Embenx Documentation 🚀¶
Universal embedding retrieval toolkit & agentic memory layer.
Embenx is a high-performance Python library designed for the 2026 AI ecosystem. It bridges the gap between raw vector indices and full-blown vector databases, providing a unified API for 15+ backends, advanced filtering, hybrid search, and specialized memory structures for AI agents.
Contents:
Key Capabilities¶
Unified Collection API: A table-like interface for managing vectors and metadata seamlessly.
Agentic Memory (MCP): Built-in Model Context Protocol server for instant integration with Claude, GPT-5, and autonomous agents.
Advanced Visuals: Built-in 3D HNSW Graph Visualizer, RAG Playground, and Embenx Explorer.
Research-Driven Optimizations: Implementation of state-of-the-art algorithms like ClusterKV, TurboQuant (1-bit quantization), and Echo (Temporal Memory).
15+ Vector Backends: Support for FAISS, ScaNN, USearch, pgvector, LanceDB, Milvus, Qdrant, and more.
Multimodal Support: Native handling of image embeddings (CLIP) and cross-modal retrieval.
Quick Start¶
Install Embenx:
pip install embenx
Simple semantic search:
from embenx import Collection
import numpy as np
# Initialize a collection with FAISS-HNSW
col = Collection(dimension=768, indexer_type="faiss-hnsw")
# Add data with metadata
vectors = np.random.rand(100, 768).astype('float32')
metadata = [{"id": i, "text": f"Document {i}", "category": "news"} for i in range(100)]
col.add(vectors, metadata)
# Search with metadata filtering
results = col.search(query_vector, top_k=5, where={"category": "news"})
for meta, dist in results:
print(f"Found: {meta['text']} (Distance: {dist:.4f})")