إتقان إخراج البيانات المنظمة مع LangChain: استراتيجيات وتقنيات لنماذج المحادثة

npm i @langchain/openai

import { ChatOpenAI } from "@langchain/openai";
 
const model = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 });

npm i @langchain/anthropic

import { ChatAnthropic } from "@langchain/anthropic";
 
const model = new ChatAnthropic({ model: "claude-3-sonnet-20240229", temperature: 0 });

OPENAI_API_KEY=your-api-key
ANTHROPIC_API_KEY=your-api-key

import { z } from "zod";
 
const joke = z.object({
  setup: z.string().describe("The setup of the joke"),
  punchline: z.string().describe("The punchline to the joke"),
  rating: z.number().optional().describe("How funny the joke is, from 1 to 10"),
});

const structuredLlm = model.withStructuredOutput(joke);
 
await structuredLlm.invoke("Tell me a joke about cats");
// Expected Output:
// { setup: "Why don't cats play poker in the wild?", punchline: "Too many cheetahs.", rating: 7 }

const structuredLlm = model.withStructuredOutput(joke, { name: "joke" });
 
await structuredLlm.invoke("Tell me a joke about cats");
// Expected Output:
// { setup: "Why don't cats play poker in the wild?", punchline: "Too many cheetahs!", rating: 7 }

const structuredLlm = model.withStructuredOutput({
  name: "joke",
  description: "Joke to tell user.",
  parameters: {
    title: "Joke",
    type: "object",
    properties: {
      setup: { type: "string", description: "The setup for the joke" },
      punchline: { type: "string", description: "The joke's punchline" },
    },
    required: ["setup", "punchline"],
  },
});
 
await structuredLlm.invoke("Tell me a joke about cats");
// Expected Output:
// { setup: "Why was the cat sitting on the computer?", punchline: "Because it wanted to keep an eye on the mouse!" }

const structuredLlm = model.withStructuredOutput(joke, { method: "json_mode", name: "joke" });
 
await structuredLlm.invoke("Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys");
// Expected Output:
// { setup: "Why don't cats play poker in the jungle?", punchline: "Too many cheetahs!" }

import { JsonOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
 
type Person = { name: string; height_in_meters: number; };
type People = { people: Person[]; };
 
const formatInstructions = `
Respond only in valid JSON. The JSON object you return should match the following schema:
{ people: [{ name: "string", height_in_meters: "number" }] }
Where people is an array of objects, each with a name and height_in_meters field.
`;
 
// Set up a parser
const parser = new JsonOutputParser();
 
// Create the prompt template
const prompt = await ChatPromptTemplate.fromMessages([
  ["system", "Answer the user query. Wrap the output in `json` tags\n{format_instructions}"],
  ["human", "{query}"]
]).partial({ format_instructions: formatInstructions });

import { AIMessage } from "@langchain/core/messages";
import { ChatPromptTemplate } from "@langchain/core/prompts";
 
type Person = { name: string; height_in_meters: number; };
type People = { people: Person[]; };
 
const schema = `
{ people: [{ name: "string", height_in_meters: "number" }] }
`;
 
// Creating the prompt template
const prompt = await ChatPromptTemplate.fromMessages([
  ["system", `Answer the user query. Output your answer as JSON that matches the given schema: \`\`\`json\n{schema}\n\`\`\`. Make sure to wrap the answer in \`\`\`json and \`\`\` tags`],
  ["human", "{query}"]
]).partial({ schema });
 
// Custom extractor to parse JSON from AI output
const extractJson = (output: AIMessage): Array => {
  const text = output.content as string;
  const pattern = /```json(.*?)```/gs;
  const matches = text.match(pattern);
 
  try {
    return (matches?.map(match => {
      const jsonStr = match.replace(/```json|```/g, "").trim();
      return JSON.parse(jsonStr);
    }) ?? []);
  } catch (error) {
    throw new Error(`Failed to parse: ${output}`);
  }
};
 
// Invoke the parsing chain
const query = "Anna is 23 years old and she is 6 feet tall";
const chain = prompt.pipe(model).pipe(new RunnableLambda({ func: extractJson }));
 
await chain.invoke({ query });
// Expected Output:
// [ { people: [ { name: "Anna", height_in_meters: 1.83 } ] } ]