Vectara API v2
REST API simplifies Retrieval Augmented Generation
Vectara API v2 launched on June 10, 2024 with 9 votes, #1,154 of 4,672 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 2 years on.
What it does
Vectara's API v2 brings an incredibly simple and easy to use retrieval augmented generation API to the market. Forget about trying to figure out text extraction, segmentation, embedding models, and hybrid search: Vectara handles it all for you
Today, we’re excited to announce that we’re introducing a brand new REST API for Vectara: API v2. Today, we’re excited to announce that we’re introducing a brand new REST API for Vectara: API v2. API v2 brings enormous quality-of-life improvements for software developers using the Vectara platform and we’d like to walk you through what some of those are, why we’ve chosen to make them, and what you can do to get the most out of the Vectara platform now. Vectara’s API has been rich and highly capable: you can define a reranking strategy, a hybrid/keyword strategy, specify multiple corpora to target your query against, provide structured filters, and so on. However, a complex JSON structure…from vectara.com
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Launching a new home-grown embedding LLM for RAG2023 · vectara.com · ▲12Hi HN! Vectara is a "batteries included" retrieval augmented generation platform. You can upload your rich text documents like PDFs, HTML pages, word docs, etc, or semi-structured JSON and Vectara handles the text and metadata extraction, segmentation, vector embedding, and vector storage, and keyword storage. You can ask a question or perform a search in the UI or via our APIs and Vectara will automatically handle the vectorization, structured metadata filtering, vector+keyword retrieval, hybrid blending, and generative summarization of the results. We're focusing on building and…
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Demystifying Advanced RAG Pipelines2023 · github.com · ▲131I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…


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