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# DEPENDENCIES
import os
import json
import time
import aiohttp
import requests
from typing import List
from typing import Dict
from typing import Optional
from typing import AsyncGenerator
from config.models import LLMProvider
from config.settings import get_settings
from config.logging_config import get_logger
from utils.error_handler import handle_errors
from utils.error_handler import LLMClientError
# Setup Settings and Logging
settings = get_settings()
logger = get_logger(__name__)
class LLMClient:
"""
Unified LLM client supporting multiple providers (Ollama, OpenAI): Provides consistent interface for text generation across different LLM services
"""
def __init__(self, provider: LLMProvider = None, model_name: str = None, api_key: str = None, base_url: str = None):
"""
Initialize LLM client
Arguments:
----------
provider { LLMProvider } : LLM provider to use
model_name { str } : Model name to use
api_key { str } : API key (for OpenAI)
base_url { str } : Base URL for API (for Ollama)
"""
self.logger = logger
self.settings = get_settings()
self.provider = provider or LLMProvider.OLLAMA
self.model_name = model_name or self.settings.OLLAMA_MODEL
self.api_key = api_key
self.base_url = base_url or self.settings.OLLAMA_BASE_URL
self.timeout = self.settings.OLLAMA_TIMEOUT
# Initialize provider-specific configurations
self._initialize_provider()
def _initialize_provider(self):
"""
Initialize provider-specific configurations
"""
# Auto-detect provider if not explicitly set
if self.settings.IS_HF_SPACE:
# In Hugging Face Space, we can't use Ollama
if (self.provider == LLMProvider.OLLAMA):
self.logger.warning("Ollama not available in Hugging Face Space. Switching provider.")
# Try to use OpenAI if API key is available
if (self.settings.USE_OPENAI and self.settings.OPENAI_API_KEY):
self.provider = LLMProvider.OPENAI
self.logger.info("HF Space detected: Using OpenAI API")
else:
# No OpenAI key either - create a dummy client or raise error
raise LLMClientError("Running in Hugging Face Space without Ollama or OpenAI. Please set OPENAI_API_KEY in Space secrets or use a different provider.")
# Provider initialization
if (self.provider == LLMProvider.OLLAMA):
if not self.base_url:
raise LLMClientError("Ollama base URL is required")
self.logger.info(f"Initialized Ollama client: {self.base_url}, model: {self.model_name}")
elif (self.provider == LLMProvider.OPENAI):
if not self.api_key:
# Try to get from environment
self.api_key = os.getenv('OPENAI_API_KEY')
if not self.api_key:
raise LLMClientError("OpenAI API key is required")
self.base_url = "https://api.openai.com/v1"
self.logger.info(f"Initialized OpenAI client, model: {self.model_name}")
else:
raise LLMClientError(f"Unsupported provider: {self.provider}")
async def generate(self, messages: List[Dict], **generation_params) -> Dict:
"""
Generate text completion (async)
Arguments:
----------
messages { list } : List of message dictionaries
**generation_params : Generation parameters (temperature, max_tokens, etc.)
Returns:
--------
{ dict } : Generation response
"""
try:
if (self.provider == LLMProvider.OLLAMA):
return await self._generate_ollama(messages, **generation_params)
elif (self.provider == LLMProvider.OPENAI):
return await self._generate_openai(messages, **generation_params)
else:
raise LLMClientError(f"Unsupported provider: {self.provider}")
except Exception as e:
self.logger.error(f"Generation failed: {repr(e)}")
raise LLMClientError(f"Generation failed: {repr(e)}")
async def generate_stream(self, messages: List[Dict], **generation_params) -> AsyncGenerator[str, None]:
"""
Generate text completion with streaming (async)
Arguments:
----------
messages { list } : List of message dictionaries
**generation_params : Generation parameters
Returns:
--------
{ AsyncGenerator } : Async generator yielding response chunks
"""
try:
if (self.provider == LLMProvider.OLLAMA):
async for chunk in self._generate_ollama_stream(messages, **generation_params):
yield chunk
elif (self.provider == LLMProvider.OPENAI):
async for chunk in self._generate_openai_stream(messages, **generation_params):
yield chunk
else:
raise LLMClientError(f"Unsupported provider: {self.provider}")
except Exception as e:
self.logger.error(f"Stream generation failed: {repr(e)}")
raise LLMClientError(f"Stream generation failed: {repr(e)}")
async def _generate_ollama(self, messages: List[Dict], **generation_params) -> Dict:
"""
Generate using Ollama API
"""
url = f"{self.base_url}/api/chat"
# Prepare request payload
payload = {"model" : self.model_name,
"messages" : messages,
"stream" : False,
"options" : {"temperature" : generation_params.get("temperature", 0.1),
"top_p" : generation_params.get("top_p", 0.9),
"num_predict" : generation_params.get("max_tokens", 1000),
}
}
async with aiohttp.ClientSession() as session:
async with session.post(url, json = payload, timeout = self.timeout) as response:
if (response.status != 200):
error_text = await response.text()
raise LLMClientError(f"Ollama API error: {response.status} - {error_text}")
result = await response.json()
return {"content" : result["message"]["content"],
"usage" : {"prompt_tokens" : result.get("prompt_eval_count", 0),
"completion_tokens" : result.get("eval_count", 0),
"total_tokens" : result.get("prompt_eval_count", 0) + result.get("eval_count", 0),
},
"finish_reason" : result.get("done_reason", "stop"),
}
async def _generate_ollama_stream(self, messages: List[Dict], **generation_params) -> AsyncGenerator[str, None]:
"""
Generate stream using Ollama API - FIXED VERSION
"""
url = f"{self.base_url}/api/chat"
payload = {"model" : self.model_name,
"messages" : messages,
"stream" : True,
"options" : {"temperature" : generation_params.get("temperature", 0.1),
"top_p" : generation_params.get("top_p", 0.9),
"num_predict" : generation_params.get("max_tokens", 1000),
}
}
async with aiohttp.ClientSession() as session:
async with session.post(url, json = payload, timeout = self.timeout) as response:
if (response.status != 200):
error_text = await response.text()
raise LLMClientError(f"Ollama API error: {response.status} - {error_text}")
async for line in response.content:
line_str = line.decode('utf-8').strip()
# Skip empty lines
if not line_str:
continue
try:
chunk_data = json.loads(line_str)
# Check if this is the final chunk
if (chunk_data.get("done", False)):
break
# Extract content regardless of whether it's empty: Ollama sends incremental content in each chunk
if ("message" in chunk_data):
content = chunk_data["message"].get("content", "")
# Only yield non-empty content
if content:
yield content
except json.JSONDecodeError as e:
self.logger.warning(f"Failed to parse streaming chunk: {line_str[:100]}")
continue
async def _generate_openai(self, messages: List[Dict], **generation_params) -> Dict:
"""
Generate using OpenAI API
"""
url = f"{self.base_url}/chat/completions"
headers = {"Authorization" : f"Bearer {self.api_key}",
"Content-Type" : "application/json",
}
payload = {"model" : self.model_name,
"messages" : messages,
"temperature" : generation_params.get("temperature", 0.1),
"top_p" : generation_params.get("top_p", 0.9),
"max_tokens" : generation_params.get("max_tokens", 1000),
"stream" : False,
}
async with aiohttp.ClientSession() as session:
async with session.post(url, headers = headers, json = payload, timeout = self.timeout) as response:
if (response.status != 200):
error_text = await response.text()
raise LLMClientError(f"OpenAI API error: {response.status} - {error_text}")
result = await response.json()
return {"content" : result["choices"][0]["message"]["content"],
"usage" : result["usage"],
"finish_reason" : result["choices"][0]["finish_reason"],
}
async def _generate_openai_stream(self, messages: List[Dict], **generation_params) -> AsyncGenerator[str, None]:
"""
Generate stream using OpenAI API
"""
url = f"{self.base_url}/chat/completions"
headers = {"Authorization" : f"Bearer {self.api_key}",
"Content-Type" : "application/json",
}
payload = {"model" : self.model_name,
"messages" : messages,
"temperature" : generation_params.get("temperature", 0.1),
"top_p" : generation_params.get("top_p", 0.9),
"max_tokens" : generation_params.get("max_tokens", 1000),
"stream" : True,
}
async with aiohttp.ClientSession() as session:
async with session.post(url, headers = headers, json = payload, timeout = self.timeout) as response:
if (response.status != 200):
error_text = await response.text()
raise LLMClientError(f"OpenAI API error: {response.status} - {error_text}")
async for line in response.content:
line = line.decode('utf-8').strip()
if (line.startswith('data: ')):
# Remove 'data: ' prefix
data = line[6:]
if (data == '[DONE]'):
break
try:
chunk_data = json.loads(data)
if ("choices" in chunk_data) and (chunk_data["choices"]):
delta = chunk_data["choices"][0].get("delta", {})
if ("content" in delta):
yield delta["content"]
except json.JSONDecodeError:
continue
def check_health(self) -> bool:
"""
Check if LLM provider is healthy and accessible
Returns:
--------
{ bool } : True if healthy
"""
try:
if (self.provider == LLMProvider.OLLAMA):
response = requests.get(f"{self.base_url}/api/tags", timeout = 30)
return (response.status_code == 200)
elif (self.provider == LLMProvider.OPENAI):
# Simple models list check
headers = {"Authorization": f"Bearer {self.api_key}"}
response = requests.get(f"{self.base_url}/models", headers=headers, timeout=10)
return (response.status_code == 200)
return False
except Exception as e:
self.logger.warning(f"Health check failed: {repr(e)}")
return False
def get_provider_info(self) -> Dict:
"""
Get provider information
Returns:
--------
{ dict } : Provider information
"""
return {"provider" : self.provider.value,
"model" : self.model_name,
"base_url" : self.base_url,
"healthy" : self.check_health(),
"timeout" : self.timeout,
}
# Global LLM client instance
_llm_client = None
def get_llm_client(provider: LLMProvider = None, **kwargs) -> LLMClient:
"""
Get global LLM client instance (singleton)
Arguments:
----------
provider { LLMProvider } : LLM provider to use
**kwargs : Additional client configuration
Returns:
--------
{ LLMClient } : LLMClient instance
"""
global _llm_client
if _llm_client is None or (provider and _llm_client.provider != provider):
_llm_client = LLMClient(provider, **kwargs)
return _llm_client
@handle_errors(error_type = LLMClientError, log_error = True, reraise = False)
async def generate_text(messages: List[Dict], provider: LLMProvider = LLMProvider.OLLAMA, **kwargs) -> str:
"""
Convenience function for text generation
Arguments:
----------
messages { list } : List of message dictionaries
provider { LLMProvider } : LLM provider to use
**kwargs : Generation parameters
Returns:
--------
{ str } : Generated text
"""
client = get_llm_client(provider, **kwargs)
response = await client.generate(messages, **kwargs)
return response["content"]
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