diff --git a/.apm/skills/benchmark-qed-setup/SKILL.md b/.apm/skills/benchmark-qed-setup/SKILL.md index 2fd56ea..52cced1 100644 --- a/.apm/skills/benchmark-qed-setup/SKILL.md +++ b/.apm/skills/benchmark-qed-setup/SKILL.md @@ -144,7 +144,7 @@ Ask in **a single `ask_user` form** (split into two if the workflow is autoq, si | Field | Type | Options / examples | Notes | |-------|------|--------------------|-------| -| `llm_provider` | enum | `openai.chat`, `azure.openai.chat`, `azure.inference.chat` | See provider table in [references/config-reference.md](references/config-reference.md). | +| `llm_provider` | enum | `openai.chat`, `azure.openai.chat`, `azure.inference.chat`, `copilot.chat` | See provider table in [references/config-reference.md](references/config-reference.md). | | `model` | string | `gpt-4.1`, `gpt-4o`, `o3-mini`, an Azure deployment name | For Azure providers this is the **deployment name**, not the base model id. | | `auth_type` | enum | `api_key` (default), `azure_managed_identity` | `azure_managed_identity` is only valid for `azure.*` providers. | | `api_key_env_var` | string | `OPENAI_API_KEY` (default), `AZURE_OPENAI_API_KEY`, … | Only ask when `auth_type=api_key`. The skill writes `${VAR_NAME}` into YAML and adds the variable to `.env`. | diff --git a/.apm/skills/benchmark-qed-setup/references/config-reference.md b/.apm/skills/benchmark-qed-setup/references/config-reference.md index c797e2f..6e0144d 100644 --- a/.apm/skills/benchmark-qed-setup/references/config-reference.md +++ b/.apm/skills/benchmark-qed-setup/references/config-reference.md @@ -481,6 +481,7 @@ significance_correction: holm | Azure OpenAI Embedding | `azure.openai.embedding` | Azure-hosted embeddings | | Azure Inference Chat | `azure.inference.chat` | Azure AI Inference chat | | Azure Inference Embedding | `azure.inference.embedding` | Azure AI Inference embeddings | +| GitHub Copilot CLI | `copilot.chat` | Chat models via the Copilot CLI (`copilot login`; no `api_key`/`auth_type`; no embeddings, so pair with another embedding provider; `temperature`/`seed` ignored). Optional `init_args`: `cli_path`, `github_token`, `timeout`, `reasoning_effort`, `validate_model`, `delete_sessions`. | ## Custom LLM Providers diff --git a/.semversioner/next-release/minor-20261023000000000000.json b/.semversioner/next-release/minor-20261023000000000000.json new file mode 100644 index 0000000..9bb7e69 --- /dev/null +++ b/.semversioner/next-release/minor-20261023000000000000.json @@ -0,0 +1,4 @@ +{ + "type": "minor", + "description": "Add copilot.chat LLM provider to use models through the GitHub Copilot CLI" +} diff --git a/benchmark_qed/cli/init_config.py b/benchmark_qed/cli/init_config.py index b91417e..e10f55e 100644 --- a/benchmark_qed/cli/init_config.py +++ b/benchmark_qed/cli/init_config.py @@ -61,7 +61,7 @@ class ConfigType(StrEnum): model: gpt-4.1 auth_type: api_key # or azure_managed_identity api_key: ${OPENAI_API_KEY} # remove this if using azure_managed_identity - llm_provider: openai.chat # or azure.openai.chat | azure.inference.chat + llm_provider: openai.chat # or azure.openai.chat | azure.inference.chat | copilot.chat concurrent_requests: 4 # The number of concurrent requests to send to the model. # init_args: # Additional initialization arguments for the LLM can be added here. diff --git a/benchmark_qed/cli/interactive.py b/benchmark_qed/cli/interactive.py index 481406f..832a074 100644 --- a/benchmark_qed/cli/interactive.py +++ b/benchmark_qed/cli/interactive.py @@ -88,6 +88,7 @@ class ProviderResult: ("openai.chat", "OpenAI", "OpenAI API (default)"), ("azure.openai.chat", "Azure OpenAI", "Azure-hosted OpenAI models"), ("azure.inference.chat", "Azure Inference", "Azure AI Inference endpoint"), + ("copilot.chat", "GitHub Copilot CLI", "Models available via GitHub Copilot"), ] EMBEDDING_PROVIDERS: list[tuple[str, str, str]] = [ @@ -106,6 +107,7 @@ class ProviderResult: ] DEFAULT_CHAT_MODEL = "gpt-4.1" +DEFAULT_COPILOT_CHAT_MODEL = "gpt-6-luna" DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large" DEFAULT_API_VERSION = "2024-12-01-preview" @@ -255,6 +257,18 @@ def prompt_provider( [(val, label) for val, label, _desc in providers], ) + if provider_value == "copilot.chat": + # The Copilot CLI handles authentication via the user's GitHub login. + model = typer.prompt( + "Model name", + default=default_model or DEFAULT_COPILOT_CHAT_MODEL, + ) + return ProviderResult( + llm_provider=provider_value, + model=model, + auth_type="copilot_cli", + ) + # Auth type auth_type = select_option("Authentication type", AUTH_TYPES) @@ -276,6 +290,12 @@ def prompt_embedding_provider( chat_result: ProviderResult, ) -> ProviderResult: """Ask whether to reuse the chat provider for embeddings, or configure separately.""" + if chat_result.llm_provider == "copilot.chat": + rich_print( + "[yellow]GitHub Copilot CLI does not provide embeddings; " + "select a separate embedding provider.[/yellow]" + ) + return prompt_provider("embedding") if typer.confirm("Use the same provider for embeddings?", default=True): # Derive the embedding provider from the chat provider mapping = { diff --git a/benchmark_qed/cli/yaml_renderer.py b/benchmark_qed/cli/yaml_renderer.py index 733826b..87fb09e 100644 --- a/benchmark_qed/cli/yaml_renderer.py +++ b/benchmark_qed/cli/yaml_renderer.py @@ -34,12 +34,17 @@ def _render_llm_section(provider_dict: dict[str, Any], indent: int = 2) -> str: provider_dict = dataclasses.asdict(provider_dict) pad = " " * indent - lines: list[str] = [] + lines: list[str] = [f"{pad}model: {provider_dict['model']}"] - lines.extend([ - f"{pad}model: {provider_dict['model']}", - f"{pad}auth_type: {provider_dict['auth_type']}", - ]) + if provider_dict["llm_provider"] == "copilot.chat": + # Copilot CLI authenticates via the user's GitHub login; no API key. + lines.extend([ + f"{pad}llm_provider: {provider_dict['llm_provider']}", + f"{pad}concurrent_requests: 4", + ]) + return "\n".join(lines) + + lines.append(f"{pad}auth_type: {provider_dict['auth_type']}") if provider_dict["auth_type"] == "api_key": lines.append(f"{pad}api_key: ${{OPENAI_API_KEY}}") diff --git a/benchmark_qed/config/llm_config.py b/benchmark_qed/config/llm_config.py index 86bf8fa..43798ac 100644 --- a/benchmark_qed/config/llm_config.py +++ b/benchmark_qed/config/llm_config.py @@ -22,6 +22,11 @@ class LLMProvider(StrEnum): AzureOpenAIEmbedding = "azure.openai.embedding" AzureInferenceChat = "azure.inference.chat" AzureInferenceEmbedding = "azure.inference.embedding" + CopilotChat = "copilot.chat" + + +# graphrag-llm completion strategy name used for the GitHub Copilot CLI provider. +COPILOT_COMPLETION_TYPE = "copilot_cli" class ModelType(StrEnum): @@ -121,9 +126,17 @@ class LLMConfig(BaseModel): description="List of custom LLM providers to register.", ) + @property + def is_copilot(self) -> bool: + """Whether this config targets the GitHub Copilot CLI provider.""" + return str(self.llm_provider) == LLMProvider.CopilotChat + @model_validator(mode="after") def check_api_key(self) -> Self: """Check if the API key is set.""" + # The Copilot CLI authenticates with the user's GitHub login or token. + if self.is_copilot: + return self if self.auth_type == "api_key" and ( self.api_key is None or self.api_key.get_secret_value().strip() == "" ): @@ -138,8 +151,14 @@ def to_model_config(self) -> ModelConfig: ``azure``, ``azure_ai``). Custom providers are passed through with their registered name as the ``ModelConfig.type`` so that ``register_completion`` / ``register_embedding`` callers can resolve - them. + them. The GitHub Copilot CLI provider is mapped to the + ``copilot_cli`` completion type registered by + :class:`benchmark_qed.llm.factory.ModelFactory`; its ``init_args`` are + forwarded to :class:`benchmark_qed.llm.copilot_completion.CopilotCLICompletion`. """ + if self.is_copilot: + return self._to_copilot_model_config() + provider_key = str(self.llm_provider) is_builtin = provider_key in _PROVIDER_TO_MODEL_PROVIDER model_provider = _PROVIDER_TO_MODEL_PROVIDER.get(provider_key, provider_key) @@ -161,17 +180,6 @@ def to_model_config(self) -> ModelConfig: azure_deployment_name = self.model if model_provider == "azure" else None - rate_limit: RateLimitConfig | None = None - if self.concurrent_requests and self.concurrent_requests > 0: - rate_limit = RateLimitConfig( - type=RateLimitType.SlidingWindow, - period_in_seconds=1, - requests_per_period=max(1, self.concurrent_requests), - ) - - # Track usage by enabling a metrics processor with no writer (silent). - metrics = MetricsConfig(writer=None) - config_kwargs: dict[str, Any] = { "model_provider": model_provider, "model": self.model, @@ -182,8 +190,9 @@ def to_model_config(self) -> ModelConfig: "azure_deployment_name": azure_deployment_name, "call_args": dict(self.call_args), "retry": self.retry, - "rate_limit": rate_limit, - "metrics": metrics, + "rate_limit": self._rate_limit_config(), + # Track usage by enabling a metrics processor with no writer (silent). + "metrics": MetricsConfig(writer=None), **init_args, } @@ -194,3 +203,32 @@ def to_model_config(self) -> ModelConfig: config_kwargs["type"] = provider_key return ModelConfig(**config_kwargs) + + def _rate_limit_config(self) -> RateLimitConfig | None: + if self.concurrent_requests and self.concurrent_requests > 0: + return RateLimitConfig( + type=RateLimitType.SlidingWindow, + period_in_seconds=1, + requests_per_period=max(1, self.concurrent_requests), + ) + return None + + def _to_copilot_model_config(self) -> ModelConfig: + """Build a ``ModelConfig`` for the GitHub Copilot CLI provider. + + Authentication is handled by the Copilot CLI (logged-in user or a + ``github_token`` in ``init_args``), so ``api_key`` is not forwarded. + ``init_args`` become extra ``ModelConfig`` fields, which graphrag-llm + passes through to the completion constructor. + """ + return ModelConfig( + type=COPILOT_COMPLETION_TYPE, + model_provider="copilot", + model=self.model, + auth_method=AuthMethod.ApiKey, + call_args=dict(self.call_args), + retry=self.retry, + rate_limit=self._rate_limit_config(), + metrics=MetricsConfig(writer=None), + **self.init_args, + ) diff --git a/benchmark_qed/llm/copilot_completion.py b/benchmark_qed/llm/copilot_completion.py new file mode 100644 index 0000000..ea1b4d4 --- /dev/null +++ b/benchmark_qed/llm/copilot_completion.py @@ -0,0 +1,474 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""``LLMCompletion`` implementation backed by the GitHub Copilot CLI. + +Requests are sent through the GitHub Copilot SDK (``github-copilot-sdk``), +which drives a Copilot CLI runtime over JSON-RPC. Each completion runs in a +fresh, tool-less Copilot session whose system prompt is fully replaced by the +caller's system message, so the model behaves like a plain chat completion. +""" + +from __future__ import annotations + +import asyncio +import atexit +import json +import logging +import re +import threading +import time +import uuid +from typing import TYPE_CHECKING, Any, Unpack + +import json_repair +from graphrag_llm.completion.completion import LLMCompletion +from graphrag_llm.middleware import with_middleware_pipeline +from graphrag_llm.types import ( + LLMChoice, + LLMCompletionMessage, + LLMCompletionResponse, + LLMCompletionUsage, +) + +if TYPE_CHECKING: + from collections.abc import AsyncIterator, Coroutine, Iterator + from concurrent.futures import Future + + from graphrag_cache import Cache, CacheKeyCreator + from graphrag_llm.config import ModelConfig + from graphrag_llm.metrics import MetricsProcessor, MetricsStore + from graphrag_llm.rate_limit import RateLimiter + from graphrag_llm.retry import Retry + from graphrag_llm.tokenizer import Tokenizer + from graphrag_llm.types import ( + LLMCompletionArgs, + LLMCompletionChunk, + Metrics, + ResponseFormat, + ) + from pydantic import BaseModel + +log: logging.Logger = logging.getLogger(__name__) + +DEFAULT_SYSTEM_PROMPT: str = "You are a helpful assistant." +DEFAULT_TIMEOUT_SECONDS: float = 600.0 + +_JSON_OBJECT_INSTRUCTION = ( + "Respond with a single valid JSON object only, with no surrounding prose " + "or Markdown code fences." +) +_CODE_FENCE_RE = re.compile(r"^\s*```(?:json)?\s*\n?(.*?)\n?\s*```\s*$", re.DOTALL) + + +class CopilotCompletionError(RuntimeError): + """Raised when the Copilot CLI fails to produce a completion.""" + + +class _CopilotRuntime: + """A Copilot SDK client running on a dedicated background event loop. + + The SDK client is bound to the event loop it was started on, while + benchmark-qed may call the model from several loops (one per + ``asyncio.run``) or from worker threads. Running the client on its own + loop thread lets every caller share a single Copilot CLI process. + """ + + def __init__(self, *, cli_path: str | None, github_token: str | None) -> None: + self._cli_path = cli_path + self._github_token = github_token + self._loop = asyncio.new_event_loop() + self._thread = threading.Thread( + target=self._loop.run_forever, + name="copilot-cli-runtime", + daemon=True, + ) + self._thread.start() + self._client: Any = None + self._models: set[str] | None = None + self._start_lock: asyncio.Lock | None = None + + def submit(self, coro: Coroutine[Any, Any, Any]) -> Future[Any]: + return asyncio.run_coroutine_threadsafe(coro, self._loop) + + async def client(self) -> Any: + if self._start_lock is None: + self._start_lock = asyncio.Lock() + async with self._start_lock: + if self._client is None: + try: + from copilot import CopilotClient, RuntimeConnection + except ImportError as e: # pragma: no cover - dependency is required + msg = ( + "The 'copilot.chat' provider requires the 'github-copilot-sdk' " + "package. Install it with `pip install github-copilot-sdk`." + ) + raise ImportError(msg) from e + + kwargs: dict[str, Any] = {} + if self._cli_path: + kwargs["connection"] = RuntimeConnection.for_stdio( + path=self._cli_path + ) + if self._github_token: + kwargs["github_token"] = self._github_token + client = CopilotClient(**kwargs) + await client.start() + self._client = client + return self._client + + async def available_models(self) -> set[str]: + if self._models is None: + client = await self.client() + self._models = {m.id for m in await client.list_models()} + return self._models + + def close(self) -> None: + if self._client is not None: + try: + self.submit(self._client.stop()).result(timeout=15) + except Exception: + log.debug("Error stopping Copilot client", exc_info=True) + self._client = None + self._loop.call_soon_threadsafe(self._loop.stop) + + +_runtimes: dict[tuple[str | None, str | None], _CopilotRuntime] = {} +_runtimes_lock = threading.Lock() + + +def _get_runtime(cli_path: str | None, github_token: str | None) -> _CopilotRuntime: + key = (cli_path, github_token) + with _runtimes_lock: + runtime = _runtimes.get(key) + if runtime is None: + runtime = _CopilotRuntime(cli_path=cli_path, github_token=github_token) + _runtimes[key] = runtime + return runtime + + +def _shutdown_runtimes() -> None: + with _runtimes_lock: + runtimes = list(_runtimes.values()) + _runtimes.clear() + for runtime in runtimes: + runtime.close() + + +# The SDK uses the loop's default executor, which refuses new work once +# interpreter shutdown begins, so stop the CLI before executors shut down. +# ``threading._register_atexit`` hooks run in reverse order, ahead of the +# executor teardown registered by ``concurrent.futures``. +try: + threading._register_atexit(_shutdown_runtimes) # type: ignore[attr-defined] # ruff: ignore[private-member-access] +except AttributeError: # pragma: no cover + atexit.register(_shutdown_runtimes) + + +def _content_to_text(content: Any) -> str: + if content is None: + return "" + if isinstance(content, str): + return content + if isinstance(content, list): + parts: list[str] = [] + for part in content: + if isinstance(part, str): + parts.append(part) + elif isinstance(part, dict) and part.get("type") == "text": + parts.append(str(part.get("text", ""))) + else: + msg = "The copilot.chat provider only supports text message content." + raise ValueError(msg) + return "\n".join(parts) + return str(content) + + +def _split_messages(messages: Any) -> tuple[str | None, str]: + """Convert chat messages into a (system prompt, user prompt) pair. + + System/developer messages become the session's system prompt. A single + user message is sent verbatim; multi-turn conversations are rendered as a + labelled transcript because each completion runs in a fresh session. + """ + if isinstance(messages, str): + return None, messages + + system_parts: list[str] = [] + turns: list[tuple[str, str]] = [] + for message in messages: + msg = message if isinstance(message, dict) else message.model_dump() + role = str(msg.get("role", "user")) + text = _content_to_text(msg.get("content")) + if role in ("system", "developer"): + system_parts.append(text) + else: + turns.append((role, text)) + + system_prompt = "\n\n".join(system_parts) if system_parts else None + if len(turns) == 1 and turns[0][0] == "user": + return system_prompt, turns[0][1] + + transcript = "\n\n".join(f"[{role.upper()}]\n{text}" for role, text in turns) + prompt = ( + "The following is the conversation so far. Write the next ASSISTANT " + f"message only.\n\n{transcript}\n\n[ASSISTANT]\n" + ) + return system_prompt, prompt + + +def _strip_code_fence(text: str) -> str: + match = _CODE_FENCE_RE.match(text) + return match.group(1).strip() if match else text.strip() + + +def _parse_structured(text: str, response_format: type[BaseModel]) -> BaseModel: + cleaned = _strip_code_fence(text) + try: + data = json.loads(cleaned) + except json.JSONDecodeError: + data = json_repair.loads(cleaned) + return response_format.model_validate(data) + + +def _deny_permission(_request: Any, _invocation: Any) -> Any: + from copilot.generated.rpc import PermissionDecisionDeniedInteractivelyByUser + + return PermissionDecisionDeniedInteractivelyByUser( + feedback="Tools are disabled for benchmark-qed completions." + ) + + +class CopilotCLICompletion(LLMCompletion): + """Chat completion through the GitHub Copilot CLI. + + Supported ``init_args`` (from ``LLMConfig.init_args``): + + - ``cli_path``: path to a Copilot CLI executable. Defaults to the runtime + bundled with the SDK (or ``COPILOT_CLI_PATH``). + - ``github_token``: GitHub token to authenticate with. Defaults to the + Copilot CLI's logged-in user / ``COPILOT_GITHUB_TOKEN`` / ``GH_TOKEN``. + - ``timeout``: seconds to wait for each completion (default 600). + - ``reasoning_effort``: optional reasoning effort for supported models. + - ``validate_model``: verify the model is available before the first + request (default ``True``). Copilot otherwise silently falls back to + its default model. + - ``delete_sessions``: delete each Copilot session from disk after it + completes (default ``True``). + + Sampling parameters in ``call_args`` (e.g. ``temperature``, ``seed``) are + not supported by the Copilot CLI and are ignored. + """ + + def __init__( + self, + *, + model_id: str, + model_config: ModelConfig, + tokenizer: Tokenizer, + metrics_store: MetricsStore, + metrics_processor: MetricsProcessor | None = None, + rate_limiter: RateLimiter | None = None, + retrier: Retry | None = None, + cache: Cache | None = None, + cache_key_creator: CacheKeyCreator, + cli_path: str | None = None, + github_token: str | None = None, + timeout: float = DEFAULT_TIMEOUT_SECONDS, + reasoning_effort: str | None = None, + validate_model: bool = True, + delete_sessions: bool = True, + **kwargs: Any, + ) -> None: + """Initialize the Copilot CLI completion.""" + if kwargs: + log.warning( + "Ignoring unsupported init_args for copilot.chat: %s", + ", ".join(sorted(kwargs)), + ) + self._model_id = model_id + self._model = model_config.model + self._tokenizer = tokenizer + self._metrics_store = metrics_store + self._track_metrics = metrics_processor is not None + self._timeout = float(timeout) + self._reasoning_effort = reasoning_effort + self._validate_model = validate_model + self._model_validated = False + self._delete_sessions = delete_sessions + self._runtime = _get_runtime(cli_path, github_token) + + self._completion, self._completion_async = with_middleware_pipeline( + model_config=model_config, + model_fn=self._base_completion, + async_model_fn=self._base_completion_async, + request_type="chat", + cache=cache, + cache_key_creator=cache_key_creator, + tokenizer=tokenizer, + metrics_processor=metrics_processor, + rate_limiter=rate_limiter, + retrier=retrier, + ) + + async def _ensure_model(self) -> None: + if not self._validate_model or self._model_validated: + return + models = await self._runtime.available_models() + if self._model not in models: + msg = ( + f"Model '{self._model}' is not available through GitHub Copilot. " + f"Available models: {', '.join(sorted(models))}" + ) + raise ValueError(msg) + self._model_validated = True + + async def _run( + self, + *, + messages: Any, + response_format: type[BaseModel] | None, + json_object: bool, + ) -> LLMCompletionResponse: + await self._ensure_model() + client = await self._runtime.client() + + system_prompt, prompt = _split_messages(messages) + system_prompt = system_prompt or DEFAULT_SYSTEM_PROMPT + if response_format is not None: + schema = json.dumps(response_format.model_json_schema()) + system_prompt += ( + f"\n\n{_JSON_OBJECT_INSTRUCTION} The JSON object must conform " + f"to this JSON schema:\n{schema}" + ) + elif json_object: + system_prompt += f"\n\n{_JSON_OBJECT_INSTRUCTION}" + + from copilot.session_events import AssistantUsageData + + session_kwargs: dict[str, Any] = { + "model": self._model, + "system_message": {"mode": "replace", "content": system_prompt}, + "available_tools": [], + "on_permission_request": _deny_permission, + "skip_custom_instructions": True, + "enable_config_discovery": False, + "enable_skills": False, + } + if self._reasoning_effort: + session_kwargs["reasoning_effort"] = self._reasoning_effort + + usage: list[Any] = [] + session = await client.create_session(**session_kwargs) + try: + session.on( + lambda event: ( + usage.append(event.data) + if isinstance(event.data, AssistantUsageData) and not event.agent_id + else None + ) + ) + event = await session.send_and_wait(prompt, timeout=self._timeout) + finally: + await session.disconnect() + if self._delete_sessions: + try: + await client.delete_session(session.session_id) + except Exception: + log.debug("Failed to delete Copilot session", exc_info=True) + + if event is None or not getattr(event.data, "content", None): + msg = "The Copilot CLI returned no assistant message." + raise CopilotCompletionError(msg) + + content: str = event.data.content + formatted = None + if response_format is not None: + formatted = _parse_structured(content, response_format) + content = formatted.model_dump_json() + elif json_object: + content = _strip_code_fence(content) + + prompt_tokens = sum(u.input_tokens or 0 for u in usage) + completion_tokens = sum(u.output_tokens or 0 for u in usage) + model = next((u.model for u in reversed(usage) if u.model), self._model) + return LLMCompletionResponse( + id=f"copilot-{uuid.uuid4()}", + object="chat.completion", + created=int(time.time()), + model=model, + choices=[ + LLMChoice( + index=0, + message=LLMCompletionMessage(role="assistant", content=content), + finish_reason="stop", + ) + ], + usage=LLMCompletionUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), + formatted_response=formatted, + ) + + def _make_request(self, kwargs: dict[str, Any]) -> Coroutine[Any, Any, Any]: + kwargs.pop("metrics", None) + if kwargs.pop("stream", False): + msg = "The copilot.chat provider does not support streaming completions." + raise ValueError(msg) + return self._run( + messages=kwargs.pop("messages"), + response_format=kwargs.pop("response_format", None), + json_object=bool(kwargs.pop("response_format_json_object", False)), + ) + + def _base_completion(self, **kwargs: Any) -> LLMCompletionResponse: + return self._runtime.submit(self._make_request(kwargs)).result() + + async def _base_completion_async(self, **kwargs: Any) -> LLMCompletionResponse: + future = self._runtime.submit(self._make_request(kwargs)) + return await asyncio.wrap_future(future) + + def _prepare(self, kwargs: dict[str, Any]) -> Metrics | None: + if kwargs.get("stream"): + msg = "The copilot.chat provider does not support streaming completions." + raise ValueError(msg) + request_metrics: Metrics | None = kwargs.pop("metrics", None) or {} + return request_metrics if self._track_metrics else None + + def completion( + self, + /, + **kwargs: Unpack[LLMCompletionArgs[ResponseFormat]], + ) -> LLMCompletionResponse[ResponseFormat] | Iterator[LLMCompletionChunk]: + """Sync completion method.""" + args: dict[str, Any] = dict(kwargs) + request_metrics = self._prepare(args) + try: + return self._completion(metrics=request_metrics, **args) + finally: + if request_metrics is not None: + self._metrics_store.update_metrics(metrics=request_metrics) + + async def completion_async( + self, + /, + **kwargs: Unpack[LLMCompletionArgs[ResponseFormat]], + ) -> LLMCompletionResponse[ResponseFormat] | AsyncIterator[LLMCompletionChunk]: + """Async completion method.""" + args: dict[str, Any] = dict(kwargs) + request_metrics = self._prepare(args) + try: + return await self._completion_async(metrics=request_metrics, **args) + finally: + if request_metrics is not None: + self._metrics_store.update_metrics(metrics=request_metrics) + + @property + def metrics_store(self) -> MetricsStore: + """The metrics store.""" + return self._metrics_store + + @property + def tokenizer(self) -> Tokenizer: + """The tokenizer.""" + return self._tokenizer diff --git a/benchmark_qed/llm/factory.py b/benchmark_qed/llm/factory.py index 8ff614b..a388959 100644 --- a/benchmark_qed/llm/factory.py +++ b/benchmark_qed/llm/factory.py @@ -9,7 +9,11 @@ from graphrag_llm.embedding import LLMEmbedding, create_embedding from graphrag_llm.embedding.embedding_factory import register_embedding -from benchmark_qed.config.llm_config import LLMConfig, ModelType +from benchmark_qed.config.llm_config import ( + COPILOT_COMPLETION_TYPE, + LLMConfig, + ModelType, +) def _register_custom_provider(model_config: LLMConfig, model_type: ModelType) -> None: @@ -59,12 +63,23 @@ def create_chat_model(cls, model_config: LLMConfig) -> LLMCompletion: """Create a chat completion client. Built-in providers (OpenAI, Azure OpenAI, Azure AI Inference) are - served by ``LiteLLMCompletion``. Custom providers declared via + served by ``LiteLLMCompletion``. The GitHub Copilot CLI provider + (``copilot.chat``) is served by + :class:`benchmark_qed.llm.copilot_completion.CopilotCLICompletion`. + Custom providers declared via :class:`benchmark_qed.config.llm_config.CustomLLMProvider` are registered with ``graphrag_llm.completion.completion_factory`` and instantiated through it. """ provider = str(model_config.llm_provider) + if ( + model_config.is_copilot + and COPILOT_COMPLETION_TYPE not in cls._registered_chat + ): + from benchmark_qed.llm.copilot_completion import CopilotCLICompletion + + register_completion(COPILOT_COMPLETION_TYPE, CopilotCLICompletion) + cls._registered_chat.add(COPILOT_COMPLETION_TYPE) custom_names = { p.name for p in model_config.custom_providers @@ -78,6 +93,13 @@ def create_chat_model(cls, model_config: LLMConfig) -> LLMCompletion: @classmethod def create_embedding_model(cls, model_config: LLMConfig) -> LLMEmbedding: """Create an embedding client (see :meth:`create_chat_model`).""" + if model_config.is_copilot: + msg = ( + "The 'copilot.chat' provider does not support embeddings. " + "Configure an embedding provider such as 'openai.embedding' or " + "'azure.openai.embedding'." + ) + raise ValueError(msg) provider = str(model_config.llm_provider) custom_names = { p.name diff --git a/docs/cli/llm_config.md b/docs/cli/llm_config.md index 25907d5..e7ab127 100644 --- a/docs/cli/llm_config.md +++ b/docs/cli/llm_config.md @@ -33,6 +33,7 @@ Defines the supported LLM providers and model types. | `azure.openai.embedding` | Azure-hosted OpenAI embedding models. | | `azure.inference.chat` | Azure Inference Service chat models. | | `azure.inference.embedding` | Azure Inference Service embedding models. | +| `copilot.chat` | Chat models served through the GitHub Copilot CLI (no embeddings). | --- @@ -115,6 +116,37 @@ llm_config: > 💡 Note: If you use azure_manager_identity make sure to be authenticated with `az login` on a terminal, if you use api_key make sure to include the api reference and the .env file. +#### GitHub Copilot CLI + +The `copilot.chat` provider routes chat completions through the [GitHub Copilot CLI](https://docs.github.com/copilot/how-tos/copilot-cli) using the [`github-copilot-sdk`](https://pypi.org/project/github-copilot-sdk/). Any model available to your Copilot subscription can be used. + +```yaml +llm_config: + model: "gpt-6-luna" # any model listed by the Copilot CLI, e.g. claude-sonnet-5.5 + llm_provider: "copilot.chat" + concurrent_requests: 4 + init_args: # all optional + # cli_path: /path/to/copilot # defaults to the SDK-bundled CLI (or $COPILOT_CLI_PATH) + # github_token: ${GITHUB_TOKEN} # defaults to the logged-in Copilot CLI user + # timeout: 600 # seconds to wait for each response + # reasoning_effort: medium # low | medium | high | xhigh, for models that support it + # validate_model: true # fail fast if the model is not available + # delete_sessions: true # remove each Copilot session after its request + retry: + type: exponential_backoff + max_retries: 6 + base_delay: 2.0 + max_delay: 30.0 + jitter: true +``` + +> 💡 Notes: +> - Authenticate once with `copilot login` (or provide `github_token`); `auth_type` and `api_key` are not used. +> - Each request runs in an isolated Copilot session with no tools, custom instructions, or skills, and with the system prompt replaced by the request's system messages. +> - The Copilot CLI does not support embeddings, so `embedding_config` must use a different provider. +> - `call_args` such as `temperature` and `seed` are not supported by the Copilot CLI and are ignored. +> - Requests count against your Copilot premium request quota. + #### Custom Provider BenchmarkQED allows for a custom implementation for a LLM, to do so first it's needed to implement the corresponding Protocol (ChatModel or EmbeddingModel) diff --git a/pyproject.toml b/pyproject.toml index 66845d7..a3095c5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ dependencies = [ "graphrag-llm==3.3.0", "graphrag-cache==3.3.0", "filelock>=3.20.3", + "github-copilot-sdk>=1.0.22", ] [dependency-groups] diff --git a/tests/test_copilot_completion.py b/tests/test_copilot_completion.py new file mode 100644 index 0000000..9fd2796 --- /dev/null +++ b/tests/test_copilot_completion.py @@ -0,0 +1,219 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""Tests for the GitHub Copilot CLI chat provider (no network access).""" + +from __future__ import annotations + +import asyncio +from types import SimpleNamespace +from typing import TYPE_CHECKING, Any + +import pytest +from copilot.session_events import AssistantUsageData +from graphrag_llm.types import LLMCompletionResponse +from pydantic import BaseModel + +from benchmark_qed.config.llm_config import ( + COPILOT_COMPLETION_TYPE, + LLMConfig, + LLMProvider, +) +from benchmark_qed.llm import copilot_completion +from benchmark_qed.llm.copilot_completion import ( + CopilotCLICompletion, + _parse_structured, + _split_messages, + _strip_code_fence, +) +from benchmark_qed.llm.factory import ModelFactory + +if TYPE_CHECKING: + from collections.abc import Iterator + + +class _Answer(BaseModel): + answer: str + score: int + + +class _FakeSession: + def __init__(self, client: _FakeClient, kwargs: dict[str, Any]) -> None: + self.client = client + self.kwargs = kwargs + self.session_id = f"session-{len(client.sessions)}" + self._handlers: list[Any] = [] + self.disconnected = False + + def on(self, handler: Any) -> None: + self._handlers.append(handler) + + async def send_and_wait(self, prompt: str, timeout: float) -> Any: + self.client.prompts.append(prompt) + usage = AssistantUsageData( + model=self.kwargs["model"], input_tokens=10, output_tokens=5 + ) + for handler in self._handlers: + handler(SimpleNamespace(data=usage, agent_id=None)) + return SimpleNamespace(data=SimpleNamespace(content=self.client.reply)) + + async def disconnect(self) -> None: + self.disconnected = True + + +class _FakeClient: + def __init__(self, reply: str) -> None: + self.reply = reply + self.sessions: list[_FakeSession] = [] + self.prompts: list[str] = [] + self.deleted: list[str] = [] + + async def create_session(self, **kwargs: Any) -> _FakeSession: + session = _FakeSession(self, kwargs) + self.sessions.append(session) + return session + + async def delete_session(self, session_id: str) -> None: + self.deleted.append(session_id) + + async def stop(self) -> None: + return None + + +@pytest.fixture +def fake_client(monkeypatch: pytest.MonkeyPatch) -> Iterator[_FakeClient]: + client = _FakeClient(reply="Hello!") + runtime = copilot_completion._CopilotRuntime(cli_path=None, github_token=None) + runtime._client = client + runtime._models = {"gpt-6-luna"} + monkeypatch.setattr(copilot_completion, "_get_runtime", lambda *_: runtime) + yield client + runtime.close() + + +def _copilot_config(**overrides: Any) -> LLMConfig: + return LLMConfig( + model="gpt-6-luna", llm_provider=LLMProvider.CopilotChat, **overrides + ) + + +class TestCopilotConfig: + def test_does_not_require_api_key(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + config = LLMConfig(model="gpt-5.6-sol", llm_provider="copilot.chat") + assert config.is_copilot + assert not config.api_key.get_secret_value() + + def test_to_model_config(self) -> None: + config = _copilot_config(init_args={"timeout": 30, "cli_path": "copilot"}) + model_config = config.to_model_config() + assert model_config.type == COPILOT_COMPLETION_TYPE + assert model_config.model == "gpt-6-luna" + assert model_config.model_extra == {"timeout": 30, "cli_path": "copilot"} + + def test_embedding_not_supported(self) -> None: + with pytest.raises(ValueError, match="embeddings"): + ModelFactory.create_embedding_model(_copilot_config()) + + +class TestHelpers: + def test_split_string_message(self) -> None: + assert _split_messages("hi") == (None, "hi") + + def test_split_single_user_message(self) -> None: + system, prompt = _split_messages([ + {"role": "system", "content": "Be terse."}, + {"role": "user", "content": [{"type": "text", "text": "Hello"}]}, + ]) + assert system == "Be terse." + assert prompt == "Hello" + + def test_split_multi_turn(self) -> None: + system, prompt = _split_messages([ + {"role": "user", "content": "Q1"}, + {"role": "assistant", "content": "A1"}, + {"role": "user", "content": "Q2"}, + ]) + assert system is None + assert "[USER]\nQ1" in prompt + assert "[ASSISTANT]\nA1" in prompt + assert prompt.rstrip().endswith("[ASSISTANT]") + + def test_rejects_non_text_content(self) -> None: + with pytest.raises(ValueError, match="text"): + _split_messages([ + {"role": "user", "content": [{"type": "image_url", "image_url": {}}]} + ]) + + def test_strip_code_fence(self) -> None: + assert _strip_code_fence('```json\n{"a": 1}\n```') == '{"a": 1}' + assert _strip_code_fence(' {"a": 1} ') == '{"a": 1}' + + def test_parse_structured_repairs_json(self) -> None: + parsed = _parse_structured( + "```json\n{'answer': 'x', 'score': 3,}\n```", _Answer + ) + assert parsed == _Answer(answer="x", score=3) + + +class TestCopilotCLICompletion: + def test_factory_creates_copilot_model(self, fake_client: _FakeClient) -> None: + llm = ModelFactory.create_chat_model(_copilot_config()) + assert isinstance(llm, CopilotCLICompletion) + + def test_completion(self, fake_client: _FakeClient) -> None: + llm = ModelFactory.create_chat_model(_copilot_config()) + response = llm.completion( + messages=[ + {"role": "system", "content": "Be terse."}, + {"role": "user", "content": "Hi"}, + ] + ) + assert isinstance(response, LLMCompletionResponse) + assert response.usage is not None + assert response.content == "Hello!" + assert response.usage.prompt_tokens == 10 + assert response.usage.completion_tokens == 5 + + session = fake_client.sessions[0] + assert session.kwargs["model"] == "gpt-6-luna" + assert session.kwargs["available_tools"] == [] + assert session.kwargs["system_message"] == { + "mode": "replace", + "content": "Be terse.", + } + assert session.disconnected + assert fake_client.deleted == [session.session_id] + assert fake_client.prompts == ["Hi"] + + def test_structured_completion_async(self, fake_client: _FakeClient) -> None: + fake_client.reply = '```json\n{"answer": "yes", "score": 7}\n```' + llm = ModelFactory.create_chat_model(_copilot_config()) + response = asyncio.run( + llm.completion_async(messages="Question?", response_format=_Answer) + ) + assert isinstance(response, LLMCompletionResponse) + assert response.formatted_response == _Answer(answer="yes", score=7) + system_prompt = fake_client.sessions[0].kwargs["system_message"]["content"] + assert '"score"' in system_prompt + + def test_unavailable_model(self, fake_client: _FakeClient) -> None: + llm = ModelFactory.create_chat_model( + LLMConfig(model="not-a-model", llm_provider="copilot.chat") + ) + with pytest.raises(ValueError, match="not available"): + llm.completion(messages="Hi") + assert fake_client.sessions == [] + + def test_options_passed_to_session(self, fake_client: _FakeClient) -> None: + llm = ModelFactory.create_chat_model( + _copilot_config( + init_args={"reasoning_effort": "high", "delete_sessions": False} + ) + ) + llm.completion(messages="Hi") + assert fake_client.sessions[0].kwargs["reasoning_effort"] == "high" + assert fake_client.deleted == [] + + def test_streaming_not_supported(self, fake_client: _FakeClient) -> None: + llm = ModelFactory.create_chat_model(_copilot_config()) + with pytest.raises(ValueError, match="streaming"): + llm.completion(messages="Hi", stream=True) diff --git a/tests/test_interactive_init.py b/tests/test_interactive_init.py index 03c8355..762b79a 100644 --- a/tests/test_interactive_init.py +++ b/tests/test_interactive_init.py @@ -11,6 +11,7 @@ from typer.testing import CliRunner from benchmark_qed.__main__ import app +from benchmark_qed.cli import interactive from benchmark_qed.cli.interactive import ( prompt_comma_list, select_option, @@ -378,6 +379,76 @@ def test_custom_indent(self): for line in section.split("\n"): assert line.startswith(" ") + def test_copilot_provider_omits_auth(self): + """Copilot CLI provider renders no auth_type or api_key.""" + provider = { + "llm_provider": "copilot.chat", + "model": "gpt-6-luna", + "auth_type": "copilot_cli", + "init_args": {}, + } + section = _render_llm_section(provider) + assert "llm_provider: copilot.chat" in section + assert "model: gpt-6-luna" in section + assert "auth_type" not in section + assert "api_key" not in section + assert "concurrent_requests: 4" in section + + def test_copilot_autoq_config_is_valid(self): + """An autoq config using Copilot for chat validates.""" + config = _default_autoq_config() + config["chat_provider"] = { + "llm_provider": "copilot.chat", + "model": "gpt-6-luna", + "auth_type": "copilot_cli", + "init_args": {}, + } + content = render_autoq_yaml(config) + validate_config(content, "autoq") + parsed = yaml.safe_load(content) + assert parsed["chat_model"]["llm_provider"] == "copilot.chat" + + +class TestCopilotProviderPrompts: + """Verify the wizard flow for the Copilot CLI provider.""" + + def test_prompt_provider_skips_auth(self, monkeypatch): + """Selecting Copilot only asks for a model name.""" + selections = iter(["copilot.chat"]) + monkeypatch.setattr( + interactive, "select_option", lambda *_a, **_k: next(selections) + ) + monkeypatch.setattr(typer, "prompt", lambda *_a, **k: k["default"]) + result = interactive.prompt_provider("chat") + assert result.llm_provider == "copilot.chat" + assert result.model == interactive.DEFAULT_COPILOT_CHAT_MODEL + assert result.init_args == {} + + def test_embedding_prompted_separately(self, monkeypatch): + """Copilot has no embeddings, so an embedding provider is prompted.""" + calls: list[str] = [] + + def fake_prompt_provider(purpose="chat", **_kwargs): + calls.append(purpose) + return interactive.ProviderResult( + llm_provider="openai.embedding", + model="text-embedding-3-large", + auth_type="api_key", + ) + + monkeypatch.setattr(interactive, "prompt_provider", fake_prompt_provider) + monkeypatch.setattr( + typer, "confirm", lambda *_a, **_k: pytest.fail("should not confirm") + ) + chat = interactive.ProviderResult( + llm_provider="copilot.chat", + model="gpt-6-luna", + auth_type="copilot_cli", + ) + result = interactive.prompt_embedding_provider(chat) + assert calls == ["embedding"] + assert result.llm_provider == "openai.embedding" + # ═══════════════════════════════════════════════════════════════════════════ # 3. Validation Tests diff --git a/uv.lock b/uv.lock index 7329e7e..0a7a349 100644 --- a/uv.lock +++ b/uv.lock @@ -1,15 +1,15 @@ version = 1 -revision = 2 +revision = 5 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and sys_platform != 'emscripten' 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