AI / LangGraph LangChain Interview questions
How do you do batch processing with LCEL?
The .batch() method on any LCEL chain processes a list of inputs and returns a list of outputs. Under the hood, LangChain runs the inputs concurrently using a thread pool (synchronous) or asyncio tasks (async), subject to an optional concurrency limit.
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
chain = ChatPromptTemplate.from_template("Summarise: {text}") | ChatOpenAI() | StrOutputParser()
texts = [
{"text": "Article 1 text..."},
{"text": "Article 2 text..."},
{"text": "Article 3 text..."},
]
# Runs concurrently, returns list in input order
summaries = chain.batch(texts)
# Limit concurrency to avoid rate limits
summaries = chain.batch(texts, config={"max_concurrency": 5})
The async equivalent is .abatch(), which is preferred in async applications:
summaries = await chain.abatch(texts, config={"max_concurrency": 5})
Batch processing is ideal for offline data pipelines: indexing document collections, running evaluations against a test set, or bulk extracting structured data from unstructured text. Results are always returned in the same order as the input list, even if individual tasks complete out of order.
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