llama-stack-mirror/llama_stack/distribution/routers/routers.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

import asyncio
import time
from collections.abc import AsyncGenerator, AsyncIterator
from typing import Annotated, Any

from openai.types.chat import ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam
from openai.types.chat import ChatCompletionToolParam as OpenAIChatCompletionToolParam
from pydantic import Field, TypeAdapter

from llama_stack.apis.common.content_types import (
    URL,
    InterleavedContent,
    InterleavedContentItem,
)
from llama_stack.apis.eval import BenchmarkConfig, Eval, EvaluateResponse, Job
from llama_stack.apis.inference import (
    BatchChatCompletionResponse,
    BatchCompletionResponse,
    ChatCompletionResponse,
    ChatCompletionResponseEventType,
    ChatCompletionResponseStreamChunk,
    CompletionMessage,
    EmbeddingsResponse,
    EmbeddingTaskType,
    Inference,
    ListOpenAIChatCompletionResponse,
    LogProbConfig,
    Message,
    OpenAICompletionWithInputMessages,
    Order,
    ResponseFormat,
    SamplingParams,
    StopReason,
    TextTruncation,
    ToolChoice,
    ToolConfig,
    ToolDefinition,
    ToolPromptFormat,
)
from llama_stack.apis.inference.inference import (
    OpenAIChatCompletion,
    OpenAIChatCompletionChunk,
    OpenAICompletion,
    OpenAIMessageParam,
    OpenAIResponseFormatParam,
)
from llama_stack.apis.models import Model, ModelType
from llama_stack.apis.scoring import (
    ScoreBatchResponse,
    ScoreResponse,
    Scoring,
    ScoringFnParams,
)
from llama_stack.apis.telemetry import MetricEvent, MetricInResponse, Telemetry
from llama_stack.apis.tools import (
    ListToolDefsResponse,
    RAGDocument,
    RAGQueryConfig,
    RAGQueryResult,
    RAGToolRuntime,
    ToolRuntime,
)
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
from llama_stack.log import get_logger
from llama_stack.models.llama.llama3.chat_format import ChatFormat
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
from llama_stack.providers.utils.inference.inference_store import InferenceStore
from llama_stack.providers.utils.inference.stream_utils import stream_and_store_openai_completion
from llama_stack.providers.utils.telemetry.tracing import get_current_span

logger = get_logger(name=__name__, category="core")


class VectorIORouter(VectorIO):
    """Routes to an provider based on the vector db identifier"""

    def __init__(
        self,
        routing_table: RoutingTable,
    ) -> None:
        logger.debug("Initializing VectorIORouter")
        self.routing_table = routing_table

    async def initialize(self) -> None:
        logger.debug("VectorIORouter.initialize")
        pass

    async def shutdown(self) -> None:
        logger.debug("VectorIORouter.shutdown")
        pass

    async def register_vector_db(
        self,
        vector_db_id: str,
        embedding_model: str,
        embedding_dimension: int | None = 384,
        provider_id: str | None = None,
        provider_vector_db_id: str | None = None,
    ) -> None:
        logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
        await self.routing_table.register_vector_db(
            vector_db_id,
            embedding_model,
            embedding_dimension,
            provider_id,
            provider_vector_db_id,
        )

    async def insert_chunks(
        self,
        vector_db_id: str,
        chunks: list[Chunk],
        ttl_seconds: int | None = None,
    ) -> None:
        logger.debug(
            f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}",
        )
        return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds)

    async def query_chunks(
        self,
        vector_db_id: str,
        query: InterleavedContent,
        params: dict[str, Any] | None = None,
    ) -> QueryChunksResponse:
        logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}")
        return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)


class InferenceRouter(Inference):
    """Routes to an provider based on the model"""

    def __init__(
        self,
        routing_table: RoutingTable,
        telemetry: Telemetry | None = None,
        store: InferenceStore | None = None,
    ) -> None:
        logger.debug("Initializing InferenceRouter")
        self.routing_table = routing_table
        self.telemetry = telemetry
        self.store = store
        if self.telemetry:
            self.tokenizer = Tokenizer.get_instance()
            self.formatter = ChatFormat(self.tokenizer)

    async def initialize(self) -> None:
        logger.debug("InferenceRouter.initialize")
        pass

    async def shutdown(self) -> None:
        logger.debug("InferenceRouter.shutdown")
        pass

    async def register_model(
        self,
        model_id: str,
        provider_model_id: str | None = None,
        provider_id: str | None = None,
        metadata: dict[str, Any] | None = None,
        model_type: ModelType | None = None,
    ) -> None:
        logger.debug(
            f"InferenceRouter.register_model: {model_id=} {provider_model_id=} {provider_id=} {metadata=} {model_type=}",
        )
        await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)

    def _construct_metrics(
        self,
        prompt_tokens: int,
        completion_tokens: int,
        total_tokens: int,
        model: Model,
    ) -> list[MetricEvent]:
        """Constructs a list of MetricEvent objects containing token usage metrics.

        Args:
            prompt_tokens: Number of tokens in the prompt
            completion_tokens: Number of tokens in the completion
            total_tokens: Total number of tokens used
            model: Model object containing model_id and provider_id

        Returns:
            List of MetricEvent objects with token usage metrics
        """
        span = get_current_span()
        if span is None:
            logger.warning("No span found for token usage metrics")
            return []
        metrics = [
            ("prompt_tokens", prompt_tokens),
            ("completion_tokens", completion_tokens),
            ("total_tokens", total_tokens),
        ]
        metric_events = []
        for metric_name, value in metrics:
            metric_events.append(
                MetricEvent(
                    trace_id=span.trace_id,
                    span_id=span.span_id,
                    metric=metric_name,
                    value=value,
                    timestamp=time.time(),
                    unit="tokens",
                    attributes={
                        "model_id": model.model_id,
                        "provider_id": model.provider_id,
                    },
                )
            )
        return metric_events

    async def _compute_and_log_token_usage(
        self,
        prompt_tokens: int,
        completion_tokens: int,
        total_tokens: int,
        model: Model,
    ) -> list[MetricInResponse]:
        metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model)
        if self.telemetry:
            for metric in metrics:
                await self.telemetry.log_event(metric)
        return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics]

    async def _count_tokens(
        self,
        messages: list[Message] | InterleavedContent,
        tool_prompt_format: ToolPromptFormat | None = None,
    ) -> int | None:
        if isinstance(messages, list):
            encoded = self.formatter.encode_dialog_prompt(messages, tool_prompt_format)
        else:
            encoded = self.formatter.encode_content(messages)
        return len(encoded.tokens) if encoded and encoded.tokens else 0

    async def chat_completion(
        self,
        model_id: str,
        messages: list[Message],
        sampling_params: SamplingParams | None = None,
        response_format: ResponseFormat | None = None,
        tools: list[ToolDefinition] | None = None,
        tool_choice: ToolChoice | None = None,
        tool_prompt_format: ToolPromptFormat | None = None,
        stream: bool | None = False,
        logprobs: LogProbConfig | None = None,
        tool_config: ToolConfig | None = None,
    ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
        logger.debug(
            f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
        )
        if sampling_params is None:
            sampling_params = SamplingParams()
        model = await self.routing_table.get_model(model_id)
        if model is None:
            raise ValueError(f"Model '{model_id}' not found")
        if model.model_type == ModelType.embedding:
            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
        if tool_config:
            if tool_choice and tool_choice != tool_config.tool_choice:
                raise ValueError("tool_choice and tool_config.tool_choice must match")
            if tool_prompt_format and tool_prompt_format != tool_config.tool_prompt_format:
                raise ValueError("tool_prompt_format and tool_config.tool_prompt_format must match")
        else:
            params = {}
            if tool_choice:
                params["tool_choice"] = tool_choice
            if tool_prompt_format:
                params["tool_prompt_format"] = tool_prompt_format
            tool_config = ToolConfig(**params)

        tools = tools or []
        if tool_config.tool_choice == ToolChoice.none:
            tools = []
        elif tool_config.tool_choice == ToolChoice.auto:
            pass
        elif tool_config.tool_choice == ToolChoice.required:
            pass
        else:
            # verify tool_choice is one of the tools
            tool_names = [t.tool_name if isinstance(t.tool_name, str) else t.tool_name.value for t in tools]
            if tool_config.tool_choice not in tool_names:
                raise ValueError(f"Tool choice {tool_config.tool_choice} is not one of the tools: {tool_names}")

        params = dict(
            model_id=model_id,
            messages=messages,
            sampling_params=sampling_params,
            tools=tools,
            tool_choice=tool_choice,
            tool_prompt_format=tool_prompt_format,
            response_format=response_format,
            stream=stream,
            logprobs=logprobs,
            tool_config=tool_config,
        )
        provider = self.routing_table.get_provider_impl(model_id)
        prompt_tokens = await self._count_tokens(messages, tool_config.tool_prompt_format)

        if stream:

            async def stream_generator():
                completion_text = ""
                async for chunk in await provider.chat_completion(**params):
                    if chunk.event.event_type == ChatCompletionResponseEventType.progress:
                        if chunk.event.delta.type == "text":
                            completion_text += chunk.event.delta.text
                    if chunk.event.event_type == ChatCompletionResponseEventType.complete:
                        completion_tokens = await self._count_tokens(
                            [
                                CompletionMessage(
                                    content=completion_text,
                                    stop_reason=StopReason.end_of_turn,
                                )
                            ],
                            tool_config.tool_prompt_format,
                        )
                        total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
                        metrics = await self._compute_and_log_token_usage(
                            prompt_tokens or 0,
                            completion_tokens or 0,
                            total_tokens,
                            model,
                        )
                        chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
                    yield chunk

            return stream_generator()
        else:
            response = await provider.chat_completion(**params)
            completion_tokens = await self._count_tokens(
                [response.completion_message],
                tool_config.tool_prompt_format,
            )
            total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
            metrics = await self._compute_and_log_token_usage(
                prompt_tokens or 0,
                completion_tokens or 0,
                total_tokens,
                model,
            )
            response.metrics = metrics if response.metrics is None else response.metrics + metrics
            return response

    async def batch_chat_completion(
        self,
        model_id: str,
        messages_batch: list[list[Message]],
        tools: list[ToolDefinition] | None = None,
        tool_config: ToolConfig | None = None,
        sampling_params: SamplingParams | None = None,
        response_format: ResponseFormat | None = None,
        logprobs: LogProbConfig | None = None,
    ) -> BatchChatCompletionResponse:
        logger.debug(
            f"InferenceRouter.batch_chat_completion: {model_id=}, {len(messages_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
        )
        provider = self.routing_table.get_provider_impl(model_id)
        return await provider.batch_chat_completion(
            model_id=model_id,
            messages_batch=messages_batch,
            tools=tools,
            tool_config=tool_config,
            sampling_params=sampling_params,
            response_format=response_format,
            logprobs=logprobs,
        )

    async def completion(
        self,
        model_id: str,
        content: InterleavedContent,
        sampling_params: SamplingParams | None = None,
        response_format: ResponseFormat | None = None,
        stream: bool | None = False,
        logprobs: LogProbConfig | None = None,
    ) -> AsyncGenerator:
        if sampling_params is None:
            sampling_params = SamplingParams()
        logger.debug(
            f"InferenceRouter.completion: {model_id=}, {stream=}, {content=}, {sampling_params=}, {response_format=}",
        )
        model = await self.routing_table.get_model(model_id)
        if model is None:
            raise ValueError(f"Model '{model_id}' not found")
        if model.model_type == ModelType.embedding:
            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
        provider = self.routing_table.get_provider_impl(model_id)
        params = dict(
            model_id=model_id,
            content=content,
            sampling_params=sampling_params,
            response_format=response_format,
            stream=stream,
            logprobs=logprobs,
        )

        prompt_tokens = await self._count_tokens(content)

        if stream:

            async def stream_generator():
                completion_text = ""
                async for chunk in await provider.completion(**params):
                    if hasattr(chunk, "delta"):
                        completion_text += chunk.delta
                    if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry:
                        completion_tokens = await self._count_tokens(completion_text)
                        total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
                        metrics = await self._compute_and_log_token_usage(
                            prompt_tokens or 0,
                            completion_tokens or 0,
                            total_tokens,
                            model,
                        )
                        chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
                    yield chunk

            return stream_generator()
        else:
            response = await provider.completion(**params)
            completion_tokens = await self._count_tokens(response.content)
            total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
            metrics = await self._compute_and_log_token_usage(
                prompt_tokens or 0,
                completion_tokens or 0,
                total_tokens,
                model,
            )
            response.metrics = metrics if response.metrics is None else response.metrics + metrics
            return response

    async def batch_completion(
        self,
        model_id: str,
        content_batch: list[InterleavedContent],
        sampling_params: SamplingParams | None = None,
        response_format: ResponseFormat | None = None,
        logprobs: LogProbConfig | None = None,
    ) -> BatchCompletionResponse:
        logger.debug(
            f"InferenceRouter.batch_completion: {model_id=}, {len(content_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
        )
        provider = self.routing_table.get_provider_impl(model_id)
        return await provider.batch_completion(model_id, content_batch, sampling_params, response_format, logprobs)

    async def embeddings(
        self,
        model_id: str,
        contents: list[str] | list[InterleavedContentItem],
        text_truncation: TextTruncation | None = TextTruncation.none,
        output_dimension: int | None = None,
        task_type: EmbeddingTaskType | None = None,
    ) -> EmbeddingsResponse:
        logger.debug(f"InferenceRouter.embeddings: {model_id}")
        model = await self.routing_table.get_model(model_id)
        if model is None:
            raise ValueError(f"Model '{model_id}' not found")
        if model.model_type == ModelType.llm:
            raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
        return await self.routing_table.get_provider_impl(model_id).embeddings(
            model_id=model_id,
            contents=contents,
            text_truncation=text_truncation,
            output_dimension=output_dimension,
            task_type=task_type,
        )

    async def openai_completion(
        self,
        model: str,
        prompt: str | list[str] | list[int] | list[list[int]],
        best_of: int | None = None,
        echo: bool | None = None,
        frequency_penalty: float | None = None,
        logit_bias: dict[str, float] | None = None,
        logprobs: bool | None = None,
        max_tokens: int | None = None,
        n: int | None = None,
        presence_penalty: float | None = None,
        seed: int | None = None,
        stop: str | list[str] | None = None,
        stream: bool | None = None,
        stream_options: dict[str, Any] | None = None,
        temperature: float | None = None,
        top_p: float | None = None,
        user: str | None = None,
        guided_choice: list[str] | None = None,
        prompt_logprobs: int | None = None,
    ) -> OpenAICompletion:
        logger.debug(
            f"InferenceRouter.openai_completion: {model=}, {stream=}, {prompt=}",
        )
        model_obj = await self.routing_table.get_model(model)
        if model_obj is None:
            raise ValueError(f"Model '{model}' not found")
        if model_obj.model_type == ModelType.embedding:
            raise ValueError(f"Model '{model}' is an embedding model and does not support completions")

        params = dict(
            model=model_obj.identifier,
            prompt=prompt,
            best_of=best_of,
            echo=echo,
            frequency_penalty=frequency_penalty,
            logit_bias=logit_bias,
            logprobs=logprobs,
            max_tokens=max_tokens,
            n=n,
            presence_penalty=presence_penalty,
            seed=seed,
            stop=stop,
            stream=stream,
            stream_options=stream_options,
            temperature=temperature,
            top_p=top_p,
            user=user,
            guided_choice=guided_choice,
            prompt_logprobs=prompt_logprobs,
        )

        provider = self.routing_table.get_provider_impl(model_obj.identifier)
        return await provider.openai_completion(**params)

    async def openai_chat_completion(
        self,
        model: str,
        messages: Annotated[list[OpenAIMessageParam], Field(..., min_length=1)],
        frequency_penalty: float | None = None,
        function_call: str | dict[str, Any] | None = None,
        functions: list[dict[str, Any]] | None = None,
        logit_bias: dict[str, float] | None = None,
        logprobs: bool | None = None,
        max_completion_tokens: int | None = None,
        max_tokens: int | None = None,
        n: int | None = None,
        parallel_tool_calls: bool | None = None,
        presence_penalty: float | None = None,
        response_format: OpenAIResponseFormatParam | None = None,
        seed: int | None = None,
        stop: str | list[str] | None = None,
        stream: bool | None = None,
        stream_options: dict[str, Any] | None = None,
        temperature: float | None = None,
        tool_choice: str | dict[str, Any] | None = None,
        tools: list[dict[str, Any]] | None = None,
        top_logprobs: int | None = None,
        top_p: float | None = None,
        user: str | None = None,
    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
        logger.debug(
            f"InferenceRouter.openai_chat_completion: {model=}, {stream=}, {messages=}",
        )
        model_obj = await self.routing_table.get_model(model)
        if model_obj is None:
            raise ValueError(f"Model '{model}' not found")
        if model_obj.model_type == ModelType.embedding:
            raise ValueError(f"Model '{model}' is an embedding model and does not support chat completions")

        # Use the OpenAI client for a bit of extra input validation without
        # exposing the OpenAI client itself as part of our API surface
        if tool_choice:
            TypeAdapter(OpenAIChatCompletionToolChoiceOptionParam).validate_python(tool_choice)
            if tools is None:
                raise ValueError("'tool_choice' is only allowed when 'tools' is also provided")
        if tools:
            for tool in tools:
                TypeAdapter(OpenAIChatCompletionToolParam).validate_python(tool)

        # Some providers make tool calls even when tool_choice is "none"
        # so just clear them both out to avoid unexpected tool calls
        if tool_choice == "none" and tools is not None:
            tool_choice = None
            tools = None

        params = dict(
            model=model_obj.identifier,
            messages=messages,
            frequency_penalty=frequency_penalty,
            function_call=function_call,
            functions=functions,
            logit_bias=logit_bias,
            logprobs=logprobs,
            max_completion_tokens=max_completion_tokens,
            max_tokens=max_tokens,
            n=n,
            parallel_tool_calls=parallel_tool_calls,
            presence_penalty=presence_penalty,
            response_format=response_format,
            seed=seed,
            stop=stop,
            stream=stream,
            stream_options=stream_options,
            temperature=temperature,
            tool_choice=tool_choice,
            tools=tools,
            top_logprobs=top_logprobs,
            top_p=top_p,
            user=user,
        )

        provider = self.routing_table.get_provider_impl(model_obj.identifier)
        if stream:
            response_stream = await provider.openai_chat_completion(**params)
            if self.store:
                return stream_and_store_openai_completion(response_stream, model, self.store, messages)
            return response_stream
        else:
            response = await self._nonstream_openai_chat_completion(provider, params)
            if self.store:
                await self.store.store_chat_completion(response, messages)
            return response

    async def list_chat_completions(
        self,
        after: str | None = None,
        limit: int | None = 20,
        model: str | None = None,
        order: Order | None = Order.desc,
    ) -> ListOpenAIChatCompletionResponse:
        if self.store:
            return await self.store.list_chat_completions(after, limit, model, order)
        raise NotImplementedError("List chat completions is not supported: inference store is not configured.")

    async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages:
        if self.store:
            return await self.store.get_chat_completion(completion_id)
        raise NotImplementedError("Get chat completion is not supported: inference store is not configured.")

    async def _nonstream_openai_chat_completion(self, provider: Inference, params: dict) -> OpenAIChatCompletion:
        response = await provider.openai_chat_completion(**params)
        for choice in response.choices:
            # some providers return an empty list for no tool calls in non-streaming responses
            # but the OpenAI API returns None. So, set tool_calls to None if it's empty
            if choice.message and choice.message.tool_calls is not None and len(choice.message.tool_calls) == 0:
                choice.message.tool_calls = None
        return response

    async def health(self) -> dict[str, HealthResponse]:
        health_statuses = {}
        timeout = 0.5
        for provider_id, impl in self.routing_table.impls_by_provider_id.items():
            try:
                # check if the provider has a health method
                if not hasattr(impl, "health"):
                    continue
                health = await asyncio.wait_for(impl.health(), timeout=timeout)
                health_statuses[provider_id] = health
            except (asyncio.TimeoutError, TimeoutError):
                health_statuses[provider_id] = HealthResponse(
                    status=HealthStatus.ERROR,
                    message=f"Health check timed out after {timeout} seconds",
                )
            except NotImplementedError:
                health_statuses[provider_id] = HealthResponse(status=HealthStatus.NOT_IMPLEMENTED)
            except Exception as e:
                health_statuses[provider_id] = HealthResponse(
                    status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}"
                )
        return health_statuses


class ScoringRouter(Scoring):
    def __init__(
        self,
        routing_table: RoutingTable,
    ) -> None:
        logger.debug("Initializing ScoringRouter")
        self.routing_table = routing_table

    async def initialize(self) -> None:
        logger.debug("ScoringRouter.initialize")
        pass

    async def shutdown(self) -> None:
        logger.debug("ScoringRouter.shutdown")
        pass

    async def score_batch(
        self,
        dataset_id: str,
        scoring_functions: dict[str, ScoringFnParams | None] = None,
        save_results_dataset: bool = False,
    ) -> ScoreBatchResponse:
        logger.debug(f"ScoringRouter.score_batch: {dataset_id}")
        res = {}
        for fn_identifier in scoring_functions.keys():
            score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch(
                dataset_id=dataset_id,
                scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
            )
            res.update(score_response.results)

        if save_results_dataset:
            raise NotImplementedError("Save results dataset not implemented yet")

        return ScoreBatchResponse(
            results=res,
        )

    async def score(
        self,
        input_rows: list[dict[str, Any]],
        scoring_functions: dict[str, ScoringFnParams | None] = None,
    ) -> ScoreResponse:
        logger.debug(f"ScoringRouter.score: {len(input_rows)} rows, {len(scoring_functions)} functions")
        res = {}
        # look up and map each scoring function to its provider impl
        for fn_identifier in scoring_functions.keys():
            score_response = await self.routing_table.get_provider_impl(fn_identifier).score(
                input_rows=input_rows,
                scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
            )
            res.update(score_response.results)

        return ScoreResponse(results=res)


class EvalRouter(Eval):
    def __init__(
        self,
        routing_table: RoutingTable,
    ) -> None:
        logger.debug("Initializing EvalRouter")
        self.routing_table = routing_table

    async def initialize(self) -> None:
        logger.debug("EvalRouter.initialize")
        pass

    async def shutdown(self) -> None:
        logger.debug("EvalRouter.shutdown")
        pass

    async def run_eval(
        self,
        benchmark_id: str,
        benchmark_config: BenchmarkConfig,
    ) -> Job:
        logger.debug(f"EvalRouter.run_eval: {benchmark_id}")
        return await self.routing_table.get_provider_impl(benchmark_id).run_eval(
            benchmark_id=benchmark_id,
            benchmark_config=benchmark_config,
        )

    async def evaluate_rows(
        self,
        benchmark_id: str,
        input_rows: list[dict[str, Any]],
        scoring_functions: list[str],
        benchmark_config: BenchmarkConfig,
    ) -> EvaluateResponse:
        logger.debug(f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows")
        return await self.routing_table.get_provider_impl(benchmark_id).evaluate_rows(
            benchmark_id=benchmark_id,
            input_rows=input_rows,
            scoring_functions=scoring_functions,
            benchmark_config=benchmark_config,
        )

    async def job_status(
        self,
        benchmark_id: str,
        job_id: str,
    ) -> Job:
        logger.debug(f"EvalRouter.job_status: {benchmark_id}, {job_id}")
        return await self.routing_table.get_provider_impl(benchmark_id).job_status(benchmark_id, job_id)

    async def job_cancel(
        self,
        benchmark_id: str,
        job_id: str,
    ) -> None:
        logger.debug(f"EvalRouter.job_cancel: {benchmark_id}, {job_id}")
        await self.routing_table.get_provider_impl(benchmark_id).job_cancel(
            benchmark_id,
            job_id,
        )

    async def job_result(
        self,
        benchmark_id: str,
        job_id: str,
    ) -> EvaluateResponse:
        logger.debug(f"EvalRouter.job_result: {benchmark_id}, {job_id}")
        return await self.routing_table.get_provider_impl(benchmark_id).job_result(
            benchmark_id,
            job_id,
        )


class ToolRuntimeRouter(ToolRuntime):
    class RagToolImpl(RAGToolRuntime):
        def __init__(
            self,
            routing_table: RoutingTable,
        ) -> None:
            logger.debug("Initializing ToolRuntimeRouter.RagToolImpl")
            self.routing_table = routing_table

        async def query(
            self,
            content: InterleavedContent,
            vector_db_ids: list[str],
            query_config: RAGQueryConfig | None = None,
        ) -> RAGQueryResult:
            logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}")
            return await self.routing_table.get_provider_impl("knowledge_search").query(
                content, vector_db_ids, query_config
            )

        async def insert(
            self,
            documents: list[RAGDocument],
            vector_db_id: str,
            chunk_size_in_tokens: int = 512,
        ) -> None:
            logger.debug(
                f"ToolRuntimeRouter.RagToolImpl.insert: {vector_db_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}"
            )
            return await self.routing_table.get_provider_impl("insert_into_memory").insert(
                documents, vector_db_id, chunk_size_in_tokens
            )

    def __init__(
        self,
        routing_table: RoutingTable,
    ) -> None:
        logger.debug("Initializing ToolRuntimeRouter")
        self.routing_table = routing_table

        # HACK ALERT this should be in sync with "get_all_api_endpoints()"
        self.rag_tool = self.RagToolImpl(routing_table)
        for method in ("query", "insert"):
            setattr(self, f"rag_tool.{method}", getattr(self.rag_tool, method))

    async def initialize(self) -> None:
        logger.debug("ToolRuntimeRouter.initialize")
        pass

    async def shutdown(self) -> None:
        logger.debug("ToolRuntimeRouter.shutdown")
        pass

    async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> Any:
        logger.debug(f"ToolRuntimeRouter.invoke_tool: {tool_name}")
        return await self.routing_table.get_provider_impl(tool_name).invoke_tool(
            tool_name=tool_name,
            kwargs=kwargs,
        )

    async def list_runtime_tools(
        self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None
    ) -> ListToolDefsResponse:
        logger.debug(f"ToolRuntimeRouter.list_runtime_tools: {tool_group_id}")
        return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint)