dsh-llm-databricks

中文

An independent, community-maintained DeepSeek Harness bundle for conversation models served through Databricks AI Gateway. It installs out of tree, uses the public Harness LLM/settings/credentials seams, and does not patch Harness or node_modules.

> This package is not an official DeepSeek AI or Databricks product.

Support matrix

Native Gemini API and embeddings are not implemented in 0.1.0. Gemini conversation models are already usable through MLflow Chat Completions. Harness's current LLM seam does not consume embeddings. Native Gemini is a roadmap item.

Requirements

• Node.js ^22.19.0 or >=24
• DeepSeek Harness 0.1.0-rc.7 compatible seams
• A Databricks personal access token, OAuth access token, or other bearer token

accepted by the configured workspace gateway

Install

From npm after a public release:

dsh plugin --profile web add dsh-llm-databricks

From the public GitHub repository:

dsh plugin --profile web add github:zhuchengde0214-ctrl/dsh-llm-databricks

For a packed local release:

pnpm pack
dsh plugin --profile web add ./dsh-llm-databricks-0.1.0.tgz

The package's dsh.bundle.patch adds a dormant llm-databricks plugin row. It registers routes only after configuration supplies providers.

Configure

Add an llm-databricks section to the isolated profile's user settings (or use a Harness configuration surface that writes the same namespace):

llm-databricks:
  providers:
    databricks-main:
      displayName: Databricks Main
      workspaceUrl: https://dbc-example.cloud.databricks.com
      protocol: mlflow-chat
      credentialRef: DATABRICKS_TOKEN
      models:
        - id: example-chat-endpoint
          name: Example Chat Model
          contextWindow: 128000
          maxTokens: 8192
          input: [text]
        - id: example-reasoning-endpoint
          name: Example Reasoning Model
          contextWindow: 200000
          maxTokens: 16384
          reasoningEfforts:
            off:
            low: low
            high: high

id is the model-serving endpoint/model service identifier sent in the model field. Capacities are deployment metadata and must match the served model. input defaults to [text]; declare [text, image] only when the endpoint actually accepts images.

Use one provider route per protocol when the same workspace exposes several wire formats:

llm-databricks:
  providers:
    databricks-anthropic:
      workspaceUrl: https://adb-example.azuredatabricks.net
      protocol: anthropic-messages
      credentialRef: DATABRICKS_TOKEN
      models:
        - id: example-anthropic-endpoint
          contextWindow: 200000
          maxTokens: 16384
    databricks-responses:
      workspaceUrl: https://workspace.gcp.databricks.com
      protocol: openai-responses
      credentialRef: DATABRICKS_TOKEN
      models:
        - id: example-responses-endpoint
          contextWindow: 128000
          maxTokens: 8192

Store the token through Harness's credentials service (for example, the web Models page) or expose the referenced environment variable to the Harness process. Settings contain only credentialRef; the plugin never stores a token in settings or logs it.

Inspect the composed bundle without sending a model request:

dsh --profile web --dump-config

Security boundary

• HTTPS is mandatory. Userinfo, query strings, fragments, and paths in

workspaceUrl are rejected.

• By default, bearer credentials are sent only to a workspace subdomain under

.cloud.databricks.com, .azuredatabricks.net, .gcp.databricks.com, or legacy .databricks.net. Matching is label-boundary safe; lookalike suffixes are rejected.

• Official hosts must use port 443.
• Each protocol is pinned to its exact gateway request path. Redirect following

is disabled by the request transport.

• Custom/private DNS requires allowCustomHost: true. This opts into forwarding

the bearer credential to that hostname. Review DNS ownership, TLS termination, proxies, and logging before enabling it.

• A non-443 custom port additionally requires allowNonStandardPort: true.

This is intended for controlled private gateways and tests.

• Native Anthropic requests delete x-api-key and send bearer authentication

only. The plugin never takes over official Anthropic or other non-Databricks provider routes.

Errors and retries

The adapter preserves HTTP status, Databricks/request IDs, and valid Retry-After/retry-after-ms delays. It maps:

• 401/403 to authentication failures
• 429 quota messages (including workspace input-tokens-per-minute limits) to

quota failures, and other 429 responses to rate limits

• explicit context-length messages to context overflow
• ordinary or bodyless 400 responses to invalid requests, never context overflow
• 5xx responses to server failures

SDK retries are disabled; Harness's provider retry policy owns retries. The default policy retries transient failures only, honors provider delays, and does not loop on deterministic 4xx responses. Configure retryPolicy per route only when a deployment needs different bounded backoff.

Troubleshooting 429

1. Read the error's retry delay and request ID; do not immediately resubmit. 2. Check the workspace AI Gateway usage and the model serving endpoint's input tokens/minute quota. 3. Reduce concurrent sessions, prompt size, or tool output retained in context. 4. Request a quota increase or route workloads across separately governed endpoints.

Migration and uninstall

Configuration is portable: copy the llm-databricks settings section and re-create the named credential in the destination Harness home. Do not copy credential values into source-controlled settings.

Remove the plugin and then delete its settings section:

dsh plugin --profile web remove dsh-llm-databricks

Remove DATABRICKS_TOKEN from the credentials service only when no other route uses it.

Development

corepack pnpm install --frozen-lockfile
pnpm run typecheck
pnpm run lint
pnpm test
pnpm run build

Tests use stub transports and an isolated HTTPS mock gateway on an ephemeral port. CI never contacts Databricks and requires no real credentials.

Release and pack

1. Update CHANGELOG.md and the package version. 2. Run all development checks above. 3. Run pnpm pack --pack-destination .artifacts. 4. Inspect the tarball with tar -tzf .artifacts/dsh-llm-databricks-<version>.tgz; it must contain only built lib, the bundle patch, package metadata, documentation, changelog, and license. 5. Install that tarball into an isolated DSH_HOME and inspect --dump-config before publishing.

Do not publish from an unreviewed workspace. This repository intentionally keeps private: false for a future public npm release, but publishing is a separate maintainer action.

License

MIT