Vector Store Configuration

Vector store settings determine where your document embeddings are stored and how they're accessed.

Available Providers

Provider	Description	Best For
Supabase	PostgreSQL with pgvector extension	Integrated solution, SQL queries
Pinecone	Managed vector database	High scale, low latency
Qdrant	Open-source vector database	Self-hosted, flexible deployment
Milvus	Distributed vector database	Enterprise scale

Supabase Configuration

When using Supabase (the default):

1. Service URL: Your Supabase project URL
2. Service Key: Service role key for server-side operations
3. Table Name: Name of the table storing vectors (default: document_chunks)

Setup Status: Shows whether the required database table exists

Initialize Database: Creates the necessary table and indexes if not present

Auto-Setup Limitation for External Supabase Projects

The Initialize Database button uses automatic setup, which requires the exec_sql helper function to be present on your Supabase database. This is a technical limitation of Supabase's REST API (PostgREST), which cannot execute DDL statements like CREATE TABLE directly.

For external Supabase projects (not the app's built-in database):

1. Click Initialize Database - it will attempt auto-setup
2. If it fails, a Manual Setup SQL box will appear
3. Copy the SQL and run it in your Supabase project's SQL Editor
4. Click Test Connection to verify the setup succeeded

This is a one-time setup. Once the table and functions are created, everything works automatically.

Auto-Setup Comparison

Provider	Auto-Setup	Notes
Pinecone	✅ Works	Native SDK supports index creation
Qdrant	✅ Works	REST API supports collection creation
Supabase	⚠️ Manual	Requires SQL execution in Supabase Dashboard

Pinecone Configuration

For Pinecone:

1. API Key: Your Pinecone API key
2. Environment: Pinecone environment (e.g., us-east-1-aws)
3. Index Name: Name of your Pinecone index

Qdrant Configuration

For Qdrant:

1. API Key: Optional authentication key
2. Server URL: Qdrant server endpoint
3. Collection Name: Target collection for vectors

Testing Connection

Click Test Connection to verify your configuration:

• Checks connectivity to the provider
• Validates credentials
• Confirms collection/table existence

Index Information

After a successful connection test, an Index Information card appears showing:

Field	Description
Dimension	Vector dimension (e.g., 1536 for OpenAI text-embedding-3-small)
Distance Metric	Similarity algorithm used (Cosine, Euclidean, or Dot Product)
Status	Current index status (Ready, Initializing, Error, or Unknown)
Vectors	Total number of vector embeddings stored

Project-Scoped Configuration

Vector store settings are configured per-project. This allows different projects to use different vector stores or configurations, enabling:

• Separation of data by client or use case
• Different performance configurations
• Testing environments