Storing

Storing writes embedded chunks to Qdrant, Pinecone, or Weaviate. In the full SDK workflow, rag.ingest() stores embedded chunks for you. Use rag.store(), rag.edit(), and rag.delete() directly for manual pipelines and maintenance.

from ragrails import RagRails

rag = RagRails(
    collection="support",
    vector_store={"provider": "qdrant", "url": "http://localhost:6333"},
    embedding={"provider": "voyage", "model": "voyage-3"},
)

Store

stored = rag.store(embedded_chunks=embedded.items, batch_size=64)

stored.inputs
stored.stored
stored.items
stored.failed
stored.provider
stored.collection
stored.errors

The collection is created automatically by default.

Parameter	Default	Description
`embedded_chunks`	required	List of chunk dicts with `embedding`.
`vector_db`	constructor default, else `"qdrant"`	`qdrant`, `qdrant_cloud`, `pinecone`, or `weaviate`.
`collection`	constructor default	Collection, index, or class name.
`url`	constructor default	Vector DB URL.
`batch_size`	`64`	Chunks per upsert batch.
`ensure_collection`	`True`	Create the collection if missing.
`options`	`None`	Provider-specific options.

Edit

edit() re-embeds replacement chunks and replaces stored records by exact ID.

edited = rag.edit(chunks=[
    {
        "id": "refund-policy-0",
        "text": "Refunds are returned to the original payment method within 3 to 5 business days.",
        "source": "files/refund-policy.pdf",
        "metadata": {"title": "Refund policy"},
    }
])

Pass embedder=... to control the re-embedding model for this edit.

Delete

deleted = rag.delete(ids=["refund-policy-0", "refund-policy-1"])

Deletes are exact-ID operations. They do not perform semantic matching.

Vector Database Names

Database	`vector_db`	Install	Notes
Qdrant local	`qdrant`	`ragrails[qdrant]`	Use a local or network Qdrant URL.
Qdrant Cloud	`qdrant_cloud`	`ragrails[qdrant]`	Set cloud URL and `QDRANT_API_KEY`.
Pinecone	`pinecone`	`ragrails[pinecone]`	Set `PINECONE_API_KEY`; index names cannot contain underscores.
Weaviate	`weaviate`	`ragrails[weaviate]`	Class names must be alphanumeric and start uppercase.

Manual Pipeline

docs = rag.parse(files="files/refund-policy.pdf")
chunks = rag.chunk(markdown=docs.outputs)
embedded = rag.embed(chunks=chunks.items)
stored = rag.store(embedded_chunks=embedded.items)

Embedding

Create embedded chunks.

Maintenance

Update or delete stored chunks.

Get Started

Workflows

Pipeline

Maintenance

Store

Edit

Delete

Vector Database Names

Manual Pipeline

Embedding

Maintenance

​Store

​Edit

​Delete

​Vector Database Names

​Manual Pipeline

Embedding

Maintenance

Store

Edit

Delete

Vector Database Names

Manual Pipeline