PromptOllama

What if you could just, actually, put an LLM on everything?

Ollama

Ollama allows you to run a variety of Large Language Models (LLMs) with minimal setup. You can also create custom models using Modelfiles and system prompts.

The PromptOllama Stage allows you to connect to a running instance of Ollama Server, which communicates with an LLM through a simple API. The Stage sends part (or all) of a Document to the LLM for generic enrichment. You’ll want to create a custom model (with a Modelfile) or provide a System Prompt in the Stage Config that is tailored to your pipeline.

We strongly recommend you have the LLM output only a JSON object for two main reasons: Firstly, LLMs tend to follow instructions better when instructed to do so. Secondly, Lucille can then parse the JSON response and fully integrate it into your Document.

Example

Let’s say you are working with Documents which represent emails, and you want to monitor them for potential signs of fraud. Lucille doesn’t have a DetectFraud Stage (at time of writing), but you can use PromptOllama to add this information with an LLM.

• Modelfile: Let’s say you created a custom model, fraud_detector, in your instance of Ollama Server. As part of the modelfile, you instruct the model to check the contents for fraud and output a JSON object containing just a boolean value (under fraud). Your Stage would be configured like so:

{
  name: "Ollama-Fraud"
  class: "com.kmwllc.lucille.stage.PromptOllama"
  hostURL: "http://localhost:9200"
  modelName: "fraud_detector"
  fields: ["email_text"]
}

• System Prompt: You can also just reference a specific LLM directly, and provide a system prompt in the Stage configuration.

{
  name: "Ollama-Fraud"
  class: "com.kmwllc.lucille.stage.PromptOllama"
  hostURL: "http://localhost:9200"
  modelName: "gemma3"
  systemPrompt: "You are to read the text inside \"email_text\" and output a JSON object containing only one field, fraud, a boolean, representing whether the text contains evidence of fraud or not."
  fields: "email_text"
}

Regardless of the approach you choose, the LLM will receive a request that looks like this:

{
  "email_text": "Let's be sure to juice the numbers in our next quarterly earnings report."
}

(Since fields: ["email_text"], any other fields on this Document are not part of the request.)

And the response from the LLM should look like this:

{
  "fraud": true
}

Lucille will then add all key-value pairs in this response JSON into your Document. So, the Document will become:

{
  "id": "emails.csv-85",
  "run-id": "f9538992-5900-459a-90ce-2e8e1a85695c",
  "email_text": "Let's be sure to juice the numbers in our next quarterly earnings report.",
  "fraud": true
}

As you can see, PromptOllama is very versatile, and can be used to enrich your Documents in a lot of ways.