rename to RAGConfiguration

[github-actions[bot]]

https://github.com/nanlabs/backend-reference/blob/bb41990a7fc544c1c025201d0175696a2d84723c/examples/langgraph-sls-fastapi-rag/app/rag_graph/configuration.py#L13

"""Define the configurable parameters for the agent."""

from __future__ import annotations

from dataclasses import dataclass, field, fields
from typing import Annotated, Any, Literal, Optional, Type, TypeVar

from langchain_core.runnables import RunnableConfig, ensure_config

from . import prompts


#TODO: rename to RAGConfiguration
@dataclass(kw_only=True)
class IndexConfiguration:
    """Configuration class for indexing and retrieval operations.

    This class defines the parameters needed for configuring the indexing and
    retrieval processes, including user identification, embedding model selection,
    retriever provider choice, and search parameters.
    """

    user_id: str = field(metadata={"description": "Unique identifier for the user."})

    # add 
    embedding_model: Annotated[
        str,
        {"__template_metadata__": {"kind": "embeddings"}},
    ] = field(
        default="openai/text-embedding-3-small",
        metadata={
            "description": "Name of the embedding model to use. Must be a valid embedding model name."
        },
    )

    retriever_provider: Annotated[
        Literal["elastic", "elastic-local", "pinecone", "mongodb"],
        {"__template_metadata__": {"kind": "retriever"}},
    ] = field(
        default="pinecone",
        metadata={
            "description": "The vector store provider to use for retrieval. Options are 'elastic', 'pinecone', or 'mongodb'."
        },
    )

    search_kwargs: dict[str, Any] = field(
        default_factory=dict,
        metadata={
            "description": "Additional keyword arguments to pass to the search function of the retriever."
        },
    )

    @classmethod
    def from_runnable_config(
        cls: Type[T], config: Optional[RunnableConfig] = None
    ) -> T:
        """Create an IndexConfiguration instance from a RunnableConfig object.

        Args:
            cls (Type[T]): The class itself.
            config (Optional[RunnableConfig]): The configuration object to use.

        Returns:
            T: An instance of IndexConfiguration with the specified configuration.
        """
        config = ensure_config(config)
        configurable = config.get("configurable") or {}
        _fields = {f.name for f in fields(cls) if f.init}
        return cls(**{k: v for k, v in configurable.items() if k in _fields})


T = TypeVar("T", bound=IndexConfiguration)


@dataclass(kw_only=True)
class Configuration(IndexConfiguration):
    """The configuration for the agent."""

    response_system_prompt: str = field(
        default=prompts.RESPONSE_SYSTEM_PROMPT,
        metadata={"description": "The system prompt used for generating responses."},
    )

    response_model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
        #default="anthropic/claude-3-5-sonnet-20240620",
        default="openai/gpt-4o-mini",
        metadata={
            "description": "The language model used for generating responses. Should be in the form: provider/model-name."
        },
    )

    query_system_prompt: str = field(
        default=prompts.QUERY_SYSTEM_PROMPT,
        metadata={
            "description": "The system prompt used for processing and refining queries."
        },
    )

    query_model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
        #default="anthropic/claude-3-haiku-20240307",
        default="openai/gpt-4o-mini",
        metadata={
            "description": "The language model used for processing and refining queries. Should be in the form: provider/model-name."
        },
    )