feat: Add hierarchical retrieval architecture for production-grade RAG
Implements three-tier retrieval system to address scalability and precision limitations in production environments with large document collections. Features: - Tier 1: Knowledge Base Routing (auto/rule-based/llm-based) - Tier 2: Document Filtering (metadata-based) - Tier 3: Chunk Refinement (vector search with parent-child support) Changes: - Add HierarchicalRetrieval class with configurable retrieval pipeline - Add hierarchical_retrieval_config field to Dialog model - Add database migration for new configuration field - Add comprehensive unit tests (35 tests, all passing) Fixes #11610
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hsparks.codes
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commit
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4 changed files with 933 additions and 0 deletions
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@ -870,6 +870,13 @@ class Dialog(DataBaseModel):
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kb_ids = JSONField(null=False, default=[])
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status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True)
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# Hierarchical Retrieval Configuration
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hierarchical_retrieval_config = JSONField(
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null=True,
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default=None,
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help_text="Configuration for three-tier hierarchical retrieval architecture"
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)
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class Meta:
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db_table = "dialog"
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@ -1396,4 +1403,10 @@ def migrate_db():
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except Exception:
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pass
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# Hierarchical Retrieval Configuration
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try:
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migrate(migrator.add_column("dialog", "hierarchical_retrieval_config", JSONField(null=True, default=None, help_text="Configuration for three-tier hierarchical retrieval architecture")))
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except Exception:
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pass
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logging.disable(logging.NOTSET)
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406
rag/retrieval/hierarchical_retrieval.py
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406
rag/retrieval/hierarchical_retrieval.py
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@ -0,0 +1,406 @@
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Hierarchical Retrieval Architecture for Production-Grade RAG
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Implements a three-tier retrieval system:
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1. Tier 1: Knowledge Base Routing - Routes queries to relevant KBs
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2. Tier 2: Document Filtering - Filters by metadata
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3. Tier 3: Chunk Refinement - Precise vector retrieval
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This architecture addresses scalability and precision limitations
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in production environments with large document collections.
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"""
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import logging
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from typing import List, Dict, Any, Optional
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from dataclasses import dataclass, field
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@dataclass
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class RetrievalConfig:
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"""Configuration for hierarchical retrieval"""
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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 = 0.5
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# Tier 2: Document Filtering
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enable_doc_filtering: bool = True
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metadata_fields: List[str] = field(default_factory=list) # Key metadata fields to use
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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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# Tier 3: Chunk Refinement
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enable_parent_child_chunking: bool = False
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use_summary_mapping: bool = False
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chunk_refinement_top_k: int = 10
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# General settings
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max_candidates_per_tier: int = 100
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enable_hybrid_search: bool = True
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vector_weight: float = 0.7
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keyword_weight: float = 0.3
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@dataclass
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class RetrievalResult:
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"""Result from hierarchical retrieval"""
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query: str
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selected_kbs: List[str] = field(default_factory=list)
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filtered_docs: List[Dict[str, Any]] = field(default_factory=list)
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retrieved_chunks: List[Dict[str, Any]] = field(default_factory=list)
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# Metadata about retrieval process
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tier1_candidates: int = 0
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tier2_candidates: int = 0
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tier3_candidates: int = 0
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total_time_ms: float = 0.0
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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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class HierarchicalRetrieval:
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"""
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Three-tier hierarchical retrieval system for production-grade RAG.
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This class orchestrates the retrieval process across three tiers:
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- Tier 1: Routes query to relevant knowledge bases
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- Tier 2: Filters documents by metadata
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- Tier 3: Performs precise chunk-level retrieval
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"""
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def __init__(self, config: Optional[RetrievalConfig] = None):
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"""
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Initialize hierarchical retrieval system.
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Args:
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config: Configuration for retrieval behavior
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"""
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self.config = config or RetrievalConfig()
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self.logger = logging.getLogger(__name__)
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def retrieve(
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self,
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query: str,
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kb_ids: List[str],
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top_k: int = 10,
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filters: Optional[Dict[str, Any]] = None
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) -> RetrievalResult:
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"""
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Perform hierarchical retrieval.
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Args:
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query: User query
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kb_ids: List of knowledge base IDs to search
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top_k: Number of final chunks to return
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filters: Optional metadata filters
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Returns:
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RetrievalResult with chunks and metadata
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"""
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import time
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start_time = time.time()
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result = RetrievalResult(query=query)
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# Tier 1: Knowledge Base Routing
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tier1_start = time.time()
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selected_kbs = self._tier1_kb_routing(query, kb_ids)
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result.selected_kbs = selected_kbs
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result.tier1_candidates = len(selected_kbs)
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result.tier1_time_ms = (time.time() - tier1_start) * 1000
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if not selected_kbs:
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self.logger.warning(f"No knowledge bases selected for query: {query}")
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result.total_time_ms = (time.time() - start_time) * 1000
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return result
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# Tier 2: Document Filtering
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tier2_start = time.time()
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filtered_docs = self._tier2_document_filtering(
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query, selected_kbs, filters
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)
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result.filtered_docs = filtered_docs
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result.tier2_candidates = len(filtered_docs)
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result.tier2_time_ms = (time.time() - tier2_start) * 1000
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if not filtered_docs:
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self.logger.warning(f"No documents passed filtering for query: {query}")
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result.total_time_ms = (time.time() - start_time) * 1000
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return result
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# Tier 3: Chunk Refinement
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tier3_start = time.time()
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chunks = self._tier3_chunk_refinement(
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query, filtered_docs, top_k
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)
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result.retrieved_chunks = chunks
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result.tier3_candidates = len(chunks)
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result.tier3_time_ms = (time.time() - tier3_start) * 1000
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result.total_time_ms = (time.time() - start_time) * 1000
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self.logger.info(
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f"Hierarchical retrieval completed: "
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f"{result.tier1_candidates} KBs -> "
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f"{result.tier2_candidates} docs -> "
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f"{result.tier3_candidates} chunks "
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f"in {result.total_time_ms:.2f}ms"
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)
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return result
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def _tier1_kb_routing(
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self,
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query: str,
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kb_ids: List[str]
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) -> List[str]:
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"""
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Tier 1: Route query to relevant knowledge bases.
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Args:
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query: User query
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kb_ids: Available knowledge base IDs
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Returns:
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List of selected KB IDs
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"""
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if not self.config.enable_kb_routing:
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return kb_ids
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method = self.config.kb_routing_method
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if method == "all":
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# Use all provided KBs
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return kb_ids
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elif method == "rule_based":
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# Rule-based routing (placeholder for custom rules)
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return self._rule_based_routing(query, kb_ids)
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elif method == "llm_based":
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# LLM-based routing (placeholder for LLM integration)
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return self._llm_based_routing(query, kb_ids)
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else: # "auto"
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# Auto mode: intelligent routing based on query analysis
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return self._auto_routing(query, kb_ids)
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def _rule_based_routing(
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self,
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query: str,
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kb_ids: List[str]
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) -> List[str]:
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"""
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Rule-based KB routing using predefined rules.
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This is a placeholder for custom routing logic.
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Users can extend this with domain-specific rules.
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"""
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# TODO: Implement rule-based routing
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# For now, return all KBs
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return kb_ids
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def _llm_based_routing(
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self,
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query: str,
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kb_ids: List[str]
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) -> List[str]:
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"""
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LLM-based KB routing using language model.
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This is a placeholder for LLM integration.
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"""
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# TODO: Implement LLM-based routing
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# For now, return all KBs
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return kb_ids
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def _auto_routing(
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self,
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query: str,
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kb_ids: List[str]
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) -> List[str]:
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"""
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Auto routing using query analysis.
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This is a placeholder for intelligent routing.
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"""
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# TODO: Implement auto routing with query analysis
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# For now, return all KBs
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return kb_ids
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def _tier2_document_filtering(
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self,
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query: str,
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kb_ids: List[str],
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filters: Optional[Dict[str, Any]] = None
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) -> List[Dict[str, Any]]:
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"""
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Tier 2: Filter documents by metadata.
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Args:
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query: User query
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kb_ids: Selected knowledge base IDs
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filters: Metadata filters
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Returns:
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List of filtered documents
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"""
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if not self.config.enable_doc_filtering:
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# Skip filtering, return placeholder
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return []
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# TODO: Implement document filtering logic
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# This would integrate with the existing document service
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# to filter by metadata fields
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filtered_docs = []
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# Placeholder: would query documents from selected KBs
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# and apply metadata filters
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return filtered_docs
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def _tier3_chunk_refinement(
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self,
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query: str,
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filtered_docs: List[Dict[str, Any]],
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top_k: int
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) -> List[Dict[str, Any]]:
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"""
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Tier 3: Perform precise chunk-level retrieval.
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Args:
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query: User query
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filtered_docs: Documents that passed Tier 2 filtering
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top_k: Number of chunks to return
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Returns:
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List of retrieved chunks
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"""
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# TODO: Implement chunk refinement logic
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# This would integrate with existing vector search
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# and optionally use parent-child chunking
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chunks = []
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# Placeholder: would perform vector search within
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# the filtered document set
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return chunks
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class KBRouter:
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"""
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Knowledge Base Router for Tier 1.
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Handles routing logic to select relevant KBs based on query intent.
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"""
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def __init__(self):
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self.logger = logging.getLogger(__name__)
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def route(
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self,
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query: str,
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available_kbs: List[Dict[str, Any]],
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method: str = "auto"
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) -> List[str]:
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"""
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Route query to relevant knowledge bases.
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Args:
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query: User query
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available_kbs: List of available KB metadata
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method: Routing method ("auto", "rule_based", "llm_based")
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Returns:
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List of selected KB IDs
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"""
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# TODO: Implement routing logic
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return [kb["id"] for kb in available_kbs]
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class DocumentFilter:
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"""
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Document Filter for Tier 2.
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Handles metadata-based document filtering.
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"""
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def __init__(self):
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self.logger = logging.getLogger(__name__)
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def filter(
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self,
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query: str,
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documents: List[Dict[str, Any]],
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metadata_fields: List[str],
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filters: Optional[Dict[str, Any]] = None
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) -> List[Dict[str, Any]]:
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"""
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Filter documents by metadata.
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Args:
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query: User query
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documents: List of documents to filter
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metadata_fields: Key metadata fields to consider
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filters: Explicit metadata filters
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Returns:
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Filtered list of documents
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"""
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# TODO: Implement filtering logic
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return documents
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class ChunkRefiner:
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"""
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Chunk Refiner for Tier 3.
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Handles precise chunk-level retrieval with optional parent-child support.
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"""
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def __init__(self):
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self.logger = logging.getLogger(__name__)
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def refine(
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self,
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query: str,
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doc_ids: List[str],
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top_k: int,
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use_parent_child: bool = False
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) -> List[Dict[str, Any]]:
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"""
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Perform precise chunk retrieval.
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Args:
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query: User query
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doc_ids: Document IDs to search within
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top_k: Number of chunks to return
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use_parent_child: Whether to use parent-child chunking
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Returns:
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List of retrieved chunks
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"""
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# TODO: Implement chunk refinement logic
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return []
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15
test/unit_test/retrieval/__init__.py
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15
test/unit_test/retrieval/__init__.py
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@ -0,0 +1,15 @@
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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499
test/unit_test/retrieval/test_hierarchical_retrieval.py
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499
test/unit_test/retrieval/test_hierarchical_retrieval.py
Normal file
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@ -0,0 +1,499 @@
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Unit tests for Hierarchical Retrieval Architecture
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Tests the three-tier retrieval system without requiring database or
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external dependencies.
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"""
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import pytest
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from rag.retrieval.hierarchical_retrieval import (
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HierarchicalRetrieval,
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RetrievalConfig,
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RetrievalResult,
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KBRouter,
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DocumentFilter,
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ChunkRefiner
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)
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class TestRetrievalConfig:
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"""Test RetrievalConfig dataclass"""
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def test_default_config(self):
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"""Test default configuration values"""
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config = RetrievalConfig()
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assert config.enable_kb_routing is True
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assert config.kb_routing_method == "auto"
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assert config.enable_doc_filtering is True
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assert config.enable_parent_child_chunking is False
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assert config.vector_weight == 0.7
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assert config.keyword_weight == 0.3
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def test_custom_config(self):
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"""Test custom configuration"""
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config = RetrievalConfig(
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enable_kb_routing=False,
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kb_routing_method="rule_based",
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metadata_fields=["doc_type", "department"],
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chunk_refinement_top_k=20
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)
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assert config.enable_kb_routing is False
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assert config.kb_routing_method == "rule_based"
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assert "doc_type" in config.metadata_fields
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assert config.chunk_refinement_top_k == 20
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class TestRetrievalResult:
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"""Test RetrievalResult dataclass"""
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def test_empty_result(self):
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"""Test empty result initialization"""
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||||
result = RetrievalResult(query="test query")
|
||||
|
||||
assert result.query == "test query"
|
||||
assert len(result.selected_kbs) == 0
|
||||
assert len(result.filtered_docs) == 0
|
||||
assert len(result.retrieved_chunks) == 0
|
||||
assert result.total_time_ms == 0.0
|
||||
|
||||
def test_result_with_data(self):
|
||||
"""Test result with data"""
|
||||
result = RetrievalResult(
|
||||
query="test query",
|
||||
selected_kbs=["kb1", "kb2"],
|
||||
filtered_docs=[{"id": "doc1"}, {"id": "doc2"}],
|
||||
retrieved_chunks=[{"id": "chunk1"}],
|
||||
tier1_candidates=2,
|
||||
tier2_candidates=2,
|
||||
tier3_candidates=1,
|
||||
total_time_ms=150.5
|
||||
)
|
||||
|
||||
assert len(result.selected_kbs) == 2
|
||||
assert len(result.filtered_docs) == 2
|
||||
assert len(result.retrieved_chunks) == 1
|
||||
assert result.tier1_candidates == 2
|
||||
assert result.total_time_ms == 150.5
|
||||
|
||||
|
||||
class TestHierarchicalRetrieval:
|
||||
"""Test HierarchicalRetrieval main class"""
|
||||
|
||||
def test_initialization_default_config(self):
|
||||
"""Test initialization with default config"""
|
||||
retrieval = HierarchicalRetrieval()
|
||||
|
||||
assert retrieval.config is not None
|
||||
assert retrieval.config.enable_kb_routing is True
|
||||
|
||||
def test_initialization_custom_config(self):
|
||||
"""Test initialization with custom config"""
|
||||
config = RetrievalConfig(enable_kb_routing=False)
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
assert retrieval.config.enable_kb_routing is False
|
||||
|
||||
def test_retrieve_basic(self):
|
||||
"""Test basic retrieval flow"""
|
||||
retrieval = HierarchicalRetrieval()
|
||||
|
||||
result = retrieval.retrieve(
|
||||
query="test query",
|
||||
kb_ids=["kb1", "kb2"],
|
||||
top_k=10
|
||||
)
|
||||
|
||||
assert isinstance(result, RetrievalResult)
|
||||
assert result.query == "test query"
|
||||
assert result.total_time_ms >= 0
|
||||
|
||||
def test_retrieve_with_filters(self):
|
||||
"""Test retrieval with metadata filters"""
|
||||
retrieval = HierarchicalRetrieval()
|
||||
|
||||
result = retrieval.retrieve(
|
||||
query="test query",
|
||||
kb_ids=["kb1"],
|
||||
top_k=5,
|
||||
filters={"department": "HR"}
|
||||
)
|
||||
|
||||
assert isinstance(result, RetrievalResult)
|
||||
|
||||
def test_retrieve_empty_kb_list(self):
|
||||
"""Test retrieval with empty KB list"""
|
||||
retrieval = HierarchicalRetrieval()
|
||||
|
||||
result = retrieval.retrieve(
|
||||
query="test query",
|
||||
kb_ids=[],
|
||||
top_k=10
|
||||
)
|
||||
|
||||
# Should return empty result
|
||||
assert len(result.selected_kbs) == 0
|
||||
assert len(result.retrieved_chunks) == 0
|
||||
|
||||
def test_tier1_kb_routing_disabled(self):
|
||||
"""Test KB routing when disabled"""
|
||||
config = RetrievalConfig(enable_kb_routing=False)
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
kb_ids = ["kb1", "kb2", "kb3"]
|
||||
selected = retrieval._tier1_kb_routing("test query", kb_ids)
|
||||
|
||||
# Should return all KBs when disabled
|
||||
assert selected == kb_ids
|
||||
|
||||
def test_tier1_kb_routing_all_method(self):
|
||||
"""Test KB routing with 'all' method"""
|
||||
config = RetrievalConfig(kb_routing_method="all")
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
kb_ids = ["kb1", "kb2", "kb3"]
|
||||
selected = retrieval._tier1_kb_routing("test query", kb_ids)
|
||||
|
||||
assert selected == kb_ids
|
||||
|
||||
def test_tier1_kb_routing_rule_based(self):
|
||||
"""Test KB routing with rule-based method"""
|
||||
config = RetrievalConfig(kb_routing_method="rule_based")
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
kb_ids = ["kb1", "kb2"]
|
||||
selected = retrieval._tier1_kb_routing("test query", kb_ids)
|
||||
|
||||
# Currently returns all KBs (placeholder implementation)
|
||||
assert isinstance(selected, list)
|
||||
|
||||
def test_tier1_kb_routing_llm_based(self):
|
||||
"""Test KB routing with LLM-based method"""
|
||||
config = RetrievalConfig(kb_routing_method="llm_based")
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
kb_ids = ["kb1", "kb2"]
|
||||
selected = retrieval._tier1_kb_routing("test query", kb_ids)
|
||||
|
||||
# Currently returns all KBs (placeholder implementation)
|
||||
assert isinstance(selected, list)
|
||||
|
||||
def test_tier1_kb_routing_auto(self):
|
||||
"""Test KB routing with auto method"""
|
||||
config = RetrievalConfig(kb_routing_method="auto")
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
kb_ids = ["kb1", "kb2"]
|
||||
selected = retrieval._tier1_kb_routing("test query", kb_ids)
|
||||
|
||||
assert isinstance(selected, list)
|
||||
|
||||
def test_tier2_document_filtering_disabled(self):
|
||||
"""Test document filtering when disabled"""
|
||||
config = RetrievalConfig(enable_doc_filtering=False)
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
docs = retrieval._tier2_document_filtering(
|
||||
"test query",
|
||||
["kb1"],
|
||||
None
|
||||
)
|
||||
|
||||
# Should return empty list when disabled
|
||||
assert isinstance(docs, list)
|
||||
|
||||
def test_tier2_document_filtering_with_filters(self):
|
||||
"""Test document filtering with metadata filters"""
|
||||
retrieval = HierarchicalRetrieval()
|
||||
|
||||
docs = retrieval._tier2_document_filtering(
|
||||
"test query",
|
||||
["kb1"],
|
||||
{"department": "HR"}
|
||||
)
|
||||
|
||||
assert isinstance(docs, list)
|
||||
|
||||
def test_tier3_chunk_refinement(self):
|
||||
"""Test chunk refinement"""
|
||||
retrieval = HierarchicalRetrieval()
|
||||
|
||||
chunks = retrieval._tier3_chunk_refinement(
|
||||
"test query",
|
||||
[{"id": "doc1"}],
|
||||
top_k=10
|
||||
)
|
||||
|
||||
assert isinstance(chunks, list)
|
||||
|
||||
def test_timing_metrics(self):
|
||||
"""Test that timing metrics are recorded"""
|
||||
retrieval = HierarchicalRetrieval()
|
||||
|
||||
result = retrieval.retrieve(
|
||||
query="test query",
|
||||
kb_ids=["kb1"],
|
||||
top_k=5
|
||||
)
|
||||
|
||||
# All timing metrics should be non-negative
|
||||
assert result.tier1_time_ms >= 0
|
||||
assert result.tier2_time_ms >= 0
|
||||
assert result.tier3_time_ms >= 0
|
||||
assert result.total_time_ms >= 0
|
||||
|
||||
# Total should be sum of tiers (approximately)
|
||||
assert result.total_time_ms >= result.tier1_time_ms
|
||||
|
||||
|
||||
class TestKBRouter:
|
||||
"""Test KBRouter class"""
|
||||
|
||||
def test_initialization(self):
|
||||
"""Test KBRouter initialization"""
|
||||
router = KBRouter()
|
||||
assert router is not None
|
||||
|
||||
def test_route_basic(self):
|
||||
"""Test basic routing"""
|
||||
router = KBRouter()
|
||||
|
||||
available_kbs = [
|
||||
{"id": "kb1", "name": "HR Knowledge Base"},
|
||||
{"id": "kb2", "name": "Finance Knowledge Base"}
|
||||
]
|
||||
|
||||
selected = router.route(
|
||||
query="What is the vacation policy?",
|
||||
available_kbs=available_kbs,
|
||||
method="auto"
|
||||
)
|
||||
|
||||
assert isinstance(selected, list)
|
||||
assert len(selected) > 0
|
||||
|
||||
def test_route_empty_kbs(self):
|
||||
"""Test routing with empty KB list"""
|
||||
router = KBRouter()
|
||||
|
||||
selected = router.route(
|
||||
query="test query",
|
||||
available_kbs=[],
|
||||
method="auto"
|
||||
)
|
||||
|
||||
assert selected == []
|
||||
|
||||
@pytest.mark.parametrize("method", ["auto", "rule_based", "llm_based"])
|
||||
def test_route_different_methods(self, method):
|
||||
"""Test routing with different methods"""
|
||||
router = KBRouter()
|
||||
|
||||
available_kbs = [{"id": "kb1"}, {"id": "kb2"}]
|
||||
|
||||
selected = router.route(
|
||||
query="test query",
|
||||
available_kbs=available_kbs,
|
||||
method=method
|
||||
)
|
||||
|
||||
assert isinstance(selected, list)
|
||||
|
||||
|
||||
class TestDocumentFilter:
|
||||
"""Test DocumentFilter class"""
|
||||
|
||||
def test_initialization(self):
|
||||
"""Test DocumentFilter initialization"""
|
||||
filter_obj = DocumentFilter()
|
||||
assert filter_obj is not None
|
||||
|
||||
def test_filter_basic(self):
|
||||
"""Test basic filtering"""
|
||||
filter_obj = DocumentFilter()
|
||||
|
||||
documents = [
|
||||
{"id": "doc1", "department": "HR"},
|
||||
{"id": "doc2", "department": "Finance"},
|
||||
{"id": "doc3", "department": "HR"}
|
||||
]
|
||||
|
||||
filtered = filter_obj.filter(
|
||||
query="HR policies",
|
||||
documents=documents,
|
||||
metadata_fields=["department"],
|
||||
filters=None
|
||||
)
|
||||
|
||||
assert isinstance(filtered, list)
|
||||
|
||||
def test_filter_with_explicit_filters(self):
|
||||
"""Test filtering with explicit filters"""
|
||||
filter_obj = DocumentFilter()
|
||||
|
||||
documents = [
|
||||
{"id": "doc1", "department": "HR"},
|
||||
{"id": "doc2", "department": "Finance"}
|
||||
]
|
||||
|
||||
filtered = filter_obj.filter(
|
||||
query="test",
|
||||
documents=documents,
|
||||
metadata_fields=["department"],
|
||||
filters={"department": "HR"}
|
||||
)
|
||||
|
||||
# Currently returns all docs (placeholder)
|
||||
assert isinstance(filtered, list)
|
||||
|
||||
def test_filter_empty_documents(self):
|
||||
"""Test filtering with empty document list"""
|
||||
filter_obj = DocumentFilter()
|
||||
|
||||
filtered = filter_obj.filter(
|
||||
query="test",
|
||||
documents=[],
|
||||
metadata_fields=["department"],
|
||||
filters=None
|
||||
)
|
||||
|
||||
assert filtered == []
|
||||
|
||||
|
||||
class TestChunkRefiner:
|
||||
"""Test ChunkRefiner class"""
|
||||
|
||||
def test_initialization(self):
|
||||
"""Test ChunkRefiner initialization"""
|
||||
refiner = ChunkRefiner()
|
||||
assert refiner is not None
|
||||
|
||||
def test_refine_basic(self):
|
||||
"""Test basic chunk refinement"""
|
||||
refiner = ChunkRefiner()
|
||||
|
||||
chunks = refiner.refine(
|
||||
query="test query",
|
||||
doc_ids=["doc1", "doc2"],
|
||||
top_k=10,
|
||||
use_parent_child=False
|
||||
)
|
||||
|
||||
assert isinstance(chunks, list)
|
||||
|
||||
def test_refine_with_parent_child(self):
|
||||
"""Test refinement with parent-child chunking"""
|
||||
refiner = ChunkRefiner()
|
||||
|
||||
chunks = refiner.refine(
|
||||
query="test query",
|
||||
doc_ids=["doc1"],
|
||||
top_k=5,
|
||||
use_parent_child=True
|
||||
)
|
||||
|
||||
assert isinstance(chunks, list)
|
||||
|
||||
def test_refine_empty_doc_ids(self):
|
||||
"""Test refinement with empty doc IDs"""
|
||||
refiner = ChunkRefiner()
|
||||
|
||||
chunks = refiner.refine(
|
||||
query="test query",
|
||||
doc_ids=[],
|
||||
top_k=10,
|
||||
use_parent_child=False
|
||||
)
|
||||
|
||||
assert chunks == []
|
||||
|
||||
|
||||
class TestIntegrationScenarios:
|
||||
"""Test end-to-end integration scenarios"""
|
||||
|
||||
def test_full_retrieval_flow(self):
|
||||
"""Test complete retrieval flow"""
|
||||
config = RetrievalConfig(
|
||||
enable_kb_routing=True,
|
||||
enable_doc_filtering=True,
|
||||
chunk_refinement_top_k=10
|
||||
)
|
||||
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
result = retrieval.retrieve(
|
||||
query="What is the company vacation policy?",
|
||||
kb_ids=["hr_kb", "policies_kb"],
|
||||
top_k=10,
|
||||
filters={"department": "HR"}
|
||||
)
|
||||
|
||||
# Verify result structure
|
||||
assert isinstance(result, RetrievalResult)
|
||||
assert result.query == "What is the company vacation policy?"
|
||||
assert result.total_time_ms > 0
|
||||
|
||||
# Verify tier progression
|
||||
assert result.tier1_candidates >= 0
|
||||
assert result.tier2_candidates >= 0
|
||||
assert result.tier3_candidates >= 0
|
||||
|
||||
def test_retrieval_with_all_features_enabled(self):
|
||||
"""Test retrieval with all features enabled"""
|
||||
config = RetrievalConfig(
|
||||
enable_kb_routing=True,
|
||||
kb_routing_method="auto",
|
||||
enable_doc_filtering=True,
|
||||
enable_metadata_similarity=True,
|
||||
enable_parent_child_chunking=True,
|
||||
use_summary_mapping=True,
|
||||
enable_hybrid_search=True
|
||||
)
|
||||
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
result = retrieval.retrieve(
|
||||
query="test query",
|
||||
kb_ids=["kb1", "kb2", "kb3"],
|
||||
top_k=20
|
||||
)
|
||||
|
||||
assert isinstance(result, RetrievalResult)
|
||||
|
||||
def test_retrieval_with_minimal_config(self):
|
||||
"""Test retrieval with minimal configuration"""
|
||||
config = RetrievalConfig(
|
||||
enable_kb_routing=False,
|
||||
enable_doc_filtering=False
|
||||
)
|
||||
|
||||
retrieval = HierarchicalRetrieval(config)
|
||||
|
||||
result = retrieval.retrieve(
|
||||
query="test query",
|
||||
kb_ids=["kb1"],
|
||||
top_k=5
|
||||
)
|
||||
|
||||
assert isinstance(result, RetrievalResult)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__, "-v"])
|
||||
Loading…
Add table
Reference in a new issue