Embedding Stores

An Embedding Store is the specialized vector database that persists and queries document embeddings, the numerical vectors generated by the Embedding Model. It enables similarity search, finding the documents most semantically related to a user’s query.

Viglet Turing supports four backends: three external services via Spring AI (ChromaDB, PgVector, Milvus) and an embedded Apache Lucene store that needs no extra infrastructure. The active backend is set globally in Administration → Settings and can be overridden per Semantic Navigation Site in its Generative AI tab.

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Supported Backends

ChromaDB

A lightweight, open-source vector database ideal for development and small to medium deployments.

• Self-hosted, connects via its HTTP API
• Zero infrastructure overhead for teams already running Python tooling
• No special schema setup required, Turing manages the collections automatically
• Multi-tenant and multi-database support

Docker Compose quickstart:

services:
  chroma:
    image: chromadb/chroma:latest
    ports:
      - "8000:8000"

Configuration	Default	Description
Base URL	http://localhost:8000	Chroma HTTP API endpoint
Collection Name	turing	Target collection name
Tenant Name	default_tenant	Chroma tenant identifier
Database Name	default_database	Chroma database name
Key Token	n/a	Bearer token for authentication
Basic Username / Password	n/a	Basic auth credentials

Authentication: ChromaDB supports two methods: Bearer token and Basic auth. Configure either via the credential field or the provider options. When both are present, the credential field takes precedence.

PgVector

PostgreSQL with the pgvector extension, the best choice for deployments that already use PostgreSQL as their primary database.

• Avoids an additional infrastructure dependency
• Embeddings live in the same database as your application data
• Supports standard PostgreSQL backup, replication, and access control
• Connection pooling via HikariCP (max 5 connections per store)

Enable the required extensions in your PostgreSQL instance:

CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS hstore;
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

Configuration	Default	Description
JDBC URL	n/a	PostgreSQL connection string (e.g., jdbc:postgresql://localhost:5432/turing)
Username	n/a	Database user
Password	n/a	Database password
Table Name	vector_store	Table where embeddings are stored
Schema Name	public	PostgreSQL schema
Dimensions	n/a	Vector dimensionality: must match the embedding model
Distance Type	n/a	COSINE_DISTANCE, EUCLIDEAN_DISTANCE, or INNER_PRODUCT
Index Type	n/a	HNSW or IVFFLAT

Table schema created by Turing:

CREATE TABLE IF NOT EXISTS "vector_store" (
    id       UUID DEFAULT uuid_generate_v4() PRIMARY KEY,
    content  TEXT,
    metadata JSON,
    embedding VECTOR
);

Milvus

A purpose-built, cloud-native vector database designed for high-scale similarity search.

• Recommended for large corpora or high-throughput deployments
• Supports distributed operation, horizontal scaling, and advanced index management
• Managed cloud offering available (Zilliz Cloud)

Configuration	Default	Description
Base URL	http://localhost:19530	Milvus service URI
Collection Name	turing	Target collection
Database Name	n/a	Optional database name
Token	n/a	Authentication token (username:password format)
Embedding Dimension	n/a	Vector dimensionality: must match the embedding model
Metric Type	n/a	COSINE, L2, or IP (inner product)
Index Type	n/a	HNSW, IVF_FLAT, IVF_SQ8, or DISKANN
Index Parameters	n/a	JSON string with index-specific params (e.g., {"M":16,"efConstruction":200})

Lucene (embedded)

An Apache Lucene 10 KNN vector store that runs inside the Turing process: no external service, no container, no network hop. It is the zero-infrastructure choice: ideal for single-node deployments, demos, air-gapped installs, and the per-site sn_<siteId> collections used by hybrid ranking. It is also the store that powers the multimodal (text→image) retrieval path.

• No service to run: vectors live in an on-disk Lucene index
• One IndexWriter per directory; collections map to Lucene directories
• Supports the same upsert / query / clear lifecycle as the external stores

Configuration	Default	Description
Index Path	(install-relative)	Filesystem directory holding the Lucene vector index
Quantization	NONE	Scalar quantization mode (see below)

Quantization & Matryoshka

Opt-in scalar quantization shrinks the index and speeds search at a measured recall cost. Set the quantization provider option on the Lucene store:

Mode	Effect
NONE (default)	Full float32: byte-for-byte legacy behaviour
INT8	8-bit scalar quantization: good size/recall balance
INT4	4-bit: smaller still, more recall cost
SEVEN_BIT	7-bit scalar encoding
BINARY	1-bit: smallest, highest recall cost

The store raises Lucene’s advertised 1024-dim cap to 4096, so 1536-/3072-dim models (OpenAI, Gemini) still index quantized. Changing the mode only affects new segments: Lucene records the codec per segment, so a full re-index converts everything. Quantization pairs with Matryoshka outputDimensionality truncation on the Voyage/Gemini embedding models to compound the shrink.

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Store Comparison

Feature	ChromaDB	PgVector	Milvus	Lucene (embedded)
Best for	Dev / small-medium	PostgreSQL shops	Large-scale production	Single-node / zero-infra
Infrastructure	Standalone container	PostgreSQL extension	Dedicated cluster	None: in-process
Scaling	Single node	PostgreSQL replication	Horizontal / distributed	Single node
Index types	Automatic	HNSW, IVFFLAT	HNSW, IVF_FLAT, IVF_SQ8, DISKANN	HNSW
Distance metrics	Cosine, L2	Cosine, L2, Inner Product	Cosine, L2, Inner Product	Cosine
Quantization	n/a	n/a	IVF_SQ8	INT8 / INT4 / 7-bit / binary
Multi-tenant	Yes (tenant + database)	Via schema	Yes (database + collection)	Via collection (directory)
Authentication	Token or Basic Auth	JDBC credentials	Token	n/a (local)
Managed cloud	n/a	Any managed PostgreSQL	Zilliz Cloud	n/a

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Create / Edit Form

Navigate to Generative AI → Embedding Stores to manage store instances.

General Information

Field	Required	Description
Title	Yes	Display name for this store instance
Description	Yes	Brief description of its purpose
Vendor	Yes	Select the backend: ChromaDB, PgVector, or Milvus. Selecting a vendor applies default values for Endpoint URL and Collection Name.

Connection

Field	Required	Description
Endpoint URL	Yes	Base URL for the store backend
Collection Name		Name of the collection or table (vendor-specific default applied)
Credential		Authentication token or password: stored encrypted. Leave blank when editing to keep the existing value.

Provider Options

Each vendor exposes additional configuration fields in the Provider Options section. These fields appear dynamically when a vendor is selected. A raw JSON editor is also available for advanced configurations.

Status

Field	Description
Enabled	Toggle to activate or deactivate this store. Disabled stores are not available for selection.

Vendor defaults applied on selection:

Vendor	Default URL	Default Collection
ChromaDB	http://localhost:8000	turing
PgVector	jdbc:postgresql://localhost:5432/turing	vector_store
Milvus	http://localhost:19530	turing
Lucene	(local index path: no URL)	turing

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Collection Management

Each store instance provides a Collections page where you can view and manage vector collections.

The collections table shows:

Column	Description
Collection Name	Name of the collection or table
ID	Internal identifier
Document Count	Number of distinct source documents embedded
Chunk Count	Total number of embedding chunks stored

Available actions per collection:

Action	Description
Create	Create a new empty collection in the store
Clear	Remove all embeddings from the collection while keeping the collection structure
Delete	Remove the entire collection and all its data

Clearing or deleting collections

Clearing or deleting a collection permanently removes all stored embeddings. A full re-indexing of all content is required to rebuild the vectors. This action cannot be undone.

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System Information

The System Info endpoint returns backend-specific metadata for monitoring and diagnostics:

Backend	Information Returned
ChromaDB	ChromaDB version
PgVector	PostgreSQL version, pgvector extension version
Milvus	Milvus server version
Lucene	Lucene version (embedded)

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REST API

Store instances are managed via the REST API at /api/store.

Store Instance Endpoints

Method	Endpoint	Description
GET	/api/store	List all store instances (ordered by title)
GET	/api/store/structure	Get the structure template for a new instance
GET	/api/store/{id}	Get a specific store instance
POST	/api/store	Create a new store instance
PUT	/api/store/{id}	Update an existing store instance
DELETE	/api/store/{id}	Delete a store instance

Collection Endpoints

Method	Endpoint	Description
GET	/api/store/{id}/collections	List all collections in a store
POST	/api/store/{id}/collections/{name}	Create a new collection
DELETE	/api/store/{id}/collections/{name}	Delete a collection
DELETE	/api/store/{id}/collections/{name}/clear	Clear all embeddings from a collection

System Info Endpoint

Method	Endpoint	Description
GET	/api/store/{id}/system-info	Get store backend version and status

Store Vendor Endpoints

Method	Endpoint	Description
GET	/api/store/vendor	List all available vendors
GET	/api/store/vendor/{id}	Get a specific vendor

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Global Configuration

Set the default embedding store in Administration → Settings:

Setting	Description
Default Embedding Store	Which vector database backend to use (ChromaDB, PgVector, or Milvus)

Individual Semantic Navigation Sites can override this setting in their Generative AI tab. The Knowledge Base always uses the global default.

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Security

Credentials are handled with care at every layer:

• Stored encrypted: credentials are encrypted before being persisted to the database
• Never returned: the credential field is transient and write-only. It flows in on save but never comes back in API responses
• Edit safely: leaving the credential field blank when editing preserves the existing encrypted value
• Per-vendor auth: ChromaDB supports Bearer token or Basic Auth; Milvus uses token-based auth; PgVector uses JDBC credentials with connection pooling

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Related Pages

Page	Description
Embedding Models	Configure the models that generate vectors stored here
What is RAG?	How embedding stores fit into the RAG pipeline
Reranking	Re-ordering retrieved chunks after the vector search
GenAI & LLM Configuration	RAG architecture, RAG sources, and system overview
LLM Instances	Configure the LLM providers that supply embedding APIs
Assets	Knowledge Base files that are indexed into the Embedding Store
Semantic Navigation	Generative AI tab: per-site embedding overrides

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