embedder.py

from sentence_transformers import SentenceTransformer

# Load the model once when this file is first imported
# This happens only one time — not every time we call the functions
model = SentenceTransformer("all-MiniLM-L6-v2")


def embed_chunks(chunks):
    """
    Converts all chunk texts into vectors.

    Input  : chunks → list of chunk dicts from chunk_text()
    Output : same list but each chunk now has an "embedding" field added
    """

    print("Embedding chunks... this may take a minute.")

    # Extract just the text from each chunk into a simple list
    texts = [chunk["text"] for chunk in chunks]

    # Convert all texts to vectors in one batch (faster than one by one)
    # show_progress_bar=True shows a progress bar so we know it is working
    embeddings = model.encode(texts, show_progress_bar=True)

    # Attach each embedding back to its chunk dict
    for i, chunk in enumerate(chunks):
        chunk["embedding"] = embeddings[i]

    print(f"Done! Each chunk is now a vector of {len(embeddings[0])} numbers.")
    return chunks


def embed_query(query):
    """
    Converts a user question into a vector.

    Input  : query → a plain string question from the user
    Output : a single vector (list of 384 numbers)

    We use this at search time to find which chunks are most
    similar to what the user is asking.
    """

    return model.encode([query])[0]  # encode returns a list, [0] gets first item