Principle:Intel Ipex llm RAG With LlamaIndex
| Knowledge Sources | |
|---|---|
| Domains | RAG, Information_Retrieval, LlamaIndex |
| Last Updated | 2026-02-09 04:00 GMT |
Overview
Pipeline pattern for retrieval-augmented generation using LlamaIndex with IPEX-LLM acceleration for both embedding and generation stages.
Description
This RAG pattern uses LlamaIndex to build a document question-answering pipeline. Documents (PDFs) are loaded, split into sentence-level chunks, embedded using IPEX-LLM-accelerated BGE embeddings, and stored in a PostgreSQL vector database. At query time, relevant chunks are retrieved via similarity search and used as context for IPEX-LLM-powered text generation. This provides a complete LlamaIndex alternative to the LangChain-based RAG pipeline.
Usage
Use this when building RAG applications with the LlamaIndex framework on Intel hardware. Choose this over the LangChain RAG pattern when LlamaIndex's node-based abstraction and built-in query engines better fit the application architecture.
Theoretical Basis
Pseudo-code Logic:
# Abstract RAG pipeline with LlamaIndex
documents = load_pdf(path)
chunks = sentence_split(documents)
embeddings = ipex_llm_embed(chunks) # IPEX-LLM accelerated
vector_store.insert(chunks, embeddings)
# Query time:
query_embedding = ipex_llm_embed(question)
relevant_chunks = vector_store.similarity_search(query_embedding)
answer = ipex_llm_generate(question, context=relevant_chunks)