mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-07-28 15:02:37 +00:00
kill older junk
This commit is contained in:
Ashwin Bharambe
parent
95781ec85d
commit
f94efcf2ee
11 changed files with 0 additions and 6898 deletions
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@ -1,98 +0,0 @@
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Any, Dict, List, Optional, Set, Union
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from model_types import (
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BuiltinTool,
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Content,
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InstructModel,
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Message,
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PretrainedModel,
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SamplingParams,
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SafetyViolation,
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StopReason,
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ToolCall,
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ToolDefinition,
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ToolResponse,
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)
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from strong_typing.schema import json_schema_type
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class ExecutionStepType(Enum):
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"""The type of execution step."""
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model_inference = "model_inference"
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tool_execution = "tool_execution"
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safety_filtering = "safety_filtering"
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memory_retrieval = "memory_retrieval"
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@dataclass
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class ExecutionStepBase:
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"""An agentic system turn can consist of one or more such execution steps."""
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step_type: ExecutionStepType
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uuid: str
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@dataclass
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class ModelInferenceStep(ExecutionStepBase):
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step_type = ExecutionStepType.model_inference
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text: str
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logprobs: Optional[Dict[str, Any]] = None
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@dataclass
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class ToolExecutionStep(ExecutionStepBase):
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step_type = ExecutionStepType.tool_execution
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# we could be calling multiple tools in a single step (in parallel)
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tool_calls: List[ToolCall]
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tool_responses: List[ToolResponse]
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@dataclass
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class SafetyFilteringStep(ExecutionStepBase):
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step_type = ExecutionStepType.safety_filtering
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violation: Optional[SafetyViolation] = None
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@json_schema_type
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@dataclass
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class MemoryBank:
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uuid: str
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name: str
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@dataclass
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class MemoryBankDocument:
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uuid: str
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content: bytes
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metadata: Dict[str, Any]
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mime_type: str
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@dataclass
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class MemoryRetrievalStep(ExecutionStepBase):
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step_type = ExecutionStepType.memory_retrieval
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documents: List[MemoryBankDocument]
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scores: List[float]
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ExecutionStep = Union[
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ModelInferenceStep,
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ToolExecutionStep,
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SafetyFilteringStep,
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MemoryRetrievalStep,
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]
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@json_schema_type
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@dataclass
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class AgenticSystemTurn:
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"""A single turn in an interaction with an Agentic System."""
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user_messages: List[Message]
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steps: List[ExecutionStep]
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response_message: Message
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@ -1,563 +0,0 @@
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from dataclasses import dataclass, field
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from datetime import datetime
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from enum import Enum
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from typing import Any, Dict, List, Optional, Protocol, Set, Tuple, Union
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import yaml
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from agentic_system_types import (
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AgenticSystemTurn,
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ExecutionStepType,
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MemoryBank,
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MemoryBankDocument,
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SafetyViolation,
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)
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from model_types import (
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BuiltinTool,
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Content,
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Dialog,
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InstructModel,
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Message,
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PretrainedModel,
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RewardModel,
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SamplingParams,
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ShieldConfig,
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StopReason,
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ToolCall,
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ToolDefinition,
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ToolResponse,
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URL,
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)
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from post_training_types import (
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Checkpoint,
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Dataset,
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DoraFinetuningConfig,
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DPOAlignmentConfig,
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FinetuningAlgorithm,
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LoraFinetuningConfig,
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OptimizerConfig,
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PostTrainingJobLogStream,
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PostTrainingJobStatus,
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QLoraFinetuningConfig,
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RLHFAlgorithm,
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TrainingConfig,
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)
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from pyopenapi import Info, Options, Server, Specification, webmethod
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from strong_typing.schema import json_schema_type
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@json_schema_type
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@dataclass
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class CompletionRequest:
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content: Content
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model: PretrainedModel
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sampling_params: SamplingParams = SamplingParams()
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max_tokens: int = 0
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stream: bool = False
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logprobs: bool = False
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@json_schema_type
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@dataclass
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class CompletionResponse:
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"""Normal completion response."""
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content: Content
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stop_reason: Optional[StopReason] = None
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logprobs: Optional[Dict[str, Any]] = None
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@json_schema_type
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@dataclass
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class CompletionResponseStreamChunk:
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"""streamed completion response."""
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text_delta: str
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stop_reason: Optional[StopReason] = None
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logprobs: Optional[Dict[str, Any]] = None
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@json_schema_type
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@dataclass
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class ChatCompletionRequest:
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model: InstructModel
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dialog: Dialog
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sampling_params: SamplingParams = SamplingParams()
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# zero-shot tool definitions as input to the model
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available_tools: List[ToolDefinition] = field(default_factory=list)
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max_tokens: int = 0
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stream: bool = False
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logprobs: bool = False
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@json_schema_type
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@dataclass
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class ChatCompletionResponse:
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"""Normal chat completion response."""
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content: Content
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# note: multiple tool calls can be generated in a single response
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tool_calls: List[ToolCall] = field(default_factory=list)
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stop_reason: Optional[StopReason] = None
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logprobs: Optional[Dict[str, Any]] = None
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@json_schema_type
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@dataclass
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class ChatCompletionResponseStreamChunk:
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"""Streamed chat completion response. The actual response is a series of such objects."""
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text_delta: str
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stop_reason: Optional[StopReason] = None
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tool_call: Optional[ToolCall] = None
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@json_schema_type
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@dataclass
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class BatchCompletionRequest:
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model: PretrainedModel
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content_batch: List[Content]
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sampling_params: SamplingParams = SamplingParams()
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max_tokens: int = 0
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logprobs: bool = False
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@json_schema_type
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@dataclass
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class BatchChatCompletionRequest:
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model: InstructModel
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batch_dialogs: List[Dialog]
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sampling_params: SamplingParams = SamplingParams()
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# zero-shot tool definitions as input to the model
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available_tools: List[ToolDefinition] = field(default_factory=list)
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max_tokens: int = 0
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logprobs: bool = False
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class Inference(Protocol):
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@webmethod(route="/inference/completion")
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def post_completion(
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self,
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request: CompletionRequest,
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) -> Union[CompletionResponse, CompletionResponseStreamChunk]: ...
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@webmethod(route="/inference/chat_completion")
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def post_chat_completion(
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self,
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request: ChatCompletionRequest,
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) -> Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]: ...
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@webmethod(route="/inference/batch_completion")
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def post_batch_completion(
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self,
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request: BatchCompletionRequest,
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) -> List[CompletionResponse]: ...
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@webmethod(route="/inference/batch_chat_completion")
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def post_batch_chat_completion(
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self,
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request: BatchChatCompletionRequest,
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) -> List[ChatCompletionResponse]: ...
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@dataclass
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class AgenticSystemCreateRequest:
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uuid: str
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instructions: str
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model: InstructModel
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# zero-shot or built-in tool configurations as input to the model
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available_tools: List[ToolDefinition] = field(default_factory=list)
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# tools which aren't executable are emitted as tool calls which the users can
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# execute themselves.
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executable_tools: Set[str] = field(default_factory=set)
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memory_bank_uuids: List[str] = field(default_factory=list)
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input_shields: List[ShieldConfig] = field(default_factory=list)
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output_shields: List[ShieldConfig] = field(default_factory=list)
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@json_schema_type
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@dataclass
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class AgenticSystemCreateResponse:
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agent_uuid: str
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@json_schema_type
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@dataclass
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class AgenticSystemExecuteRequest:
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agent_uuid: str
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messages: List[Message]
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turn_history: List[AgenticSystemTurn] = None
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stream: bool = False
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@json_schema_type
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@dataclass
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class AgenticSystemExecuteResponse:
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"""non-stream response from the agentic system."""
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turn: AgenticSystemTurn
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class AgenticSystemExecuteResponseEventType(Enum):
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"""The type of event."""
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step_start = "step_start"
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step_end = "step_end"
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step_progress = "step_progress"
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@json_schema_type
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@dataclass
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class AgenticSystemExecuteResponseStreamChunk:
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"""Streamed agent execution response."""
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event_type: AgenticSystemExecuteResponseEventType
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step_uuid: str
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step_type: ExecutionStepType
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# TODO(ashwin): maybe add more structure here and do this as a proper tagged union
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violation: Optional[SafetyViolation] = None
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tool_call: Optional[ToolCall] = None
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tool_response_delta: Optional[ToolResponse] = None
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response_text_delta: Optional[str] = None
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retrieved_document: Optional[MemoryBankDocument] = None
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stop_reason: Optional[StopReason] = None
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class AgenticSystem(Protocol):
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@webmethod(route="/agentic_system/create")
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def create_agentic_system(
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self,
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request: AgenticSystemCreateRequest,
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) -> AgenticSystemCreateResponse: ...
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@webmethod(route="/agentic_system/execute")
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def create_agentic_system_execute(
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self,
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request: AgenticSystemExecuteRequest,
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) -> Union[
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AgenticSystemExecuteResponse, AgenticSystemExecuteResponseStreamChunk
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]: ...
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@webmethod(route="/agentic_system/delete")
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def delete_agentic_system(
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self,
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agent_id: str,
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) -> None: ...
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class MemoryBanks(Protocol):
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@webmethod(route="/memory_banks/create")
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def post_create_memory_bank(
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self,
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bank_uuid: str,
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bank_name: str,
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documents: List[MemoryBankDocument],
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) -> None: ...
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@webmethod(route="/memory_banks/get")
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def get_memory_banks(
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self
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) -> List[MemoryBank]: ...
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@webmethod(route="/memory_banks/drop")
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def delete_memory_bank(
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self,
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bank_uuid: str,
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) -> str: ...
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@webmethod(route="/memory_bank/insert")
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def post_insert_memory_documents(
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self,
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bank_uuid: str,
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documents: List[MemoryBankDocument],
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) -> None: ...
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@webmethod(route="/memory_bank/update")
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def post_update_memory_documents(
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self,
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bank_uuid: str,
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documents: List[MemoryBankDocument],
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) -> None: ...
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@webmethod(route="/memory_bank/get")
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def get_memory_documents(
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self,
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bank_uuid: str,
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document_uuids: List[str],
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) -> List[MemoryBankDocument]: ...
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@webmethod(route="/memory_bank/delete")
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def delete_memory_documents(
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self,
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bank_uuid: str,
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document_uuids: List[str],
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) -> List[str]: ...
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@dataclass
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class KPromptGenerations:
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dialog: Dialog
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k_generations: List[Message]
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@json_schema_type
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@dataclass
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class ScoredMessage:
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message: Message
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score: float
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@json_schema_type
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@dataclass
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class KScoredPromptGenerations:
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prompt: Message
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k_scored_generations: List[ScoredMessage]
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@json_schema_type
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@dataclass
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class RewardScoringRequest:
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"""Request to score a reward function. A list of prompts and a list of responses per prompt."""
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prompt_generations: List[KPromptGenerations]
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model: RewardModel
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@json_schema_type
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@dataclass
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class RewardScoringResponse:
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"""Response from the reward scoring. Batch of (prompt, response, score) tuples that pass the threshold."""
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scored_generations: List[KScoredPromptGenerations]
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class RewardScoring(Protocol):
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@webmethod(route="/reward_scoring/score")
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def post_score(
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self,
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request: RewardScoringRequest,
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) -> Union[RewardScoringResponse]: ...
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|
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|
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class FilteringFunction(Enum):
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"""The type of filtering function."""
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none = "none"
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random = "random"
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top_k = "top_k"
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top_p = "top_p"
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top_k_top_p = "top_k_top_p"
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sigmoid = "sigmoid"
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|
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|
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@json_schema_type
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@dataclass
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class SyntheticDataGenerationRequest:
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"""Request to generate synthetic data. A small batch of prompts and a filtering function"""
|
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|
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prompts: List[Message]
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filtering_function: FilteringFunction = FilteringFunction.none
|
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reward_scoring: Optional[RewardScoring] = None
|
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|
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|
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@json_schema_type
|
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@dataclass
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class SyntheticDataGenerationResponse:
|
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"""Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold."""
|
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|
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synthetic_data: List[KScoredPromptGenerations]
|
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statistics: Optional[Dict[str, Any]] = None
|
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|
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|
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class SyntheticDataGeneration(Protocol):
|
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@webmethod(route="/synthetic_data_generation/generate")
|
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def post_generate(
|
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self,
|
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request: SyntheticDataGenerationRequest,
|
||||
) -> Union[SyntheticDataGenerationResponse]: ...
|
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|
||||
|
||||
@json_schema_type
|
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@dataclass
|
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class CreateDatasetRequest:
|
||||
"""Request to create a dataset."""
|
||||
|
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uuid: str
|
||||
dataset: Dataset
|
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|
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|
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class Datasets(Protocol):
|
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@webmethod(route="/datasets/create")
|
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def create_dataset(
|
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self,
|
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request: CreateDatasetRequest,
|
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) -> None: ...
|
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|
||||
@webmethod(route="/datasets/get")
|
||||
def get_dataset(
|
||||
self,
|
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dataset_id: str,
|
||||
) -> Dataset: ...
|
||||
|
||||
@webmethod(route="/datasets/delete")
|
||||
def delete_dataset(
|
||||
self,
|
||||
dataset_id: str,
|
||||
) -> None: ...
|
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|
||||
|
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@json_schema_type
|
||||
@dataclass
|
||||
class PostTrainingSFTRequest:
|
||||
"""Request to finetune a model."""
|
||||
|
||||
job_uuid: str
|
||||
|
||||
model: PretrainedModel
|
||||
dataset: Dataset
|
||||
validation_dataset: Dataset
|
||||
|
||||
algorithm: FinetuningAlgorithm
|
||||
algorithm_config: Union[
|
||||
LoraFinetuningConfig, QLoraFinetuningConfig, DoraFinetuningConfig
|
||||
]
|
||||
|
||||
optimizer_config: OptimizerConfig
|
||||
training_config: TrainingConfig
|
||||
|
||||
# TODO: define these
|
||||
hyperparam_search_config: Dict[str, Any]
|
||||
logger_config: Dict[str, Any]
|
||||
|
||||
|
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@json_schema_type
|
||||
@dataclass
|
||||
class PostTrainingRLHFRequest:
|
||||
"""Request to finetune a model."""
|
||||
|
||||
job_uuid: str
|
||||
|
||||
finetuned_model: URL
|
||||
|
||||
dataset: Dataset
|
||||
validation_dataset: Dataset
|
||||
|
||||
algorithm: RLHFAlgorithm
|
||||
algorithm_config: Union[DPOAlignmentConfig]
|
||||
|
||||
optimizer_config: OptimizerConfig
|
||||
training_config: TrainingConfig
|
||||
|
||||
# TODO: define these
|
||||
hyperparam_search_config: Dict[str, Any]
|
||||
logger_config: Dict[str, Any]
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class PostTrainingJobStatusResponse:
|
||||
"""Status of a finetuning job."""
|
||||
|
||||
job_uuid: str
|
||||
status: PostTrainingJobStatus
|
||||
|
||||
scheduled_at: Optional[datetime] = None
|
||||
started_at: Optional[datetime] = None
|
||||
completed_at: Optional[datetime] = None
|
||||
|
||||
resources_allocated: Optional[Dict[str, Any]] = None
|
||||
|
||||
checkpoints: List[Checkpoint] = field(default_factory=list)
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class PostTrainingJobArtifactsResponse:
|
||||
"""Artifacts of a finetuning job."""
|
||||
|
||||
job_uuid: str
|
||||
checkpoints: List[Checkpoint] = field(default_factory=list)
|
||||
|
||||
# TODO(ashwin): metrics, evals
|
||||
|
||||
|
||||
class PostTraining(Protocol):
|
||||
@webmethod(route="/post_training/supervised_fine_tune/")
|
||||
def post_supervised_fine_tune(
|
||||
self,
|
||||
request: PostTrainingSFTRequest,
|
||||
) -> None: ...
|
||||
|
||||
@webmethod(route="/post_training/preference_optimize/")
|
||||
def post_preference_optimize(
|
||||
self,
|
||||
request: PostTrainingRLHFRequest,
|
||||
) -> None: ...
|
||||
|
||||
# sends SSE stream of logs
|
||||
@webmethod(route="/post_training/job/logs")
|
||||
def get_training_log_stream(self, job_uuid: str) -> PostTrainingJobLogStream: ...
|
||||
|
||||
@webmethod(route="/post_training/job/status")
|
||||
def get_training_job_status(
|
||||
self, job_uuid: str
|
||||
) -> PostTrainingJobStatusResponse: ...
|
||||
|
||||
@webmethod(route="/post_training/job/cancel")
|
||||
def cancel_training_job(self, job_uuid: str) -> None: ...
|
||||
|
||||
@webmethod(route="/post_training/job/artifacts")
|
||||
def get_training_job_artifacts(
|
||||
self, job_uuid: str
|
||||
) -> PostTrainingJobArtifactsResponse: ...
|
||||
|
||||
|
||||
class LlamaStackEndpoints(
|
||||
Inference,
|
||||
AgenticSystem,
|
||||
RewardScoring,
|
||||
SyntheticDataGeneration,
|
||||
Datasets,
|
||||
PostTraining,
|
||||
MemoryBanks,
|
||||
): ...
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Converting the spec to YAML (openapi.yaml) and HTML (openapi.html)")
|
||||
spec = Specification(
|
||||
LlamaStackEndpoints,
|
||||
Options(
|
||||
server=Server(url="http://any-hosted-llama-stack.com"),
|
||||
info=Info(
|
||||
title="[DRAFT] Llama Stack Specification",
|
||||
version="0.0.1",
|
||||
description="""This is the specification of the llama stack that provides
|
||||
a set of endpoints and their corresponding interfaces that are tailored to
|
||||
best leverage Llama Models. The specification is still in draft and subject to change.""",
|
||||
),
|
||||
),
|
||||
)
|
||||
with open("openapi.yaml", "w", encoding="utf-8") as fp:
|
||||
yaml.dump(spec.get_json(), fp, allow_unicode=True)
|
||||
|
||||
with open("openapi.html", "w") as fp:
|
||||
spec.write_html(fp, pretty_print=True)
|
||||
|
|
@ -1,59 +0,0 @@
|
|||
import requests
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from typing import List, Set, Optional, Union, Protocol
|
||||
from enum import Enum
|
||||
|
||||
import json
|
||||
|
||||
from model_types import *
|
||||
from agentic_system_types import *
|
||||
from api_definitions import *
|
||||
|
||||
class EnumEncoder(json.JSONEncoder):
|
||||
def default(self, obj):
|
||||
if isinstance(obj, Enum):
|
||||
return obj.value
|
||||
elif isinstance(obj, set):
|
||||
return list(obj)
|
||||
return json.JSONEncoder.default(self, obj)
|
||||
|
||||
|
||||
class AgenticSystemClient:
|
||||
def __init__(self, base_url: str):
|
||||
self.base_url = base_url
|
||||
|
||||
def create_agentic_system(self, request: AgenticSystemCreateRequest) -> AgenticSystemCreateResponse:
|
||||
response = requests.post(f"{self.base_url}/agentic_system/create", data=json.dumps(asdict(request), cls=EnumEncoder), headers={'Content-Type': 'application/json'})
|
||||
response.raise_for_status()
|
||||
return AgenticSystemCreateResponse(**response.json())
|
||||
|
||||
def execute_agentic_system(self, request: AgenticSystemExecuteRequest) -> Union[AgenticSystemExecuteResponse, AgenticSystemExecuteResponseStreamChunk]:
|
||||
response = requests.post(f"{self.base_url}/agentic_system/execute", data=json.dumps(asdict(request), cls=EnumEncoder), headers={'Content-Type': 'application/json'})
|
||||
response.raise_for_status()
|
||||
response_json = response.json()
|
||||
if 'turn' in response_json:
|
||||
return AgenticSystemExecuteResponse(**response_json)
|
||||
else:
|
||||
return AgenticSystemExecuteResponseStreamChunk(**response_json)
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
client = AgenticSystemClient("http://localhost:5000")
|
||||
|
||||
# Create a new agentic system
|
||||
create_request = AgenticSystemCreateRequest(
|
||||
instructions="Your instructions here",
|
||||
model=InstructModel.llama3_8b_chat,
|
||||
)
|
||||
create_response = client.create_agentic_system(create_request)
|
||||
print("Agent ID:", create_response.agent_id)
|
||||
|
||||
# Execute the agentic system
|
||||
execute_request = AgenticSystemExecuteRequest(
|
||||
agent_id=create_response.agent_id,
|
||||
messages=[Message(role="user", content="Tell me a joke")],
|
||||
turn_history=[],
|
||||
stream=False
|
||||
)
|
||||
execute_response = client.execute_agentic_system(execute_request)
|
||||
print("Execute Response:", execute_response)
|
||||
Binary file not shown.
|
|
@ -1,14 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
set -euo pipefail
|
||||
set -x
|
||||
|
||||
export JAVA_HOME=/usr/local/java-runtime/impl/11
|
||||
|
||||
$JAVA_HOME/bin/java -jar codegen/openapi-generator-cli.jar \
|
||||
generate \
|
||||
-i openapi.yaml \
|
||||
-g python-flask \
|
||||
-o /tmp/foo \
|
||||
--log-to-stderr \
|
||||
--global-property debugModels,debugOperations,debugOpenAPI,debugSupportingFiles
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
PYTHONPATH=. python3 api_definitions.py
|
||||
|
|
@ -1,149 +0,0 @@
|
|||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, Set, Union
|
||||
|
||||
from strong_typing.schema import json_schema_type
|
||||
|
||||
|
||||
class ShieldType(Enum):
|
||||
"""The type of safety shield."""
|
||||
|
||||
llama_guard = "llama_guard"
|
||||
prompt_guard = "prompt_guard"
|
||||
code_guard = "code_guard"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class ShieldConfig:
|
||||
shield_type: ShieldType
|
||||
params: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
@dataclass
|
||||
class SafetyViolation:
|
||||
violation_type: str
|
||||
details: str
|
||||
suggested_user_response: Optional[str] = None
|
||||
|
||||
|
||||
@json_schema_type(
|
||||
schema={"type": "string", "format": "uri", "pattern": "^(https?://|file://|data:)"}
|
||||
)
|
||||
@dataclass
|
||||
class URL:
|
||||
url: str
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.url
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class Attachment:
|
||||
"""
|
||||
Attachments are used to refer to external resources, such as images, videos, audio, etc.
|
||||
|
||||
"""
|
||||
|
||||
url: URL
|
||||
mime_type: str
|
||||
|
||||
# TODO(ashwin): make this better named maybe InterleavedTextMedia
|
||||
Content = Union[
|
||||
str,
|
||||
Attachment,
|
||||
List[Union[str, Attachment]],
|
||||
]
|
||||
|
||||
|
||||
class Role(Enum):
|
||||
system = "system"
|
||||
user = "user"
|
||||
assistant = "assistant"
|
||||
tool = "tool"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolCall:
|
||||
"""
|
||||
A tool call is a request to a tool.
|
||||
"""
|
||||
|
||||
tool_name: str
|
||||
arguments: Dict[str, Any]
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolResponse:
|
||||
tool_name: str
|
||||
content: Content
|
||||
|
||||
|
||||
# TODO: we need to document the parameters for the tool calls
|
||||
class BuiltinTool(Enum):
|
||||
web_search = "web_search"
|
||||
math = "math"
|
||||
image_gen = "image_gen"
|
||||
code_interpreter = "code_interpreter"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolDefinition:
|
||||
tool_name: Union[BuiltinTool, str]
|
||||
parameters: Optional[Dict[str, Any]] = None
|
||||
input_shields: List[ShieldConfig] = field(default_factory=list)
|
||||
output_shields: List[ShieldConfig] = field(default_factory=list)
|
||||
|
||||
|
||||
class StopReason(Enum):
|
||||
"""
|
||||
Stop reasons are used to indicate why the model stopped generating text.
|
||||
"""
|
||||
|
||||
not_stopped = "not_stopped"
|
||||
finished_ok = "finished_ok"
|
||||
max_tokens = "max_tokens"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class Message:
|
||||
role: Role
|
||||
|
||||
# input to the model or output from the model
|
||||
content: Content
|
||||
|
||||
# output from the model
|
||||
tool_calls: List[ToolCall] = field(default_factory=list)
|
||||
|
||||
# input to the model
|
||||
tool_responses: List[ToolResponse] = field(default_factory=list)
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class Dialog:
|
||||
message: Message
|
||||
message_history: List[Message] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class SamplingParams:
|
||||
temperature: float = 0.0
|
||||
strategy: str = "greedy"
|
||||
top_p: float = 0.95
|
||||
top_k: int = 0
|
||||
|
||||
|
||||
class PretrainedModel(Enum):
|
||||
llama3_8b = "llama3_8b"
|
||||
llama3_70b = "llama3_70b"
|
||||
|
||||
|
||||
class InstructModel(Enum):
|
||||
llama3_8b_chat = "llama3_8b_chat"
|
||||
llama3_70b_chat = "llama3_70b_chat"
|
||||
|
||||
class RewardModel(Enum):
|
||||
llama3_405b_reward = "llama3_405b_reward"
|
||||
3597
source/openapi.html
3597
source/openapi.html
File diff suppressed because it is too large
Load diff
2249
source/openapi.yaml
2249
source/openapi.yaml
File diff suppressed because it is too large
Load diff
|
|
@ -1,119 +0,0 @@
|
|||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, Set, Union
|
||||
|
||||
from model_types import Message, URL
|
||||
|
||||
from strong_typing.schema import json_schema_type
|
||||
|
||||
|
||||
class DatasetColumnType(Enum):
|
||||
dialog = "dialog"
|
||||
text = "text"
|
||||
media = "media"
|
||||
number = "number"
|
||||
json = "json"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class Dataset:
|
||||
"""Dataset to be used for training or evaluating language models."""
|
||||
|
||||
# TODO(ashwin): figure out if we need to add an enum for a "dataset type"
|
||||
|
||||
columns: Dict[str, DatasetColumnType]
|
||||
content_url: URL
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
class OptimizerType(Enum):
|
||||
adam = "adam"
|
||||
adamw = "adamw"
|
||||
sgd = "sgd"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class OptimizerConfig:
|
||||
optimizer_type: OptimizerType
|
||||
lr: float
|
||||
lr_min: float
|
||||
weight_decay: float
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class TrainingConfig:
|
||||
n_epochs: int
|
||||
batch_size: int
|
||||
shuffle: bool
|
||||
n_iters: int
|
||||
|
||||
enable_activation_checkpointing: bool
|
||||
memory_efficient_fsdp_wrap: bool
|
||||
fsdp_cpu_offload: bool
|
||||
|
||||
|
||||
class FinetuningAlgorithm(Enum):
|
||||
full = "full"
|
||||
lora = "lora"
|
||||
qlora = "qlora"
|
||||
dora = "dora"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class LoraFinetuningConfig:
|
||||
lora_attn_modules: List[str]
|
||||
apply_lora_to_mlp: bool
|
||||
apply_lora_to_output: bool
|
||||
rank: int
|
||||
alpha: int
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class QLoraFinetuningConfig(LoraFinetuningConfig):
|
||||
pass
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class DoraFinetuningConfig(LoraFinetuningConfig):
|
||||
pass
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class PostTrainingJobLogStream:
|
||||
"""Stream of logs from a finetuning job."""
|
||||
|
||||
job_uuid: str
|
||||
log_lines: List[str]
|
||||
|
||||
|
||||
class PostTrainingJobStatus(Enum):
|
||||
running = "running"
|
||||
completed = "completed"
|
||||
failed = "failed"
|
||||
scheduled = "scheduled"
|
||||
|
||||
|
||||
@dataclass
|
||||
class Checkpoint:
|
||||
iters: int
|
||||
path: URL
|
||||
|
||||
|
||||
class RLHFAlgorithm(Enum):
|
||||
dpo = "dpo"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
@dataclass
|
||||
class DPOAlignmentConfig:
|
||||
reward_scale: float
|
||||
reward_clip: float
|
||||
epsilon: float
|
||||
gamma: float
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
from flask import Flask, request, jsonify
|
||||
from dataclasses import dataclass, field
|
||||
from typing import List, Set, Optional, Union, Protocol
|
||||
from enum import Enum
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
from model_types import *
|
||||
from agentic_system_types import *
|
||||
from api_definitions import *
|
||||
|
||||
class AgenticSystemImpl(AgenticSystem):
|
||||
def create_agentic_system(self, request: AgenticSystemCreateRequest) -> AgenticSystemCreateResponse:
|
||||
# Mock implementation
|
||||
return AgenticSystemCreateResponse(agent_id="12345")
|
||||
|
||||
def create_agentic_system_execute(self, request: AgenticSystemExecuteRequest) -> Union[AgenticSystemExecuteResponse, AgenticSystemExecuteResponseStreamChunk]:
|
||||
# Mock implementation
|
||||
return AgenticSystemExecuteResponse(
|
||||
turn=AgenticSystemTurn(
|
||||
user_messages=[],
|
||||
steps=[],
|
||||
response_message=Message(
|
||||
role="assistant",
|
||||
content="Hello, I am an agent. I can help you with your tasks. What can I help you with?",
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
agentic_system = AgenticSystemImpl()
|
||||
|
||||
@app.route("/agentic_system/create", methods=["POST"])
|
||||
def create_agentic_system():
|
||||
data = request.json
|
||||
create_request = AgenticSystemCreateRequest(**data)
|
||||
response = agentic_system.create_agentic_system(create_request)
|
||||
return jsonify(response)
|
||||
|
||||
@app.route("/agentic_system/execute", methods=["POST"])
|
||||
def create_agentic_system_execute():
|
||||
data = request.json
|
||||
execute_request = AgenticSystemExecuteRequest(**data)
|
||||
response = agentic_system.create_agentic_system_execute(execute_request)
|
||||
return jsonify(response)
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(debug=True)
|
||||
Loading…
Add table
Add a link
Reference in a new issue