Vector Store

Zeus uses PostgreSQL pgvector as its vector storage engine, wrapped via langchain-postgres’s PGVector, providing a unified vector storage service for both Knowledge Base (RAG retrieval) and Memory (long-term memory).

Storage Architecture

Collection Design

Collection	Purpose	Filter Fields
`knowledge`	Knowledge base document chunks	`user_id`, `knowledge_base_id`, `document_id`, `chunk_index`
`memory`	User long-term memory	`user_id`, `scope`, `project_id`, `session_id`

Each collection is obtained via VectorStore.get_instance(collection_name, embeddings), and the same collection + embeddings combination shares a singleton instance.

Embedding Configuration

Zeus supports user-level embedding configuration, allowing different users to use different embedding models. Configuration is resolved in the following priority order:

Priority

Priority	Source	Resolution Method
1 (Highest)	User database config	`GET /api/config/embedding` (X-User-Id header), selects the config with `isDefault=true` and `enabled=true`
2	Environment variables	`OPENAI_API_KEY` + `OPENAI_BASE_URL` + `EMBEDDING_MODEL`
3	System default	Next.js API system embedding configuration

Supported Models

Any model compatible with the OpenAI Embeddings API format can be used:

Model	Description
`text-embedding-3-small`	OpenAI default, 1536 dimensions, best cost-performance ratio
`text-embedding-3-large`	OpenAI high-precision, 3072 dimensions
BGE series	Available via compatible API
Jina Embeddings	Available via compatible API
Self-hosted models	Accessible via custom `base_url`

Core Operations

Write

# Write during document processing
docs_to_add = [
    LCDocument(
        page_content="Chunk content...",
        metadata={
            "user_id": "user_123",
            "knowledge_base_id": "kb_456",
            "document_id": "doc_789",
            "chunk_index": 0,
            "source": "example.pdf",
            "page": 1,
            "created_at": "2025-01-01T00:00:00",
        }
    )
]
chunk_ids = vectorstore.add_documents(docs_to_add)

Retrieval

# Vector similarity search + metadata filtering
results = vectorstore.similarity_search_with_score(
    query="How to configure Zeus?",
    k=5,
    filter={
        "user_id": "user_123",
        "knowledge_base_id": "kb_456",
    },
)

Deletion

# Delete all chunks for a document
vectorstore.delete(filter={"document_id": "doc_789"})

# Delete all data for a knowledge base
vectorstore.delete(filter={"knowledge_base_id": "kb_456"})

Get Started

Fundamentals

Core Capabilities

Infra

Channels

Vector Store

Storage Architecture

Collection Design

Embedding Configuration

Priority

Supported Models

Core Operations

Write

Retrieval

Deletion

​Storage Architecture

​Collection Design

​Embedding Configuration

​Priority

​Supported Models

​Core Operations

​Write

​Retrieval

​Deletion

Storage Architecture

Collection Design

Embedding Configuration

Priority

Supported Models

Core Operations

Write

Retrieval

Deletion