38 lines
No EOL
1.7 KiB
TypeScript
38 lines
No EOL
1.7 KiB
TypeScript
import { Ollama } from "ollama-node";
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import { readFile } from "fs/promises";
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async function main() {
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const ollama = new Ollama();
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// Set the system prompt to prepare the model to receive a prompt and a schema and set some rules for the output.
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const systemprompt = `You will be given a text along with a prompt and a schema. You will have to extract the information requested in the prompt from the text and generate output in JSON observing the schema provided. If the schema shows a type of integer or number, you must only show a integer for that field. A string should always be a valid string. If a value is unknown, leave it empty. Output the JSON with extra spaces to ensure that it pretty prints.`
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const schema = {
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"people": [{
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"name": {
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"type": "string",
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"description": "Name of the person"
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},
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"title": {
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"type": "string",
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"description": "Title of the person"
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}
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}],
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}
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// Depending on the model chosen, you may be limited by the size of the context window, so limit the context to 2000 words.
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const textcontent = await readFile("./wp.txt", "utf-8").then((text) => text.split(" ").slice(0, 2000).join(" "));
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// Specific instructions for this task
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const prompt = `Review the source text and determine the 10 most important people to focus on. Then extract the name and title for those people. Output should be in JSON.\n\nSchema: \n${JSON.stringify(schema, null, 2)}\n\nSource Text:\n${textcontent}`
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await ollama.setModel("neural-chat");
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ollama.setSystemPrompt(systemprompt);
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// setJSONFormat is the equivalent of setting 'format: json' in the API
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ollama.setJSONFormat(true);
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await ollama.streamingGenerate(prompt, (word) => { process.stdout.write(word) })
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}
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main(); |