dbx-tools-model-proxy

Rust proxy between OpenAI or Anthropic clients and Databricks model-serving protocols.

Install the public binary from crates.io:

cargo install dbx-tools-model-proxy

Release builds also publish dbx-model-proxy as a GitHub release asset for each configured platform.

The prototype uses aigw-openai and aigw-anthropic as protocol adapters. OpenAI Chat Completions and Anthropic Messages requests can target either Databricks Chat Completions or Responses. Native Responses input currently targets Responses without conversion.

Authentication comes from dbx-tools-client. The proxy resolves the selected Databricks profile, obtains cached or refreshed credentials, and retries one upstream 401 after refreshing the rejected token. dbx-tools-model discovers the workspace's serving endpoints, caches the catalogue for five minutes, and resolves loose model values before forwarding. For example, "model": "gpt" selects the highest-ranked deployed GPT model.

Run

cargo run --manifest-path packages/rs/model-proxy/Cargo.toml -- \
  --profile PROFILE --target auto

The server listens on 127.0.0.1:4000 by default. --port reads DATABRICKS_APP_PORT when present.

Import postman/model-proxy.postman_collection.json into Postman. The collection has separate folders for --target chat and --target responses; restart the proxy with the folder's documented command before running it. Set chatModel and responsesModel to endpoints available in the selected workspace.

Supported routes:

• GET /v1/models
• POST /v1/chat/completions
• POST /v1/responses
• POST /v1/messages
• GET /healthz

GET /v1/models reads the cached live serving-endpoint catalogue. Standard requests receive an OpenAI object / data envelope. An originator header whose value starts with codex receives a Codex models envelope. Both use the identities returned by Databricks directly: OpenAI uses the serving endpoint name, while Codex maps databricks-<model> to the gateway's system.ai.<model> identity. No databricks/ or dbx/ namespace is added.

Use ?search=gpt to apply the same fuzzy scoring and ordering as model resolution. Add ?extended=true to include the score, service names, capability class, profile, task, state, and other catalogue metadata. Extended output defaults to false.

--target responses forces Chat Completions or Anthropic Messages input through the Responses request translator. --target chat sends canonical Chat Completions. --target auto selects Responses for Responses clients, Codex models, and GPT 5.4 or newer.

Streaming requests use SSE without buffering the upstream response. Matching protocols pass the upstream byte stream through directly, including Chat Completions to Chat Completions and Responses to Responses for Codex clients. Cross-protocol streams pass through aigateway's stateful Chat Completions or Responses parser. Anthropic output uses aigateway's native SSE encoder, while OpenAI Chat Completions output uses the prototype's canonical event encoder.

Responses input currently targets only Responses, so Responses-to-Chat translation is outside this prototype's supported route matrix.