322 lines
10 KiB
Python
322 lines
10 KiB
Python
#!/usr/bin/env python3
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# This is free software for the public good of a permacomputer hosted at
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# permacomputer.com, an always-on computer by the people, for the people.
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# One which is durable, easy to repair, & distributed like tap water
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# for machine learning intelligence.
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#
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# The permacomputer is community-owned infrastructure optimized around
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# four values:
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#
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# TRUTH First principles, math & science, open source code freely distributed
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# FREEDOM Voluntary partnerships, freedom from tyranny & corporate control
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# HARMONY Minimal waste, self-renewing systems with diverse thriving connections
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# LOVE Be yourself without hurting others, cooperation through natural law
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#
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# This software contributes to that vision by making machine learning
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# accessible to everyone through a free, open, embeddable chat interface.
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# Code is seeds to sprout on any abandoned technology.
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"""
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uncloseai. - Python Client using OpenAI SDK
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A Python client library for OpenAI-compatible APIs with streaming support
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Compatible with vLLM, Ollama, and OpenAI-compatible endpoints
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"""
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from openai import OpenAI
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import os
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import requests
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from typing import List, Dict, Optional, Iterator
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class uncloseai:
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"""Client for OpenAI-compatible API endpoints using OpenAI SDK"""
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def __init__(
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self,
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model_endpoints: Optional[List[str]] = None,
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tts_endpoints: Optional[List[str]] = None,
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api_key: str = "dummy-key",
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timeout: int = 30
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):
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"""
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Initialize uncloseai. client with automatic model discovery
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Args:
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model_endpoints: List of model endpoint URLs (defaults to MODEL_ENDPOINT_* env vars)
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tts_endpoints: List of TTS endpoint URLs (defaults to TTS_ENDPOINT_* env vars)
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api_key: API key for authentication (default: "dummy-key")
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timeout: Request timeout in seconds
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"""
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self.timeout = timeout
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self.api_key = api_key
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self.models: List[Dict] = []
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self.tts_endpoints: List[str] = []
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# Discover endpoints from environment or use provided
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if model_endpoints is None:
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model_endpoints = self._discover_env_endpoints("MODEL_ENDPOINT")
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if tts_endpoints is None:
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tts_endpoints = self._discover_env_endpoints("TTS_ENDPOINT")
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# Discover models from each endpoint
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for endpoint in model_endpoints:
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self._discover_models_from_endpoint(endpoint)
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self.tts_endpoints = tts_endpoints
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def _discover_env_endpoints(self, prefix: str) -> List[str]:
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"""Discover endpoints from environment variables like PREFIX_1, PREFIX_2, ..."""
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endpoints = []
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for i in range(1, 10000):
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endpoint = os.getenv(f"{prefix}_{i}")
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if not endpoint:
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break
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endpoints.append(endpoint)
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return endpoints
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def _discover_models_from_endpoint(self, endpoint: str) -> None:
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"""Discover available models from an endpoint"""
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try:
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response = requests.get(f"{endpoint}/models", timeout=10)
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if response.status_code == 200:
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data = response.json()
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for model in data.get("data", []):
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model_id = model["id"]
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# Filter out modelperm-* and chatcmpl-* entries
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if model_id.startswith("modelperm-") or model_id.startswith("chatcmpl-"):
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continue
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self.models.append({
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"id": model_id,
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"endpoint": endpoint,
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"max_tokens": model.get("max_model_len", 8192)
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})
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except Exception:
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# Silently skip failed endpoints
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pass
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def list_models(self) -> List[Dict]:
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"""Return list of discovered models with their metadata"""
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return self.models.copy()
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def chat(
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self,
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messages: List[Dict[str, str]],
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model: Optional[str] = None,
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max_tokens: int = 100,
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temperature: float = 0.7,
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**kwargs
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) -> Dict:
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"""
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Non-streaming chat completion
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Args:
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messages: List of message dicts with 'role' and 'content'
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model: Model ID (defaults to first available model)
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max_tokens: Maximum tokens in response
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temperature: Sampling temperature
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**kwargs: Additional parameters to pass to the API
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Returns:
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Response dict with 'choices' containing the completion
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"""
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model_info = self._get_model_info(model)
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client = OpenAI(
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base_url=model_info['endpoint'],
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api_key=self.api_key,
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timeout=self.timeout
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)
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response = client.chat.completions.create(
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model=model_info["id"],
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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**kwargs
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)
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# Convert OpenAI response to dict format
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return {
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"id": response.id,
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"model": response.model,
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"choices": [
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{
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"index": choice.index,
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"message": {
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"role": choice.message.role,
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"content": choice.message.content
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},
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"finish_reason": choice.finish_reason
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}
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for choice in response.choices
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],
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"usage": {
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"prompt_tokens": response.usage.prompt_tokens,
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"completion_tokens": response.usage.completion_tokens,
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"total_tokens": response.usage.total_tokens
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}
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}
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def chat_stream(
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self,
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messages: List[Dict[str, str]],
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model: Optional[str] = None,
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max_tokens: int = 500,
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temperature: float = 0.7,
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**kwargs
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) -> Iterator[str]:
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"""
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Streaming chat completion using OpenAI SDK
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Args:
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messages: List of message dicts with 'role' and 'content'
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model: Model ID (defaults to first available model)
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max_tokens: Maximum tokens in response
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temperature: Sampling temperature
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**kwargs: Additional parameters to pass to the API
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Yields:
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Content strings as they arrive
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"""
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model_info = self._get_model_info(model)
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client = OpenAI(
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base_url=model_info['endpoint'],
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api_key=self.api_key,
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timeout=self.timeout
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)
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stream = client.chat.completions.create(
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model=model_info["id"],
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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stream=True,
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**kwargs
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)
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for chunk in stream:
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if chunk.choices[0].delta.content:
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yield chunk.choices[0].delta.content
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def tts(
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self,
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text: str,
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voice: str = "alloy",
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model: str = "tts-1",
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output_file: str = "speech.mp3"
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) -> str:
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"""
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Generate speech from text
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Args:
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text: Input text to convert to speech
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voice: Voice name (alloy, echo, fable, onyx, nova, shimmer)
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model: TTS model (tts-1 or tts-1-hd)
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output_file: Path to save the audio file
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Returns:
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Path to the saved audio file
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"""
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if not self.tts_endpoints:
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raise ValueError("No TTS endpoints available")
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endpoint = self.tts_endpoints[0]
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client = OpenAI(
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base_url=endpoint,
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api_key=self.api_key,
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timeout=self.timeout
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)
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with client.audio.speech.with_streaming_response.create(
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model=model,
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voice=voice,
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input=text
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) as response:
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response.stream_to_file(output_file)
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return output_file
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def _get_model_info(self, model: Optional[str] = None) -> Dict:
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"""Get model info by ID or return first available model"""
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if not self.models:
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raise ValueError("No models available. Check endpoint configuration.")
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if model is None:
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return self.models[0]
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for m in self.models:
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if m["id"] == model:
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return m
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raise ValueError(f"Model '{model}' not found in discovered models")
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# Demo usage when run as script
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if __name__ == "__main__":
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print("=== uncloseai. Python Client (OpenAI SDK) ===\n")
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# Initialize client (auto-discovers from environment)
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client = uncloseai()
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if not client.models:
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print("ERROR: No models discovered. Set environment variables:")
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print(" MODEL_ENDPOINT_1, MODEL_ENDPOINT_2, etc.")
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exit(1)
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print(f"Discovered {len(client.models)} model(s)")
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for model in client.models:
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print(f" - {model['id']} (max_tokens: {model['max_tokens']})")
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print()
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# Non-streaming chat example
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print("=== Non-Streaming Chat ===")
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response = client.chat(
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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{"role": "user", "content": "Explain quantum computing in one sentence."}
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],
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max_tokens=100
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)
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print(f"Model: {response['model']}")
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print(f"Response: {response['choices'][0]['message']['content']}\n")
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# Streaming chat example
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print("=== Streaming Chat ===")
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if len(client.models) > 1:
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model_id = client.models[1]["id"]
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else:
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model_id = None
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print(f"Model: {model_id or client.models[0]['id']}")
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print("Response: ", end="", flush=True)
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for content in client.chat_stream(
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messages=[
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{"role": "system", "content": "You are a coding assistant."},
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{"role": "user", "content": "Write a Python function to check if a number is prime"}
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],
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model=model_id,
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max_tokens=200
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):
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print(content, end="", flush=True)
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print("\n")
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# TTS example
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if client.tts_endpoints:
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print("=== TTS Speech Generation ===")
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output_path = client.tts(
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text="Hello from uncloseai. Python client with OpenAI SDK! This demonstrates text to speech with streaming support.",
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voice="alloy",
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output_file="speech.mp3"
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)
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if os.path.exists(output_path):
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file_size = os.path.getsize(output_path)
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print(f"[OK] Speech file created: {output_path} ({file_size} bytes)\n")
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print("=== Examples Complete ===")
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