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from sentence_transformers import SentenceTransformer # 📌 library
import numpy as np # 📌 library (for math)
# 📌 variable: load a free embedding model (runs locally in Codespaces)
model = SentenceTransformer('all-MiniLM-L6-v2')
def generate_embeddings(chunks: list) -> list:
"""
📌 Function: Generate embeddings for a list of text chunks using Hugging Face.
Args:
chunks (list): List of text chunks.
Returns:
list: List of embedding vectors (one per chunk).
"""
embeddings = model.encode(chunks, convert_to_numpy=True) # numpy array
return embeddings.tolist() # convert to plain Python list
# ----------------------------
# OLD OPENAI EMBEDDINGS CODE
# (kept for reference only)
# ----------------------------
# from openai import OpenAI # 📌 library + class
# client = OpenAI() # 📌 variable (needs API key in env)
# def generate_embeddings(chunks: list, model: str = "text-embedding-3-small") -> list:
# embeddings = [] # 📌 variable: holds all vectors
# for chunk in chunks:
# response = client.embeddings.create( # 📌 function (method) call
# input=chunk,
# model=model
# )
# embeddings.append(response.data[0].embedding) # vector = list of floats
# return embeddings |