Integrating LlamaIndex for RAG Pipelines in Mobile App
RAG (Retrieval-Augmented Generation) solves LLM's fundamental flaw: model doesn't know your data. LlamaIndex is specialized RAG framework unlike LangChain's broad scope. Document parsing, chunking, indexing, retrieval — LlamaIndex handles deeper.
RAG Architecture for Mobile App
Mobile client works with backend via REST API. LlamaIndex lives on server handling full cycle: document indexing → retrieval per request → answer generation with context.
Document Indexing
LlamaIndex parses PDF, Word, Notion, Google Docs, HTML via SimpleDirectoryReader or specialized readers. Chunking — document fragmentation for indexing.
Configure: embed model (OpenAI Embeddings), LLM (gpt-4o-mini), node parser (SentenceSplitter with chunk size/overlap), vector store (PGVector or Pinecone).
Chunk size critical. 512 tokens fits documentation with varied sections. Long narrative text — 1024–2048 with larger overlap (100–200 tokens).
Advanced Retrieval: Problems and Solutions
Naive RAG — top-K by cosine similarity — often returns irrelevant chunks on complex questions. LlamaIndex offers strategies:
Hybrid search (BM25 + vector): keywords for exact search, embeddings for semantic. Helps with specific terms (SKUs, names, dates).
Re-ranking: primary retrieval returns top-20, cross-encoder re-ranks, keeps top-4. Cohere Rerank — managed option, cross-encoder/ms-marco-MiniLM-L-6-v2 — open-source.
HyDE (Hypothetical Document Embeddings): generate hypothetical answer before retrieval, search by its embedding instead of question embedding. Works when questions and documents phrased differently.
Multi-Document Retrieval and Routing
If knowledge base split by type (policies, instructions, FAQ) — router directs query to right sub-index. Reduces noise in retrieved context.
Index Updates
Documents change. Update strategies: full re-index (cheap for small corpora, daily), incremental new document addition, remove stale by metadata. LlamaIndex supports refresh_ref_docs() for incremental updates without full rebuild.
Process
Document base audit → chunking strategy selection → indexing → retrieval pipeline tuning → A/B test naive vs hybrid search → mobile client API.
Timeline Estimates
Basic RAG with pgvector — 3–5 days. Hybrid search with re-ranker — 1–2 weeks. Multi-document router with incremental updates — 2–3 weeks.