GraphAcademy

GenAI Workshop - TypeScript

Project Setup

What is Neo4j?

Writing Data to Neo4j

Building LLM Applications

Congratulations!

Using LLMs for Summarisation

In this lesson, you will learn how to:

  1. Scrape the conference website homepage to obtain links to all talks

  2. Create a Prompt to extract talk and speaker information from HTML markup

  3. Summarise each talk webpage and import the data into Neo4j

Steps

Scraping Content

Create a new file called src/setup/summarise.mjs.

Inside that file, import the necessary modules.

import
typescript
import { load } from "cheerio";
import { PromptTemplate } from "@langchain/core/prompts";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
import { Neo4jGraph } from "@langchain/community/graphs/neo4j_graph";
import { OpenAI } from "@langchain/openai";
import {
  RunnablePassthrough,
  RunnableSequence,
} from "@langchain/core/runnables";
import { z } from "zod";
import { config } from "dotenv";

Create a getTalkUrls() function to load the conference website and extract all of the talk links.

getTalkUrls()
typescript
async function getTalkUrls() {
  const res = await fetch("https://athens.cityjsconf.org");
  const html = await res.text();
  const $ = load(html);

  return $('a[href*="talk"]')
    .map((i, el) => $(el).attr("href"))
    .get()
    .filter((el, index, data) => data.indexOf(el) === index);
}

Extract from HTML

Write a function to load an individual talk webpage and load the content into a Cheerio instance. Next, use LCEL to create a chain that takes the HTML and extracts information about the talk and speaker based on instructions from a StructuredOutputParser.

extractTalkInformation()
typescript
async function extractTalkInformation(url) {
  const res = await fetch(`https://athens.cityjsconf.org${url}`);
  const html = await res.text();

  const $ = load(html);
  const main = $("main").html();

  const summarise_prompt = PromptTemplate.fromTemplate(`
  Use the following HTML from a conference website to
  identify information about the talk and speaker.

  {format_instructions}

  HTML:
  ----
  {html}
  ----`);

  const parser = StructuredOutputParser.fromZodSchema(
    z.object({
      title: z.string().describe("title of the talk"),
      time: z
        .string()
        .describe("time of the talk in 24 hour format, eg: 12:34:00"),
      room: z.string().describe("the room in which the talk will take place"),
      description: z
        .string()
        .describe("a one sentence summary of the talk abstract"),
      // ...
      tags: z
        .string()
        .array()
        .describe(
          "a set of tags to use to categorise the talk in lower snake case, eg: graph-database"
        ),
      speaker: z.object({
        name: z.string().describe("name of the speaker"),
        company: z.string().describe("the company that the speaker works for"),
        x_handle: z
          .string()
          .describe("their handle for X (formerly Twitter) beginning with @"),
        bio: z.string().describe("the bio for the speaker"),
      }),
    })
  );

  const format_instructions = parser.getFormatInstructions();

  const llm = new OpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    model: "gpt-4-turbo",
  });

  const summarise_chain = RunnableSequence.from([
    summarise_prompt,
    llm,
    parser,
  ]);

  const summary = await summarise_chain.invoke({
    html: main,
    format_instructions: format_instructions,
  });

  return summary;
}

Summarise & Load

Create a main() function to fetch the talks, extract the information and save it to Neo4j.

main()
typescript
async function main() {
  config({ path: "./.env.local" });

  const graph = await Neo4jGraph.initialize({
    url: process.env.NEO4J_URI,
    username: process.env.NEO4J_USERNAME,
    password: process.env.NEO4J_PASSWORD,
  });

  const details = [];
  const errors = [];

  const talks = await getTalkUrls();

  for (const talk of talks) {
    try {
      const info = await extractTalkInformation(talk);

      await graph.query(
        `
        MERGE (t:Talk {url: $talk})
        SET t.title = $info.title, t.time = $info.time,
            t.description = $info.description

        MERGE (r:Room {name: $info.room})
        MERGE (t)-[:IN_ROOM]->(r)

        MERGE (s:Speaker {name: $info.speaker.name})
        SET s += $info.speaker

        MERGE (t)-[:GIVEN_BY]->(s)

        FOREACH (tag IN $info.tags |
            MERGE (tg:Tag {name: toLower(tag)})
            MERGE (t)-[:HAS_TAG]->(tg)
        )
      `,
        { talk, info }, "WRITE"
      );

      details.push(info);
    } catch (e) {
      errors.push({
        talk,
        error: e.message,
      });
    }
  }

  console.log(`Loaded ${details.length} talks`)

  console.log(errors);
}

Call the main() function

typescript
main()

Run the code

Run the file using node:

sh
node src/setup/summarise.mjs

It may take a few minutes to run. You should see a message stating the number of talks loaded and any errors.

Verify Challenge

Verifying the Test

Once you have executed the code, click the Verify button and we will check that the code has been executed successfully.

Summary

Well done!