| 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 |