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+**Table of Contents**
+1. [Agent Evaluation](#agent-evaluation)
+2. [RAG Evaluation](#rag-evaluation)
+
+# Agent Evaluation
+
+**TODO**
+
+# RAG Evaluation
+
+## Introduction
+
+Our objective is to monitor and improve the RAG pipeline for **AI-OPS**, that requires context-specific data from 
+*Cybersecurity* and *Penetration Testing* fields; also we want the evaluation process to be as automated as possible.
+
+The evaluation workflow is split in two steps:
+
+1. **Dataset Generation** ([dataset_generation.ipynb](./test/benchmarks/rag/dataset_generation.ipynb)):
+uses Ollama and the data that is ingested into Qdrant (RAG Vector Database) to generate *question* and *ground truth* 
+ (Q&A dataset).
+
+2. **Evaluation** ([evaluation.py](./test/benchmarks/rag/evaluation.py)):
+builds the RAG pipeline with the same used to generate the synthetic Q&A dataset, leverages the pipeline to provide
+ an *answer* to the questions (given *contex*), then performs evaluation of the full evaluation dataset using LLM as a
+judge; for performance reasons the evaluation is performed using HuggingFace Inference API.
+
+## Results
+
+### Context Precision
+
+**TODO:** *describe the metric and the prompts used* 
+
+![Context Precision Plot](data/rag_eval/results/plots/context_precision.png)
+
+### Context Recall
+
+**TODO:** *describe the metric and the prompts used* 
+
+
+![Context Precision Plot](data/rag_eval/results/plots/context_recall.png)
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