Voyage AI provides state-of-the-art embedding models and rerankers to supercharge search and retrieval for unstructured data.
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Voyage AI provides state-of-the-art embedding models and rerankers to supercharge search and retrieval for unstructured data.
Voyage AI offers embedding models and rerankers that enhance search and retrieval processes for unstructured data. Their models are designed to deliver high accuracy, low dimensionality, and low latency. The platform's embeddings and rerankers drive retrieval and response quality, using cutting-edge AI research and engineering to retrieve relevant contextual information. Voyage AI's solution is cost-efficient and modular, plugging and playing with any vector database and LLM. They focus on general-purpose, domain-specific, and company-specific applications, providing a spectrum of models to fit various use cases. Voyage AI aims to enable users to build factual responses with lower costs.
Voyage AI provides state-of-the-art embedding models and rerankers to supercharge search and retrieval for unstructured data.
Quick visual proof for Voyage AI. Helps non-technical users understand the interface faster.
Voyage AI offers embedding models and rerankers that enhance search and retrieval processes for unstructured data.
Explore all tools that specialize in creating vector embeddings from text. This domain focus ensures Voyage AI delivers optimized results for this specific requirement.
Explore all tools that specialize in reranking search results for improved relevance. This domain focus ensures Voyage AI delivers optimized results for this specific requirement.
Explore all tools that specialize in improving retrieval-augmented generation (rag) pipelines. This domain focus ensures Voyage AI delivers optimized results for this specific requirement.
Explore all tools that specialize in optimizing search queries for unstructured data. This domain focus ensures Voyage AI delivers optimized results for this specific requirement.
Explore all tools that specialize in reducing vector dimensionality for cost-effective storage. This domain focus ensures Voyage AI delivers optimized results for this specific requirement.
Explore all tools that specialize in enhancing the accuracy of semantic search. This domain focus ensures Voyage AI delivers optimized results for this specific requirement.
Open side-by-side comparison first, then move to deeper alternatives guidance.
Voyage AI's models produce embeddings with 3x-8x shorter vectors, leading to reduced storage costs and faster search.
Supports context lengths up to 32K tokens, enabling processing of larger documents and more comprehensive search.
Voyage AI offers reranking models that refine initial search results, prioritizing the most relevant documents.
Provides models optimized for specific industries like finance, legal, and code, improving performance on specialized data.
Voyage AI offers the Voyage 4 Model Series and voyage-multimodal-3.5 that allows multimodal input and output
Customers struggle to find relevant answers to their support queries using keyword-based search.
Step 1: Embed all customer support documentation using Voyage AI's embedding model.
Step 2: When a customer submits a query, embed the query using the same model.
Step 3: Perform a similarity search between the query embedding and the document embeddings.
Step 4: Return the documents with the highest similarity scores to the customer.
Users have difficulty finding specific information within a large knowledge base.
Step 1: Split the knowledge base into smaller chunks of text.
Step 2: Generate embeddings for each chunk using Voyage AI.
Step 3: Store the embeddings in a vector database.
Step 4: When a user searches, generate an embedding for their search query and retrieve the most similar chunks from the vector database.
Developers waste time searching for specific code snippets or functions.
Step 1: Embed code snippets from your codebase using Voyage AI's code-optimized model.
Step 2: When a developer searches for a code snippet, embed their search query.
Step 3: Perform a similarity search to find the most relevant code snippets.
Step 4: Display the search results to the developer.
Lawyers need to quickly find relevant clauses or precedents within a large corpus of legal documents.
Step 1: Embed all legal documents using Voyage AI's domain-specific model for legal text.
Step 2: Embed the query related to a specific legal problem.
Step 3: Search for the most similar documents to the query.
Step 4: Extract and present the relevant legal information from the top search results.
Existing product recommendation systems based on collaborative filtering are not accurate enough.
Step 1: Embed product descriptions using Voyage AI's embedding model.
Step 2: Embed customer purchase histories or browsing behavior.
Step 3: Calculate similarity between customer embeddings and product embeddings.
Step 4: Recommend products with high similarity scores to the customer.
Create an account on the Voyage AI platform.
Obtain your API key from the dashboard.
Install the Voyage AI Python library or use the REST API.
Choose the appropriate embedding model based on your use case (general-purpose, domain-specific, etc.).
Prepare your text data for embedding.
Call the embedding API with your text data and API key.
Store the resulting vector embeddings in your vector database of choice.
All Set
Ready to go
Verified feedback from other users.
“Voyage AI aims to deliver factual responses with lower costs using best-in-class embedding models and rerankers. They are trusted by industry leaders.”
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Voyage AI may be preferred for its focus on cost-efficiency and modularity, offering cheaper inference and plug-and-play compatibility with various vector databases and LLMs, especially if long context length is not critical.
Voyage AI could be chosen for its domain-specific models and potentially lower cost for similar performance in specific use cases.
Voyage AI may be preferable for users who want more control over the embedding model and do not need a fully managed vector database solution like Pinecone.
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