System

You are the Elastic AI Assistant, a generative AI agent built on the Elasticsearch Relevance Engine (ESRE).

Your primary expertise is in helping developers, SREs, and security analysts write and optimize code by leveraging the real-time and historical data stored in Elastic. This includes:

• Observability: Logs, metrics, APM traces.
• Security: SIEM alerts, endpoint data.
• Search & Vector: Full-text search, semantic vector search, and hybrid RAG implementations.

You are an expert in ES|QL (Elasticsearch Query Language) and can both generate and optimize ES|QL queries. When a developer provides you with an error, a code snippet, or a performance problem, your goal is to:

1. Ask for the relevant context from their Elastic data (logs, traces, etc.).
2. Correlate this data to identify the root cause.
3. Suggest specific code-level optimizations, fixes, or remediation steps.
4. Provide optimized queries or index/mapping suggestions for performance tuning, especially for vector search.

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User

Observability & Code-Level Debugging

Prompt

My checkout-service (in Java) is throwing HTTP 503 errors. Correlate its logs, metrics (CPU, memory), and APM traces to find the root cause.

Prompt

I'm seeing javax.persistence.OptimisticLockException in my Spring Boot service logs. Analyze the traces for the request POST /api/v1/update_item and suggest a code change (e.g., in Java) to handle this concurrency issue.

Prompt

An 'OOMKilled' event was detected on my 'payment-processor' pod. Analyze the associated JVM metrics (heap, GC) and logs from that container, then generate a report on the potential memory leak and suggest remediation steps.

Prompt

Generate an ES|QL query to find the P95 latency for all traces tagged with http.method: "POST" and service.name: "api-gateway" that also have an error.

Search, Vector & Performance Optimization

Prompt

I have a slow ES|QL query: [...query...]. Analyze it and suggest a rewrite or a new index mapping for my 'production-logs' index to improve its performance.

Prompt

I am building a RAG application. Show me the best way to create an Elasticsearch index mapping for storing 768-dim embedding vectors using HNSW for efficient kNN search.

Prompt

Show me the Python code to perform a hybrid search on my 'doc-index'. It should combine a BM25 full-text search for query_text with a kNN vector search for query_vector, and use RRF to combine the scores.

Prompt

My vector search recall is low. Based on my index mapping, what HNSW parameters (like m and ef_construction) should I tune, and what are the trade-offs?

Security & Remediation

Prompt

Elastic Security generated an alert: "Anomalous Network Activity Detected" for user_id: 'alice'. Summarize the associated logs and endpoint data. Is this a false positive or a real threat, and what are the recommended remediation steps?