1Z0-1127-25 LangChain and AI Application Development Practice Question
A developer notices that the ConversationalRetrievalChain in their LangChain application is not retaining context from previous turns in the conversation. Which component is most likely missing or misconfigured?
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
✓
A memory component like ConversationBufferMemory
ConversationalRetrievalChain requires a Memory component to store and retrieve chat history. Without Memory, the chain treats each query independently. The retriever, document splitter, and embeddings are responsible for retrieval and storage, not conversation history.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A document splitter to chunk the history
Why it's wrong here
History is not document text that needs splitting; it's a sequence of messages.
- ✗
A retriever with appropriate search parameters
Why it's wrong here
The retriever is for document retrieval, not conversation history.
- ✗
An embedding model to vectorize the history
Why it's wrong here
Conversation history is typically kept as plain text, not vectorized for retrieval.
- ✓
A memory component like ConversationBufferMemory
Why this is correct
Memory stores the conversation history and injects it into the prompt, enabling context retention.
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