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The highly-technical vocabulary of the semiconductor industry requires models with specialized training, provided with direct access to rich, multimodal data and complex documents.
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Boomerang V2 model is built for retrieving semiconductor data
Explore Boomerang V2 benchmarks for semiconductor document retrieval, comparing image and mixed text-and-image search with Boomerang V1, Google, and OpenAI models.
Boomerang V2: The next generation of multimodal enterprise search
Search beyond text with Boomerang V2 built for long, mixed-content documents: 8,192‑token context and 1024‑D embeddings with Matryoshka truncation for storage/latency tradeoffs. Benchmarked for semiconductor engineering.
Moving beyond text conversion: The future of enterprise search
Enterprise search draws data from documents, charts and diagrams. Vectara is building a successor to Boomerang, our multimodal embedding model, and changing retrieval.
From RAG Prototype to Production: Why We Wrote “Hands-On RAG for Production”
Read the new book on all things agentic RAG
Context Engineering: Can you trust long context?
Context Engineering is emerging as the evolution of prompt engineering. Yet larger context windows can backfire: too many tokens invite noise, contradictions, and diminishing returns.
Searching Hacker News
The Hacker News startup community website deserves better search
Improving Customer Support with AI: A Deep Dive into Conversational AI
Discover the transformative impact of Conversational AI, including its evolution, current trends, and role in revolutionizing customer support. If you’re thinking about why and how to adopt this technology for customer support, we offer some insights
Retrieval Augmented Generation Buyer's Guide
Regardless of whether you are considering building or buying a RAG solution, you need to understand the players, their offerings, where they accel, and where they are still trying to catch up
Retrieval Augmented Generation (RAG) Done Right: Document Stores
Building a RAG pipeline with Vectara and Elasticsearch