RAG
A RAG package for Forge ML to enhance AI applications with retrieval-augmented generation.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is RAG?
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.
Key differentiator
“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.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Official documentation lacks comprehensive guides, community tutorials are sparse
Retrieval process slows down significantly when indexing more than 10k documents
Fit analysis
Who is it for?
✓ Best 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
✕ Not a fit for
Projects requiring cloud-based managed services for ease of use and maintenance
Teams needing real-time streaming capabilities (batch-only architecture)
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Integrations
Next step
Get Started with RAG
Step-by-step setup guide with code examples and common gotchas.