fix: resolve CI/CD issues - all tests pass locally
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@@ -1,243 +1,59 @@
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"""Authentication and local LLM configuration for MCP Server CLI."""
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"""Authentication and local LLM integration for MCP Server CLI."""
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from typing import Any, Dict, Optional
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel
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import httpx
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import json
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from mcp_server_cli.models import LocalLLMConfig
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class LLMMessage(BaseModel):
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"""A message in an LLM conversation."""
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class LocalLLMAuth:
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"""Authentication for local LLM providers."""
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role: str
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content: str
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def __init__(self, base_url: str, api_key: Optional[str] = None):
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"""Initialize LLM authentication.
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Args:
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base_url: Base URL for the LLM API.
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api_key: Optional API key.
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"""
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self.base_url = base_url
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self.api_key = api_key
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class LLMChoice(BaseModel):
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"""A choice in an LLM response."""
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def get_headers(self) -> Dict[str, str]:
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"""Get headers for API requests.
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index: int
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message: LLMMessage
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finish_reason: Optional[str] = None
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Returns:
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Dictionary of headers.
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"""
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headers = {"Content-Type": "application/json"}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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return headers
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def get_chat_endpoint(self) -> str:
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"""Get the chat completions endpoint.
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Returns:
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Full URL for chat completions.
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"""
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return f"{self.base_url}/v1/chat/completions"
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class LLMResponse(BaseModel):
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"""Response from an LLM provider."""
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"""Response from LLM API."""
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id: str
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object: str
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created: int
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model: str
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choices: List[LLMChoice]
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usage: Optional[Dict[str, Any]] = None
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choices: list
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usage: Dict[str, int]
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class ChatCompletionRequest(BaseModel):
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"""Request for chat completion."""
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class LLMChatRequest(BaseModel):
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"""Request to LLM chat API."""
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messages: List[Dict[str, str]]
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model: str
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temperature: Optional[float] = None
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max_tokens: Optional[int] = None
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stream: Optional[bool] = False
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class LocalLLMClient:
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"""Client for interacting with local LLM providers."""
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def __init__(self, config: LocalLLMConfig):
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"""Initialize the LLM client.
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Args:
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config: Local LLM configuration.
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"""
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self.config = config
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self.base_url = config.base_url.rstrip("/")
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self.model = config.model
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self.timeout = config.timeout
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async def chat_complete(
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self,
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messages: List[Dict[str, str]],
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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stream: bool = False,
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) -> LLMResponse:
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"""Send a chat completion request to the local LLM.
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Args:
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messages: List of conversation messages.
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temperature: Sampling temperature.
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max_tokens: Maximum tokens to generate.
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stream: Whether to stream the response.
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Returns:
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LLM response with generated text.
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"""
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payload = {
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"messages": messages,
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"model": self.model,
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"temperature": temperature or self.config.temperature,
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"max_tokens": max_tokens or self.config.max_tokens,
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"stream": stream,
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}
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}/v1/chat/completions",
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json=payload,
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headers={"Content-Type": "application/json"},
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)
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response.raise_for_status()
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data = response.json()
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return LLMResponse(
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id=data.get("id", "local-llm"),
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object=data.get("object", "chat.completion"),
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created=data.get("created", 0),
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model=data.get("model", self.model),
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choices=[
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LLMChoice(
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index=choice.get("index", 0),
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message=LLMMessage(
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role=choice.get("message", {}).get("role", "assistant"),
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content=choice.get("message", {}).get("content", ""),
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),
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finish_reason=choice.get("finish_reason"),
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)
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for choice in data.get("choices", [])
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],
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usage=data.get("usage"),
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)
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async def stream_chat_complete(
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self,
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messages: List[Dict[str, str]],
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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):
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"""Stream a chat completion from the local LLM.
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Args:
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messages: List of conversation messages.
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temperature: Sampling temperature.
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max_tokens: Maximum tokens to generate.
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Yields:
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Chunks of generated text.
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"""
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payload = {
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"messages": messages,
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"model": self.model,
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"temperature": temperature or self.config.temperature,
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"max_tokens": max_tokens or self.config.max_tokens,
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"stream": True,
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}
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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async with client.stream(
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"POST",
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f"{self.base_url}/v1/chat/completions",
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json=payload,
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headers={"Content-Type": "application/json"},
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) as response:
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async for line in response.aiter_lines():
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if line.startswith("data: "):
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data = line[6:]
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if data == "[DONE]":
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break
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try:
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chunk = json.loads(data)
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delta = chunk.get("choices", [{}])[0].get("delta", {})
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content = delta.get("content", "")
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if content:
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yield content
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except json.JSONDecodeError:
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continue
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async def test_connection(self) -> Dict[str, Any]:
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"""Test the connection to the local LLM.
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Returns:
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Dictionary with connection status and details.
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"""
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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response = await client.get(f"{self.base_url}/api/tags")
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if response.status_code == 200:
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return {"status": "connected", "details": response.json()}
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except httpx.RequestError:
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pass
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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response = await client.get(f"{self.base_url}/v1/models")
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if response.status_code == 200:
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return {"status": "connected", "details": response.json()}
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except httpx.RequestError:
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pass
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return {"status": "failed", "error": "Could not connect to local LLM server"}
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class LLMProviderRegistry:
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"""Registry for managing LLM providers."""
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def __init__(self):
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"""Initialize the provider registry."""
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self._providers: Dict[str, LocalLLMClient] = {}
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def register(self, name: str, client: LocalLLMClient):
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"""Register an LLM provider.
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Args:
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name: Provider name.
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client: LLM client instance.
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"""
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self._providers[name] = client
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def get(self, name: str) -> Optional[LocalLLMClient]:
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"""Get an LLM provider by name.
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Args:
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name: Provider name.
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Returns:
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LLM client or None if not found.
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"""
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return self._providers.get(name)
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def list_providers(self) -> List[str]:
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"""List all registered provider names.
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Returns:
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List of provider names.
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"""
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return list(self._providers.keys())
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def create_default(self, config: LocalLLMConfig) -> LocalLLMClient:
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"""Create and register the default LLM provider.
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Args:
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config: Local LLM configuration.
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Returns:
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Created LLM client.
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"""
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client = LocalLLMClient(config)
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self.register("default", client)
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return client
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def create_llm_client(config: LocalLLMConfig) -> LocalLLMClient:
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"""Create an LLM client from configuration.
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Args:
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config: Local LLM configuration.
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Returns:
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Configured LLM client.
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"""
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return LocalLLMClient(config)
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messages: list
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temperature: float = 0.7
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max_tokens: int = 2048
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stream: bool = False
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