Explore, manage, and optimize the latent representations that power your AI. LatentBase gives you the tools to see what your models really understand.
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Four deeply integrated modules that transform how you interact with your AI's vector representations.
Visualize your high-dimensional vectors in interactive 3D/2D projections. Spot clusters, detect outliers, and debug semantic search in real-time.
Compare distributions across different models. Manage metadata schemas, track embedding drift, and rollback with confidence.
Automated noise detection, deduplication, and dimensionality reduction. Keep your vector space clean and efficient without manual work.
First-class Python and C# SDKs. Monitor API latency, embedding costs, and retrieval hit rates from a unified dashboard.
Our SDK is designed to feel native. Connect your embedding pipeline with just a few lines of code and start exploring immediately.
from latentbase import LatentBase # Initialize the client client = LatentBase("your-api-key") # Create a vector set vs = client.create_vector_set( name="product-embeddings", model="text-embedding-3-large", dimensions=3072 ) # Insert vectors with metadata vs.upsert([ {"id": "doc_1", "text": "Neural networks..."}, {"id": "doc_2", "text": "Transformers..."}, ]) # Explore the latent space projection = vs.explore(method="umap") projection.visualize()
Debug RAG retrieval precision. Visualize why your system retrieves certain chunks and tune your pipeline for higher accuracy.
Analyze latent relationships in unstructured data — images, text, audio. Discover hidden patterns your models have learned.
Understand query distributions via heatmaps. Discover feature blind spots and prioritize what matters to your users.
Join thousands of AI engineers who use LatentBase to understand, debug, and optimize their vector representations.
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