Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Marqo stands out not just as a vector database, but as a comprehensive vector search engine. It simplifies the entire process of vector generation, storage, and retrieval through a unified API, eliminating the necessity of providing your own embeddings. By utilizing Marqo, you can expedite your development timeline significantly, as indexing documents and initiating searches can be accomplished with just a few lines of code. Additionally, it enables the creation of multimodal indexes, allowing for the seamless combination of image and text searches. Users can select from an array of open-source models or implement their own, making it flexible and customizable. Marqo also allows for the construction of intricate queries with multiple weighted elements, enhancing its versatility. With features that incorporate input pre-processing, machine learning inference, and storage effortlessly, Marqo is designed for convenience. You can easily run Marqo in a Docker container on your personal machine or scale it to accommodate numerous GPU inference nodes in the cloud. Notably, it is capable of handling low-latency searches across multi-terabyte indexes, ensuring efficient data retrieval. Furthermore, Marqo assists in configuring advanced deep-learning models like CLIP to extract semantic meanings from images, making it a powerful tool for developers and data scientists alike. Its user-friendly nature and scalability make Marqo an excellent choice for those looking to leverage vector search capabilities effectively.
Description
Vectara offers LLM-powered search as-a-service. The platform offers a complete ML search process, from extraction and indexing to retrieval and re-ranking as well as calibration. API-addressable for every element of the platform. Developers can embed the most advanced NLP model for site and app search in minutes.
Vectara automatically extracts text form PDF and Office to JSON HTML XML CommonMark, and many other formats. Use cutting-edge zero-shot models that use deep neural networks to understand language to encode at scale. Segment data into any number indexes that store vector encodings optimized to low latency and high recall. Use cutting-edge, zero shot neural network models to recall candidate results from millions upon millions of documents. Cross-attentional neural networks can increase the precision of retrieved answers. They can merge and reorder results. Focus on the likelihood that the retrieved answer is a probable answer to your query.
API Access
Has API
API Access
Has API
Integrations
Amazon S3
Datavolo
Docker
Hugging Face
IBM watsonx.data
Langflow
Integrations
Amazon S3
Datavolo
Docker
Hugging Face
IBM watsonx.data
Langflow
Pricing Details
$86.58 per month
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Marqo
Website
www.marqo.ai/
Vendor Details
Company Name
Vectara
Founded
2020
Country
United States
Website
vectara.com
Product Features
Product Features
Enterprise Search
AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery