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HIMANSHU KUMAR
HIMANSHU KUMAR

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Building a Production-Ready RAG Evaluation Framework

#ai #opensource #productivity #discuss

Evaluating a RAG application is harder than measuring a few metrics.

Many existing tools focus on generating scores, but when something goes wrong, they rarely answer the questions developers actually have:

  • Why is retrieval failing?
  • Is the corpus the problem?
  • Which component is responsible?
  • Can I evaluate without production data?
  • How can I switch vector databases without rewriting my evaluation pipeline?

These questions motivated the latest release of OpenAgent Eval v0.3.0.

What's New

Production CLI

Instead of writing Python scripts for every task, OpenAgent Eval now includes a complete CLI.

openagent-eval configure
openagent-eval version
openagent-eval providers
openagent-eval list-collections
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The goal is to make evaluation accessible directly from the terminal.

Corpus Health Auditor

Evaluation results are only as good as the data behind them.

The new Corpus Health Auditor analyzes your knowledge base before evaluation and reports potential issues that could reduce retrieval quality.

Instead of discovering problems after deployment, you can identify them during development.

Component Diagnosis

A poor evaluation score doesn't explain where the failure happened.

OpenAgent Eval now helps diagnose problems at the component level, making it easier to identify whether the issue originates from retrieval, embeddings, chunking, or another stage of the RAG pipeline.

Synthetic Test Data

Many teams cannot evaluate using production documents because of privacy or compliance requirements.

OpenAgent Eval can now generate synthetic datasets for benchmarking and testing, making it easier to build repeatable evaluation workflows.

Retriever Provider Abstraction

Modern RAG systems use many different vector databases.

OpenAgent Eval now supports 11+ providers, including:

  • Pinecone
  • Weaviate
  • Qdrant
  • ChromaDB
  • FAISS
  • Elasticsearch
  • Milvus
  • pgvector
  • Redis
  • and more.

The abstraction layer allows developers to evaluate different retrieval backends through a unified interface.

NLI-Based Semantic Evaluation

Traditional similarity metrics often fail to capture semantic correctness.

Version 0.3.0 introduces DeBERTa-based Natural Language Inference (NLI) scoring, enabling more meaningful evaluation through entailment rather than simple embedding similarity.

Unified Embedding Interface

Embedding providers are no longer tied to specific implementations.

OpenAgent Eval now supports a common interface for:

  • OpenAI
  • Cohere
  • Local embedding models

This makes switching providers straightforward without changing evaluation logic.

Why We Built It

Most RAG evaluation tools fall into one of two categories:

  • Simple metric libraries that provide limited insights.
  • Full enterprise platforms that are difficult to integrate into existing workflows.

Our goal is different.

OpenAgent Eval is designed as a lightweight, developer-first framework with production-grade capabilities and a simple CLI.

Getting Started 

pip install openagent-eval

openagent-eval configure

openagent-eval version
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Open Source

We're actively building OpenAgent Eval in the open and welcome feedback, feature requests, and contributions from the community.

GitHub: https://github.com/OpenAgentHQ/openagent-eval

PyPI: https://pypi.org/project/openagent-eval/

If you're building RAG applications, AI agents, or retrieval systems, we'd love to hear how you're evaluating them and what features you'd like to see next.

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