Gaussian
Native vector and graph database

ChironDB

Retrieve the right context for AI and operational decisions.

Gaussian ▸ ChironDB

Find meaning with dense vector search.

Keep exact terms with sparse retrieval.

Combine relevance with business context.

Follow relationships with native graph queries.

Built in Rust, ChironDB combines native vector search and property graphs. Use dense, sparse, and hybrid retrieval with metadata filters and graph relationships to find the context your application needs.

Native vectorNative graphHybrid retrievalSelf hosted
Book DemoView on GitHub
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Use case & CLI

Our supply chain POC and ChironQL console.

Gaussian Supply Chain POC built with ChironDB, showing supplier, disruption, manufacturer tracing, and release readiness scenarios. The captured connection status reads ChironDB unavailable.
Gaussian Supply Chain is our proof of concept built with ChironDB. It brings suppliers, disruptions, manufacturer relationships, and release readiness into one workspace to explore how vector retrieval and graph context can support operational decisions.
ChironDB terminal showing ChironQL 1.1 help for retrieval, collection inspection, writes, and collection management.
The ChironQL console provides a terminal interface for retrieval, collection inspection, and data management. This view shows the ChironDB console and ChironQL 1.1 help.

From relevant context to connected decisions.

01
Retrieve

Find relevant context

Bring semantic similarity and exact term relevance into one retrieval engine. Dense search, sparse lexical retrieval, and hybrid fusion help AI applications find useful context, while payload filters keep results aligned with the task.

  • Dense ANN and sparse BM25/WAND retrieval
  • RRF or weighted fusion with metadata filters
  • Named vectors for text and image embeddings generated by external models
Gaussian ▸ ChironDB
Retrieval workflow

Relevant context

  1. 01

    Represent

    Externally generated dense and sparse vectors

  2. 02

    Retrieve

    Semantic similarity and lexical matching

  3. 03

    Combine

    Hybrid fusion with filters for your task

Named vectors can hold multiple representations, such as text and image embeddings.

02
Native graph

Explore connected operations

Connect suppliers, disruptions, and manufacturers through native graph relationships. Traverse those connections and use graph constraints with dense or hybrid retrieval. Our supply chain POC brings these capabilities into an operational workspace.

  • Native property graphs with typed relationships
  • Graph traversal and constraints for dense and hybrid retrieval
  • A supply chain proof of concept built with ChironDB
Gaussian ▸ ChironDB
Native graph

Connected operations

  1. 01

    Suppliers

    Explore alternatives and relationships

  2. 02

    Disruptions

    Trace connected operational context

  3. 03

    Retrieval

    Constrain dense or hybrid search with the graph

Our supply chain proof of concept uses ChironDB for vector retrieval and graph context.

03
Query

Work through ChironQL and APIs

Inspect collections and query data through ChironQL 1.1, using an attached console or a standalone terminal client. Integrate through HTTP, gRPC, or ChironWire, with Python and Rust SDKs available from source.

  • Retrieval, collection inspection, and data management
  • Attached server console or separate terminal client
  • PostgreSQL wire supports a subset of vector queries; relational queries remain in PostgreSQL
Gaussian ▸ ChironDB
ChironQL 1.1

A terminal for retrieval

  1. 01

    Inspect

    SHOW COLLECTIONS · DESCRIBE · USE

  2. 02

    Retrieve

    SEARCH · HYBRID · MULTI · RECOMMEND

  3. 03

    Manage

    UPSERT · UPDATE · DELETE

Use the attached server console or connect with the standalone chironql client.

04
Operate

Operate on your infrastructure

Run ChironDB on a single node using infrastructure you control. A write ahead log, snapshots, and point in time recovery support data durability. Access controls and observability help teams understand and manage their retrieval service.

  • Docker or local binary deployment on a single node
  • Durable writes, snapshots, and point in time recovery
  • API keys, RBAC, TLS/mTLS, audit logs, Prometheus, and OpenTelemetry
Gaussian ▸ ChironDB
Single node deployment

Infrastructure you control

  1. 01

    Persist

    A write ahead log and immutable segments

  2. 02

    Recover

    Snapshots and point in time recovery

  3. 03

    Observe

    Access controls, audit logs, metrics, and traces

Run a local binary or Docker deployment on a single node.

Build your first twin

Start with one operation worth improving.

Share a complex workflow. We’ll explore where a focused Digital Twin could improve visibility, test changes, and support better decisions.

Explore use cases
  • Focused operational discovery
  • Deployment aligned to your data policy
  • contact@gaussian.id

Tell us where to start

Share a little context and our team will follow up.