zediot regular nolink
ZedIoT Logo

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? […]

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 Outdated Knowledge: Traditional knowledge systems cannot keep up with fast-evolving business needs.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.

Data TypeExamplesProcessing Method
StructuredSQL Databases, ERP SystemsETL tools, database connectors
Semi-structuredJSON, XML FilesField mapping, standardisation
UnstructuredPDFs, Word Docs, Web DataOCR, entity recognition

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.

TaskTechniqueTools
Entity RecognitionNamed Entity Recognition (NER)Hugging Face Models
Relationship MappingSyntax and Dependency ParsingSpaCy, BERT
Knowledge StorageRDF/OWL RepresentationNeo4j, GraphDB

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%.
MetricBeforeAfterImprovement
Data Processing Time20 hours12 hours40% faster
Query Speed5 seconds1.5 seconds3x faster
Q&A Accuracy70%90%20% better

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.


Start Free!

Get Free Trail Before You Commit.