How to Build a Voice-Enabled Knowledge Base Assistant for Notion with ElevenLabs and RAG

The onboarding email was friendly enough, but the link inside sent a shiver of dread down Alex’s spine: a link to the company’s Notion workspace. It was a digital labyrinth. Hundreds of pages, databases, and stray notes from meetings that happened years ago. His simple question—”What is the process for submitting a travel expense report?”—unleashed a search query that returned 27 different pages, each with conflicting or outdated information. He spent the next hour clicking through a maze of links, feeling more lost than when he started. The company’s single source of truth felt more like a source of total confusion. This scenario is painfully familiar in modern organizations. Tools like Notion have become indispensable for centralizing knowledge, but they often become victims of their own success, growing into unwieldy digital archives where finding specific, accurate information is a full-time job. Studies have shown that knowledge workers can spend nearly 20% of their workweek—a full day—just searching for and gathering internal information.

The core challenge isn’t the existence of information, but its accessibility. Static text, buried in nested pages, creates a significant barrier to productivity. It forces employees into a tedious cycle of searching, scanning, and cross-referencing, leading to wasted hours, repetitive questions for senior staff, and a frustrating employee experience. But what if you could transform that static knowledge base into a dynamic, conversational expert? Imagine if Alex could simply ask his computer, “Hey, how do I submit a travel expense report?” and receive an instant, clear, and spoken answer sourced directly from the most up-to-date official document. This is the promise of combining Retrieval-Augmented Generation (RAG) with state-of-the-art voice AI.

This article provides a complete technical walkthrough for building a voice-enabled knowledge base assistant for your Notion workspace. We will move beyond simple text-based chatbots to create a sophisticated system that understands user queries, retrieves relevant information from your company’s Notion pages, and delivers the answer using a natural, human-like voice. We will detail the entire process, from setting up a Notion integration and ingesting your data, to constructing a robust RAG pipeline with LangChain, and finally, integrating the world-class voice synthesis of ElevenLabs to bring your documentation to life. Get ready to turn your company’s wiki from a silent library into an interactive, on-demand expert.

The Architectural Blueprint: Combining Notion, RAG, and ElevenLabs

Before diving into the code, it’s crucial to understand the three core components of our system and how they interact. This architecture is designed to be modular and powerful, turning a passive content repository into an active, conversational resource. Each piece plays a distinct and vital role in the final user experience.

Why Notion is the Perfect Enterprise Knowledge Hub

Notion has rapidly evolved from a note-taking app to a comprehensive platform for enterprise knowledge management. Its strength lies in its unique blend of structured and unstructured data. With databases, pages, properties, and a block-based editor, it allows teams to create rich, interconnected documentation. This structure, which can be a challenge for human navigation, is a significant advantage for an AI system. Furthermore, Notion’s robust API provides the programmatic access we need to ingest this data for our RAG pipeline, making it an ideal foundation for a ‘single source of truth’ that our AI can tap into.

The RAG Pipeline at a Glance

Retrieval-Augmented Generation is the engine of our assistant. It enhances the capabilities of a Large Language Model (LLM) by providing it with specific, relevant information from an external knowledge source—in our case, the Notion workspace. The process works as follows:

1. Ingestion & Vectorization: All the content from your designated Notion pages is loaded, broken into smaller, manageable chunks, and converted into numerical representations (embeddings) using a machine learning model. These embeddings are stored in a specialized vector database.
2. Retrieval: When a user asks a question, their query is also converted into an embedding. The system then searches the vector database to find the chunks of text with the most similar embeddings, effectively retrieving the most relevant information from the Notion workspace.
3. Augmentation & Generation: The retrieved text chunks are combined with the original user query and passed to an LLM as context. The LLM then uses this context to generate a precise, coherent, and accurate answer, rather than relying solely on its generalized pre-trained knowledge.

The Power of Voice: Why ElevenLabs Changes the Game

Integrating a voice interface elevates the entire experience from a simple chatbot to a true digital assistant. Audio output offers several distinct advantages over text. It supports multitasking, allowing an employee to listen to instructions while performing a task. It enhances accessibility for users with visual impairments. Most importantly, a natural-sounding voice creates a more engaging and human-centric interaction. We use ElevenLabs for this critical step because of its industry-leading realism and low-latency API. Its AI-powered voices are virtually indistinguishable from human speech, which is essential for building trust and ensuring a high-quality user experience. The ability to clone voices or select from a vast library of styles allows for deep customization to match a company’s brand. To get started with these powerful AI voices, you can try for free now.

Step 1: Setting Up Your Notion Integration and Data Ingestion

With the architecture defined, our first practical step is to establish a connection to Notion and pull the knowledge base content into our application. This process requires creating an API integration and then using a document loader to parse the content.

Creating a Notion Integration

To allow our application to read your workspace data, you need to create an internal integration in Notion.

1. Navigate to https://www.notion.so/my-integrations.
2. Click “+ New integration”. Give it a name, like “RAG Knowledge Assistant”.
3. Ensure the integration has “Read content” capabilities. You can leave other settings as default for now.
4. Once created, Notion will provide you with an “Internal Integration Secret”. This is your API key. Copy it and store it securely, for example, as an environment variable (NOTION_API_KEY).
5. Finally, go to the top-level Notion page that contains the knowledge base you want the AI to access. Click the “…” menu, select “+ Add connections”, and add your newly created integration. This grants it permission to read that page and all its sub-pages.

Structuring Your Knowledge Base for Optimal Retrieval

Garbage in, garbage out. The quality of your RAG system’s answers depends heavily on the quality and organization of your Notion documents. For best results:

• Use Clear Hierarchies: Organize information logically with parent pages and sub-pages.
• Leverage Headings: Use H1, H2, and H3 headings within your pages to structure content. This helps the document chunking process maintain logical context.
• Be Concise: Break down massive walls of text into smaller paragraphs. Write clear, unambiguous sentences.
• Keep It Updated: Regularly archive or delete outdated pages to prevent the AI from retrieving conflicting information.

Programmatically Accessing and Parsing Notion Content

We will use LangChain’s dedicated Notion document loaders to simplify data ingestion. The NotionDirectoryLoader is excellent for this task. First, install the necessary libraries:

pip install langchain-community beautifulsoup4 notion-client

Next, you can write a simple Python script to load your documents. This script reads the content from your specified Notion page (and its children) and prepares it for the next stage.

import os
from langchain_community.document_loaders import NotionDirectoryLoader

# Set your Notion API key as an environment variable
# os.environ["NOTION_API_KEY"] = "your_secret_key_here"

# Export your Notion page to Markdown/CSV and place it in a directory
# Unzip the file and an organised folder structure will be there
loader = NotionDirectoryLoader("path/to/your/notion_export_directory")
docs = loader.load()

print(f"Loaded {len(docs)} documents from Notion.")
# Example output of a document's content
print(docs[0].page_content[:500])

This script loads the documents into a format that LangChain can work with, preserving metadata like the source page. The next step is to chunk this text into digestible pieces for the embedding model.

Step 2: Building the Core RAG Pipeline with LangChain

Now that we have our documents loaded, we can construct the RAG pipeline. This involves embedding the document chunks, storing them in a vector database, and creating a chain that retrieves them to answer user questions.

Choosing and Configuring Your Vector Store

A vector store is a database designed to efficiently store and search high-dimensional vectors (our document embeddings). For local development and simplicity, Chroma is an excellent choice. It’s lightweight and integrates seamlessly with LangChain.

First, install the required libraries:

pip install langchain-openai chromadb tiktoken

Next, we’ll chunk our documents and embed them using OpenAI’s models.

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma

# 1. Split documents into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)

# 2. Create embeddings and store in Chroma
# Requires OPENAI_API_KEY environment variable
embedding_model = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)

print("Vector store created successfully.")

This code splits the loaded Notion documents into 1000-character chunks with a 200-character overlap to maintain context. It then uses OpenAI’s embedding model to convert these chunks into vectors and stores them in a Chroma vector store in memory.

Setting Up the Retriever

The retriever is the component responsible for fetching the relevant document chunks from the vector store based on a user’s query. LangChain makes this incredibly simple:

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

# Test the retriever
query = "What is the travel expense policy?"
retrieved_docs = retriever.invoke(query)
print(f"Retrieved {len(retrieved_docs)} documents for the query.")

Here, k=3 tells the retriever to fetch the top 3 most relevant document chunks for any given query. This provides the LLM with focused, high-quality context.

Constructing the Question-Answering Chain

Finally, we assemble the components into a complete question-answering chain using the LangChain Expression Language (LCEL). This modern approach provides more transparency and control over the data flow.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

# Define the LLM
llm = ChatOpenAI(model_name="gpt-4o", temperature=0)

# Define the prompt template
prompt_template = """Answer the question based only on the following context:

{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(prompt_template)

# Create the RAG chain
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Run the chain
answer = rag_chain.invoke("What is the process for submitting a travel expense report?")
print(answer)

This chain elegantly orchestrates the entire RAG flow. It takes a question, passes it to the retriever to get context, formats that context and the question into a prompt, sends it to the LLM for an answer, and parses the output into a clean string.

Step 3: Integrating ElevenLabs for Lifelike Voice Responses

With a functional text-based RAG system, the final step is to add the voice component that will set our assistant apart. We will use the ElevenLabs API to convert the text response from our RAG chain into high-quality, spoken audio.

Getting Your ElevenLabs API Key

First, you’ll need an ElevenLabs account and an API key. The platform offers a generous free tier that is perfect for development and testing.

1. Sign up at the ElevenLabs website.
2. Navigate to your profile section and find your API key.
3. Store this key securely as an environment variable (ELEVEN_API_KEY).

The Voice Synthesis API Call

ElevenLabs provides a straightforward Python SDK that makes API calls trivial. First, install the SDK:

pip install elevenlabs

Now, let’s create a function that takes a text string, sends it to the ElevenLabs API, and plays the resulting audio stream.

from elevenlabs.client import ElevenLabs
from elevenlabs import play

# Set your API key
# Note: The client automatically looks for the ELEVEN_API_KEY environment variable
client = ElevenLabs()

def generate_and_play_audio(text: str):
    """Generates audio from text using ElevenLabs API and plays it."""
    try:
        audio = client.generate(
            text=text,
            voice="Rachel",  # You can choose from many available voices or clone your own
            model="eleven_multilingual_v2"
        )
        print("Generating speech from text...")
        play(audio)
    except Exception as e:
        print(f"Error generating audio: {e}")

# Example usage:
generate_and_play_audio("Hello, I am your Notion knowledge assistant.")

This function is simple but powerful. It uses a pre-made voice named “Rachel” but can be easily customized with different voices or settings available through the ElevenLabs API to perfectly match your desired tone.

Tying It All Together: From Text Query to Spoken Answer

Now we integrate this audio function into our main application loop. The complete workflow is as follows: the user provides a query, the RAG chain generates a text answer, and that answer is immediately passed to our generate_and_play_audio function.

# ... (all the RAG chain setup from Step 2)

# Main application loop
user_query = "What is the process for submitting a travel expense report?"

# 1. Get the text answer from the RAG chain
print(f"User Query: {user_query}")
text_answer = rag_chain.invoke(user_query)
print(f"Generated Text Answer: {text_answer}")

# 2. Convert the text answer to speech
if text_answer:
    generate_and_play_audio(text_answer)

With this final integration, you have a fully functional prototype. You have successfully built an intelligent assistant that can readyour Notion knowledge base and speak the answers aloud with stunning realism.

We’ve come a long way from a static, silent documentation library. By following these steps, we have successfully transformed a sprawling Notion workspace into a dynamic, interactive knowledge assistant. We connected to Notion’s data, built a sophisticated RAG pipeline to understand and retrieve information, and integrated a world-class voice AI to deliver answers in a natural, human-like way. The power of this approach extends far beyond simple internal Q&A. Imagine automated audio summaries of meeting notes being generated moments after a call ends, fully voice-driven onboarding modules for new hires, or mission-critical documentation made accessible to visually impaired team members. The barrier between information and the people who need it is dissolving.

Let’s go back to our new employee, Alex. Instead of being lost in a maze of links, he now has an on-demand expert at his disposal. He can confidently ask complex questions about company policy, technical procedures, or project history and get an immediate, clear, and spoken response. He’s not just retrieving information anymore; he’s having a conversation with the collective knowledge of his entire organization. The tools to build these transformative experiences are more powerful and accessible than ever before. To start bringing your own documentation to life with market-leading voice AI, begin your journey with the ElevenLabs API. Click here to sign up and explore the future of interactive knowledge bases.