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Client Ui Elasticsearch — DSH Plugin for DeepSeek Harness
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@asuka1121/dsh-client-ui-elasticsearch

Client Ui Elasticsearch

Elasticsearch connection settings for DeepSeek Harness: shared settings section (URIs/username/password) plus the es_query_logs model tool

The plugin will be installed here. Keep web if you are unsure.

npx -y @deepseek-ai/dsh plugin --profile web add github:SaitoAsuka1121/dsh-client-ui-elasticsearch#c6b1668fff471915d2c57791a7cffee2eae28eef
READMECompatibilityVersions

Compatibility and provenance

Client Ui Elasticsearch is published as @asuka1121/dsh-client-ui-elasticsearch and currently resolves to version 0.2.1. The Hub verifies its manifest and preserves the exact installation source for reproducible installs.

DSH compatibility
*
Runtime surfaces
web
Release source
github
Registry updated
8/31/2026

Versions

0.2.1stable
8/31/2026
0.2.0stable
8/24/2026

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0.2.1
DSH
*
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Process restart
Tree shaking
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Unavailable
Files
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Surface
web
License
MIT
Source
github
GitHub
★ 0
Weekly downloads
0
Last push
8/31/2026
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README

dsh-client-ui-elasticsearch

English | 中文

A DeepSeek Harness plugin that adds an Elasticsearch section to Settings and a host-side es_query_logs model tool.

  • Settings → Elasticsearch: configure uris (one per line), username, and password. Values are stored in the shared DSH settings document and shared across sessions.
  • es_query_logs tool: search logs by trace_id (exact match) and/or by natural-language search terms via query (matched against message, exception, and other log text fields), with optional fields, index, and size. It reuses the connection configured above.
  • Auto-guidance: a system-prompt section teaches every session to use the tool when a trace_id or a log-content question is asked — no dedicated agent preset required.

Install

dsh plugin --profile web add @asuka1121/dsh-client-ui-elasticsearch@latest

Then restart the deployment. The package declares dsh.bundle.patch, so dsh plugin registers it as a profile bundle automatically.

Requires pnpm on PATH. The plugin resolves its @deepseek-ai/* runtime dependencies as peer dependencies from the DSH installation, so it shares the installation's single Cordis instance.

Tool

es_query_logs parameters:

nametyperequireddefaultnotes
querystringno*—natural-language search terms matched against log text fields (e.g. "connection refused", "NullPointerException")
trace_idstringno*—exact trace id to search across trace fields
fieldsstring[]nocommon log text fieldsexplicit fields the query matches against; wildcards unsupported
indexstringno*index pattern to target
sizeintegerno100max hits, capped at 500

* at least one of query or trace_id is required.

  • trace_id runs exact term queries on trace_id, trace.id, traceId, and traceID.
  • query runs a multi_match (best_fields) over the resolved fields, avoiding wildcard field expansion that fails on wide indices.
  • Both together narrow by trace and content; results are sorted by @timestamp desc, trying each configured URI in order.

License

MIT

Development

The compiled lib/ ships with this repo, so consumers can install from the directory directly. Building from source requires the deepseek-harness monorepo: tsdown.config.ts imports the clientBundle preset from packages/client/tsdown.client.ts. The published npm package (@asuka1121/dsh-client-ui-elasticsearch) is the canonical build artifact.

Future Plans

Vision. The real payoff of a log-search plugin is correlation: not just finding log lines, but tracing each one back to the project code that produced it. The long-term goal is exactly that — log-to-code traceability.

Current boundary. Today the agent correlates logs only against the local workspace. Code-side integration stays on hold until DSH officially exposes a remote/network workspace (e.g. binding 0.0.0.0) — correlating logs against a workspace the agent cannot reach would be meaningless.

Open problem — acquiring the code. The leading idea is to clone the owning project on the server side, on demand, before each query, keyed by the app_name found in the logs, so answers stay self-contained and always based on the latest code. Storing full clones for every app is expensive, so lighter approaches are under evaluation: shallow/partial clones, lazily fetching only the files a trace references, and cache/eviction policies.