File size: 4,745 Bytes
9f50319 | 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 | import os
from openai import OpenAI
from anthropic import Anthropic
import google.generativeai as genai
import requests
from dotenv import load_dotenv
load_dotenv()
class LLMCaller:
def __init__(self, model_name: str = "gpt-4.1-mini-2025-04-14"):
"""
Initialize LLM model with unified interface
Parameters:
- model_name: Model name to use, defaults to "gpt-4.1-mini-2025-04-14"
"""
self.model_name = model_name
self._setup_model()
def _setup_model(self):
"""Setup model configuration based on model name"""
if self.model_name.startswith("gpt"):
self.client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY"),
base_url="https://api.openai.com/v1"
)
self.api_type = "openai"
elif self.model_name.startswith("claude"):
self.client = Anthropic(
api_key=os.getenv("ANTHROPIC_API_KEY")
)
self.api_type = "anthropic"
elif self.model_name.startswith("deepseek"):
self.api_key = os.getenv("DEEPSEEK_API_KEY")
self.api_url = "https://api.deepseek.com/v1/chat/completions"
self.api_type = "deepseek"
elif self.model_name.startswith("gemini"):
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
self.model = genai.GenerativeModel(self.model_name)
self.api_type = "gemini"
elif self.model_name in ["llama-3-70b", "mixtral-8x7b", "qwen-72b"]:
self.api_url = "http://localhost:8000/v1"
self.api_type = "local"
else:
raise ValueError(f"Unsupported model: {self.model_name}")
def call(self, prompt: str) -> str:
"""
Call LLM API with a single prompt
Parameters:
- prompt: Input prompt string
Returns:
- response: Model's response
"""
try:
if self.api_type == "openai":
response = self.client.chat.completions.create(
model=self.model_name,
messages=[
{"role": "system", "content": "You are a mathematical expert specializing in graph theory and persistent homology."},
{"role": "user", "content": prompt}
]
)
return response.choices[0].message.content
elif self.api_type == "anthropic":
response = self.client.messages.create(
model=self.model_name,
system="You are a helpful assistant.",
messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
elif self.api_type == "deepseek":
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
data = {
"model": self.model_name,
"messages": [
{"role": "user", "content": "/no_think" + prompt}
]
}
response = requests.post(self.api_url, headers=headers, json=data)
response.raise_for_status()
return response.json()["choices"][0]["message"]["content"]
elif self.api_type == "gemini":
response = self.model.generate_content(prompt)
return response.text
elif self.api_type == "local":
data = {
"model": self.model_name,
"messages": [
{"role": "user", "content": prompt}
]
}
response = requests.post(self.api_url, json=data)
response.raise_for_status()
return response.json()["choices"][0]["message"]["content"]
except Exception as e:
print(f"Error calling {self.model_name} API: {e}")
return f"API call error: {str(e)}"
def batch_call(self, prompts: list[str]) -> list[str]:
"""
Batch call LLM API with multiple prompts
Parameters:
- prompts: List of input prompts
Returns:
- responses: List of model responses
"""
responses = []
for idx, prompt in enumerate(prompts, start=1):
print(f"Processing prompt {idx}/{len(prompts)}")
response = self.call(prompt)
responses.append(response)
return responses |