LLM Embeddings

Convert text into a vector embedding for search, similarity, or retrieval workflows.

Overview#

LLM Embeddings turns text into a numeric vector using OpenAI embedding models. The output is infrastructure data , not something customers read directly.

Use it inside larger workflows for semantic search, memory, indexing, or comparing how similar two pieces of text are.

Workflow Preview

LLM Embeddings

Read-only builder graph

Ports#

Input , Text: The string to embed , connected upstream content or fallback text from the inspector.
Output , Embedding: An array-like vector for downstream similarity or storage steps.

Inspector#

Text (fallback): Used when the Text input is not connected.
Model: Default small model is cheap and strong for most retrieval jobs; large model suits premium memory workflows.

Tips#

Feed clean, meaningful text , garbage in produces weak vectors.
Keep this block inside the graph; pair it with Workflow Output only if customers need to see raw vectors.
Do not confuse embeddings with visible copy from LLM Chat.
Requires an OpenAI key in the run modal (or Edgaze-hosted run where applicable).

Workflow Studio

More blocks

Workflow Input

Ask the customer one clear question and turn their answer into data your workflow can use.

Workflow Form

Collect several run-modal fields in one grouped form, each with a stable key for downstream steps.

Workflow Output

Define what the customer receives when the workflow finishes.

Block reference

LLM Embeddings

Convert text into a vector embedding for search, similarity, or retrieval workflows.

Overview#

LLM Embeddings turns text into a numeric vector using OpenAI embedding models. The output is infrastructure data , not something customers read directly.

Use it inside larger workflows for semantic search, memory, indexing, or comparing how similar two pieces of text are.

Workflow Preview

LLM Embeddings

Read-only builder graph

Ports#

Input , Text: The string to embed , connected upstream content or fallback text from the inspector.
Output , Embedding: An array-like vector for downstream similarity or storage steps.

Inspector#

Text (fallback): Used when the Text input is not connected.
Model: Default small model is cheap and strong for most retrieval jobs; large model suits premium memory workflows.

Tips#

Feed clean, meaningful text , garbage in produces weak vectors.
Keep this block inside the graph; pair it with Workflow Output only if customers need to see raw vectors.
Do not confuse embeddings with visible copy from LLM Chat.
Requires an OpenAI key in the run modal (or Edgaze-hosted run where applicable).