Production-ready LLM pipelines – prompt structures and API blocks for semantic intelligence.
# OpenAI SDK v1.0+ Integration
from openai import OpenAI
client = OpenAI(api_key="your_mak_studio_key")
def ai_analyze_semantics(user_text):
response = client.chat.completions.create(
model="gpt-4o-mini",
response_format={ "type": "json_object" },
messages=[
{"role": "system", "content": "Analyze semantic intent. Output raw JSON format with keys: 'intent', 'confidence_score', 'emotional_tone'."},
{"role": "user", "content": user_text}
],
temperature=0.1
)
return response.choices[0].message.content
# Fetch model token parsing payload
print(ai_analyze_semantics("I want to upgrade my subscription matrix right now."))# System prompt template designed for deep token classifications
AI_SEMANTIC_PROMPT = """
You are a fine-tuned zero-shot semantic parser. Analyze the input statement inside triple backticks.
Provide a clean output containing:
1. Core Polarity Vector (Scale from -1.0 to +1.0)
2. Underlying Urgency Indicator (Low / Medium / High)
Input Statement:
```{}```
"""
def format_ai_prompt(user_input):
# Returns raw prompt setup injection block
return AI_SEMANTIC_PROMPT.format(user_input)
# Feed prompt straight into your pipeline completion handler
print(format_ai_prompt("I hate waiting in queues."))Get 50+ enterprise AI snippets (Vector database indexing, RAG embedding chains, token counters) inside the Pro Pack.
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