from __future__ import annotations

from functools import partial
from typing import TYPE_CHECKING, Literal, Optional, Union

from pydantic import BaseModel, Field

from langchain_core.prompts import (
    BasePromptTemplate,
    PromptTemplate,
    aformat_document,
    format_document,
)
from langchain_core.tools.simple import Tool

if TYPE_CHECKING:
    from langchain_core.callbacks import Callbacks
    from langchain_core.documents import Document
    from langchain_core.retrievers import BaseRetriever


class RetrieverInput(BaseModel):
    """Input to the retriever."""

    query: str = Field(description="query to look up in retriever")


def _get_relevant_documents(
    query: str,
    retriever: BaseRetriever,
    document_prompt: BasePromptTemplate,
    document_separator: str,
    callbacks: Callbacks = None,
    response_format: Literal["content", "content_and_artifact"] = "content",
) -> Union[str, tuple[str, list[Document]]]:
    docs = retriever.invoke(query, config={"callbacks": callbacks})
    content = document_separator.join(
        format_document(doc, document_prompt) for doc in docs
    )
    if response_format == "content_and_artifact":
        return (content, docs)

    return content


async def _aget_relevant_documents(
    query: str,
    retriever: BaseRetriever,
    document_prompt: BasePromptTemplate,
    document_separator: str,
    callbacks: Callbacks = None,
    response_format: Literal["content", "content_and_artifact"] = "content",
) -> Union[str, tuple[str, list[Document]]]:
    docs = await retriever.ainvoke(query, config={"callbacks": callbacks})
    content = document_separator.join(
        [await aformat_document(doc, document_prompt) for doc in docs]
    )

    if response_format == "content_and_artifact":
        return (content, docs)

    return content


def create_retriever_tool(
    retriever: BaseRetriever,
    name: str,
    description: str,
    *,
    document_prompt: Optional[BasePromptTemplate] = None,
    document_separator: str = "\n\n",
    response_format: Literal["content", "content_and_artifact"] = "content",
) -> Tool:
    """Create a tool to do retrieval of documents.

    Args:
        retriever: The retriever to use for the retrieval
        name: The name for the tool. This will be passed to the language model,
            so should be unique and somewhat descriptive.
        description: The description for the tool. This will be passed to the language
            model, so should be descriptive.
        document_prompt: The prompt to use for the document. Defaults to None.
        document_separator: The separator to use between documents. Defaults to "\n\n".
        response_format: The tool response format. If "content" then the output of
                the tool is interpreted as the contents of a ToolMessage. If
                "content_and_artifact" then the output is expected to be a two-tuple
                corresponding to the (content, artifact) of a ToolMessage (artifact
                being a list of documents in this case). Defaults to "content".

    Returns:
        Tool class to pass to an agent.
    """
    document_prompt = document_prompt or PromptTemplate.from_template("{page_content}")
    func = partial(
        _get_relevant_documents,
        retriever=retriever,
        document_prompt=document_prompt,
        document_separator=document_separator,
        response_format=response_format,
    )
    afunc = partial(
        _aget_relevant_documents,
        retriever=retriever,
        document_prompt=document_prompt,
        document_separator=document_separator,
        response_format=response_format,
    )
    return Tool(
        name=name,
        description=description,
        func=func,
        coroutine=afunc,
        args_schema=RetrieverInput,
        response_format=response_format,
    )

from __future__ import annotations

from functools import partial
from typing import Literal, Optional, Union

from pydantic import BaseModel, Field

from langchain_core.callbacks import Callbacks
from langchain_core.documents import Document
from langchain_core.prompts import (
    BasePromptTemplate,
    PromptTemplate,
    aformat_document,
    format_document,
)
from langchain_core.retrievers import BaseRetriever
from langchain_core.tools.simple import Tool


class RetrieverInput(BaseModel):
    """Input to the retriever."""

    query: str = Field(description="query to look up in retriever")


def _get_relevant_documents(
    query: str,
    retriever: BaseRetriever,
    document_prompt: BasePromptTemplate,
    document_separator: str,
    callbacks: Callbacks = None,
    response_format: Literal["content", "content_and_artifact"] = "content",
) -> Union[str, tuple[str, list[Document]]]:
    docs = retriever.invoke(query, config={"callbacks": callbacks})
    content = document_separator.join(
        format_document(doc, document_prompt) for doc in docs
    )
    if response_format == "content_and_artifact":
        return (content, docs)

    return content


async def _aget_relevant_documents(
    query: str,
    retriever: BaseRetriever,
    document_prompt: BasePromptTemplate,
    document_separator: str,
    callbacks: Callbacks = None,
    response_format: Literal["content", "content_and_artifact"] = "content",
) -> Union[str, tuple[str, list[Document]]]:
    docs = await retriever.ainvoke(query, config={"callbacks": callbacks})
    content = document_separator.join(
        [await aformat_document(doc, document_prompt) for doc in docs]
    )

    if response_format == "content_and_artifact":
        return (content, docs)

    return content


def create_retriever_tool(
    retriever: BaseRetriever,
    name: str,
    description: str,
    *,
    document_prompt: Optional[BasePromptTemplate] = None,
    document_separator: str = "\n\n",
    response_format: Literal["content", "content_and_artifact"] = "content",
) -> Tool:
    """Create a tool to do retrieval of documents.

    Args:
        retriever: The retriever to use for the retrieval
        name: The name for the tool. This will be passed to the language model,
            so should be unique and somewhat descriptive.
        description: The description for the tool. This will be passed to the language
            model, so should be descriptive.
        document_prompt: The prompt to use for the document. Defaults to None.
        document_separator: The separator to use between documents. Defaults to "\n\n".
        response_format: The tool response format. If "content" then the output of
                the tool is interpreted as the contents of a ToolMessage. If
                "content_and_artifact" then the output is expected to be a two-tuple
                corresponding to the (content, artifact) of a ToolMessage (artifact
                being a list of documents in this case). Defaults to "content".

    Returns:
        Tool class to pass to an agent.
    """
    document_prompt = document_prompt or PromptTemplate.from_template("{page_content}")
    func = partial(
        _get_relevant_documents,
        retriever=retriever,
        document_prompt=document_prompt,
        document_separator=document_separator,
        response_format=response_format,
    )
    afunc = partial(
        _aget_relevant_documents,
        retriever=retriever,
        document_prompt=document_prompt,
        document_separator=document_separator,
        response_format=response_format,
    )
    return Tool(
        name=name,
        description=description,
        func=func,
        coroutine=afunc,
        args_schema=RetrieverInput,
        response_format=response_format,
    )
