Knowledge Graph Powered Qdrant FAQ Assistant with cognee

The Challenge: Making Documentation Truly Accessible

We've all been there. You're working with a new technology, diving deep into documentation, and you find yourself jumping between dozens of pages trying to piece together the information you need. Traditional search often falls short—it might find the right keywords, but it doesn't understand the relationships between concepts or provide the contextual understanding you're looking for. You can ask ChatGPT or directly to an LLM with Cursor but you are probably famililar with the "now I know the issue" hallucinations.

What if we could build something smarter? What if we could create an FAQ assistant that doesn't just search for keywords, but actually understands the whole documentation and can reason about the relationships between different concepts?

That's exactly what we set out to build with this project—an intelligent FAQ assistant powered by Cognee. In this tutorial, we will transform raw documentation into structured, queryable knowledge.

The Vision: Beyond Traditional RAG

Most documentation assistants today rely on Retrieval-Augmented Generation (RAG)—a technique that searches for relevant text chunks and feeds them to a language model. While effective, RAG has limitations:

No understanding of relationships: It treats each piece of text in isolation
Limited context: It can miss connections between related concepts
Shallow reasoning: It struggles with complex, multi-step questions

Our approach is different. Instead of just retrieving text chunks, we build a knowledge graph—a structured representation of concepts and their relationships. This allows our FAQ assistant to:

✨ Understand connections between different parts of the documentation

✨ Reason about relationships between concepts

✨ Provide more contextual and comprehensive answers

✨ Handle complex queries that span multiple documentation sections

The Architecture: A Four-Stage Pipeline

We chose Qdrant as our demonstration platform because it's one of the well-documented vector databases available, with rich, comprehensive documentation that showcases advanced concepts in similarity search, vector indexing, and distributed systems—making it an excellent showcase for the power of knowledge graph-driven assistance.

Our FAQ assistant follows a elegant four-stage pipeline:

1. 🕷️ Intelligent Web Scraping

We start by systematically crawling Qdrant's documentation using a breadth-first search approach. Our scrape_docs.py script:

Discovers all documentation pages automatically
Uses Firecrawl API to extract clean markdown content
Handles rate limiting and retries gracefully
Combines everything into a single, comprehensive document

# The scraper intelligently navigates the entire docs site
def crawl_qdrant_docs(start_url: str, max_depth: int, output_file: str):
    domain_prefix = "<https://qdrant.tech/documentation>"
    visited = set()
    queue = deque([(start_url, 0)])
    # BFS to discover and scrape all documentation pages

2. 🧹 Content Cleaning & Preprocessing

Raw scraped content often contains noise—cookie banners, privacy notices, navigation elements. Our clean_docs_qdrant.py script removes these distractions:

This ensures our knowledge graph focuses on the actual technical content.

3. 🧠 Knowledge Graph Construction

Here's where the magic happens. Using Cognee, we transform the cleaned documentation into a structured knowledge graph:

# Load content into Cognee
await cognee.add([md_content], dataset_name)

# Build the knowledge graph
await cognee.cognify([dataset_name])

Cognee automatically:

Extracts entities (concepts, technologies, features)
Identifies relationships between entities
Creates a queryable graph structure
Enables semantic understanding of the content

4. 🔍 Intelligent Querying

Our query.py script demonstrates three different approaches to answering questions:

Graph Completion: Leverages the knowledge graph structure for contextual answers

graph_completion_answer = await cognee.search(
    query_type=SearchType.GRAPH_COMPLETION,
    query_text=query_text,
    datasets=[dataset_name]
)

Traditional RAG: For comparison with conventional approaches

search_results_traditional_rag = await cognee.search(
    query_type=SearchType.RAG_COMPLETION,
    query_text=query_text,
    datasets=[dataset_name]
)

Direct LLM: Baseline comparison without any retrieval

Why Knowledge Graphs Matter

The power of our approach lies in the knowledge graph. Unlike traditional text search, knowledge graphs understand that:

Concepts are connected: Understanding vector databases requires knowing about embeddings, similarity search, and indexing
Context matters: The same term might mean different things in different contexts
Relationships are key: Knowing how concepts relate is often more important than knowing what they are

When you ask "How do I optimize Qdrant's performance?", our system doesn't just find pages with those keywords. It understands the relationships between performance optimization, indexing strategies, memory usage, and distributed deployment—providing a comprehensive answer that draws from multiple related concepts.

Real-World Impact

This approach has several practical advantages:

🎯 More Accurate Answers: By understanding relationships, the system provides more contextually relevant responses

⚡ Faster Discovery: Users can find information faster because the system understands what they're really asking

🔗 Better Connections: The system can suggest related topics and help users discover relevant information they might not have thought to ask about

📈 Scalable: As documentation grows, the knowledge graph automatically incorporates new relationships

The Technology Stack

Our implementation leverages several cutting-edge technologies:

Cognee: The core knowledge graph framework that handles entity extraction and relationship mapping
Firecrawl: For clean, reliable web scraping that extracts markdown content
Neo4j & Qdrant: Backend storage for the knowledge graph (via Cognee)
OpenAI GPT: For natural language understanding and generation

Getting Started

Want to build your own documentation FAQ assistant? Here's how:

The full code is available on our cognee-community repo

Clone the project and install dependencies:

pip install cognee[neo4j,qdrant]>=0.1.40

Scrape your documentation:

python scrape_docs.py  # Customize for your docs site

Clean the content:
```
python clean_docs_qdrant.py
```
Build the knowledge graph:
```
python faq_assistant_with_cognee.py
```
Start querying:
```
python query.py
```

What's Next?

This project opens up exciting possibilities:

Multi-modal support: Incorporating images, videos, and code examples
Real-time updates: Automatically updating the knowledge graph as documentation changes
Interactive exploration: Building a UI that lets users explore the knowledge graph visually
Cross-documentation search: Connecting knowledge graphs from multiple projects
Agent memory: Setting up cognee MCP server and connecting it via Cursor for coding assistant (watch our short walkthrough here)

Conclusion

We've moved beyond simple keyword search to create a truly intelligent documentation assistant. By leveraging knowledge graphs, we've built a system that doesn't just find information—it understands it.

The combination of systematic web scraping, intelligent content processing, and knowledge graph construction creates a powerful foundation for next-generation documentation tools. Whether you're building internal tools for your team or creating public-facing documentation assistants, this approach offers a path toward more intelligent, more helpful AI systems.

Ready to transform your documentation into an intelligent knowledge base? The code is open, the approach is proven, and the possibilities are endless.

Want to dive deeper? Check out the cognee repo and start building your own knowledge graph-powered FAQ assistant today!