AI - Vector Context Add

Type identifier: ai:vectorContext:add Category: AI Operations

Description

The AI - Vector Context Add node stores text embeddings in a vector context, enabling later semantic search operations. It automatically computes embeddings and indexes them with optional labels for filtering.

Input Handles

Output Handles

Configuration Options

Behaviour

1. Receives text content and unique ID
2. Computes embedding vector for the content
3. Resolves label values from dynamic handles

4. Stores the content in the vector context with:

   • The original text content
   • The unique ID
   • Label key-value pairs
5. Triggers success or error control flow

Labels

Labels enable filtered vector queries:

• Each configured label creates a dynamic input handle
• Label values are stored alongside embeddings
• Queries can filter by label values

ID Handling

The ID uniquely identifies each embedding:

• Duplicate IDs update existing embeddings

Embedding Model

Uses OpenAI text-embedding-ada-002 (1536 dimensions) for embedding computation. For the canonical model list, see AI - Model Reference — Compute Embedding Models.

Examples

Store Document Chunks

Configuration:

• Labels: ["documentId", "chunkIndex"]

Flow:

1. Text - Recursive Split chunks a document
2. List - Map iterates chunks
3. AI - Vector Context Add stores each chunk

For each chunk:

• content: The chunk text
• id: <documentId>-<chunkIndex>
• documentId: Source document ID
• chunkIndex: Position in document

Store Categorised Content

Configuration:

• Labels: ["category", "source"]

Use case: Build a knowledge base with categorised entries for filtered retrieval.

Update Existing Content

Providing an existing ID updates the embedding:

• New embedding computed
• Previous embedding replaced
• Labels updated