mirror of
https://github.com/infiniflow/ragflow.git
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c08ced09a7
### What problem does this PR solve? add retrieval fallback comments ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
813 lines
34 KiB
Python
813 lines
34 KiB
Python
#
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# Copyright 2024 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 json
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import logging
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import re
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import math
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from collections import OrderedDict, defaultdict
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from dataclasses import dataclass
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from rag.nlp import rag_tokenizer, query
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import numpy as np
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from common.doc_store.doc_store_base import MatchDenseExpr, FusionExpr, OrderByExpr, DocStoreConnection
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from common.string_utils import remove_redundant_spaces
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from common.float_utils import get_float
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from common.constants import PAGERANK_FLD, TAG_FLD
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from common.tag_feature_utils import parse_tag_features
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from common import settings
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from common.misc_utils import thread_pool_exec
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def index_name(uid): return f"ragflow_{uid}"
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class Dealer:
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def __init__(self, dataStore: DocStoreConnection):
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self.qryr = query.FulltextQueryer()
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self.dataStore = dataStore
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@dataclass
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class SearchResult:
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total: int
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ids: list[str]
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query_vector: list[float] | None = None
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field: dict | None = None
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highlight: dict | None = None
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aggregation: list | dict | None = None
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keywords: list[str] | None = None
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group_docs: list[list] | None = None
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async def get_vector(self, txt, emb_mdl, topk=10, similarity=0.1):
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qv, _ = await thread_pool_exec(emb_mdl.encode_queries, txt)
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shape = np.array(qv).shape
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if len(shape) > 1:
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raise Exception(
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f"Dealer.get_vector returned array's shape {shape} doesn't match expectation(exact one dimension).")
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embedding_data = [get_float(v) for v in qv]
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vector_column_name = f"q_{len(embedding_data)}_vec"
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return MatchDenseExpr(vector_column_name, embedding_data, 'float', 'cosine', topk, {"similarity": similarity})
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async def _existing_doc_ids(self, doc_ids: list[str]) -> set[str]:
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if not doc_ids:
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return set()
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unique_doc_ids = list(dict.fromkeys(doc_ids))
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def _load():
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from api.db.services.document_service import DocumentService
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return {row["id"] for row in DocumentService.get_by_ids(unique_doc_ids).dicts()}
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return await thread_pool_exec(_load)
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async def _prune_deleted_chunks(self, sres: SearchResult) -> SearchResult:
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# Temporary safety net:
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# Some delete paths can leave stale chunks in the doc store if the DB row
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# is removed but the vector record is not fully cleaned up. We filter those
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# chunks here so chat/retrieval does not surface content from deleted docs.
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# Keep this as a fallback, not as the primary delete mechanism.
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chunk_doc_ids = [chunk.get("doc_id") for chunk in sres.field.values() if chunk and chunk.get("doc_id")]
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if not chunk_doc_ids:
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return sres
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existing_doc_ids = await self._existing_doc_ids(chunk_doc_ids)
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if len(existing_doc_ids) == len(set(chunk_doc_ids)):
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return sres
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filtered_ids = []
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filtered_field = {}
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filtered_highlight = {} if sres.highlight else sres.highlight
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removed = 0
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for chunk_id in sres.ids:
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chunk = sres.field.get(chunk_id)
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if not chunk or chunk.get("doc_id") not in existing_doc_ids:
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removed += 1
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continue
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filtered_ids.append(chunk_id)
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filtered_field[chunk_id] = chunk
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if sres.highlight and chunk_id in sres.highlight:
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filtered_highlight[chunk_id] = sres.highlight[chunk_id]
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if removed:
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logging.warning("Pruned %s stale chunks whose documents no longer exist.", removed)
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return self.SearchResult(
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total=len(filtered_ids),
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ids=filtered_ids,
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query_vector=sres.query_vector,
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field=filtered_field,
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highlight=filtered_highlight,
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aggregation=sres.aggregation,
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keywords=sres.keywords,
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group_docs=sres.group_docs,
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)
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def get_filters(self, req):
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condition = dict()
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for key, field in {"kb_ids": "kb_id", "doc_ids": "doc_id"}.items():
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if key in req and req[key] is not None:
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condition[field] = req[key]
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# TODO(yzc): `available_int` is nullable however infinity doesn't support nullable columns.
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for key in ["knowledge_graph_kwd", "available_int", "entity_kwd", "from_entity_kwd", "to_entity_kwd",
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"removed_kwd"]:
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if key in req and req[key] is not None:
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condition[key] = req[key]
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return condition
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async def search(self, req, idx_names: str | list[str],
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kb_ids: list[str],
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emb_mdl=None,
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highlight: bool | list | None = None,
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rank_feature: dict | None = None
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):
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if highlight is None:
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highlight = False
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filters = self.get_filters(req)
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orderBy = OrderByExpr()
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pg = int(req.get("page", 1)) - 1
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topk = int(req.get("topk", 1024))
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ps = int(req.get("size", topk))
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offset, limit = pg * ps, ps
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src = req.get("fields",
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["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd", "position_int",
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"doc_id", "chunk_order_int", "page_num_int", "top_int", "create_timestamp_flt", "knowledge_graph_kwd",
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"question_kwd", "question_tks", "doc_type_kwd",
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"available_int", "content_with_weight", "mom_id", PAGERANK_FLD, TAG_FLD, "row_id()"])
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kwds = set([])
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qst = req.get("question", "")
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q_vec = []
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if not qst:
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if req.get("sort"):
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orderBy.asc("chunk_order_int")
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orderBy.asc("page_num_int")
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orderBy.asc("top_int")
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orderBy.desc("create_timestamp_flt")
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res = self.dataStore.search(src, [], filters, [], orderBy, offset, limit, idx_names, kb_ids)
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total = self.dataStore.get_total(res)
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logging.debug("Dealer.search TOTAL: {}".format(total))
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else:
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highlightFields = ["content_ltks", "title_tks"]
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if not highlight:
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highlightFields = []
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elif isinstance(highlight, list):
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highlightFields = highlight
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matchText, keywords = self.qryr.question(qst, min_match=0.3)
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if emb_mdl is None:
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matchExprs = [matchText]
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res = await thread_pool_exec(self.dataStore.search, src, highlightFields, filters, matchExprs, orderBy, offset, limit,
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idx_names, kb_ids, rank_feature=rank_feature)
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total = self.dataStore.get_total(res)
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logging.debug("Dealer.search TOTAL: {}".format(total))
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else:
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matchDense = await self.get_vector(qst, emb_mdl, topk, req.get("similarity", 0.1))
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q_vec = matchDense.embedding_data
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if not settings.DOC_ENGINE_INFINITY:
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src.append(f"q_{len(q_vec)}_vec")
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fusionExpr = FusionExpr("weighted_sum", topk, {"weights": "0.05,0.95"})
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matchExprs = [matchText, matchDense, fusionExpr]
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res = await thread_pool_exec(self.dataStore.search, src, highlightFields, filters, matchExprs, orderBy, offset, limit,
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idx_names, kb_ids, rank_feature=rank_feature)
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total = self.dataStore.get_total(res)
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logging.debug("Dealer.search TOTAL: {}".format(total))
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# If result is empty, try again with lower min_match
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if total == 0:
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if filters.get("doc_id"):
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res = await thread_pool_exec(self.dataStore.search, src, [], filters, [], orderBy, offset, limit, idx_names, kb_ids)
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total = self.dataStore.get_total(res)
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else:
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matchText, _ = self.qryr.question(qst, min_match=0.1)
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matchDense.extra_options["similarity"] = 0.17
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res = await thread_pool_exec(self.dataStore.search, src, highlightFields, filters, [matchText, matchDense, fusionExpr],
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orderBy, offset, limit, idx_names, kb_ids,
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rank_feature=rank_feature)
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total = self.dataStore.get_total(res)
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logging.debug("Dealer.search 2 TOTAL: {}".format(total))
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for k in keywords:
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kwds.add(k)
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for kk in rag_tokenizer.fine_grained_tokenize(k).split():
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if len(kk) < 2:
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continue
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if kk in kwds:
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continue
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kwds.add(kk)
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logging.debug(f"TOTAL: {total}")
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ids = self.dataStore.get_doc_ids(res)
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keywords = list(kwds)
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highlight = self.dataStore.get_highlight(res, keywords, "content_with_weight")
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aggs = self.dataStore.get_aggregation(res, "docnm_kwd")
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return self.SearchResult(
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total=total,
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ids=ids,
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query_vector=q_vec,
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aggregation=aggs,
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highlight=highlight,
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field=self.dataStore.get_fields(res, src + ["_score"]),
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keywords=keywords
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)
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@staticmethod
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def trans2floats(txt):
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return [get_float(t) for t in txt.split("\t")]
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def insert_citations(self, answer, chunks, chunk_v,
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embd_mdl, tkweight=0.1, vtweight=0.9):
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assert len(chunks) == len(chunk_v)
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if not chunks:
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return answer, set([])
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pieces = re.split(r"(```)", answer)
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if len(pieces) >= 3:
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i = 0
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pieces_ = []
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while i < len(pieces):
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if pieces[i] == "```":
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st = i
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i += 1
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while i < len(pieces) and pieces[i] != "```":
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i += 1
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if i < len(pieces):
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i += 1
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pieces_.append("".join(pieces[st: i]) + "\n")
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else:
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# Sentence boundary regex includes Arabic punctuation (، ؛ ؟ ۔)
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pieces_.extend(
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re.split(
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r"([^\|][；。？!！،؛؟۔\n]|[a-z\u0600-\u06FF][.?;!،؛؟][ \n])",
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pieces[i]))
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i += 1
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pieces = pieces_
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else:
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# Sentence boundary regex includes Arabic punctuation (، ؛ ؟ ۔)
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pieces = re.split(r"([^\|][；。？!！،؛؟۔\n]|[a-z\u0600-\u06FF][.?;!،؛؟][ \n])", answer)
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for i in range(1, len(pieces)):
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if re.match(r"([^\|][；。？!！،؛؟۔\n]|[a-z\u0600-\u06FF][.?;!،؛؟][ \n])", pieces[i]):
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pieces[i - 1] += pieces[i][0]
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pieces[i] = pieces[i][1:]
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idx = []
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pieces_ = []
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for i, t in enumerate(pieces):
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if len(t) < 5:
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continue
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idx.append(i)
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pieces_.append(t)
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logging.debug("{} => {}".format(answer, pieces_))
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if not pieces_:
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return answer, set([])
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ans_v, _ = embd_mdl.encode(pieces_)
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for i in range(len(chunk_v)):
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if len(ans_v[0]) != len(chunk_v[i]):
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chunk_v[i] = [0.0] * len(ans_v[0])
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logging.warning(
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"The dimension of query and chunk do not match: {} vs. {}".format(len(ans_v[0]), len(chunk_v[i])))
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assert len(ans_v[0]) == len(chunk_v[0]), "The dimension of query and chunk do not match: {} vs. {}".format(
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len(ans_v[0]), len(chunk_v[0]))
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chunks_tks = [rag_tokenizer.tokenize(self.qryr.rmWWW(ck)).split()
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for ck in chunks]
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cites = {}
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thr = 0.63
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while thr > 0.3 and len(cites.keys()) == 0 and pieces_ and chunks_tks:
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for i, a in enumerate(pieces_):
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sim, tksim, vtsim = self.qryr.hybrid_similarity(ans_v[i],
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chunk_v,
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rag_tokenizer.tokenize(
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self.qryr.rmWWW(pieces_[i])).split(),
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chunks_tks,
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tkweight, vtweight)
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mx = np.max(sim) * 0.99
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logging.debug("{} SIM: {}".format(pieces_[i], mx))
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if mx < thr:
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continue
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cites[idx[i]] = list(
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set([str(ii) for ii in range(len(chunk_v)) if sim[ii] > mx]))[:4]
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thr *= 0.8
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res = ""
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seted = set([])
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for i, p in enumerate(pieces):
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res += p
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if i not in idx:
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continue
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if i not in cites:
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continue
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for c in cites[i]:
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assert int(c) < len(chunk_v)
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for c in cites[i]:
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if c in seted:
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continue
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res += f" [ID:{c}]"
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seted.add(c)
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return res, seted
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def _rank_feature_scores(self, query_rfea, search_res):
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## For rank feature(tag_fea) scores.
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rank_fea = []
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pageranks = []
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for chunk_id in search_res.ids:
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pageranks.append(search_res.field[chunk_id].get(PAGERANK_FLD, 0))
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pageranks = np.array(pageranks, dtype=float)
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if not query_rfea:
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return np.array([0 for _ in range(len(search_res.ids))]) + pageranks
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q_denor = np.sqrt(np.sum([s * s for t, s in query_rfea.items() if t != PAGERANK_FLD]))
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if q_denor == 0:
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return np.array([0 for _ in range(len(search_res.ids))]) + pageranks
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for i in search_res.ids:
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nor, denor = 0, 0
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if not search_res.field[i].get(TAG_FLD):
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rank_fea.append(0)
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continue
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tag_feas = parse_tag_features(search_res.field[i].get(TAG_FLD), allow_json_string=True, allow_python_literal=True)
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if not tag_feas:
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rank_fea.append(0)
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continue
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for t, sc in tag_feas.items():
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if t in query_rfea:
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nor += query_rfea[t] * sc
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denor += sc * sc
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if denor == 0:
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rank_fea.append(0)
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else:
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rank_fea.append(nor / np.sqrt(denor) / q_denor)
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return np.array(rank_fea) * 10. + pageranks
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def rerank(self, sres, query, tkweight=0.3,
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vtweight=0.7, cfield="content_ltks",
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rank_feature: dict | None = None
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):
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_, keywords = self.qryr.question(query)
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vector_size = len(sres.query_vector)
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vector_column = f"q_{vector_size}_vec"
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zero_vector = [0.0] * vector_size
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ins_embd = []
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for chunk_id in sres.ids:
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vector = sres.field[chunk_id].get(vector_column, zero_vector)
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if isinstance(vector, str):
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vector = [get_float(v) for v in vector.split("\t")]
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ins_embd.append(vector)
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if not ins_embd:
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return [], [], []
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for i in sres.ids:
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if isinstance(sres.field[i].get("important_kwd", []), str):
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sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
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ins_tw = []
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for i in sres.ids:
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content_ltks = list(OrderedDict.fromkeys(sres.field[i][cfield].split()))
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title_tks = [t for t in sres.field[i].get("title_tks", "").split() if t]
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question_tks = [t for t in sres.field[i].get("question_tks", "").split() if t]
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important_kwd = sres.field[i].get("important_kwd", [])
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tks = content_ltks + title_tks * 2 + important_kwd * 5 + question_tks * 6
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ins_tw.append(tks)
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## For rank feature(tag_fea) scores.
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rank_fea = self._rank_feature_scores(rank_feature, sres)
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sim, tksim, vtsim = self.qryr.hybrid_similarity(sres.query_vector,
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ins_embd,
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keywords,
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ins_tw, tkweight, vtweight)
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return sim + rank_fea, tksim, vtsim
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def rerank_by_model(self, rerank_mdl, sres, query, tkweight=0.3,
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vtweight=0.7, cfield="content_ltks",
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rank_feature: dict | None = None):
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print(f"[DEBUG rerank_by_model] query={query}, tkweight={tkweight}, vtweight={vtweight}")
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_, keywords = self.qryr.question(query)
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print(f"[DEBUG rerank_by_model] keywords={keywords}")
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for i in sres.ids:
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if isinstance(sres.field[i].get("important_kwd", []), str):
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sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
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ins_tw = []
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for i in sres.ids:
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content_ltks = sres.field[i][cfield].split()
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title_tks = [t for t in sres.field[i].get("title_tks", "").split() if t]
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important_kwd = sres.field[i].get("important_kwd", [])
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tks = content_ltks + title_tks + important_kwd
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ins_tw.append(tks)
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print(f"[DEBUG rerank_by_model] chunk id={i}, content_ltks={len(content_ltks)}, title_tks={len(title_tks)}, important_kwd={len(important_kwd)}")
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doc_text = remove_redundant_spaces(" ".join(tks))
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if len(doc_text) > 100:
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print(f"[DEBUG rerank_by_model] chunk id={i}, doc_text (first 100)={doc_text[:100]}...")
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else:
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print(f"[DEBUG rerank_by_model] chunk id={i}, doc_text={doc_text}")
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docs = [remove_redundant_spaces(" ".join(tks)) for tks in ins_tw]
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print(f"[DEBUG rerank_by_model] docs sent to reranker: {len(docs)} docs")
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for idx, doc in enumerate(docs[:2]): # Print first 2
|
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print(f"[DEBUG rerank_by_model] doc[{idx}] len={len(doc)}, full={doc}")
|
||
if len(doc) > 100:
|
||
print(f"[DEBUG rerank_by_model] doc[{idx}] (first 100)={doc[:100]}...")
|
||
else:
|
||
print(f"[DEBUG rerank_by_model] doc[{idx}]={doc}")
|
||
|
||
tksim = self.qryr.token_similarity(keywords, ins_tw)
|
||
print(f"[DEBUG rerank_by_model] tksim={tksim}")
|
||
vtsim, _ = rerank_mdl.similarity(query, docs)
|
||
print(f"[DEBUG rerank_by_model] vtsim from reranker={vtsim}")
|
||
## For rank feature(tag_fea) scores.
|
||
rank_fea = self._rank_feature_scores(rank_feature, sres)
|
||
print(f"[DEBUG rerank_by_model] rank_fea={rank_fea}")
|
||
|
||
return tkweight * np.array(tksim) + vtweight * vtsim + rank_fea, tksim, vtsim
|
||
|
||
def hybrid_similarity(self, ans_embd, ins_embd, ans, inst):
|
||
return self.qryr.hybrid_similarity(ans_embd,
|
||
ins_embd,
|
||
rag_tokenizer.tokenize(ans).split(),
|
||
rag_tokenizer.tokenize(inst).split())
|
||
|
||
async def retrieval(
|
||
self,
|
||
question,
|
||
embd_mdl,
|
||
tenant_ids,
|
||
kb_ids,
|
||
page,
|
||
page_size,
|
||
similarity_threshold=0.2,
|
||
vector_similarity_weight=0.3,
|
||
top=1024,
|
||
doc_ids=None,
|
||
aggs=True,
|
||
rerank_mdl=None,
|
||
highlight=False,
|
||
rank_feature: dict | None = {PAGERANK_FLD: 10},
|
||
):
|
||
ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
|
||
if not question:
|
||
return ranks
|
||
|
||
# Keep the historical windowing strategy by default, but when an external
|
||
# reranker is enabled cap candidate count by both top_k and provider-safe 64.
|
||
RERANK_LIMIT = math.ceil(64 / page_size) * page_size if page_size > 1 else 1
|
||
RERANK_LIMIT = max(30, RERANK_LIMIT)
|
||
if rerank_mdl and top > 0:
|
||
RERANK_LIMIT = min(RERANK_LIMIT, top, 64)
|
||
page = max(page, 1)
|
||
global_offset = (page - 1) * page_size
|
||
req = {
|
||
"kb_ids": kb_ids,
|
||
"doc_ids": doc_ids,
|
||
"page": global_offset // RERANK_LIMIT + 1,
|
||
"size": RERANK_LIMIT,
|
||
"question": question,
|
||
"vector": True,
|
||
"topk": top,
|
||
"similarity": similarity_threshold,
|
||
"available_int": 1,
|
||
}
|
||
logging.debug(f"[Search] global_offset={global_offset}, rerank_limit={RERANK_LIMIT}, page_size={page_size}, page={page}")
|
||
|
||
if isinstance(tenant_ids, str):
|
||
tenant_ids = tenant_ids.split(",")
|
||
|
||
sres = await self.search(req, [index_name(tid) for tid in tenant_ids], kb_ids, embd_mdl, highlight,
|
||
rank_feature=rank_feature)
|
||
# Temporary retrieval-side guard: prune chunks whose parent document no
|
||
# longer exists before reranking and returning results.
|
||
sres = await self._prune_deleted_chunks(sres)
|
||
if sres.total == 0:
|
||
ranks["doc_aggs"] = []
|
||
return ranks
|
||
|
||
if rerank_mdl and sres.total > 0:
|
||
sim, tsim, vsim = self.rerank_by_model(
|
||
rerank_mdl,
|
||
sres,
|
||
question,
|
||
1 - vector_similarity_weight,
|
||
vector_similarity_weight,
|
||
rank_feature=rank_feature,
|
||
)
|
||
else:
|
||
if settings.DOC_ENGINE_INFINITY:
|
||
# Don't need rerank here since Infinity normalizes each way score before fusion.
|
||
sim = [sres.field[id].get("_score", 0.0) for id in sres.ids]
|
||
sim = [s if s is not None else 0.0 for s in sim]
|
||
tsim = sim
|
||
vsim = sim
|
||
else:
|
||
# ElasticSearch doesn't normalize each way score before fusion.
|
||
sim, tsim, vsim = self.rerank(
|
||
sres,
|
||
question,
|
||
1 - vector_similarity_weight,
|
||
vector_similarity_weight,
|
||
rank_feature=rank_feature,
|
||
)
|
||
|
||
sim_np = np.array(sim, dtype=np.float64)
|
||
if sim_np.size == 0:
|
||
ranks["doc_aggs"] = []
|
||
return ranks
|
||
|
||
sorted_idx = np.argsort(sim_np * -1)
|
||
|
||
# When vector_similarity_weight is 0, similarity_threshold is not meaningful for term-only scores.
|
||
post_threshold = 0.0 if vector_similarity_weight <= 0 else similarity_threshold
|
||
|
||
# When doc_ids is explicitly provided (metadata or document filtering), bypass threshold
|
||
# User wants those specific documents regardless of their relevance score
|
||
if doc_ids:
|
||
post_threshold = 0.0
|
||
|
||
valid_idx = [int(i) for i in sorted_idx if sim_np[i] >= post_threshold]
|
||
filtered_count = len(valid_idx)
|
||
ranks["total"] = int(filtered_count)
|
||
|
||
if filtered_count == 0:
|
||
ranks["doc_aggs"] = []
|
||
return ranks
|
||
|
||
begin = global_offset % RERANK_LIMIT
|
||
end = begin + page_size
|
||
page_idx = valid_idx[begin:end]
|
||
|
||
dim = len(sres.query_vector)
|
||
vector_column = f"q_{dim}_vec"
|
||
zero_vector = [0.0] * dim
|
||
|
||
for i in page_idx:
|
||
id = sres.ids[i]
|
||
chunk = sres.field[id]
|
||
dnm = chunk.get("docnm_kwd", "")
|
||
did = chunk.get("doc_id", "")
|
||
|
||
position_int = chunk.get("position_int", [])
|
||
d = {
|
||
"chunk_id": id,
|
||
"content_ltks": chunk["content_ltks"],
|
||
"content_with_weight": chunk["content_with_weight"],
|
||
"doc_id": did,
|
||
"docnm_kwd": dnm,
|
||
"kb_id": chunk["kb_id"],
|
||
"important_kwd": chunk.get("important_kwd", []),
|
||
"tag_kwd": chunk.get("tag_kwd", []),
|
||
"image_id": chunk.get("img_id", ""),
|
||
"similarity": float(sim_np[i]),
|
||
"vector_similarity": float(vsim[i]),
|
||
"term_similarity": float(tsim[i]),
|
||
"vector": chunk.get(vector_column, zero_vector),
|
||
"positions": position_int,
|
||
"doc_type_kwd": chunk.get("doc_type_kwd", ""),
|
||
"mom_id": chunk.get("mom_id", ""),
|
||
"row_id": chunk.get("row_id()"),
|
||
}
|
||
if highlight and sres.highlight:
|
||
if id in sres.highlight:
|
||
d["highlight"] = remove_redundant_spaces(sres.highlight[id])
|
||
else:
|
||
d["highlight"] = d["content_with_weight"]
|
||
ranks["chunks"].append(d)
|
||
|
||
if aggs:
|
||
for i in valid_idx:
|
||
id = sres.ids[i]
|
||
chunk = sres.field[id]
|
||
dnm = chunk.get("docnm_kwd", "")
|
||
did = chunk.get("doc_id", "")
|
||
if dnm not in ranks["doc_aggs"]:
|
||
ranks["doc_aggs"][dnm] = {"doc_id": did, "count": 0}
|
||
ranks["doc_aggs"][dnm]["count"] += 1
|
||
|
||
ranks["doc_aggs"] = [
|
||
{
|
||
"doc_name": k,
|
||
"doc_id": v["doc_id"],
|
||
"count": v["count"],
|
||
}
|
||
for k, v in sorted(
|
||
ranks["doc_aggs"].items(),
|
||
key=lambda x: x[1]["count"] * -1,
|
||
)
|
||
]
|
||
else:
|
||
ranks["doc_aggs"] = []
|
||
|
||
return ranks
|
||
|
||
def sql_retrieval(self, sql, fetch_size=128, format="json"):
|
||
tbl = self.dataStore.sql(sql, fetch_size, format)
|
||
return tbl
|
||
|
||
def chunk_list(self, doc_id: str, tenant_id: str,
|
||
kb_ids: list[str], max_count=1024,
|
||
offset=0,
|
||
fields=["docnm_kwd", "content_with_weight", "img_id"],
|
||
sort_by_position: bool = False):
|
||
condition = {"doc_id": doc_id}
|
||
|
||
fields_set = set(fields or [])
|
||
if sort_by_position:
|
||
for need in ("page_num_int", "position_int", "top_int"):
|
||
if need not in fields_set:
|
||
fields_set.add(need)
|
||
fields = list(fields_set)
|
||
|
||
orderBy = OrderByExpr()
|
||
if sort_by_position:
|
||
orderBy.asc("page_num_int")
|
||
orderBy.asc("position_int")
|
||
orderBy.asc("top_int")
|
||
|
||
res = []
|
||
bs = 128
|
||
for p in range(offset, max_count, bs):
|
||
limit = min(bs, max_count - p)
|
||
if limit <= 0:
|
||
break
|
||
es_res = self.dataStore.search(fields, [], condition, [], orderBy, p, limit, index_name(tenant_id),
|
||
kb_ids)
|
||
dict_chunks = self.dataStore.get_fields(es_res, fields)
|
||
for id, doc in dict_chunks.items():
|
||
doc["id"] = id
|
||
if dict_chunks:
|
||
res.extend(dict_chunks.values())
|
||
chunk_count = len(dict_chunks)
|
||
if chunk_count == 0 or chunk_count < limit:
|
||
break
|
||
return res
|
||
|
||
def all_tags(self, tenant_id: str, kb_ids: list[str], S=1000):
|
||
if not self.dataStore.index_exist(index_name(tenant_id), kb_ids[0]):
|
||
return []
|
||
res = self.dataStore.search([], [], {}, [], OrderByExpr(), 0, 0, index_name(tenant_id), kb_ids, ["tag_kwd"])
|
||
return self.dataStore.get_aggregation(res, "tag_kwd")
|
||
|
||
def all_tags_in_portion(self, tenant_id: str, kb_ids: list[str], S=1000):
|
||
res = self.dataStore.search([], [], {}, [], OrderByExpr(), 0, 0, index_name(tenant_id), kb_ids, ["tag_kwd"])
|
||
res = self.dataStore.get_aggregation(res, "tag_kwd")
|
||
total = np.sum([c for _, c in res])
|
||
return {t: (c + 1) / (total + S) for t, c in res}
|
||
|
||
def tag_content(self, tenant_id: str, kb_ids: list[str], doc, all_tags, topn_tags=3, keywords_topn=30, S=1000):
|
||
idx_nm = index_name(tenant_id)
|
||
match_txt = self.qryr.paragraph(doc["title_tks"] + " " + doc["content_ltks"], doc.get("important_kwd", []),
|
||
keywords_topn)
|
||
res = self.dataStore.search([], [], {}, [match_txt], OrderByExpr(), 0, 0, idx_nm, kb_ids, ["tag_kwd"])
|
||
aggs = self.dataStore.get_aggregation(res, "tag_kwd")
|
||
if not aggs:
|
||
return False
|
||
cnt = np.sum([c for _, c in aggs])
|
||
tag_fea = sorted([(a, round(0.1 * (c + 1) / (cnt + S) / max(1e-6, all_tags.get(a, 0.0001)))) for a, c in aggs],
|
||
key=lambda x: x[1] * -1)[:topn_tags]
|
||
doc[TAG_FLD] = {a.replace(".", "_"): c for a, c in tag_fea if c > 0}
|
||
return True
|
||
|
||
def tag_query(self, question: str, tenant_ids: str | list[str], kb_ids: list[str], all_tags, topn_tags=3, S=1000):
|
||
if isinstance(tenant_ids, str):
|
||
idx_nms = index_name(tenant_ids)
|
||
else:
|
||
idx_nms = [index_name(tid) for tid in tenant_ids]
|
||
match_txt, _ = self.qryr.question(question, min_match=0.0)
|
||
res = self.dataStore.search([], [], {}, [match_txt], OrderByExpr(), 0, 0, idx_nms, kb_ids, ["tag_kwd"])
|
||
aggs = self.dataStore.get_aggregation(res, "tag_kwd")
|
||
if not aggs:
|
||
return {}
|
||
cnt = np.sum([c for _, c in aggs])
|
||
tag_fea = sorted([(a, round(0.1 * (c + 1) / (cnt + S) / max(1e-6, all_tags.get(a, 0.0001)))) for a, c in aggs],
|
||
key=lambda x: x[1] * -1)[:topn_tags]
|
||
return {a.replace(".", "_"): max(1, c) for a, c in tag_fea}
|
||
|
||
async def retrieval_by_toc(self, query: str, chunks: list[dict], tenant_ids: list[str], chat_mdl, topn: int = 6):
|
||
from rag.prompts.generator import relevant_chunks_with_toc # moved from the top of the file to avoid circular import
|
||
if not chunks:
|
||
return []
|
||
idx_nms = [index_name(tid) for tid in tenant_ids]
|
||
ranks, doc_id2kb_id = {}, {}
|
||
for ck in chunks:
|
||
if ck["doc_id"] not in ranks:
|
||
ranks[ck["doc_id"]] = 0
|
||
ranks[ck["doc_id"]] += ck["similarity"]
|
||
doc_id2kb_id[ck["doc_id"]] = ck["kb_id"]
|
||
doc_id = sorted(ranks.items(), key=lambda x: x[1] * -1.)[0][0]
|
||
kb_ids = [doc_id2kb_id[doc_id]]
|
||
es_res = self.dataStore.search(["content_with_weight"], [], {"doc_id": doc_id, "toc_kwd": "toc"}, [],
|
||
OrderByExpr(), 0, 128, idx_nms,
|
||
kb_ids)
|
||
toc = []
|
||
dict_chunks = self.dataStore.get_fields(es_res, ["content_with_weight"])
|
||
for _, doc in dict_chunks.items():
|
||
try:
|
||
toc.extend(json.loads(doc["content_with_weight"]))
|
||
except Exception as e:
|
||
logging.exception(e)
|
||
if not toc:
|
||
return chunks
|
||
|
||
ids = await relevant_chunks_with_toc(query, toc, chat_mdl, topn * 2)
|
||
if not ids:
|
||
return chunks
|
||
|
||
vector_size = 1024
|
||
id2idx = {ck["chunk_id"]: i for i, ck in enumerate(chunks)}
|
||
for cid, sim in ids:
|
||
if cid in id2idx:
|
||
chunks[id2idx[cid]]["similarity"] += sim
|
||
continue
|
||
chunk = self.dataStore.get(cid, idx_nms[0], kb_ids)
|
||
if not chunk:
|
||
continue
|
||
d = {
|
||
"chunk_id": cid,
|
||
"content_ltks": chunk["content_ltks"],
|
||
"content_with_weight": chunk["content_with_weight"],
|
||
"doc_id": doc_id,
|
||
"docnm_kwd": chunk.get("docnm_kwd", ""),
|
||
"kb_id": chunk["kb_id"],
|
||
"important_kwd": chunk.get("important_kwd", []),
|
||
"image_id": chunk.get("img_id", ""),
|
||
"similarity": sim,
|
||
"vector_similarity": sim,
|
||
"term_similarity": sim,
|
||
"vector": [0.0] * vector_size,
|
||
"positions": chunk.get("position_int", []),
|
||
"doc_type_kwd": chunk.get("doc_type_kwd", "")
|
||
}
|
||
for k in chunk.keys():
|
||
if k[-4:] == "_vec":
|
||
d["vector"] = chunk[k]
|
||
vector_size = len(chunk[k])
|
||
break
|
||
chunks.append(d)
|
||
|
||
return sorted(chunks, key=lambda x: x["similarity"] * -1)[:topn]
|
||
|
||
def retrieval_by_children(self, chunks: list[dict], tenant_ids: list[str]):
|
||
if not chunks:
|
||
return []
|
||
idx_nms = [index_name(tid) for tid in tenant_ids]
|
||
mom_chunks = defaultdict(list)
|
||
i = 0
|
||
while i < len(chunks):
|
||
ck = chunks[i]
|
||
mom_id = ck.get("mom_id")
|
||
if not isinstance(mom_id, str) or not mom_id.strip():
|
||
i += 1
|
||
continue
|
||
mom_chunks[ck["mom_id"]].append(chunks.pop(i))
|
||
|
||
if not mom_chunks:
|
||
return chunks
|
||
|
||
if not chunks:
|
||
chunks = []
|
||
|
||
vector_size = 1024
|
||
for id, cks in mom_chunks.items():
|
||
chunk = self.dataStore.get(id, idx_nms[0], [ck["kb_id"] for ck in cks])
|
||
d = {
|
||
"chunk_id": id,
|
||
"content_ltks": " ".join([ck["content_ltks"] for ck in cks]),
|
||
"content_with_weight": chunk["content_with_weight"],
|
||
"doc_id": chunk["doc_id"],
|
||
"docnm_kwd": chunk.get("docnm_kwd", ""),
|
||
"kb_id": chunk["kb_id"],
|
||
"important_kwd": [kwd for ck in cks for kwd in ck.get("important_kwd", [])],
|
||
"image_id": chunk.get("img_id", ""),
|
||
"similarity": np.mean([ck["similarity"] for ck in cks]),
|
||
"vector_similarity": np.mean([ck["similarity"] for ck in cks]),
|
||
"term_similarity": np.mean([ck["similarity"] for ck in cks]),
|
||
"vector": [0.0] * vector_size,
|
||
"positions": chunk.get("position_int", []),
|
||
"doc_type_kwd": chunk.get("doc_type_kwd", "")
|
||
}
|
||
for k in cks[0].keys():
|
||
if k[-4:] == "_vec":
|
||
d["vector"] = cks[0][k]
|
||
vector_size = len(cks[0][k])
|
||
break
|
||
chunks.append(d)
|
||
|
||
return sorted(chunks, key=lambda x: x["similarity"] * -1)
|