import pandas as pd
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings


class SemanticSearchAgent:
    def __init__(self, vector_store_path: str) -> None:
        self._vector_store = FAISS.load_local(vector_store_path, HuggingFaceEmbeddings(), allow_dangerous_deserialization=True)

    def run(self, query: str, limit: int = 10, score_threshold: int = 1.2) -> pd.DataFrame:
        docs_with_scores = self._vector_store.similarity_search_with_score(query, k=limit)

        results = []
        for doc, score in docs_with_scores:
            if score >= score_threshold:
                continue

            result = doc.metadata
            result["summary"] = doc.page_content
            result["score"] = score

            results.append(result)

        df = pd.DataFrame(results)
        df.rename(columns={"_id": "id", "full_text": "content"}, inplace=True)

        return df