Which vector database and embedding model combination currently yields the lowest hallucination rate for enterprise knowledge retrieval systems?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Which vector database and embedding model combination currently yields the lowest hallucination rate for enterprise knowledge retrieval systems?
3 real searches · 1 real-search pass
2026 enterprise RAG benchmark embedding models vector databases retrieval hallucination evaluationGoogle ↗
OBSERVEDOther / navigational
MTEB benchmark retrieval embeddings 2025 2026 enterprise RAG hallucination vector database comparisonGoogle ↗
OBSERVEDComparison
RAG evaluation faithfulness retrieval reranking benchmark 2025 enterprise knowledge retrievalGoogle ↗
OBSERVEDOther / navigational
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (3)
Classes come from the query-classifier (a local Laya model). 2 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
2026 enterprise RAG benchmark embedding models vector databases retrieval hallucination evaluation Google ↗
MTEB benchmark retrieval embeddings 2025 2026 enterprise RAG hallucination vector database comparison Google ↗
RAG evaluation faithfulness retrieval reranking benchmark 2025 enterprise knowledge retrieval Google ↗
- An Empirical Evaluation of Retrieval, Reranking, and Similarity for a Q&A-Based Retrieval Augmented Generation System | IEEE Journals & Magazine · ieeexplore.ieee.org
- Systematic Evaluation of Similarity Metrics for Retrieval, Reranking, and Completion in Retrieval Augmented Generation Systems | IEEE Conference Publication · ieeexplore.ieee.org
- Embedding Benchmarks · aimultiple.com
- Gemini Embedding 2 model | Gemini API | Google AI for Developers · ai.google.dev
- Gemini Embedding 2: Our first natively multimodal embedding model · blog.google
- Hybrid Search with Reranking · qdrant.tech
- Hybrid Search in Qdrant · qdrant.tech
- Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards · aclanthology.org
- Frontiers | Bayesian RAG: uncertainty-aware retrieval for reliable financial question answering · doi.org
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
2026 enterprise RAG benchmark embedding models vector databases retrieval hallucination evaluation OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
MTEB benchmark retrieval embeddings 2025 2026 enterprise RAG hallucination vector database comparison OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
RAG evaluation faithfulness retrieval reranking benchmark 2025 enterprise knowledge retrieval OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |