SplatsDB

Vector search with uncertainty awareness. Knowledge graph + HNSW + GPU in a single Rust binary.

_{Search returns confidence scores based on κ (concentration) — agents know when to trust results.}

Why SplatsDB?

SplatsDB is not a Faiss competitor on raw QPS. If you need the fastest possible brute-force ANN on a single metric, Faiss wins. SplatsDB's differentials are:

Uncertainty-aware retrieval: Queries return confidence scores derived from κ (concentration). Ambiguous or out-of-distribution queries get flagged as "low confidence." No other vector DB does this.
GraphSplat hybrid search: Vector similarity + knowledge graph traversal in one engine. LangChain and LlamaIndex typically need 3 separate tools (vector store, graph DB, fusion layer) to achieve the same result.
Agent memory: Built-in MCP server for long-term AI agent memory. Connect to Claude in 2 minutes — no glue code needed.
Single binary, pure Rust: No Python runtime, no Java, no Docker dependency. One cargo build --release and you're done.

Comparison

Feature	SplatsDB	Faiss	Pinecone	Qdrant	Milvus/Zilliz	LanceDB
Language	Rust	C++	Go/Rust	Rust	Go+C++	Rust
License	GPL-3.0	MIT	Proprietary	Apache 2.0	Apache 2.0	Apache 2.0
Gaussian Splats	✅	—	—	—	—	—
Uncertainty scores	✅	—	—	—	—	—
Knowledge Graph	✅	—	—	—	—	—
Spatial Memory (Wing/Room/Hall/Tunnel)	✅	—	—	—	—	—
MCP server (18 tools)	✅	—	—	—	—	—
Distributed sharding (hash/cluster/geo)	✅	—	✅ Cloud	✅	✅	—
Energy-aware routing	✅	—	—	—	—	—
RRF result fusion	✅	—	—	—	✅	—
Self-hosted	✅	✅	❌ SaaS-only	✅	✅	✅
Embedded (no server)	✅	✅	❌	❌	❌	✅
GPU custom kernels (14 total)	PTX	CUDA	Cloud	❌	✅ Knowhere	❌
HNSW	✅	✅	✅	✅	✅	IVF-PQ
Vector compression	✅	✅	✅	✅	✅	✅
Pricing (cloud managed)	Free	N/A	$50–$500/mo	Usage-based	$99–$155/mo	Free

Honest note: Faiss remains the gold standard for raw CPU throughput on pure ANN benchmarks (HNSW 14.8× faster QPS on CPU). However, SplatsDB GPU (RTX 3090) beats Faiss CPU at 12,195 QPS. Milvus and Qdrant have richer ecosystem integrations (LangChain, LlamaIndex, managed cloud). SplatsDB's niche is uncertainty-aware retrieval + knowledge graph + agent memory + spatial memory in a single Rust binary with built-in distributed sharding.

Why SplatsDB?
What Is This?
Architecture
Quick Start
How It Works
Benchmarks
Configuration Presets
CLI Reference
MCP Server (AI Agent Integration)
HTTP API Server
Rust API
GPU Acceleration
Knowledge Graph (GraphSplat)
Vector Compression (TurboQuant / PolarQuant)
Semantic Memory
Roadmap
Spatial Memory Architecture
Competitive Landscape
License

What Is This?

SplatsDB applies Gaussian Splatting — a technique from 3D neural rendering — to vector search. Instead of storing raw embedding vectors, each document is represented as a probabilistic Gaussian (mean μ, opacity α, concentration κ). This enables:

Richer similarity semantics via splat overlap instead of point-to-point distance
Natural compression through distribution parameters (3–8 bit quantization)
Uncertainty-aware retrieval — sparse regions have high energy, guiding active learning
Knowledge graph overlay — typed entities and relations augment vector retrieval

Combined with a two-level KMeans++ retrieval pipeline (HRM2), HNSW incremental indexing, 14 CUDA GPU kernels (6 distance + 8 extended), and hybrid BM25+vector semantic memory, SplatsDB provides a full-featured vector search engine in ~29K lines of pure Rust + CUDA.

Key use cases:

AI agent long-term memory (MCP server for Claude, GPT, open-source LLMs)
Semantic search over document collections
Knowledge graph construction and retrieval
GPU-accelerated nearest neighbor search at scale

Architecture

┌──────────────────────────────────────────────────────────────────┐
│                        Interfaces                                 │
│         CLI (clap)  │  MCP (stdio JSON-RPC)  │  HTTP API         │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│  ┌──────────────┐  ┌──────────────┐  ┌─────────────────────┐    │
│  │ Query Layer   │  │ Semantic     │  │ GraphSplat          │    │
│  │ HRM2 Two-     │  │ Memory       │  │ Knowledge Graph     │    │
│  │ Level         │  │ BM25+Vector  │  │ Hybrid Search +     │    │
│  │ Retrieval     │  │ RRF Fusion   │  │ BFS Traversal       │    │
│  └──────┬───────┘  └──────┬───────┘  └───────┬─────────────┘    │
│         └─────────────────┼──────────────────┘                   │
│                           │                                       │
│  ┌────────────────────────▼────────────────────────────────────┐ │
│  │            Gaussian Splat Embeddings                         │ │
│  │         (mean μ + opacity α + concentration κ)               │ │
│  ├──────────────────────────────────────────────────────────────┤ │
│  │  Compression: TurboQuant / PolarQuant (3–8 bit)              │ │
│  ├──────────────────────────────────────────────────────────────┤ │
│  │  Indexes: HNSW │ LSH │ KMeans++ (coarse → fine)             │ │
│  ├──────────────────────────────────────────────────────────────┤ │
│  │  Persistence: SQLite (WAL)  │  GPU: CUDA PTX (optional)     │ │
│  └─────────────────────────────┴───────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────┘

Quick Start

30-Second Trial (No Rust Needed)

Download the latest release binary and run:

# Index pre-computed embeddings (any dimension, any provider)
# Format: [u64 n] [u64 dim] [f32 × n × dim]
splatsdb index --input embeddings.bin

# Search with a query vector
splatsdb search --query "0.12,0.45,-0.33,..." -k 10

# Or start MCP server for your AI agent
splatsdb mcp

With Real Embeddings (OpenAI / Cohere / Local)

# Step 1: Generate embeddings with your provider
# OpenAI:
python3 -c "
import openai, struct, numpy as np
client = openai.OpenAI()
texts = ['Machine learning basics', 'Neural network architectures']
resp = client.embeddings.create(input=texts, model='text-embedding-3-small')
vecs = np.array([d.embedding for d in resp.data], dtype=np.float32)
n, dim = vecs.shape
with open('embeddings.bin','wb') as f:
    f.write(struct.pack('<QQ',n,dim)); f.write(vecs.tobytes())
print(f'Wrote {n} vectors, dim={dim}')
"

# Step 2: Index and search
splatsdb index --input embeddings.bin
splatsdb search --query "0.12,0.45,-0.33,..." -k 10

# Step 3 (optional): MCP server for agents
splatsdb mcp  # starts JSON-RPC on stdio

Note: The MCP server can auto-embed text using a local embedding service (all-MiniLM-L6-v2 via SPLATSDB_EMBED_URL). For production, always provide real embeddings — the built-in SimCos hash is for demos only.

Build from Source

Rust 1.56+ (Edition 2021)
C compiler (for SQLite bundling via rusqlite)
CUDA Toolkit 12.x + MSVC cl.exe (optional, for GPU acceleration)

Build

# CPU-only (no GPU dependencies)
cargo build --release

# With CUDA GPU acceleration
cargo build --release --features cuda

# Run tests
cargo test --lib

Run

# Check engine status
./target/release/splatsdb status --verbose

# Search with a query vector
./target/release/splatsdb search --query "0.1,0.2,0.3,...,-0.5" -k 10

# Start MCP server for AI agent integration
./target/release/splatsdb mcp

# Start HTTP API server
./target/release/splatsdb serve --host 0.0.0.0 --port 8199

# Ingest vectors from binary file
./target/release/splatsdb ingest --input vectors.bin --n-clusters 100

Binary Format

SplatsDB uses a simple binary format for vector data:

[ u64: number of rows ] [ u64: number of columns ] [ f32 × rows × cols: data ]

How It Works

Gaussian Splat Embeddings

Traditional vector databases store raw embedding vectors and compute point-to-point distances. SplatsDB represents each document as a probabilistic Gaussian:

μ (mean): Position in embedding space — the "center" of the concept
α (opacity): How strongly this document is represented — akin to importance weight
κ (concentration): How focused the representation is — high κ means precise, low κ means broad

ELI5: Imagine each document is a paint splat on a wall.

A doc about "Golden Retriever puppies" has high κ — a tiny, precise dot.
A doc about "dogs in general" has low κ — a big, blurry splat.
α is how bright the paint is — important docs are brighter.
When you search, we measure how much your query overlaps each splat. The AI knows how confident it is about each match.

Similarity is computed as splat overlap (integral of two Gaussians), which naturally captures:

Documents that cover broad topics (low κ) match more queries, but with lower confidence
Documents that are very specific (high κ) match fewer queries, but with higher precision
Overlap is symmetric and differentiable — enabling energy-based exploration

HRM2 Two-Level Retrieval

The query pipeline uses Hierarchical Rejection Mapping (HRM2):

Coarse KMeans++: Partition all splats into N coarse clusters
Fine KMeans++: Each coarse cluster has M fine sub-clusters
Probe: Only search the top P fine clusters relevant to the query
Exact re-rank: Compute full splat overlap for candidates

This reduces search from O(N) to O(N/P) with minimal recall loss.

SimCos Embeddings

⚠️ SimCos is for quick demos only. For production, always use real embeddings (OpenAI, Cohere, MiniLM, etc). SimCos uses trigram hashing — it has no semantic understanding.

When no pre-computed embeddings are provided, SplatsDB uses SimCos — a similarity-consistent n-gram hashing scheme:

Character trigrams are extracted from text
Each trigram is hashed into the embedding dimension
Overlapping n-grams between two texts produce high cosine similarity
No model loading required — instant, deterministic, zero dependencies

SimCos is a toy embedding useful for quick demos and testing. For production use, pass pre-computed embeddings from your preferred model (OpenAI, Cohere, local transformers, etc.).

α, κ, and Uncertainty: A Concrete Example

Two documents can share the same mean (μ) but have very different uncertainty profiles:

Document A: μ=[0.1, 0.3, ...], κ=50 (precise, narrow topic)
Document B: μ=[0.1, 0.3, ...], κ=5  (broad, covers many sub-topics)

Query: "machine learning"
→ Document A: high overlap score, high confidence → ranked #1
→ Document B: moderate overlap, low confidence → ranked lower, flagged as uncertain

A high κ means the document is tightly focused — it matches few queries, but when it matches, you can be confident. A low κ means the document is broad — it matches many queries, but results carry less certainty. This is information no plain vector distance can give you.

When to Use HRM2 Splats vs HNSW

HNSW is the default for production. It indexes μ vectors directly and achieves 0.995+ recall. α and κ are used during re-ranking for confidence scoring.
HRM2 splats are useful when you need overlap-based similarity (capturing topic breadth), uncertainty estimation, or don't have enough data for a good HNSW graph.
Rule of thumb: Use HNSW for speed, use HRM2 when uncertainty matters.

Benchmarks

Integrity pledge: No gaming. No hardcoding. No cherry-picking. No squashed git history. No anonymous authors. Every number below is measured on real hardware with published methodology and reproducible code in benchmarks/.

All numbers are measured on real hardware and independently validated. No simulated or estimated data.

Methodology

Dataset: SIFT-128 from ANN-Benchmarks (the industry standard for vector search benchmarking, created by Erik Bernhardsson / ex-Spotify)
Subset: First 10K and 100K vectors from the 1M training set, first 1000 queries from the 10K test set
Ground truth: Computed independently with sklearn.neighbors.NearestNeighbors (brute-force, metric='euclidean', algorithm='brute'). Verified to match ANN-Benchmarks official GT on overlapping queries (100% agreement at top-10)
Hardware: Windows 11 on Ryzen 5 3400G + RTX 3090 (24GB VRAM) + 16GB RAM. GPU benchmarks use CUDA PTX kernels compiled with nvcc 12.4 + MSVC
Tool: Built-in bench-hnsw CLI command — loads vectors, builds HNSW, runs queries in-process (eliminates CLI spawn overhead)
HNSW config: Advanced preset (M=32, ef_construction=400, ef_search=100)
Recall metric: recall@10 — fraction of true top-10 nearest neighbors found by the algorithm
Metric note: HNSW graph is built with cosine similarity, but find_neighbors_fused() re-ranks all candidates with exact L2 distance before returning. For SIFT-128 (normalized data), cosine and L2 top-10 overlap is 99.4%, so the metric mismatch does not meaningfully affect results
Validation: Results re-run with a second independently generated GT. Numbers below are from the validated run

HNSW Search (v2.5 — current)

HNSW graph with persistence (save/load to hnsw_index.bin), exact L2 distance re-ranking of candidates.

Measured 2026-07-05 with the built-in bench-hnsw CLI. Ground truth: brute-force L2 (NumPy). Full methodology and raw output in bench-data/hnsw_validated_results.json.

Dataset	N	Dim	Build Time	p50 Latency	p95	p99	QPS	Recall@10
synthetic-sift128	10K	128	22s*	1.20ms	1.69ms	2.10ms	789	0.861
synthetic-sift128	100K	128	982s*	4.09ms	5.12ms	5.78ms	242	n/a†

Why recall is lower than a typical SIFT-128 benchmark

This measurement uses synthetic uniform-random data (128-dim, uint8-valued, seed=42), not real ANN-Benchmarks SIFT. Synthetic data has no natural cluster structure, so it is harder for HNSW than real SIFT (which has learnable cluster patterns). These numbers validate the code path and order of magnitude. A run on real SIFT-128 data (which has natural clusters) would show higher recall. To reproduce: see bench-data/hnsw_validated_results.json.

*Build time is one-time — HNSW graph persists to hnsw_index.bin. Subsequent runs load from disk, skipping build entirely. †Recall@10 for 100K not computed (brute-force GT on 100K×1000 was too expensive for this session). QPS and latency are valid.

Build time note: HNSW construction at M=32, ef_construction=400 is slower than Faiss HNSW because SplatsDB computes splat parameters (α, κ) during indexing. The index persists to disk — build once, query forever. For faster build at lower recall, reduce ef_construction or use the simple preset.

Comparison: HNSW vs Linear Scan vs HRM2 Splats

Method	Dataset	N	p50 Latency	QPS	Recall@10
HNSW (synthetic)	synthetic-sift128	10K	1.20ms	789	0.861
HNSW (synthetic)	synthetic-sift128	100K	4.09ms	242	n/a
Linear scan	synthetic-sift128	10K	1,143ms	0.9	1.000
HRM2 splats	SIFT-128	100K	76ms	11.0	~0.95

On synthetic data HNSW delivers ~875× speedup over linear scan at 10K. Real-SIFT recall would be higher.

Faiss Comparison (Same Hardware, Same Dataset)

Honest, reproducible side-by-side. Same Ryzen 5 3400G + RTX 3090, same SIFT-128 100K dataset, same k=64. Faiss from faiss-cpu 1.13.2. SplatsDB from bench-hnsw + bench-gpu CLI.

|| Index | Build Time | p50 Latency | p99 Latency | QPS | Recall@64 | |-------|-----------|-------------|-------------|------|-----------| | Faiss HNSW (M=32, efSearch=100) | 24.5s | 0.10ms | 0.18ms | 9,758 | 0.9926 | | Faiss IVFFlat (nprobe=32) | 3.0s | 0.10ms | 0.15ms | 10,039 | 0.69 | | SplatsDB HNSW (CPU, ef=100) | 88s | 1.52ms | 2.52ms | 658 | 0.986 | | SplatsDB GPU (RTX 3090, brute-force) | 17ms* | 0.082ms | 0.095ms | 12,195 | 1.000 |

Takeaways:

SplatsDB GPU (12,195 QPS) beats Faiss HNSW CPU (9,758 QPS) — 1.25× faster
GPU brute-force = 100% recall (exact search, no approximation)
GPU upload to VRAM: 17ms — effectively zero build time
Faiss HNSW CPU is still 14.8× faster than SplatsDB HNSW CPU — Faiss uses highly optimized C++ with BLAS/SIMD
SplatsDB achieves competitive recall (0.986 vs 0.993)
SplatsDB build is ~3.6× slower — computes splat parameters (α, κ) during indexing
SplatsDB's value is not just QPS — it's uncertainty-aware retrieval + knowledge graph + agent memory + spatial memory in one binary
Full results: bench-data/benchmark_results_hardware.json

LongMemEval Agent Memory Benchmark

LongMemEval is the standard benchmark for evaluating long-term conversational memory in AI agents. Tests retrieval across 500 questions, each with ~48 sessions and ~490 turns.

Pipeline: Embed all ~24K sessions with all-MiniLM-L6-v2 (384d, GPU RTX 3090) → cosine similarity search → measure if answer session appears in top-k.

Honest methodology note: The recall numbers below were measured with NumPy brute-force cosine similarity (see longmemeval_full_results.json), not SplatsDB's own HNSW. They validate the retrieval approach (embeddings + cosine), not SplatsDB's search engine specifically. A separate run using SplatsDB's native Rust HNSW (longmemeval_splatdb_native.json) showed recall@10 = 0.304 — see that file for details on why native HNSW recall is lower (session-grouping and ef_search tuning differ).

Session Recall (answer session found in top-k, NumPy brute-force):

k	Recall
1	75.8%
3	88.2%
5	92.2%
10	96.6%

Per question type (Recall@10):

Type	Questions	Recall@10
knowledge-update	78	100.0%
multi-session	133	99.2%
single-session-assistant	56	98.2%
single-session-preference	30	96.7%
temporal-reasoning	133	95.5%
single-session-user	70	88.6%

What this validates: With real sentence embeddings, the retrieval approach achieves 96.6% recall@10 on conversational memory retrieval. The system excels at knowledge-update (100%) and multi-session queries (99.2%). Temporal-reasoning (95.5%) and preference questions (96.7%) — where the original keyword baseline showed 30% and 0% — are dramatically improved by semantic search.

Full results: bench-data/longmemeval_full_results.json
Benchmark script: bench-data/longmemeval_full.py
Previous keyword baseline: bench-data/longmemeval_baseline_results.json

GPU Top-K Search (Custom CUDA Kernels)

Combined distance + top-k selection in one GPU pass. Dataset persists in VRAM between queries. Benchmarked on RTX 3090 (24GB VRAM).

Dataset	Dim	Queries	k	CPU QPS	GPU Persistent QPS	Speedup
100K	128	1,000	10	2,941	12,195	4.1×

Upload bandwidth: ~3 GB/s (PCIe)
Per-query latency with dataset in VRAM: 0.082 ms
GPU results verified against CPU: identical top-k indices and order
Custom PTX l2_topk_kernel compiled with nvcc 12.4 + MSVC

CUDA Kernel Optimizations

Distance kernels (kernels/distance.cu — 6 kernels):

float4 vectorized loads (640D = 160 × float4 per vector)
Shared memory query cache (avoids redundant global reads per thread)
Thread-local sorted top-k (max K=32) with shared memory merge
__launch_bounds__(256) for optimal sm_86 occupancy
PTX compiled with --use_fast_math -O3

Extended kernels (kernels/extended_kernels.cu — 8 kernels, added v2.5):

rotation_gemv_kernel / rotation_gemv_inverse_kernel — batch R·x and R^T·x with float4 loads + shared mem query cache
qjl_batch_sketch_kernel — batch sign(G·x) for QJL quantization (N vectors × G projections)
qjl_batch_ip_estimate_kernel — batch inner product estimation from sketches with precomputed proj·query
kmeans_assign_kernel — N×K×D assignment in single kernel (KMeans++ hot path)
cosine_similarity_kernel — all-pairs cosine sim with tile-based 16×16 upper triangle computation
batch_geodesic_kernel — pairwise arccos distance between matched vector pairs
lsh_hash_kernel — batch LSH hash sign bits across T tables × K projections

All extended kernels use float4 vectorized loads, shared memory caching, and __launch_bounds__(256). Each has a CPU fallback via Option-returning functions — no CUDA dependency required at runtime.

Rust GPU layer (src/gpu/cuda_extended.rs):

GpuExtended — stateless kernel launcher (no persistent GPU state)
Public API in gpu/mod.rs: rotation_gemv(), rotation_gemv_inverse(), qjl_batch_sketch(), qjl_batch_ip_estimate(), kmeans_assign(), cosine_similarity_matrix(), batch_geodesic(), lsh_batch_hash()
Auto-fallback: if CUDA unavailable, returns None and caller uses CPU path

🔄 Planned Benchmarks

The following benchmarks are implemented in benchmarks/benchmark_reproducible.py but have not yet been executed on our hardware. They will be run and results published once the full pipeline is validated.

Benchmark	Domain	What It Tests	Status
BEIR	5 IR domains (scifact, trec-covid, fiqa, arguana, nfcorpus)	Standard information retrieval: NDCG@10, Recall@10, MAP across diverse text corpora	✅ 0.461 NDCG@10
LOCOMO	Long-form conversational memory	Cross-session recall in multi-turn dialogs with temporal/counterfactual/causal reasoning	✅ 68.2% Recall@10
MemoBench-style	Multi-agent dialog memory	Cross-session recall of agent preferences and facts (synthetic data — labeled accordingly)	✅ 100% Recall@10

BEIR uses the standard evaluation protocol from beir.ai — no per-dataset tuning, no cherry-picking domains. The implementation downloads datasets, encodes with all-MiniLM-L6-v2, builds Faiss index, and computes NDCG@10, Recall@10, MAP using pytrec-eval.

BEIR Results (5 domains, model: all-MiniLM-L6-v2):

Domain	Corpus	Queries	NDCG@10	Recall@10	MAP@10	QPS
scifact	5,183	300	0.645	0.783	0.596	6,990
arguana	8,674	1,406	0.502	0.790	0.412	6,416
trec-covid	171,332	50	0.473	0.013	0.011	283
fiqa	57,638	648	0.369	0.441	0.291	1,031
nfcorpus	3,633	323	0.316	0.155	0.111	13,683
Average			0.461	0.437	0.284	5,681

LOCOMO (KimmoZZZ/locomo on HuggingFace) evaluates long-form conversational memory across 19 sessions per conversation, with QA pairs spanning factoid, temporal, counterfactual, and causal categories.

MemoBench-style is a synthetic benchmark (seed=42, deterministic) since the official MemoBench dataset is not yet publicly available. It tests the same capabilities — cross-session agent memory recall — with clearly labeled synthetic data. We will replace it with the official dataset when available.

LOCOMO Results (1,982 questions across 10 conversations):

Category	Questions	Recall@10
cat_5 (session matching)	446	100.0%
causal	841	73.7%
factoid	282	52.8%
counterfactual	92	38.0%
temporal	321	31.8%
Overall	1,982	68.2%

MemoBench-style Results (15 questions, 5 agents, 20 sessions):

k	Recall@k
1	93.3%
5	100.0%
10	100.0%

Run any benchmark:

python benchmarks/benchmark_reproducible.py --suite beir       # BEIR (5 domains)
python benchmarks/benchmark_reproducible.py --suite locomo     # LOCOMO
python benchmarks/benchmark_reproducible.py --suite memobench  # MemoBench-style
python benchmarks/benchmark_reproducible.py --suite all        # Everything

Integration points (GPU-accelerated hot paths):

clustering.rs — KMeans assignment step: GPU → rayon CPU fallback
geometry.rs — cosine_similarity_matrix + geodesic_distance_batch: GPU → ndarray CPU
quant/rotation.rs — batch rotation GEMV: GPU → sequential CPU

HRM2 vs Linear Scan (Python Prototype — Historical)

An earlier Python prototype showed 32x speedup with HRM2 vs linear scan on 100K vectors (100K splats, 1K queries, k=64, CPU). Referenced for historical context only. Validated Rust result:

Method	Latency	QPS	Speedup
Linear scan	94.79 ms	10.55	baseline
SplatsDB HRM2	0.99 ms	1,012.77	32.4x

Configuration Presets

Seven presets cover edge devices to GPU clusters. Each preset configures all subsystems at once — no manual flag toggling required.

Preset	Use Case	Capacity	Quantization	Graph	Semantic	GPU	HNSW	LSH
`default`	General purpose	100K	8-bit TurboQuant	✅	✅ RRF	—	—	—
`simple`	Edge / IoT	10K	—	—	—	—	—	—
`mcp`	AI agent memory	100K	8-bit TurboQuant	✅	✅ RRF	Auto	—	—
`advanced`	AI agents	1M	4-bit TurboQuant	✅	✅ RRF	Optional	✅ M=32	—
`training`	Model research	500K	—	—	—	Optional	—	—
`distributed`	Multi-node	10M	4-bit TurboQuant	✅	✅ RRF	Optional	—	—
`gpu`	CUDA servers	5M	4-bit TurboQuant	✅	✅	✅	✅ M=48	—

use splatsdb::config::SplatsDBConfig;

let edge    = SplatsDBConfig::simple(None);              // Raspberry Pi
let agent   = SplatsDBConfig::mcp(None);                 // AI agent memory (auto GPU)
let heavy   = SplatsDBConfig::advanced(Some("cuda"));    // Full-featured with GPU
let server  = SplatsDBConfig::gpu(None);                 // Maximum GPU performance
let cluster = SplatsDBConfig::distributed(Some("cpu"));  // Multi-node deployment

Device auto-detection: Pass None to auto-detect (CUDA → Vulkan → CPU). Pass Some("cuda"), Some("vulkan"), or Some("cpu") to force a specific device.

Choosing a Preset

Start
  ├─ <10K docs, no GPU? → simple
  ├─ AI agent, need memory? → mcp (auto GPU)
  ├─ >100K docs, need speed? → advanced (HNSW M=32)
  ├─ >5M docs + CUDA? → gpu (M=48, VRAM-persistent)
  └─ Multi-node? → distributed

CLI Reference

SplatsDB provides a comprehensive CLI for all operations. Global options apply to every command.

Global Options

Flag	Default	Description
`--data-dir <DIR>`	`./splatsdb_data`	Storage directory
`--dim <DIM>`	`64`	Vector dimensionality
`--max-splats <N>`	`100000`	Maximum splat capacity
`--backend <BACKEND>`	`sqlite`	Metadata storage backend (`sqlite` or `json`)

Core Commands

Command	Description
`status`	Show store statistics (active splats, entropy, index status)
`search --query "0.1,0.2,..." -k 10`	Search by comma-separated query vector
`search-file --input query.bin -k 10`	Search using query vector from binary file
`index --input data.bin --shard default`	Add vectors from binary file
`append --input data.bin`	Incrementally add vectors to existing HNSW graph (no full rebuild)
`save`	Save current state to disk
`load`	Load state from disk
`list`	List all stored shards
`backup --output ./backup/`	Backup all data to directory

Ingestion Commands

Command	Description
`ingest --input data.bin --n-clusters 100`	KMeans splat centroids via DatasetTransformer
`ingest-hierarchical --input data.bin --n-clusters 10`	Two-level hierarchical KMeans
`ingest-leader --input data.bin --target-clusters 50`	O(n) single-pass leader clustering

Search Backend Commands

Command	Description
`fused-search --query "0.1,..." -k 10`	Search all enabled backends with score fusion
`hnsw-search --query "0.1,..." -k 10`	HNSW index only
`lsh-search --query "0.1,..." -k 10`	LSH index only

Quantization Commands

Command	Description
`quant-index --algorithm turbo --bits 4`	Build compressed index (TurboQuant or PolarQuant)
`quant-search --query "0.1,..." --top-k 10`	Search compressed codes
`quant-status`	Show quantization statistics

GPU Commands (requires `--features cuda`)

Command	Description
`gpu-info`	Show CUDA device information and VRAM status
`bench-gpu --n-vectors 100000 --dim 640`	Benchmark GPU vs CPU search
`bench-gpu-ingest --n-vectors 100000 --dim 640`	Benchmark full GPU ingest + search pipeline

Energy / Self-Organized Criticality

Command	Description
`soc-check`	Check system criticality state
`soc-avalanche --seed 42`	Trigger avalanche reorganization
`soc-relax --iterations 50`	Relax the system toward lower energy

Document Commands

Command	Description
`doc-add --id my-doc --text "content"`	Add document with metadata
`doc-get --id my-doc`	Retrieve document by ID
`doc-del --id my-doc`	Soft-delete a document

Knowledge Graph Commands

Command	Description
`graph-add-doc --text "content"`	Add document node to graph
`graph-add-entity --name "entity" --entity-type person`	Add entity node
`graph-add-relation --source-id 1 --target-id 2 --relation-type MENTIONS`	Add edge
`graph-traverse --text \"query\" --max-depth 3`	BFS traversal from embedding-matched node
`graph-search --query "search text" --k 10`	Hybrid graph+vector search
`graph-stats`	Graph statistics

Server Commands

Command	Description
`serve --host 0.0.0.0 --port 8199`	Start HTTP API server
`mcp`	Start MCP server (stdio JSON-RPC 2.0)
`preset-info`	Show which subsystems each preset enables

ML & Entity Commands

Command	Description
`extract-entities --text "content" --min-score 0.3`	Extract entities via structural patterns and n-grams
`eval-embeddings --dim 64 --n-queries 10`	Evaluate embedding quality with synthetic benchmark

Data Lake Commands

Command	Description
`lake-list`	List datasets in the data lake
`lake-register --id ds1 --name "My Dataset" --n-vectors 10000 --dim 640`	Register a dataset in the data lake

Benchmark Commands

Command	Description
`bench-hnsw --train data.bin --queries q.bin --gt ground.bin --dim 128`	HNSW benchmark with recall measurement

Spatial Memory Commands

Command	Description
`spatial-search --query "text" --wing project-x --room auth --hall decisions`	Search with spatial filters (Wing/Room/Hall)
`spatial-info`	Show spatial memory structure (wings, rooms, tunnels)

MCP Server (AI Agent Integration)

SplatsDB includes a built-in Model Context Protocol server for direct integration with AI agents. The MCP server uses stdio transport with JSON-RPC 2.0 and auto-loads the mcp preset with GPU auto-detection.

Environment Variables

Variable	Default	Description
`SPLATSDB_DOC_PATH`	`~/.hermes/splatsdb_docs.db`	SQLite database path for document persistence
`SPLATSDB_EMBED_URL`	`http://127.0.0.1:8788/embed`	Embedding service URL (MiniLM or compatible)

Security & Stability Features

No panics: All .unwrap() / .expect() calls replaced with safe error handling
Input validation: Text length capped at 100KB, entity names at 10KB, k capped at 1000
No info leakage: Mutex poisoning logged to stderr only, generic error returned to client
Incremental indexing: Uses hnsw_sync_incremental() instead of full rebuild on every insert
LRU text cache: In-memory document text cache with 10K entry cap and automatic eviction
Warm start: Progress logging every 100 docs, capped at 50K docs, interruptible by SIGINT
Graceful shutdown: SIGINT triggers clean exit (completes current request, persists state)
JSON-RPC validation: Rejects requests with jsonrpc != "2.0" with proper error code

Configuration

Add to your AI agent's MCP configuration (e.g., Claude Code, Cursor, Hermes):

{
  "mcp_servers": {
    "splatsdb": {
      "command": "/path/to/splatsdb",
      "args": ["mcp"],
      "timeout": 30
    }
  }
}

Available Tools

Vector Store

Tool	Required Params	Optional Params	Description
`splatsdb_store`	`text`	`id`, `category`, `embedding`, `wing`, `room`, `hall`	Store a memory. Auto-embeds text via SimCos, or accepts pre-computed embedding. Spatial params (`wing`/`room`/`hall`) enable pre-filter search via `splatsdb_spatial_search`. For production, always provide real embeddings via the `embedding` field.
`splatsdb_search`	`query`	`top_k`, `embedding`	Semantic search. Returns ranked results with similarity scores.
`splatsdb_status`	—	—	Engine status: dimension, doc count, active indexes, quantization state.

Document Store (SQLite-Backed)

Tool	Required Params	Optional Params	Description
`splatsdb_doc_add`	`id`, `text`	`metadata` (JSON)	Store document with metadata. Persists to SQLite. Survives restarts.
`splatsdb_doc_get`	`id`	—	Retrieve document by ID. Returns text, metadata, timestamps.
`splatsdb_doc_del`	`id`	—	Soft-delete a document.

Knowledge Graph (GraphSplat)

Tool	Required Params	Optional Params	Description
`splatsdb_graph_add_doc`	`text`	—	Add document node. Auto-embeds text via SimCos (use real embeddings in production).
`splatsdb_graph_add_entity`	`name`, `entity_type`	—	Add entity node (person, org, location, concept). Auto-embeds name via SimCos (use real embeddings in production).
`splatsdb_graph_add_relation`	`source_id`, `target_id`, `relation_type`	`weight`	Add directed edge between nodes.
`splatsdb_graph_traverse`	`text`	`max_depth`, `add_doc`	BFS traversal. Auto-embeds query, finds nearest node, traverses graph. Use `--add-doc` to add the text as a document first.
`splatsdb_graph_search`	`query`	`k`	Hybrid search: vector similarity + graph context boost.
`splatsdb_graph_search_entities`	`query`	`k`	Search entity nodes by embedding similarity.
`splatsdb_graph_stats`	—	—	Node counts, edge counts, entity/document breakdown.
`splatsdb_spatial_search`	`query`	`wing`, `room`, `hall`, `top_k`	Search with spatial filters. Pre-filters by wing/room/hall metadata before vector search for higher recall.
`splatsdb_spatial_info`	—	—	Show spatial memory structure: wings, rooms, halls, tunnels (auto-detected cross-wing connections).

Example: AI Agent Session

Agent: splatsdb_store(text="User prefers dark mode and Spanish language")
→ {"id": "mem_1", "status": "stored"}

Agent: splatsdb_graph_add_entity(name="Brian", entity_type="person")
→ {"entity_type": "person", "name": "Brian", "node_id": 1}

Agent: splatsdb_graph_add_doc(text="Brian works on SplatsDB, a vector search engine")
→ {"node_id": 2, "node_type": "document"}

Agent: splatsdb_graph_add_relation(source_id=2, target_id=1, relation_type="MENTIONS")
→ {"ok": true, "relation_type": "MENTIONS"}

Agent: splatsdb_search(query="user preferences", top_k=5)
→ {"results": [{"id": "mem_1", "score": 0.95, "text": "User prefers dark mode..."}]}

Agent: splatsdb_graph_search(query="who works on vector search", k=3)
→ {"results": [{"content": "Brian works on SplatsDB...", "score": 0.49}]}

Persistence

The MCP server uses SQLite with WAL mode for document storage. On restart, all documents are automatically reloaded (warm start) with progress logging. HNSW indices use incremental insertion — new vectors are added to the existing graph without rebuilding. Vector indices persist to hnsw_index.bin and are loaded from disk on startup.

HTTP API Server

Start an HTTP server for integration with Python, Node.js, or any HTTP client:

./target/release/splatsdb serve --host 0.0.0.0 --port 8199 --data-dir ./data

Flag	Default	Description
`--host`	`127.0.0.1`	Bind address
`--port`	`8199`	Listen port
`--data-dir`	`./splatsdb_data`	Storage directory
`--dim`	`64`	Vector dimensionality
`--max-splats`	`100000`	Max capacity
`--backend`	`sqlite`	Metadata backend

Endpoints

`GET /health` — Health Check

curl http://localhost:8199/health

{"status": "ok", "version": "2.5.0"}

`POST /status` — Store Statistics

curl -X POST http://localhost:8199/status

{
  "n_active": 1500,
  "max_splats": 100000,
  "dimension": 640,
  "has_hnsw": false,
  "has_lsh": false,
  "has_quantization": true,
  "has_semantic_memory": false
}

`POST /store` — Store a Memory

curl -X POST http://localhost:8199/store \
  -H "Content-Type: application/json" \
  -d '{"text": "my memory text", "category": "notes", "id": "mem_42"}'

Field	Type	Required	Description
`text`	string	Yes	Text content (auto-hashed to embedding if no `embedding`)
`embedding`	float[]	No	Pre-computed embedding vector
`category`	string	No	Category tag
`id`	string	No	Custom ID (auto-generated if omitted)

Response:

{"id": "mem_42", "status": "stored"}

`POST /search` — Search Memories

curl -X POST http://localhost:8199/search \
  -H "Content-Type: application/json" \
  -d '{"query": "my search", "top_k": 5}'

Field	Type	Required	Description
`query`	string	Yes	Query text (auto-hashed to embedding if no `embedding`)
`embedding`	float[]	No	Pre-computed query embedding
`top_k`	int	No	Number of results (default: 10)

Response:

{
  "results": [
    {"index": 0, "score": 0.023, "metadata": null},
    {"index": 5, "score": 0.145, "metadata": null}
  ]
}

Note: The default HTTP server uses SimCos hash-based pseudo-embeddings (trigram hashing — no semantic understanding). For production use, provide real embeddings from a model (e.g. OpenAI, Cohere, MiniLM, BGE) via the embedding field.

Rust API

use splatsdb::{SplatsDBConfig, SplatStore};

fn main() {
    // Choose a preset (or use SplatsDBConfig::default())
    let config = SplatsDBConfig::mcp(None);  // AI agent memory, auto GPU
    let mut store = SplatStore::new(config);

    // Insert vectors
    let id = store.insert(&vec![0.1f32; 640]);

    // Search
    let results = store.search(&vec![0.1f32; 640], 10);
    for r in &results {
        println!("id={} dist={:.4}", r.index, r.distance);
    }

    // Build index for fast retrieval
    store.build_index();

    // Fast approximate search via HRM2
    let fast_results = store.find_neighbors_fast(&query.view(), k);
}

Key Methods

Method	Description
`SplatStore::new(config)`	Create store with given configuration
`store.insert(&vec)`	Insert a vector, return its index
`store.insert_with_hnsw(&vec)`	Insert a vector and incrementally add to HNSW graph
`store.add_batch_with_hnsw(&arr)`	Batch insert vectors with incremental HNSW sync
`store.search(&vec, k)`	Exact k-nearest neighbor search
`store.find_neighbors(&row, k)`	Search using ndarray row reference
`store.find_neighbors_fast(&view, k)`	HRM2 approximate search (requires `build_index()`)
`store.build_index()`	Build HRM2 coarse + fine index (full build)
`store.hnsw_sync_incremental()`	Sync new vectors into HNSW graph (incremental, no rebuild)
`store.hnsw_needs_sync()`	Check if new vectors need HNSW indexing
`store.n_active()`	Number of active splats
`store.hnsw_indexed_count()`	Number of vectors currently in the HNSW graph
`store.entropy()`	Current entropy of the system

GPU Acceleration

SplatsDB supports CUDA GPU acceleration via custom PTX kernels compiled at build time.

Requirements

NVIDIA GPU (Compute Capability 7.0+, tested on RTX 3090 sm_86)
CUDA Toolkit 12.x
MSVC cl.exe in PATH (Windows) or GCC (Linux)

Build

cargo build --release --features cuda

How It Works

Upload: Dataset vectors are copied to GPU VRAM as a flat float array
Persistent: Data stays in VRAM between queries — no re-upload overhead
Query: Each query launches a CUDA kernel that:
- Loads the query into shared memory
- Each thread block processes a chunk of vectors using float4 loads
- Thread-local top-k maintained in registers
- Final merge across thread blocks
Download: Only the top-k indices and distances are copied back to CPU

Performance

Measured on RTX 3090 (24GB VRAM), SIFT-128 100K vectors, k=10. Full results: bench-data/benchmark_results_hardware.json.

Metric	Value
GPU QPS (persistent)	12,195
CPU QPS (same data)	2,941
Speedup	4.1×
Per-query latency (GPU)	0.082 ms
Upload time (100K → VRAM)	17 ms

GPU QPS measured with dataset resident in VRAM between queries (persistent mode). First-query upload cost is amortized over the query batch. GPU results verified identical to CPU brute-force (100% recall).

GPU Commands

# Show CUDA device info
./target/release/splatsdb gpu-info

# Benchmark GPU vs CPU
./target/release/splatsdb bench-gpu --n-vectors 100000 --dim 640 --n-queries 100 --top-k 10

# Full pipeline benchmark (ingest + index + search + GPU)
./target/release/splatsdb bench-gpu-ingest --n-vectors 100000 --dim 640 --n-queries 100

Knowledge Graph (GraphSplat)

SplatsDB includes a built-in knowledge graph that augments vector search with structured relationships.

Node Types

Document nodes: Auto-embedded from text content
Entity nodes: Named entities with types (person, organization, location, concept)

Edge Types

Any string label works. Common patterns:

MENTIONS — document references an entity
RELATED_TO — general semantic connection
PART_OF — hierarchical containment
LOCATED_IN — spatial relationship

Search

Hybrid search combines vector similarity with graph context:

Find top-k similar nodes by embedding cosine similarity
Boost scores for nodes connected to highly-ranked neighbors
Return fused ranking

This means: a document that is similar to your query AND is connected to other relevant documents will rank higher than a similar but isolated document.

Retrieve + 2-Hop Expansion Example

This is where GraphSplat outperforms plain vector search — it doesn't just find the closest document, it discovers the context around it:

Vector-only search for "database indexing strategy":
  → Doc #47 (score: 0.91) — "Use HNSW for approximate nearest neighbor..."

GraphSplat search for same query (2-hop expansion):
  → Doc #47   (score: 0.91) — "Use HNSW for approximate nearest neighbor..."
  → Entity #3 (score: 0.82) — "HNSW algorithm" [connected via MENTIONS]
  → Doc #12   (score: 0.78) — "HNSW build time tradeoffs: ef_construction..."
                           [connected to Entity #3 via MENTIONS]
  → Doc #89   (score: 0.74) — "Faiss IVF-PQ comparison with HNSW..."
                           [connected to Doc #12 via RELATED_TO]

Result: Agent gets the full context — the strategy, the tradeoffs, and the alternatives.
        A flat vector search would have returned Doc #47 and stopped.

// The code behind the example above
let results = graph.hybrid_search(&query_embedding, 10);

// For each result, expand 2 hops to discover related context
for result in &results {
    let context = graph.traverse(result.node_id, 2); // 2-hop BFS
    // Returns: connected docs, entities, relations — full context graph
}

This is what LangChain and LlamaIndex try to do with multiple tools (vector store + graph DB + fusion). GraphSplat does it natively in a single query.

API

use splatsdb::graph_splat::GaussianGraphStore;

let mut graph = GaussianGraphStore::new();

// Add nodes
let doc_id = graph.add_document("SplatsDB is a vector search engine", &embedding)?;
let entity_id = graph.add_entity("SplatsDB", "software", &name_embedding)?;

// Add relation
graph.add_relation(doc_id, entity_id, "MENTIONS", 1.0)?;

// Hybrid search
let results = graph.hybrid_search(&query_embedding, 10);

// BFS traversal
let connected = graph.traverse(entity_id, 3);

// Entity search
let entities = graph.search_entities(&query_embedding, 5);

Vector Compression (TurboQuant / PolarQuant)

SplatsDB supports data-oblivious vector compression that requires no training data, no codebooks, no fine-tuning.

Algorithms

Algorithm	Bits	Method	Best For
TurboQuant	3–8	Random projection + quantization	General purpose, high throughput
PolarQuant	3–8	Polar coordinate quantization	Directional similarity

Usage

# Build compressed index
./target/release/splatsdb quant-index --algorithm turbo --bits 4

# Search compressed codes
./target/release/splatsdb quant-search --query "0.1,..." --top-k 10

# Check status
./target/release/splatsdb quant-status

Compression Ratio

With 4-bit TurboQuant on 640D vectors:

Raw: 2,560 bytes per vector
Compressed: 320 bytes per vector (8x reduction)
Recall: >95% top-10 recall at 5% search fraction

Semantic Memory

SplatsDB combines BM25 text search with vector similarity using Reciprocal Rank Fusion (RRF).

How RRF Works

Run BM25 search over document text → get ranking R₁
Run vector similarity search → get ranking R₂
Fuse: score(d) = w_vec × 1/(k + rank_R₂(d)) + w_bm25 × 1/(k + rank_R₁(d))

Default weights: w_vec = 0.6, w_bm25 = 0.4 (configurable per preset).

This gives the best of both worlds: vector search captures semantic meaning, BM25 captures exact keyword matches, and RRF merges them without score normalization issues.

Temporal Decay

Optional: recent memories can be weighted higher via exponential decay with configurable half-life.

Verbatim Storage (Planned)

Inspired by MemorySpaces's principle: "Store everything verbatim, never let an LLM decide what to remember."

Most vector DBs store only embeddings — the original text is lost or truncated. SplatsDB's planned verbatim storage keeps three layers per document:

Layer	Content	Size	Purpose
Splat	μ, α, κ + compressed vector	~320 bytes (4-bit TurboQuant)	Fast retrieval
Summary	Key facts extracted by LLM	~200 bytes	Quick context for agent decision-making
Drawer	Original document verbatim	Variable	Source of truth — never summarized

How it works in practice:

MCP query → Vector search finds top-5 splats
         → Agent reads summaries (cheap, ~1K tokens)
         → Agent decides: "I need the full detail on doc #3"
         → Drawer lookup: exact original text returned

This is the opposite of how Pinecone/Qdrant handle text — they truncate or discard it. SplatsDB keeps the source of truth because for agent memory, losing why a decision was made kills future recall.

Status: Planned. The building blocks exist (SQLite persistence, BM25 text index, MCP server). Pending: summary extraction pipeline and drawer API.

Text Compression (Planned)

Vector compression (TurboQuant/PolarQuant) handles embeddings. But the text itself also needs compression for efficient agent memory.

The concept: a compressed representation that any LLM can read natively without a decoder — similar to how MemorySpaces's AAAK achieves ~30x text compression by stripping everything except semantic content.

Original text (1,200 tokens):
  "On January 15th, 2025, the team decided to migrate from Faiss to SplatsDB
   for the production vector search pipeline. The primary motivation was the
   need for uncertainty-aware retrieval and knowledge graph integration.
   Brian will lead the migration, targeting completion by end of Q1."

Compressed (~40 tokens, 30x):
  "2025-01-15: migrate Faiss→SplatsDB prod. reason: uncertainty+KG.
   lead: Brian. target: Q1 end."

Why this matters for SplatsDB:

TurboQuant gives 8x on vectors → 30x on text is the bigger win for agent memory
MCP server can return compressed summaries within token budgets
Agent only fetches full drawer when needed → saves ~90% tokens on average query

Status: Planned. Will be implemented as a post-ingestion compression pass, independent of the vector pipeline.

Energy-Based Model

SplatsDB models the embedding space as an energy landscape:

E(x) = −log(Σᵢ αᵢ · exp(−κᵢ · ‖x − μᵢ‖²))

Low energy: Well-covered regions — many documents nearby, high confidence
High energy: Sparse regions — few documents, high uncertainty

Applications

Active learning: Focus labeling effort on high-energy regions
Exploration: Boltzmann sampling from the energy landscape
Self-organized criticality (SOC): The system can self-tune via avalanche reorganization and relaxation

CLI

# Check current energy state
./target/release/splatsdb soc-check

# Trigger avalanche (reorganize splats for better coverage)
./target/release/splatsdb soc-avalanche --iterations 100

# Relax toward lower energy
./target/release/splatsdb soc-relax --iterations 50

Module Map

26,465 lines of Rust across these modules:

Module	Source	Description
`splats`	`src/splats.rs`	Core API — insert, search, batch ops, incremental HNSW, statistics
`hrm2_engine`	`src/hrm2_engine.rs`	Two-level hierarchical retrieval (coarse → fine)
`engine`	`src/engine.rs`	CPU L2 distance with rayon parallelism
`gpu`	`src/gpu/`	CUDA PTX kernels, `GpuIndex` with persistent VRAM
`quantization`	`src/quantization.rs`	TurboQuant / PolarQuant data-oblivious compression with recall measurement
`clustering`	`src/clustering.rs`	KMeans++ with ChaCha8 RNG
`graph_splat`	`src/graph_splat.rs`	Knowledge graph overlay with hybrid search
`semantic_memory`	`src/semantic_memory.rs`	BM25 + vector RRF fusion with temporal decay
`hnsw_index`	`src/hnsw_index.rs`	HNSW approximate nearest neighbor with incremental insertion
`lsh_index`	`src/lsh_index.rs`	Locality-sensitive hashing
`energy`	`src/energy.rs`	Energy landscape computation
`ebm`	`src/ebm/`	Boltzmann exploration, self-organized criticality
`storage`	`src/storage/`	SQLite persistence (WAL), JSON store
`api_server`	`src/api_server.rs`	HTTP REST API (4 endpoints: health, status, store, search)
`mcp_server`	`src/mcp_server.rs`	MCP JSON-RPC 2.0 server (18 tools, production-hardened)
`config`	`src/config/`	7 presets, device auto-detection
`cli`	`src/cli/`	Command-line interface (clap)

Dependencies

Crate	Purpose	Optional
`ndarray`	N-dimensional array operations	No
`rayon`	Data parallelism (CPU search)	No
`rand` / `rand_chacha` / `rand_distr`	Random number generation, KMeans++	No
`serde` / `serde_json`	Serialization	No
`rusqlite`	SQLite persistence (bundled)	No
`libc`	Signal handling (graceful shutdown)	No
`clap`	CLI argument parsing	No
`regex`	Pattern matching	No
`ureq`	HTTP client (embedding service)	No
`axum` / `tokio` / `tower-http`	HTTP API server	No
`parking_lot`	High-performance synchronization primitives	No
`bytemuck`	Zero-cost byte casting for GPU	No
`cudarc`	CUDA GPU kernels	Yes (`--features cuda`)

Roadmap

✅ Done

Faiss benchmark comparison: Same hardware, same dataset, honest side-by-side numbers → See Faiss Comparison
LongMemEval benchmark: Validate agent memory use case with the conversational memory standard → See LongMemEval Agent Memory Benchmark
~~Spatial memory structure: Wings/Rooms/Halls/Tunnels**~~ → See Spatial Memory
GPU CUDA kernels: 14 PTX kernels for RTX 3090 → See GPU Acceleration

🔜 Planned

Docker image: docker run splatsdb for instant trial without Rust/CUDA setup
Test coverage reporting: CI integration with tarpaulin or llvm-cov
CI with GPU: GitHub Actions runner with CUDA for integration testing
Verbatim storage: Store original document text alongside splats for exact recall (alpha — concept defined, implementation pending)
Text compression (AAAK): ~30× compressed text any LLM reads natively (concept defined, implementation pending)

Spatial Memory Architecture

Inspired by MemorySpaces — organizing memory like a physical space.

SplatsDB's KMeans++ clusters are currently pure geometry — they group vectors by proximity but carry no semantic meaning. The spatial memory architecture maps these clusters to navigable structures:

┌─────────────────────────────────────────────────┐
│  Wing = Project / Persona / Domain              │
│  ┌──────────────┐  ┌──────────────┐            │
│  │ Room = Theme  │  │ Room = Theme  │            │
│  │ (auth,        │  │ (billing,     │            │
│  │  migration)   │  │  payments)    │            │
│  │  ┌──────────┐ │  │  ┌──────────┐ │            │
│  │  │ Hall =   │ │  │  │ Hall =   │ │            │
│  │  │ Type     │ │  │  │ Type     │ │            │
│  │  │ facts    │ │  │  │ events   │ │            │
│  │  │ decisions│ │  │  │ errors   │ │            │
│  │  └──────────┘ │  │  └──────────┘ │            │
│  └──────────────┘  └──────────────┘            │
│         │                │                      │
│         └─── Tunnel ─────┘                      │
│         (shared room across wings)              │
└─────────────────────────────────────────────────┘

How It Maps to SplatsDB

Spatial Concept	SplatsDB Equivalent	Purpose
Wing	Top-level metadata tag	Scope queries to a project/persona
Room	KMeans++ coarse cluster + label	Semantic grouping within a wing
Hall	Memory type (fact, decision, event, error)	Filter by what kind of memory
Tunnel	GraphSplat edge between wings	Cross-project connections

Why This Matters

Flat vector search searches everything equally. Spatial search filters first:

Query: "auth decisions from project X"

1. Filter by wing: "project-x"           → reduces to ~10% of corpus
2. Filter by room: "auth"                 → reduces to ~2% of corpus
3. Filter by hall: "decisions"            → reduces to ~0.5% of corpus
4. Vector search within that subspace     → high recall, minimal noise

This is structurally identical to how MemorySpaces achieves +34% retrieval improvement over flat search — you reduce the search space before computing distances, so the signal-to-noise ratio improves dramatically.

Tunnels = Cross-Wing Knowledge Graph

When the same room (e.g., "auth") appears in two wings (e.g., "project-x" and "project-y"), a tunnel connects them automatically via GraphSplat:

// Auto-detected: "auth" room exists in both wings
graph.add_relation(wing_x_auth_room, wing_y_auth_room, "TUNNEL", 1.0);

// Query that benefits:
// "How did we handle auth in other projects?" → follows tunnel → cross-project insight

This turns your knowledge graph from a flat adjacency list into a navigable spatial structure — exactly what human memory does naturally.

Status

Spatial memory has an initial implementation. The building blocks exist:

✅ SpatialIndex with Wing/Room/Hall/Tunnel structures (src/spatial.rs)
✅ Spatial query filter (AND logic on wing + room + hall)
✅ Auto-tunnel detection between wings sharing the same room
✅ CLI commands: spatial-search --wing X --room Y --hall Z, spatial-info
✅ KMeans++ coarse/fine clustering (becomes Rooms)
✅ GraphSplat with BFS traversal (becomes Tunnels)
✅ Metadata tags on documents (becomes Wings)
✅ BM25 + vector hybrid (can filter by hall type)
✅ Named cluster labels auto-assigned from document content (TF keyword extraction, EN+ES stopwords)
✅ Full search pipeline integration (spatial pre-filter → vector search via find_neighbors_filtered)
✅ MCP tools: splatsdb_spatial_search, splatsdb_spatial_info
✅ Spatial query API (search --wing project-x --room auth --hall decisions)

Competitive Landscape

Market Position (DB-Engines Vector DBMS Ranking, April 2026)

Rank	Product	Score	Type
1	Elasticsearch	99.51	Multi-model (not pure vector)
4	Pinecone	8.18	SaaS-only, proprietary
6	Milvus	6.39	Cloud-native, Apache 2.0
7	Qdrant	4.98	Rust, Apache 2.0
9	Weaviate	4.42	Go, BSD-3
11	Chroma	—	Python+Rust, embedded
16	LanceDB	—	Rust, embedded, emergent
—	SplatsDB	—	Rust, embedded+cluster, GPL-3.0

Cloud Pricing Comparison

Product	Free Tier	Paid Tier	Enterprise
Pinecone	Limited	$50/mo min	$500/mo min
Zilliz/Milvus Cloud	5GB, 2.5M vCUs	$99/GB/mo	$155/mo
Qdrant Cloud	1GB RAM	Usage-based	Custom
Weaviate Cloud	14-day trial	$45/mo Flex	$400/mo
Chroma Cloud	$0 + usage	$250/mo Team	Custom
LanceDB	Free (self-hosted)	—	—
SplatsDB	Free forever	—	—

Where SplatsDB Wins

Spatial Memory — Wing/Room/Hall/Tunnel with auto-labeling and auto-tunnel detection. No other vector DB has hierarchical spatial navigation. Qdrant has generic payload filters but no spatial hierarchy. On LongMemEval, the retrieval approach (embeddings + cosine) achieves 96.6% recall@10 (NumPy brute-force baseline; see methodology note in benchmarks section) vs 86.2% best in original paper.
MCP Server — 18 tools ready to use with any MCP-compatible agent (Claude, GPT, etc.). Zero glue code. Competitors require custom SDK integrations or REST wrappers.
Distributed Sharding — 3 strategies (hash/cluster/geo), load balancing (round-robin/least-loaded/broadcast), RRF result fusion, energy-aware routing, offline sync with retry. All built-in, no external dependencies.
Embedded + Cluster — Works as CLI tool, Rust library, MCP server, or distributed cluster. Milvus and Qdrant require a separate server process. Chroma is embedded but limited to single-node.
Cost — Free, self-hosted, no usage limits. Pinecone charges $50/mo minimum. Zilliz starts at $99/GB. SplatsDB runs on a $5 VPS.

Where Competitors Win (Honest Assessment)

Area	Leader	Gap
Raw QPS (single-node)	Faiss	4–6x faster (optimized C++ + SIMD)
Ecosystem integrations	Milvus, Qdrant	LangChain, LlamaIndex, Haystack, 50+ connectors
Managed cloud	Pinecone, Qdrant	Zero-ops deployments, auto-scaling
Dataset scale (proven)	Milvus	Billions of vectors (SplatsDB tested to 100K)
Multi-tenancy	Qdrant	Native sharding per tenant
Maturity	All competitors	Years of production use, large communities

SplatsDB's Niche

AI agents with long-term conversational memory, RAG pipelines, and knowledge management — where spatial pre-filtering, MCP integration, and zero-cost self-hosting matter more than raw QPS at billion-scale.

The closest competitor in philosophy is LanceDB (Rust, embedded, Apache 2.0, 9.9k GitHub stars). Key differences: LanceDB uses IVF-PQ (not HNSW), has no spatial memory, no MCP server, no knowledge graph, no distributed sharding. SplatsDB trades ecosystem maturity for deeper agent memory features.

Security Audit

A comprehensive pentest and architecture review was conducted across five attack surfaces: binary file parsing, path traversal, text compression, fuzzing (3 000 iterations), and MCP injection.

Vulnerability Findings

Severity	Issue	Mitigation
HIGH	OOM crash via malicious `.bin` file in `cli/helpers.rs`	`MAX_LOAD_ELEMENTS` cap (1 B elements) + `MAX_DECOMPRESS_SIZE` (100 MB)
MEDIUM	Panic on `dim=0` vectors	Input validation rejects zero-dimension vectors
LOW	`u32` size header in text compression decompressor	Bounded length check before allocation

3 vulnerabilities found, 0 critical for data confidentiality.

Patches Applied

persistence.rs — copy_dir_recursive now enforces a depth limit of 128 to prevent path-traversal escalation.
mcp_server.rs — Embedding fetch moved outside Mutex guard to eliminate a concurrency deadlock surface.

Hardening Measures

API-key authentication is optional by default, enabled via the SPLATSDB_API_KEY environment variable.
API-key comparison uses constant-time equality to prevent timing side-channel attacks.

Audit Test Suite

Category	Tests	Description
Integration	3	End-to-end security workflows
QA validation	31	Input validation and edge-case coverage
Chaos / stress	8	Concurrency, memory pressure, WAL under load
Total	42	All passing (127 s)

The 8 chaos tests cover: concurrent stress (10 threads × 1 000 ops), memory pressure, large vectors, rapid-fire writes, interleaved operations, edge cases, WAL under load, and heavy compaction.

Reproducible Benchmarks

All benchmarks can be reproduced independently. Full code in benchmarks/benchmark_reproducible.py.

# Run full suite (ANN-Benchmarks + LongMemEval)
python benchmarks/benchmark_reproducible.py --suite all

# Run single dataset
python benchmarks/benchmark_reproducible.py --suite ann --dataset sift-128-euclidean

Benchmark Suite

Benchmark	What it tests	Status
ANN-Benchmarks	Vector search quality (SIFT, GloVe, NYTimes)	✅ Published
BEIR	Information retrieval (5 domains)	✅ Published
LOCOMO	Long-form conversational memory	✅ Published
MemoBench (synthetic)	Multi-agent dialog memory	✅ Published
LongMemEval	Agent memory recall	🔄 Planned

BEIR Information Retrieval

Standard BEIR protocol — NDCG@10, Recall@10 via pytrec-eval. Same evaluator, same qrels, same queries.

v1 — Baseline (all-MiniLM-L6-v2, dense only)

Domain	Docs	Queries	NDCG@10	Recall@10
scifact	5,183	300	0.6451	0.7833
trec-covid	171,332	50	0.4725	0.0128
fiqa	57,638	648	0.3687	0.4413
arguana	8,674	1,406	0.5017	0.7902
nfcorpus	3,633	323	0.3159	0.1550
Average			0.4608	0.4365

v2 — Improved (BGE-small + BM25 + RRF fusion + entity boost)

Uses SplatsDB's full pipeline: BM25 from bm25_index.rs, RRF from cluster/aggregator.rs, entity extraction from entity_extractor.rs, graph boost from graph_splat.rs.

Domain	NDCG@10	vs v1	Recall@10	vs v1
scifact	0.7200	+0.075	0.8452	+0.064
arguana	0.5950	+0.093	0.8542	+0.043
nfcorpus	0.3371	-0.005	0.1582	+0.024
Average (3/5)	0.5507	+0.054	0.6192	+0.044

Note: trec-covid and fiqa v2 results not completed due to BM25 scoring performance on >50K corpora in Python. The Rust implementation would handle this efficiently.

Source: v1 results from bench-data/beir_results.json (BEIR v1.0, all-MiniLM-L6-v2, standard protocol). v2 results from internal benchmark runs — result file not yet published.

Alternative Approaches — A/B tested on scifact/arguana/nfcorpus

After v2, we tested 4 alternative approaches to identify what actually improves retrieval:

Approach	NDCG@10 avg	vs Baseline	Verdict
Baseline (BGE-small dense)	0.5507	—	Reference
Passage-level retrieval	0.5432	-0.0075	❌ No benefit (short docs)
Title-only cross-encoder	0.1422	-0.4085	❌ Titles ≠ content in BEIR
BGE-base (larger model)	0.5806	+0.0299	✅ Best improvement
BM25 + RRF fusion	~0.55	~0	Neutral on semantic queries

Key insight: the biggest improvement (+3% NDCG) comes from using a better embedding model, not from retrieval tricks. This confirms the bottleneck is the semantic gap — the embedding model's ability to match query intent with document content.

LOCOMO Conversational Memory

1,982 questions across 10 conversations, 5 question categories.

Category	v1 Recall@10	v2 Recall@10	Delta
Session matching (cat_5)	100.0%	100.0%	=
Causal	73.7%	77.3%	+3.6%
Factoid	52.8%	52.5%	-0.3%
Temporal	31.8%	50.8%	+19.0%
Counterfactual	38.0%	41.3%	+3.3%
Overall	68.2%	72.9%	+4.7%

v2 uses BGE-small + session-level retrieval + BM25 hybrid via RRF. The +19% improvement on temporal queries shows BM25 helps when queries contain specific keywords (dates, names).

HotpotQA Multi-hop Reasoning

We tested whether the knowledge graph helps with multi-hop question answering — questions that require finding MULTIPLE documents and connecting information across them.

Example: "Were Scott Derrickson and Ed Wood of the same nationality?" → Need docs about BOTH people.

Method	k	SP Recall	All Found	vs Dense
Dense BGE-small	4	86.7%	224/300	Reference
Dense BGE-small	2	71.3%	144/300	-
Graph 1-hop + dense seed	2	67.0%	120/300	-4.3%
Graph 2-hop + dense seed	4	83.8%	209/300	-2.9%
Entity lookup + dense hybrid	4	—	176/300	-16.0%

Conclusion: The knowledge graph does NOT improve retrieval, even for multi-hop questions where you'd expect it to help. Dense embeddings with BGE-small are already strong enough to find relevant documents. Graph expansion adds topologically-connected but semantically irrelevant docs.

When the graph IS useful: Exploration and discovery — finding connected topics you didn't search for, tracing entity relationships, content recommendation. These are different tasks from retrieval ranking.

Negative Results — What Doesn't Work

We publish negative results because they're more valuable than positive ones. They tell you what NOT to waste time on.

Technique	Tested on	NDCG change	Why it failed
Graph traversal	scifact, arguana	0.0000	Dense already finds relevant docs; graph expansion adds topologically-connected but irrelevant docs
Query expansion via entities	scifact, arguana	-0.0474	Weighted average of query + entity embeddings dilutes original query signal
Full-text cross-encoder	scifact	-0.1511	BEIR docs > 512 tokens → model truncates → loses key evidence
Title-only cross-encoder	scifact, arguana, nfcorpus	-0.4085	BEIR titles are paper names, not content descriptions; queries match body text
Passage-level chunking	scifact, arguana, nfcorpus	-0.0075	Most BEIR docs are short (avg 200 words); chunking adds noise
Graph 1-hop expansion	HotpotQA (300 questions)	-4.3%	Topologically-connected ≠ semantically relevant
Graph 2-hop expansion	HotpotQA (300 questions)	-2.9%	More hops = more noise
Entity lookup hybrid	HotpotQA (300 questions)	-16.0%	Entities match docs that MENTION the entity, not ABOUT it

Takeaway: For standard information retrieval, the ranking of improvement levers is:

Better embedding model (BGE-base > BGE-small > MiniLM) — biggest single gain
BM25 + RRF fusion — helps keyword-heavy queries, neutral otherwise
Session/context grouping — helps conversational memory tasks
Everything else — graph, expansion, chunking, reranking — either neutral or harmful

The knowledge graph remains SplatsDB's unique strength for exploration and discovery (traverse connections between entities), just not for standard query-answer retrieval.

Integrity Checklist

We publish this checklist because the vector search industry has a credibility problem. If you see a vector DB making benchmark claims, ask:

Is the benchmark code open-source and reproducible?
Are ground truth labels computed independently (not by the index itself)?
Is k_search smaller than the default pull size? (If k=50 and you pull 50, recall is trivially 100%)
Were any questions or queries hard-coded with known-good answers?
Is the git history transparent (not squashed to hide changes)?
Are the authors identifiable (not anonymous)?
Can you reproduce the exact numbers on your own hardware?

If a project can't answer "yes" to all of these, their benchmarks are not trustworthy.

License

Licensed under the GPL-3.0 License — use commercially, modify, distribute freely. Copyleft — derivatives must also be GPL-3.0.

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
assets		assets
bench-data		bench-data
benchmarks		benchmarks
kernels		kernels
src		src
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
deny.toml		deny.toml

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SplatsDB

Why SplatsDB?

Comparison

Table of Contents

What Is This?

Architecture

Quick Start

30-Second Trial (No Rust Needed)

With Real Embeddings (OpenAI / Cohere / Local)

Build from Source

Build

Run

Binary Format

How It Works

Gaussian Splat Embeddings

HRM2 Two-Level Retrieval

SimCos Embeddings

α, κ, and Uncertainty: A Concrete Example

When to Use HRM2 Splats vs HNSW

Benchmarks

Methodology

HNSW Search (v2.5 — current)

Comparison: HNSW vs Linear Scan vs HRM2 Splats

Faiss Comparison (Same Hardware, Same Dataset)

LongMemEval Agent Memory Benchmark

GPU Top-K Search (Custom CUDA Kernels)

CUDA Kernel Optimizations

🔄 Planned Benchmarks

HRM2 vs Linear Scan (Python Prototype — Historical)

Configuration Presets

Choosing a Preset

CLI Reference

Global Options

Core Commands

Ingestion Commands

Search Backend Commands

Quantization Commands

GPU Commands (requires --features cuda)

Energy / Self-Organized Criticality

Document Commands

Knowledge Graph Commands

Server Commands

ML & Entity Commands

Data Lake Commands

Benchmark Commands

Spatial Memory Commands

MCP Server (AI Agent Integration)

Environment Variables

Security & Stability Features

Configuration

Available Tools

Vector Store

Document Store (SQLite-Backed)

Knowledge Graph (GraphSplat)

Example: AI Agent Session

Persistence

HTTP API Server

Endpoints

GET /health — Health Check

POST /status — Store Statistics

POST /store — Store a Memory

POST /search — Search Memories

Rust API

Key Methods

GPU Acceleration

Requirements

Build

How It Works

Performance

GPU Commands

Knowledge Graph (GraphSplat)

Node Types

Edge Types

Search

Retrieve + 2-Hop Expansion Example

API

GPU Commands (requires `--features cuda`)

`GET /health` — Health Check

`POST /status` — Store Statistics

`POST /store` — Store a Memory

`POST /search` — Search Memories