ai-engineer-rag-implementation-using-sdks-directly

# ai-engineer-rag-implementation-using-sdks-directly

## Contents

__Roadmap info from [ roadmap website ] (https://roadmap.sh/ai-engineer/using-sdks-directly@WZVW8FQu6LyspSKm1C_sl) __

  ## Using SDKs Directly

  While
  tools
  like
  Langchain
  and
  LlamaIndex
  make
  it
  easy
  to
  implement
  RAG, you don’t have to necessarily learn and use them. If you know about the different steps of implementing RAG you can simply do it all yourself e.g. do the chunking using `@langchain/textsplitters` package, create embeddings using any LLM e.g. use OpenAI Embedding API through their SDK, save the embeddings to any vector database e.g. if you are using Supabase Vector DB, you can use their SDK and similarly you can use the relevant SDKs for the rest of the steps as well.