| name | sap-rpt1-oss-predictor |
| description | Use SAP-RPT-1-OSS open source tabular foundation model for predictive analytics on SAP business data. Handles classification and regression tasks including customer churn prediction, delivery delay forecasting, payment default risk, demand planning, and financial anomaly detection. Use when asked to predict, forecast, classify, or analyze patterns in SAP tabular data exports (CSV/DataFrame). Runs locally via Hugging Face model. |
SAP-RPT-1-OSS Predictor
SAP-RPT-1-OSS is SAP's open source tabular foundation model (Apache 2.0) for predictions on structured business data. Unlike LLMs that predict text, RPT-1 predicts field values in table rows using in-context learning—no model training required.
Repository: https://github.com/SAP-samples/sap-rpt-1-oss
Model: https://huggingface.co/SAP/sap-rpt-1-oss
Setup
1. Install Package
pip install git+https://github.com/SAP-samples/sap-rpt-1-oss
2. Hugging Face Authentication
Model weights require HF login and license acceptance:
pip install huggingface_hub
huggingface-cli login
Then accept model terms at: https://huggingface.co/SAP/sap-rpt-1-oss
3. Hardware Requirements
| Config | GPU Memory | Context Size | Bagging | Use Case |
|---|
| Optimal | 80GB (A100) | 8192 | 8 | Production, best accuracy |
| Standard | 40GB (A6000) | 4096 | 4 | Good balance |
| Minimal | 24GB (RTX 4090) | 2048 | 2 | Development |
| CPU | N/A | 1024 | 1 | Testing only (slow) |
Quick Start
Classification (Customer Churn, Payment Default)
import pandas as pd
from sap_rpt_oss import SAP_RPT_OSS_Classifier
df = pd.read_csv("sap_customers.csv")
X = df.drop(columns=["CHURN_STATUS"])
y = df["CHURN_STATUS"]
X_train, X_test = X[:400], X[400:]
y_train, y_test = y[:400], y[400:]
clf = SAP_RPT_OSS_Classifier(max_context_size=4096, bagging=4)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
probabilities = clf.predict_proba(X_test)
Regression (Delivery Delay Days, Demand Quantity)
from sap_rpt_oss import SAP_RPT_OSS_Regressor
reg = SAP_RPT_OSS_Regressor(max_context_size=4096, bagging=4)
reg.fit(X_train, y_train)
predictions = reg.predict(X_test)
Core Workflow
- Extract SAP data → Export to CSV from relevant tables
- Prepare dataset → Include 50-500 rows with known outcomes
- Rename fields → Use semantic names (see Data Preparation)
- Run prediction → Fit on training data, predict on new data
- Interpret results → Probabilities for classification, values for regression
SAP Use Cases
See references/sap-use-cases.md for detailed extraction queries:
- FI-AR: Payment default probability (BSID, BSAD, KNA1)
- FI-GL: Journal entry anomaly detection (ACDOCA, BKPF)
- SD: Delivery delay prediction (VBAK, VBAP, LIKP)
- SD: Customer churn likelihood (VBRK, VBRP, KNA1)
- MM: Vendor performance scoring (EKKO, EKPO, EBAN)
- PP: Production delay risk (AFKO, AFPO)
Data Preparation
Semantic Column Names (Important!)
RPT-1-OSS uses an LLM to embed column names and values. Descriptive names improve accuracy:
CUSTOMER_CREDIT_LIMIT, DAYS_SINCE_LAST_ORDER, PAYMENT_DELAY_DAYS
COL1, VALUE, FIELD_A
Use scripts/prepare_sap_data.py to rename SAP technical fields:
from scripts.prepare_sap_data import SAPDataPrep
prep = SAPDataPrep()
df = prep.rename_sap_fields(df)
Dataset Size
- Minimum: 50 training examples
- Recommended: 200-500 examples
- Maximum context: 8192 rows (GPU dependent)
Scripts
scripts/rpt1_oss_predict.py - Local model prediction wrapperscripts/prepare_sap_data.py - SAP field renaming and SQL templatesscripts/batch_predict.py - Chunked processing for large datasets
Alternative: RPT Playground API
For users with SAP access, the closed-source RPT-1 is available via API:
from scripts.rpt1_api import RPT1Client
client = RPT1Client(token="YOUR_RPT_TOKEN")
result = client.predict(data="data.csv", target_column="TARGET", task_type="classification")
See references/api-reference.md for RPT Playground API documentation.
Limitations
- Tabular data only (no images, text documents)
- Requires labeled examples for in-context learning
- First prediction is slow (model loading)
- GPU strongly recommended for production use
