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May 6, 2024
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Vectorize

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Turn your unstructured data into optimized vector search indexes.

Vectorize is an AI tool designed to turn unstructured data into optimally structured vector search indexes. This technology is built for Retrieval Augmented Generation, meaning it operates on the principle of retrieving and utilising relevant information to improve AI task performance.

Vectorize can be used in a variety of applications such as question answering systems, AI copilots, call center automation, content automation, and hyper-personalization.

The tool involves a three-step process comprising import, experiment, and deploy. During the import phase, users can upload documents or link to external knowledge management systems from which Vectorize extract natural language for AI use.

The experiment phase involves determining the most beneficial chunking and embedding strategies. Once a vector configuration is selected, it can be converted into a real-time vector pipeline through the deploy phase.

This pipeline can automatically update when changes in data occur, thereby ensuring high accuracy. Vectorize includes built-in support for various AI platforms such as Hugging Face, Google Vertex, LangChain, AWS Bedrock, OpenAI, Microsoft Azure, Jina AI, Voyage AI, and Mistral AI.

It also offers automatic creation and updates of vector indexes in a user's favorite vector database. Vectorize automates the process of turning data into AI-ready vectors, which can then be stored into a user's selected vector database.

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Pricing

Pricing model
Freemium
Paid options from
$99/month
Billing frequency
Monthly
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Reviews

3.6
Average from 5 ratings.
3
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0
1
1
Comments(3)
Rated it
it was very good untill they make it paid
Rated it
We still have a forever free tier you can use.
Rated it
The experimentation features are very useful in figuring out how different embedding models/chunking strategies perform.

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Vectorize was manually vetted by our editorial team and was first featured on May 6th 2024.

Pros and Cons

Pros

Automated vector index creation
Real-time vector pipeline
Optimizes unstructured data
Supports several vector databases
Auto-updates based on data changes
Experiment phase for best strategy
Supports large language models
Import data from various platforms
Fast and accurate results
Supports knowledge extraction
Built for Retrieval Augmented Generation
Useful for diverse applications
3-step deployment process
Supports document uploading

View 9 more pros

Cons

Three-step process required
Extract natural language limitation
Limited database compatibility
Depends on external platforms
Updating may affect accuracy
Requires constant data input
No guaranteed optimization strategies
Dependent on data quality

View 3 more cons

Q&A

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