1Z0-1127-25 LangChain and AI Application Development Practice Question
A developer is building a RAG pipeline using LangChain and Oracle AI Vector Search. After loading and splitting PDF documents, they generate embeddings and store them in Oracle Database using OracleVS. Which method should they call on the vector store object to create a retriever that uses similarity search with a configurable number of results?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
as_retriever()
The as_retriever() method on a vector store returns a retriever object that can be configured with search_kwargs like 'k'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
as_retriever()
Why this is correct
as_retriever() creates a retriever that uses the vector store's search method.
- ✗
from_texts()
Why it's wrong here
from_texts() is used to create a vector store from texts, not to create a retriever.
- ✗
max_marginal_relevance_search()
Why it's wrong here
This is a search method, not a retriever factory.
- ✗
similarity_search()
Why it's wrong here
similarity_search() returns the documents directly, not a retriever object.
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