Migration Guide

Moving from LangChain or LlamaIndex? The full guide with 5 real, runnable side-by-side comparisons lives at MIGRATION.md in the repo root. This page is a short teaser.

What's covered in the full guide

1. Basic ingest + query — LangChain/LlamaIndex need a text splitter, embedding client, vectorstore, and chain/query-engine assembled separately; ragleap-rag is one RagLeap(...) object.
2. Provider fallback — LangChain has no first-class automatic fallback between providers; ragleap-rag has fallbacks= built in, plus result["provider_used"] to know which one actually answered.
3. Cost tracking — real per-call cost, monthly budgets, and automatic downgrade to a cheaper fallback when a budget is hit.
4. Swapping vector backends — one line changes (vector_backend=FAISSBackend(...) vs PgVectorBackend(...)), rest of your ingest/query code is identical.
5. What you gain vs. give up — an honest comparison table, including where LangChain's larger ecosystem still wins (600+ integrations vs. ragleap-rag's 8 embedding providers + 6 vector backends).

Quick taste

# Before (LangChain): splitter, embeddings, vectorstore, chain — 4 objects
# After (ragleap-rag):
from ragleap import RagLeap, ProviderConfig, EmbeddingConfig

rag = RagLeap(
    database_url="postgresql://user:pass@localhost:5432/mydb",
    primary=ProviderConfig(provider="gemini", model="gemini-3.6-flash", api_key="..."),
    embedder=EmbeddingConfig(provider="gemini", model="models/gemini-embedding-001",
                              dimensions=3072, api_key="..."),
)
rag.init_schema()
rag.ingest_text(filename="doc.txt", text=raw_text)
answer = rag.ask("What is the budget?")

A note on accuracy

LangChain's and LlamaIndex's APIs change fairly often between versions. The comparisons in the full guide reflect patterns that have been broadly stable, but verify against your own installed versions before relying on them for a real migration.

Read the full guide: MIGRATION.md