What is Vectorize
Vectorize is a cutting-edge platform designed to streamline the creation of AI-powered applications using Retrieval Augmented Generation (RAG). It transforms unstructured data into optimized vector search indexes, specifically tailored for RAG, ensuring fast, accurate, and production-ready results.
Features of Vectorize
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Comprehensive Data Integration: Vectorize connects seamlessly with various data sources including content management systems, file systems, CRMs, and collaboration tools, extracting natural language data for use by Large Language Models (LLMs).
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Parallel Experimentation: The platform conducts multiple chunking and embedding strategies simultaneously, providing quantified results for each, allowing users to choose the best approach or follow recommended strategies.
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Real-Time Deployment: Once a vector configuration is selected, Vectorize converts it into a real-time vector pipeline that updates automatically, ensuring consistent and accurate search results.
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Built-in Support for Leading AI Platforms: Vectorize supports top AI platforms like Hugging Face, Google Vertex, LangChain, AWS Bedrock, OpenAI, Microsoft Azure, Jina AI, Voyage AI, and Mistral AI, leveraging their powerful embedding models and chunking strategies.
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Automatic Vector Index Creation: The platform automatically creates and updates vector indexes in your preferred vector database, making data ready for AI applications.
How to use Vectorize
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Import Data: Upload documents or connect to external knowledge management systems to extract natural language data for your LLM.
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Experiment: Utilize Vectorize's parallel processing to test various chunking and embedding strategies, selecting the most effective approach.
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Deploy: Convert your chosen vector configuration into a real-time pipeline, ensuring automatic updates for always accurate search results.
Pricing of Vectorize
Vectorize offers flexible pricing options tailored to different business needs. For specific pricing details, please visit the Pricing section or Request a Demo for a personalized quote.
Useful tips for using Vectorize
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Leverage Multiple Data Sources: Integrate data from various sources to enrich your AI applications with diverse information.
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Optimize Chunking Strategies: Experiment with different chunking methods to find the optimal balance between granularity and relevance.
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Stay Updated with AI Trends: Regularly update your knowledge on AI advancements to maximize the potential of Vectorize.
Frequently asked questions about Vectorize
What data sources can Vectorize connect to?
Vectorize can connect to a wide range of data sources including content management systems, file systems, CRMs, and collaboration tools.
How does Vectorize ensure accurate search results?
Vectorize ensures accuracy by automatically updating vector indexes in real-time and providing parallel experimentation to optimize chunking and embedding strategies.
Can Vectorize be integrated with existing AI platforms?
Yes, Vectorize supports integration with leading AI platforms such as Hugging Face, Google Vertex, LangChain, AWS Bedrock, OpenAI, and Microsoft Azure.
Is there a free trial available for Vectorize?
Yes, Vectorize offers a Free Trial to allow users to explore its features and capabilities before making a commitment.
How can I get support for using Vectorize?
For support, you can visit the Documentation section, join the Community, or Contact Us directly for personalized assistance.