Spaces:
Sleeping
Sleeping
File size: 13,003 Bytes
646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 b7db63d 646ba30 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 | import os
from typing import Optional, Dict, Any
from dataclasses import dataclass
import openai
import anthropic
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
# Force reload environment variables
load_dotenv(override=True)
@dataclass
class LLMResponse:
content: str
model: str
success: bool
error: Optional[str] = None
class LLMClientManager:
"""Manages connections to different LLM providers."""
def __init__(self):
self.clients = {}
self._initialize_clients()
def _initialize_clients(self):
"""Initialize available LLM clients based on API keys."""
# Debug: Print available API keys
print("🔍 Initializing LLM clients...")
# OpenAI
openai_key = os.getenv("OPENAI_API_KEY")
if openai_key:
print(f"✅ OpenAI API key found: {openai_key[:8]}...{openai_key[-4:]}")
self.clients["openai"] = openai.OpenAI(api_key=openai_key)
else:
print("❌ OpenAI API key not found")
# Anthropic
anthropic_key = os.getenv("ANTHROPIC_API_KEY")
if anthropic_key:
print(f"✅ Anthropic API key found: {anthropic_key[:8]}...{anthropic_key[-4:]}")
self.clients["anthropic"] = anthropic.Anthropic(api_key=anthropic_key)
else:
print("❌ Anthropic API key not found")
# DeepSeek (uses OpenAI-compatible API)
deepseek_key = os.getenv("DEEPSEEK_API_KEY")
if deepseek_key:
print(f"✅ DeepSeek API key found: {deepseek_key[:8]}...{deepseek_key[-4:]}")
self.clients["deepseek"] = openai.OpenAI(
api_key=deepseek_key,
base_url="https://api.deepseek.com/v1"
)
else:
print("❌ DeepSeek API key not found")
# Mercury API (OpenAI-compatible via Inception Labs)
# Support both MERCURY_API_KEY and INCEPTION_API_KEY
mercury_key = os.getenv("MERCURY_API_KEY") or os.getenv("INCEPTION_API_KEY")
if mercury_key:
print(f"✅ Mercury API key found: {mercury_key[:8]}...{mercury_key[-4:]}")
try:
# Prefer explicit base URL envs; default to Inception Labs documented endpoint
base_url = (
os.getenv("MERCURY_BASE_URL")
or os.getenv("INCEPTION_BASE_URL")
or "https://api.inceptionlabs.ai/v1"
)
self.clients["mercury"] = openai.OpenAI(api_key=mercury_key, base_url=base_url)
print("✅ Mercury client initialized successfully")
except Exception as e:
print(f"⚠️ Mercury client initialization failed: {e}")
# Still add to clients so it appears in UI, but will show error when used
self.clients["mercury"] = None
else:
print("❌ Mercury API key not found")
# Check for Hugging Face API key with multiple possible names
hf_token = os.getenv("HUGGINGFACE_API_KEY") or os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
if hf_token:
self.clients["huggingface"] = InferenceClient(token=hf_token)
def get_available_models(self) -> Dict[str, str]:
"""Return available models with display names."""
models = {}
if "openai" in self.clients:
models["openai"] = "OpenAI GPT-4o-mini"
if "anthropic" in self.clients:
models["anthropic"] = "Claude 4.5 Haiku"
if "deepseek" in self.clients:
models["deepseek"] = "DeepSeek Coder V2"
if "mercury" in self.clients:
models["mercury"] = "Mercury Fast LLM"
if "huggingface" in self.clients:
models["huggingface"] = "Hugging Face (Mixtral)"
return models
def query(self, model: str, prompt: str, temperature: float = 0.1) -> LLMResponse:
"""Query a specific LLM model."""
try:
if model == "openai" and "openai" in self.clients:
response = self.clients["openai"].chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}],
temperature=temperature
)
return LLMResponse(
content=response.choices[0].message.content,
model="OpenAI GPT-4o-mini",
success=True
)
elif model == "anthropic" and "anthropic" in self.clients:
response = self.clients["anthropic"].messages.create(
model="claude-3-5-haiku-20241022",
messages=[{"role": "user", "content": prompt}],
max_tokens=2000,
temperature=temperature
)
return LLMResponse(
content=response.content[0].text,
model="Claude 4.5 Haiku",
success=True
)
elif model == "deepseek" and "deepseek" in self.clients:
try:
response = self.clients["deepseek"].chat.completions.create(
model="deepseek-coder-v2",
messages=[{"role": "user", "content": prompt}],
temperature=temperature
)
return LLMResponse(
content=response.choices[0].message.content,
model="DeepSeek Coder V2",
success=True
)
except Exception as deepseek_error:
# Try with alternative model name if the first one fails
try:
response = self.clients["deepseek"].chat.completions.create(
model="deepseek-coder",
messages=[{"role": "user", "content": prompt}],
temperature=temperature
)
return LLMResponse(
content=response.choices[0].message.content,
model="DeepSeek Coder V2",
success=True
)
except Exception as second_error:
return LLMResponse(
content="",
model="DeepSeek Coder V2",
success=False,
error=f"DeepSeek API Error: {str(deepseek_error)}. Also tried alternative model: {str(second_error)}"
)
elif model == "mercury" and "mercury" in self.clients:
# Check if Mercury client is properly initialized
if self.clients["mercury"] is None:
return LLMResponse(
content="",
model="Mercury Fast LLM",
success=False,
error="Mercury API client not properly initialized. Check your API key and endpoint configuration."
)
# Build candidate base URLs (env first, then known defaults)
candidate_base_urls = []
if os.getenv("MERCURY_BASE_URL"):
candidate_base_urls.append(os.getenv("MERCURY_BASE_URL"))
if os.getenv("INCEPTION_BASE_URL"):
candidate_base_urls.append(os.getenv("INCEPTION_BASE_URL"))
candidate_base_urls.extend([
"https://api.inceptionlabs.ai/v1",
"https://api.mercury.ai/v1",
"https://api.mercury.ai",
])
# Candidate model names (env first, then fallbacks)
candidate_models = []
env_model = os.getenv("MERCURY_MODEL_NAME") or os.getenv("INCEPTION_MODEL_NAME")
if env_model:
candidate_models.append(env_model)
candidate_models.extend(["mercury", "mercury-fast", "mercury-pro", "gpt-4", "gpt-3.5-turbo"])
last_error: Optional[str] = None
for base_url in candidate_base_urls:
try:
client = openai.OpenAI(
api_key=(os.getenv("MERCURY_API_KEY") or os.getenv("INCEPTION_API_KEY")),
base_url=base_url,
)
for mercury_model in candidate_models:
try:
response = client.chat.completions.create(
model=mercury_model,
messages=[{"role": "user", "content": prompt}],
temperature=temperature,
max_tokens=2000,
)
return LLMResponse(
content=response.choices[0].message.content,
model="Mercury Fast LLM",
success=True,
)
except Exception as model_error:
last_error = f"{type(model_error).__name__}: {str(model_error)}"
continue
except Exception as client_error:
last_error = f"{type(client_error).__name__}: {str(client_error)}"
continue
# If all attempts failed, provide a consolidated error
if last_error and "503" in last_error:
return LLMResponse(
content="",
model="Mercury Fast LLM",
success=False,
error=(
"Mercury/Inception API returned 503 across endpoints. Service may be down. "
"Tried endpoints: " + ", ".join(candidate_base_urls)
),
)
return LLMResponse(
content="",
model="Mercury Fast LLM",
success=False,
error=(
"Mercury API request failed after trying multiple endpoints and models. "
f"Last error: {last_error or 'unknown error'}"
),
)
elif model == "huggingface" and "huggingface" in self.clients:
try:
# Use chat completion API for Mixtral model (most compatible)
response = self.clients["huggingface"].chat_completion(
messages=[{"role": "user", "content": prompt}],
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
max_tokens=2000,
temperature=temperature if temperature > 0 else 0.1,
)
return LLMResponse(
content=response.choices[0].message.content,
model="Hugging Face (Mixtral)",
success=True
)
except Exception as hf_error:
# Fallback to text generation with a simpler model
try:
response = self.clients["huggingface"].text_generation(
prompt,
model="microsoft/DialoGPT-medium",
max_new_tokens=2000,
temperature=temperature if temperature > 0 else 0.1,
)
return LLMResponse(
content=response,
model="Hugging Face (DialoGPT)",
success=True
)
except Exception as fallback_error:
return LLMResponse(
content="",
model="Hugging Face (Mixtral)",
success=False,
error=f"Hugging Face API Error: {str(hf_error)}. Fallback also failed: {str(fallback_error)}"
)
else:
return LLMResponse(
content="",
model=model,
success=False,
error=f"Model {model} not available or not configured"
)
except Exception as e:
return LLMResponse(
content="",
model=model,
success=False,
error=str(e)
) |