mirror of
https://github.com/TheBlewish/Automated-AI-Web-Researcher-Ollama.git
synced 2025-09-01 01:59:58 +00:00
111 lines
4.8 KiB
Python
111 lines
4.8 KiB
Python
from llama_cpp import Llama
|
|
import requests
|
|
import json
|
|
import os
|
|
from llm_config import get_llm_config
|
|
from openai import OpenAI
|
|
|
|
class LLMWrapper:
|
|
def __init__(self):
|
|
self.llm_config = get_llm_config()
|
|
self.llm_type = self.llm_config.get('llm_type', 'llama_cpp')
|
|
|
|
if self.llm_type == 'llama_cpp':
|
|
self.llm = self._initialize_llama_cpp()
|
|
elif self.llm_type == 'ollama':
|
|
self.base_url = self.llm_config.get('base_url', 'http://localhost:11434')
|
|
self.model_name = self.llm_config.get('model_name', 'your_model_name')
|
|
elif self.llm_type == 'openai':
|
|
self._initialize_openai()
|
|
else:
|
|
raise ValueError(f"Unsupported LLM type: {self.llm_type}")
|
|
|
|
def _initialize_openai(self):
|
|
"""Initialize OpenAI client"""
|
|
api_key = self.llm_config.get('api_key') or os.getenv('OPENAI_API_KEY')
|
|
base_url = self.llm_config.get('base_url', 'https://api.openai.com/v1')
|
|
if not api_key:
|
|
raise ValueError("OpenAI API key not found. Set it in config or OPENAI_API_KEY environment variable")
|
|
self.client = OpenAI(api_key=api_key, base_url=base_url)
|
|
self.model_name = self.llm_config.get('model_name', 'gpt-4o-mini')
|
|
|
|
def generate(self, prompt, **kwargs):
|
|
if self.llm_type == 'llama_cpp':
|
|
llama_kwargs = self._prepare_llama_kwargs(kwargs)
|
|
response = self.llm(prompt, **llama_kwargs)
|
|
return response['choices'][0]['text'].strip()
|
|
elif self.llm_type == 'ollama':
|
|
return self._ollama_generate(prompt, **kwargs)
|
|
elif self.llm_type == 'openai':
|
|
return self._openai_generate(prompt, **kwargs)
|
|
else:
|
|
raise ValueError(f"Unsupported LLM type: {self.llm_type}")
|
|
|
|
def _openai_generate(self, prompt, **kwargs):
|
|
"""Generate text using OpenAI API"""
|
|
try:
|
|
response = self.client.chat.completions.create(
|
|
model=self.model_name,
|
|
messages=[{"role": "user", "content": prompt}],
|
|
temperature=kwargs.get('temperature', self.llm_config.get('temperature', 0.7)),
|
|
max_tokens=kwargs.get('max_tokens', self.llm_config.get('max_tokens', 4096)),
|
|
top_p=kwargs.get('top_p', self.llm_config.get('top_p', 0.9)),
|
|
stop=kwargs.get('stop', self.llm_config.get('stop', None))
|
|
)
|
|
return response.choices[0].message.content.strip()
|
|
except Exception as e:
|
|
raise Exception(f"OpenAI API error: {str(e)}")
|
|
|
|
def _initialize_llama_cpp(self):
|
|
return Llama(
|
|
model_path=self.llm_config.get('model_path'),
|
|
n_ctx=self.llm_config.get('n_ctx', 55000),
|
|
n_gpu_layers=self.llm_config.get('n_gpu_layers', 0),
|
|
n_threads=self.llm_config.get('n_threads', 8),
|
|
verbose=False
|
|
)
|
|
|
|
def _ollama_generate(self, prompt, **kwargs):
|
|
url = f"{self.base_url}/api/generate"
|
|
data = {
|
|
'model': self.model_name,
|
|
'prompt': prompt,
|
|
'options': {
|
|
'temperature': kwargs.get('temperature', self.llm_config.get('temperature', 0.7)),
|
|
'top_p': kwargs.get('top_p', self.llm_config.get('top_p', 0.9)),
|
|
'stop': kwargs.get('stop', self.llm_config.get('stop', [])),
|
|
'num_predict': kwargs.get('max_tokens', self.llm_config.get('max_tokens', 55000)),
|
|
'num_ctx': self.llm_config.get('n_ctx', 55000)
|
|
}
|
|
}
|
|
response = requests.post(url, json=data, stream=True)
|
|
if response.status_code != 200:
|
|
raise Exception(f"Ollama API request failed with status {response.status_code}: {response.text}")
|
|
text = ''.join(json.loads(line)['response'] for line in response.iter_lines() if line)
|
|
return text.strip()
|
|
|
|
def _cleanup(self):
|
|
"""Force terminate any running LLM processes"""
|
|
if self.llm_type == 'ollama':
|
|
try:
|
|
# Force terminate Ollama process
|
|
requests.post(f"{self.base_url}/api/terminate")
|
|
except:
|
|
pass
|
|
|
|
try:
|
|
# Also try to terminate via subprocess if needed
|
|
import subprocess
|
|
subprocess.run(['pkill', '-f', 'ollama'], capture_output=True)
|
|
except:
|
|
pass
|
|
|
|
def _prepare_llama_kwargs(self, kwargs):
|
|
llama_kwargs = {
|
|
'max_tokens': kwargs.get('max_tokens', self.llm_config.get('max_tokens', 55000)),
|
|
'temperature': kwargs.get('temperature', self.llm_config.get('temperature', 0.7)),
|
|
'top_p': kwargs.get('top_p', self.llm_config.get('top_p', 0.9)),
|
|
'stop': kwargs.get('stop', self.llm_config.get('stop', [])),
|
|
'echo': False,
|
|
}
|
|
return llama_kwargs
|