deep-searcher/deepsearcher/agent/rag_router.py

from typing import List, Tuple

from deepsearcher.agent import RAGAgent
from deepsearcher.vector_db import RetrievalResult

RAG_ROUTER_PROMPT = """Given a list of agent indexes and corresponding descriptions, each agent has a specific function. 
Given a query, select only one agent that best matches the agent handling the query, and return the index without any other information.

## Question
{query}

## Agent Indexes and Descriptions
{description_str}

Only return one agent index number that best matches the agent handling the query:
"""


class RAGRouter(RAGAgent):
    """
    Routes queries to the most appropriate RAG agent implementation.

    This class analyzes the content and requirements of a query and determines
    which RAG agent implementation is best suited to handle it.
    """

    def __init__(
        self,
        agent: RAGAgent
    ):
        """
        Initialize the RAGRouter.

        Args:
            llm: The language model to use for analyzing queries.
            rag_agents: A list of RAGAgent instances.
            agent_descriptions (list, optional): A list of descriptions for each agent.
        """
        self.agent = agent

    def retrieve(self, query: str, **kwargs) -> Tuple[List[RetrievalResult], int, dict]:
        retrieved_results, n_token_retrieval, metadata = self.agent.retrieve(query, **kwargs)
        return retrieved_results, n_token_retrieval, metadata

    def query(self, query: str, **kwargs) -> Tuple[str, List[RetrievalResult], int]:
        answer, retrieved_results, n_token_retrieval = self.agent.query(query, **kwargs)
        return answer, retrieved_results, n_token_retrieval