Embedding

Embedding adds vector representations to chunks so the vector database can perform semantic search. In the full SDK workflow, rag.ingest() embeds chunks for you. Use rag.embed() directly when you need to inspect vectors or control batching/model choice.

pip install "ragrails[voyage]"
export VOYAGE_API_KEY="..."

from ragrails import RagRails

rag = RagRails(embedding={"provider": "voyage", "model": "voyage-3"})

Embed Chunks

embedded = rag.embed(
    chunks=chunks.items,
    batch_size=64,
)

Result

embedded.inputs
embedded.embedded
embedded.items
embedded.failed
embedded.errors

An embedded chunk keeps the chunk fields and adds embedding:

{
  "id": "refund-policy-0",
  "text": "Customers can request a refund within 30 days of purchase.",
  "source": "files/refund-policy.pdf",
  "metadata": {"title": "Refund policy", "chunk_index": 0},
  "embedding": [0.0132, -0.0719, 0.0241]
}

Create an Embedder

embedder = rag.embedder(provider="voyage", model="voyage-3", input_type="document")
embedded = rag.embed(chunks=chunks.items, embedder=embedder)

Use input_type="document" while indexing and input_type="query" for retrieval. rag.query(), rag.retrieve(), and rag.chat() create query embedders automatically from constructor defaults.

Override the Model

embedded = rag.embed(
    chunks=chunks.items,
    embedder=rag.embedder(model="voyage-3-large"),
    batch_size=32,
)

Parameters

`embedder()`

Parameter	Default	Description
`provider`	constructor default, else `"voyage"`	Embedding provider.
`model`	constructor default, else `"voyage-3"`	Embedding model.
`input_type`	`"document"`	`"document"` or `"query"`.
`options`	`None`	Provider-specific options.

`embed()`

Parameter	Default	Description
`chunks`	required	List of chunk dicts with `id` and `text`.
`embedder`	auto-created	Explicit embedding model object.
`input_type`	`"document"`	Used only when `embed()` creates the embedder.
`batch_size`	`64`	Chunks per embedding batch.

Next Stage

stored = rag.store(embedded_chunks=embedded.items)

Chunking

Create chunks.

Storing

Store embedded chunks.

Get Started

Workflows

Pipeline

Maintenance

Embed Chunks

Result

Create an Embedder

Override the Model

Parameters

`embedder()`

`embed()`

Next Stage

Chunking

Storing

​Embed Chunks

​Result

​Create an Embedder

​Override the Model

​Parameters

​embedder()

​embed()

​Next Stage

Chunking

Storing

Embed Chunks

Result

Create an Embedder

Override the Model

Parameters

`embedder()`

`embed()`

Next Stage