Production-Grade Engineering Stacks & AI

# Pydantic Structured Output Enforcement with Instructor
from pydantic import BaseModel, Field
import instructor
from openai import OpenAI

class EnterpriseContractSummary(BaseModel):
    client_name: str = Field(description="Party signing the contract")
    total_value_usd: float = Field(description="Total monetary value")
    sla_percentage: float = Field(description="Service level agreement guaranteed")

client = instructor.from_openai(OpenAI())
response = client.chat.completions.create(
    model="gpt-4o",
    response_model=EnterpriseContractSummary,
    messages=[{"role": "user", "content": raw_contract_text}]
)

# Two-Stage Hybrid Vector Search with Re-Ranking
from qdrant_client import QdrantClient
import cohere

qdrant = QdrantClient(url="https://vector.sibiri.internal")
co = cohere.Client("cohere-key")

async def retrieve_grounded_context(query: str):
    # Step 1: Hybrid Vector + Keyword Retrieval
    candidates = qdrant.query(collection_name="enterprise_docs", query_text=query, limit=25)
    
    # Step 2: Cross-Encoder Re-Ranking
    reranked = co.rerank(model="rerank-english-v3.0", query=query, documents=[c.payload["text"] for c in candidates], top_n=4)
    return [r.document.text for r in reranked.results]

# Production LCEL Chain with Vector Retrieval & Streaming Output
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("""Answer based strictly on the context:
{context}
Question: {question}""")

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | ChatOpenAI(model="gpt-4o", temperature=0.1)
    | StrOutputParser()
)

# LangGraph Multi-Agent State Machine with Reflection Loop
from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated

class AgentState(TypedDict):
    task: str
    draft_code: str
    review_passed: bool
    iterations: int

builder = StateGraph(AgentState)
builder.add_node("developer", write_code_node)
builder.add_node("reviewer", review_code_node)
builder.add_edge("developer", "reviewer")

builder.add_conditional_edges(
    "reviewer",
    lambda s: END if s["review_passed"] or s["iterations"] >= 3 else "developer"
)
builder.set_entry_point("developer")
graph = builder.compile()

# CrewAI Multi-Agent Task Orchestration
from crewai import Agent, Task, Crew, Process

architect = Agent(
    role="Enterprise Software Architect",
    goal="Design scalable cloud architectures for incoming client requirements",
    backstory="Senior software architect with 15+ years in high-concurrency cloud systems",
    tools=[cloud_pricing_tool, benchmark_tool],
    verbose=True
)

qa_engineer = Agent(
    role="Security & Resilience Auditor",
    goal="Audit architecture designs against OWASP top 10 and cloud resilience standards",
    backstory="Strict cybersecurity auditor focused on zero-trust and data privacy",
    verbose=True
)

crew = Crew(
    agents=[architect, qa_engineer],
    tasks=[design_task, audit_task],
    process=Process.sequential
)
result = crew.kickoff()

# PyTorch Inference Pipeline with ONNX Runtime Acceleration
import onnxruntime as ort
import numpy as np

class OptimizedInferenceEngine:
    def __init__(self, model_path: str):
        self.session = ort.InferenceSession(
            model_path, 
            providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
        )
        self.input_name = self.session.get_inputs()[0].name

    def predict(self, feature_vector: np.ndarray) -> dict:
        inputs = {self.input_name: feature_vector.astype(np.float32)}
        raw_outputs = self.session.run(None, inputs)
        probabilities = np.exp(raw_outputs[0]) / np.sum(np.exp(raw_outputs[0]))
        return {'predicted_class': int(np.argmax(probabilities)), 'confidence': float(np.max(probabilities))}

# LangGraph Multi-Agent Cyclic State Machine
from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

class AgentWorkflowState(TypedDict):
    query: str
    retrieved_docs: list[str]
    draft_response: str
    critique: str
    iteration: int

def retrieve_node(state: AgentWorkflowState):
    docs = qdrant_hybrid_search(state['query'], top_k=5)
    return {'retrieved_docs': docs}

def generate_node(state: AgentWorkflowState):
    draft = llm_synthesize(state['query'], state['retrieved_docs'])
    return {'draft_response': draft, 'iteration': state.get('iteration', 0) + 1}

def critique_node(state: AgentWorkflowState):
    verdict = llm_verify_facts(state['draft_response'], state['retrieved_docs'])
    return {'critique': verdict}

workflow = StateGraph(AgentWorkflowState)
workflow.add_node('retrieve', retrieve_node)
workflow.add_node('generate', generate_node)
workflow.add_node('critique', critique_node)
workflow.set_entry_point('retrieve')
workflow.add_edge('retrieve', 'generate')
workflow.add_edge('generate', 'critique')
workflow.add_conditional_edges('critique', lambda s: END if s['critique'] == 'PASS' or s['iteration'] > 3 else 'generate')
agent_app = workflow.compile()