How to Build an Automated Video Announcement System in Slack with HeyGen and RAG

Alex, a project lead at a fast-growing tech firm, stared at her screen with a familiar sense of dread. Her team, distributed across three continents and five time zones, was brilliant but increasingly disconnected. Her carefully crafted weekly updates, posted in the main project Slack channel, were being lost in a relentless stream of notifications, code snippets, and memes. Engagement was dropping, and a critical pivot in project direction last week was missed by two key developers until it was almost too late. The challenge wasn’t a lack of information, but a failure of communication’s medium. Text is efficient but lacks impact. Pre-recorded videos are engaging but time-consuming to produce, making them impractical for frequent, ad-hoc updates. The team was suffering from terminal communication fatigue.

This scenario is playing out in thousands of organizations globally. As remote and hybrid work become permanent fixtures, the limitations of text-based asynchronous communication are glaringly obvious. How can you ensure critical information is not just delivered, but received, understood, and retained? The demand for more dynamic, personal, and scalable communication tools has never been higher. Standard video creation workflows are too slow and cumbersome for the rapid pace of modern project management. You can’t spend an hour setting up a camera, recording, and editing a video for a 90-second update on a minor scope change. What leaders like Alex need is a way to combine the speed of text with the impact of video, without the manual overhead.

Imagine a system where Alex could simply type a command in Slack, like /announce Our latest user feedback requires us to reprioritize the auth module, and moments later, a polished video of her AI-powered avatar appears in the channel, delivering the message with context and clarity. This isn’t science fiction; it’s the power of combining Retrieval-Augmented Generation (RAG), generative AI video, and modern collaboration platforms. This solution bridges the gap, allowing for the creation of contextual, on-brand video messages on the fly. It transforms mundane text updates into engaging, can’t-miss announcements that cut through the digital noise. In this guide, we’ll provide a complete technical walkthrough to build this very system, integrating HeyGen’s video generation with a custom RAG pipeline and the Slack API. Get ready to revolutionize your internal communications.

The Architectural Blueprint: Combining RAG, HeyGen, and Slack

At its core, this system is an elegant orchestration of three powerful technologies. It’s not just about piping text into a video generator; it’s about creating intelligent videos. The RAG component is the brain of the operation, ensuring each announcement is not just a recitation of input text but an informed, contextual summary.

Why RAG is the Secret Sauce for Contextual Updates

Traditionally, RAG is seen as a tool for question-answering systems, where it fetches relevant documents to answer a user’s query. However, its potential is far broader. In our use case, RAG acts as a dynamic context provider. When a project manager types an update, the RAG system doesn’t just see that one sentence. It performs a similarity search against a knowledge base—a collection of project briefs, meeting notes, Jira tickets, and past announcements—to retrieve relevant background information.

This retrieved context is then passed to a Large Language Model (LLM) along with the original update. The LLM’s task is to synthesize these two inputs into a coherent, comprehensive script for the video. For instance, if the update is about reprioritizing a module, the RAG system might pull up the original project requirements for that module and the names of the engineers assigned to it. The resulting video script could then say, “Team, quick update. Based on new user feedback, we’re reprioritizing the auth module. I know this impacts the work that Sarah and Tom have been doing, and we’ll connect with them directly. This aligns with our Q3 goal of improving user onboarding security.”

System Components and Data Flow

The workflow is designed to be lean and event-driven, perfect for a serverless architecture:

1. Trigger: A user initiates the process with a slash command in Slack (e.g., /announce <update text>).
2. Ingestion & Processing: A cloud function (like AWS Lambda or Google Cloud Functions) receives the payload from Slack.
3. Retrieval: The function queries a vector database (our knowledge base) with the user’s text to find relevant project documents and context.
4. Generation: The original text and the retrieved context are fed into an LLM with a specific prompt to generate a polished video script.
5. Video Synthesis: The generated script is sent to the HeyGen API, which begins rendering the video with a pre-selected AI avatar.
6. Delivery: Because video generation is asynchronous, the system waits for a webhook from HeyGen or periodically polls for the video’s status. Once the video URL is ready, the cloud function posts a final message back to the designated Slack channel, where the video unfurls for immediate viewing.

Setting Up Your Knowledge Base

Your RAG system is only as good as the data it can access. For this project, you’ll need a vector database to store your project documentation. You can start simply by gathering key documents (e.g., project plans, strategy docs, team charters) as .txt or .md files.

Tools like Pinecone, Weaviate, or the open-source ChromaDB are excellent choices. For this guide, we’ll assume a simple implementation where documents are chunked, converted into vector embeddings using a model like OpenAI’s text-embedding-3-small, and stored in your chosen database. This setup allows for lightning-fast semantic search to find the most relevant context for any given update.

Step 1: Configuring Your HeyGen and Slack Environments

Before writing any code, you need to set up the two main external services: HeyGen for video generation and Slack for communication. This involves obtaining API keys and configuring permissions.

Getting Started with the HeyGen API

HeyGen is the engine that will turn your generated scripts into polished videos. Their API allows you to programmatically create content using a variety of stock or custom-trained avatars.

1. Sign Up and Get Your API Key: First, you’ll need a HeyGen account. The platform offers a range of plans to get you started. You can try for free now to explore the features. Once your account is set up, navigate to the API settings in your account dashboard to retrieve your unique API key. Store this securely.
2. Select Your Avatar: In the HeyGen portal, you can choose from a wide library of realistic avatars. For a more personal touch, you can create a custom avatar of yourself. For our system, you will need the avatar_id of your chosen avatar, which you can find through the API or within the platform’s interface.

Creating a Slack App and Obtaining Credentials

Next, you’ll create a Slack app to handle the slash command and post messages back to your workspace.

1. Create a New App: Go to api.slack.com/apps and click “Create New App”. Choose “From scratch” and give it a name like “Video Announcer Bot”.
2. Enable Slash Commands: In the app’s settings, go to “Slash Commands” and create a new command. Let’s call it /announce. For the “Request URL”, you’ll need a public endpoint for your cloud function, which we’ll create later. For now, you can use a placeholder.
3. Set Permissions (OAuth & Permissions): Go to the “OAuth & Permissions” page. Under “Scopes”, you’ll need to add the following Bot Token Scopes:
   • commands: To allow your app to register and respond to slash commands.
   • chat:write: To allow the bot to post messages in channels.
4. Install the App: Install the app to your workspace. After installation, you’ll be provided with a “Bot User OAuth Token” (starts with xoxb-). This is your Slack bot token. Store it securely alongside your HeyGen API key.

Step 2: Building the RAG-Powered Script Generator

This is where the intelligence of our system comes to life. We’ll build a Python function that takes the raw text from Slack, enriches it with context from our vector database, and uses an LLM to generate a perfect video script.

Implementing the Retrieval Logic

Using a framework like LlamaIndex or LangChain simplifies this process. Here’s a conceptual Python snippet using LlamaIndex to query a ChromaDB vector store. First, ensure you have the necessary libraries installed: pip install llama-index chromadb openai.

import chromadb
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext

# Initialize ChromaDB client and create a collection
chroma_client = chromadb.Client()
chroma_collection = chroma_client.get_or_create_collection("project_docs")

# Load documents and build the index (do this once offline)
documents = SimpleDirectoryReader("./knowledge_base").load_data()
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Create a query engine for retrieval
query_engine = index.as_query_engine()

def retrieve_context(update_text: str) -> str:
    """Retrieves relevant context from the vector store."""
    response = query_engine.query(update_text)
    return str(response)

This function, retrieve_context, takes the user’s raw update text and returns a string of relevant information retrieved from your knowledge base.

Crafting the Prompt for the Generator (LLM)

A well-crafted prompt is crucial for getting a high-quality script. The prompt needs to instruct the LLM on its role, the desired output format, tone, and length. Here is an effective prompt template:

PROMPT_TEMPLATE = """
You are a helpful assistant for a project lead. Your task is to convert a raw project update into a clear, engaging, and professional script for a 30-second video announcement. Use the provided context to add detail and clarity.

Keep the tone positive and direct. The final script should be natural-sounding and ready to be read by the AI avatar.

--- CONTEXT ---
{context}

--- RAW UPDATE ---
{update}

--- VIDEO SCRIPT ---
"""

def generate_script(context: str, update: str) -> str:
    """Generates a video script using an LLM."""
    import openai

    response = openai.ChatCompletion.create(
        model="gpt-4-turbo-preview",
        messages=[
            {"role": "system", "content": PROMPT_TEMPLATE.format(context=context, update=update)}
        ]
    )
    return response.choices[0].message.content

This function takes the context from the retrieval step and the original update, then returns a finished script.

Step 3: Orchestrating the Video Generation and Slack Posting

With the script ready, the final step is to create the video with HeyGen and post it to Slack. This involves making API calls to both services.

Calling the HeyGen API to Create the Video

The HeyGen API has an endpoint for video generation. You’ll send a POST request containing your script, avatar ID, and other parameters. Remember that video generation is not instantaneous.

import requests
import time

HEYGEN_API_KEY = "YOUR_HEYGEN_API_KEY"
AVATAR_ID = "YOUR_AVATAR_ID"

def create_video(script: str) -> str:
    """Initiates video generation and polls for the result."""
    # 1. Initiate generation
    start_url = "https://api.heygen.com/v2/video/generate"
    headers = {"X-Api-Key": HEYGEN_API_KEY, "Content-Type": "application/json"}
    data = {
        "video_inputs": [{
            "character": {"type": "avatar", "avatar_id": AVATAR_ID},
            "voice": {"type": "text", "input_text": script}
        }],
        "test": False
    }
    response = requests.post(start_url, headers=headers, json=data)
    video_id = response.json()["data"]["video_id"]

    # 2. Poll for status
    status_url = f"https://api.heygen.com/v1/video_status.get?video_id={video_id}"
    while True:
        status_response = requests.get(status_url, headers=headers).json()
        if status_response["data"]["status"] == "completed":
            return status_response["data"]["video_url"]
        time.sleep(10) # Wait 10 seconds before checking again

This function returns the URL of the finished video file. A more robust production system would use webhooks instead of polling to be notified upon completion.

Posting the Final Video to Slack

Finally, use the Slack API to post the video URL back to the channel where the command was initiated. Slack automatically unfurls video links, creating an embedded player.

SLACK_BOT_TOKEN = "YOUR_SLACK_BOT_TOKEN"

def post_to_slack(channel_id: str, video_url: str, original_update: str):
    """Posts the video announcement to a Slack channel."""
    url = "https://slack.com/api/chat.postMessage"
    headers = {"Authorization": f"Bearer {SLACK_BOT_TOKEN}"}
    data = {
        "channel": channel_id,
        "text": f"Here's a video update about: '{original_update}'",
        "blocks": [
            {
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": f"Here's a video update about: *{original_update}*"
                }
            },
            {
                "type": "video",
                "title": {"type": "plain_text", "text": "Project Update"},
                "video_url": video_url,
                # You might need to host the video on a public URL that Slack can access
                # And provide a thumbnail URL for optimal display
            }
        ]
    }
    requests.post(url, headers=headers, json=data)

Now, all that’s left is to wrap these functions in a single cloud function endpoint that Slack can call. This full integration turns a simple text command into a powerful, automated communication workflow.

This system does more than just save time; it fundamentally enhances the quality and impact of internal communications. By automating the creation of contextual video announcements, you ensure that every update is engaging, informative, and hard to ignore. We’ve just walked through how to build a powerful tool that transforms the way teams stay aligned in a remote-first world.

Remember Alex, our project lead struggling with disconnected teams? Now, instead of her updates getting lost in the scroll, Alex’s team gets engaging video summaries that keep everyone in sync and motivated, all from a simple Slack command. This is the future of work—smarter, not harder. Ready to transform your own team’s communication? The first step is creating your AI avatar. Try HeyGen for free now and start exploring the future of automated video messaging.