A model adapter allows converting models with different input/output formats into a common schema.

To interact with model adapter using the CLI, use the lf model-adapter command.

Model Adapter Overview

Properties

key Key required

Reference to an existing entity in AI GO!.

Pattern: ^[a-zA-Z0-9_\-\$]+$
Max Length: 250

display_name string required

The model adapter's name displayed to the user.

description string

Short description of the model adapter.

Default: None

long_description string

Long description of the model adapter. Supports Markdown formatting.

Default: None

provider enum ModelAdapterProviderId

Provider of the model adapter.

Default: user

Possible ModelAdapterProviderId values

The provider of the model adapter.

Allowed Values:

latticeflow
user

task enum MLTask

The ML task of the model. Default is: chat_completion.

Default: chat_completion

Possible MLTask values

The type of machine learning task to be performed.

Allowed Values:

chat_completion
embeddings
custom

process_input ModelAdapterCodeSnippet required

The transform of the model inputs in AI GO! format into the body of the HTTP request.

process_output ModelAdapterCodeSnippet required

The transform of the model's HTTP response body into the AI GO! format.

display_name: "RAGFlow Chat Completion"
key: "adapter-ragflow"
description: "Adapter for RAGFlow models."
long_description: >
  Adapter for RAGFlow OpenAI compatible messages. See
  [documentation](https://ragflow.io/docs/http_api_reference#openai-compatible-api).
task: "chat_completion"
process_input:
  language: "jinja"
  source_code: !include "./ragflow_input.jinja"
process_output:
  language: "jinja"
  source_code: !include "./ragflow_output.jinja"

{
    "model": "{{ model_info.model_key }}",
    "messages": {{ input.messages | tojson }},
    "stream": false,
    "reference": true
}

{% set body = body | fromjson %}
{
    "choices": [
        {% for choice in body.choices %}
            {% set raw_msg = choice.message if choice.message is defined else {} %}
            {% set msg = raw_msg if raw_msg is mapping else {} %}
            {% set clean_msg = {} %}
            {% for k, v in msg.items() %}
                {% if k != "reference" %}
                {% set _ = clean_msg.update({k: v}) %}
                {% endif %}
            {% endfor %}
            {
                "message": {{ clean_msg | tojson }},
                "references": {{ (msg.reference if msg.reference is defined else []) | tojson }}
            }{% if not loop.last %},{% endif %}
        {% endfor %}
    ]
}

Definitions

`ModelAdapterCodeSnippet`

Properties

language enum ModelAdapterCodeLanguage required

The programming language of the code snippet.

Possible ModelAdapterCodeLanguage values

The programming language used in the code snippet.

Allowed Values:

jinja

source_code string required

The source code of the model adapter.

display_name: "RAGFlow Chat Completion"
key: "adapter-ragflow"
description: "Adapter for RAGFlow models."
long_description: >
  Adapter for RAGFlow OpenAI compatible messages. See
  [documentation](https://ragflow.io/docs/http_api_reference#openai-compatible-api).
task: "chat_completion"
process_input:
  language: "jinja"
  source_code: !include "./ragflow_input.jinja"
process_output:
  language: "jinja"
  source_code: !include "./ragflow_output.jinja"

💡
!include directive is a convenient way to include the code snippet from a separate file. Alternatively, you can inline the code snippet directly in the YAML file.