from langchain_core.retrievers import BaseRetriever
from langchain_core.documents.base import Document
from langchain_core.vectorstores import VectorStore
from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun

from typing import List

class GraphRetriever(BaseRetriever):
    vectorstore:VectorStore
    sources:list = ["OWID"] # plus tard ajouter OurWorldInData # faudra integrate avec l'autre retriever
    threshold:float = 0.5
    k_total:int = 10

    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:

        # Check if all elements in the list are IEA or OWID
        assert isinstance(self.sources,list)
        assert self.sources
        assert any([x in ["OWID"] for x in self.sources])

        # Prepare base search kwargs
        filters = {}

        filters["source"] = {"$in": self.sources}

        docs = self.vectorstore.similarity_search_with_score(query=query, filter=filters, k=self.k_total)
        
        # Filter if scores are below threshold
        docs = [x for x in docs if x[1] > self.threshold]

        # Remove duplicate documents
        unique_docs = []
        seen_docs = []
        for i, doc in enumerate(docs):
            if doc[0].page_content not in seen_docs:
                unique_docs.append(doc)
                seen_docs.append(doc[0].page_content)

        # Add score to metadata
        results = []
        for i,(doc,score) in enumerate(unique_docs):
            doc.metadata["similarity_score"] = score
            doc.metadata["content"] = doc.page_content
            results.append(doc)

        return results