45 lines
1.9 KiB
Python
45 lines
1.9 KiB
Python
"""
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adapters/llm/anthropic.py
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Anthropic Claude adapter — Phase 2 stub.
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TODO (Phase 2):
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- Implement complete() using the anthropic SDK (anthropic.Anthropic client).
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- Implement resolve_model() by reading config/team.yaml capability_map.
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- Handle streaming responses, rate-limit retries, and token counting.
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- Support system-prompt injection via context["system_prompt"].
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- Map capability → model using the provider's capability_map config.
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"""
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from __future__ import annotations
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from adapters.base.llm import LLMAdapter
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class AnthropicAdapter(LLMAdapter):
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"""
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LLM adapter for Anthropic Claude models.
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Reads model configuration from config/team.yaml:
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models.provider: anthropic
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models.capability_map.reasoning-heavy.anthropic: claude-opus-4-6
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models.capability_map.capable.anthropic: claude-sonnet-4-6
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models.capability_map.fast-cheap.anthropic: claude-haiku-3-5
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"""
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def __init__(self, config: dict) -> None:
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# TODO (Phase 2): Accept loaded team.yaml config dict.
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# Extract API key from environment (ANTHROPIC_API_KEY).
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# Initialise the anthropic.Anthropic() client.
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raise NotImplementedError("AnthropicAdapter.__init__ is not yet implemented.")
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def complete(self, prompt: str, capability: str, context: dict) -> str:
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# TODO (Phase 2): Call anthropic client messages.create().
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# Use resolve_model(capability) to pick the model.
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# Support context keys: system_prompt, max_tokens, temperature.
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# Return response text as a plain string.
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raise NotImplementedError("AnthropicAdapter.complete is not yet implemented.")
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def resolve_model(self, capability: str) -> str:
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# TODO (Phase 2): Look up capability in team.yaml capability_map.
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# Fall back to "capable" tier model if capability is unknown.
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raise NotImplementedError("AnthropicAdapter.resolve_model is not yet implemented.")
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