Dify+RAGFlow creates an enterprise-level intelligent knowledge base: PDF tables become structured data in seconds, and search accuracy increases dramatically

Create an efficient solution for enterprise-level intelligent knowledge base, turn PDF tables into structured data in seconds, and greatly improve search accuracy.
Core content:
1. Hardware and software architecture requirements and environment preparation
2. Detailed deployment steps, including RAGFlow and Dify configuration
3. System integration and configuration, core strategies to improve document parsing optimization and search accuracy
Detailed tutorial and principle analysis of combining dify with RAGFlow to deploy local knowledge base and improve retrieval accuracy:
1. Environment Preparation and Deployment Architecture
Hardware requirements :
CPU ≥ 4 cores (AVX instruction set support recommended) Memory ≥ 16GB Disk ≥ 50 GB (for storing vector indexes) GPU is not required but can speed up processing (NVIDIA T4 or above is recommended)
Software Architecture :
User side → Dify application layer (workflow orchestration) → RAGFlow engine (document parsing/retrieval) → Local LLM (Ollama, etc.)
This architecture achieves decoupled deployment of Dify and RAGFlow through API interfaces, ensuring the professionalism of document processing while maintaining the flexibility of application development.
2. Detailed deployment steps
1. RAGFlow deployment (document processing layer)
# Clone the repository and start the container (Docker must be installed in advance)
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/deploy/docker
docker-compose up -d
Key configuration :
Revise docker-compose.yml
middleMINIO_ROOT_PASSWORD
(Object Storage Key)Adjustment elasticsearch
Memory allocation to 8GB or more
2. Dify deployment (application development layer)
# Modify environment variables (key steps)
vim dify-main/docker/.env
# Enable custom models and configure Ollama
CUSTOM_MODEL_ENABLED = true
OLLAMA_API_BASE_URL=http://[localhost IP]:11434
Deployment command :
cd dify-main/docker
docker compose -p dify_docker up -d
This configuration implements local model calls and avoids cloud API delays.
3. System Integration and Configuration
1. API connection process
http://[IP]:9380 | ||
Special note : The following processing needs to be completed in RAGFlow in advance:
Enable "deep layout parsing" mode for PDF documents Select "Cell Level Segmentation" in Excel Set multi-language support parameters (Chinese requires special configuration)
2. Hybrid retrieval configuration
In the Dify workflow, set:
retrieval_strategy:
- vector_search:
model: jina-embeddings-v2-base-zh
top_k: 8
- full_text:
analyzer: ik_max_word
rerank:
model: bge-reranker-large
score_threshold: 0.35
This configuration combines semantic retrieval with keyword matching. Tests have shown that it can improve the recall rate of table data.
4. Core Strategies for Improving Accuracy
1. Document parsing optimization
Layout-aware technology : RAGFlow uses the CV algorithm to identify the position of tables in PDF, avoiding the misalignment problem of traditional OCR (tests show that the completeness of table parsing in scanned documents has increased by 62%) Intelligent block algorithm : Use "." to separate Chinese characters (28% more accurate than line breaks) The table uses "title + cell" association storage Automatically generate AltText for images and create cross-modal indexes
2. Retrieval Enhancement Mechanism
Multi-way recall strategy :
Vector Retrieval: Capturing Semantic Similarity Full-text search: Ensure keyword matching Graph recall: Expanding on document internal associations
Dynamic re-ranking : Use the BGE model to re-rank the Top 50 results to eliminate the "semantic drift" phenomenon TopK dynamic adjustment : set according to the average length of the document (recommended 6-12) Score threshold : Start testing from 0.3 and adjust in steps of 0.05 Segment overlap rate : set to 10-15% to avoid information fragmentation Deep document understanding : RAGFlow's layout parsing algorithm breaks through the limitations of traditional NLP tools, especially when processing scanned documents and complex tables. Hybrid search mechanism : Combined with Dify's flexible workflow arrangement, it realizes the three-dimensional matching of "keywords + semantics + associations" Dynamic optimization strategy : continuous optimization closed loop based on reordering model and parameter adaptation Local deployment : Eliminate API transmission loss and ensure original data security RAGFlow Official Deployment Guide Dify External Knowledge Base Configuration Manual White Paper on Hybrid Search Parameter Optimization
3. Workflow Optimization
5. Effect Verification and Tuning
1. Case comparison
2. Parameter Tuning Guide
6. Summary of the principles of improving accuracy
Operation document reference :