ColBERT

ColBERT

Future Data Systems
Milvus

Milvus

Zilliz
+
+

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About

ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. It relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings. At search time, it embeds every query into another matrix and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. These rich interactions allow ColBERT to surpass the quality of single-vector representation models while scaling efficiently to large corpora. The toolkit includes components for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. ColBERT integrates with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. It also includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts.

About

Vector database built for scalable similarity search. Open-source, highly scalable, and blazing fast. Store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models. With Milvus vector database, you can create a large-scale similarity search service in less than a minute. Simple and intuitive SDKs are also available for a variety of different languages. Milvus is hardware efficient and provides advanced indexing algorithms, achieving a 10x performance boost in retrieval speed. Milvus vector database has been battle-tested by over a thousand enterprise users in a variety of use cases. With extensive isolation of individual system components, Milvus is highly resilient and reliable. The distributed and high-throughput nature of Milvus makes it a natural fit for serving large-scale vector data. Milvus vector database adopts a systemic approach to cloud-nativity, separating compute from storage.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Academic researchers and developers seeking a tool for implementing and evaluating listwise reranking with large language models

Audience

Enterprises in need of a solution to manage their vector data and create a large scale similarity search service

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Future Data Systems
United States
github.com/stanford-futuredata/ColBERT

Company Information

Zilliz
Founded: 2020
United States
milvus.io

Alternatives

TILDE

TILDE

ielab

Alternatives

RankLLM

RankLLM

Castorini
RankGPT

RankGPT

Weiwei Sun
BERT

BERT

Google
RoBERTa

RoBERTa

Meta

Categories

Categories

Integrations

Amazon S3
Apache Kafka
Apache Pulsar
Azure Blob Storage
Cognee
Database Mart
Docker
IBM watsonx.data
Kubernetes
MinIO
Prompt Security
Zilliz Cloud

Integrations

Amazon S3
Apache Kafka
Apache Pulsar
Azure Blob Storage
Cognee
Database Mart
Docker
IBM watsonx.data
Kubernetes
MinIO
Prompt Security
Zilliz Cloud
Claim ColBERT and update features and information
Claim ColBERT and update features and information
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Claim Milvus and update features and information