Launching a new home-grown embedding LLM for RAG

Hacker News

Hi 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 operationalizing the complex infrastructure for vector storage, hybrid retrieval, and generative summarization so you can use fairly high-level APIs and focus on building your own applications. We know that retrieval accuracy is incredibly important for RAG: garbage in, garbage out. We've seen a lot of projects not spend enough time on really getting the retrieval model right and wasting a lot of time/money with poor outcomes. We've spent about the past 6 months working on a new embedding model named Boomerang and just released it on the Vectara platform. We've run it through standard evaluations like BEIR (though we know many models over-fit against BEIR) as well as multi-domain evaluations. We've published the details of our tests for those that really want to dive in, but the TL;DR is that Boomerang beats most/all publicly available models in many/most situations and is particularly strong at cross-lingual and multi-domain tests. We'd love any and all feedback!

Visit product View on Hacker NewsSep 28, 2023

Share card

Actual performance

12points

1comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: just released, lua, io · Missing: https docs, excited, exist

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Mockingbird is an LLM that outperforms GPT4 on RAG57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Mockingbird is an LLM that outperforms GPT4 on RAG

Hacker News9

Boomerang, a new embedding model for RAG and semantic search60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Boomerang, a new embedding model for RAG and semantic search

Hacker News18

RAG App Example with Self-hosted Embedding and LLM Services59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RAG App Example with Self-hosted Embedding and LLM Services

Hacker News5

Embedding visualizations for bloggers and journalists – VizFiddle52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Embedding visualizations for bloggers and journalists – VizFiddle

Hacker News1

Launching Flurly Affiliates41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Launching Flurly Affiliates

Hacker News1

Visualizing and Comparing Embedding Vectors as Heatmaps56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualizing and Comparing Embedding Vectors as Heatmaps

Hacker News3

Implementing Embedding Gemma in PyTorch28%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Implementing Embedding Gemma in PyTorch

Hacker News3

CentUp - Launching in late February53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

CentUp - Launching in late February

Hacker News8

Disciple – Launching our new community platform44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Disciple – Launching our new community platform

Hacker News2

EmbedFlow –> Upgrade embedding models without re-embedding your corpus56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

EmbedFlow –> Upgrade embedding models without re-embedding your corpus

Hacker News6

Browse all products Analyze your launch