Building an Internal AI Knowledge Base with Dify: A Case Study of A Medical Company

This guide demonstrates how a healthcare company developed an internal AI-powered knowledge hub using the Dify platform. It covers the end-to-end process of data management, knowledge graph creation, AI model fine-tuning, and practical application. This solution supports smart business transformation by addressing inefficiencies in knowledge sharing and data silos.

Why Build an AI Knowledge Base?

Challenges Faced:

1. Data Silos: Dispersed data across multiple systems makes unified management difficult.
2. Outdated Knowledge: Traditional knowledge systems cannot keep up with fast-evolving business needs.
3. Low Efficiency: Searching for information is slow and frustrating for users.

Objectives:

• Efficient Data Integration: Unify structured and unstructured data for seamless access.
• Smart Knowledge Modelling: Transform data into actionable knowledge using semantic tools.
• Dynamic Interactions: Enable intelligent Q&A and real-time recommendations powered by AI.

How Dify Streamlines Knowledge Base Creation

Dify is a one-stop platform that simplifies creating AI-powered systems with pre-built workflows, custom app tools, and model integrations. Here's how it works:

Step 1: Collecting and Preparing Data

Dify supports importing various data types and cleaning them with automated workflows.

Key Techniques:

1. ETL (Extract-Transform-Load):

• Streamlines data extraction, conversion, and loading into the knowledge hub.
• Dify's ETL module automates these tasks.

1. OCR and Semantic Analysis:

• Extracts information from scanned documents for better structuring.

Step 2: Creating a Knowledge Graph

Processed data is converted into meaningful relationships using AI models and graph databases.

Workflow Highlights:

• Automated Entity Mapping: Leverages pre-trained AI models for seamless identification of key concepts.
• Graph Storage: Uses Neo4j to store interconnected knowledge for fast retrieval.

Step 3: Integrating Knowledge with AI Models

Dify connects knowledge graphs to large language models (LLMs) for interactive applications.

Implementation:

1. API Integration: Use RESTful or gRPC APIs to link models and knowledge graphs.
2. Fine-Tuning Models: Optimise LLMs like LLaMA 3.2 or Qwen for domain-specific use cases.
3. Enhanced Q&A: Combine search with generative AI for precise and dynamic responses.

Practical Use Cases

1. Knowledge Sharing System

• Need: Share knowledge across the organisation efficiently.
• Solution:

1. Collect internal technical documents and client case studies.
2. Build a graph database with searchable relationships.
3. Implement real-time Q&A using AI models.

2. Personalised Recommendation Engine

• Need: Recommend resources based on user behaviour.
• Solution:

1. Gather behaviour data from CRM logs.
2. Analyse preferences using semantic AI.
3. Generate recommendations based on the knowledge graph.

Performance Optimisations

Model Optimisation:

• Quantisation: Reduce computation with ONNX Runtime (e.g., FP16, INT8).
• Distillation: Use smaller models to mimic larger ones for efficiency.

Query and Data Optimisation:

• Indexing: Speed up graph queries by indexing key nodes and relations.
• Caching: Improve repeat query performance with Redis.

Results

By implementing Dify’s AI-powered solution, the company achieved:

• 40% Faster Data Integration: Automated workflows reduced manual processing time.
• 3x Faster Knowledge Queries: From 5 seconds to 1.5 seconds per query.
• Improved Q&A Accuracy: From 70% to over 90%.

Conclusion

The Dify platform is a powerful tool for transforming enterprise knowledge management. Its capabilities in workflow automation, knowledge graph integration, and AI enhancement enable businesses to make smarter decisions and improve efficiency. Whether for Q&A systems or personalised recommendations, Dify provides a solid foundation for digital transformation.