1Z0-1127-25 LangChain and AI Application Development Practice Question
A developer is building a LangChain RAG pipeline with OCI Generative AI. Which TWO components are needed to create embeddings from documents and store them for retrieval?
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
✓
OCIGenAIEmbeddings
OCIGenAIEmbeddings converts text into embeddings, and a vector store (e.g., FAISS, Chroma, OracleVS) stores those embeddings for similarity search. Document loaders and text splitters are used before embedding but are not part of the embedding/storage step itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DocumentLoader
Why it's wrong here
Loads documents but not part of embedding/storage.
- ✓
OCIGenAIEmbeddings
Why this is correct
This wraps OCI's embedding models.
- ✗
ChatOCIGenAI
Why it's wrong here
Chat model, not embeddings.
- ✓
Vector store (e.g., FAISS, OracleVS)
Why this is correct
Stores embeddings for retrieval.
- ✗
TextSplitter
Why it's wrong here
Splits documents but not part of embedding/storage.
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