Vectara's models
Vectara's advanced end-to-end RAG models have pioneered enterprise-grade RAG.
Try our modelsBoomerang
Retrieval model
Learn moreMockingbird
Generation model
Learn moreSlingshot
Multilingual reranker
Learn moreHughes HEM
Evaluation model
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Best-of-breed models at every stage
A RAG pipeline is only as good as the models that power it.
So we maintain state-of-the-art models for every step in the process.
Boomerang
Vectara’s advanced retrieval and embedding model delivers high-precision, contextually relevant search results
Multilingual semantic search
See announcement
Boomerang manages retrieval in RAG, grounding results in datasets and supporting Hybrid Search by mapping concepts to vectors, not keywords.
Boomerang supports hundreds of languages and dialects and uses zero-shot learning to quickly find relevant information across diverse domains.
Boomerang’s performance benchmarks
Performance details (June 2024)
Slingshot
Vectara’s reranking model reorganizes search results to prioritize highly relevant and useful information.
Reranking for relevance and variety
See documentation
The Slingshot model reorganizes search results to ensure relevance and avoid redundancy, delivering accurate outcomes across queries.
It can be combined with inputs based on your priorities, such as recency or keyword frequency, to ensure the most relevant information rises to the top.
Slingshot’s performance benchmarks
Performance details (May 2024)
Mockingbird
Vectara’s multilingual generative model is fine-tuned for RAG and structured output tasks.
Context-aware multilingual generation
Explore documentation
The Mockingbird LLM is the “G” in our RAG platform. We’ve optimized it to deliver highly relevant answers and cite sources for each claim.
Mockingbird handles queries and results from diverse domains and languages. It can generate everything modern AI applications need.
Mockingbird’s performance benchmarks
Performance details (July 2024)
Hughes Hallucination Evaluation Model (HHEM)
Vectara’s HHEM model is the most production-ready generative AI hallucination detector
Hallucination detection for any LLM
View documentation
The Hughes Hallucination Evaluation Model assesses LLMs for hallucinated results versus factual consistency.
The free version is on Hugging Face, with a more advanced version used in our platform's Factual Consistency Score.
Our Hallucination Evaluation Model is a first-of-its-kind initiative to proffer a commercially available and open-source model that addresses the accuracy and level of hallucination in LLMs.
HHEM’s hallucination detection performance benchmarks
Performance details (August 2024)
Remembering a champion of responsible AI
The Hughes Hallucination Evaluation Model honors Dr. Simon Mark Hughes, a pioneering machine learning engineer at Vectara. His research on AI hallucination detection, before his passing in 2023, continues to shape industry efforts toward truthful and reliable AI outputs.
Experience innovative retrieval augmented generation via API
Vectara is the shortest path between question and answer, delivering true business value in the shortest time.