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Given the code snippet:

medium📝 component behavior Q13 of 15
LangChain - Embeddings and Vector Stores
Given the code snippet:
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

texts = ["apple", "banana", "cherry"]
embeddings = OpenAIEmbeddings()
faiss_store = FAISS.from_texts(texts, embeddings)
results = faiss_store.similarity_search("apple", k=1)
print(results)

What will be printed?
AA runtime error due to missing API key
B["banana"]
CAn empty list []
D["apple"]
Step-by-Step Solution
Solution:

  1. Step 1: Understand similarity_search behavior

    The method searches for the closest vector to the query "apple" and returns the top 1 similar text.

  2. Step 2: Check input texts and query

    Since "apple" is in the texts, it will be the closest match and returned in the results list.

  3. Step 3: Consider runtime environment

    However, OpenAIEmbeddings requires an API key to function. Without it, a runtime error will occur.

  4. Final Answer:

    A runtime error due to missing API key -> Option A

  5. Quick Check:

    OpenAIEmbeddings needs API key; missing key causes error [OK]
Quick Trick: Search returns closest text; query matches exact text [OK]
Common Mistakes:
  • Assuming it returns unrelated items like banana
  • Expecting empty list if query matches
  • Ignoring possibility of runtime errors from setup

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