LLM Orchestration

RAG

A RAG package for Forge ML to enhance AI applications with retrieval-augmented generation.

Status
Not yet determined
Last checked
Unverified
License
Open source
Pricing
Free tier · from $0
Visit RAG Compare with alternatives

What it is

This package integrates retrieval-augmented generation capabilities into Forge ML, enabling developers to build more sophisticated and context-aware AI applications. It is particularly useful for scenarios where the model needs access to external knowledge bases or documents.

The @forge-ml/rag package stands out by offering a local, flexible solution for integrating retrieval-augmented generation into Forge ML applications, providing developers with full control over their infrastructure.

When it fits — and when it doesn’t

A good fit for

  • Teams building RAG apps who need a robust integration with Forge ML

  • Developers working on projects that require local deployment and control over the infrastructure

  • Data scientists looking to enhance their models with retrieval-augmented generation capabilities

Look elsewhere if

  • Projects requiring cloud-based managed services for ease of use and maintenance

  • Teams needing real-time streaming capabilities (batch-only architecture)

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited documentation and examples

    Official documentation lacks comprehensive guides, community tutorials are sparse

  2. 02

    Performance issues with large datasets

    Retrieval process slows down significantly when indexing more than 10k documents

What it does well

  • Integration with Forge ML for seamless AI application development

  • Retrieval-augmented generation capabilities to enhance model context awareness

  • Supports local deployment, providing flexibility in infrastructure choices

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up RAG

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →