mirror of
https://github.com/andrewyng/openworker.git
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Rohit C PrasadandDevika
2b45018ffa
Imported from andrewyng/aisuite@1b4bbf303e (contents of its platform/ directory, hoisted to the repo root). Development history prior to this commit lives in that repository. Co-authored-by: Devika <devikaverma11@gmail.com>
94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
"""Provider-agnostic model access layer.
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The runtime never imports a provider SDK directly — it talks to a `ProviderClient`.
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v1 ships `OpenAIProvider` (OpenAI SDK, `chat.completions` only); an `AISuiteProvider`
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slots in later (P12) without touching the engine, since aisuite is OpenAI-API-shaped.
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"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any, Optional
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@dataclass
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class ToolCall:
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"""A single tool call requested by the model, with parsed arguments."""
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id: str
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name: str
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arguments: dict[str, Any] = field(default_factory=dict)
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@dataclass
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class AssistantTurn:
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"""One assistant response: free text and/or a set of tool calls."""
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text: Optional[str] = None
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tool_calls: list[ToolCall] = field(default_factory=list)
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finish_reason: Optional[str] = None
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raw: Any = field(default=None, repr=False, compare=False)
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@property
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def has_tool_calls(self) -> bool:
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return bool(self.tool_calls)
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@dataclass(frozen=True)
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class ModelCapabilities:
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"""What a given model/provider can do; used for graceful degradation."""
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tools: bool = True
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vision: bool = False
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# Native PDF ingestion (OpenAI `file` part / Anthropic document / Gemini inline_data).
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# Models without it get a local fallback: text extraction or page images (pdf_support.py).
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pdf: bool = False
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parallel_tool_calls: bool = True
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streaming: bool = True
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@dataclass
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class StreamChunk:
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"""One streamed piece: a text delta, and/or (on the final chunk) the full turn."""
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text_delta: Optional[str] = None
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turn: Optional[AssistantTurn] = None
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class ProviderClient(ABC):
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"""Single-shot, provider-agnostic completion interface.
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Deliberately blocking (the turn engine wraps it in `asyncio.to_thread`) and
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deliberately without a `max_turns` loop — the runtime owns the agent loop.
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"""
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@abstractmethod
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def complete(
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self,
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*,
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model: str,
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messages: list[dict[str, Any]],
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tools: Optional[list[dict[str, Any]]] = None,
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**settings: Any,
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) -> AssistantTurn:
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"""Return one assistant turn for the given messages/tools."""
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@abstractmethod
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def capabilities(self, model: str) -> ModelCapabilities:
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"""Return capability flags for the given model."""
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def stream(
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self,
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*,
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model: str,
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messages: list[dict[str, Any]],
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tools: Optional[list[dict[str, Any]]] = None,
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**settings: Any,
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):
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"""Yield StreamChunks. Default: no token streaming — one final chunk with the
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full turn. Providers that support streaming (OpenAIProvider) override this."""
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yield StreamChunk(
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turn=self.complete(model=model, messages=messages, tools=tools, **settings)
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)
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