from langchain.vectorstores import VectorStore
from typing import Iterable, List, Any
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.embeddings.fake import FakeEmbeddings as FakeEmbeddingsBase
from langchain.chat_models.fake import FakeListChatModel
from typing import Optional


class FakeChatModel(FakeListChatModel):
    def __init__(self, **kwargs):
        responses = ["The answer is 42. SOURCES: 1, 2, 3, 4"]
        super().__init__(responses=responses, **kwargs)


class FakeEmbeddings(FakeEmbeddingsBase):
    def __init__(self, **kwargs):
        super().__init__(size=4, **kwargs)


class FakeVectorStore(VectorStore):
    """Fake vector store for testing purposes."""

    def __init__(self, texts: List[str]):
        self.texts: List[str] = texts

    def add_texts(
        self, texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any
    ) -> List[str]:
        self.texts.extend(texts)
        return self.texts

    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> "FakeVectorStore":
        return cls(texts=list(texts))

    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        return [
            Document(page_content=text, metadata={"source": f"{i+1}-{1}"})
            for i, text in enumerate(self.texts)
        ]