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