Bird
0
0

When creating a Chroma vector store in Langchain, which argument must you provide to enable embedding storage?

easy📝 Conceptual Q2 of 15
LangChain - Embeddings and Vector Stores
When creating a Chroma vector store in Langchain, which argument must you provide to enable embedding storage?
Asimilarity_metric
Bcollection_name
Cpersist_directory
Dembedding_function
Step-by-Step Solution
Solution:

  1. Step 1: Review Chroma initialization parameters

    Chroma requires an embedding function to convert text into vectors.

  2. Step 2: Identify the essential argument

    The 'embedding_function' parameter is mandatory for vector creation.

  3. Final Answer:

    embedding_function -> Option D

  4. Quick Check:

    Without embedding_function, Chroma cannot generate vectors. [OK]
Quick Trick: embedding_function is required to create vectors [OK]
Common Mistakes:
  • Providing only collection_name without embedding_function
  • Assuming persist_directory is mandatory at init

Want More Practice?

15+ quiz questions · All difficulty levels · Free

Free Signup - Practice All Questions
More LangChain Quizzes
Conversational RAG - Memory-augmented retrieval - Quiz 5medium Document Loading - Directory loader for bulk documents - Quiz 8hard Document Loading - Directory loader for bulk documents - Quiz 10hard Embeddings and Vector Stores - OpenAI embeddings - Quiz 8hard Embeddings and Vector Stores - Metadata filtering in vector stores - Quiz 7medium RAG Chain Construction - Source citation in RAG responses - Quiz 6medium Text Splitting - Overlap and chunk boundaries - Quiz 13medium Text Splitting - Code-aware text splitting - Quiz 1easy Text Splitting - RecursiveCharacterTextSplitter - Quiz 7medium Text Splitting - Why chunk size affects retrieval quality - Quiz 4medium