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	Open-source distributed vector database	Self-hosted or Zilliz Cloud

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
Milvus	✅ Works	REST API v2 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

Milvus Configuration

Milvus is an open-source distributed vector database designed for high-performance similarity search. It supports both self-hosted deployment and Zilliz Cloud, the fully managed Milvus service.

Configuration fields:

1. Address: Milvus server URL. Self-hosted: http://localhost:19530. Zilliz Cloud: your cluster's Public Endpoint (e.g. https://in03-xxxx.serverless.aws-us-east-1.cloud.zilliz.com).
2. Collection Name: Name of the collection to store vectors (default: document_chunks). The collection is created automatically when you click Initialize.
3. Token: Authentication token. Required for Zilliz Cloud; optional for self-hosted deployments that have authentication enabled. Leave blank for unauthenticated self-hosted instances.

Getting Started with Milvus

Self-hosted (Docker):

1. Start Milvus standalone:

   docker run -d --name milvus-standalone -p 19530:19530 milvusdb/milvus:latest standalone

2. Enter http://localhost:19530 as the Address.
3. Leave Token blank.
4. Click Initialize — the collection is created automatically.

Zilliz Cloud (managed Milvus):

1. Create a cluster at cloud.zilliz.com.
2. Copy the Public Endpoint URL and paste it as the Address.
3. Copy the API Key and paste it as the Token.
4. Click Initialize — the collection is created automatically.

Initialize Database

Unlike Supabase, Milvus auto-setup works out of the box. The collection is created automatically via the Milvus REST API v2 when you click Initialize. No manual SQL execution is required.

Troubleshooting Milvus

• Connection refused: Verify Milvus is running and port 19530 is accessible from the server.
• Unauthorized: Check that your token is correct. Zilliz Cloud always requires a token; self-hosted without authentication should have a blank token.
• Collection not found: Click Initialize to create the collection automatically.

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