mirror of
https://github.com/infiniflow/ragflow.git
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395 lines
16 KiB
Python
395 lines
16 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import 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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from abc import abstractmethod
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from elasticsearch import BadRequestError, NotFoundError
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from elasticsearch_dsl import Index
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from elastic_transport import ConnectionTimeout
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from elasticsearch.client import IndicesClient
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from common.file_utils import get_project_base_directory
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from common.misc_utils import convert_bytes
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from common.doc_store.doc_store_base import DocStoreConnection, OrderByExpr, MatchExpr
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from rag.nlp import is_english, rag_tokenizer
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from common import settings
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ATTEMPT_TIME = 2
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class ESConnectionBase(DocStoreConnection):
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def __init__(self, mapping_file_name: str = "mapping.json", logger_name: str = "ragflow.es_conn"):
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from common.doc_store.es_conn_pool import ES_CONN
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self.logger = logging.getLogger(logger_name)
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self.info = {}
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self.logger.info(f"Use Elasticsearch {settings.ES['hosts']} as the doc engine.")
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self.es = ES_CONN.get_conn()
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fp_mapping = os.path.join(get_project_base_directory(), "conf", mapping_file_name)
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if not os.path.exists(fp_mapping):
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msg = f"Elasticsearch mapping file not found at {fp_mapping}"
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self.logger.error(msg)
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raise Exception(msg)
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with open(fp_mapping, "r") as f:
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self.mapping = json.load(f)
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self.logger.info(f"Elasticsearch {settings.ES['hosts']} is healthy.")
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def _connect(self):
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from common.doc_store.es_conn_pool import ES_CONN
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if self.es.ping():
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return True
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self.es = ES_CONN.refresh_conn()
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return True
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"""
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Database operations
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"""
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def db_type(self) -> str:
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return "elasticsearch"
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def health(self) -> dict:
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health_dict = dict(self.es.cluster.health())
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health_dict["type"] = "elasticsearch"
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return health_dict
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def get_cluster_stats(self):
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"""
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curl -XGET "http://{es_host}/_cluster/stats" -H "kbn-xsrf: reporting" to view raw stats.
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"""
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raw_stats = self.es.cluster.stats()
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self.logger.debug(f"ESConnection.get_cluster_stats: {raw_stats}")
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try:
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res = {"cluster_name": raw_stats["cluster_name"], "status": raw_stats["status"]}
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indices_status = raw_stats["indices"]
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res.update({"indices": indices_status["count"], "indices_shards": indices_status["shards"]["total"]})
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doc_info = indices_status["docs"]
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res.update({"docs": doc_info["count"], "docs_deleted": doc_info["deleted"]})
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store_info = indices_status["store"]
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res.update({"store_size": convert_bytes(store_info["size_in_bytes"]), "total_dataset_size": convert_bytes(store_info["total_data_set_size_in_bytes"])})
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mappings_info = indices_status["mappings"]
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res.update(
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{
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"mappings_fields": mappings_info["total_field_count"],
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"mappings_deduplicated_fields": mappings_info["total_deduplicated_field_count"],
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"mappings_deduplicated_size": convert_bytes(mappings_info["total_deduplicated_mapping_size_in_bytes"]),
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}
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)
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node_info = raw_stats["nodes"]
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res.update(
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{
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"nodes": node_info["count"]["total"],
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"nodes_version": node_info["versions"],
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"os_mem": convert_bytes(node_info["os"]["mem"]["total_in_bytes"]),
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"os_mem_used": convert_bytes(node_info["os"]["mem"]["used_in_bytes"]),
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"os_mem_used_percent": node_info["os"]["mem"]["used_percent"],
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"jvm_versions": node_info["jvm"]["versions"][0]["vm_version"],
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"jvm_heap_used": convert_bytes(node_info["jvm"]["mem"]["heap_used_in_bytes"]),
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"jvm_heap_max": convert_bytes(node_info["jvm"]["mem"]["heap_max_in_bytes"]),
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}
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)
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return res
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except Exception as e:
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self.logger.exception(f"ESConnection.get_cluster_stats: {e}")
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return None
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"""
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Table operations
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"""
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def create_idx(self, index_name: str, dataset_id: str, vector_size: int, parser_id: str = None):
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# parser_id is used by Infinity but not needed for ES (kept for interface compatibility)
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if self.index_exist(index_name, dataset_id):
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return True
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try:
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return IndicesClient(self.es).create(index=index_name, settings=self.mapping["settings"], mappings=self.mapping["mappings"])
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except Exception:
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self.logger.exception("ESConnection.createIndex error %s" % index_name)
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def create_doc_meta_idx(self, index_name: str):
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"""
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Create a document metadata index.
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Index name pattern: ragflow_doc_meta_{tenant_id}
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- Per-tenant metadata index for storing document metadata fields
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"""
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if self.index_exist(index_name, ""):
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return True
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try:
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fp_mapping = os.path.join(get_project_base_directory(), "conf", "doc_meta_es_mapping.json")
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if not os.path.exists(fp_mapping):
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self.logger.error(f"Document metadata mapping file not found at {fp_mapping}")
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return False
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with open(fp_mapping, "r") as f:
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doc_meta_mapping = json.load(f)
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return IndicesClient(self.es).create(index=index_name, settings=doc_meta_mapping["settings"], mappings=doc_meta_mapping["mappings"])
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except Exception as e:
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self.logger.exception(f"Error creating document metadata index {index_name}: {e}")
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def refresh_idx(self, index_name: str) -> bool:
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"""
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Refresh an index so that recently inserted documents become searchable.
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Service layers should call this dispatch method instead of reaching
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into ``self.es`` directly, so the OpenSearch and Elasticsearch
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connections present a uniform abstract API.
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"""
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try:
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self.es.indices.refresh(index=index_name)
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return True
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except NotFoundError:
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return False
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except Exception as e:
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self.logger.warning(f"ESConnection.refresh_idx({index_name}) failed: {e}")
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return False
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def count_idx(self, index_name: str) -> int:
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"""
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Return the document count for an index, or -1 if the call fails.
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Used to decide whether a per-tenant metadata index is empty without
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paying a full search.
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"""
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try:
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response = self.es.count(index=index_name)
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return int(response.get("count", 0))
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except NotFoundError:
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return 0
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except Exception as e:
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self.logger.warning(f"ESConnection.count_idx({index_name}) failed: {e}")
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return -1
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def replace_meta_fields(self, index_name: str, doc_id: str, meta_fields: dict) -> bool:
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"""
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Fully replace the ``meta_fields`` object on a single document.
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Using ES.update with a ``doc`` body would deep-merge object fields,
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retaining old keys that should be removed. A scripted update assigns
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the new meta_fields outright, matching delete-key semantics.
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"""
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body = {
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"script": {
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"source": "ctx._source.meta_fields = params.meta_fields",
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"params": {"meta_fields": meta_fields},
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}
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}
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try:
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self.es.update(index=index_name, id=doc_id, refresh=True, body=body)
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return True
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except NotFoundError:
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return False
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except Exception as e:
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self.logger.warning(f"ESConnection.replace_meta_fields({index_name}, {doc_id}) failed: {e}")
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return False
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def delete_idx(self, index_name: str, dataset_id: str):
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if len(dataset_id) > 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.es.indices.delete(index=index_name, 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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self.logger.exception("ESConnection.deleteIdx error %s" % index_name)
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def index_exist(self, index_name: str, dataset_id: str = None) -> bool:
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s = Index(index_name, self.es)
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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 ConnectionTimeout:
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self.logger.exception("ES request timeout")
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time.sleep(3)
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self._connect()
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continue
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except Exception as e:
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self.logger.exception(e)
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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 get(self, doc_id: str, index_name: str, dataset_ids: 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.es.get(
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index=index_name,
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id=doc_id,
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source=True,
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)
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if str(res.get("timed_out", "")).lower() == "true":
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raise Exception("Es Timeout.")
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doc = res["_source"]
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doc["id"] = doc_id
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return doc
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except NotFoundError:
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return None
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except Exception as e:
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self.logger.exception(f"ESConnection.get({doc_id}) got exception")
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raise e
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self.logger.error(f"ESConnection.get timeout for {ATTEMPT_TIME} times!")
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raise Exception("ESConnection.get timeout.")
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@abstractmethod
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def search(
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self,
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select_fields: list[str],
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highlight_fields: list[str],
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condition: dict,
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match_expressions: list[MatchExpr],
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order_by: OrderByExpr,
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offset: int,
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limit: int,
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index_names: str | list[str],
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dataset_ids: list[str],
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agg_fields: list[str] | None = None,
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rank_feature: dict | None = None,
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):
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def insert(self, documents: list[dict], index_name: str, dataset_id: str = None) -> list[str]:
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def update(self, condition: dict, new_value: dict, index_name: str, dataset_id: str) -> bool:
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raise NotImplementedError("Not implemented")
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@abstractmethod
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def delete(self, condition: dict, index_name: str, dataset_id: str) -> int:
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raise NotImplementedError("Not implemented")
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"""
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Helper functions for search result
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"""
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def get_total(self, res):
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if isinstance(res["hits"]["total"], type({})):
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return res["hits"]["total"]["value"]
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return res["hits"]["total"]
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def get_doc_ids(self, res):
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return [d["_id"] for d in res["hits"]["hits"]]
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def get_scores(self, res) -> dict[str, float]:
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"""
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Map hit `_id` to its raw `_score`. Used to recover the cosine
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similarity returned by a KNN-only search without pulling the
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chunk vectors out of the index.
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"""
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out = {}
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for d in res.get("hits", {}).get("hits", []):
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doc_id = d.get("_id")
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if doc_id is None:
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continue
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score = d.get("_score")
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out[doc_id] = float(score) if score is not None else 0.0
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return out
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def _get_source(self, res):
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rr = []
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for d in res["hits"]["hits"]:
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d["_source"]["id"] = d["_id"]
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d["_source"]["_score"] = d["_score"]
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rr.append(d["_source"])
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return rr
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@abstractmethod
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def get_fields(self, res, fields: list[str]) -> dict[str, dict]:
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raise NotImplementedError("Not implemented")
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def get_highlight(self, res, keywords: list[str], field_name: str):
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ans = {}
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for d in res["hits"]["hits"]:
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highlights = d.get("highlight")
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if not highlights:
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continue
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txt = "...".join([a for a in list(highlights.items())[0][1]])
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if not is_english(txt.split()):
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ans[d["_id"]] = txt
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continue
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txt = d["_source"][field_name]
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txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE)
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txt_list = []
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for t in re.split(r"[.?!;\n]", txt):
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for w in keywords:
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t = re.sub(r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w), r"\1<em>\2</em>\3", t, flags=re.IGNORECASE | re.MULTILINE)
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if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE):
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continue
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txt_list.append(t)
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ans[d["_id"]] = "...".join(txt_list) if txt_list else "...".join([a for a in list(highlights.items())[0][1]])
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return ans
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def get_aggregation(self, res, field_name: str):
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agg_field = "aggs_" + field_name
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if "aggregations" not in res or agg_field not in res["aggregations"]:
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return list()
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buckets = res["aggregations"][agg_field]["buckets"]
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return [(b["key"], b["doc_count"]) for b in buckets]
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"""
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SQL
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"""
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def sql(self, sql: str, fetch_size: int, format: str):
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self.logger.debug(f"ESConnection.sql get sql: {sql}")
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sql = re.sub(r"[ `]+", " ", sql)
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sql = sql.replace("%", "")
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replaces = []
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for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql):
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fld, v = r.group(1), r.group(3)
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match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format(fld, rag_tokenizer.fine_grained_tokenize(rag_tokenizer.tokenize(v)))
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replaces.append(("{}{}'{}'".format(r.group(1), r.group(2), r.group(3)), match))
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for p, r in replaces:
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sql = sql.replace(p, r, 1)
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self.logger.debug(f"ESConnection.sql to es: {sql}")
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for i in range(ATTEMPT_TIME):
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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="2s")
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return res
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except ConnectionTimeout:
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self.logger.exception("ES request timeout")
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time.sleep(3)
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self._connect()
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continue
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except BadRequestError as e:
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# LLM-generated SQL routinely references columns that don't exist
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# (e.g. unknown_column / verification_exception). The caller in
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# api/db/services/dialog_service.py:use_sql catches this and either
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# re-prompts the LLM with the error or falls back to vector search,
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# so a full ERROR-level traceback is misleading — see #15409.
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self.logger.warning(f"ESConnection.sql rejected by ES (likely invalid LLM-generated SQL). SQL:\n{sql}\nError: {e}")
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raise Exception(f"SQL error: {e}\n\nSQL: {sql}")
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except Exception as e:
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self.logger.exception(f"ESConnection.sql got exception. SQL:\n{sql}")
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raise Exception(f"SQL error: {e}\n\nSQL: {sql}")
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self.logger.error(f"ESConnection.sql timeout for {ATTEMPT_TIME} times!")
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return None
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