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Ollama

Usage

import { Ollama, Settings } from "llamaindex";

Settings.llm = ollamaLLM;
Settings.embedModel = ollamaLLM;

Load and index documents

For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.

const document = new Document({ text: essay, id_: "essay" });

const index = await VectorStoreIndex.fromDocuments([document]);

Query

const queryEngine = index.asQueryEngine();

const query = "What is the meaning of life?";

const results = await queryEngine.query({
query,
});

Full Example

import { Ollama, Document, VectorStoreIndex, Settings } from "llamaindex";

import fs from "fs/promises";

const ollama = new Ollama({ model: "llama2", temperature: 0.75 });

// Use Ollama LLM and Embed Model
Settings.llm = ollama;
Settings.embedModel = ollama;

async function main() {
const essay = await fs.readFile("./paul_graham_essay.txt", "utf-8");

const document = new Document({ text: essay, id_: "essay" });

// Load and index documents
const index = await VectorStoreIndex.fromDocuments([document]);

// get retriever
const retriever = index.asRetriever();

// Create a query engine
const queryEngine = index.asQueryEngine({
retriever,
});

const query = "What is the meaning of life?";

// Query
const response = await queryEngine.query({
query,
});

// Log the response
console.log(response.response);
}