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https://github.com/meta-llama/llama-stack.git
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c9ab72fa82
# What does this PR do? The current default system prompt for llama3.2 tends to overindex on tool calling and doesn't work well when the prompt does not require tool calling. This PR adds an option to override the default system prompt, and organizes tool-related configs into a new config object. - [ ] Addresses issue (#issue) ## Test Plan python -m unittest llama_stack.providers.tests.inference.test_prompt_adapter ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests. --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/937). * #938 * __->__ #937
454 lines
14 KiB
Python
454 lines
14 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any, AsyncGenerator, Dict, List, Optional
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from llama_stack.apis.common.content_types import InterleavedContent, URL
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from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
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from llama_stack.apis.eval import (
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AppEvalTaskConfig,
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Eval,
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EvalTaskConfig,
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EvaluateResponse,
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Job,
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JobStatus,
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)
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from llama_stack.apis.inference import (
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EmbeddingsResponse,
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Inference,
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LogProbConfig,
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Message,
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ResponseFormat,
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SamplingParams,
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ToolChoice,
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ToolConfig,
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ToolDefinition,
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ToolPromptFormat,
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)
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from llama_stack.apis.models import ModelType
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from llama_stack.apis.safety import RunShieldResponse, Safety
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from llama_stack.apis.scoring import (
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ScoreBatchResponse,
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ScoreResponse,
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Scoring,
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ScoringFnParams,
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)
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from llama_stack.apis.shields import Shield
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from llama_stack.apis.tools import (
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RAGDocument,
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RAGQueryConfig,
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RAGQueryResult,
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RAGToolRuntime,
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ToolDef,
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ToolRuntime,
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)
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from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
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from llama_stack.providers.datatypes import RoutingTable
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class VectorIORouter(VectorIO):
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"""Routes to an provider based on the vector db identifier"""
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def register_vector_db(
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self,
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vector_db_id: str,
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embedding_model: str,
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embedding_dimension: Optional[int] = 384,
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provider_id: Optional[str] = None,
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provider_vector_db_id: Optional[str] = None,
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) -> None:
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await self.routing_table.register_vector_db(
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vector_db_id,
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embedding_model,
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embedding_dimension,
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provider_id,
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provider_vector_db_id,
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)
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async def insert_chunks(
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self,
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vector_db_id: str,
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chunks: List[Chunk],
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ttl_seconds: Optional[int] = None,
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) -> None:
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return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds)
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async def query_chunks(
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self,
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vector_db_id: str,
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query: InterleavedContent,
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params: Optional[Dict[str, Any]] = None,
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) -> QueryChunksResponse:
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return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
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class InferenceRouter(Inference):
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"""Routes to an provider based on the model"""
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def register_model(
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self,
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model_id: str,
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provider_model_id: Optional[str] = None,
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provider_id: Optional[str] = None,
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metadata: Optional[Dict[str, Any]] = None,
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model_type: Optional[ModelType] = None,
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) -> None:
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await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)
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async def chat_completion(
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self,
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model_id: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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response_format: Optional[ResponseFormat] = None,
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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tool_config: Optional[ToolConfig] = None,
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) -> AsyncGenerator:
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model = await self.routing_table.get_model(model_id)
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if model is None:
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raise ValueError(f"Model '{model_id}' not found")
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if model.model_type == ModelType.embedding:
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raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
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if tool_config:
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if tool_choice != tool_config.tool_choice:
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raise ValueError("tool_choice and tool_config.tool_choice must match")
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if tool_prompt_format != tool_config.tool_prompt_format:
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raise ValueError("tool_prompt_format and tool_config.tool_prompt_format must match")
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else:
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tool_config = ToolConfig(
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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)
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params = dict(
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model_id=model_id,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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response_format=response_format,
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stream=stream,
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logprobs=logprobs,
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tool_config=tool_config,
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)
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provider = self.routing_table.get_provider_impl(model_id)
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if stream:
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return (chunk async for chunk in await provider.chat_completion(**params))
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else:
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return await provider.chat_completion(**params)
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async def completion(
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self,
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model_id: str,
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content: InterleavedContent,
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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model = await self.routing_table.get_model(model_id)
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if model is None:
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raise ValueError(f"Model '{model_id}' not found")
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if model.model_type == ModelType.embedding:
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raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
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provider = self.routing_table.get_provider_impl(model_id)
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params = dict(
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model_id=model_id,
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content=content,
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sampling_params=sampling_params,
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response_format=response_format,
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stream=stream,
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logprobs=logprobs,
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)
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if stream:
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return (chunk async for chunk in await provider.completion(**params))
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else:
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return await provider.completion(**params)
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async def embeddings(
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self,
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model_id: str,
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contents: List[InterleavedContent],
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) -> EmbeddingsResponse:
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model = await self.routing_table.get_model(model_id)
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if model is None:
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raise ValueError(f"Model '{model_id}' not found")
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if model.model_type == ModelType.llm:
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raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
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return await self.routing_table.get_provider_impl(model_id).embeddings(
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model_id=model_id,
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contents=contents,
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)
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class SafetyRouter(Safety):
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def register_shield(
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self,
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shield_id: str,
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provider_shield_id: Optional[str] = None,
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provider_id: Optional[str] = None,
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params: Optional[Dict[str, Any]] = None,
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) -> Shield:
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return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params)
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async def run_shield(
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self,
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shield_id: str,
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messages: List[Message],
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params: Dict[str, Any] = None,
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) -> RunShieldResponse:
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return await self.routing_table.get_provider_impl(shield_id).run_shield(
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shield_id=shield_id,
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messages=messages,
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params=params,
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)
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class DatasetIORouter(DatasetIO):
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def get_rows_paginated(
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self,
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dataset_id: str,
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rows_in_page: int,
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page_token: Optional[str] = None,
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filter_condition: Optional[str] = None,
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) -> PaginatedRowsResult:
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return await self.routing_table.get_provider_impl(dataset_id).get_rows_paginated(
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dataset_id=dataset_id,
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rows_in_page=rows_in_page,
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page_token=page_token,
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filter_condition=filter_condition,
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)
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async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None:
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return await self.routing_table.get_provider_impl(dataset_id).append_rows(
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dataset_id=dataset_id,
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rows=rows,
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)
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class ScoringRouter(Scoring):
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def score_batch(
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self,
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dataset_id: str,
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scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
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save_results_dataset: bool = False,
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) -> ScoreBatchResponse:
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res = {}
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for fn_identifier in scoring_functions.keys():
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score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch(
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dataset_id=dataset_id,
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scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
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)
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res.update(score_response.results)
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if save_results_dataset:
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raise NotImplementedError("Save results dataset not implemented yet")
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return ScoreBatchResponse(
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results=res,
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)
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async def score(
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self,
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input_rows: List[Dict[str, Any]],
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scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
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) -> ScoreResponse:
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res = {}
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# look up and map each scoring function to its provider impl
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for fn_identifier in scoring_functions.keys():
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score_response = await self.routing_table.get_provider_impl(fn_identifier).score(
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input_rows=input_rows,
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scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
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)
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res.update(score_response.results)
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return ScoreResponse(results=res)
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class EvalRouter(Eval):
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def run_eval(
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self,
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task_id: str,
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task_config: AppEvalTaskConfig,
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) -> Job:
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return await self.routing_table.get_provider_impl(task_id).run_eval(
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task_id=task_id,
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task_config=task_config,
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)
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async def evaluate_rows(
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self,
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task_id: str,
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input_rows: List[Dict[str, Any]],
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scoring_functions: List[str],
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task_config: EvalTaskConfig,
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) -> EvaluateResponse:
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return await self.routing_table.get_provider_impl(task_id).evaluate_rows(
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task_id=task_id,
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input_rows=input_rows,
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scoring_functions=scoring_functions,
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task_config=task_config,
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)
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async def job_status(
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self,
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task_id: str,
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job_id: str,
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) -> Optional[JobStatus]:
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return await self.routing_table.get_provider_impl(task_id).job_status(task_id, job_id)
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async def job_cancel(
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self,
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task_id: str,
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job_id: str,
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) -> None:
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await self.routing_table.get_provider_impl(task_id).job_cancel(
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task_id,
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job_id,
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)
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async def job_result(
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self,
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task_id: str,
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job_id: str,
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) -> EvaluateResponse:
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return await self.routing_table.get_provider_impl(task_id).job_result(
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task_id,
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job_id,
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)
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class ToolRuntimeRouter(ToolRuntime):
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class RagToolImpl(RAGToolRuntime):
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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async def query(
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self,
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content: InterleavedContent,
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vector_db_ids: List[str],
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query_config: Optional[RAGQueryConfig] = None,
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) -> RAGQueryResult:
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return await self.routing_table.get_provider_impl("query_from_memory").query(
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content, vector_db_ids, query_config
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)
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async def insert(
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self,
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documents: List[RAGDocument],
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vector_db_id: str,
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chunk_size_in_tokens: int = 512,
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) -> None:
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return await self.routing_table.get_provider_impl("insert_into_memory").insert(
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documents, vector_db_id, chunk_size_in_tokens
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)
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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self.routing_table = routing_table
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# HACK ALERT this should be in sync with "get_all_api_endpoints()"
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self.rag_tool = self.RagToolImpl(routing_table)
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for method in ("query", "insert"):
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setattr(self, f"rag_tool.{method}", getattr(self.rag_tool, method))
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> Any:
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return await self.routing_table.get_provider_impl(tool_name).invoke_tool(
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tool_name=tool_name,
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kwargs=kwargs,
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)
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async def list_runtime_tools(
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self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None
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) -> List[ToolDef]:
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return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint)
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