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0ccfe4a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import os
from abc import ABC, abstractmethod
import anthropic
import openai
from config import SYNTHESIS_MODEL
def cached_system(text: str) -> list[dict]:
"""Wrap a system prompt string for Anthropic prompt caching."""
return [{"type": "text", "text": text, "cache_control": {"type": "ephemeral"}}]
def cached_tools(tools: list[dict]) -> list[dict]:
"""Mark the last tool with cache_control so the full tool list is cached."""
if not tools:
return tools
return [*tools[:-1], {**tools[-1], "cache_control": {"type": "ephemeral"}}]
def _normalize_messages(messages: list) -> list[dict]:
"""Convert message objects or dicts to {role, content} dicts."""
result = []
for msg in messages:
if hasattr(msg, "type"):
raw_role, content = msg.type, msg.content
else:
raw_role, content = msg.get("role", "user"), msg.get("content", "")
if raw_role in ("human", "user"):
role = "user"
elif raw_role in ("ai", "assistant"):
role = "assistant"
else:
continue
result.append({"role": role, "content": content})
return result
class LLMProvider(ABC):
@abstractmethod
def complete(self, messages: list, system: str = "") -> str: ...
class AnthropicProvider(LLMProvider):
def __init__(self):
self._client = anthropic.Anthropic()
self._model = SYNTHESIS_MODEL
def complete(self, messages: list, system: str = "") -> str:
kwargs = dict(
model=self._model,
max_tokens=8192,
messages=_normalize_messages(messages),
)
if system:
kwargs["system"] = cached_system(system)
response = self._client.messages.create(**kwargs)
return next((b.text for b in response.content if b.type == "text"), "")
class OpenAIProvider(LLMProvider):
MODEL = "gpt-4o"
def __init__(self):
self._client = openai.OpenAI()
def complete(self, messages: list, system: str = "") -> str:
normalized = _normalize_messages(messages)
if system:
normalized = [{"role": "system", "content": system}] + normalized
response = self._client.chat.completions.create(
model=self.MODEL,
messages=normalized,
)
return response.choices[0].message.content or ""
def get_provider(name: str | None = None) -> LLMProvider:
name = name or os.getenv("LLM_PROVIDER", "anthropic")
if name == "openai":
return OpenAIProvider()
if name == "anthropic":
return AnthropicProvider()
raise ValueError(f"Unknown LLM provider: {name!r}. Choose 'anthropic' or 'openai'.")
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