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What is Vectara?

Vectara provides a search-as-a-service solution powered by large language models (LLMs). This platform encompasses the entire machine learning search workflow, including steps such as extraction, indexing, retrieval, re-ranking, and calibration, all of which are accessible via API. Developers can swiftly integrate state-of-the-art natural language processing (NLP) models for search functionality within their websites or applications within just a few minutes. The system automatically converts text from various formats, including PDF and Office documents, into JSON, HTML, XML, CommonMark, and several others. Leveraging advanced zero-shot models that utilize deep neural networks, Vectara can efficiently encode language at scale. It allows for the segmentation of data into multiple indexes that are optimized for low latency and high recall through vector encodings. By employing sophisticated zero-shot neural network models, the platform can effectively retrieve potential results from vast collections of documents. Furthermore, cross-attentional neural networks enhance the accuracy of the answers retrieved, enabling the system to intelligently merge and reorder results based on the probability of relevance to user queries. This capability ensures that users receive the most pertinent information tailored to their needs.

What is TILDE?

TILDE (Term Independent Likelihood moDEl) functions as a framework designed for the re-ranking and expansion of passages, leveraging BERT to enhance retrieval performance by combining sparse term matching with sophisticated contextual representations. The original TILDE version computes term weights across the entire BERT vocabulary, which often leads to extremely large index sizes. To address this limitation, TILDEv2 introduces a more efficient approach by calculating term weights exclusively for words present in the expanded passages, resulting in indexes that can be 99% smaller than those produced by the initial TILDE model. This improved efficiency is achieved by deploying TILDE as a passage expansion model, which enriches passages with top-k terms (for instance, the top 200) to improve their content quality. Furthermore, it provides scripts that streamline the processes of indexing collections, re-ranking BM25 results, and training models using datasets such as MS MARCO, thus offering a well-rounded toolkit for enhancing information retrieval tasks. In essence, TILDEv2 signifies a major leap forward in the management and optimization of passage retrieval systems, contributing to more effective and efficient information access strategies. This progression not only benefits researchers but also has implications for practical applications in various domains.

Media

Media

Integrations Supported

Datavolo
Hugging Face
IBM watsonx.data
Langflow
Python

Integrations Supported

Datavolo
Hugging Face
IBM watsonx.data
Langflow
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Vectara

Date Founded

2020

Company Location

United States

Company Website

vectara.com

Company Facts

Organization Name

ielab

Company Location

United States

Company Website

github.com/ielab/TILDE/tree/main

Categories and Features

Enterprise Search

AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery

Categories and Features

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