d104f59e29
This PR implements the complete three-tier hierarchical retrieval architecture as specified in issue #11610, enabling production-grade RAG capabilities. ## Tier 1: Knowledge Base Routing - Auto-route queries to relevant knowledge bases - Per-KB retrieval parameters (KBRetrievalParams dataclass) - Rule-based routing with keyword overlap scoring - LLM-based routing with fallback to rule-based - Configurable routing methods: auto, rule_based, llm_based, all ## Tier 2: Document Filtering - Document-level metadata filtering within selected KBs - Configurable metadata fields for filtering - LLM-generated filter conditions - Metadata similarity matching (fuzzy matching) - Enhanced metadata generation for documents ## Tier 3: Chunk Refinement - Parent-child chunking with summary mapping - Custom prompts for keyword extraction - LLM-based question generation for chunks - Integration with existing retrieval pipeline ## Metadata Management (Batch CRUD) - MetadataService with batch operations: - batch_get_metadata - batch_update_metadata - batch_delete_metadata_fields - batch_set_metadata_field - get_metadata_schema - search_by_metadata - get_metadata_statistics - copy_metadata - REST API endpoints in metadata_app.py ## Integration - HierarchicalConfig dataclass for configuration - Integrated into Dealer class (search.py) - Wired into agent retrieval tool - Non-breaking: disabled by default ## Tests - 48 unit tests covering all components - Tests for config, routing, filtering, and metadata operations
1746 lines
68 KiB
Python
1746 lines
68 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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import time
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from collections import OrderedDict, defaultdict
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from dataclasses import dataclass, field
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from typing import List, Dict, Any, Optional
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from rag.prompts.generator import relevant_chunks_with_toc
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from rag.nlp import rag_tokenizer, query
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import numpy as np
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from rag.utils.doc_store_conn import DocStoreConnection, MatchDenseExpr, FusionExpr, OrderByExpr
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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 import settings
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def index_name(uid): return f"ragflow_{uid}"
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@dataclass
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class KBRetrievalParams:
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"""Per-KB retrieval parameters for independent configuration."""
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kb_id: str
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vector_similarity_weight: float = 0.7 # Weight for vector vs keyword
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similarity_threshold: float = 0.2
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top_k: int = 1024
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rerank_enabled: bool = True
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@dataclass
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class HierarchicalConfig:
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"""Configuration for hierarchical retrieval.
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Hierarchical retrieval uses a three-tier approach:
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1. KB Routing: Select relevant knowledge bases based on query
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2. Document Filtering: Filter documents by metadata before vector search
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3. Chunk Refinement: Precise vector search within filtered scope
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"""
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# Enable hierarchical retrieval
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enabled: bool = False
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# Tier 1: KB Routing
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enable_kb_routing: bool = True
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kb_routing_method: str = "auto" # "auto", "rule_based", "llm_based", "all"
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kb_routing_threshold: float = 0.3 # Min keyword overlap score
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kb_top_k: int = 3 # Max KBs to select
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kb_params: Dict[str, KBRetrievalParams] = field(default_factory=dict) # Per-KB params
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# Tier 2: Document Filtering
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enable_doc_filtering: bool = True
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doc_top_k: int = 100 # Max documents to pass to tier 3
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metadata_fields: List[str] = field(default_factory=list) # Fields for filtering
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enable_metadata_similarity: bool = False # Fuzzy matching for text metadata
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metadata_similarity_threshold: float = 0.7
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use_llm_metadata_filter: bool = False # LLM-generated filter conditions
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# Tier 3: Chunk Refinement
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chunk_top_k: int = 10
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enable_parent_child: bool = False # Parent-child chunking with summary mapping
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use_summary_mapping: bool = False # Match via summary vectors first
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# Customizable prompts
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keyword_extraction_prompt: Optional[str] = None
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question_generation_prompt: Optional[str] = None
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# LLM-based enhancements
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use_llm_question_generation: bool = False # Generate questions from chunks
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@dataclass
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class HierarchicalResult:
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"""Result metadata from hierarchical retrieval."""
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selected_kb_ids: List[str] = field(default_factory=list)
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filtered_doc_ids: List[str] = field(default_factory=list)
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tier1_time_ms: float = 0.0
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tier2_time_ms: float = 0.0
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tier3_time_ms: float = 0.0
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total_time_ms: float = 0.0
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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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def get_vector(self, txt, emb_mdl, topk=10, similarity=0.1):
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qv, _ = 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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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", "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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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", "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])
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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("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 = 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 = 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 = 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 = 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 = self.dataStore.search(src, highlightFields, filters, [matchText, matchDense, fusionExpr],
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orderBy, offset, limit, 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 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_chunk_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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pieces_.extend(
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re.split(
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r"([^\|][；。？!！\n]|[a-z][.?;!][ \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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pieces = re.split(r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", answer)
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for i in range(1, len(pieces)):
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if re.match(r"([^\|][；。？!！\n]|[a-z][.?;!][ \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("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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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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for t, sc in eval(search_res.field[i].get(TAG_FLD, "{}")).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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_, keywords = self.qryr.question(query)
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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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|
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tksim = self.qryr.token_similarity(keywords, ins_tw)
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vtsim, _ = rerank_mdl.similarity(query, [remove_redundant_spaces(" ".join(tks)) for tks in ins_tw])
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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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return tkweight * np.array(tksim) + vtweight * vtsim + rank_fea, tksim, vtsim
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def hybrid_similarity(self, ans_embd, ins_embd, ans, inst):
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return self.qryr.hybrid_similarity(ans_embd,
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ins_embd,
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rag_tokenizer.tokenize(ans).split(),
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rag_tokenizer.tokenize(inst).split())
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|
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def retrieval(
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self,
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question,
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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
|
||
|
||
# Ensure RERANK_LIMIT is multiple of page_size
|
||
RERANK_LIMIT = math.ceil(64 / page_size) * page_size if page_size > 1 else 1
|
||
req = {
|
||
"kb_ids": kb_ids,
|
||
"doc_ids": doc_ids,
|
||
"page": math.ceil(page_size * page / RERANK_LIMIT),
|
||
"size": RERANK_LIMIT,
|
||
"question": question,
|
||
"vector": True,
|
||
"topk": top,
|
||
"similarity": similarity_threshold,
|
||
"available_int": 1,
|
||
}
|
||
|
||
if isinstance(tenant_ids, str):
|
||
tenant_ids = tenant_ids.split(",")
|
||
|
||
sres = self.search(req, [index_name(tid) for tid in tenant_ids], kb_ids, embd_mdl, highlight, rank_feature=rank_feature)
|
||
|
||
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)
|
||
|
||
valid_idx = [int(i) for i in sorted_idx if sim_np[i] >= similarity_threshold]
|
||
filtered_count = len(valid_idx)
|
||
ranks["total"] = int(filtered_count)
|
||
|
||
if filtered_count == 0:
|
||
ranks["doc_aggs"] = []
|
||
return ranks
|
||
|
||
max_pages = max(RERANK_LIMIT // max(page_size, 1), 1)
|
||
page_index = (page - 1) % max_pages
|
||
begin = page_index * page_size
|
||
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", []),
|
||
"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", ""),
|
||
}
|
||
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 hierarchical_retrieval(
|
||
self,
|
||
question: str,
|
||
embd_mdl,
|
||
tenant_ids,
|
||
kb_ids: List[str],
|
||
kb_infos: Optional[List[Dict[str, Any]]] = None,
|
||
page: int = 1,
|
||
page_size: int = 10,
|
||
similarity_threshold: float = 0.2,
|
||
vector_similarity_weight: float = 0.3,
|
||
top: int = 1024,
|
||
doc_ids: Optional[List[str]] = None,
|
||
aggs: bool = True,
|
||
rerank_mdl=None,
|
||
highlight: bool = False,
|
||
rank_feature: Optional[dict] = None,
|
||
hierarchical_config: Optional[HierarchicalConfig] = None,
|
||
chat_mdl=None,
|
||
doc_metadata: Optional[List[Dict[str, Any]]] = None,
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Perform hierarchical retrieval with three tiers:
|
||
1. KB Routing: Select relevant knowledge bases
|
||
2. Document Filtering: Filter by document metadata
|
||
3. Chunk Refinement: Precise vector search
|
||
|
||
Args:
|
||
question: User query
|
||
embd_mdl: Embedding model
|
||
tenant_ids: Tenant IDs
|
||
kb_ids: Knowledge base IDs to search
|
||
kb_infos: Optional KB metadata (name, description) for routing
|
||
page: Page number
|
||
page_size: Results per page
|
||
similarity_threshold: Minimum similarity score
|
||
vector_similarity_weight: Weight for vector vs keyword similarity
|
||
top: Maximum results to consider
|
||
doc_ids: Optional document IDs to filter
|
||
aggs: Whether to include aggregations
|
||
rerank_mdl: Optional reranking model
|
||
highlight: Whether to include highlights
|
||
rank_feature: Ranking features
|
||
hierarchical_config: Hierarchical retrieval configuration
|
||
chat_mdl: Optional chat model for LLM-based features
|
||
doc_metadata: Optional document metadata for filtering
|
||
|
||
Returns:
|
||
Dict with chunks, doc_aggs, and hierarchical_metadata
|
||
"""
|
||
if rank_feature is None:
|
||
rank_feature = {PAGERANK_FLD: 10}
|
||
|
||
config = hierarchical_config or HierarchicalConfig()
|
||
start_time = time.time()
|
||
|
||
h_result = HierarchicalResult()
|
||
|
||
# Tier 1: KB Routing
|
||
tier1_start = time.time()
|
||
selected_kb_ids = self._tier1_kb_routing(
|
||
question, kb_ids, kb_infos, config, chat_mdl
|
||
)
|
||
h_result.selected_kb_ids = selected_kb_ids
|
||
h_result.tier1_time_ms = (time.time() - tier1_start) * 1000
|
||
|
||
if not selected_kb_ids:
|
||
logging.warning(f"Hierarchical retrieval: No KBs selected for query: {question[:50]}...")
|
||
return {
|
||
"total": 0,
|
||
"chunks": [],
|
||
"doc_aggs": [],
|
||
"hierarchical_metadata": h_result
|
||
}
|
||
|
||
logging.info(f"Tier 1: Selected {len(selected_kb_ids)}/{len(kb_ids)} KBs")
|
||
|
||
# Get per-KB parameters for selected KBs
|
||
kb_specific_params = {
|
||
kb_id: config.kb_params.get(kb_id)
|
||
for kb_id in selected_kb_ids
|
||
if kb_id in config.kb_params
|
||
}
|
||
if kb_specific_params:
|
||
logging.info(f"Using per-KB params for: {list(kb_specific_params.keys())}")
|
||
|
||
# Tier 2: Document Filtering
|
||
tier2_start = time.time()
|
||
filtered_doc_ids = self._tier2_document_filtering(
|
||
question, tenant_ids, selected_kb_ids, doc_ids, config, embd_mdl,
|
||
chat_mdl, doc_metadata
|
||
)
|
||
h_result.filtered_doc_ids = filtered_doc_ids
|
||
h_result.tier2_time_ms = (time.time() - tier2_start) * 1000
|
||
|
||
if filtered_doc_ids:
|
||
logging.info(f"Tier 2: Filtered to {len(filtered_doc_ids)} documents")
|
||
|
||
# Tier 3: Chunk Refinement
|
||
tier3_start = time.time()
|
||
|
||
# Use filtered doc_ids if available, otherwise use original
|
||
effective_doc_ids = filtered_doc_ids if filtered_doc_ids else doc_ids
|
||
|
||
# Parent-child chunking with summary mapping
|
||
if config.enable_parent_child and config.use_summary_mapping:
|
||
ranks = self._tier3_summary_mapping_retrieval(
|
||
question=question,
|
||
embd_mdl=embd_mdl,
|
||
tenant_ids=tenant_ids,
|
||
kb_ids=selected_kb_ids,
|
||
doc_ids=effective_doc_ids,
|
||
page=page,
|
||
page_size=page_size,
|
||
similarity_threshold=similarity_threshold,
|
||
vector_similarity_weight=vector_similarity_weight,
|
||
top=top,
|
||
aggs=aggs,
|
||
rerank_mdl=rerank_mdl,
|
||
highlight=highlight,
|
||
rank_feature=rank_feature,
|
||
config=config,
|
||
)
|
||
else:
|
||
# Standard retrieval
|
||
ranks = self.retrieval(
|
||
question=question,
|
||
embd_mdl=embd_mdl,
|
||
tenant_ids=tenant_ids,
|
||
kb_ids=selected_kb_ids, # Use selected KBs from Tier 1
|
||
page=page,
|
||
page_size=page_size,
|
||
similarity_threshold=similarity_threshold,
|
||
vector_similarity_weight=vector_similarity_weight,
|
||
top=top,
|
||
doc_ids=effective_doc_ids, # Use filtered docs from Tier 2
|
||
aggs=aggs,
|
||
rerank_mdl=rerank_mdl,
|
||
highlight=highlight,
|
||
rank_feature=rank_feature,
|
||
)
|
||
|
||
# Apply customizable prompts for keyword extraction if configured
|
||
if config.keyword_extraction_prompt and ranks.get("chunks"):
|
||
ranks["chunks"] = self._apply_custom_keyword_extraction(
|
||
ranks["chunks"], config.keyword_extraction_prompt, embd_mdl
|
||
)
|
||
|
||
# Apply LLM-based question generation if configured
|
||
if config.use_llm_question_generation and ranks.get("chunks") and chat_mdl:
|
||
ranks["chunks"] = self._apply_llm_question_generation(
|
||
ranks["chunks"], config.question_generation_prompt, chat_mdl
|
||
)
|
||
|
||
h_result.tier3_time_ms = (time.time() - tier3_start) * 1000
|
||
h_result.total_time_ms = (time.time() - start_time) * 1000
|
||
|
||
# Add hierarchical metadata to result
|
||
ranks["hierarchical_metadata"] = {
|
||
"selected_kb_ids": h_result.selected_kb_ids,
|
||
"filtered_doc_ids": h_result.filtered_doc_ids,
|
||
"tier1_time_ms": h_result.tier1_time_ms,
|
||
"tier2_time_ms": h_result.tier2_time_ms,
|
||
"tier3_time_ms": h_result.tier3_time_ms,
|
||
"total_time_ms": h_result.total_time_ms,
|
||
"kb_params_used": {kb_id: True for kb_id in selected_kb_ids if kb_id in config.kb_params},
|
||
}
|
||
|
||
logging.info(
|
||
f"Hierarchical retrieval: {len(selected_kb_ids)} KBs -> "
|
||
f"{len(filtered_doc_ids) if filtered_doc_ids else 'all'} docs -> "
|
||
f"{len(ranks.get('chunks', []))} chunks in {h_result.total_time_ms:.1f}ms"
|
||
)
|
||
|
||
return ranks
|
||
|
||
def _tier1_kb_routing(
|
||
self,
|
||
question: str,
|
||
kb_ids: List[str],
|
||
kb_infos: Optional[List[Dict[str, Any]]],
|
||
config: HierarchicalConfig,
|
||
chat_mdl=None
|
||
) -> List[str]:
|
||
"""
|
||
Tier 1: Route query to relevant knowledge bases.
|
||
|
||
Supports multiple routing methods:
|
||
- "all": Return all KBs (no filtering)
|
||
- "rule_based": Keyword overlap matching
|
||
- "llm_based": LLM-powered intent analysis
|
||
- "auto": Combines rule-based with LLM fallback
|
||
"""
|
||
if not config.enable_kb_routing or not kb_infos:
|
||
return kb_ids
|
||
|
||
if len(kb_ids) <= config.kb_top_k:
|
||
return kb_ids
|
||
|
||
method = config.kb_routing_method
|
||
|
||
if method == "all":
|
||
return kb_ids
|
||
elif method == "llm_based":
|
||
return self._llm_kb_routing(question, kb_ids, kb_infos, config, chat_mdl)
|
||
elif method == "rule_based":
|
||
return self._rule_based_kb_routing(question, kb_ids, kb_infos, config)
|
||
else: # "auto" - try rule-based first, fall back to LLM if needed
|
||
rule_result = self._rule_based_kb_routing(question, kb_ids, kb_infos, config)
|
||
# If rule-based found good matches, use them
|
||
if rule_result and len(rule_result) < len(kb_ids):
|
||
return rule_result
|
||
# Otherwise try LLM if available
|
||
if chat_mdl:
|
||
return self._llm_kb_routing(question, kb_ids, kb_infos, config, chat_mdl)
|
||
return rule_result
|
||
|
||
def _rule_based_kb_routing(
|
||
self,
|
||
question: str,
|
||
kb_ids: List[str],
|
||
kb_infos: List[Dict[str, Any]],
|
||
config: HierarchicalConfig
|
||
) -> List[str]:
|
||
"""Rule-based KB routing using keyword overlap."""
|
||
# Tokenize query
|
||
query_tokens = set(rag_tokenizer.tokenize(question.lower()).split())
|
||
if not query_tokens:
|
||
return kb_ids
|
||
|
||
# Score each KB
|
||
kb_scores = []
|
||
for kb_info in kb_infos:
|
||
kb_id = kb_info.get('id')
|
||
if kb_id not in kb_ids:
|
||
continue
|
||
|
||
# Combine name and description
|
||
kb_text = ' '.join([
|
||
kb_info.get('name', ''),
|
||
kb_info.get('description', '')
|
||
]).lower()
|
||
|
||
kb_tokens = set(rag_tokenizer.tokenize(kb_text).split())
|
||
|
||
if kb_tokens:
|
||
overlap = len(query_tokens & kb_tokens)
|
||
score = overlap / len(query_tokens)
|
||
kb_scores.append((kb_id, score))
|
||
|
||
if not kb_scores:
|
||
return kb_ids
|
||
|
||
# Sort by score descending
|
||
kb_scores.sort(key=lambda x: x[1], reverse=True)
|
||
|
||
# Filter by threshold
|
||
selected = [
|
||
kb_id for kb_id, score in kb_scores
|
||
if score >= config.kb_routing_threshold
|
||
]
|
||
|
||
# If none pass threshold, take top K
|
||
if not selected:
|
||
selected = [kb_id for kb_id, _ in kb_scores[:config.kb_top_k]]
|
||
|
||
return selected[:config.kb_top_k] if selected else kb_ids
|
||
|
||
def _llm_kb_routing(
|
||
self,
|
||
question: str,
|
||
kb_ids: List[str],
|
||
kb_infos: List[Dict[str, Any]],
|
||
config: HierarchicalConfig,
|
||
chat_mdl=None
|
||
) -> List[str]:
|
||
"""LLM-based KB routing using semantic understanding."""
|
||
if not chat_mdl:
|
||
logging.warning("LLM routing requested but no chat model provided, falling back to rule-based")
|
||
return self._rule_based_kb_routing(question, kb_ids, kb_infos, config)
|
||
|
||
try:
|
||
# Build KB descriptions for LLM
|
||
kb_descriptions = []
|
||
kb_id_map = {}
|
||
for i, kb_info in enumerate(kb_infos):
|
||
kb_id = kb_info.get('id')
|
||
if kb_id not in kb_ids:
|
||
continue
|
||
name = kb_info.get('name', f'KB_{i}')
|
||
desc = kb_info.get('description', 'No description')
|
||
kb_descriptions.append(f"{i+1}. {name}: {desc}")
|
||
kb_id_map[i+1] = kb_id
|
||
|
||
if not kb_descriptions:
|
||
return kb_ids
|
||
|
||
# Create prompt for LLM
|
||
prompt = f"""Given the following user query and available knowledge bases, select the most relevant knowledge bases that would contain information to answer the query.
|
||
|
||
User Query: {question}
|
||
|
||
Available Knowledge Bases:
|
||
{chr(10).join(kb_descriptions)}
|
||
|
||
Instructions:
|
||
- Return ONLY the numbers of the most relevant knowledge bases (up to {config.kb_top_k})
|
||
- Format: comma-separated numbers, e.g., "1, 3, 5"
|
||
- If unsure, include more rather than fewer
|
||
|
||
Selected knowledge bases:"""
|
||
|
||
# Call LLM
|
||
response = chat_mdl.chat(prompt, [], {"temperature": 0.1})
|
||
if not response:
|
||
return self._rule_based_kb_routing(question, kb_ids, kb_infos, config)
|
||
|
||
# Parse response - extract numbers
|
||
import re
|
||
numbers = re.findall(r'\d+', response)
|
||
selected = []
|
||
for num_str in numbers:
|
||
num = int(num_str)
|
||
if num in kb_id_map:
|
||
selected.append(kb_id_map[num])
|
||
|
||
if selected:
|
||
logging.info(f"LLM routing selected {len(selected)} KBs: {selected[:3]}...")
|
||
return selected[:config.kb_top_k]
|
||
|
||
# Fallback to rule-based if LLM response was unparseable
|
||
return self._rule_based_kb_routing(question, kb_ids, kb_infos, config)
|
||
|
||
except Exception as e:
|
||
logging.error(f"LLM KB routing failed: {e}, falling back to rule-based")
|
||
return self._rule_based_kb_routing(question, kb_ids, kb_infos, config)
|
||
|
||
def _tier2_document_filtering(
|
||
self,
|
||
question: str,
|
||
tenant_ids,
|
||
kb_ids: List[str],
|
||
doc_ids: Optional[List[str]],
|
||
config: HierarchicalConfig,
|
||
embd_mdl=None,
|
||
chat_mdl=None,
|
||
doc_metadata: Optional[List[Dict[str, Any]]] = None
|
||
) -> List[str]:
|
||
"""
|
||
Tier 2: Filter documents by relevance before chunk search.
|
||
|
||
Supports multiple filtering strategies:
|
||
- Vector-based: Lightweight search to find relevant documents
|
||
- Metadata filtering: Filter by specified metadata fields
|
||
- Similarity matching: Fuzzy matching on document names/summaries
|
||
- LLM-based: Use LLM to generate filter conditions
|
||
"""
|
||
if not config.enable_doc_filtering:
|
||
return doc_ids or []
|
||
|
||
if doc_ids:
|
||
# Already have doc_ids filter, just limit count
|
||
return doc_ids[:config.doc_top_k]
|
||
|
||
try:
|
||
if isinstance(tenant_ids, str):
|
||
tenant_ids = tenant_ids.split(",")
|
||
|
||
filtered_docs = set()
|
||
|
||
# Strategy 1: LLM-based metadata filtering
|
||
if config.use_llm_metadata_filter and chat_mdl and doc_metadata:
|
||
llm_filtered = self._llm_metadata_filter(
|
||
question, doc_metadata, config, chat_mdl
|
||
)
|
||
if llm_filtered:
|
||
filtered_docs.update(llm_filtered)
|
||
logging.info(f"LLM metadata filter selected {len(llm_filtered)} docs")
|
||
|
||
# Strategy 2: Metadata similarity matching
|
||
if config.enable_metadata_similarity and doc_metadata:
|
||
similarity_filtered = self._metadata_similarity_filter(
|
||
question, doc_metadata, config, embd_mdl
|
||
)
|
||
if similarity_filtered:
|
||
if filtered_docs:
|
||
filtered_docs.intersection_update(similarity_filtered)
|
||
else:
|
||
filtered_docs.update(similarity_filtered)
|
||
logging.info(f"Metadata similarity filter: {len(similarity_filtered)} docs")
|
||
|
||
# Strategy 3: Vector-based document search (default)
|
||
if not filtered_docs:
|
||
req = {
|
||
"kb_ids": kb_ids,
|
||
"question": question,
|
||
"size": config.doc_top_k,
|
||
"topk": config.doc_top_k * 2,
|
||
"similarity": 0.1, # Lower threshold for document filtering
|
||
"available_int": 1,
|
||
}
|
||
|
||
idx_names = [index_name(tid) for tid in tenant_ids]
|
||
|
||
# Search with embedding if available
|
||
sres = self.search(req, idx_names, kb_ids, embd_mdl, highlight=False)
|
||
|
||
if sres and sres.field:
|
||
for chunk_id in sres.ids:
|
||
doc_id = sres.field[chunk_id].get("doc_id")
|
||
if doc_id:
|
||
filtered_docs.add(doc_id)
|
||
|
||
return list(filtered_docs)[:config.doc_top_k]
|
||
|
||
except Exception as e:
|
||
logging.error(f"Tier 2 document filtering error: {e}")
|
||
return []
|
||
|
||
def _llm_metadata_filter(
|
||
self,
|
||
question: str,
|
||
doc_metadata: List[Dict[str, Any]],
|
||
config: HierarchicalConfig,
|
||
chat_mdl
|
||
) -> List[str]:
|
||
"""Use LLM to generate and apply metadata filter conditions."""
|
||
try:
|
||
# Get available metadata fields
|
||
if not doc_metadata:
|
||
return []
|
||
|
||
# Sample metadata to show LLM what's available
|
||
sample_fields = set()
|
||
sample_values = {}
|
||
for doc in doc_metadata[:10]:
|
||
for field in config.metadata_fields or doc.keys():
|
||
if field in doc and field not in ['id', 'doc_id']:
|
||
sample_fields.add(field)
|
||
if field not in sample_values:
|
||
sample_values[field] = []
|
||
if len(sample_values[field]) < 3:
|
||
sample_values[field].append(str(doc[field])[:50])
|
||
|
||
if not sample_fields:
|
||
return []
|
||
|
||
# Build prompt
|
||
field_examples = "\n".join([
|
||
f"- {field}: examples = {sample_values.get(field, [])}"
|
||
for field in sample_fields
|
||
])
|
||
|
||
prompt = f"""Given a user query and available document metadata fields, generate filter conditions to select relevant documents.
|
||
|
||
User Query: {question}
|
||
|
||
Available Metadata Fields:
|
||
{field_examples}
|
||
|
||
Instructions:
|
||
- Return filter conditions as JSON: {{"field": "value"}} or {{"field": {{"operator": "value"}}}}
|
||
- Supported operators: "contains", "equals", "starts_with"
|
||
- Only filter on fields that are clearly relevant to the query
|
||
- Return empty {{}} if no clear filter applies
|
||
|
||
Filter conditions (JSON only):"""
|
||
|
||
response = chat_mdl.chat(prompt, [], {"temperature": 0.1})
|
||
if not response:
|
||
return []
|
||
|
||
# Parse JSON response
|
||
try:
|
||
# Extract JSON from response
|
||
json_match = re.search(r'\{[^}]*\}', response)
|
||
if json_match:
|
||
filters = json.loads(json_match.group())
|
||
else:
|
||
return []
|
||
except json.JSONDecodeError:
|
||
return []
|
||
|
||
if not filters:
|
||
return []
|
||
|
||
# Apply filters to documents
|
||
filtered_ids = []
|
||
for doc in doc_metadata:
|
||
doc_id = doc.get('id') or doc.get('doc_id')
|
||
if not doc_id:
|
||
continue
|
||
|
||
matches = True
|
||
for field, condition in filters.items():
|
||
doc_value = str(doc.get(field, '')).lower()
|
||
|
||
if isinstance(condition, dict):
|
||
op = list(condition.keys())[0]
|
||
val = str(list(condition.values())[0]).lower()
|
||
if op == "contains":
|
||
matches = val in doc_value
|
||
elif op == "equals":
|
||
matches = val == doc_value
|
||
elif op == "starts_with":
|
||
matches = doc_value.startswith(val)
|
||
else:
|
||
# Simple equality
|
||
matches = str(condition).lower() in doc_value
|
||
|
||
if not matches:
|
||
break
|
||
|
||
if matches:
|
||
filtered_ids.append(doc_id)
|
||
|
||
return filtered_ids
|
||
|
||
except Exception as e:
|
||
logging.error(f"LLM metadata filter error: {e}")
|
||
return []
|
||
|
||
def _metadata_similarity_filter(
|
||
self,
|
||
question: str,
|
||
doc_metadata: List[Dict[str, Any]],
|
||
config: HierarchicalConfig,
|
||
embd_mdl=None
|
||
) -> List[str]:
|
||
"""Filter documents by metadata text similarity."""
|
||
try:
|
||
if not embd_mdl or not doc_metadata:
|
||
return []
|
||
|
||
# Get query embedding
|
||
query_vec, _ = embd_mdl.encode_queries(question)
|
||
if not query_vec or len(query_vec) == 0:
|
||
return []
|
||
|
||
query_vec = np.array(query_vec)
|
||
|
||
# Score each document by metadata similarity
|
||
doc_scores = []
|
||
for doc in doc_metadata:
|
||
doc_id = doc.get('id') or doc.get('doc_id')
|
||
if not doc_id:
|
||
continue
|
||
|
||
# Combine relevant text fields
|
||
text_parts = []
|
||
for field in ['name', 'title', 'summary', 'description', 'docnm_kwd']:
|
||
if field in doc and doc[field]:
|
||
text_parts.append(str(doc[field]))
|
||
|
||
if not text_parts:
|
||
continue
|
||
|
||
doc_text = ' '.join(text_parts)
|
||
|
||
# Get document text embedding
|
||
doc_vec, _ = embd_mdl.encode([doc_text])
|
||
if not doc_vec or len(doc_vec) == 0:
|
||
continue
|
||
|
||
doc_vec = np.array(doc_vec[0])
|
||
|
||
# Cosine similarity
|
||
similarity = np.dot(query_vec, doc_vec) / (
|
||
np.linalg.norm(query_vec) * np.linalg.norm(doc_vec) + 1e-8
|
||
)
|
||
|
||
if similarity >= config.metadata_similarity_threshold:
|
||
doc_scores.append((doc_id, float(similarity)))
|
||
|
||
# Sort by similarity and return top docs
|
||
doc_scores.sort(key=lambda x: x[1], reverse=True)
|
||
return [doc_id for doc_id, _ in doc_scores[:config.doc_top_k]]
|
||
|
||
except Exception as e:
|
||
logging.error(f"Metadata similarity filter error: {e}")
|
||
return []
|
||
|
||
def _tier3_summary_mapping_retrieval(
|
||
self,
|
||
question: str,
|
||
embd_mdl,
|
||
tenant_ids,
|
||
kb_ids: List[str],
|
||
doc_ids: Optional[List[str]],
|
||
page: int,
|
||
page_size: int,
|
||
similarity_threshold: float,
|
||
vector_similarity_weight: float,
|
||
top: int,
|
||
aggs: bool,
|
||
rerank_mdl,
|
||
highlight: bool,
|
||
rank_feature: Optional[dict],
|
||
config: HierarchicalConfig
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Tier 3 with parent-child chunking and summary mapping.
|
||
|
||
First matches macro-themes via summary/parent vectors,
|
||
then maps to original child chunks for detailed information.
|
||
"""
|
||
try:
|
||
if isinstance(tenant_ids, str):
|
||
tenant_ids = tenant_ids.split(",")
|
||
|
||
idx_names = [index_name(tid) for tid in tenant_ids]
|
||
|
||
# Step 1: Search for parent/summary chunks first
|
||
# Look for chunks with mom_id (parent chunks) or summary content
|
||
parent_req = {
|
||
"kb_ids": kb_ids,
|
||
"doc_ids": doc_ids,
|
||
"question": question,
|
||
"size": config.chunk_top_k * 3, # Get more parents to find children
|
||
"topk": top,
|
||
"similarity": similarity_threshold * 0.8, # Slightly lower for parents
|
||
"available_int": 1,
|
||
}
|
||
|
||
parent_sres = self.search(
|
||
parent_req, idx_names, kb_ids, embd_mdl,
|
||
highlight=False, rank_feature=rank_feature
|
||
)
|
||
|
||
if not parent_sres or not parent_sres.ids:
|
||
# Fallback to standard retrieval
|
||
return self.retrieval(
|
||
question=question,
|
||
embd_mdl=embd_mdl,
|
||
tenant_ids=tenant_ids,
|
||
kb_ids=kb_ids,
|
||
page=page,
|
||
page_size=page_size,
|
||
similarity_threshold=similarity_threshold,
|
||
vector_similarity_weight=vector_similarity_weight,
|
||
top=top,
|
||
doc_ids=doc_ids,
|
||
aggs=aggs,
|
||
rerank_mdl=rerank_mdl,
|
||
highlight=highlight,
|
||
rank_feature=rank_feature,
|
||
)
|
||
|
||
# Step 2: Find child chunks for matched parents
|
||
parent_ids = set()
|
||
child_doc_ids = set()
|
||
|
||
for chunk_id in parent_sres.ids:
|
||
chunk = parent_sres.field.get(chunk_id, {})
|
||
mom_id = chunk.get("mom_id")
|
||
doc_id = chunk.get("doc_id")
|
||
|
||
if mom_id:
|
||
# This is a child chunk, get its parent
|
||
parent_ids.add(mom_id)
|
||
else:
|
||
# This might be a parent, use its ID
|
||
parent_ids.add(chunk_id)
|
||
|
||
if doc_id:
|
||
child_doc_ids.add(doc_id)
|
||
|
||
# Step 3: Retrieve child chunks for the matched parents
|
||
# Search within the documents that contain matched parents
|
||
child_req = {
|
||
"kb_ids": kb_ids,
|
||
"doc_ids": list(child_doc_ids) if child_doc_ids else doc_ids,
|
||
"question": question,
|
||
"size": page_size * 2,
|
||
"topk": top,
|
||
"similarity": similarity_threshold,
|
||
"available_int": 1,
|
||
}
|
||
|
||
child_sres = self.search(
|
||
child_req, idx_names, kb_ids, embd_mdl,
|
||
highlight=highlight, rank_feature=rank_feature
|
||
)
|
||
|
||
if not child_sres or not child_sres.ids:
|
||
# Use parent results if no children found
|
||
child_sres = parent_sres
|
||
|
||
# Step 4: Rerank and format results
|
||
if rerank_mdl and child_sres.total > 0:
|
||
sim, tsim, vsim = self.rerank_by_model(
|
||
rerank_mdl, child_sres, question,
|
||
1 - vector_similarity_weight, vector_similarity_weight,
|
||
rank_feature=rank_feature,
|
||
)
|
||
else:
|
||
sim, tsim, vsim = self.rerank(
|
||
child_sres, question,
|
||
1 - vector_similarity_weight, vector_similarity_weight,
|
||
rank_feature=rank_feature,
|
||
)
|
||
|
||
# Format results
|
||
ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
|
||
|
||
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)
|
||
valid_idx = [int(i) for i in sorted_idx if sim_np[i] >= similarity_threshold]
|
||
ranks["total"] = len(valid_idx)
|
||
|
||
if not valid_idx:
|
||
ranks["doc_aggs"] = []
|
||
return ranks
|
||
|
||
# Get page of results
|
||
begin = (page - 1) * page_size
|
||
end = begin + page_size
|
||
page_idx = valid_idx[begin:end]
|
||
|
||
dim = len(child_sres.query_vector) if child_sres.query_vector else 0
|
||
vector_column = f"q_{dim}_vec" if dim else ""
|
||
zero_vector = [0.0] * dim if dim else []
|
||
|
||
for i in page_idx:
|
||
chunk_id = child_sres.ids[i]
|
||
chunk = child_sres.field[chunk_id]
|
||
|
||
d = {
|
||
"chunk_id": chunk_id,
|
||
"content_ltks": chunk.get("content_ltks", ""),
|
||
"content_with_weight": chunk.get("content_with_weight", ""),
|
||
"doc_id": chunk.get("doc_id", ""),
|
||
"docnm_kwd": chunk.get("docnm_kwd", ""),
|
||
"kb_id": chunk.get("kb_id", ""),
|
||
"important_kwd": chunk.get("important_kwd", []),
|
||
"image_id": chunk.get("img_id", ""),
|
||
"similarity": float(sim_np[i]),
|
||
"vector_similarity": float(vsim[i]) if i < len(vsim) else 0.0,
|
||
"term_similarity": float(tsim[i]) if i < len(tsim) else 0.0,
|
||
"vector": chunk.get(vector_column, zero_vector),
|
||
"positions": chunk.get("position_int", []),
|
||
"doc_type_kwd": chunk.get("doc_type_kwd", ""),
|
||
"mom_id": chunk.get("mom_id", ""),
|
||
"parent_matched": chunk_id in parent_ids or chunk.get("mom_id") in parent_ids,
|
||
}
|
||
|
||
if highlight and child_sres.highlight and chunk_id in child_sres.highlight:
|
||
d["highlight"] = remove_redundant_spaces(child_sres.highlight[chunk_id])
|
||
|
||
ranks["chunks"].append(d)
|
||
|
||
# Build doc aggregations
|
||
if aggs:
|
||
for i in valid_idx:
|
||
chunk = child_sres.field[child_sres.ids[i]]
|
||
dnm = chunk.get("docnm_kwd", "")
|
||
did = chunk.get("doc_id", "")
|
||
if dnm and dnm not in ranks["doc_aggs"]:
|
||
ranks["doc_aggs"][dnm] = {"doc_id": did, "count": 0}
|
||
if dnm:
|
||
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"] = []
|
||
|
||
logging.info(f"Summary mapping retrieval: {len(parent_ids)} parents -> {len(ranks['chunks'])} chunks")
|
||
return ranks
|
||
|
||
except Exception as e:
|
||
logging.error(f"Summary mapping retrieval error: {e}")
|
||
# Fallback to standard retrieval
|
||
return self.retrieval(
|
||
question=question,
|
||
embd_mdl=embd_mdl,
|
||
tenant_ids=tenant_ids,
|
||
kb_ids=kb_ids,
|
||
page=page,
|
||
page_size=page_size,
|
||
similarity_threshold=similarity_threshold,
|
||
vector_similarity_weight=vector_similarity_weight,
|
||
top=top,
|
||
doc_ids=doc_ids,
|
||
aggs=aggs,
|
||
rerank_mdl=rerank_mdl,
|
||
highlight=highlight,
|
||
rank_feature=rank_feature,
|
||
)
|
||
|
||
def _apply_custom_keyword_extraction(
|
||
self,
|
||
chunks: List[Dict[str, Any]],
|
||
prompt_template: str,
|
||
embd_mdl=None
|
||
) -> List[Dict[str, Any]]:
|
||
"""
|
||
Apply customizable prompts for chunk keyword extraction.
|
||
|
||
Allows users to configure custom prompts for domain-specific
|
||
keyword extraction to better align with their semantics.
|
||
"""
|
||
try:
|
||
for chunk in chunks:
|
||
content = chunk.get("content_with_weight", "") or chunk.get("content_ltks", "")
|
||
if not content:
|
||
continue
|
||
|
||
# Extract keywords using tokenizer with custom weighting
|
||
tokens = rag_tokenizer.tokenize(content).split()
|
||
|
||
# Apply custom prompt logic if it contains extraction rules
|
||
if "important" in prompt_template.lower():
|
||
# Prioritize capitalized words and technical terms
|
||
important_tokens = [
|
||
t for t in tokens
|
||
if len(t) > 3 and (t[0].isupper() or '_' in t or '-' in t)
|
||
]
|
||
if important_tokens:
|
||
existing = chunk.get("important_kwd", [])
|
||
if isinstance(existing, str):
|
||
existing = [existing]
|
||
chunk["important_kwd"] = list(set(existing + important_tokens[:10]))
|
||
|
||
if "question" in prompt_template.lower():
|
||
# Generate potential questions from content
|
||
# This is a simplified version - full implementation would use LLM
|
||
if "?" not in content and len(content) > 50:
|
||
# Add a generated question hint
|
||
chunk["question_hint"] = f"What does this section explain about {tokens[0] if tokens else 'the topic'}?"
|
||
|
||
return chunks
|
||
|
||
except Exception as e:
|
||
logging.error(f"Custom keyword extraction error: {e}")
|
||
return chunks
|
||
|
||
def _apply_llm_question_generation(
|
||
self,
|
||
chunks: List[Dict[str, Any]],
|
||
prompt_template: Optional[str],
|
||
chat_mdl
|
||
) -> List[Dict[str, Any]]:
|
||
"""
|
||
Apply LLM-based question generation for chunks.
|
||
|
||
Generates potential questions that each chunk could answer,
|
||
improving retrieval by enabling question-to-question matching.
|
||
"""
|
||
if not chat_mdl:
|
||
return chunks
|
||
|
||
try:
|
||
default_prompt = """Based on the following content, generate 2-3 concise questions that this content could answer.
|
||
Return only the questions, one per line.
|
||
|
||
Content: {content}
|
||
|
||
Questions:"""
|
||
|
||
prompt = prompt_template or default_prompt
|
||
|
||
for chunk in chunks:
|
||
content = chunk.get("content_with_weight", "") or chunk.get("content_ltks", "")
|
||
if not content or len(content) < 50:
|
||
continue
|
||
|
||
# Truncate content if too long
|
||
content_truncated = content[:1500] if len(content) > 1500 else content
|
||
|
||
try:
|
||
# Format prompt with content
|
||
formatted_prompt = prompt.replace("{content}", content_truncated)
|
||
|
||
# Call LLM
|
||
response = chat_mdl.chat(formatted_prompt, [], {"temperature": 0.3, "max_tokens": 200})
|
||
|
||
if response:
|
||
# Parse questions from response
|
||
questions = [
|
||
q.strip() for q in response.strip().split('\n')
|
||
if q.strip() and '?' in q
|
||
]
|
||
|
||
if questions:
|
||
chunk["generated_questions"] = questions[:3]
|
||
logging.debug(f"Generated {len(questions)} questions for chunk")
|
||
|
||
except Exception as e:
|
||
logging.warning(f"Question generation failed for chunk: {e}")
|
||
continue
|
||
|
||
return chunks
|
||
|
||
except Exception as e:
|
||
logging.error(f"LLM question generation error: {e}")
|
||
return chunks
|
||
|
||
def generate_document_metadata(
|
||
self,
|
||
doc_id: str,
|
||
content: str,
|
||
chat_mdl=None,
|
||
embd_mdl=None
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Generate enhanced metadata for a document.
|
||
|
||
This is a Data Pipeline enhancement hook that generates:
|
||
- Document summary
|
||
- Key topics/themes
|
||
- Suggested questions
|
||
- Category classification
|
||
|
||
Can be called during document ingestion to enrich metadata.
|
||
"""
|
||
metadata = {
|
||
"doc_id": doc_id,
|
||
"generated": True,
|
||
"summary": "",
|
||
"topics": [],
|
||
"suggested_questions": [],
|
||
"category": "",
|
||
}
|
||
|
||
if not chat_mdl or not content:
|
||
return metadata
|
||
|
||
try:
|
||
# Truncate content for LLM
|
||
content_truncated = content[:3000] if len(content) > 3000 else content
|
||
|
||
prompt = f"""Analyze the following document content and provide:
|
||
1. A brief summary (2-3 sentences)
|
||
2. Key topics/themes (comma-separated list)
|
||
3. 3 questions this document could answer
|
||
4. A category classification
|
||
|
||
Content:
|
||
{content_truncated}
|
||
|
||
Respond in this exact format:
|
||
SUMMARY: <summary>
|
||
TOPICS: <topic1>, <topic2>, <topic3>
|
||
QUESTIONS:
|
||
- <question1>
|
||
- <question2>
|
||
- <question3>
|
||
CATEGORY: <category>"""
|
||
|
||
response = chat_mdl.chat(prompt, [], {"temperature": 0.2, "max_tokens": 500})
|
||
|
||
if response:
|
||
# Parse response
|
||
lines = response.strip().split('\n')
|
||
for line in lines:
|
||
line = line.strip()
|
||
if line.startswith("SUMMARY:"):
|
||
metadata["summary"] = line[8:].strip()
|
||
elif line.startswith("TOPICS:"):
|
||
topics = line[7:].strip().split(',')
|
||
metadata["topics"] = [t.strip() for t in topics if t.strip()]
|
||
elif line.startswith("- ") and "?" in line:
|
||
metadata["suggested_questions"].append(line[2:].strip())
|
||
elif line.startswith("CATEGORY:"):
|
||
metadata["category"] = line[9:].strip()
|
||
|
||
# Generate summary embedding if embedding model available
|
||
if embd_mdl and metadata["summary"]:
|
||
try:
|
||
summary_vec, _ = embd_mdl.encode([metadata["summary"]])
|
||
if summary_vec and len(summary_vec) > 0:
|
||
metadata["summary_vector"] = summary_vec[0]
|
||
except Exception:
|
||
pass
|
||
|
||
logging.info(f"Generated metadata for doc {doc_id}: {len(metadata['topics'])} topics, {len(metadata['suggested_questions'])} questions")
|
||
return metadata
|
||
|
||
except Exception as e:
|
||
logging.error(f"Document metadata generation error: {e}")
|
||
return metadata
|
||
|
||
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):
|
||
es_res = self.dataStore.search(fields, [], condition, [], orderBy, p, bs, 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())
|
||
# FIX: Solo terminar si no hay chunks, no si hay menos de bs
|
||
if len(dict_chunks.values()) == 0:
|
||
break
|
||
return res
|
||
|
||
def all_tags(self, tenant_id: str, kb_ids: list[str], S=1000):
|
||
if not self.dataStore.indexExist(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}
|
||
|
||
def retrieval_by_toc(self, query:str, chunks:list[dict], tenant_ids:list[str], chat_mdl, topn: int=6):
|
||
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 = 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, kb_ids)
|
||
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, [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)
|