Reference Architectures — Chat, RAG, Classification, Forecasting (catalog)

Overview

This catalog gives pragmatic, runnable reference architectures and implementation recipes for four high-value AI use cases: chat assistants, RAG (vector search + retrieval planning), classification pipelines, and forecasting pipelines. Each entry is intentionally concrete: core components, recommended open-source and commercial tools, a minimal deploy recipe, monitoring and validation checks, and notes on security, cost, and scale.

Use these patterns as starting points you can copy and tailor to your data, compliance needs, latency targets, and budget.

1. Chat Assistant Reference Stack

Purpose: Low-latency conversational interface with context management, tool execution, and session safety.

Core components

• Client UI (web or mobile) — session and message UI
• Session manager — stores conversation state, system prompts, and user preferences
• Prompt / policy layer — templates, guardrails, and temperature/response controls
• LLM layer — hosted model or on‑prem model (OpenAI, Anthropic, Azure, Hugging Face, local Llama family, etc.)
• Tooling layer — domain tools the assistant can call (search, database queries, calculators, ticket creation)
• Execution orchestration — planner/agent controller (LangChain, custom agent) for tool calls
• Observability — logs, latency metrics, cost tracking, and conversation analytics

Minimal runnable recipe

1. Start with a simple web UI that posts messages to a session API.
2. Store last N turns and system prompt in a session store (Redis or database).
3. Send concatenated prompt to an LLM provider with a conservative token budget.
4. If tool use is needed, implement a small agent loop: detect intent → call tool → summarize result → continue.
5. Add logging of user messages, prompts, and model responses for debugging and drift analysis.

Recommended tech

• Agent frameworks: LangChain, LlamaIndex, or Haystack
• Session store: Redis, PostgreSQL
• LLM providers: OpenAI, Anthropic, Azure OpenAI, Hugging Face + local inference
• Orchestration: Kubernetes, serverless functions
• Monitoring: Prometheus, Grafana, application logs, cost dashboards

Monitoring & validation

• Latency, error rate, token cost per session
• Quality checks: periodic human review sampling, NPS or satisfaction score
• Safety: flag unsafe outputs, implement rejection/rewriting policies

Security & privacy

• Mask/omit sensitive data from prompts when possible
• Encrypt session data at rest and in transit
• Apply RBAC to tool endpoints and logs

2. RAG (Retrieval-Augmented Generation) — Vector Store + Retrieval Planner

Purpose: Combine document retrieval with LLM generation so outputs are grounded in source content.

Core components

• Document ingestion & chunking — convert docs to text chunks with metadata
• Embedding model — convert chunks to vectors (OpenAI embeddings, Sentence Transformers)
• Vector store / index — FAISS, Milvus, Weaviate, Pinecone, or proprietary
• Retriever — nearest-neighbor search, optionally reranker
• Retrieval planner — decides which documents to fetch and how to assemble context
• LLM prompt composer — constructs a prompt with retrieved context and question
• Attribution layer — returns source passages and citations with the answer

Minimal runnable recipe

1. Ingest a small corpus (wiki pages, docs) and chunk at 500–1,000 tokens with overlap.
2. Compute embeddings for chunks and store in a vector store.
3. Implement a retriever that returns top-K chunks for a query.
4. Compose a prompt: system instruction + user question + retrieved chunks (with separators) + answer template.
5. Return the LLM output plus the list of source chunks (with highlights) for attribution.

Recommended tech

• Ingestion: Apache NiFi, Python scripts, or cloud functions
• Embeddings: OpenAI embeddings, Hugging Face sentence-transformers
• Vector stores: FAISS (local), Milvus, Weaviate, Pinecone
• RAG frameworks: LangChain, LlamaIndex, Haystack

Monitoring & validation

• Retrieval relevance: precision@K, human evaluation of citation correctness
• Freshness: timestamp metadata and staleness alarms
• Hallucination checks: sample queries with known answers and measure correctness

Operational notes

• Plan re-ingestion cadence for changing content and version vectors
• Manage vector store capacity and shard strategy for scale
• Store provenance metadata to enable quick audits and corrections

3. Classification Pipeline (Supervised) — Labeling to Monitoring

Purpose: Reliable classification (binary/multi-class/multi-label) with repeatable training, evaluation, and drift detection.

Core components

• Data ingestion & validation — schema checks, dedupe
• Labeling interface — human labeling and consensus (Label Studio, Prodigy)
• Feature store / preprocessing — consistent feature transforms in train and serving
• Model training & experiments — training orchestration, experiment tracking (MLflow)
• Model registry & deployment — versioning and canary or shadow deploys
• Monitoring — accuracy, precision/recall, data drift, concept drift

Minimal runnable recipe

1. Collect labeled examples and hold out a test set. Start small (1k–5k labeled rows is often enough to iterate).
2. Build a baseline model (logistic regression or a small transformer) and log experiments.
3. Deploy a model behind a simple inference endpoint with request logging.
4. Monitor key metrics and set thresholds for retraining or rollback.

Recommended tech

• Labeling: Label Studio, Prodigy, custom forms
• Training: scikit-learn, PyTorch, TensorFlow, Hugging Face
• Experiment tracking: MLflow, Weights & Biases
• Serving: Seldon, KServe, TorchServe, serverless endpoints
• Feature store: Feast or managed cloud feature stores

Monitoring & governance

• Data schema checks and alerting on missing fields
• Model performance monitoring and label drift detection
• Human-in-the-loop escalation for low-confidence predictions

4. Forecasting Pipeline — Time-series & Retraining Cadence

Purpose: Predict future values with repeatable backtesting, automated retraining, and robust evaluation for operational decisions.

Core components

• Data ingestion & windowing — consistent time alignment and resampling
• Feature engineering — seasonality, lag features, external regressors
• Modeling & backtesting — holdout windows, rolling forecasts
• Model selection & ensembling — classical and ML models
• Retraining & deployment cadence — scheduled retrain with validation
• Alerting & business KPIs — track forecast error against thresholds

Minimal runnable recipe

1. Define the forecasting frequency and horizon (daily, weekly, 90-day horizon, etc.).
2. Prepare a historic series with any external regressors; create rolling windows for validation.
3. Start with a baseline (Prophet or ARIMA) and add a more advanced model (TFT, N-BEATS) if needed.
4. Backtest with rolling windows and deploy the best model; schedule retraining based on error drift.

Recommended tech

• Libraries: Prophet, statsmodels, darts, sktime, PyTorch Lightning for deep models
• Orchestration: Airflow, Prefect, or cron for scheduled retrains and data pipelines
• Monitoring: track MAPE, RMSE, bias, and business-impact KPIs

Retraining cadence guidance

• Stable environments: weekly or monthly retrain
• Volatile data: daily or event-driven retrain when external signals change
• Always validate with backtesting and require human sign-off for production model changes that materially affect business outcomes

Quick adaptation checklist (copy & run)

• Define the outcome you measure (e.g., reduce support time, improve accuracy, reduce forecast error).
• Choose a minimal technology stack (one embedding provider, one vector store, one LLM provider).
• Implement robust logging and provenance for inputs/outputs.
• Start small with a pilot dataset and expand as you validate value.
• Automate retraining and add human review gates for safety and quality.

Starter repos, frameworks, and references

Useful projects and frameworks to copy or study: LangChain, LlamaIndex, Haystack, FAISS, Milvus, Weaviate, Pinecone, MLflow, Label Studio, Prophet, Darts, Feast, Seldon/KServe. Use them as the building blocks of the recipes above.

Next steps for teams

1. Pick one architecture that maps to a prioritized business hunger and run a 2–4 week pilot with clear success metrics.
2. Document data sources, access policies, and cost/latency targets before scaling.
3. Plan for observability and human review from day one.
4. Iterate: improve retrieval, labeling quality, or feature engineering depending on which metric matters most.

If you want, this catalog can be converted into copyable templates, interactive checklists, and starter repos that a team can acquire and tailor to their environment.