from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

model = SentenceTransformer(
    "sentence-transformers/all-MiniLM-L6-v2"
)


def retrieve_context(query, papers):

    try:

        texts = [
            p["abstract"]
            for p in papers
            if p["abstract"]
        ]

        if len(texts) == 0:
            return ""

        embeddings = model.encode(
            texts
        )

        dimension = embeddings.shape[1]

        index = faiss.IndexFlatL2(
            dimension
        )

        index.add(
            np.array(embeddings).astype(
                "float32"
            )
        )

        query_embedding = model.encode(
            [query]
        )

        _, indices = index.search(
            np.array(query_embedding).astype(
                "float32"
            ),
            2
        )

        retrieved = [
            texts[i]
            for i in indices[0]
        ]

        return "\n".join(retrieved)

    except Exception as e:

        print("RAG Error:", e)

        return ""