Map the semantic
universe.
Keywords are obsolete. FyBrain converts unstructured data into 1536-dimensional coordinate arrays, allowing your applications to search by concept, cluster by meaning, and power Enterprise RAG instantly.
Understand the math.
Keywords are dead.
1536-Dimensional Mapping
Every document, paragraph, and query is translated into a dense 1536-dimension floating-point array, capturing infinite layers of semantic nuance.
HNSW Continuous Indexing
Add millions of vectors per hour without locking the database. We utilize custom Hierarchical Navigable Small World graphs for sub-millisecond retrieval.
Cosine Similarity Engine
Distance equals meaning. Our hardware-accelerated engine calculates the exact angular distance between vectors to find semantic nearest neighbors instantly.
Multimodal Projection
Text, images, and structured JSON are all projected into the exact same unified semantic space, allowing cross-modal search natively.
Stop searching for words.
Start searching for meaning.
Query the space.
Retrieve the intent.
1import fybrain23# Initialize the Vector Cluster4client = fybrain.Client(api_key="fb_live_*******************")56# Search by semantic intent rather than keywords7results = client.vector_db.query(8 query="Show me all contracts regarding office lease terms",9 top_k=3,10 min_similarity=0.85,11 include_metadata=True12)1314for doc in results:15 print(f"Match: {doc.filename} | Score: {doc.score}")Powering the next
generation of search.
Map the
latent space.
Stop fighting with synonym dictionaries and rigid keyword rules. Initialize your vector cluster and upgrade your enterprise to true semantic search.