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"""
Utilities module: LLM client wrapper and shared helpers.
"""
import os
import openai
from typing import List
from openai import AzureOpenAI
from langchain_openai import AzureOpenAIEmbeddings
try:
from src.utils import logger
except ImportError:
import structlog
logger = structlog.get_logger()
class LLMClient:
"""
Simple wrapper around OpenAI (or any other) LLM API.
Reads API key from environment and exposes `generate(prompt)`.
"""
@staticmethod
def generate(prompt: str, model: str = None, max_tokens: int = 512, **kwargs) -> str:
azure_api_key = os.getenv('AZURE_API_KEY')
azure_endpoint = os.getenv('AZURE_ENDPOINT')
azure_api_version = os.getenv('AZURE_API_VERSION')
openai_model_name = model or os.getenv('OPENAI_MODEL', 'gpt-4o')
if not (azure_api_key or azure_endpoint or azure_api_version or openai_model_name):
logger.error('OPENAI_API_KEY is not set')
raise EnvironmentError('Missing OPENAI_API_KEY')
client = AzureOpenAI(
api_key=azure_api_key,
azure_endpoint=azure_endpoint,
api_version=azure_api_version
)
try:
resp = client.chat.completions.create(
model=openai_model_name,
messages=[{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}],
max_tokens=max_tokens,
temperature=0.0,
**kwargs
)
text = resp.choices[0].message.content.strip()
return text
except Exception as e:
logger.exception('LLM generation failed')
raise
class OpenAIEmbedder:
"""
Wrapper around OpenAI Embeddings API.
Usage: embedder = OpenAIEmbedder(model_name)
embs = embedder.embed([str1, str2, ...])
"""
def __init__(self, model_name: str):
self.model = model_name
openai.api_key = os.getenv("OPENAI_API_KEY")
def embed(self, texts: List[str]) -> List[List[float]]:
embeddings = AzureOpenAIEmbeddings(model=self.model)
resp = embeddings.embed_documents(texts)
# return list of embedding vectors
return resp |