PromptOllama
Connect to Ollama Server and send a Document to an LLM for enrichment.
Stages are the building blocks of a Lucille pipeline. Each Stage performs a specific transformation on a Document.
Lucille Stages should have JavaDocs that describe their purpose and the parameters acceptable in their Config. On this site, you’ll find more in-depth documentation for some more advanced / complex Lucille Stages.
To configure a stage, you have to provide its class (under class
) in its config. You can also specify a name
for the Stage as well,
in addition to conditions
and conditionPolicy
(described below).
You’ll also provide the parameters needed by the Stage as well. For example, the AddRandomBoolean
Stage accepts two optional parameters -
field_name
and percent_true
. So, an AddRandomBoolean
Config would look something like this:
{
name: "AddRandomBoolean-First"
class: "com.kmwllc.lucille.stage.AddRandomBoolean"
field_name: "rand_bool_1"
percent_true: 65
}
For any Stage, you can specify “conditions” in its Config, controlling when the Stage will process a Document. Each
condition has a required parameter, fields
, and two optional parameters, operator
and values
.
fields
is a list of field names that will determine whether the Stage applies to a Document.
values
is a list of values that the conditional fields will be searched for. (If not specified, only the existence of fields is checked.)
operator
is either "must"
or "must_not"
(defaults to "must"
).
In the root of the Stage’s Config, you can also specify a conditionPolicy
- either "any"
or "all"
, specifying whether
any or all of your conditions must be met for the Stage to process a Document. (Defaults to "any"
.)
Let’s say we are running the Print
Stage, but we only want it to execute on a Document where city = Boston
or city = New York
.
Our Config for this Stage would look something like this:
{
name: "print-1"
class: "com.kmwllc.lucille.stage.Print"
conditions: [
{
fields: ["city"]
values: ["Boston", "New York"]
}
]
}
Connect to Ollama Server and send a Document to an LLM for enrichment.
Execute an OpenSearch Template using information from a Document, and add the response to it.
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