encourage-rag 0.3.1

ENCOURAGE

EncouRAGe

the all-in one solution for evaluate RAG methods.

This repository provides a flexible library for running local inference with or without context, leveraging a variety of popular LLM libraries for enhanced functionality:

• ⚙️ jinja2
  • Offers a template engine for dynamic prompt generation.
• 📝 mlflow
  • Designed to ensure observability of the model performance and tracing.
• 🔄 chroma
  • Strong in-memory vector database for efficient data retrieval.
• 🧭 qdrant
  • Supports robust vector search for efficient data retrieval.

---

🚀 Getting Started

pip install encourage

To initialize the environment using uv, run the following command:

uv sync

⚡ Usage Inference Runners

For understanding how to use the inference runners, refer to the following tutorials:

• ChatInferenceRunner
• BatchInferenceRunner

🔍 RAG Methods

Encourage provides several RAG (Retrieval-Augmented Generation) methods to enhance your LLM responses with relevant context:

• Overview of RAG Methods

📊 Evaluation Metrics

Encourage offers a comprehensive set of metrics for evaluating LLM and RAG performance:

• Metrics Overview - Table of all available metrics
• Metrics Explanation - Detailed explanations and formulas
• Metrics Tutorial - Step-by-step guide to using metrics

⚙️ Custom Templates

To use a custom template for the inference, follow the steps below:

• Create a custom template

📈 Model Tracking

For tracking the model performance, use the following commands:

• Track the model