Bird
0
0

You want to improve recall for a complex topic by using multi-query retrieval with three related queries. Which approach best ensures you get the most relevant documents without duplicates?

hard📝 Application Q15 of 15
LangChain - RAG Chain Construction
You want to improve recall for a complex topic by using multi-query retrieval with three related queries. Which approach best ensures you get the most relevant documents without duplicates?
AUse MultiQueryRetriever.from_llm(retriever, llm) to generate multiple queries and deduplicate results
BRun each query separately and pick the first result from each
CCombine all queries into one long string and run a single query
DUse only the query with the highest expected relevance
Step-by-Step Solution
Solution:

  1. Step 1: Understand multi-query retrieval goal

    It aims to combine results from multiple related queries to improve recall.

  2. Step 2: Identify best practice to avoid duplicates

    Using MultiQueryRetriever.from_llm(retriever, llm) generates multiple queries via LLM and deduplicates results for completeness without repeats.

  3. Final Answer:

    Use MultiQueryRetriever.from_llm(retriever, llm) to generate multiple queries and deduplicate results -> Option A

  4. Quick Check:

    MultiQueryRetriever generates + deduplicates = best recall [OK]
Quick Trick: MultiQueryRetriever generates queries then deduplicates for best recall [OK]
Common Mistakes:
  • Running queries separately without combining results
  • Merging queries into one string loses query specificity
  • Using only one query misses relevant documents

Want More Practice?

15+ quiz questions · All difficulty levels · Free

Free Signup - Practice All Questions
More LangChain Quizzes
Conversational RAG - Why conversation history improves RAG - Quiz 11easy Document Loading - Loading CSV and Excel files - Quiz 14medium Document Loading - Custom document loaders - Quiz 12easy Embeddings and Vector Stores - Metadata filtering in vector stores - Quiz 7medium Embeddings and Vector Stores - Open-source embedding models - Quiz 6medium Embeddings and Vector Stores - Open-source embedding models - Quiz 15hard Embeddings and Vector Stores - Pinecone cloud vector store - Quiz 7medium Embeddings and Vector Stores - Why embeddings capture semantic meaning - Quiz 11easy Text Splitting - RecursiveCharacterTextSplitter - Quiz 8hard Text Splitting - Token-based splitting - Quiz 9hard