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Ollama

Usage

import { Ollama, serviceContextFromDefaults } from "llamaindex";

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

const serviceContext = serviceContextFromDefaults({
llm: ollamaLLM,
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], {
serviceContext,
});

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,
serviceContextFromDefaults,
} from "llamaindex";

import fs from "fs/promises";

async function main() {
// Create an instance of the LLM
const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });

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

// Create a service context
const serviceContext = serviceContextFromDefaults({
embedModel: ollamaLLM, // prevent 'Set OpenAI Key in OPENAI_API_KEY env variable' error
llm: ollamaLLM,
});

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

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

// 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);
}