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from __future__ import annotations
import logging
from typing import Any, Dict, Generator, List
from urllib.parse import urlsplit, urlunsplit
import requests
from dotenv import load_dotenv
from .base_api import BaseAPI
from ..core import APICallSample, ToolCall
_logger = logging.getLogger(f"sr_harness.{__name__}")
@BaseAPI.register("lmstudio")
class LMStudioAPI(BaseAPI):
"""LLM provider adapter for a local or remote LM Studio server.
Automatically normalizes LM Studio's ``/api/v1/chat`` endpoint to the
OpenAI-compatible ``/v1/chat/completions`` route required for multi-turn
conversations and function calling.
"""
def __init__(self, model: str = "qwen_qwen3-4b-instruct-2507", **kwargs: Any) -> None:
"""Initialize the LM Studio API adapter.
Args:
model: Local model identifier loaded in LM Studio.
**kwargs: Additional arguments forwarded to :class:`BaseAPI`.
"""
super().__init__(model=model, **kwargs)
def cancel(self) -> str:
"""Request cancellation and note that synchronous LM Studio HTTP calls finish the active request before stopping.
Returns:
str: Human-readable notice describing cancellation behavior.
"""
return (
"LMStudioAPI does not support force-interrupting an active non-streaming request. "
"The Agent will pause at its next safe boundary."
)
def format_response(
self, message: dict[str, Any], tool_calls: list[ToolCall], content: str | None = None
) -> APICallSample:
"""Convert an LM Studio completion message into a normalized :class:`~sr_harness.core.APICallSample`.
Args:
message: Raw message dictionary returned by LM Studio.
tool_calls: Normalized tool calls extracted from the message.
content: Optional explicit text content override.
Returns:
APICallSample: Normalized assistant response sample.
"""
return self._build_response(message, tool_calls, content)
@property
def model_list(self) -> list[str]:
"""Return the configured model identifier for this LM Studio instance.
Returns:
list[str]: Single-element list containing the active model name.
"""
return ["qwen_qwen3-4b-instruct-2507"]
@staticmethod
def normalize_endpoint(endpoint: str) -> str:
"""Convert an LM Studio base URL or native chat path into the ``/v1/chat/completions`` endpoint URL.
Args:
endpoint: Raw endpoint URL from ``LMSTUDIO_ENDPOINT`` or settings.
Returns:
str: Normalized OpenAI-compatible chat completions URL.
"""
endpoint = endpoint.strip()
parts = urlsplit(endpoint)
if parts.scheme not in {"http", "https"} or not parts.netloc:
raise ValueError(f"Invalid LMSTUDIO_ENDPOINT: {endpoint!r}")
path = (
parts.path.rstrip("/").removesuffix("/api/v1/chat").removesuffix("/v1/chat/completions")
+ "/v1/chat/completions"
)
return urlunsplit((parts.scheme, parts.netloc, path, "", ""))
def _request_samples(
self,
messages: List[Dict[str, str]],
n: int = 1,
max_tokens: int | None = None,
temperature: float = 1.0,
top_p: float = 1.0,
callback=None,
) -> Generator[APICallSample, None, dict[str, Any]]:
yield from []
load_dotenv()
endpoint = self.normalize_endpoint(self.get_env("LMSTUDIO_ENDPOINT", required=True))
api_key = self.get_env("LMSTUDIO_API_KEY", required=True)
payload = {
"model": self.model,
"messages": [dict(message) for message in messages],
"n": n,
"top_p": top_p,
**({"max_tokens": max_tokens} if max_tokens is not None else {}),
"temperature": temperature,
"stream": False,
}
if self.tool_box is None:
pass
elif not self.tool_box.native_tool_calls:
description = (
"Use the following tools when a tool call is needed. "
"Return tool calls in the specified format.\n\n"
f"{self.tool_box.format_tools()}"
)
if payload["messages"][0]["role"] in {"system", "developer"}:
first = payload["messages"][0]
payload["messages"][0] = {
**first,
"content": f"{first['content']}\n\n{description}",
}
else:
payload["messages"].insert(0, {"role": "system", "content": description})
else:
tools = self.tool_box.format_tools()
if not isinstance(tools, list):
raise TypeError("Native tool parser must return JSON tool descriptions")
payload["tools"] = tools
payload["tool_choice"] = "auto"
# LM Studio is normally on the local network. Ignore shell proxy
# variables so a loopback proxy cannot intercept or block the request.
session = requests.Session()
session.trust_env = False
timeout = float(self.get_env("LMSTUDIO_TIMEOUT", "300"))
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
max_retry = 3
self.report_progress(callback, {"type": "start", "sample": 1})
for attempt in range(1, max_retry + 1):
try:
response = session.post(endpoint, headers=headers, json=payload, timeout=timeout)
response.raise_for_status()
response_dict = response.json()
choices = response_dict.get("choices") or []
if not choices:
raise ValueError(f"LMStudioAPI({self.model}) returned no choices.")
break
except InterruptedError:
raise
except Exception as error:
public_error = self.error_message(error)
if attempt == max_retry:
self.report_progress(callback, {
"type": "failed",
"sample": 1,
"error": public_error,
})
return {
"ok": False,
"error": public_error,
"token_usage": {},
"money_usage": {},
"extra": {},
}
if callback is not None:
self.report_progress(callback, {
"type": "retry",
"sample": 1,
"attempt": attempt,
"error": public_error,
})
delay = min(float(2 ** (attempt - 1)), 30.0)
_logger.info(
f"Retrying LMStudioAPI({self.model}) in {delay:g}s "
f"(attempt {attempt}/{max_retry})."
)
self.wait_before_retry(delay)
details = []
for sample, choice in enumerate(choices, 1):
message = choice.get("message") or {}
content = message.get("content") or ""
truncated = choice.get("finish_reason") == "length"
if truncated:
message = {key: value for key, value in message.items() if key != "tool_calls"}
tool_call = [] if truncated else self.parse_tool_calls(message, content)
if not (content.strip() or tool_call):
_logger.warning(f"LMStudioAPI({self.model}) returned an empty choice.")
details.append({"content": content, "tool_call": tool_call, "message": message})
sample_response = self.format_response(message, tool_call, content)
sample_response.finish_reason = choice.get("finish_reason")
if callback is not None:
self.report_progress(callback, {
"type": "complete",
"sample": sample,
"content": sample_response.content,
"reasoning": sample_response.reasoning,
"tool_calls": [call.raw for call in sample_response.tool_calls],
})
if sample < len(choices):
self.report_progress(callback, {
"type": "start",
"sample": sample + 1,
})
yield sample_response
raw_usage = response_dict.get("usage") or {}
prompt_tokens = int(raw_usage.get("prompt_tokens") or 0)
completion_tokens = int(raw_usage.get("completion_tokens") or 0)
reasoning_tokens = int(
(raw_usage.get("completion_tokens_details") or {}).get("reasoning_tokens") or 0
)
token_usage = {
"prompt": prompt_tokens,
"answer": max(completion_tokens - reasoning_tokens, 0),
}
if reasoning_tokens:
token_usage["reason"] = reasoning_tokens
total_tokens = int(raw_usage.get("total_tokens") or prompt_tokens + completion_tokens)
accounted = prompt_tokens + completion_tokens
if total_tokens > accounted:
token_usage["other"] = total_tokens - accounted
return {
"ok": True,
"error": None,
"token_usage": token_usage,
"money_usage": {"total": 0.0},
"extra": {"responses": [response_dict]},
}
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