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Yyds dry goods inventory building knowledge map from scratch with neo4j (I)

2022-06-28 23:34:00 【wb59770b05237ae】

One 、 install Neo4j

Here are the official documents ：https://neo4j.com/download/ Download through the above website , Remember what pops up key, This is required for subsequent software activation key.

Two 、 Basic usage

New projects

#yyds Dry inventory # Use from scratch Neo4j Build a map of knowledge （ One ）_python

2. Change project name

#yyds Dry inventory # Use from scratch Neo4j Build a map of knowledge （ One ）_python_02

Set up local database or remote database

#yyds Dry inventory # Use from scratch Neo4j Build a map of knowledge （ One ）_ Reference documents _03

Start the local database

#yyds Dry inventory # Use from scratch Neo4j Build a map of knowledge （ One ）_python_04

adopt Neo4j Broswer To operate

#yyds Dry inventory # Use from scratch Neo4j Build a map of knowledge （ One ）_python_05

Node operation

Create nodes

      
      
       
       create(: university {name:
       
       " Xi'an University of Electronic Science and Technology "})
      
      
      
      
       
       1.

View the built node graph

#yyds Dry inventory # Use from scratch Neo4j Build a map of knowledge （ One ）_python_06

Adjust the size of the node display font

#yyds Dry inventory # Use from scratch Neo4j Build a map of knowledge （ One ）_ database _07

Get all nodes

      
      
       
       match(n) return (n)
      
      
      
      
       
       1.

The following focuses on how to pass py2neo Come on Neo4j To operate .

Query nodes

       
       created_node 
       
       = 
       
       matcher[
       
       node_id]
       
       print(
       
       created_node)
      
1.
2.

Conditional query

       
       matcher.
       
       match(
       
       ' Judgment document ').
       
       where(
       
       name 
       
       = 
       
       'XXX A criminal judgment ').
       
       first()
      
1.

Return a node that meets the conditions

       
       temp 
       
       = 
       
       selector.
       
       match(
       
       'Person').
       
       first()
      
1.

Add and modify the attribute value of the node

labeling

       
       temp.
       
       add_label(
       
       ' case ')
       
       graph.
       
       push(
       
       temp)
      
1.
2.

Delete node

       
       graph.
       
       delete(
       
       node)
      
1.

Create nodes

       
       node 
       
       = 
       
       Node(
       
       ' people ', 
       
       name 
       
       = 
       
       ' Zhang San ')
       
       graph.
       
       create(
       
       node)
      
1.
2.

Use py2neo Connect to database

       
       from 
       
       py2neo 
       
       import 
       
       Graph,
       
       Node,
       
       Relationship,
       
       Subgraph
       
       from 
       
       py2neo.
       
       matching 
       
       import 
       
       *
       
       graph 
       
       = 
       
       Graph(
       
       "bolt://localhost:7687", 
       
       auth
       
       =(
       
       "neo4j", 
       
       "1234"))
       
       success 
       
       = 
       
       graph.
       
       run(
       
       "UNWIND range(1, 3) AS n RETURN n, n * n as n_sq")
       
       if (
       
       success):
       
       print(
       
       " Successful connection ")
      
1.
2.
3.
4.
5.
6.
7.
8.

summary

py2neo It's an operation Neo4j Of python Third party Library , We can query and use , Very convenient , Focus on the official documents and some key reference documents below .