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# inference.py
from typing import List, Dict, Optional
from hf_client import get_inference_client
from models import find_model
def chat_completion(
model_id: str,
messages: List[Dict[str, str]],
provider: Optional[str] = None,
max_tokens: int = 4096
) -> str:
"""
Send a chat completion request to the appropriate inference provider.
Args:
model_id: The model identifier to use.
messages: A list of OpenAI-style {'role','content'} messages.
provider: Optional override for provider; uses model default if None.
max_tokens: Maximum tokens to generate.
Returns:
The assistant's response content.
"""
# resolve default provider from registry if needed
if provider is None:
meta = find_model(model_id)
provider = meta.default_provider if meta else "auto"
client = get_inference_client(model_id, provider)
resp = client.chat.completions.create(
model=model_id,
messages=messages,
max_tokens=max_tokens
)
return resp.choices[0].message.content
def stream_chat_completion(
model_id: str,
messages: List[Dict[str, str]],
provider: Optional[str] = None,
max_tokens: int = 4096
):
"""
Generator for streaming chat completions.
Yields partial message chunks as strings.
"""
if provider is None:
meta = find_model(model_id)
provider = meta.default_provider if meta else "auto"
client = get_inference_client(model_id, provider)
stream = client.chat.completions.create(
model=model_id,
messages=messages,
max_tokens=max_tokens,
stream=True
)
for chunk in stream:
delta = getattr(chunk.choices[0].delta, "content", None)
if delta:
yield delta
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