mirror of
https://github.com/openswarm-ai/openswarm.git
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ciregenz
ebea25f0c2
- PostHog integration: collector, analytics subapp, opt-in UI, Analytics page - 9Router: auto-start, OAuth subscription flow, /v1/messages Anthropic format support - Settings overhaul: multi-provider API keys, subscription connect UI, onboarding modal - Unified usage: merge 9Router cost/token data into Settings Usage tab - Provider system: providers/, agent_loop, tools/ (unused, for future non-Anthropic support) - Agent SDK: restored as primary with 9Router ANTHROPIC_BASE_URL fallback - Updated system prompt, credential resolution, dashboard analytics
136 lines
3.7 KiB
Python
136 lines
3.7 KiB
Python
"""Provider-agnostic base classes for multi-model support.
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All provider adapters (Anthropic, OpenAI, Gemini, OpenAI-compatible)
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implement BaseProvider, translating their native APIs into these
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common data structures.
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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, AsyncIterator
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@dataclass
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class ToolSchema:
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"""Provider-agnostic tool definition."""
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name: str
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description: str
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input_schema: dict[str, Any]
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@dataclass
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class ToolCall:
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"""A tool invocation requested by the model."""
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id: str
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name: str
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input: dict[str, Any]
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@dataclass
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class ContentBlock:
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"""A block of content from the model response."""
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type: str # "text" | "tool_use"
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text: str = ""
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tool_call: ToolCall | None = None
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@dataclass
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class ModelResponse:
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"""Complete (non-streaming) response from a provider."""
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content: list[ContentBlock]
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stop_reason: str # "end_turn" | "tool_use" | "max_tokens"
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usage: dict[str, int] = field(default_factory=dict)
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@dataclass
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class StreamEvent:
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"""A single streaming event, normalized across providers.
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The event types match what the frontend already expects via WebSocket:
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content_block_start, content_block_delta, content_block_stop, message_stop.
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"""
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type: str
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index: int = 0
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block_type: str = "" # "text" | "tool_use"
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delta_type: str = "" # "text_delta" | "input_json_delta"
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text: str = ""
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tool_name: str = ""
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tool_id: str = ""
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usage: dict[str, int] = field(default_factory=dict)
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@dataclass
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class ProviderMessage:
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"""Provider-agnostic message for conversation history.
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Each provider adapter converts these to/from its native format.
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"""
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role: str # "user" | "assistant" | "tool_result"
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content: Any # str, list[dict], or provider-specific content
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class BaseProvider(ABC):
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"""Abstract base for LLM provider adapters."""
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@abstractmethod
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async def stream_message(
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self,
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model: str,
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system: str | None,
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messages: list[ProviderMessage],
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tools: list[ToolSchema],
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max_tokens: int = 8192,
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) -> AsyncIterator[StreamEvent]:
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"""Stream a model response, yielding normalized StreamEvents."""
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...
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@abstractmethod
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async def create_message(
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self,
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model: str,
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system: str | None,
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messages: list[ProviderMessage],
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tools: list[ToolSchema],
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max_tokens: int = 8192,
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) -> ModelResponse:
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"""Non-streaming message creation."""
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...
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@abstractmethod
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def format_tool_result(
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self,
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tool_use_id: str,
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content: list[dict],
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) -> dict:
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"""Format a tool result in this provider's expected message format."""
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...
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@abstractmethod
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def format_user_message(self, content: Any) -> ProviderMessage:
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"""Wrap user content (str or multimodal blocks) into a ProviderMessage."""
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...
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@abstractmethod
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def format_assistant_message(self, response: ModelResponse) -> ProviderMessage:
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"""Convert a ModelResponse into a ProviderMessage for conversation history."""
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...
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@abstractmethod
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def get_model_id(self, short_name: str) -> str:
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"""Resolve a short model name to the full API model ID."""
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...
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def clean_tool_schema(self, schema: ToolSchema) -> dict:
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"""Convert a ToolSchema to the provider's native tool format.
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Default: Anthropic-style format. Override for providers that need
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different formats or schema cleaning (e.g. Gemini).
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"""
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return {
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"name": schema.name,
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"description": schema.description,
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"input_schema": schema.input_schema,
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}
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