How do you load and split documents lazily (streaming) to handle very large files without running out of memory?

Q: How do you load and split documents lazily (streaming) to handle very large files without running out of memory?

LangChain provides a `lazy_load()` method on document loaders that returns a generator (iterator) rather than loading all documents into memory at once. This is crucial for large files or large numbers of documents. You can then iterate over the generator, and for each document, apply your splitter using `split_documents`. This creates a streaming pipeline where only one document chunk is held in memory at a time. Some loaders, like the database loaders, also support `lazy_load()` natively. ```python from langchain_community.document_loaders import TextLoader from langchain_text_splitters import RecursiveCharacterTextSplitter # Loader (large file) loader = TextLoader("huge_document.txt") # Splitter configuration splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) # Stream process: only one document at a time in memory for doc in loader.lazy_load(): # Split the single document into chunks chunks = splitter.split_documents([doc]) for chunk in chunks: # Process each chunk (e.g., add to vector store) vector_store.add_documents([chunk]) ``` This pattern is essential for handling very large documents (e.g., multi-gigabyte log files) or directories with thousands of files. Without lazy loading, the entire dataset would be loaded into memory, leading to memory exhaustion and slow performance. The `lazy_load()` method is implemented by all LangChain loaders that support streaming (most do). If a loader doesn't implement it, you can create a custom loader by subclassing `BaseLoader` and implementing the `lazy_load` generator method.

1What is a Document object in LangChain — what are pageContent and metadata fields and why does metadata matter in RAG?

2What are Document Loaders in LangChain and how do you choose the right loader for PDFs, web pages, Notion, Google Drive, or SQL databases?

3What is RAG and why is it preferred over fine-tuning for domain-specific knowledge in production applications?

4How do you implement RAG evaluation — what metrics like faithfulness, answer relevancy, and context recall do you measure using RAGAS?

5What is HNSW indexing and why does it make approximate nearest neighbor search fast at scale?

6What are the core components of a RAG pipeline in LangChain — Document Loaders, Text Splitters, Embeddings, Vector Stores, Retrievers, and Chains?

7What is the difference between dense embeddings and sparse embeddings (BM25) — when would you combine both in a hybrid search?

8What is an Embedding in the context of RAG — what does it represent and why is cosine similarity used to compare them?

9What is a Vector Store and how does it differ from a traditional relational or document database?

10How do you choose the right embedding model — what tradeoffs exist between OpenAI embeddings, Cohere, HuggingFace, and local models like nomic-embed?

11What is a MultiQueryRetriever and how does it improve recall by generating multiple phrasings of the same question?

12How do you optimize retrieval latency in production — what caching, pre-fetching, or index optimization strategies do you apply?

13What is HyDE (Hypothetical Document Embedding) and how does it improve retrieval for vague or abstract queries?

14How do you preserve and propagate source metadata (filename, page number, URL, timestamp) through the loading and splitting pipeline?

15What is chunk size and chunk overlap in text splitting — how do you decide the right values for your use case?

16What is a Re-ranker (cross-encoder) and where does it fit in the RAG pipeline after initial retrieval?

17How do you implement namespace or tenant isolation in a vector store for a multi-tenant RAG application?

18What is a ParentDocumentRetriever — how does it index small chunks but return larger parent chunks to the LLM?

19How do you handle incremental updates to a vector store — adding, updating, and deleting documents without full re-indexing?

20How do you build a citation system that maps each sentence in the LLM's answer back to the exact source chunk it came from?

21How do you implement Step-Back Prompting in a RAG pipeline to improve retrieval for highly specific questions?

22How do you handle embedding model upgrades in production — what happens to your existing vectors when you switch models?

23How do you handle multilingual RAG — embedding and retrieving documents in multiple languages for a global user base?

24How do you handle long context RAG — when retrieved chunks exceed the LLM's context window, what strategies do you apply?

25How do you detect and mitigate hallucination in RAG outputs — what role does citation and source grounding play?

26How do you implement hybrid search combining dense vector search with BM25 keyword search using EnsembleRetriever?

27What is Self-Query Retrieval and how does it allow the LLM to generate structured metadata filters alongside the semantic query?

28How do you design a RAG pipeline with LangGraph — turning retrieval, grading, and generation into discrete stateful graph nodes?

29What is the difference between semantic search and keyword search and why does RAG rely on semantic similarity?

30What are the tradeoffs between vector stores like Pinecone, Weaviate, Chroma, pgvector, and FAISS — how do you choose for production?

31What is the difference between RecursiveCharacterTextSplitter and CharacterTextSplitter — when would you use one over the other?

32How do you handle structured documents like tables, code blocks, or markdown files during the splitting phase to avoid breaking semantic meaning?

33What is the difference between a Retriever and a Vector Store in LangChain — why is the abstraction separation important?

34What is CRAG (Corrective RAG) and how does it add a grading step to decide whether retrieved docs are relevant before answering?

35How do you implement a fallback strategy when retrieval returns no relevant documents — how do you avoid hallucination in this case?

36What is Self-RAG and how does the LLM decide when to retrieve, whether retrieved docs are relevant, and whether the answer is grounded?

37What is the difference between Stuff, MapReduce, Refine, and MapRerank document chain strategies — when do you use each?

38What is query decomposition and how do you break a complex multi-part question into sub-queries for better retrieval?

39How do you build a Conversational RAG chain that maintains chat history and reformulates follow-up questions into standalone queries?

40What is a similarity score threshold in retrieval and how do you use it to filter out low-confidence results?

41What is a SemanticChunker and how does it differ from fixed-size character-based splitting?

42How do you implement access control at the retrieval layer — ensuring users only retrieve documents they are authorized to see?

43What is Contextual Compression in LangChain retrieval and how does it reduce noise in retrieved chunks?

44What is MMR (Maximal Marginal Relevance) retrieval and how does it balance relevance with diversity of results?

45How do you load and split documents lazily (streaming) to handle very large files without running out of memory?

46How do you efficiently batch embed a large corpus of documents without hitting rate limits or memory constraints?

How do you load and split documents lazily (streaming) to handle very large files without running out of memory?

Use `lazy_load()` instead of `load()` to stream documents from a loader as a generator, processing each document one at a time, and chain it with a splitter using `split_documents` to avoid loading all data into memory at once.

How do you load and split documents lazily (streaming) to handle very large files without running out of memory?

Use `lazy_load()` instead of `load()` to stream documents from a loader as a generator, processing each document one at a time, and chain it with a splitter using `split_documents` to avoid loading all data into memory at once.

Scenario Questions

Follow-up Questions

How do you load and split documents lazily (streaming) to handle very large files without running out of memory?

Use lazy_load() instead of load() to stream documents from a loader as a generator, processing each document one at a time, and chain it with a splitter using split_documents to avoid loading all data into memory at once.

How do you load and split documents lazily (streaming) to handle very large files without running out of memory?

Use lazy_load() instead of load() to stream documents from a loader as a generator, processing each document one at a time, and chain it with a splitter using split_documents to avoid loading all data into memory at once.

Scenario Questions

Follow-up Questions

Use `lazy_load()` instead of `load()` to stream documents from a loader as a generator, processing each document one at a time, and chain it with a splitter using `split_documents` to avoid loading all data into memory at once.

Use `lazy_load()` instead of `load()` to stream documents from a loader as a generator, processing each document one at a time, and chain it with a splitter using `split_documents` to avoid loading all data into memory at once.