adding langchain output parser

action/create-course.ts

@@ -0,0 +1,51 @@
+"use server";
+import { OpenAI } from "@langchain/openai";
+import { RunnableSequence } from "@langchain/core/runnables";
+import { StructuredOutputParser } from "langchain/output_parsers";
+import { PromptTemplate } from "@langchain/core/prompts";
+
+export const createCourse = async ({
+  unit,
+  title,
+}: {
+  unit: string;
+  title: string;
+}) => {
+  try {
+    const parser = StructuredOutputParser.fromNamesAndDescriptions({
+      chapter_title: "one chapter in the unit",
+      youtube_search_query:
+        "best youtube query for the chapter_title so he can confirm his concepts",
+    });
+
+    const chain = RunnableSequence.from([
+      PromptTemplate.fromTemplate(
+        "Answer the users question as best as possible.\n{format_instructions}\n{question}"
+      ),
+      new OpenAI({ temperature: 0 }),
+      parser,
+    ]);
+
+    const request1 = chain.invoke({
+      question: `The title of course is ${title}, It has a unit named ${unit}. I want to create a chapter which tell about the basics of the unit , so name chapter accordingly`,
+      format_instructions: parser.getFormatInstructions(),
+    });
+
+    const request2 = chain.invoke({
+      question: `The title of course is ${title}, It has a unit named ${unit}. I want to create a chapter which tell about the intermediate stuff of the unit , so name chapter accordingly`,
+      format_instructions: parser.getFormatInstructions(),
+    });
+
+    const request3 = chain.invoke({
+      question: `The title of course is ${title}, It has a unit named ${unit}. I want to create a chapter which tell about the advance stuff of the unit , so name chapter accordingly`,
+      format_instructions: parser.getFormatInstructions(),
+    });
+
+    const courses = await Promise.all([request1, request2, request3]);
+
+    return { success: "success", courses: courses };
+  } catch (error) {
+    console.log(error);
+    return { error: error };
+  }
+};

action/create-image-query.ts

@@ -0,0 +1,32 @@
+"use server";
+import { RunnableSequence } from "@langchain/core/runnables";
+import { StructuredOutputParser } from "langchain/output_parsers";
+import { PromptTemplate } from "@langchain/core/prompts";
+import { OpenAI } from "@langchain/openai";
+
+export const createImageQuery = async ({ title }: { title: string }) => {
+  try {
+    const parser = StructuredOutputParser.fromNamesAndDescriptions({
+      image_url:
+        "image url which suited my title and i can search it on unsplash",
+    });
+
+    const chain = RunnableSequence.from([
+      PromptTemplate.fromTemplate(
+        "Answer the users question as best as possible.\n{format_instructions}\n{question}"
+      ),
+      new OpenAI({ temperature: 0 }),
+      parser,
+    ]);
+
+    const request = await chain.invoke({
+      question: `create image for the titlr : ${title}`,
+      format_instructions: parser.getFormatInstructions(),
+    });
+
+    return { success: "success", image: request };
+  } catch (error) {
+    console.log(error);
+    return { error: error };
+  }
+};

action/create-questions.ts

@@ -0,0 +1,59 @@
+"use server";
+import { RunnableSequence } from "@langchain/core/runnables";
+import { StructuredOutputParser } from "langchain/output_parsers";
+import { PromptTemplate } from "@langchain/core/prompts";
+import { OpenAI } from "@langchain/openai";
+
+export const createQuestion = async ({
+  transcript,
+  segment,
+}: {
+  transcript: string;
+  segment: "first" | "second" | "third" | "fourth";
+}) => {
+  try {
+    const parser = StructuredOutputParser.fromNamesAndDescriptions({
+      question:
+        "relevant question for this chapter , create 4 option , out of which one is corrct and all other is wrong and put those into below keys ",
+      answer: "1st option ,  and this is right answer",
+      option1: "2nd option , and this option is wrong",
+      option2: "3rd option , and this option is wrong",
+      option3: "4th option , and this option is wrong",
+    });
+
+    const segmentLength = Math.floor(transcript.length / 4);
+    let segmentText = "";
+    switch (segment) {
+      case "first":
+        segmentText = transcript.slice(0, segmentLength);
+        break;
+      case "second":
+        segmentText = transcript.slice(segmentLength, 2 * segmentLength);
+        break;
+      case "third":
+        segmentText = transcript.slice(2 * segmentLength, 3 * segmentLength);
+        break;
+      case "fourth":
+        segmentText = transcript.slice(3 * segmentLength);
+        break;
+    }
+
+    const chain = RunnableSequence.from([
+      PromptTemplate.fromTemplate(
+        "Answer the users question as best as possible.\n{format_instructions}\n{question}"
+      ),
+      new OpenAI({ temperature: 0, modelName: "gpt-3.5-turbo-1106" }),
+      parser,
+    ]);
+
+    const request = await chain.invoke({
+      question: `Create a question and answer object with 4 options out of which one is correct. The topic is based on the following content segment: ${segmentText}`,
+      format_instructions: parser.getFormatInstructions(),
+    });
+
+    return { success: "success", questions: request };
+  } catch (error) {
+    console.log(error);
+    return { error: error };
+  }
+};

action/create-summary.ts

@@ -0,0 +1,33 @@
+"use server";
+import { RunnableSequence } from "@langchain/core/runnables";
+import { StructuredOutputParser } from "langchain/output_parsers";
+import { PromptTemplate } from "@langchain/core/prompts";
+import { OpenAI } from "@langchain/openai";
+
+export const createSummary = async ({ transcript }: { transcript: string }) => {
+  try {
+    const parser = StructuredOutputParser.fromNamesAndDescriptions({
+      summary: "a crisp summary of transcript that i will provide",
+    });
+
+    const chain = RunnableSequence.from([
+      PromptTemplate.fromTemplate(
+        "Answer the users question as best as possible.\n{format_instructions}\n{question}"
+      ),
+      new OpenAI({ temperature: 0, modelName: "gpt-3.5-turbo-1106" }),
+      parser,
+    ]);
+
+    const request = await chain.invoke({
+      question: `Create summary of youtube transcript in less than 300 words. Do not talk of sponsor or anything unrealted to main topic , also do not introduce what the summary is about.\n${transcript}`,
+      format_instructions: parser.getFormatInstructions(),
+    });
+
+    console.log(request);
+
+    return { success: "success", summary: request };
+  } catch (error) {
+    console.log(error);
+    return { error: error };
+  }
+};

next.config.js

@@ -13,6 +13,9 @@ const nextConfig = {
   typescript: {
     ignoreBuildErrors: true,
   },
+  experimental: {
+    serverActions: true,
+  },
 
   output: "standalone",
 };