Configuration

Configuration is set through YAML. In most cases, YAML keys map to fields names in Python. The example in the previous section gave a full-featured example covering a wide array of configuration options.

Each section below describes the available configuration settings.

Embeddings

The configuration parser expects a top level embeddings key to be present in the YAML. All embeddings configuration is supported.

The following example defines an embeddings index.

path: index path
writable: true

embeddings:
  path: vector model
  content: true

Three top level settings are available to control where indexes are saved and if an index is a read-only index.

path

path: string

Path to save and load the embeddings index. Each API instance can only access a single index at a time.

writable

writable: boolean

Determines if the input embeddings index is writable (true) or read-only (false). This allows serving a read-only index.

cloud

Cloud storage settings can be set under a cloud top level configuration group.

Agent

Agents are defined under a top level agent key. Each key under the agent key is the name of the agent. Constructor parameters can be passed under this key.

The following example defines an agent.

agent:
    researcher:
        tools:
            - websearch

llm:
    path: hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4

Pipeline

Pipelines are loaded as top level configuration parameters. Pipeline names are automatically detected in the YAML configuration and created upon startup. All pipelines are supported.

The following example defines a series of pipelines. Note that entries below are the lower-case names of the pipeline class.

caption:

extractor:
  path: model path

labels:

summary:

tabular:

translation:

Under each pipeline name, configuration settings for the pipeline can be set.

Workflow

Workflows are defined under a top level workflow key. Each key under the workflow key is the name of the workflow. Under that is a tasks key with each task definition.

The following example defines a workflow.

workflow:
  sumtranslate:
    tasks:
        - action: summary
        - action: translation

schedule

Schedules a workflow using a cron expression.

workflow:
  index:
    schedule:
      cron: 0/10 * * * * *
      elements: ["api params"] 
    tasks:
      - task: service
        url: api url
      - action: index

tasks

tasks: list

Expects a list of workflow tasks. Each element defines a single workflow task. All task configuration is supported.

A shorthand syntax for creating tasks is supported. This syntax will automatically map task strings to an action:value pair.

Example below.

workflow:
  index:
    tasks:
      - action1
      - action2

Each task element supports the following additional arguments.

action

action: string|list

Both single and multi-action tasks are supported.

The action parameter works slightly different when passed via configuration. The parameter(s) needs to be converted into callable method(s). If action is a pipeline that has been defined in the current configuration, it will use that pipeline as the action.

There are three special action names index, upsert and search. If index or upsert are used as the action, the task will collect workflow data elements and load them into defined the embeddings index. If search is used, the task will execute embeddings queries for each input data element.

Otherwise, the action must be a path to a callable object or function. The configuration parser will resolve the function name and use that as the task action.

task

task: string

Optionally sets the type of task to create. For example, this could be a file task or a retrieve task. If this is not specified, a generic task is created. The list of workflow tasks can be found here.

args

args: list

Optional list of static arguments to pass to the workflow task. These are combined with workflow data to pass to each __call__.