Knowledge Base Retrieval Architecture: Why Vector Search + Full-Text Search + Rerank Are All Indispensable
Enterprise knowledge base retrieval is not a single-algorithm problem — it is a multi-layer coordination problem.
Public sources are already very sufficient for this topic. Dify’s official and legacy documentation have clearly made public concepts and configuration methods for indexing methods, vector search, full-text search, hybrid search, rerank, and more. Therefore, this article can fully exist as a “public-facing retrieval architecture explanation” without relying on internal materials.
1. Retrieval Layers Confirmed by Public Sources
1. The Official Documentation Clearly Distinguishes Multiple Retrieval Methods
Dify’s official documentation explicitly breaks down retrieval methods under high-quality indexing into:
- Vector search
- Full-text search
- Hybrid search
- Rerank
This means the three-layer structure is not external speculation but part of Dify’s publicly available knowledge system.
2. Different Retrieval Methods Address Different Problems
Official documentation and articles related to hybrid search have already explained that keyword-type queries, semantic-type queries, and complex document ranking problems correspond to different layers of capability.
3. The Core of Retrieval Architecture Is Not “Algorithm Stacking” but Clear Division of Responsibility
If all problems are handed to a single retrieval method, enterprise document scenarios will inevitably encounter issues such as missed recall of technical terms, missed matches for vague expressions, or excessive noise. Public sources are sufficient to support this judgment.
2. What Vector Search Solves
Vector search excels at handling semantically similar but differently worded queries. It is well-suited for colloquial and fuzzy queries.
3. What Full-Text Search Solves
Full-text search excels at handling exact match problems involving keywords, reference numbers, proper nouns, alarm codes, and clause numbers.
4. What Rerank Solves
When recall results are plentiful, Rerank is responsible for reordering fragments that “all seem relevant,” reducing noise from entering the LLM.
5. Why All Three Layers Matter
With only vector search, precise technical terms are easily missed. With only full-text search, semantic expression differences are easily missed. Without Rerank, complex documents tend to carry noise into the answer.
6. Conclusion
The three-layer retrieval approach is not about adding complexity — it is about enabling the knowledge base to simultaneously achieve both recall rate and precision in real enterprise documents.
Public Source References
note.com
- 【世界一わかりやすい】Dify初心者のためのRAG完全ガイド | https://note.com/ai_app_pro/n/n440e26749bde
- 【徹底解説】Difyでのナレッジの使い方 | https://note.com/ai_dev_lab/n/n222b025fe3c3
zenn.dev / Official Documentation / Other Public Sources
- ハイブリッド検索 | 日本語 | https://legacy-docs.dify.ai/ja-jp/learn-more/extended-reading/retrieval-augment/hybrid-search
- インデックス方法と検索設定を指定 | https://docs.dify.ai/ja/use-dify/knowledge/create-knowledge/setting-indexing-methods
- Re-ranking | https://legacy-docs.dify.ai/learn-more/extended-reading/retrieval-augment/rerank
Verified Information from Public Sources for This Article
- Dify’s public knowledge system clearly distinguishes between vector, full-text, hybrid search, and rerank
- Different retrieval layers each address different problems and cannot simply replace one another
- This article has sufficient public source support and can be retained as a formal methodology piece