A LangChain RAG pipeline consists of six core components: Document Loaders to ingest data from various sources, Text Splitters to chunk documents into manageable pieces, Embeddings to convert text into numerical vectors, Vector Stores to index and store these embeddings, Retrievers to fetch relevant chunks based on a query, and Chains to orchestrate the retrieval and generation steps.
A typical RAG pipeline in LangChain is built from a sequence of specialized components. First, Document Loaders ingest data from various sources like PDFs, websites, or databases, converting them into LangChain Document objects[reference:3]. Next, Text Splitters break long documents into smaller, semantically coherent chunks (e.g., using RecursiveCharacterTextSplitter) to manage context window limits and improve retrieval precision[reference:4]. Then, Embeddings are generated for each chunk, converting the text into a numerical vector that captures its semantic meaning. These embeddings are stored and indexed in a Vector Store, which enables fast semantic similarity searches[reference:5]. A Retriever uses the vector store to fetch the most relevant chunks for a user's query. Finally, a Chain (often a RetrievalQA chain) combines the retrieved context with the original query and sends it to an LLM to generate a final, grounded answer[reference:6].
Scenario Questions
0-2 years experience
- 1How would you build a minimal LangChain RAG pipeline to answer questions from a handful of PDF files?
- 2If the answers you get are unrelated to the query, which part of the pipeline would you inspect first and what would you look for?
- 3What happens if your text splitter creates chunks that are larger than the maximum token limit of your embedding model?