Ollama

import { Ollama } from "@llamaindex/ollama";
import { Settings } from "llamaindex";

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

import { Document, VectorStoreIndex } from "llamaindex";

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

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

const queryEngine = index.asQueryEngine();

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

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

import { Ollama } from "@llamaindex/llms/ollama";
import { z } from "zod";

// Simple JSON format
const llm = new Ollama({ 
  model: "llama2", 
  temperature: 0,
  responseFormat: { type: "json_object" }
});

// Using Zod schema for validation
const responseSchema = z.object({
  summary: z.string(),
  topics: z.array(z.string()),
  sentiment: z.enum(["positive", "negative", "neutral"])
});

const llm = new Ollama({ 
  model: "llama2", 
  temperature: 0,
  responseFormat: responseSchema  
});

import { Ollama } from "@llamaindex/ollama";
import { 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);
}