Case Study: Knowledge Assistant for Technical Documentation

Most documentation assistants today rely on simple keyword matching or basic RAG, which treats each piece of text in isolation.

We introduce a paradigm shift in how we approach documentation assistance, moving beyond simple text retrieval to understanding the intricate relationships between concepts.

Scenario: Intelligent Documentation Assistant to Built with Qdrant

Imagine a developer trying to optimize their Qdrant vector database implementation. Instead of jumping between dozens of documentation pages, picking the relevant ones and adding them manually to their coding assistant; they can ask natural language questions like:

“How do I optimize Qdrant’s performance for high-throughput scenarios?”

“What’s the relationship between indexing strategies and memory usage?”

“How does distributed deployment affect query latency?”

A knowledge graph-powered assistant doesn’t just find pages with those keywords—it understands the relationships between performance optimization, indexing strategies, memory usage, and distributed deployment, providing comprehensive answers that draw from multiple related concepts.

Four-Stage Solution Pipeline

In this example, we transform raw documentation into structured, queryable knowledge through a four-stage pipeline:

1. 🕷️ Intelligent Web Scraping

• Systematic crawling using breadth-first search to discover all documentation pages
• Clean content extraction using tools like Firecrawl API to get markdown content
• Rate limiting and retry handling for robust data collection
• Comprehensive aggregation into a single, structured document

2. 🧹 Content Cleaning & Preprocessing

• Noise removal including cookie banners, privacy notices, and navigation elements
• Content normalization to ensure consistent formatting
• Focus on technical content by filtering out non-essential elements

3. 🧠 Knowledge Graph Construction

Using Cognee, the cleaned documentation is transformed 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 queryable graph structure
• Enables semantic understanding of the content

4. 🔍 Intelligent Querying

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]
)

Why Knowledge Graphs Matter

The power of this approach lies in understanding 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

✨ Comprehensive reasoning: Can handle complex queries that span multiple documentation sections

Real-World Benefits

This approach delivers 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

Technology Stack

We used several cutting-edge technologies for this use demo:

• Cognee: Core knowledge graph framework for entity extraction, relationship mapping, retrieving
• Firecrawl: Clean web scraping that extracts markdown content
• Neo4j & Qdrant: Backend storage for the knowledge graph
• OpenAI GPT: As our LLM provider

Getting Started

Ready to build your own documentation assistant? You can follow this example . Here’s how it works:

1. Install Cognee with the necessary providers:

pip install cognee[neo4j,qdrant]>=0.1.40

2. Scrape your documentation:

# Customize the scraping for your docs site
python scrape_docs.py

3. Clean the content:

# Remove noise and normalize content
python clean_docs.py

4. Build the knowledge graph:

# Transform content into structured knowledge
python build_knowledge_graph.py

5. Start querying:

# Begin asking intelligent questions
python query_assistant.py

Advanced Applications

This approach 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 UIs that let users explore the knowledge graph visually
• Cross-documentation search: Connecting knowledge graphs from multiple projects
• Agent memory: Integrating with coding assistants through cognee MCP server.

In this example, we connected our knowledge graph generated by cognee again with cognee’s mcp server via Cursor. Thus, while building our solution with Qdrant, we didn’t have to go back and forth with documentation tabs - all the knowledge were available to us on the Cursor’s chat interface without manually adding docs pages.

Next Steps

Want to dive deeper into building intelligent documentation assistants? Check out:

• Cognee GitHub Repository  for the core framework
• Community Examples  for practical implementations
• Tutorial: Build Custom Graphs for advanced customization

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