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Distributed multimodal database

Four data models.
One database.

Query records, vectors, relationships and text in one engine.

Start free Read the quickstart

One record. Four ways to use it.

Keep business fields, embeddings, searchable text and relationships in one engine, linked by record ID.

Explore the architecture
One record, four representationsIllustrative example
FOLLOW A RECORD
SHARED RECORD ID prod-042 Alpine shell jacket commerce.products

A product your application can retrieve in four ways.

OriginChainDB · one engine
01 / SQL↗

Read the facts.

id
prod-042
category
outdoor
in_stock
true

Filter and query business fields.

02 / VECTOR↗

Find the meaning.

query

“A lightweight layer for wet trails”

03 / GRAPH↗

Follow the context.

prod-042belongs_toOutdoor gear

Traverse declared record relationships.

04 / FULL-TEXT↗

Match the words.

waterproofjacket
prod-042term match

Search the content you index.

Linked by ID. Queried through the model that fits.Example representations · no live queries

  1. Model and index

    Define fields and graph relations. Write rows, then add your embeddings and search entries using the same record ID.

  2. Query your way

    Filter with SQL, find similar meaning with vectors, match keywords with full-text search and follow connections with graph queries.

The current API uses separate row, vector and full-text calls; each call is atomic.

See an ingestion example
An application workflow

From a search
to a useful result.

Product discovery can use all four models. Your application coordinates the separate requests and decides what to return.

Example customer search
“Lightweight shoes for the trail”
Meaning + keywords + business context
Explore retrieval and ranking
  1. VectorFull-text

    Find candidate products

    Search embeddings for similar meaning and indexed text for matching words. Each query returns candidate record IDs.

  2. Your application

    Combine the candidates

    Merge and deduplicate those IDs in your application. Choose how the two ranked lists contribute to the result.

  3. SQL

    Apply business rules

    Fetch the candidate rows with SQL, filtering for available stock and the customer's price range.

  4. Graph

    Add relationship context

    Follow each product's declared supplier relation, then assemble the product and supplier details in your application.

Before your first integration

Make three decisions early.

How will models connect?

Choose a stable primary key and reuse it for vector IDs and full-text document IDs.

Define the schema
Which embeddings will you use?

Set the model, dimensions and distance metric. Store the metadata needed for vector filtering.

Plan vector data
What happens when data changes?

Handle row, vector and text-index writes separately. Update affected indexes and track calls that need retrying.

Follow the ingestion example

Why OriginChainDB?

Bring application data and AI retrieval into the same database.

Less integration work

Keep records, search indexes and relationships in one database, reducing the services and synchronization pipelines you maintain.

Inside the engine

Richer retrieval

Combine semantic matches, exact keywords and relationship context in your application for RAG, search and recommendations.

Build a retrieval flow

Results you can inspect

Review published throughput, recall and retrieval-quality measurements, with the hardware, datasets and commands used to produce them.

View the benchmarks

Start small.
Scale with dedicated resources.

Deployment guide

Optional standby replication is asynchronous. Choose your recovery requirements alongside compute and storage.

Understand the topology

Build with OriginChainDB.

Start free Read the quickstart