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Description
Enhance your embedding metadata and tokens through an intuitive user interface. By employing sophisticated NLP cleansing methods such as TF-IDF, you can normalize and enrich your embedding tokens, which significantly boosts both efficiency and accuracy in applications related to large language models. Furthermore, optimize the pertinence of the content retrieved from a vector database by intelligently managing the structure of the content, whether by splitting or merging, and incorporating void or hidden tokens to ensure that the chunks remain semantically coherent. With Embedditor, you gain complete command over your data, allowing for seamless deployment on your personal computer, within your dedicated enterprise cloud, or in an on-premises setup. By utilizing Embedditor's advanced cleansing features to eliminate irrelevant embedding tokens such as stop words, punctuation, and frequently occurring low-relevance terms, you have the potential to reduce embedding and vector storage costs by up to 40%, all while enhancing the quality of your search results. This innovative approach not only streamlines your workflow but also optimizes the overall performance of your NLP projects.
Description
Your organization's knowledge is scattered across various formats such as PDFs, spreadsheets, wikis, and ERP exports. Traditional retrieval augmented generation (RAG) techniques can only extract the top-K similar snippets, which is adequate for brief summaries but inadequate when precise and comprehensive answers are required.
Vedana adopts a structure-first methodology, allowing you to define your domain through anchors, attributes, and connections. It systematically ingests your data into a categorized knowledge graph, enabling AI agents to investigate it incrementally: performing graph queries, vector searches, and compiling answers from actual data. In this setup, the language model interprets the information while the data remains the authoritative source.
The advantages you gain include:
- Precise figures: obtaining specific prices, dates, and statuses directly from the graph
- Comprehensive outcomes: guaranteeing that all relevant records are included without omissions
- Multi-step reasoning: connecting product information to categories, regulations, and documents seamlessly
- Traceability: ensuring that every answer is linked back to specific nodes, edges, and data segments
- Consistency: delivering the same results with identical queries and processes
Additionally, the system comes with built-in evaluation using gold-standard datasets and is compatible with any language model. It can be deployed as open-core, managed cloud, or on-premises solutions, with pilot implementations available in just four weeks. This approach not only enhances accuracy but also fosters a deep understanding of the interconnected data landscape.
API Access
Has API
API Access
Has API
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Integrations
Docker
GitHub
IngestAI
Pricing Details
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Free Trial
Free Version
Pricing Details
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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
Embedditor
Website
embedditor.ai/
Vendor Details
Company Name
Epoch8
Founded
2017
Country
Georgia
Website
e8.team/
Product Features
Product Features
Alternatives
Alternatives
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