infigraph-confluence 1.5.2

Confluence ingestion for Infigraph — fetch, parse, and index Confluence pages into the document graph
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

use std::io::{self, BufRead, Write};

use serde_json::{json, Value};

use infigraph_confluence::template::{fill_with_llm, parse_pipeline_template};

fn main() {
    let stdout = io::stdout();
    let mut out = stdout.lock();

    writeln!(
        out,
        "{}",
        json!({"ready": true, "plugin_id": "intuit", "version": "1.0"})
    )
    .unwrap();
    out.flush().unwrap();

    let stdin = io::stdin();
    for line in stdin.lock().lines() {
        let line = match line {
            Ok(l) => l,
            Err(_) => break,
        };
        let line = line.trim().to_string();
        if line.is_empty() {
            continue;
        }

        let cmd: Value = match serde_json::from_str(&line) {
            Ok(v) => v,
            Err(e) => {
                writeln!(
                    out,
                    "{}",
                    json!({"status": "error", "message": format!("Invalid JSON: {e}")})
                )
                .unwrap();
                out.flush().unwrap();
                continue;
            }
        };

        let command = cmd.get("command").and_then(|v| v.as_str()).unwrap_or("");

        match command {
            "extract" => {
                let content = cmd.get("content").and_then(|v| v.as_str()).unwrap_or("");
                let title = cmd.get("title").and_then(|v| v.as_str()).unwrap_or("");
                let doc_id = cmd.get("doc_id").and_then(|v| v.as_str()).unwrap_or("");

                match parse_pipeline_template(content, title, doc_id) {
                    Some(mut record) => {
                        fill_with_llm(&mut record, content, title);

                        let inputs = split_csv(&record.source_systems);
                        let outputs = split_csv(&record.dest_tables);

                        let response = json!({
                            "status": "ok",
                            "data": {
                                "core": {
                                    "name": record.name,
                                    "inputs": inputs,
                                    "outputs": outputs,
                                },
                                "properties": {
                                    "source_systems": record.source_systems,
                                    "dest_tables": record.dest_tables,
                                    "scheduler_type": record.scheduler_type,
                                    "scheduler_config": record.scheduler_config,
                                    "compliance": record.compliance,
                                    "github_repo": record.github_repo,
                                    "daci": record.daci,
                                    "idempotent": record.idempotent,
                                    "business_logic_summary": record.business_logic_summary,
                                    "data_quality": record.data_quality,
                                }
                            }
                        });
                        writeln!(out, "{response}").unwrap();
                    }
                    None => {
                        writeln!(out, "{}", json!({"status": "skip", "reason": "No pipeline template sections detected"})).unwrap();
                    }
                }
                out.flush().unwrap();
            }
            "schema" => {
                let response = json!({
                    "status": "ok",
                    "schema": {
                        "columns": [
                            {"name": "source_systems", "col_type": "STRING"},
                            {"name": "dest_tables", "col_type": "STRING"},
                            {"name": "scheduler_type", "col_type": "STRING"},
                            {"name": "scheduler_config", "col_type": "STRING"},
                            {"name": "compliance", "col_type": "STRING"},
                            {"name": "github_repo", "col_type": "STRING"},
                            {"name": "daci", "col_type": "STRING"},
                            {"name": "idempotent", "col_type": "STRING"},
                            {"name": "business_logic_summary", "col_type": "STRING"},
                            {"name": "data_quality", "col_type": "STRING"},
                        ],
                        "dependency_fields": {"inputs": "core.inputs", "outputs": "core.outputs"},
                        "searchable_fields": ["compliance", "business_logic_summary", "daci", "scheduler_type"],
                    }
                });
                writeln!(out, "{response}").unwrap();
                out.flush().unwrap();
            }
            _ => {
                writeln!(
                    out,
                    "{}",
                    json!({"status": "error", "message": format!("Unknown command: {command}")})
                )
                .unwrap();
                out.flush().unwrap();
            }
        }
    }
}

fn split_csv(s: &str) -> Vec<&str> {
    if s.is_empty() {
        return Vec::new();
    }
    s.split(',')
        .map(|p| p.trim())
        .filter(|p| !p.is_empty())
        .collect()
}