Generating Video Summaries with Go and the OpenAI API

Welcome back! In the previous lessons, we explored downloading videos from both Google Drive and LinkedIn using Go. Building on those skills, we're now going to delve deeper into generating video summaries — an essential skill for transforming lengthy transcriptions into concise and insightful content. This lesson takes you a step further by utilizing the capabilities of the OpenAI API to create detailed yet succinct summaries on the fly.

Today, you will:

• Understand how to generate a summary from a transcription.
• Explore the use of the OpenAI API to craft well-structured summaries.
• Gain insights into designing effective prompts for better results.
• Learn about system and user roles in OpenAI API requests and their importance.

Summarizing transcriptions involves distilling the core messages from extensive spoken content, ensuring key points are retained while unnecessary details are filtered out. When dealing with long videos or lectures, extracting the main themes allows you to quickly grasp the essentials without listening to every word. The OpenAI API facilitates this by leveraging advanced language models capable of understanding context and summarizing long texts.

In our context, the OpenAI API will help us convert raw transcriptions into coherent summaries. The prompts you provide to the API play a crucial role in shaping the quality and structure of the summary.

Here’s how you might structure the prompts for the API in Go:

systemPrompt := `You are an expert content analyst and summarizer with these capabilities:
- Extracting key points while maintaining context
- Identifying main themes and core messages
- Preserving critical details while reducing length
- Maintaining the original tone and intent
- Organizing information hierarchically

Format your summaries with:
1. A one-sentence overview
2. 2-3 key takeaways
3. Important details or quotes (if any)`
userPrompt := fmt.Sprintf(
    "Create a structured summary of this transcription. Focus on the core message and key points while maintaining context and critical details.\n\nTranscription:\n%s",
    transcriptionText,
)

• The system prompt sets the stage by instructing the model on its role and the style and structure of summary you expect.
• The user prompt provides the actual content to summarize and specifies the desired focus and format.

Let's walk through summarizing a transcription using the OpenAI API in Go, leveraging the official OpenAI Go SDK for a more streamlined and idiomatic approach.

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/openai/openai-go"
    "github.com/openai/openai-go/option"
)

func main() {
    apiKey := os.Getenv("OPENAI_API_KEY")
    if apiKey == "" {
        log.Fatal("Please set the OPENAI_API_KEY environment variable.")
    }

    client := openai.NewClient(option.WithAPIKey(apiKey))

    // Replace this with your transcription text
    transcriptionText := `Your transcription text goes here.`

    // System and user prompts (from the initial lesson)
    systemPrompt := `You are an expert content analyst and summarizer with these capabilities:
- Extracting key points while maintaining context
- Identifying main themes and core messages
- Preserving critical details while reducing length
- Maintaining the original tone and intent
- Organizing information hierarchically

Format your summaries with:
1. A one-sentence overview
2. 2-3 key takeaways
3. Important details or quotes (if any)`

    userPrompt := fmt.Sprintf(
        "Create a structured summary of this transcription. Focus on the core message and key points while maintaining context and critical details.\n\nTranscription:\n%s",
        transcriptionText,
    )

    messages := []openai.ChatCompletionMessageParamUnion{
        openai.SystemMessage(systemPrompt),
        openai.UserMessage(userPrompt),
    }

    chatCompletion, err := client.Chat.Completions.New(
        context.Background(),
        openai.ChatCompletionNewParams{
            Messages: messages,
            Model:    "gpt-4o",
        },
    )
    if err != nil {
        log.Fatalf("Error getting summary: %v", err)
    }
    if len(chatCompletion.Choices) == 0 {
        log.Fatal("No summary returned")
    }

    fmt.Println(chatCompletion.Choices[0].Message.Content)
}

Explanation of the process:

• Environment Setup: The API key is read from the OPENAI_API_KEY environment variable.
• Prompt Construction: The system and user prompts are defined as strings, guiding the model on how to summarize the content, with clear instructions and formatting.
• Request Preparation: The prompts are added to a message slice and sent to the OpenAI API using the Go SDK.
• Response Handling: The summary is extracted from the API response and printed to the console.

This lesson highlighted the importance of efficiently distilling information from video transcriptions using the OpenAI API. By converting lengthy transcripts into concise summaries, the process facilitates quick decision-making and enhances comprehension across various fields, such as education and business analytics. Mastering video summarization enables you to create accessible, information-rich content while eliminating unnecessary details. By transforming raw data into actionable insights, you bridge a critical gap in information synthesis. You can now apply these concepts in practical exercises, reinforcing your skills through hands-on coding tasks.