Build retrieval-augmented generation pipelines with DocumentLoader, TextSplitter, VectorStore, LCEL pipe operator, and retrievers
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What does a DocumentLoader do in LangChain?
DocumentLoader: LangChain provides loaders such as PDFLoader, CheerioWebBaseLoader, CSVLoader, and DirectoryLoader. Each loader returns Document[] where each document has pageContent: string and metadata: Record<string, any>. These are the input to the text splitter stage of a RAG pipeline.
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What is the purpose of a TextSplitter in a RAG pipeline?
TextSplitter: Common choice is RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 }). Overlap ensures that context spanning chunk boundaries is captured in both chunks. The chunks are then embedded and stored. Without splitting, documents too long for the embedding model get truncated, losing information.
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How does a VectorStore enable semantic search in LangChain?
VectorStore: Add documents: await vectorStore.addDocuments(chunks). Retrieve: const docs = await vectorStore.similaritySearch(query, 4). LangChain supports Chroma, Pinecone, Weaviate, pgvector, and FAISS. Similarity is measured by cosine distance between the query embedding and stored chunk embeddings.
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What does LCEL (LangChain Expression Language) enable with the pipe operator?
LCEL: Example: const chain = prompt | llm | outputParser. Each component must implement invoke() (and optionally stream()). The | operator creates a RunnableSequence. LCEL chains support .stream(), .batch(), and .invoke() uniformly. LangSmith tracing integrates automatically with LCEL chains.
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In a LangChain RAG chain, what role does the retriever play between the user query and the LLM?
Retriever in RAG: The standard LCEL RAG pattern: const chain = RunnableMap.from({ context: retriever, question: new RunnablePassthrough() }) | prompt | llm | parser. The retriever's output fills the {context} slot in the prompt template. The LLM then grounds its answer in the retrieved chunks, reducing hallucination and enabling responses about private or recent data.