Create embeddings.py

src/embeddings.py

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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