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You have a list of documents and want to find which document is most similar to a query using OpenAI embeddings in Langchain. Which sequence of steps correctly achieves this?

hard📝 Conceptual Q8 of 15
LangChain - Embeddings and Vector Stores
You have a list of documents and want to find which document is most similar to a query using OpenAI embeddings in Langchain. Which sequence of steps correctly achieves this?
AUse embed_query on documents and embed_documents on the query, then compare vectors.
BEmbed the query only, then compare it directly to raw document texts.
CEmbed all documents, embed the query, then compute cosine similarity between query and each document embedding.
DEmbed documents and query, then use string matching on the embeddings.
Step-by-Step Solution
Solution:

  1. Step 1: Embed documents

    Use embed_documents to convert all documents into vectors.

  2. Step 2: Embed query

    Use embed_query to convert the query into a vector.

  3. Step 3: Compute similarity

    Calculate cosine similarity between the query vector and each document vector.

  4. Step 4: Select most similar

    The document with highest similarity score is the most relevant.

  5. Final Answer:

    Embed all documents, embed the query, then compute cosine similarity between query and each document embedding. -> Option C

  6. Quick Check:

    Similarity requires vector comparison, not raw text. [OK]
Quick Trick: Embed docs and query, then compare vectors [OK]
Common Mistakes:
  • Comparing query embedding to raw text
  • Mixing embed_query and embed_documents incorrectly
  • Using string matching on embeddings

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