Publish a Model (Embedding)

Target Outcome

The embedding model passes protocol testing, is published to the intended scope, and returns vectors with the expected dimensions.

Applicable Roles

• Model Provider

Before You Start

• Prepare the model source, model identifier, API credential, and embedding endpoint.
• Confirm the input format, vector dimensions, billing unit, and rate-limit policy.

Procedure

1. From the platform home page, select My Models in the left navigation.
2. Open My Publications. Use Public Models / Private Models to switch publication areas, or open Overview and My Aggregations when needed.
3. Select Publish Model in the upper-right corner.
4. Select a publication area:
   • Publish to Private Area makes the model visible only within the team or tenant and keeps it out of the public catalog.
   • Publish to Public Area lists the model in the public catalog for all tenants and allows independent pricing and rate limits.
5. Select Publish to Public Area to open Step 1.

Step 1: Basic Information

• Under Model Source / Meta-Model Information:
  • Select a meta-model, such as text-embedding-v1.
  • Select a model source, such as Alibaba - China.
  • Enter the request URL, such as https://dashscope.aliyuncs.com.
  • Enter the API key in the protected field, such as sk-***.
  • Enter the exact upstream Model Source ID, such as text-embedding-v1.

• Confirm that Model Type is Embedding Model.

• Under Request Headers, keep the default Authorization: Bearer <key> template and add only headers required by the upstream service.

• Under Model Parameters:
  • Set the input modality to Text.
  • Embedding models have no output-modality option because the output is a vector.
  • Set maximum context and maximum input, such as 8192K. Leave maximum output empty when the embedding service does not use output tokens.

• Under Supported Protocols and Default Parameters, select OpenAI-Embeddings, run the connectivity test, enter the endpoint, and configure inputs such as Encoding Format and Input. Embedding models use synchronous calls with a fixed response structure and therefore have no callback or result-parsing section.

• Enter the public Custom Identifier and description.

• Select Publish Immediately or Scheduled Publication.

• Select Next to open Step 2.

Step 2: Billing Configuration

• Select Token Billing or Free.
• For token billing:
  • Enable Show Price Comparison when a reference price should be displayed.
  • Enter the input sale price and optional original price. Embedding models do not use output-price, cache, or tier configuration.
  • Optionally configure a free quota, eligible-user count, and total amount.

• Select Next to open Step 3.

Step 3: Rate-Limit Configuration

• Select Enable Rate Limiting or Disabled.
• Configure default limits:
  • RPM: requests per minute, or Unlimited.
  • TPM: tokens per minute, or Unlimited.

• Select Save Only or Submit for Review.

Parameter Reference - Embedding Model

Field	Type	Example	Description
Meta-Model	Select	text-embedding-v1	Required; base meta-model
Model Source	Select	Alibaba - China	Required; upstream Model Provider
Request URL	URL	https://dashscope.aliyuncs.com	Required; model-service base URL
API Key	Password	sk-***	Required; protected upstream credential
Model Source ID	Text	text-embedding-v1	Required; exact upstream model name
Model Type	Single select	Embedding Model	Required; no subtype
Request Headers	Key-value pairs	Authorization: Bearer <key>	Optional; authentication and custom headers
Input Modality	Multi-select	Text	Required; accepted input type
Output Modality	Not applicable	None	Embedding output is a vector
Maximum Context	Number	8192K	Required; context-token limit
Maximum Input	Number	8192K	Required; input-token limit
Maximum Output	Number	Empty	Embedding models do not use output-token limits
Supported Protocol	Multi-select	OpenAI-Embeddings	Required; test connectivity before continuing
Endpoint	URL	https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings	Required; protocol endpoint
Input Parameters	Parameter list	Encoding Format / Input	Optional; protocol inputs and required-state settings
Custom Identifier	Text	text-embedding-v1	Required; model identifier shown to users
Description	Text	Text embedding...	Optional; model description
Publication Method	Single select	Immediate / Scheduled	Required; publication time
Billing Method	Single select	Token Billing / Free	Required; billing method
Show Price Comparison	Switch	On / Off	Optional; displays an original reference price
Input Sale Price	Number	7 Credits/1M tokens	Required for paid models
Original Price	Number	14 Credits/1M tokens	Optional; input reference price
Free Quota	Switch	On / Off	Optional; configures free usage quota
Rate Limiting	Single select	Enabled / Disabled	Optional; controls invocation limits
RPM	Number / Unlimited	2 requests/minute	Optional; request limit per minute
TPM	Number / Unlimited	100 tokens/minute	Optional; token limit per minute

Completion Checklist

Purpose: These are the exit criteria for the current feature task. Use them to decide whether the result is observable and reviewable and whether you can continue to the next step in the scenario. They do not repeat the procedure; if any item fails, follow the troubleshooting section below.

Check	Pass Criteria
1	Protocol connectivity passes and the model source and identifier are accurate.
2	Publication or review status is correct.
3	A controlled call returns vectors with the expected dimensions and the call log is traceable.

Troubleshooting

Symptom	Check First
Protocol test fails	Endpoint, credential, model identifier, request body, and source network access
Vector response is invalid	Input format, dimensions, response mapping, and selected protocol

User Manual

Review complete My Models fields and publication-result validation