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https://github.com/NousResearch/hermes-agent.git
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731f4fbae6
Add ProviderTransport ABC (4 abstract methods: convert_messages, convert_tools, build_kwargs, normalize_response) plus optional hooks (validate_response, extract_cache_stats, map_finish_reason). Add transport registry with lazy discovery — get_transport() auto-imports transport modules on first call. Add AnthropicTransport — delegates to existing anthropic_adapter.py functions, wired to ALL Anthropic code paths in run_agent.py: - Main normalize loop (L10775) - Main build_kwargs (L6673) - Response validation (L9366) - Finish reason mapping (L9534) - Cache stats extraction (L9827) - Truncation normalize (L9565) - Memory flush build_kwargs + normalize (L7363, L7395) - Iteration-limit summary + retry (L8465, L8498) Zero direct adapter imports remain for transport methods. Client lifecycle, streaming, auth, and credential management stay on AIAgent. 20 new tests (ABC contract, registry, AnthropicTransport methods). 359 anthropic-related tests pass (0 failures). PR 3 of the provider transport refactor.
89 lines
3 KiB
Python
89 lines
3 KiB
Python
"""Abstract base for provider transports.
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A transport owns the data path for one api_mode:
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convert_messages → convert_tools → build_kwargs → normalize_response
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It does NOT own: client construction, streaming, credential refresh,
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prompt caching, interrupt handling, or retry logic. Those stay on AIAgent.
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"""
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional
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from agent.transports.types import NormalizedResponse
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class ProviderTransport(ABC):
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"""Base class for provider-specific format conversion and normalization."""
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@property
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@abstractmethod
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def api_mode(self) -> str:
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"""The api_mode string this transport handles (e.g. 'anthropic_messages')."""
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...
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@abstractmethod
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def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
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"""Convert OpenAI-format messages to provider-native format.
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Returns provider-specific structure (e.g. (system, messages) for Anthropic,
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or the messages list unchanged for chat_completions).
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"""
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...
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@abstractmethod
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def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
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"""Convert OpenAI-format tool definitions to provider-native format.
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Returns provider-specific tool list (e.g. Anthropic input_schema format).
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"""
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...
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@abstractmethod
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def build_kwargs(
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self,
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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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**params,
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) -> Dict[str, Any]:
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"""Build the complete API call kwargs dict.
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This is the primary entry point — it typically calls convert_messages()
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and convert_tools() internally, then adds model-specific config.
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Returns a dict ready to be passed to the provider's SDK client.
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"""
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...
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@abstractmethod
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def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
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"""Normalize a raw provider response to the shared NormalizedResponse type.
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This is the only method that returns a transport-layer type.
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"""
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...
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def validate_response(self, response: Any) -> bool:
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"""Optional: check if the raw response is structurally valid.
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Returns True if valid, False if the response should be treated as invalid.
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Default implementation always returns True.
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"""
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return True
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def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
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"""Optional: extract provider-specific cache hit/creation stats.
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Returns dict with 'cached_tokens' and 'creation_tokens', or None.
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Default returns None.
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"""
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return None
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def map_finish_reason(self, raw_reason: str) -> str:
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"""Optional: map provider-specific stop reason to OpenAI equivalent.
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Default returns the raw reason unchanged. Override for providers
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with different stop reason vocabularies.
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"""
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return raw_reason
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