Section 4

Developer Toolkit

Production-ready API guides, library references, and copy-paste code snippets for shipping AI/ML applications.

4.1

OpenAI API

GPT-4o integration with function calling and streaming.

🐍openai_integration.py
from openai import OpenAI

client = OpenAI(api_key="your-key")

# Chat completion with function calling
tools = [{
    "type": "function",
    "function": {
        "name": "search_database",
        "description": "Search the DoD financial database",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string"},
                "fiscal_year": {"type": "integer"}
            },
            "required": ["query"]
        }
    }
}]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Find FY2025 Army modernization data"}],
    tools=tools,
    tool_choice="auto",
    temperature=0.3
)

# Streaming
with client.chat.completions.stream(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain RAG in 3 sentences"}]
) as stream:
    for chunk in stream.text_stream:
        print(chunk, end="", flush=True)

4.2

scikit-learn Production Pipeline

End-to-end ML pipeline with preprocessing, cross-validation, and model persistence.

🐍ml_pipeline.py
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
import joblib

# Production ML Pipeline
numeric_features = ['transaction_amount', 'days_outstanding', 'obligation_rate']
categorical_features = ['appropriation_type', 'vendor_category', 'service_branch']

numeric_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler()),
])

preprocessor = ColumnTransformer([
    ('num', numeric_transformer, numeric_features),
    ('cat', SimpleImputer(strategy='most_frequent'), categorical_features),
])

pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', GradientBoostingClassifier(random_state=42))
])

# Hyperparameter tuning
param_grid = {
    'classifier__n_estimators': [100, 200],
    'classifier__max_depth': [3, 5, 7],
    'classifier__learning_rate': [0.05, 0.1, 0.2],
}

search = GridSearchCV(pipeline, param_grid, cv=5, scoring='f1', n_jobs=-1)
search.fit(X_train, y_train)

print(f"Best params: {search.best_params_}")
print(f"Best F1: {search.best_score_:.4f}")

# Save the model
joblib.dump(search.best_estimator_, 'audit_risk_model_v1.pkl')

Production Tip

Always use Pipeline to prevent data leakage during cross-validation. The preprocessor should be fit only on training data — Pipeline handles this automatically.

4.3

LangChain Guide

Framework for building LLM-powered applications with chains, agents, and memory.

LangChainPopular

Most mature, extensive integrations, large community. Best for complex chains.

LlamaIndexRAG-first

Optimized for RAG and document indexing. Better structured data handling.

AutoGenAgents

Multi-agent conversations. Microsoft's framework for agent orchestration.

4.4

Vector Databases

Semantic search infrastructure for RAG systems.

DB	Type	Scale	Best For	Cost
Chroma	Local/Cloud	Small-Med	Development, prototypes	Free
Pinecone	Managed	Large	Production, real-time	Paid
Weaviate	Self/Cloud	Large	GraphQL + vectors	Open source
pgvector	PostgreSQL	Med-Large	Existing Postgres users	Free
Qdrant	Self/Cloud	Large	High performance Rust	Open source

🐍vectordb_chroma.py
# Chroma (local) - great for development
import chromadb
from chromadb.utils import embedding_functions

# Initialize local DB
client = chromadb.PersistentClient(path="./chroma_db")

# Use OpenAI or custom embeddings
openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-key",
    model_name="text-embedding-3-small"
)

collection = client.get_or_create_collection(
    name="policy_documents",
    embedding_function=openai_ef
)

# Add documents
collection.add(
    documents=["OMB Circular A-11 requires budget justification...",
               "FIAR requires detailed transaction-level data..."],
    metadatas=[{"source": "OMB A-11", "year": 2024},
               {"source": "FIAR", "year": 2024}],
    ids=["omb-a11-001", "fiar-001"]
)

# Semantic search
results = collection.query(
    query_texts=["budget submission requirements"],
    n_results=3
)

for doc, meta in zip(results['documents'][0], results['metadatas'][0]):
    print(f"[{meta['source']}] {doc[:100]}...")

4.5

Production Code Snippets

Copy-paste ready patterns for common ML engineering tasks.

🐍streaming_api.py
# Streaming responses for better UX
from anthropic import Anthropic

client = Anthropic()

async def stream_analysis(document: str):
    """Stream AI analysis with real-time output"""
    with client.messages.stream(
        model="claude-3-5-sonnet-20241022",
        max_tokens=2000,
        messages=[{
            "role": "user",
            "content": f"Analyze this document: {document}"
        }]
    ) as stream:
        for text in stream.text_stream:
            yield text  # Send to frontend via SSE

# FastAPI streaming endpoint
from fastapi import FastAPI
from fastapi.responses import StreamingResponse

app = FastAPI()

@app.post("/analyze-stream")
async def analyze_stream(request: dict):
    async def generate():
        async for chunk in stream_analysis(request["document"]):
            yield f"data: {chunk}\n\n"
    
    return StreamingResponse(generate(), media_type="text/event-stream")