Construct a Log Evaluation Multi-Agent Self-Corrective RAG System with NVIDIA Nemotron

• QA and test automation teams: Testing pipelines generate massive logs which are often tricky to parse. Our AI system supports log summarization, clustering, and root-cause detection, helping QA engineers quickly pinpoint flaky tests, faulty logic, or unexpected behaviors.
• Engineering and DevOps teams: Engineers cope with heterogeneous log sources—application, system, service—all in several formats. Our AI agents unify these streams, perform hybrid retrieval (semantic and keyword), and surface essentially the most relevant snippets. The result: faster root-cause discovery and fewer late-night firefights.
• CloudOps and ITOps teams: Cloud environments add layers of complexity with distributed services, and configurations. AI log evaluation enables cross-service ingestion, centralized evaluation, and early anomaly detection anomalies for misconfigurations or bottlenecks.
• Platform and observability managers: For leaders driving observability, visibility is all the things. As an alternative of raw data floods, our solution delivers clear, actionable summaries—helpingprioritize fixes and improve product experiences.

Introduction to the log evaluation agent architecture

The log evaluation agent is a self-corrective, multi-agent RAG system designed to extract insights from logs using large language models (LLMs). It orchestrates a LangGraph workflow that features: 

1. Hybrid retrieval: BM25 for lexical matching + FAISS vector store with NVIDIA NeMo Retriever embeddings for semantic similarity.
2. Reranking: NeMo Retriever reranks results to surface essentially the most relevant log lines.
3. Grading: Candidate snippets are scored for contextual relevance.
4. Generation: Produces context-aware answers as a substitute of raw log dumps.
5. Self-correction loop: If results aren’t sufficient, the system rewrites queries and retries. 

Multi-agent intelligence: divide, conquer, correct

The answer implements a directed graph where each node is a specialized agent: retrieval, reranking, grading, generation, or transformation. Edges encode decision logic to steer the workflow dynamically. 

• Agents act autonomously on specific subtasks.
• Conditional edges make sure the system adapts, looping back for self-correction when needed.

Key components:

Component	File	Purpose
StateGraph	bat_ai.py	Defines the workflow graph using LangGraph
Nodes	graphnodes.py	Implements retrieval, reranking, grading, generation, and query transformation
Edges	graphedges.py	Encodes transitions logic
Hybrid Retriever	multiagent.py	Combines BM25 and FAISS retrieval
Output Models	binary_score_models.py	Structured outputs for grading
Utilities	utils.py and prompt.json	Prompts and NVIDIA AI endpoint integration

All source files can be found within the GenerativeAIExamples GitHub repository.

Behind the scenes: retrieval, reranking, and self-correction 

Hybrid retrieval:

The HybridRetriever class in multiagent.py combines: 

• BM25Retriever for precise lexical scoring. 
• FAISS Vectorstore for semantic similarity, using embeddings from an NVIDIA NeMo Retriever model (llama-3.2-nv-rerankqa-1b-v2). 

This dual strategy balances precision and recall, ensuring that each keyword matches and semantically related log snippets are captured. 

LLM integration and reranking:

Prompt templates loaded from prompt.json guide each LLM task. NVIDIA AI endpoints power: 

These models are orchestrated inside workflow nodes to handle retrieval, reranking, and answer generation seamlessly. 

Self-correction loop:

If initial retrieval results are weak, the transform_query node rewrites the user’s query to refine the search. Conditional edges similar to decide_to_generate and grade_generation_vs_documents_and_question evaluate results. Based on grading, the workflow either advances to final response generation, or loops back into the retrieval pipeline for one more pass. 

Quick-start guide

Clone the repo:

git clone https://github.com/NVIDIA/GenerativeAIExamples.git
cd GenerativeAIExamples/community/log_analysis_multi_agent_rag

Run an example query:

python example.py --log-file /path/to/your.log --question "What caused the timeout errors?"

The system will run Retrieval → Reranking → Grading → Generation producing a transparent explanation of the error source.

Make it yours: customization and extensions

• Advantageous-tuning: Swap in custom LLMs or adjust prompts on your logs. 
• Industry adaptations: Similar multi-agent workflows already power cybersecurity pipeline and self-healing IT systems. 
• Cross-domain potential: QA, DevOps, CloudOps, and Observability can all profit.

From logs to insights: why it matters

The log evaluation agent demonstrates how multi-agent RAG systems can turn unstructured logs into actionable insights, reducing the mean time to resolve (MTTR) and improving developer productivity:

• Faster debugging: Diagnose problems in seconds, not hours.
• Smarter root cause detection: Contextual answers, not raw dumps.
• Cross-domain value: Adaptable to QA, DevOps, CloudOps, and cybersecurity.

Beyond log evaluation

That is just the start. The identical multi-agent workflow that powers log evaluation could be prolonged into:

• Bug reproduction automation: Turning logs into est cases.
• Observability dashboards: Merging logs, metrics and traces.
• Cybersecurity pipelines: Automating anomaly and vulnerability checks.

Try it yourself: Run the sample query in your logs and explore how multi-agent RAG can change your debugging workflowFork, extend, and contribute your personal agents—the system is modular by design.

Curious how generative AI and NVIDIA NeMo Retriever are getting used? Explore additional examples and applications.

References

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