception as e:
                logger.exception("OSConnection.indexExist got exception")
                if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
                    continue
                break
        return False

    """
    CRUD operations
    """

    def search(
            self, select_fields: list[str],
            highlight_fields: list[str],
            condition: dict,
            match_expressions: list[MatchExpr],
            order_by: OrderByExpr,
            offset: int,
            limit: int,
            index_names: str | list[str],
            knowledgebase_ids: list[str],
            agg_fields: list[str] = [],
            rank_feature: dict | None = None
    ):
        """
        Refers to https://github.com/opensearch-project/opensearch-py/blob/main/guides/dsl.md
        """
        use_knn = False
        use_text = False
        if isinstance(index_names, str):
            index_names = index_names.split(",")
        assert isinstance(index_names, list) and len(index_names) > 0
        assert "_id" not in condition

        bqry = Q("bool", must=[])
        condition["kb_id"] = knowledgebase_ids
        for k, v in condition.items():
            if k == "available_int":
                if v == 0:
                    bqry.filter.append(Q("range", available_int={"lt": 1}))
                else:
                    bqry.filter.append(
                        Q("bool", must_not=Q("range", available_int={"lt": 1})))
                continue
            if 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.")

        s = Search()
        vector_similarity_weight = 0.5
        for m in match_expressions:
            if isinstance(m, FusionExpr) and m.method == "weighted_sum" and "weights" in m.fusion_params:
                assert len(match_expressions) == 3 and isinstance(match_expressions[0], MatchTextExpr) and isinstance(match_expressions[1],
                                                                                                        MatchDenseExpr) and isinstance(
                    match_expressions[2], FusionExpr)
                weights = m.fusion_params["weights"]
                vector_similarity_weight = float(weights.split(",")[1])
        knn_query = {}
        for m in match_expressions:
            if isinstance(m, MatchTextExpr):
                use_text = True
                minimum_should_match = m.extra_options.get("minimum_should_match", 0.0)
                if isinstance(minimum_should_match, float):
                    minimum_should_match = str(int(minimum_should_match * 100)) + "%"
                bqry.must.append(Q("query_string", fields=m.fields,
                                   type="best_fields", query=m.matching_text,
                                   minimum_should_match=minimum_should_match,
                                   boost=1))
                bqry.boost = 1.0 - vector_similarity_weight

            # Elasticsearch has the encapsulation of KNN_search in python sdk
            # while the Python SDK for OpenSearch does not provide encapsulation for KNN_search,
            # the following codes implement KNN_search in OpenSearch using DSL
            # Besides, Opensearch's DSL for KNN_search query syntax differs from that in Elasticsearch, I also made some adaptions for it
            elif isinstance(m, MatchDenseExpr):
                assert (bqry is not None)
                similarity = 0.0
                if "similarity" in m.extra_options:
                    similarity = m.extra_options["similarity"]
                use_knn = True
                vector_column_name = m.vector_column_name
                knn_query[vector_column_name] = {}
                knn_query[vector_column_name]["vector"] = list(m.embedding_data)
                knn_query[vector_column_name]["k"] = m.topn
                # The knn filter holds only the structural filters (kb_id,
                # available_int, ...). The text query is deliberately kept out of it:
                # it's scored as its own leg in the hybrid query below, not used to
                # pre-filter knn candidates.
                bool_inner = bqry.to_dict().get("bool", {})
                if bool_inner.get("filter"):
                    knn_query[vector_column_name]["filter"] = {"bool": {"filter": bool_inner["filter"]}}
                knn_query[vector_column_name]["boost"] = similarity

        if bqry and rank_feature:
            for fld, sc in rank_feature.items():
                if fld != PAGERANK_FLD:
                    fld = f"{TAG_FLD}.{fld}"
                bqry.should.append(Q("rank_feature", field=fld, linear={}, boost=sc))

        if bqry:
            s = s.query(bqry)
        for field in highlight_fields:
            s = s.highlight(field, force_source=True, no_match_size=30, require_field_match=False)

        if order_by:
            orders = list()
            for field, order in order_by.fields:
                order = "asc" if order == 0 else "desc"
                if field in ["page_num_int", "top_int"]:
                    order_info = {"order": order, "unmapped_type": "float",
                                  "mode": "avg", "numeric_type": "double"}
                elif field.endswith("_int") or field.endswith("_flt"):
                    order_info = {"order": order, "unmapped_type": "float"}
                else:
                    order_info = {"order": order, "unmapped_type": "text"}
                orders.append({field: order_info})
            s = s.sort(*orders)

        for fld in agg_fields:
            s.aggs.bucket(f'aggs_{fld}', 'terms', field=fld, size=1000000)

        if limit > 0:
            s = s[offset:offset + limit]
        q = s.to_dict()
        logger.debug(f"OSConnection.search {str(index_names)} query: " + json.dumps(q))

        hybrid_search = use_knn and use_text and getattr(self, "hybrid_search_enabled", False)
        if use_knn:
            if hybrid_search:
                # both legs + a pipeline available: send a real hybrid query so the
                # keyword (BM25) and vector (knn) legs are scored separately and
                # merged by the pipeline.
                keyword_query = q.get("query")
                q["query"] = {"hybrid": {"queries": [keyword_query, {"knn": knn_query}]}}
            else:
                # vector-only, or no pipeline available: fall back to a plain knn query.
                del q["query"]
                q["query"] = {"knn": knn_query}

        search_kwargs = {}
        if hybrid_search:
            search_kwargs["params"] = {"search_pipeline": self._hybrid_pipeline}

        for i in range(ATTEMPT_TIME):
            try:
                res = self.os.search(index=index_names,
                                     body=q,
                                     timeout=600,
                                     # search_type="dfs_query_then_fetch",
                                     track_total_hits=True,
                                     _source=True,
                                     **search_kwargs)
                if str(res.get("timed_out", "")).lower() == "true":
                    raise Exception("OpenSearch Timeout.")
                logger.debug(f"OSConnection.search {str(index_names)} res: " + str(res))
                return res
            except Exception as e:
                logger.exception(f"OSConnection.search {str(index_names)} query: " + str(q))
                if str(e).find("Timeout") > 0:
                    continue
                raise e
        logger.error(f"OSConnection.search timeout for {ATTEMPT_TIME} times!")
        raise Exception("OSConnection.search timeout.")

    def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None:
        for i in range(ATTEMPT_TIME):
            try:
                res = self.os.get(index=(indexName),
                                  id=chunkId, _source=True, )
                if str(res.get("timed_out", "")).lower() == "true":
                    raise Exception("Es Timeout.")
                chunk = res["_source"]
                chunk["id"] = chunkId
                return chunk
            except NotFoundError:
                return None
            except Exception as e:
                logger.exception(f"OSConnection.get({chunkId}) got exception")
                if str(e).find("Timeout") > 0:
                    continue
                raise e
        logger.error(f"OSConnection.get timeout for {ATTEMPT_TIME} times!")
        raise Exception("OSConnection.get timeout.")

    def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]:
        # Refers to https://opensearch.org/docs/latest/api-reference/document-apis/bulk/
        operations = []
        for d in documents:
            assert "_id" not in d
            assert "id" in d
            d_copy = copy.deepcopy(d)
            # Use id as _id for uniqueness, but keep "id" in the document so the
            # doc-meta read path (DocMetadataService filters on / sorts by the
            # "id" field) can find it, mirroring ESConnection.insert().
            meta_id = d_copy.get("id", "")
            operations.append(
                {"index": {"_index": indexName, "_id": meta_id}})
            operations.append(d_copy)

        res = []
        for _ in range(ATTEMPT_TIME):
            try:
                res = []
                r = self.os.bulk(index=(indexName), body=operations,
                                 refresh="wait_for", timeout=60)
                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:
                res.append(str(e))
                logger.warning("OSConnection.insert got exception: " + str(e))
                res = []
                if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
                    res.append(str(e))
                    time.sleep(3)
                    continue
        return res

    def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool:
        doc = copy.deepcopy(newValue)
        doc.pop("id", None)
        if "id" in condition and isinstance(condition["id"], str):
            # update specific single document
            chunkId = condition["id"]
            for i in range(ATTEMPT_TIME):
                doc_part = copy.deepcopy(doc)
                remove_value = doc_part.pop("remove", None)
                remove_field = remove_value if isinstance(remove_value, str) else None
                remove_dict = remove_value if isinstance(remove_value, dict) else None
                try:
                    if remove_field is not None:
                        self.os.update(
                            index=indexName,
                            id=chunkId,
                            body={"script": {"source": f"ctx._source.remove('{remove_field}');"}},
                        )
                    if remove_dict is not None:
                        scripts = []
                        params = {}
                        for kk, vv in remove_dict.items():
                            scripts.append(
                                f"if (ctx._source.containsKey('{kk}') && ctx._source.{kk} != null) "
                                f"{{ int i = ctx._source.{kk}.indexOf(params.p_{kk}); "
                                f"if (i >= 0) {{ ctx._source.{kk}.remove(i); }} }}"
                            )
                            params[f"p_{kk}"] = vv
                        if scripts:
                            self.os.update(
                                index=indexName,
                                id=chunkId,
                                body={"script": {"source": "".join(scripts), "params": params}},
                            )
                    if doc_part:
                        self.os.update(index=indexName, id=chunkId, body={"doc": doc_part})
                    if remove_field is not None or remove_dict is not None or doc_part:
                        return True
                except Exception as e:
                    logger.exception(
                        f"OSConnection.update(index={indexName}, id={id}, doc={json.dumps(condition, ensure_ascii=False)}) got exception")
                    if re.search(r"(timeout|connection)", str(e).lower()):
                        continue
                    break
            return False

        # update unspecific maybe-multiple documents
        bqry = Q("bool")
        for k, v in condition.items():
            if not isinstance(k, str) or not v:
                continue
            if k == "exists":
                bqry.filter.append(Q("exists", field=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 = []
        params = {}
        for k, v in newValue.items():
            if k == "remove":
                if isinstance(v, str):
                    scripts.append(f"ctx._source.remove('{v}');")
                if isinstance(v, dict):
                    for kk, vv in v.items():
                        scripts.append(f"int i=ctx._source.{kk}.indexOf(params.p_{kk});ctx._source.{kk}.remove(i);")
                        params[f"p_{kk}"] = vv
                continue
            if k == "add":
                if isinstance(v, dict):
                    for kk, vv in v.items():
                        scripts.append(f"ctx._source.{kk}.add(params.pp_{kk});")
                        params[f"pp_{kk}"] = vv.strip()
                continue
            if (not isinstance(k, str) or not v) and k != "available_int":
                continue
            if isinstance(v, str):
                v = re.sub(r"(['\n\r]|\\.)", " ", v)
                params[f"pp_{k}"] = v
                scripts.append(f"ctx._source.{k}=params.pp_{k};")
            elif isinstance(v, int) or isinstance(v, float):
                scripts.append(f"ctx._source.{k}={v};")
            elif isinstance(v, list):
                scripts.append(f"ctx._source.{k}=params.pp_{k};")
                params[f"pp_{k}"] = json.dumps(v, ensure_ascii=False)
            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.os).query(bqry)
        ubq = ubq.script(source="".join(scripts), params=params)
        ubq = ubq.params(refresh=True)
        ubq = ubq.params(slices=5)
        ubq = ubq.params(conflicts="proceed")

        for _ in range(ATTEMPT_TIME):
            try:
                _ = ubq.execute()
                return True
            except Exception as e:
                logger.error("OSConnection.update got exception: " + str(e) + "\n".join(scripts))
                if re.search(r"(timeout|connection|conflict)", str(e).lower()):
                    continue
                break
        return False

    def adjust_chunk_pagerank_fea(
        self,
        chunk_id: str,
        indexName: str,
        knowledgebaseId: str,
        delta: float,
        min_w: float = 0.0,
        max_w: float = 100.0,
        row_id: int | None = None,
    ) -> bool:
        """Atomically adjust pagerank_fea on one chunk (painless script)."""
        _ = row_id
        try:
            self.os.update(
                index=indexName,
                id=chunk_id,
                retry_on_conflict=3,
                body={
                    "script": {
                        "source": _PAGERANK_FEA_ADJUST_SCRIPT.strip(),
                        "lang": "painless",
                        "params": {
                            "pf": PAGERANK_FLD,
                            "delta": float(delta),
                            "min_w": float(min_w),
                            "max_w": float(max_w),
                        },
                    }
                },
            )
            logger.debug(
                "OSConnection.adjust_chunk_pagerank_fea(index=%s, id=%s, delta=%s) succeeded",
                indexName,
                chunk_id,
                delta,
            )
            return True
        except Exception as e:
            logger.exception(
                "OSConnection.adjust_chunk_pagerank_fea(index=%s, id=%s): %s",
                indexName,
                chunk_id,
                e,
            )
        return False

    def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int:
        assert "_id" not in condition
        condition["kb_id"] = knowledgebaseId

        # Build a bool query that combines id filter with other conditions
        bool_query = Q("bool")

        # Handle chunk IDs if present
        if "id" in condition:
            chunk_ids = condition["id"]
            if not isinstance(chunk_ids, list):
                chunk_ids = [chunk_ids]
            if chunk_ids:
                # Filter by specific chunk IDs
                bool_query.filter.append(Q("ids", values=chunk_ids))
            # If chunk_ids is empty, we don't add an ids filter - rely on other conditions

        # Add all other conditions as filters
        for k, v in condition.items():
            if k == "id":
                continue  # Already handled above
            if k == "exists":
                bool_query.filter.append(Q("exists", field=v))
            elif k == "must_not":
                if isinstance(v, dict):
                    for kk, vv in v.items():
                        if kk == "exists":
                            bool_query.must_not.append(Q("exists", field=vv))
            elif isinstance(v, list):
                bool_query.must.append(Q("terms", **{k: v}))
            elif isinstance(v, str) or isinstance(v, int):
                bool_query.must.append(Q("term", **{k: v}))
            elif v is not None:
                raise Exception("Condition value must be int, str or list.")

        # If no filters were added, use match_all (for tenant-wide operations)
        if not bool_query.filter and not bool_query.must and not bool_query.must_not:
            qry = Q("match_all")
        else:
            qry = bool_query
        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 get_total(self, res):
        if isinstance(res["hits"]["total"], type({})):
            return res["hits"]["total"]["value"]
        return res["hits"]["total"]

    def get_doc_ids(self, res):
        return [d["_id"] for d in res["hits"]["hits"]]

    def get_scores(self, res) -> dict[str, float]:
        """
        Map hit `_id` to its raw `_score`. Used by rag/nlp/search.py:_knn_scores()
        to recover the cosine similarity returned by a KNN-only second-pass search
        without pulling the chunk vectors out of the index. OpenSearch hit headers
        carry `_score` exactly like Elasticsearch, so this mirrors
        ESConnectionBase.get_scores.
        """
        out = {}
        for d in res.get("hits", {}).get("hits", []):
            doc_id = d.get("_id")
            if doc_id is None:
                continue
            score = d.get("_score")
            out[doc_id] = float(score) if score is not None else 0.0
        return out

    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 get_fields(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] = remove_redundant_spaces(m[n])

            if m:
                res_fields[d["id"]] = m
        return res_fields

    def get_highlight(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 get_aggregation(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