EdsClient Refactor into EdsRestClient and EdsSoapClient
Date: 2025 12-December 01
oolong@CIT-36NZRL3 ~/dev/pipeline dev poetry run python -m pipeline.api.eds.soap.demo demo_soap_tabular
2025-12-01 15:38:03 [INFO] __main__: CLI started
plant_name = Stiles
Attempting to connect to WSDL at: http://172.19.4.128:43080/eds.wsdl
SOAP client created successfully.
Logging in as user: 'admin'...
Login successful. Received authstring: ffc2f5a6-75cf-5457-b113-39e60e360126
--- Example 1: Pinging server ---
Ping successful.
--- Example 2: Requesting server time ---
Received server time response:
(reply){
time =
(Timestamp){
second = 1764625086
}
zone =
(TimeDuration){
seconds = -21600
}
offset =
(TimeDuration){
seconds = -21600
}
}
--- Example 3: Requesting server status ---
Received server status response:
(reply){
startTime =
(Timestamp){
second = 1764107412
}
soapConnectionCount = 0
soapHttpsConnectionCount = 0
sessionCount = 1
requestCount = 2
requestRunningCount = 0
srvConnection = "LOGGED_IN | SYNCHRONIZED | STATIC_CHANGED | DYNAMIC_CHANGED | UPDATE_CYCLE"
objConnection = "CONNECTED"
globalObjectCount = 1314
pendingObjectCount = 0
liveDataConnectionCount = 0
}
--- Example 4: Requesting point by IESS name ('{}') ---
Received getPoints response (by IESS):
(reply){
points[] =
(Point){
id =
(PointId){
sid = 87
iess = "I-0300A.UNIT1@NET1"
idcs = "I-0300A"
zd = "WWTF"
}
rt = "POINT-TYPE-ANALOG"
value =
(PointValue){
av = 184.57614
}
quality = "QUALITY-GOOD"
ts =
(Timestamp){
second = 1764625084
}
lts =
(Timestamp){
second = 0
}
tss =
(TimeDuration){
seconds = -54
}
at =
(Timestamp){
second = 1764027271
}
atss =
(TimeDuration){
seconds = -54
}
desc = "RAW PUMP STATION WELL LEVEL"
st = 0
xst1 = 524288
ar = "ARCHIVING-LOCAL"
artd = "ARCHIVING-DEADBAND-PCT-RANGE"
sg[] =
0,
1,
tg[] =
0,
1,
df = 292
ap = 17
aux = "DROP21 3Y=0 3Z=0 4Y=0 4Z=0 HW=1.6.7-6 KR=-------- LB=132 ZH=0 ZL=0"
un = "FT"
dp = 2
ard = 0.5
tb = 187.0
bb = 177.0
hl = 184.0
ll = 182.0
foreground =
(Color){
argb = 4278190080
}
background =
(Color){
argb = 16777215
}
},
matchCount = 1
totalCount = 7986
}
Received getPoints response (by IESS):
(reply){
points[] =
(Point){
id =
(PointId){
sid = 5395
iess = "I-0301A.UNIT1@NET1"
idcs = "I-0301A"
zd = "WWTF"
}
rt = "POINT-TYPE-ANALOG"
value =
(PointValue){
av = 188.08508
}
quality = "QUALITY-GOOD"
ts =
(Timestamp){
second = 1764625084
}
lts =
(Timestamp){
second = 0
}
tss =
(TimeDuration){
seconds = -54
}
at =
(Timestamp){
second = 1764625026
}
atss =
(TimeDuration){
seconds = -54
}
desc = "RAW PUMP LASER LEVEL"
st = 1192
xst1 = 540842
ar = "ARCHIVING-LOCAL"
artd = "ARCHIVING-DEADBAND-PCT-RANGE"
sg[] =
0,
1,
tg[] =
0,
1,
df = 292
ap = 17
aux = "DROP21 3Y=0 3Z=0 4Y=0 4Z=0 HW=1.6.7-7 KR=-------- LB=128 ZH=0 ZL=0"
un = "FT"
dp = 2
ard = 0.5
tb = 244.0
bb = 172.0
hl = 188.0
ll = 0.0
foreground =
(Color){
argb = 4278190080
}
background =
(Color){
argb = 4294901760
}
},
matchCount = 1
totalCount = 7986
}
--- Example 6: Requesting tabular data for 'I-0300A.UNIT1@NET1' ---
Requesting data from 1764624486 to 1764625086
Submitting tabular data request...
2025-12-01 15:38:07 [ERROR] suds.client: <?xml version="1.0" encoding="UTF-8"?>
<SOAP-ENV:Envelope xmlns:SOAP-ENV="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ns0="http://tt.com.pl/eds/" xmlns:ns1="http://schemas.xmlsoap.org/soap/envelope/">
<SOAP-ENV:Header/>
<ns1:Body>
<ns0:requestTabular>
<ns0:authString>ffc2f5a6-75cf-5457-b113-39e60e360126</ns0:authString>
<ns0:request>
<ns0:period>
<ns0:from>
<ns0:second>1764624486</ns0:second>
</ns0:from>
<ns0:till>
<ns0:second>1764625086</ns0:second>
</ns0:till>
</ns0:period>
<ns0:step>
<ns0:seconds>60</ns0:seconds>
</ns0:step>
<ns0:items>
<ns0:pointId>
<ns0:iess>I-0300A.UNIT1@NET1</ns0:iess>
</ns0:pointId>
<ns0:shadePriority/>
<ns0:function>AVG</ns0:function>
</ns0:items>
</ns0:request>
</ns0:requestTabular>
</ns1:Body>
</SOAP-ENV:Envelope>
Error during tabular data request: b"Server raised fault: 'Validation constraint violation: empty value provided where a value is required in element 'ns0:shadePriority''"
Logging out with authstring: ffc2f5a6-75cf-5457-b113-39e60e360126...
Logout successful.
plant_name = Maxson
Attempting to connect to WSDL at: http://172.19.4.127:43080/eds.wsdl
SOAP client created successfully.
Logging in as user: 'operator'...
Login successful. Received authstring: b83940cb-1254-51fa-9abd-653c933ab7a5
--- Example 1: Pinging server ---
Ping successful.
--- Example 2: Requesting server time ---
Received server time response:
(reply){
time =
(Timestamp){
second = 1764625088
}
zone =
(TimeDuration){
seconds = -21600
}
offset =
(TimeDuration){
seconds = -21600
}
}
--- Example 3: Requesting server status ---
Received server status response:
(reply){
startTime =
(Timestamp){
second = 1764107402
}
soapConnectionCount = 0
soapHttpsConnectionCount = 0
sessionCount = 1
requestCount = 157
requestRunningCount = 0
srvConnection = "LOGGED_IN | SYNCHRONIZED | STATIC_CHANGED | DYNAMIC_CHANGED | UPDATE_CYCLE"
objConnection = "CONNECTED"
globalObjectCount = 1481
pendingObjectCount = 0
liveDataConnectionCount = 0
}
--- Example 4: Requesting point by IESS name ('{}') ---
Received getPoints response (by IESS):
(reply){
points[] =
(Point){
id =
(PointId){
sid = 8528
iess = "FI8001.UNIT0@NET0"
idcs = "FI8001"
zd = "Maxson"
}
rt = "POINT-TYPE-ANALOG"
value =
(PointValue){
av = 66.357529
}
quality = "QUALITY-GOOD"
ts =
(Timestamp){
second = 1764625088
}
lts =
(Timestamp){
second = 0
}
tss =
(TimeDuration){
seconds = 9
}
at =
(Timestamp){
second = 1764601849
}
atss =
(TimeDuration){
seconds = 9
}
desc = "Sum of FI7065 and FI7080 (Total Effluent Flow)"
st = 1056
xst1 = 524320
ar = "ARCHIVING-FILLIN"
artd = "ARCHIVING-DEADBAND-STANDARD"
sg[] =
0,
1,
tg[] =
0,
1,
df = 292
ap = 17
aux = "DROP5 3Y=0 3Z=0 4Y=0 4Z=0 DG=272 KR=A------- LB=128 WDPF_RT=91 WDPF_SID=0x80004301 ZH=0 ZL=0"
un = "MGD"
dp = 3
ard = 0.5
tb = 200.0
bb = 0.0
hl = 180.0
ll = 0.0
foreground =
(Color){
argb = 4278190080
}
background =
(Color){
argb = 4278255360
}
},
matchCount = 1
totalCount = 11061
}
Received getPoints response (by IESS):
(reply){
points[] =
(Point){
id =
(PointId){
sid = 2382
iess = "M310LI.UNIT0@NET0"
idcs = "M310LI"
zd = "Maxson"
}
rt = "POINT-TYPE-ANALOG"
value =
(PointValue){
av = 81.042473
}
quality = "QUALITY-GOOD"
ts =
(Timestamp){
second = 1764625088
}
lts =
(Timestamp){
second = 0
}
tss =
(TimeDuration){
seconds = 9
}
at =
(Timestamp){
second = 1764444892
}
atss =
(TimeDuration){
seconds = 9
}
desc = "INFLU WETWELL LVL"
st = 197664
xst1 = 32
ar = "ARCHIVING-FILLIN"
artd = "ARCHIVING-DEADBAND-STANDARD"
sg[] =
0,
1,
tg[] =
0,
1,
df = 292
ap = 68
aux = "DROP1 3Y=0 3Z=0 4Y=0 4Z=0 DG=148 HW=1.3.4-4 KR=-------- LB=133 WDPF_RT=91 WDPF_SID=0x80002A78 ZH=0 ZL=0"
un = "INCHES"
dp = 2
ard = 0.5
tb = 200.0
bb = 0.0
hl = 175.0
ll = 13.0
foreground =
(Color){
argb = 4278190080
}
background =
(Color){
argb = 4278255360
}
},
matchCount = 1
totalCount = 11061
}
--- Example 6: Requesting tabular data for 'I-0300A.UNIT1@NET1' ---
Requesting data from 1764624489 to 1764625089
Submitting tabular data request...
2025-12-01 15:38:09 [ERROR] suds.client: <?xml version="1.0" encoding="UTF-8"?>
<SOAP-ENV:Envelope xmlns:SOAP-ENV="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ns0="http://tt.com.pl/eds/" xmlns:ns1="http://schemas.xmlsoap.org/soap/envelope/">
<SOAP-ENV:Header/>
<ns1:Body>
<ns0:requestTabular>
<ns0:authString>b83940cb-1254-51fa-9abd-653c933ab7a5</ns0:authString>
<ns0:request>
<ns0:period>
<ns0:from>
<ns0:second>1764624489</ns0:second>
</ns0:from>
<ns0:till>
<ns0:second>1764625089</ns0:second>
</ns0:till>
</ns0:period>
<ns0:step>
<ns0:seconds>60</ns0:seconds>
</ns0:step>
<ns0:items>
<ns0:pointId>
<ns0:iess>I-0300A.UNIT1@NET1</ns0:iess>
</ns0:pointId>
<ns0:shadePriority/>
<ns0:function>AVG</ns0:function>
</ns0:items>
</ns0:request>
</ns0:requestTabular>
</ns1:Body>
</SOAP-ENV:Envelope>
Error during tabular data request: b"Server raised fault: 'Validation constraint violation: empty value provided where a value is required in element 'ns0:shadePriority''"
Logging out with authstring: b83940cb-1254-51fa-9abd-653c933ab7a5...
Logout successful.
oolong@CIT-36NZRL3 ~/dev/pipeline dev git add .
oolong@CIT-36NZRL3 ~/dev/pipeline dev ✚ git commit -m "continue refactor, correctly code that uses the eds rest api and the eds soap api: demo"
[dev 073c320] continue refactor, correctly code that uses the eds rest api and the eds soap api: demo
1 file changed, 15 deletions(-)
oolong@CIT-36NZRL3 ~/dev/pipeline ↱ dev git push gitEnumerating objects: 15, done.
Counting objects: 100% (15/15), done.
Delta compression using up to 12 threads
Compressing objects: 100% (8/8), done.
Writing objects: 100% (8/8), 664 bytes | 664.00 KiB/s, done.
Total 8 (delta 7), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (7/7), completed with 7 local objects.
chTo github.com:City-of-Memphis-Wastewater/pipeline.git
8343f54..073c320 dev -> dev
oolong@CIT-36NZRL3 ~/dev/pipeline dev git checkout main
Switched to branch 'main'
Your branch is up to date with 'origin/main'.
oolong@CIT-36NZRL3 ~/dev/pipeline main git merge dev
Updating 2fa0b2e..073c320
Fast-forward
src/pipeline/api/eds/database.py | 31 +++++++++++++++----------------
src/pipeline/api/eds/rest/config.py | 1 +
src/pipeline/api/eds/rest/demo.py | 2 +-
src/pipeline/api/eds/soap/client.py | 10 ++++++----
src/pipeline/api/eds/soap/config.py | 29 +++++++++++------------------
src/pipeline/api/eds/soap/demo.py | 1 +
src/pipeline/core/eds.py | 2 +-
7 files changed, 36 insertions(+), 40 deletions(-)
oolong@CIT-36NZRL3 ~/dev/pipeline ↱ main git push
Total 0 (delta 0), reused 0 (delta 0), pack-reused 0
To github.com:City-of-Memphis-Wastewater/pipeline.git
2fa0b2e..073c320 main -> main
oolong@CIT-36NZRL3 ~/dev/pipeline main git checkout dev
Switched to branch 'dev'
Your branch is up to date with 'origin/dev'.
oolong@CIT-36NZRL3 ~/dev/pipeline dev poetry run python -m pipeline.api.eds.soap.demo 2025-12-01 15:40:47 [INFO] __main__: CLI started
Usage options:
poetry run python -m pipeline.api.eds.soap.demo demo_soap_tabular
oolong@CIT-36NZRL3 ~/dev/pipeline dev poetry run python -m pipeline.api.eds.rest.demo
2025-12-01 15:40:57 [INFO] __main__: CLI started
Usage options:
poetry run python -m pipeline.api.eds.rest.demo demo-point-export
poetry run python -m pipeline.api.eds.rest.demo demo-tabular-export
poetry run python -m pipeline.api.eds.rest.demo demo-live
poetry run python -m pipeline.api.eds.rest.demo demo-live-alt
poetry run python -m pipeline.api.eds.rest.demo demo-trend
poetry run python -m pipeline.api.eds.rest.demo demo-plot-live
poetry run python -m pipeline.api.eds.rest.demo demo-webplot-live
poetry run python -m pipeline.api.eds.rest.demo demo-plot-trend
poetry run python -m pipeline.api.eds.rest.demo demo-db
poetry run python -m pipeline.api.eds.rest.demo ping
poetry run python -m pipeline.api.eds.rest.demo license
poetry run python -m pipeline.api.eds.rest.demo export-graphics
poetry run python -m pipeline.api.eds.rest.demo access-workspace
oolong@CIT-36NZRL3 ~/dev/pipeline dev poetry run python -m pipeline.api.eds.rest.demo export-graphics
2025-12-01 15:41:04 [INFO] __main__: CLI started
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/home/oolong/dev/pipeline/src/pipeline/api/eds/rest/demo.py", line 309, in <module>
demo_eds_save_graphics_export()
File "/home/oolong/dev/pipeline/src/pipeline/decorators.py", line 13, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/oolong/dev/pipeline/src/pipeline/api/eds/rest/graphics.py", line 17, in demo_eds_save_graphics_export
workspace_manager, sessions = demo_eds_start_session_CoM_WWTPs()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/oolong/dev/pipeline/src/pipeline/decorators.py", line 13, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/oolong/dev/pipeline/src/pipeline/api/eds/rest/demo.py", line 20, in demo_eds_start_session_CoM_WWTPs
workspace_name = WorkspaceManager.identify_default_workspace_name()
^^^^^^^^^^^^^^^^
NameError: name 'WorkspaceManager' is not defined. Did you mean: 'workspace_manager'?
✘ oolong@CIT-36NZRL3 ~/dev/pipeline dev nano src/pipeline/api/eds/core.py oolong@CIT-36NZRL3 ~/dev/pipeline dev code src/pipeline/api/eds/core.py
oolong@CIT-36NZRL3 ~/dev/pipeline dev cat src/pipeline/core/eds.py
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± rm src/pipeline/core/eds.py
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± git add .
oolong@CIT-36NZRL3 ~/dev/pipeline dev ✚ git commit -m "src.pipeline.core.eds as eds_core, altered to src.pipeline.api.eds.core as eds_core"
[dev 0886bcc] src.pipeline.core.eds as eds_core, altered to src.pipeline.api.eds.core as eds_core
3 files changed, 2 insertions(+), 2 deletions(-)
rename src/pipeline/{core/eds.py => api/eds/core.py} (100%)
oolong@CIT-36NZRL3 ~/dev/pipeline ↱ dev git push Enumerating objects: 21, done.
Counting objects: 100% (21/21), done.
Delta compression using up to 12 threads
Compressing objects: 100% (10/10), done.
Writing objects: 100% (11/11), 945 bytes | 236.00 KiB/s, done.
Total 11 (delta 9), reused 1 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (9/9), completed with 9 local objects.
To github.com:City-of-Memphis-Wastewater/pipeline.git
073c320..0886bcc dev -> dev
oolong@CIT-36NZRL3 ~/dev/pipeline dev
these apples
i need to alter my approach for credential-providing. I have a repacement solution, and have not yet completed the migration - in some demo functions, and even in some core functions, the old plaintext credential location is still called, based on workspace.
i like the workspace pattern, and i think it should be well understood, with an approriate plan for use. In our current case however, we will proceed with the more pythonic approach: config references as a plaintext single file config with a site-id and a four-part hyphenated key, and secondly for credentials, use the keyring and crypotegraphy solution to reference the windows credential manager, etc, which leverages a username and password.
These values are provided through typer CLI inputs, one-by-one tkinter pops, or by a uvicorn severed browser tab for each necessary input, using alpine js, vanialla html, . This just-in-yime, as-needed credential and config value seeking does not suit a SPA as well, and we do aim to utlimately serve a svelte app. I am open to the possible ways to seek config and credentializing through a svelte SPA, especially with containers or sections. We had originally though of using iframe, with a dynamic popup as value were requested. I frame is outdated, but this at least identifies an approach with multiple panes and a way to feed config, input request values, and visualize data in a single tab rather than separation-of-concerns = separation-of-tabs and separation-of-servers.
here is the new approach:
# src/pipeline/api/eds/soap/config.py
from __future__ import annotations
from typing import Dict
import logging
from pipeline.security_and_config import SecurityAndConfig, get_base_url_config_with_prompt, not_enough_info
from pipeline.variable_clarity import Redundancy
def get_eds_soap_api_credentials(plant_name: str, overwrite: bool = False, forget: bool = False) -> Dict[str, str]:
"""Retrieves API credentials for a given plant, prompting if necessary."""
service_name = f"pipeline-eds-api-{plant_name}"
overwrite = False
eds_base_url = get_base_url_config_with_prompt(service_name = f"{plant_name}_eds_base_url", prompt_message = f"Enter {plant_name} EDS base url (e.g., http://000.00.0.000, or just 000.00.0.000)")
eds_soap_api_port = SecurityAndConfig.get_config_with_prompt(config_key = f"{plant_name}_eds_soap_api_port", prompt_message = f"Enter {plant_name} EDS SOAP API port (e.g., 43080)", overwrite=overwrite)
eds_soap_api_sub_path = SecurityAndConfig.get_config_with_prompt(config_key = f"{plant_name}_eds_soap_api_sub_path", prompt_message = f"Enter {plant_name} EDS SOAP API WSDL path (e.g., 'eds.wsdl')", overwrite=overwrite)
username = SecurityAndConfig.get_credential_with_prompt(service_name = service_name, item_name = "username", prompt_message = f"Enter your EDS API username for {plant_name} (e.g. admin)", hide=False, overwrite=overwrite)
password = SecurityAndConfig.get_credential_with_prompt(service_name = service_name, item_name = "password", prompt_message = f"Enter your EDS API password for {plant_name} (e.g. '')", overwrite=overwrite)
idcs_to_iess_suffix = SecurityAndConfig.get_config_with_prompt(config_key = f"{plant_name}_eds_api_iess_suffix", prompt_message = f"Enter iess suffix for {plant_name} (e.g., .UNIT0@NET0)", overwrite=overwrite)
zd = SecurityAndConfig.get_config_with_prompt(config_key = f"{plant_name}_eds_api_zd", prompt_message = f"Enter {plant_name} ZD (e.g., 'Maxson' or 'WWTF')", overwrite=overwrite)
#if not all([username, password]):
# raise CredentialsNotFoundError(f"API credentials for '{plant_name}' not found. Please run the setup utility.")
eds_soap_api_port = int(eds_soap_api_port)
eds_soap_api_sub_path = eds_soap_api_sub_path
# Comparable SOAP API function, for documentation:
eds_soap_api_url = get_eds_soap_api_url(base_url = eds_base_url,
eds_soap_api_port = str(eds_soap_api_port),
eds_soap_api_sub_path = eds_soap_api_sub_path)
if eds_soap_api_url is None:
not_enough_info()
return {
'url': eds_soap_api_url,
'username': username,
'password': password,
'zd': zd,
'idcs_to_iess_suffix': idcs_to_iess_suffix
# The URL and other non-secret config would come from a separate config file
# or be prompted just-in-time as we discussed previously.
}
also note that the EDS SOAP API has more breadth and use cases than the EDS REST API, so many current REST API calls need to be migrated to the growing SOAP API codebase. To make room and the best modularize the exisitng code, i converted ./src/pipeline/api/eds.py into ./src/pipeline/api/eds/rest/, ./src/pipeline/api/eds/soap/, ./src/pipeline/api/eds/database.py , and ./src/pipeline/api/eds/core/
oolong@CIT-36NZRL3 ~/dev/pipeline dev tree src/pipeline/api
src/pipeline/api
├── __init__.py
├── __pycache__
│ ├── __init__.cpython-312.pyc
│ └── eds.cpython-312.pyc
├── eds
│ ├── __init__.py
│ ├── __pycache__
│ │ ├── __init__.cpython-312.pyc
│ │ ├── config.cpython-312.pyc
│ │ ├── database.cpython-312.pyc
│ │ └── exceptions.cpython-312.pyc
│ ├── config.py
│ ├── core.py
│ ├── database.py
│ ├── exceptions.py
│ ├── imports_historic.py
│ ├── mock
│ │ ├── build_eds_dir.sh
│ │ ├── by_grok_edited_clay.md
│ │ ├── client.py
│ │ ├── docs.md
│ │ ├── hold__init__.py
│ │ ├── points.py
│ │ ├── refactor_20November2025.md
│ │ ├── session.py
│ │ ├── status_1763706013.md
│ │ └── trend.py
│ ├── rest
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ │ ├── __init__.cpython-312.pyc
│ │ │ ├── client.cpython-312.pyc
│ │ │ ├── config.cpython-312.pyc
│ │ │ ├── demo.cpython-312.pyc
│ │ │ └── graphics.cpython-312.pyc
│ │ ├── alarm.py
│ │ ├── client.py
│ │ ├── config.py
│ │ ├── demo.py
│ │ ├── demo_
│ │ │ └── __init__.py
│ │ └── graphics.py
│ └── soap
│ ├── __init__.py
│ ├── __pycache__
│ │ ├── __init__.cpython-312.pyc
│ │ ├── client.cpython-312.pyc
│ │ ├── config.cpython-312.pyc
│ │ └── demo.cpython-312.pyc
│ ├── client.py
│ ├── config.py
│ ├── demo.py
│ └── demo_
│ └── __init__.py
├── mission.py
├── mission_opcua.py
├── rjn.py
└── status_api.py
11 directories, 48 files
oolong@CIT-36NZRL3 ~/dev/pipeline dev
mission.py and rjn.py are comparable to the original eds.py, and include respectively MissionClient and RjnClient
we now have EdsRestClient and EdsSoapClient
make sense?
That all makes sense. You've provided a lot of information covering:
-
A SOAP API error in the demo code related to a missing or empty
ns0:shadePriorityelement in therequestTabularSOAP request. -
A Python
NameErrorin the REST API demo code (NameError: name 'WorkspaceManager' is not defined) due to an incorrect import or change in module structure. -
A refactoring and migration plan to move away from the old plaintext/workspace-based credential approach towards a more robust, Pythonic configuration/credential management system (
keyring/SecurityAndConfig) and a new modular structure for the EDS API clients (EdsRestClient,EdsSoapClient). -
The new structure for the EDS API modules under
src/pipeline/api/eds/.
Here is a summary and a breakdown of the next steps based on the information and your stated goals.
🛠️ Summary of Refactoring and Issues
1. SOAP API Issue (Stiles & Maxson)
The demo failed on requestTabular for both plants with the error:
Server raised fault: 'Validation constraint violation: empty value provided where a value is required in element 'ns0:shadePriority''
-
Cause: The XML request for
requestTabularexplicitly included an empty tag:<ns0:shadePriority/>. The EDS SOAP server likely requires a value for this element, or perhaps it shouldn't be included if a default/zero value is not being used. -
Action: Review the
requestTabularimplementation in yourEdsSoapClientto ensureshadePriorityis handled correctly (e.g., provided a default value, or omitted if not needed and the SOAP server allows omission).
2. REST API Demo Issue (export-graphics)
The demo failed with a NameError:
NameError: name 'WorkspaceManager' is not defined. Did you mean: 'workspace_manager'?
-
Cause: The code
demo_eds_start_session_CoM_WWTPs()insrc/pipeline/api/eds/rest/demo.pyis callingWorkspaceManager.identify_default_workspace_name(), butWorkspaceManageris not imported or no longer exists at that name/location following your refactor ofsrc/pipeline/core/eds.pytosrc/pipeline/api/eds/core.py. -
Action: Update the imports in the relevant REST demo and core files to correctly reference the class that manages workspaces (likely renamed or moved during the refactoring).
3. Credential/Config Migration
You are moving from a workspace/plaintext config model to a keyring/prompt-based config model, utilizing the functions in src/pipeline/api/eds/soap/config.py.
-
New Approach: Uses
SecurityAndConfigfor:-
Base URL/Port/Sub-path/Suffix/ZD: Stored as config (likely plaintext/single file).
-
Username/Password: Stored securely via a keyring/credential manager (leveraging Windows Credential Manager, etc.).
-
-
Goal: Provide credentials just-in-time via CLI, Tkinter, or a web-served browser tab, which you acknowledge needs further consideration for your Svelte SPA goal (moving away from an outdated
iframeapproach).
4. Code Structure
The refactoring to modularize the EDS API clients is complete:
-
Old:
./src/pipeline/api/eds.py -
New:
-
./src/pipeline/api/eds/rest/(forEdsRestClient) -
./src/pipeline/api/eds/soap/(forEdsSoapClient) -
./src/pipeline/api/eds/database.py -
./src/pipeline/api/eds/core.py(which likely holds common logic, configuration, and the movedWorkspaceManagerclass).
-
🎯 Proposed Next Steps
I recommend addressing the immediate errors caused by the refactor and then implementing your new credential pattern completely.
1. Fix the REST API NameError
Check where the WorkspaceManager class is now defined (most likely in src/pipeline/api/eds/core.py) and ensure it's imported correctly in src/pipeline/api/eds/rest/demo.py.
2. Fix the SOAP API requestTabular Fault
You need to modify how the requestTabular call is constructed in src/pipeline/api/eds/soap/client.py (or a helper function).
-
Analyze the XML: The server is complaining about
<ns0:shadePriority/>. This element is part of thens0:itemscomplex type:XML
<ns0:items> <ns0:pointId> <ns0:iess>I-0300A.UNIT1@NET1</ns0:iess> </ns0:pointId> <ns0:shadePriority/> <ns0:function>AVG</ns0:function> </ns0:items> -
Potential Fixes (in
client.py):-
Option A (Omit): If
sudsis being used, try omitting theshadePriorityparameter entirely in the Python call if the server definition allows it to be optional. -
Option B (Default Value): If it must be present, look up the required data type. If it's an integer representing a priority level (e.g., 0 for no shading), pass that value explicitly (e.g.,
shadePriority=0).
-
3. Complete Credential Migration
Ensure all demo and core functions that currently rely on the old workspace/plaintext credential retrieval are updated to use the new get_eds_soap_api_credentials() function or its equivalent for the REST API.
Would you like me to help you debug the NameError or the Validation constraint violation first, or perhaps review the logic for handling shadePriority?
here you go
# src/pipeline/workspace_manager.py
from __future__ import annotations # Delays annotation evaluation, allowing modern 3.10+ type syntax and forward references in older Python versions 3.8 and 3.9
import os
import toml
import logging
from pathlib import Path
import sys
'''
Goal:
Implement default-workspace.toml variable: use-most-recently-edited-workspace-directory
'''
# Configure logging (adjust level as needed)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
class WorkspaceManager:
# It has been chosen to not make the WorkspaceManager a singleton if there is to be batch processing.
WORKSPACES_DIR_NAME = 'workspaces'
QUERIES_DIR_NAME = 'queries'
IMPORTS_DIR_NAME = 'imports'
EXPORTS_DIR_NAME = 'exports'
SCRIPTS_DIR_NAME = 'scripts'
CONFIGURATIONS_DIR_NAME = 'configurations'
SECRETS_DIR_NAME ='secrets'
LOGS_DIR_NAME = 'logs'
CONFIGURATION_FILE_NAME = 'configuration.toml'
SECRETS_YAML_FILE_NAME ='secrets.yaml'
SECRETS_EXAMPLE_YAML_FILE_NAME ='secrets-example.yaml'
DEFAULT_WORKSPACE_TOML_FILE_NAME = 'default-workspace.toml'
APP_NAME = "pipeline"
TIMESTAMPS_JSON_FILE_NAME = 'timestamps_success.json'
# Detect if running in a dev repo vs installed package
if getattr(sys, "frozen", False):
# Running from a pipx/executable environment
ROOT_DIR = None
else:
# Running from a cloned repo
ROOT_DIR = Path(__file__).resolve().parents[2] # root directory
# This climbs out of /src/pipeline/ to find the root.
# parents[0] → The directory that contains the (this) Python file.
# parents[1] → The parent of that directory.
# parents[2] → The grandparent directory (which should be the root), if root_pipeline\src\pipeline\
# This organization anticipates PyPi packaging.
def __init__(self, workspace_name):
self.workspace_name = workspace_name
self.workspaces_dir = self.get_workspaces_dir()
self.workspace_dir = self.get_workspace_dir()
self.configurations_dir = self.get_configurations_dir()
self.exports_dir = self.get_exports_dir()
self.imports_dir = self.get_imports_dir()
self.queries_dir = self.get_queries_dir()
self.secrets_dir = self.get_secrets_dir()
self.scripts_dir = self.get_scripts_dir()
self.logs_dir = self.get_logs_dir()
self.aggregate_dir = self.get_aggregate_dir()
self.check_and_create_dirs(list_dirs =
[self.workspace_dir,
self.exports_dir,
self.imports_dir,
self.secrets_dir,
self.scripts_dir,
self.logs_dir,
self.aggregate_dir])
@classmethod
def get_workspaces_dir(cls):
"""
Return workspaces directory depending on environment:
- If ROOT_DIR is defined (repo clone), use that
- Else use AppData/local platform-specific location
"""
if cls.ROOT_DIR and (cls.ROOT_DIR / cls.WORKSPACES_DIR_NAME).exists():
workspaces_dir = cls.ROOT_DIR / cls.WORKSPACES_DIR_NAME
else:
workspaces_dir = cls.get_appdata_dir() / cls.WORKSPACES_DIR_NAME
workspaces_dir.mkdir(parents=True, exist_ok=True)
default_file = workspaces_dir / cls.DEFAULT_WORKSPACE_TOML_FILE_NAME
if not default_file.exists():
# auto-populate default TOML with most recent workspace
recent_ws = cls.most_recent_workspace_name() or "default"
default_file.write_text(f"[default-workspace]\nworkspace = '{recent_ws}'\n")
return workspaces_dir
@classmethod
def most_recent_workspace_name(cls):
workspaces_dir = cls.get_workspaces_dir()
all_dirs = [p for p in workspaces_dir.iterdir() if p.is_dir() and not p.name.startswith('.')]
if not all_dirs:
return None
latest = max(all_dirs, key=lambda p: p.stat().st_mtime)
return latest.name
def get_workspace_dir(self):
# workspace_name is established at instantiation. You want a new name? Initialize a new WorkspaceManager(). It manages one workpspace.
return self.get_workspaces_dir() / self.workspace_name
def get_exports_dir(self):
return self.workspace_dir / self.EXPORTS_DIR_NAME
def get_exports_file_path(self, filename):
# Return the full path to the export file
return self.exports_dir / filename
def get_aggregate_dir(self):
# This is for five-minute aggregation data to be stored between hourly bulk passes
# This should become defunct once the tabular trend data request is functional
return self.exports_dir / 'aggregate'
def get_configurations_dir(self):
return self.workspace_dir / self.CONFIGURATIONS_DIR_NAME
def get_configuration_file_path(self):
# Return the full path to the config file or create it from the fallback copy if it exists
file_path = self.get_configurations_dir() / self.CONFIGURATION_FILE_NAME
return file_path
def get_logs_dir(self):
return self.workspace_dir / self.LOGS_DIR_NAME
def get_imports_dir(self):
return self.workspace_dir / self.IMPORTS_DIR_NAME
def get_imports_file_path(self, filename):
# Return the full path to the export file
return self.imports_dir / filename
def get_secrets_dir(self):
return self.workspace_dir / self.SECRETS_DIR_NAME
def get_secrets_file_path(self):
# Return the full path to the config file
file_path = self.secrets_dir / self.SECRETS_YAML_FILE_NAME
if not file_path.exists():
logging.warning(f"Secrets sonfiguration file {self.SECRETS_YAML_FILE_NAME} not found in:\n{self.secrets_dir}.\nHint: Copy and edit the {self.SECRETS_YAML_FILE_NAME}.")
print("\n")
choice = str(input(f"Auto-copy {self.SECRETS_EXAMPLE_YAML_FILE_NAME} [Y] or sys.exit() [n] ? "))
if choice.lower().startswith("y"):
file_path = self.get_secrets_file_path_or_copy()
else:
# edge case, expected once per machine, or less, if the user knows to set up a secrets.yaml file.
import sys
sys.exit()
return file_path
def get_secrets_file_path_or_copy(self):
# Return the full path to the config file or create it from the fallback copy if it exists
file_path = self.secrets_dir / self.SECRETS_YAML_FILE_NAME
fallback_file_path = self.secrets_dir / self.SECRETS_EXAMPLE_YAML_FILE_NAME
if not file_path.exists() and fallback_file_path.exists():
import shutil
shutil.copy(fallback_file_path, file_path)
print(f"{self.SECRETS_YAML_FILE_NAME} not found, copied from {self.SECRETS_YAML_FILE_NAME}")
elif not file_path.exists() and not fallback_file_path.exists():
raise FileNotFoundError(f"Configuration file {self.SECRETS_YAML_FILE_NAME} nor {self.SECRETS_EXAMPLE_YAML_FILE_NAME} not found in directory '{self.secrets_dir}'.")
return file_path
def get_scripts_dir(self):
return self.workspace_dir / self.SCRIPTS_DIR_NAME
def get_scripts_file_path(self, filename):
# Return the full path to the config file
return self.get_scripts_dir() / filename
def get_queries_dir(self):
return self.workspace_dir / self.QUERIES_DIR_NAME
def get_queries_file_path(self,filename): #
# Return the full path to the config file
filepath = self.get_queries_dir() / filename
if not filepath.exists():
raise FileNotFoundError(f"Query filepath={filepath} not found. \nPossible reason: You are in the wrong project directory.")
return filepath
def get_timestamp_success_file_path(self):
# Return the full path to the timestamp file
filepath = self.get_queries_dir() / self.TIMESTAMPS_JSON_FILE_NAME
logging.info(f"WorkspaceManager.get_timestamp_success_file_path() = {filepath}")
return filepath
def check_and_create_dirs(self, list_dirs):
for dir_path in list_dirs:
if not dir_path.exists():
dir_path.mkdir(parents=True, exist_ok=True)
@classmethod
def get_all_workspaces_names(cls):
"""
Return a list of all workspace names found in the workspaces directory.
"""
workspaces_dir = cls.get_workspaces_dir()
if not workspaces_dir.exists():
raise FileNotFoundError(f"Workspaces directory not found at: {workspaces_dir}")
workspace_dirs = [
p.name for p in workspaces_dir.iterdir()
if p.is_dir() and not p.name.startswith('.') # skip hidden/system folders
]
return workspace_dirs
@classmethod
def identify_default_workspace_path(cls):
"""
Class method that reads default-workspace.toml to identify the default-workspace path.
"""
workspaces_dir = cls.get_workspaces_dir()
workspace_name = cls.identify_default_workspace_name()
if workspace_name is None:
workspace_name = cls.most_recent_workspace_name() # if
if workspace_name is None:
workspace_name = 'eds'
workspace_path = workspaces_dir / workspace_name
if not workspace_path.exists():
workspace_path.mkdir(parents=True, exist_ok=True)
return workspace_path
@classmethod
def identify_default_workspace_name(cls, workspaces_dir = None):
"""
Class method that reads default-workspace.toml to identify the default-workspace.
"""
if workspaces_dir is None:
workspaces_dir = cls.get_workspaces_dir()
logging.info(f"workspaces_dir = {workspaces_dir}\n")
default_toml_path = workspaces_dir / cls.DEFAULT_WORKSPACE_TOML_FILE_NAME
if not default_toml_path.exists():
#print("No default_workspace.toml file to identify a default workspace folder, so the most recently edited folder will be used.")
return None
with open(default_toml_path, 'r') as f:
data = toml.load(f)
logging.debug(f"data = {data}")
try:
return data['default-workspace']['workspace'] # This dictates the proper formatting of the TOML file.
except KeyError as e:
recent_ws = cls.most_recent_workspace_name() or "default"
default_toml_path.write_text(f"[default-workspace]\nworkspace = '{recent_ws}'\n")
return recent_ws
def get_default_query_file_paths_list(self):
default_query_path = self.get_queries_dir()/ 'default-queries.toml'
with open(default_query_path, 'r') as f:
query_config = toml.load(f)
if 'default-query' not in query_config or 'files' not in query_config['default-query']:
raise ValueError("Missing ['default-query']['files'] in default-queries.toml")
filenames = query_config['default-query']['files']
if not isinstance(filenames, list):
raise ValueError("Expected a list under ['default-query']['files'] in default-queries.toml")
paths = [self.get_queries_file_path(fname) for fname in filenames]
for path in paths:
if not os.path.exists(path):
raise FileNotFoundError(f"Query file not found: {path}")
return paths
@property
def name(self):
return self.workspace_name
@classmethod
def get_appdata_dir(cls) -> Path:
"""Return platform-appropriate appdata folder."""
if os.name == "nt": # Windows
base = Path(os.getenv("APPDATA", Path.home() / "AppData" / "Roaming")) ## configuration-example
elif os.name == "posix" and "ANDROID_ROOT" in os.environ: # Termux
base = Path.home() / ".local" / "share"
else: # macOS/Linux
base = Path(os.getenv("XDG_DATA_HOME", Path.home() / ".local" / "share"))
return base / cls.APP_NAME
def establish_default_workspace():
workspace_name = WorkspaceManager.identify_default_workspace_name()
logging.info(f"workspace_name = {workspace_name}")
workspace_manager = WorkspaceManager(workspace_name)
logging.info(f"WorkspaceManager.get_workspace_dir() = {WorkspaceManager.get_workspace_dir()}")
return
def demo_establish_default_workspace():
establish_default_workspace()
if __name__ == "__main__":
import sys
cmd = sys.argv[1] if len(sys.argv) > 1 else "default"
if cmd == "demo-default":
demo_establish_default_workspace()
else:
print("Usage options: \n"
"poetry run python -m pipeline.api.eds demo-default \n")
this was hard won, and we are ultimately replacing it the keyring credential and single config file approach which was recommended as a replacement
this
@app.command(name="config", help="Configure and store API and database credentials.")
def configure_credentials(
overwrite: bool = typer.Option(False, "--overwrite", "-o", help="Overwrite existing credentials, with confirmation protection."),
textedit: bool = typer.Option(False, "--textedit", "-t", help = "Open the config file in a text editor instead of using the guided prompt.")
):
"""
Guides the user through a guided credential setup process. This is not necessary, as necessary credentials will be prompted for as needed, but this is a convenient way to set up multiple credentials at once. This command with the `--overwrite` flag is the designed way to edit existing credentials.
"""
if textedit:
typer.echo(F"Config filepath: {CONFIG_PATH}")
ph.edit_textfile(CONFIG_PATH)
return
typer.echo("")
typer.echo("--- Pipeline-EDS Credential Setup ---")
#typer.echo("This will securely store your credentials in the system keyring and a local config file.")
typer.echo("You can skip any step by saying 'no' or 'n' when prompted.")
typer.echo("You can quit editing credentials at any time by escaping with `control+C`.")
typer.echo("You can run this command again later to add or modify credentials.")
typer.echo("If you are not prompted for a credential, it is likely already configured. To change it, use the --overwrite flag.")
typer.echo("")
if overwrite:
typer.echo("⚠️ Overwrite mode is enabled. Existing credentials will shown and you will be prompted to confirm overwriting them.")
typer.echo(f"Alternatively, edit the configuration file directly in a text editor with the `--textedit` flag.")
typer.echo(f"Config file path: {CONFIG_PATH}", color=typer.colors.MAGENTA)
# Get a list of plant names from the user
#num_plants = typer.prompt("How many EDS plants do you want to configure?", type=int, default=1)
num_plants = 1
plant_names = []
for i in range(num_plants):
plant_name = typer.prompt(f"Enter a unique name for Plant (e.g., 'Maxson' or 'Stiles')")
plant_names.append(plant_name)
# Loop through each plant to configure its credentials
for name in plant_names:
typer.echo(f"\nConfiguring credentials for {name}...")
# Configure API for this plant
if typer.confirm(f"Do you want to configure the EDS API for '{name}'?", default=True):
get_eds_rest_api_credentials(plant_name=name, overwrite=overwrite)
# Configure DB for this plant
if False and typer.confirm(f"Do you want to configure the EDS database for '{name}'?", default=False):
get_eds_local_db_credentials(plant_name=name, overwrite=overwrite)
# Configure any other external APIs
if False and typer.confirm("Do you want to configure external API credentials? (e.g., RJN)"):
external_api_name = typer.prompt("Enter a name for the external API (e.g., 'RJN')")
get_external_api_credentials(party_name=external_api_name, overwrite=overwrite)
typer.echo("\nSetup complete. You can now use the commands that require these credentials.")
typer.echo("If a question was skipped, it is because the credential is already configured.")
typer.echo("Run this command again with --overwrite to change it.")
This is an ultimately defunct command in cli.py, which called get_external_api_credentials and get_eds_rest_api_credentials and get_eds_local_db_credentials to encourage uses to frontload fill these all at once rather than waiting for the just-in-time
---
here is the list_workspaces function in cli.py, which is very cool but is not typical in python
@app.command()
def list_workspaces():
"""
List all available workspaces detected in the workspaces folder.
"""
# Determine workspace name
from pipeline.workspace_manager import WorkspaceManager
workspaces_path = WorkspaceManager.get_workspaces_dir()
typer.echo(f"Workspaces directory: {workspaces_path}", color=typer.colors.MAGENTA)
workspaces_list = WorkspaceManager.get_all_workspaces_names()
typer.echo("📦 Available workspaces:")
for name in workspaces_list:
typer.echo(f" - {name}")
to support this would basically mean supporting a novel library that offered something fundamentally different than cookiecutter or dynaconf and would ebody a directory by directory approach to loadable state, with various imports ,export ,and file based program configuration. we abadnon this in favor of one slush config file
✘ oolong@CIT-36NZRL3 ~/dev/pipeline dev ls
Control-Room.ipynb main_eds_to_rjn_once.ps1
Dockerfile main_eds_to_rjn_quiet.ps1
Dockerfile.dev media
Dockerfile.multi-dev output.json
INSTALLATION.md package.py
LICENSE package.yml
QUICKSTART.md pipeline-eds-0.3.82-py312-ubuntu24.04-x86_64.spec
README.md poetry.lock
build poetry_add.sh
build_docker.sh pyproject.toml
build_executable.py requirements.txt
build_executable_v0.3.71.py scripts
config setup.yaml
daemon_eds_to_rjn_quiet_311.ps1 src
daemon_eds_to_rjn_quiet_311.py start_daemon_eds_to_rjn_311.bat
desktop.ini termux-setup-oldhat.sh
dist termux-setup.sh
docker_push.sh tests
docs tools
launch-termux.sh uv.lock
logs version.rc.template
main.bat workspaces
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± cd ..
oolong@CIT-36NZRL3 ~/dev cd ..
oolong@CIT-36NZRL3 ~ ls -a
. .ipython .python_history .zshrc.pre-oh-my-zsh
.. .jupyter .shell.pre-oh-my-zsh Desktop
.aws .landscape .shiv Documents
.azure .lesshst .ssh Downloads
.bash_history .local .sudo_as_admin_successful Pictures
.bash_logout .motd_shown .vscode-remote-containers Templates
.bashrc .nanorc .wget-hsts Videos
.cache .npm .yarn cookiecutter-playground
.config .oh-my-zsh .zcompdump dev
.cookiecutter_replay .pipeline-eds .zcompdump-CIT-36NZRL3-5.9 microsoft.gpg
.cookiecutters .pipeline_eds_history.json .zcompdump-CIT-36NZRL3-5.9.zwc mulch-template
.docker .pki .zprofile pyhabitat-1.0.37
.gitconfig .profile .zsh_history
.inputrc .psql_history .zshrc
oolong@CIT-36NZRL3 ~ ls .pipeline_eds_history.json
.pipeline_eds_history.json
oolong@CIT-36NZRL3 ~ cat .pipeline_eds_history.json
[
"m100fi fi8001 m310li",
"D-331W D-321E FI8001",
"m100fi fi8001 m310fi",
"m100fi"
]% oolong@CIT-36NZRL3 ~ cat .pipeline-eds/config.json
{
"configurable_plantname_eds_api": "Maxson",
"Maxson_eds_rest_api_sub_path": "api/v1",
"Maxson_eds_api_iess_suffix": ".UNIT0@NET0",
"Maxson_eds_api_zd": "Maxson",
"Maxson_eds_rest_api_port": "43084",
"Stiles_eds_soap_api_port": "43080",
"Stiles_eds_soap_api_sub_path": "eds.wsdl",
"Stiles_eds_api_iess_suffix": ".UNIT1@NET1",
"Maxson_eds_soap_api_port": "43080",
"Maxson_eds_soap_api_sub_path": "eds.wsdl"
}% oolong@CIT-36NZRL3 ~ ls .pipeline-eds/
config.json
oolong@CIT-36NZRL3 ~
i am on wsl2, which might mean that i am not using windows credential manager but rather, what keyring manifestation? the KDE or the Gnome, i suspect. Which is standard on ubuntu? I am using wslg and miscrosoft-edge for localhost web stack gui.
also,
(paste partial contents of src/pipeline/security_and_config.py)
This is a very insightful look into the evolution of your configuration and workspace management approach, transitioning from a workspace directory model (WorkspaceManager) using a default-workspace.toml file to a single, secure configuration file model (SecurityAndConfig) paired with the system's keyring.
Here is an analysis of your code, the shift in methodology, and an answer to your specific query about the WSL2 keyring manifestation.
1. Analysis of the Configuration Shift
You've documented a clear shift from a complex, directory-based state management system to a centralized, more standard approach for credentials and configuration:
A. Original Model (Defunct: WorkspaceManager)
This approach tried to encapsulate the entire program state (queries, imports, exports, logs, configs, secrets) within a named "workspace" directory, with the default workspace chosen via:
-
Reading
default-workspace.toml. -
Falling back to the most recently edited workspace directory (
most_recent_workspace_name). -
Falling back to a hardcoded name (e.g.,
'eds').
Pros: Self-contained environments for different projects.
Cons: Overly complex state management, reliance on filesystem modification times, custom solution prone to maintenance issues, and non-standard way of handling credentials (using secrets.yaml inside the workspace).
B. New Model (Current: SecurityAndConfig)
This approach is significantly cleaner and more standard:
-
Configuration (
config.json): A single file (Path.home() / ".pipeline-eds" / "config.json") stores non-secret configuration variables (like API paths, ports, plant names). -
Credentials (Keyring): All sensitive data (passwords, tokens) are stored using the Python
keyringlibrary, which interfaces with the OS's native secret storage. -
Prompting (
_prompt_for_value): Implements a robust, platform-agnostic, and user-friendly fallback chain for prompting for missing or overwritten values:-
Terminal (CLI) - If interactive.
-
GUI (Tkinter) - If non-interactive CLI is unavailable.
-
Web Browser - As a final fallback using a temporary web server.
-
This new model is excellent as it aligns with best practices: separation of configuration and secrets, and use of OS-native secret storage.
2. WSL2 Keyring Manifestation
You are asking which keyring backend is likely being used on your WSL2 (Ubuntu) environment.
On a standard Ubuntu installation (which is what WSL2 runs), the default keyring manager for the Python keyring library is often:
-
Gnome Keyring (Secret Service): This is the most common default if you are running a GNOME desktop environment (which you are via WSLg). This service is the standard cross-desktop mechanism on Linux for storing secrets.
-
KDE KWallet: Less common unless you explicitly install KDE components.
🔑 Keyring Behavior on WSL2
The behavior of keyring in your specific setup is determined by how WSL2 and WSLg are configured:
| Environment | Keyring Manager Used | Details |
|---|---|---|
| WSL2 (Ubuntu) with WSLg | Gnome Keyring (Secret Service) | This is the most likely answer. Since you are running a GUI application (like the web browser and potentially Tkinter) via WSLg, a full Linux desktop session is running in the background, which includes the Gnome Keyring service. |
| WSL2 (Ubuntu) CLI Only | Plaintext/File Backend (Fallback) | If no GUI session is running (no WSLg) and no TTY or X-server is available, keyring often falls back to a non-secure local file or will prompt you to set up a password for an encrypted file backend (e.g., keyrings.cryptfile.CryptFileKeyring), but this is highly dependent on the installed packages and keyring configuration. |
Conclusion: Given you are using WSLg (implied by using microsoft-edge for localhost web stack gui), your keyring library is almost certainly accessing the Gnome Keyring (Secret Service), which is the standard for Linux GUI environments.
3. Recommended Implementation of the Goal
The goal in your initial comment was:
Implement default-workspace.toml variable: use-most-recently-edited-workspace-directory
Since you have decided to abandon the WorkspaceManager directory-by-directory approach in favor of the single configuration file model (SecurityAndConfig), this goal is effectively obsolete.
If you were to keep a minor aspect of the workspace concept, the best replacement for storing the "last used workspace/plant name" would be to:
-
Store the name in the
config.jsonfile managed bySecurityAndConfig, not in a TOML file inside the defunctworkspacesdirectory. -
Use a new config key, e.g.,
"last_used_plant_name", which you update on successful execution.
This is much simpler and fits your new architecture.
Would you like me to help you migrate the logic for getting the "last used workspace name" (or now, the "last used plant name") from the old WorkspaceManager logic into your new SecurityAndConfig class?
Yes, this is exactly what must be done
'''migrate the logic for getting the "last used workspace name" (or now, the "last used plant name") from the old WorkspaceManager logic into your new SecurityAndConfig class'''
we must document the current uses of each. Once we have done this, we can generated a standard example, and clean up all demos. Then, we see where there is overlap in the demos and we question what can be collapsed down and simplified, or whether the current redundant rigor is fair play examples of how to use the existing approach. One aspect of the current approach is that the value key (AKA config_key AKA service_name) is referenced when the value is needed. I.e. there is no sure way to keep the various scripts that use the same just in time value consistent - this must be maintained manually, and if there is diversion, then the new reference is made and provided but might differ slightly, and so might the prompt text, which is also provided at point of sale and rises to meet the user at either the CLI input prompt, the tkinter pop up prompt, or the html approach. A fourth approach is necessary to achieve the svelted SPA, but it should reference the same variables, know to either credentials or the plaintext config file.
That's a great plan. Migrating the "last used" logic to SecurityAndConfig will unify your state management and help eliminate the reliance on the outdated default-workspace.toml.
Let's approach this in three steps:
-
Define the new functions in
SecurityAndConfigfor reading and writing the "last used plant name." -
Modify
WorkspaceManagerto use the newSecurityAndConfigfunctions (for backward compatibility during the migration phase). -
Document and discuss the existing logic and the new approach.
1. Defining the New Configuration Functions
We will introduce two methods to SecurityAndConfig: get_last_used_plant_name and set_last_used_plant_name. These will use the existing config.json structure.
In src/pipeline/security_and_config.py, add the following (or modify your existing SecurityAndConfig class):
Python
# src/pipeline/security_and_config.py (Add to SecurityAndConfig class)
class SecurityAndConfig:
# ... existing methods ...
@staticmethod
def _load_config() -> Dict[str, Any]:
"""Loads the configuration from CONFIG_PATH."""
if CONFIG_PATH.exists():
try:
with open(CONFIG_PATH, "r") as f:
return json.load(f)
except json.JSONDecodeError:
# Attempt self-healing or return empty dict on failure
logging.warning(f"Config file is corrupted: {CONFIG_PATH}")
# Assuming json_heal is defined elsewhere, if not, this needs error handling
# if json_heal(CONFIG_PATH):
# with open(CONFIG_PATH, "r") as f:
# return json.load(f)
return {}
return {}
@staticmethod
def _save_config(config: Dict[str, Any]):
"""Saves the configuration to CONFIG_PATH."""
CONFIG_PATH.parent.mkdir(parents=True, exist_ok=True)
try:
with open(CONFIG_PATH, "w") as f:
json.dump(config, f, indent=4)
except Exception as e:
logging.error(f"Failed to save config to {CONFIG_PATH}: {e}")
@classmethod
def get_last_used_plant_name(cls) -> str | None:
"""Retrieves the name of the last successfully used plant/workspace."""
config = cls._load_config()
# Use a new key for this purpose
return config.get('last_used_plant_name')
@classmethod
def set_last_used_plant_name(cls, plant_name: str):
"""Sets the name of the last successfully used plant/workspace."""
if not plant_name:
logging.warning("Attempted to set an empty 'last_used_plant_name'. Skipping.")
return
config = cls._load_config()
config['last_used_plant_name'] = plant_name
cls._save_config(config)
logging.info(f"Updated 'last_used_plant_name' in config to: {plant_name}")
# ... existing methods ...
2. Modifying WorkspaceManager
The WorkspaceManager's primary function for selecting a default workspace is identify_default_workspace_name. We need to modify this method to prioritize the new, single source of truth in SecurityAndConfig while keeping the legacy fallbacks for the time being.
In src/pipeline/workspace_manager.py, modify identify_default_workspace_name:
Python
# src/pipeline/workspace_manager.py (Modified identify_default_workspace_name)
# Add import:
from pipeline.security_and_config import SecurityAndConfig
# ... inside WorkspaceManager class ...
@classmethod
def identify_default_workspace_name(cls, workspaces_dir = None):
"""
Class method that identifies the default workspace name using a unified fallback strategy:
1. Config file (New, preferred source: 'last_used_plant_name').
2. default-workspace.toml (Legacy source).
3. Most recently edited directory (Legacy fallback).
"""
# 1. NEW SOURCE (Preferred): Read from SecurityAndConfig's unified config file
last_used_name = SecurityAndConfig.get_last_used_plant_name()
if last_used_name:
logging.info(f"Default workspace found in config.json: {last_used_name}")
return last_used_name
# --- LEGACY FALLBACK STARTS HERE ---
if workspaces_dir is None:
workspaces_dir = cls.get_workspaces_dir()
logging.info(f"workspaces_dir = {workspaces_dir}\n")
default_toml_path = workspaces_dir / cls.DEFAULT_WORKSPACE_TOML_FILE_NAME
if not default_toml_path.exists():
# 2. Legacy fallback to most recently edited workspace
recent_ws = cls.most_recent_workspace_name()
if recent_ws:
logging.warning(f"No default-workspace.toml, falling back to most recently edited workspace: {recent_ws}")
return recent_ws
# 3. Final fallback
return None # The caller (identify_default_workspace_path) will use 'eds'
with open(default_toml_path, 'r') as f:
data = toml.load(f)
logging.debug(f"data = {data}")
try:
# 4. Legacy source: Read from default-workspace.toml
legacy_name = data['default-workspace']['workspace']
logging.warning(f"Default workspace found in LEGACY default-workspace.toml: {legacy_name}")
return legacy_name
except KeyError as e:
# 5. Handle corrupted TOML by auto-populating it (Legacy logic preserved for stability)
recent_ws = cls.most_recent_workspace_name() or "default"
default_toml_path.write_text(f"[default-workspace]\nworkspace = '{recent_ws}'\n")
logging.warning(f"Corrupted default-workspace.toml repaired. Using: {recent_ws}")
return recent_ws
3. Documentation and Standardization
By introducing the new functions, you now have a standard, consistent way to handle the "last used" state.
📜 State Management Comparison
| Component | Variable Key/Location | Purpose | Consistency/Risk |
|---|---|---|---|
Old: WorkspaceManager |
default-workspace.toml |
Select the initial project directory. | Low consistency. Based on filesystem dates. Custom and non-standard. |
New: SecurityAndConfig |
config.json: last_used_plant_name |
Store the last successful target plant/service name. | High consistency. Centralized in the main config file. |
🎯 Enforcing Consistency and Reducing Redundancy
Your concern about manual consistency is valid:
"I.e. there is no sure way to keep the various scripts that use the same just in time value consistent - this must be maintained manually..."
You are correct. The power of the new SecurityAndConfig model is not just where it stores the data, but the get_config_with_prompt pattern:
To fix the manual consistency issue, you should introduce Config Keys Constants that are shared across all modules that request the same value.
Example:
In a new file (e.g., src/pipeline/constants.py):
Python
# src/pipeline/constants.py
# Plant Configuration Keys
MAXSON_URL_KEY = "Maxson_eds_rest_api_url"
STILES_PORT_KEY = "Stiles_eds_soap_api_port"
# Global State Keys
LAST_USED_PLANT_NAME_KEY = "last_used_plant_name"
Then, in any calling code:
Python
from pipeline.constants import LAST_USED_PLANT_NAME_KEY
from pipeline.security_and_config import SecurityAndConfig
# To read the name:
name = SecurityAndConfig.get_last_used_plant_name()
# To write the name after a successful run:
SecurityAndConfig.set_last_used_plant_name("new_plant_name")
This enforces consistency across all scripts because they all refer to the same constant key string ("last_used_plant_name").
Would you like to proceed with defining the constants.py file to hold these universal configuration keys?
the example functions you provided as part of the SecurityAndConfig class - it seems like they mimick CRUD operations.
i have existing functions and i am not sure i need what you provided. Explain in only two paragraphs why you provided these function, and how they fill my needs
The functions _load_config(), _save_config(), get_last_used_plant_name(), and set_last_used_plant_name() were provided because they implement the specific and generalized read/write mechanisms necessary to interact with your single, centralized config.json file. While your existing get_config_with_prompt() handles the complex flow of Create/Update/Read/Prompting for secure or volatile config values, it doesn't offer simple, non-prompting, pure Read (get) and Update (set) operations for a simple, persistent program state variable like "last_used_plant_name". The new methods separate the simple state management logic from the complex credential/user-prompting logic, allowing any part of your application (e.g., a batch job, an API handler, or a CLI command) to silently and efficiently update the last used name without invoking the entire interactive, multi-channel _prompt_for_value fallback chain.
By implementing these helper functions, you achieve separation of concerns and modularity. _load_config and _save_config centralize the complex, error-prone tasks of file I/O and JSON corruption handling, which otherwise would need to be replicated in multiple places. The public methods get_last_used_plant_name and set_last_used_plant_name then provide a clean, high-level interface (an API) to the state management, decoupling the WorkspaceManager and future components from the low-level details of how the .pipeline-eds/config.json file is structured or managed. This ensures that any logic that simply needs to know or record the last successful plant name doesn't risk accidentally triggering a user prompt via get_config_with_prompt(), which is crucial for non-interactive environments like cron jobs or automated tasks.
Right this is interesting - you are right that prompting the user needs to be decoupled from retrieved stored credentials. One-off requests differ from cron jobs.
I like a just-in-time design because it is inherently stable and asks for what it needs if it is missing something. But i agree there is room for providing for cron jobs in a way that might treat the current approach as overcoupled.
That's an excellent way to summarize the design tension: Just-In-Time (JIT) stability versus Decoupled Cron Job Flexibility.
You are seeking a pattern that preserves the robustness of JIT prompting (ask-if-missing, multi-channel fallback) for interactive use cases, while allowing non-interactive environments (cron, batch) to access and update simple state data without crashing or hanging due to an unexpected prompt.
Here is the proposed way to structure your new SecurityAndConfig class to support both needs, using explicit methods for each use case:
🏗️ Structure for Decoupled Configuration
We can define three distinct categories of methods within SecurityAndConfig to clearly separate the use cases:
1. Simple State Management (Non-Credential)
-
Purpose: Read/write simple, non-sensitive program state variables (e.g.,
last_used_plant_name). -
Behavior: Never prompts the user. Reads directly from
config.json. -
Use Case: Ideal for cron jobs, batch processes, or any internal logic that needs to track state quietly.
2. Secure Credential Management (JIT-Optional)
-
Purpose: Retrieve sensitive credentials (passwords, tokens) or complex, mandatory config (e.g., API URL).
-
Behavior:
-
Interactive (Default): Tries to read from Keyring/Config file; prompts the user if missing or
overwrite=True. -
Non-Interactive (Cron): If an interactive prompt is needed but the environment is determined to be non-interactive (e.g., a flag is passed, or no TTY is detected), it raises a specific exception (
CredentialsNotFoundError).
-
-
Use Case: The primary way your CLI and interactive scripts run.
3. Temporary Input (One-Off)
-
Purpose: Get a value that is not stored (e.g., a temporary password or a run-specific filter).
-
Behavior: Always prompts the user if an interactive session is detected. Does not save to any file/keyring.
-
Use Case: Your existing
get_temporary_inputfunction.
🛠️ Refined SecurityAndConfig Implementation
Based on this, the functions I provided (_load_config, _save_config, get_last_used_plant_name, set_last_used_plant_name) are key to establishing the first category (Simple State Management), which directly addresses your need for decoupled cron job flexibility.
The modification to your existing code focuses on isolating the file I/O operations (_load_config/_save_config) so that the simple state setters/getters can use them without triggering any of the complex prompting logic found in get_config_with_prompt().
For the cron jobs to work cleanly, the logic inside the job would look like this:
Python
from pipeline.security_and_config import SecurityAndConfig
# 1. Start by retrieving the last successful plant name (no prompt triggered)
plant_name = SecurityAndConfig.get_last_used_plant_name() or 'default'
try:
# 2. Retrieve credentials (This *would* prompt if missing, but cron jobs must
# ensure the config/keyring is pre-filled, or fail gracefully.)
api_key = SecurityAndConfig.get_secure_credential_value(
key=f"{plant_name}_api_key",
prompt_message="Enter API Key for {plant_name}"
)
# ... cron job runs successfully ...
# 3. Update the last used name (no prompt triggered)
SecurityAndConfig.set_last_used_plant_name(plant_name)
except CredentialsNotFoundError:
# Handle failure gracefully for non-interactive mode
logging.error(f"Cannot run job: Credentials for {plant_name} are missing and interactive prompt is unavailable.")
sys.exit(1)
This clear separation ensures that simple state tracking is truly non-interactive, while the robust JIT prompting is reserved for mandatory configuration steps.
# src/pipeline/api/eds/soap/demo.py
from __future__ import annotations # Delays annotation evaluation, allowing modern 3.10+ type syntax and forward references in older Python versions 3.8 and 3.9
import logging
logger = logging.getLogger(__name__)
from pipeline.decorators import log_function_call
from pipeline.api.eds.soap.client import EdsSoapClient
@log_function_call(level=logging.DEBUG)
def demo_eds_soap_api_tabular():
EdsSoapClient.soap_api_iess_request_tabular(plant_name = "Stiles",idcs = ['I-0300A','I-0301A'])
EdsSoapClient.soap_api_iess_request_tabular(plant_name = "Maxson",idcs = ['FI8001','M310LI'])
if __name__ == "__main__":
'''
- auto id current function name. solution: decorator, @log_function_call
- print only which vars succeed
'''
import sys
from pipeline.logging_setup import setup_logging
cmd = sys.argv[1] if len(sys.argv) > 1 else "default"
setup_logging()
logger = logging.getLogger(__name__)
logger.info("CLI started")
if cmd == "demo_soap_tabular":
demo_eds_soap_api_tabular()
else:
print("Usage options: \n"
"poetry run python -m pipeline.api.eds.soap.demo demo_soap_tabular"
)
check this out
this is what we have in the most modern way
from __future__ import annotations # Delays annotation evaluation, allowing modern 3.10+ type syntax and forward references in older Python versions 3.8 and 3.9import time
import sys
import logging
import time
from suds.client import Client as SudsClient # uses suds-py3
from pipeline.api.eds.rest.client import EdsRestClient
from pipeline.security_and_config import SecurityAndConfig, get_base_url_config_with_prompt
from pipeline.variable_clarity import Redundancy
class EdsSoapClient:
def __init__(self):
pass
# --- Context Management (Pattern 2) ---
def __enter__(self):
"""Called upon entering the 'with' block."""
return self
def __exit__(self, exc_type, exc_val, exc_tb):
"""Called upon exiting the 'with' block (for cleanup)."""
# Logout from SOAP (if login was performed)
if self.authstring:
print(f"[{self.plant_name}] Attempting SOAP logout...")
try:
# We need a SOAP client instance to perform the logout
if self.soapclient is None:
# Initialize just to logout, if not done already
self.soapclient = SudsClient(self.soap_url)
self.soapclient.service.logout(self.authstring)
print(f"[{self.plant_name}] Logout successful.")
except Exception as e:
print(f"[{self.plant_name}] Error during SOAP logout: {e}")
# Return False to propagate exceptions, or True to suppress them
return False
@classmethod
@Redundancy.set_on_return_hint(recipient=None,attribute_name="tabular_data")
def soap_api_iess_request_tabular(cls, plant_name: str | None= None, idcs: list[str] | None = None):
from pipeline.api.eds.soap.config import get_eds_soap_api_url
from pipeline.api.eds.config import get_service_name
tabular_data = None
soapclient = None
authstring = None
use_default_idcs = True
if plant_name is None:
plant_name = SecurityAndConfig.get_configurable_default_plant_name()
print(f"plant_name = {plant_name}")
service_name = get_service_name(plant_name = plant_name) # for secure credentials
if idcs is None:
if use_default_idcs:
idcs = SecurityAndConfig.get_configurable_idcs_list(plant_name)
else:
idcs = SecurityAndConfig.get_temporary_input()
base_url = get_base_url_config_with_prompt(service_name = f"{plant_name}_eds_base_url", prompt_message=f"Enter {plant_name} EDS base url (e.g., http://000.00.0.000, or just 000.00.0.000)")
if base_url is None: return
eds_soap_api_port = SecurityAndConfig.get_config_with_prompt(config_key = f"{plant_name}_eds_soap_api_port", prompt_message=f"Enter {plant_name} EDS SOAP API port (e.g., 43080)")
if eds_soap_api_port is None: return
eds_soap_api_sub_path = SecurityAndConfig.get_config_with_prompt(config_key = f"{plant_name}_eds_soap_api_sub_path", prompt_message=f"Enter {plant_name} EDS SOAP API WSDL PATH (e.g., 'eds.wsdl')")
if eds_soap_api_sub_path is None: return
username = SecurityAndConfig.get_credential_with_prompt(service_name, "username", f"Enter your EDS API username for {plant_name} (e.g. admin)", hide=False)
if username is None: return
password = SecurityAndConfig.get_credential_with_prompt(service_name, "password", f"Enter your EDS API password for {plant_name} (e.g. '')")
if password is None: return
idcs_to_iess_suffix = SecurityAndConfig.get_config_with_prompt(f"{plant_name}_eds_api_iess_suffix", f"Enter iess suffix for {plant_name} (e.g., .UNIT0@NET0)")
if idcs_to_iess_suffix is None: return
#session = EdsRestClient.login_to_session_with_api_credentials(api_credentials)
eds_soap_api_url = get_eds_soap_api_url(base_url = base_url,
eds_soap_api_port = eds_soap_api_port,
eds_soap_api_sub_path = eds_soap_api_sub_path)
if eds_soap_api_url is None:
logging.info("Not enough information provided to build: eds_soap_api_url.")
logging.info("Please rerun your last command or try something else.")
return
try:
# 1. Create the SOAP client
print(f"Attempting to connect to WSDL at: {eds_soap_api_url}")
soapclient = SudsClient(eds_soap_api_url)
print("SOAP client created successfully.")
# You can uncomment the line below to see all available services
# print(soapclient)
# 2. Login to get the authstring
# This is the "authstring assignment" you asked for.
print(f"Logging in as user: '{username}'...")
authstring = soapclient.service.login(username, password)
if not authstring:
print("Login failed. Received an empty authstring.")
return
print(f"Login successful. Received authstring: {authstring}")
# 3. Use the authstring to make other API calls
# Example 1: ping (to keep authstring valid)
print("\n--- Example 1: Pinging server ---")
soapclient.service.ping(authstring)
print("Ping successful.")
# Example 2: getServerTime
print("\n--- Example 2: Requesting server time ---")
server_time_response = soapclient.service.getServerTime(authstring)
print("Received server time response:")
print(server_time_response)
# Example 3: getServerStatus
print("\n--- Example 3: Requesting server status ---")
server_status_response = soapclient.service.getServerStatus(authstring)
print("Received server status response:")
print(server_status_response)
# --- NEW EXAMPLES BASED ON YOUR CSV DATA ---
# Example 4: Get a specific point by IESS name
# We will use 'I-0300A.UNIT1@NET1' from your latest output
print("\n--- Example 4: Requesting point by IESS name ('{}') ---")
try:
# Create a PointFilter object
point_filter_iess = soapclient.factory.create('PointFilter')
# Set the iessRe (IESS regular expression) filter
# We use the exact name, but it also accepts wildcards
idcs = [s.upper() for s in idcs]
iess_list = [x+idcs_to_iess_suffix for x in idcs]
for iess in iess_list:
point_filter_iess.iessRe = iess
# Call getPoints(authstring, filter, order, startIdx, maxCount)
# We set order, startIdx, and maxCount to None
points_response_iess = soapclient.service.getPoints(authstring, point_filter_iess, None, None, None)
print("Received getPoints response (by IESS):")
print(points_response_iess)
except Exception as e:
print(f"Error during getPoints (by IESS): {e}")
# -----------------------------------------------
# Example 6: Request Tabular (Trend) Data
# This will request historical data for 'I-0300A.UNIT1@NET1'
print("\n--- Example 6: Requesting tabular data for 'I-0300A.UNIT1@NET1' ---")
request_id = None # Initialize request_id
try:
# 1. Define time range (e.g., last 10 minutes)
end_time = int(time.time())
start_time = end_time - 600 # 600 seconds = 10 minutes
print(f"Requesting data from {start_time} to {end_time}")
# 2. Create the main TabularRequest object (see PDF page 32)
tab_request = soapclient.factory.create('TabularRequest')
# 3. Create and set the time period
period = soapclient.factory.create('TimePeriod')
# Use getattr() for 'from' as it's a Python keyword
getattr(period, 'from').second = start_time
period.till.second = end_time
tab_request.period = period
# 4. Set the step (e.g., one value every 60 seconds)
tab_request.step = soapclient.factory.create('TimeDuration')
tab_request.step.seconds = 60
# 5. Create a request item for the point
item = soapclient.factory.create('TabularRequestItem')
item.pointId = soapclient.factory.create('PointId')
item.pointId.iess = 'I-0300A.UNIT1@NET1' # Using point from Example 4
# 6. Set the function (e.g., 'AVG', 'RAW', 'MIN', 'MAX')
# 'AVG' gives averages. Use 'RAW' to get raw recorded samples.
item.function = 'AVG'
# 7. Add the item to the request
tab_request.items.append(item)
# 8. Send the request
print("Submitting tabular data request...")
request_id = soapclient.service.requestTabular(authstring, tab_request)
print(f"Request submitted. Got request_id: {request_id}")
# 9. Poll for request status (see PDF page 30)
status = None
max_retries = 10
retries = 0
while status != 'REQUEST-SUCCESS' and retries < max_retries:
retries += 1
time.sleep(1) # Wait 1 second before checking
status_response = soapclient.service.getRequestStatus(authstring, request_id)
status = status_response.status
print(f"Polling status (Attempt {retries}): {status}")
if status == 'REQUEST-FAILURE':
print(f"Request failed: {status_response.message}")
break
# 10. Get the data if successful (see PDF page 40)
if status == 'REQUEST-SUCCESS':
print("Request successful. Fetching data...")
tabular_data = soapclient.service.getTabular(authstring, request_id)
print("Received tabular data:")
print(tabular_data)
else:
print(f"Failed to get tabular data after {max_retries} retries.")
except Exception as e:
print(f"Error during tabular data request: {e}")
# If the request was made but failed mid-poll, try to drop it
if request_id and authstring and soapclient:
try:
print(f"Attempting to drop request {request_id} after error...")
soapclient.service.dropRequest(authstring, request_id)
print(f"Dropped request {request_id}.")
except Exception as drop_e:
print(f"Error trying to drop request {request_id}: {drop_e}")
except Exception as e:
from pipeline.api.eds.exceptions import connection_error_message
connection_error_message(e, url = eds_soap_api_url)
finally:
# Removed diagram close logic
# 5. Logout using the authstring
if authstring and soapclient:
print(f"\nLogging out with authstring: {authstring}...")
try:
soapclient.service.logout(authstring)
print("Logout successful.")
except Exception as e:
print(f"Error during logout: {e}")
else:
print("\nSkipping logout (was not logged in).")
return tabular_data
@staticmethod
def soap_api_iess_request_single(plant_name: str|None, idcs:list[str]|None):
from pipeline.api.eds.soap.config import get_eds_soap_api_url
from pipeline.api.eds.config import get_service_name
# --- Initialize vars ---
soapclient = None
authstring = None
# --- Get encrypted credentials and plaintext configuration values ---
if plant_name is None:
plant_name = SecurityAndConfig.get_configurable_default_plant_name()
service_name = get_service_name(plant_name = plant_name) # for secure credentials
base_url = get_base_url_config_with_prompt(service_name=f"{plant_name}_eds_base_url", prompt_message=f"Enter {plant_name} EDS base url (e.g., http://000.00.0.000, or just 000.00.0.000)")
if base_url is None: return
username = SecurityAndConfig.get_credential_with_prompt(service_name, "username", f"Enter your EDS API username for {plant_name} (e.g. admin)", hide=False)
if username is None: return
password = SecurityAndConfig.get_credential_with_prompt(service_name, "password", f"Enter your EDS API password for {plant_name} (e.g. '')")
if password is None: return
idcs_to_iess_suffix = SecurityAndConfig.get_config_with_prompt(f"{plant_name}_eds_api_iess_suffix", f"Enter iess suffix for {plant_name} (e.g., .UNIT0@NET0)")
if idcs_to_iess_suffix is None: return
# Let API Port and the sub path be None, such that the defaults will be used.
eds_soap_api_url = get_eds_soap_api_url(base_url = base_url)
if eds_soap_api_url is None:
logging.info("Not enough information provided to build: eds_soap_api_url.")
logging.info("Please rerun your last command or try something else.")
sys.exit()
try:
# 1. Create the SOAP client
print(f"Attempting to connect to WSDL at: {eds_soap_api_url}")
soapclient = SudsClient(eds_soap_api_url)
print("SOAP client created successfully.")
# You can uncomment the line below to see all available services
# print(soapclient)
# 2. Login to get the authstring
# This is the "authstring assignment" you asked for.
print(f"Logging in as user: '{username}'...")
authstring = soapclient.service.login(username, password)
if not authstring:
print("Login failed. Received an empty authstring.")
return
print(f"Login successful. Received authstring: {authstring}")
# 3. Use the authstring to make other API calls
# Example 1: ping (to keep authstring valid)
print("\n--- Example 1: Pinging server ---")
soapclient.service.ping(authstring)
print("Ping successful.")
# Example 2: getServerTime
print("\n--- Example 2: Requesting server time ---")
server_time_response = soapclient.service.getServerTime(authstring)
print("Received server time response:")
print(server_time_response)
# Example 3: getServerStatus
print("\n--- Example 3: Requesting server status ---")
server_status_response = soapclient.service.getServerStatus(authstring)
print("Received server status response:")
print(server_status_response)
# --- EXAMPLES OF CSV DATA ---
# Example 4: Get a specific point by IESS name
# We will use 'I-0300A.UNIT1@NET1' from your CSV
## WWTF,I-0300A,I-0300A.UNIT1@NET1,87,WELL,47EE48FD-904F-4EDA-9ED9-C622D1944194,eefe228a-39a2-4742-a9e3-c07314544ada,229,Wet Well
print("\n--- Example 4: Requesting point by IESS name ('I-0300A.UNIT1@NET1') ---")
try:
# Create a PointFilter object
point_filter_iess = soapclient.factory.create('PointFilter')
# Set the iessRe (IESS regular expression) filter
# We use the exact name, but it also accepts wildcards
point_filter_iess.iessRe = 'I-0300A.UNIT1@NET1'
# Call getPoints(authstring, filter, order, startIdx, maxCount)
# We set order, startIdx, and maxCount to None
points_response_iess = soapclient.service.getPoints(authstring, point_filter_iess, None, None, None)
print("Received getPoints response (by IESS):")
print(points_response_iess)
except Exception as e:
print(f"Error during getPoints (by IESS): {e}")
# Example 5: Get a specific point by SID
# We will use '5395' (for I-5005A.UNIT1@NET1) from your CSV
print("\n--- Example 5: Requesting point by SID ('5392') ---")
try:
# Create another PointFilter object
point_filter_sid = soapclient.factory.create('PointFilter')
# Add the SID to the 'sid' array in the filter
# (PointFilter definition on page 19 shows sid[] = <empty>)
point_filter_sid.sid.append(5395)
# Call getPoints
points_response_sid = soapclient.service.getPoints(authstring, point_filter_sid, None, None, None)
print("Received getPoints response (by SID):")
print(points_response_sid)
except Exception as e:
print(f"Error during getPoints (by SID): {e}")
# -----------------------------------------------
except Exception as e:
from pipeline.api.eds.exceptions import connection_error_message
EdsRestClient.connection_error_message(e, url = eds_soap_api_url)
finally:
# 4. Logout using the authstring
if authstring and soapclient:
print(f"\nLogging out with authstring: {authstring}...")
try:
soapclient.service.logout(authstring)
print("Logout successful.")
except Exception as e:
print(f"Error during logout: {e}")
else:
print("\nSkipping logout (was not logged in).")
# ---- end file ----
notice that the calls use prompts and credential/config keys at point of sale in the both scripted tabular and single timestamp functions. These are somewhere between scripts and methods. We really might be looking at the stable form, the way that they keys and prompts are defined, but it would be good if these were at least logged using a decortator, or there was a formalized way to inspect the config.json file and the Gnome credentials
how do i find the Gnome credentials in my WSL2 Ubuntu dev environment?
That's a very detailed look at your EdsSoapClient logic. You're absolutely right that the current design, where configuration keys and prompt messages are defined directly at the point of use within soap_api_iess_request_tabular and soap_api_iess_request_single, creates a high degree of coupling and makes centralized management/inspection difficult.
The functions are indeed performing a crucial middle-ground role, acting as robust, JIT-configured service request handlers.
Before addressing the credential inspection, let's briefly reinforce your observation on the coupling, as it guides the solution:
-
Problem: Each function calls
SecurityAndConfig.get_config_with_prompt()repeatedly, duplicating the string keys (e.g.,f"{plant_name}_eds_base_url") and prompt messages across every client method. -
Solution Path (Future): As discussed, introduce a set of Config Key Constants (
constants.py) and centralize the configuration retrieval logic into a single method (e.g.,EdsSoapClient.get_configuration(plant_name)) that returns a fully validated configuration object. This object is then passed to the service methods.
🔒 Finding Gnome Credentials in WSL2/Ubuntu
The Gnome credentials on a Linux system like your WSL2 Ubuntu environment are typically managed by the Secret Service API, often implemented by Gnome Keyring or Kwallet. The keyring library you are likely using interfaces with the system's Secret Service provider.
In your WSL2/Ubuntu environment, accessing these credentials manually can be tricky because WSL2 runs without a full graphical desktop environment (Gnome) by default. The keyring library often defaults to using a plaintext file (like a Basic File Keyring) or relies on the Secret Service which might not be running correctly.
1. Identify the Keyring Backend
First, you need to determine which backend your keyring library is actually using. This tells you where to look for the stored data.
Run a quick test script in your environment:
Python
import keyring
import os
print(f"Keyring backend in use: {keyring.get_keyring().__module__}")
print(f"Home directory: {os.path.expanduser('~')}")
Common Outcomes in WSL2:
| Backend Module | Where Credentials Are Stored | How to View Manually |
|---|---|---|
keyrings.cryptfile |
Encrypted file in your home directory (e.g., ~/.local/share/python_keyring/user_secrets.crypted). |
Not easily viewable. Requires decryption via the master password (or reading via your Python script). |
keyring.backends.SecretService |
Managed by Gnome Keyring or similar. | Requires a running Secret Service daemon and a graphical tool like seahorse (Keyring Manager). Difficult in headless WSL2. |
keyring.backends.fail |
Nowhere. Credentials fail to save. | N/A |
2. Manual Inspection using a Command-Line Tool
If your system is using the standard Secret Service API (Gnome Keyring), you can often inspect or test it using the command-line utility secret-tool.
-
Install the tool (if needed):
Bash
sudo apt update sudo apt install libsecret-tools -
List items by service name (where "service_name" matches the first argument you pass to
get_credential_with_prompt):The service name you are using is generated by:
service_name = get_service_name(plant_name = plant_name). Assumingget_service_namereturns a consistent string (e.g.,"eds_soap_api_credentials"or similar), you can query it:Bash
# Replace 'your_service_name' with the actual output of get_service_name('Stiles') secret-tool search --all service your_service_name -
Retrieve a specific item (less reliable, but possible):
Bash
secret-tool lookup service your_service_name username your_username
3. Programmatic Inspection (The Best Method)
The safest and most reliable way to inspect the values is by writing a utility function in your Python code that deliberately retrieves the stored credentials without triggering the prompt:
-
Add a non-prompting getter to
SecurityAndConfig(similar to what we discussed for state management):Python
# In SecurityAndConfig class @classmethod def get_credential_value_silent(cls, service_name: str, key: str) -> str | None: """Retrieves a credential value from the keyring/keychain without prompting.""" try: value = keyring.get_password(service_name, key) return value except Exception as e: logging.error(f"Error accessing keyring for {service_name}:{key}: {e}") return None -
Call this new function in a separate debug script:
Python
# debug_creds.py from pipeline.security_and_config import SecurityAndConfig # from pipeline.api.eds.config import get_service_name # If needed plant_name = "Stiles" service = "Stiles_eds_api_credentials" # or whatever get_service_name returns username = SecurityAndConfig.get_credential_value_silent(service, "username") password = SecurityAndConfig.get_credential_value_silent(service, "password") print(f"[{plant_name}] Username: {username}") print(f"[{plant_name}] Password: {password}") # Use caution when printing sensitive data!
⏭️ Next Step
Now that you have the path to inspect your credentials (either manually or programmatically), we should focus on the documentation and cleanup you requested.
Would you like to:
-
Document and standardize the configuration keys (e.g., create
constants.py)? -
Refactor one of the demo methods (
soap_api_iess_request_tabular) to use the new standardized keys and a dedicated configuration object?
I am not sure i like your constants.py idea, but it could be an intermediate filebased option to something that accomplished some of what we lost when we torched the workspace manager approach. Maybe databasing could be good. sqlite3. but then in a sense we have multiple sources of truth: the keys used in the keyring, the keys in config.json, the refernces in the database, and the code itself that makes the calls. How can we harden and formalize the available keys, to not be arbitary but stable and knowable?
That's a very insightful objection. You've hit on the central problem created by the loss of the centralized WorkspaceManager: how to enforce stability and prevent configuration drift when the config keys are defined arbitrarily at the point of consumption.
Your concern about introducing another source of truth (like SQLite) is spot on—it would just add another layer of complexity to the existing mess of keyring, config.json, and the code itself.
The solution is not another storage mechanism, but a formal, codified Schema that defines every available key and its associated metadata (prompt, type, security level).
💡 The Service/Key Registry Pattern
Instead of a simple constants.py file full of strings, we create a structured Registry within your Python code. This registry acts as the single source of truth for every configuration item needed by your API clients, replacing the arbitrary strings in your client methods.
This pattern is known as Configuration-as-Code and allows you to enforce stability and eliminate manual key/prompt duplication.
1. Define the Registry
We'll define a new class, perhaps EdsConfigKeys, that holds all the necessary configuration definitions.
Python
# src/pipeline/config/key_registry.py (New File)
from dataclasses import dataclass, field
from typing import Literal, Dict, Any, List
# Define the structure for a single configuration item
@dataclass(frozen=True)
class ConfigKey:
"""Defines a single, non-arbitrary configuration key and its metadata."""
key_stem: str
description: str
security_level: Literal["PLAINTEXT", "CREDENTIAL", "STATE"]
prompt_message: str = ""
default_value: Any = None
plant_dependent: bool = True # True if the actual key includes the plant name (e.g., Maxson_url)
# --- The Central Key Registry ---
class EdsConfigKeys:
"""A formal registry of all known EDS API configuration and credential keys."""
# --- 1. CORE API ENDPOINT KEYS (Required for connection) ---
EDS_BASE_URL = ConfigKey(
key_stem="eds_base_url",
description="Base URL for the EDS API.",
security_level="PLAINTEXT",
prompt_message="Enter {plant_name} EDS base url (e.g., http://000.00.0.000)",
)
EDS_SOAP_PORT = ConfigKey(
key_stem="eds_soap_api_port",
description="SOAP API Port.",
security_level="PLAINTEXT",
prompt_message="Enter {plant_name} EDS SOAP API port (e.g., 43080)",
default_value="43080" # Example Default
)
# --- 2. CREDENTIAL KEYS (Stored in Keyring) ---
EDS_USERNAME = ConfigKey(
key_stem="username",
description="EDS API Username.",
security_level="CREDENTIAL",
prompt_message="Enter your EDS API username for {plant_name}",
plant_dependent=False # Keyring stores this globally per service_name
)
EDS_PASSWORD = ConfigKey(
key_stem="password",
description="EDS API Password.",
security_level="CREDENTIAL",
prompt_message="Enter your EDS API password for {plant_name}",
plant_dependent=False
)
# --- 3. STATE KEYS (Simple, non-prompting state tracking) ---
LAST_USED_PLANT = ConfigKey(
key_stem="last_used_plant_name",
description="The name of the last successfully used plant/workspace.",
security_level="STATE",
plant_dependent=False # This is a single, global value
)
# --- 4. DATA/SERVICE SPECIFIC KEYS ---
IESS_SUFFIX = ConfigKey(
key_stem="eds_api_iess_suffix",
description="IESS suffix for point IDs.",
security_level="PLAINTEXT",
prompt_message="Enter IESS suffix for {plant_name} (e.g., .UNIT0@NET0)",
)
# You would continue this list for all other configuration items...
@classmethod
def get_all_keys(cls) -> List[ConfigKey]:
"""Returns a list of all defined ConfigKey objects."""
return [
getattr(cls, name) for name in dir(cls)
if not name.startswith('_') and isinstance(getattr(cls, name), ConfigKey)
]
2. Refactor SecurityAndConfig
You modify the SecurityAndConfig methods to accept the ConfigKey object instead of arbitrary strings, ensuring stability:
Python
# src/pipeline/security_and_config.py (Partial Refactor)
from pipeline.config.key_registry import ConfigKey # New Import
class SecurityAndConfig:
# ... existing methods ...
@classmethod
def get_config_with_prompt(cls, config_key_def: ConfigKey, plant_name: str) -> Any | None:
"""
Retrieves a non-credential value, falling back to prompt if missing.
Uses the definition in ConfigKey for keys and prompts.
"""
if config_key_def.security_level != "PLAINTEXT":
raise ValueError(f"Use get_credential_with_prompt or dedicated state methods for {config_key_def.key_stem}")
# Construct the final, stable key string
final_key = f"{plant_name}_{config_key_def.key_stem}"
# 1. Try to get value from config.json (using your existing logic)
value = cls._get_plaintext_config_value(final_key)
if value is None:
# 2. If missing, prompt the user
# Use the defined prompt message, filling in the plant name
prompt_msg = config_key_def.prompt_message.format(plant_name=plant_name)
value = cls._prompt_for_value(final_key, prompt_msg) # Your existing prompt logic
# 3. Save the newly acquired value
if value is not None:
cls._set_plaintext_config_value(final_key, value)
return value
# --- Simple State Management (Updated to use ConfigKey) ---
@classmethod
def get_last_used_plant_name(cls) -> str | None:
"""Retrieves the name of the last successfully used plant/workspace."""
# Note: This is now driven by the registry definition
key_def = EdsConfigKeys.LAST_USED_PLANT
config = cls._load_config()
return config.get(key_def.key_stem) # Use the key_stem defined in the registry
@classmethod
def set_last_used_plant_name(cls, plant_name: str):
"""Sets the name of the last successfully used plant/workspace."""
# Note: This is now driven by the registry definition
key_def = EdsConfigKeys.LAST_USED_PLANT
if not plant_name: return
config = cls._load_config()
config[key_def.key_stem] = plant_name # Use the key_stem defined in the registry
cls._save_config(config)
3. Refactor Client Code
The confusing, error-prone strings are gone! Your client code is now clean, readable, and relies entirely on the stable registry:
Python
# src/pipeline/api/eds/soap/client.py (Refactored)
from pipeline.config.key_registry import EdsConfigKeys # New Import
# ... inside soap_api_iess_request_tabular ...
# The key names and prompts are defined ONCE in EdsConfigKeys
# 1. Base URL
base_url = SecurityAndConfig.get_config_with_prompt(
EdsConfigKeys.EDS_BASE_URL, plant_name)
if base_url is None: return
# 2. SOAP Port
eds_soap_api_port = SecurityAndConfig.get_config_with_prompt(
EdsConfigKeys.EDS_SOAP_PORT, plant_name)
if eds_soap_api_port is None: return
# 3. Username (Uses a dedicated credential getter, passing the service name)
# Note: Credential getters are slightly different since the key isn't plant-prefixed
username = SecurityAndConfig.get_credential_with_prompt(
service_name,
EdsConfigKeys.EDS_USERNAME.key_stem, # Use the defined stem
EdsConfigKeys.EDS_USERNAME.prompt_message.format(plant_name=plant_name)
)
if username is None: return
# ... and so on for all other keys ...
This Registry Pattern provides the formalization and stability you need:
-
Hardened Keys: Any key you use must be defined in the
EdsConfigKeysregistry. -
Single Source of Truth: All prompt messages, default values, and security classifications are defined in one place.
-
Inspectability: You can easily write a CLI command that iterates through
EdsConfigKeys.get_all_keys()and prints a full, documented list of every configuration item your system relies on.
Would you like to start by creating the src/pipeline/config/key_registry.py file with the definitions needed for your EdsSoapClient?
the seems like building up a lot of framework to harden the approach. Defend this apparoach as futureproof. Talk about maintenance. Reference exisiting repositories that use this battle-tested approach. Why is this the best way in python, and how might this be handled differently using svelte or even a rust ffi? is your approach able to be captured in a single pyz or exe, or in git cloning the source code, or in pipx installing the pipeline-eds library, to effectively rollout onto a system, and also to be stood up as a localhost web app and potentially linked to a domain
This Registry Pattern (Configuration-as-Code) is not just a framework; it’s a necessary architectural shift from implicit, scattered configuration logic to explicit, verifiable state management. It is the best way to future-proof your application against complexity and maintenance nightmares.
🛡️ Defense of the Registry Pattern as Future-Proof
1. Hardened Maintenance and Stability
-
Eliminates Magic Strings: The primary maintenance burden you face now is tracking down every arbitrary key string and prompt message across dozens of files. By forcing every consumer (like
EdsSoapClient) to reference an object from the central registry (EdsConfigKeys.EDS_BASE_URL), you achieve refactorability. If you need to change the key string (e.g., fromeds_base_urltoapi_base_url), you change it once in the registry, and static analysis tools instantly show you every place theEdsConfigKeysclass is used. -
Enforced Constraints: The
ConfigKeyobject formalizes constraints (e.g.,security_level,plant_dependent). When a developer adds a new configuration item, they are forced to explicitly define its characteristics. This prevents errors like accidentally saving a password to the plaintextconfig.jsonbecause the security level is explicitly defined asCREDENTIALin the registry. -
Self-Documentation: The registry becomes the living, authoritative documentation for your application's external requirements. New developers don't have to read code across multiple files to understand what configuration is needed—they just inspect
EdsConfigKeys.
2. Battle-Tested Precedents
This pattern is a cornerstone of modern, scalable software design, where configuration needs to be defined once and consumed everywhere.
-
Django Settings: The Django framework uses a centralized
settings.py(which acts as a registry) where every configuration variable is explicitly defined, often with comments and types, before being consumed by middleware, database connectors, and template engines. -
Cloud Development Kits (CDKs): Tools like the AWS CDK or Terraform use configuration files/objects to define infrastructure, ensuring all dependencies are explicit and verifiable before deployment.
-
Open-Source Configuration Libraries: Python libraries like Pydantic Settings (or similar dataclass-based approaches) are built on this principle, allowing developers to define a configuration schema (your registry) and automatically load/validate settings from environment variables, files, or arguments.
🌐 Cross-Technology Handling
The Registry Pattern provides an elegant abstraction layer that allows different client technologies to access the configuration data tailored to their needs.
| Technology | How Registry is Handled |
|---|---|
| Python (Best Way) | Configuration-as-Code. The registry is defined natively using Python features (dataclasses, classes), allowing direct object access (EdsConfigKeys.EDS_BASE_URL) and compile-time (or type-checker) validation. |
| Svelte (SPA) | The Python registry would need to be the authority for the API Schema. The keys and metadata must be translated into a format the frontend can consume (e.g., TypeScript interfaces or a JSON schema). A build step could read your key_registry.py and auto-generate the necessary Svelte/TypeScript files to maintain consistency between the frontend's expected API and the backend's configuration keys. |
| Rust FFI (Foreign Function Interface) | The registry could be the Core Service Layer. Instead of the Python code having its own independent configuration logic, the Rust FFI function would export a canonical function (get_config_value(key_stem)) that queries the Rust-side configuration engine (which also uses a registry structure). This ensures that even when a highly performant Rust module needs a configuration value, it uses the same authority. |
📦 Deployment and Distribution
The Registry Pattern is highly beneficial for all your deployment scenarios because it consolidates dependencies.
-
Single File Executables (.pyz or .exe): Since the registry is just Python code, it is effortlessly bundled into a single file executable. The complexity (I/O, Keyring access, prompting) is contained within the
SecurityAndConfigclass, which uses the defined registry. -
Git Cloning/pipx Installation: When developers clone the source or install the package, they automatically receive the complete definition of the application's required state. They can now easily generate the configuration template or inspect requirements using the registry.
Your approach is able to be stood up as a localhost web app. In that scenario, the key benefit is that the registry provides the blueprint for the web form itself. You can easily write a function that iterates through the EdsConfigKeys registry and automatically generates the HTML/Svelte fields, labels, and validation rules for a SPA configuration page, completely eliminating redundancy between your CLI/backend and the web client.
This approach isn't over-engineering; it's robust, defensive design that ensures your configuration scales with your ambition to support multiple clients (CLI, cron, web) and maintains a stable code base.
Would you like to draft the src/pipeline/config/key_registry.py now and begin the refactoring of SecurityAndConfig?
You say that the Pythonic approach would need to be the authority for the API schema. How is this usually handled when there is not a Python backend?
That is a critical question, especially for maintaining a consistent frontend (like Svelte) when your configuration authority resides in a non-web language (Python) or when you eventually decouple the configuration service entirely.
When the configuration authority (your Python key_registry.py) is not running the main backend serving the frontend, the configuration definitions must be translated and exposed via a standard, universal format that all other technologies can consume.
Here is how the translation and consumption are typically handled without a Python backend:
1. The Build-Step Translation (Code Generation)
The most robust and common approach is to use a build step to generate artifacts from your single Python source of truth.
Process Steps:
-
Define the Source: Your Python
key_registry.pyis the ultimate source of truth, defining thekey_stem,description,security_level, andprompt_messagefor every item. -
Generate a Universal Schema: A lightweight Python script (often run via a command in your
Makefileor CI/CD pipeline) executes, iterates through theEdsConfigKeysclass, and serializes the metadata into a JSON Schema or a simple YAML file.-
Example JSON Output:
JSON
[ { "key_stem": "eds_base_url", "type": "string", "description": "Base URL for the EDS API.", "security": "PLAINTEXT", "prompt": "Enter {plant_name} EDS base url..." }, // ... other keys ... ]
-
-
Cross-Language Artifact Generation: The serialized file is then used to generate language-specific artifacts for client applications:
-
For Svelte/TypeScript: A utility reads the JSON schema and auto-generates a TypeScript Interface or Enum containing the exact key names and types. This prevents runtime errors and provides intelligent autocompletion for frontend developers.
-
For Rust FFI: The schema is used to define the Rust struct or Enum that represents the external configuration, ensuring the FFI boundary uses identical key names and formats.
-
Benefits:
-
Offline Consistency: The frontend and other services are always built against a recent, verified snapshot of the configuration schema.
-
Compile-Time Safety: Languages like TypeScript and Rust can validate that configuration fields exist and are of the correct type before deployment.
2. API Endpoint for Dynamic Configuration (Runtime Fetch)
In addition to the build-step approach (which is best for key names and validation), your backend API can expose an endpoint for dynamic configuration data itself.
-
Metadata Endpoint: Your Python backend service could expose a simple, unauthenticated endpoint (e.g.,
/api/config/schema) that returns the JSON Schema generated in step 2 above. This is used by the Svelte app at startup to know which fields to render on the configuration page. -
Configuration Retrieval Endpoint: This is for the actual values. Instead of the Svelte app trying to talk to the local Keyring/Config file (which it can't, unless running on localhost), it asks the Python backend running as a server: "What is the value for
Maxson_eds_base_url?"-
The Python server runs its JIT logic (tries
config.json, then Keyring, then falls back to a prompt or fails gracefully). -
It securely returns the value to the Svelte app, which uses it for subsequent API calls.
-
This dual approach ensures static consistency at build time and dynamic access to values at runtime, preserving the benefits of your JIT approach even in a service architecture.
Since the primary hurdle is migrating to the centralized key definition to enable these advanced architectures, I recommend we proceed with drafting the src/pipeline/config/key_registry.py file.
at the end of the day there must be some xml or json config, or a set of functions that can output these, possibly translated from a python config file. EIther way, there is some stable file that is the ultimate source of truth. So, character that ideal file and its aspects, compared to the characterists of .pipeline-eds/config.json
That is an excellent way to frame the problem: What is the nature of the ideal, stable artifact that guarantees the schema's integrity, compared to the current, unstructured reality of .pipeline-eds/config.json?
Your current config.json is a storage mechanism, while the desired stable file is a definition and validation schema.
Here is a comparison of the character and characteristics of the two files:
📄 Comparison of Configuration Artifacts
| Characteristic | Your Current .pipeline-eds/config.json | The Ideal Schema Artifact (e.g., config_schema.json) |
|---|---|---|
| Primary Character | Runtime Data Store. A simple key-value file used to save user inputs, caching the values of configuration settings. | Definitional Schema. A structural blueprint that defines the rules, names, and metadata for every configuration setting. |
| Content | Arbitrary key-value pairs ("Maxson_eds_base_url": "http://0.0.0.0") and simple state ("last_used_plant_name": "Maxson"). |
Structured definitions for each key (name, type, default, prompt, security level, description). |
| Structure | Flat, simple JSON dictionary. | Hierarchical structure, often JSON Schema or a custom YAML/JSON format that allows for array and object definitions. |
| Source of Truth | Consumer. The keys are defined ad hoc by the code that uses them. High risk of string drift. | Authority. The single, stable source of truth that generates all client code, documentation, and validation logic. |
| Purpose | Caching. Prevents the user from having to re-enter plaintext values during runtime. | Validation & Standardization. Ensures consistency across all clients (CLI, web, Rust FFI). |
| Maintenance Risk | High. Changing a key in one place (e.g., in a prompt) won't update the saved value's key, breaking the next retrieval attempt. | Low. All consumers use the same definition. Maintenance is centralized to the schema itself. |
| Cross-Client Utility | Low. The raw keys are meaningless without the Python logic that generates the prompts. | High. Can be read by any language (TypeScript, Rust, etc.) to auto-generate forms or validate inputs. |
🌟 Character of the Ideal Schema Artifact
The ideal configuration schema file must move beyond simple key-value storage and embody rich metadata. Your Python key_registry.py is the code-based definition of this ideal schema. The resulting JSON/YAML file is the translatable artifact used in non-Python environments.
The key characteristics of this artifact are:
1. Complete Decoupling from Values
The ideal schema file contains zero actual configuration values (usernames, URLs, ports). Its only concern is defining what is needed, not what the values are.
2. Rich, Hierarchical Metadata
The schema must define, for every key:
-
Key Name (
key_stem): The canonical, stable, base identifier for the config item. -
Data Type (
type): String, Integer, Boolean, Array, etc. (Enables cross-language type-checking). -
Security Context (
security_level): Specifies where the value should be stored (PLAINTEXTinconfig.json,CREDENTIALin Keyring). -
Prompt/Label: The user-facing string for GUIs or CLIs.
-
Scope (
plant_dependent): Whether the key needs to be prefixed by the plant name (e.g.,Maxson_). -
Validation Rules: (Advanced) Regex patterns, min/max length, required status.
3. Machine Readability (JSON Schema)
To ensure all technologies can consume it, the schema should conform to a standard like JSON Schema. This allows you to leverage existing ecosystem tools for validation, documentation, and frontend form generation.
How to Achieve the Ideal File
The most Pythonic and future-proof strategy is to use the Python key_registry.py as the true Source of Truth, and use a small utility script to generate the stable config_schema.json artifact at build time:
This approach minimizes the maintenance burden while providing the stable, cross-language artifact that addresses all your concerns about stability and formalization.
i need to get better at diagramming my code.
how might the ideal JSON schema fit into my codebase, as represented by diagramming?
provide references to approaches of ideal diagramming that is the most helpful for modern python and for generic software development in a way that will stand the test of time.
who is known for being excellent at diagramming?
That is a fantastic goal. Diagramming is crucial for conveying architectural intent and ensuring your codebase remains maintainable, especially as you decouple configuration and manage multiple clients (CLI, web, cron).
1. Diagramming the Ideal JSON Schema Flow
The "Ideal JSON Schema Artifact" doesn't replace your Python code; it's an output artifact of your Python code, designed for consumption by other technologies.
Here is how the ideal JSON Schema artifact fits into your larger codebase architecture, represented by two key diagrams:
A. Static (Build-Time) Configuration Flow
This diagram shows how your Python registry generates the static schema used by other environments:
| Component | Role | Artifact |
|---|---|---|
1. Python Key Registry (key_registry.py) |
Source of Truth (Configuration-as-Code). | Python ConfigKey objects. |
| 2. Schema Generator (Build Script) | Translates Python objects to universal format. | |
3. Ideal Schema Artifact (config_schema.json) |
Translatable Blueprint. Defines every key, type, and rule. | Standard JSON Schema. |
| 4. Code Generator (e.g., Svelte Build) | Reads the schema and auto-generates client code. | TypeScript Interfaces, Form Field Data. |
B. Dynamic (Runtime) JIT Configuration Flow
This shows the operational path, demonstrating that the schema is used to govern the JIT fetching, but the actual values still flow through the SecurityAndConfig class.
| Component | Role | Configuration Input | Value Source |
|---|---|---|---|
1. API Client (EdsSoapClient) |
Requests value by Registry Key (e.g., EdsConfigKeys.EDS_BASE_URL). |
||
| 2. SecurityAndConfig | Uses the Key's metadata (from the Registry) to determine search strategy. | Security Level (PLAINTEXT, CREDENTIAL). |
Keyring, config.json, or Prompt. |
| 3. Web Client (Svelte SPA) | Renders UI based on the config_schema.json. |
Prompt Message (from the Schema). | Submits the value to an API endpoint for persistent saving by the Python backend. |
2. Ideal Diagramming Approaches for Modern Software
To create diagrams that stand the test of time, you need to prioritize meaning and standardization over visual flair. The goal is to document the architecture, not the implementation details (like variables).
A. C4 Model for Software Architecture
The C4 model, created by Simon Brown, is widely considered the most effective approach for diagramming modern software architecture because it offers a set of hierarchical views that allow you to zoom in and out of complexity.
| View | Purpose | Focus |
|---|---|---|
| C1: Context | The highest level. Shows the system's relationship to its users and external systems. | The Big Picture: Your pipeline, users, EDS API, and keyring service. |
| C2: Container | Shows the major structural elements of the system (executables, applications, databases). | Your Deployable Units: pipeline-eds Python library, CLI interface, Svelte SPA, and .pipeline-eds filesystem. |
| C3: Component | Shows the breakdown of a single Container into smaller functional units. | Your Key Classes: EdsSoapClient, SecurityAndConfig, KeyRegistry, WorkspaceManager. |
| C4: Code | (Optional) Shows a detailed view of a single class or function. | Low Level: UML class diagrams (often better suited to IDEs). |
Why it stands the test of time: It uses a common, consistent notation that focuses on abstraction and hierarchy, making it easy to share and maintain across teams and projects.
B. UML (Unified Modeling Language) - Class Diagrams
While C4 handles architecture, UML Class Diagrams are still the best way to visualize the static structure of your Python code and the relationships between classes (inheritance, composition, etc.).
- Best Use: Diagramming the relationship between
EdsConfigKeys(Composition),SecurityAndConfig(Uses/Delegation), and the various client classes.
C. Sequence Diagrams (UML)
This is excellent for showing your JIT logic.
- Best Use: Diagramming the flow of execution:
API ClientcallsSecurityAndConfig, which attempts toconfig.json, then callsKeyring, then callsPrompt Utility, and finally returns a value. This effectively documents the complex conditional logic in a clear, linear way.
3. Who is Known for Being Excellent at Diagramming?
Simon Brown (The C4 Model Creator)
- Known for: Advocating for "lite" architecture documentation and creating the C4 Model. His work emphasizes that diagrams must be simple, consistent, and useful, moving away from overly detailed and proprietary notations. He is a primary reference for modern, agile diagramming.
Martin Fowler
- Known for: Being one of the leading voices in software architecture and design patterns. His website and books contain countless high-quality, clear diagrams that explain complex patterns (like "Registry Pattern" or "Dependency Injection"). His diagrams are often simple, focused, and directly supportive of the text.
The best diagrammers are not those who use the fanciest tools, but those who can choose the right view (C1, C2, Sequence, etc.) to clearly answer a specific architectural question for the intended audience.
can you generate a node red flow that demonstrates this? i will create a folder ./node-red/json-schema/ and another, ./node-red/code-diagram/
this is specific and general respectively to the current problem. Write files ./node-red/built-scripts/json-schema-flow-build.sh and ./node-red/built-scripts/code-diagram-flow-build.sh which use cat EOF blocks and no backticks and no triple backticks to stand up these two node red flows
oolong@CIT-36NZRL3 ~/dev cd pipeline
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± mkdir node-red oolong@CIT-36NZRL3 ~/dev/pipeline dev ± ls node-red/
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± mkdir node-red/json-schema/
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± mkdir node-red/code-diagram/
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± npx node-red
1 Dec 20:44:11 - [info]
Welcome to Node-RED
===================
1 Dec 20:44:11 - [info] Node-RED version: v4.1.1
1 Dec 20:44:11 - [info] Node.js version: v24.11.1
1 Dec 20:44:11 - [info] Linux 6.6.87.2-microsoft-standard-WSL2 x64 LE
1 Dec 20:44:11 - [info] Loading palette nodes
1 Dec 20:44:12 - [info] Settings file : /home/oolong/.node-red/settings.js
1 Dec 20:44:12 - [info] Context store : 'default' [module=memory]
1 Dec 20:44:12 - [info] User directory : /home/oolong/.node-red
1 Dec 20:44:12 - [warn] Projects disabled : editorTheme.projects.enabled=false
1 Dec 20:44:12 - [info] Flows file : /home/oolong/.node-red/flows.json
1 Dec 20:44:12 - [info] Creating new flow file
1 Dec 20:44:12 - [warn]
---------------------------------------------------------------------
Your flow credentials file is encrypted using a system-generated key.
If the system-generated key is lost for any reason, your credentials
file will not be recoverable, you will have to delete it and re-enter
your credentials.
You should set your own key using the 'credentialSecret' option in
your settings file. Node-RED will then re-encrypt your credentials
file using your chosen key the next time you deploy a change.
---------------------------------------------------------------------
1 Dec 20:44:12 - [info] Server now running at http://127.0.0.1:1880/
1 Dec 20:44:12 - [warn] Encrypted credentials not found
1 Dec 20:44:12 - [info] Starting flows
1 Dec 20:44:12 - [info] Started flows
^C1 Dec 20:44:14 - [info] Stopping flows
1 Dec 20:44:14 - [info] Stopped flows
✘ oolong@CIT-36NZRL3 ~/dev/pipeline dev ±
This is a fantastic request, as Node-RED is an ideal tool for visualizing data flows, which is exactly what the Configuration-as-Code pattern is: a data flow from definition to consumption.
I will generate two shell scripts for you, which you can run to create the necessary Node-RED flow files using cat EOF. These scripts will create the following files in your specified directories:
-
./node-red/json-schema/flow.json: Demonstrates the Configuration Registry generating a schema, and a client (Svelte/Web UI) consuming it to build a form. -
./node-red/code-diagram/flow.json: A generic flow demonstrating the Code-to-Diagram process, useful for improving your overall diagramming skills.
Here are the two build scripts:
JSON Schema Flow Builder
Dec 1, 8:44 PM
Code Diagramming Flow Builder
Dec 1, 8:44 PM
Open
You can run these scripts in your terminal, then import the resulting flow.json files into your Node-RED environment to see the flows in action. The DEBUG nodes will show the transformation of data at each stage of the process.
Can i use these node red flows as a source of truth, to feed the python codebase which ultimately outputs the JSON schema file, to be referenced as a source of truth, but it is truly the node red setup which can be easily edited by a super user that is the ultimate source of editable, tunable truth. Cool, eh?
this might only be true for the json schema. For the code diagram, obviously it is only an exercise in representation and cannot drive the codebase (right )? The JSON schema flow might also only be an exercise in diagramming, without building an input or a driver mechanism.
how is node-red leveraged as a control or editing mechanism, with values that can feed into a model? is this the typical use?
i havent ran your two shell scripts yet and i am open to discussing changes.
That's a profoundly insightful question about control, governance, and the separation of concerns. You are asking if we can flip the source of truth from Code (Python) to Configuration (Node-RED/GUI).
Just acknowledging your request to discuss changes and the functionality of the existing flows before you run them.
The Node-RED-as-Source-of-Truth Model
Your proposal to use Node-RED as the ultimate, editable source of truth for the JSON Schema is cool, and entirely viable, but it flips the typical pattern.
JSON Schema Flow: Viability as a Driver
The existing Config-as-Code Flow is currently an exercise in representation. The key data is hardcoded in the first function node:
JavaScript
// 1. Python Key Registry (Source of Truth)
msg.registry = [
{
"key_stem": "eds_base_url",
// ... metadata ...
},
// ...
];
To make Node-RED the driver (the editable source of truth), you would need to change this:
-
Replace the Hardcoded Function: Replace the function node with a combination of an HTTP Input node and a Database (or a File node writing to your filesystem).
-
External Storage: The true configuration metadata (the
key_stem,prompt_message, etc.) would be stored in an external system like a local SQLite database, or more simply, a JSON file in your project directory (e.g.,config_definitions.json). -
Node-RED as the Editor: You could build a small dashboard in Node-RED that lets a "super user" edit the database/JSON file via a form.
-
The New Flow: A flow would trigger when the user saves the form, updating the
config_definitions.json. A separate Python script would then read that file to build the final library structure.
Conclusion for JSON Schema Flow: Yes, you can successfully leverage Node-RED as the front-end control mechanism to drive the configuration schema file, making it the ultimate editable truth. However, the Python code would then need to read this external file instead of defining the schema internally using dataclasses.
Code Diagram Flow: Viability as a Driver
For the code diagram, obviously it is only an exercise in representation and cannot drive the codebase (right)?
You are absolutely correct. The Code-to-Diagram Flow is purely an exercise in analysis and representation. It follows this chain:
-
Code is the Input: The source code must exist first.
-
Diagram is the Output: The diagram is a visualization of the existing code structure.
You cannot edit the Mermaid DSL output and expect your Python code to magically change. That would require an extremely complex, two-way transformation (round-trip engineering) that is fragile and never recommended for simple dependency changes.
Node-RED as a Control/Editing Mechanism
How is node-red leveraged as a control or editing mechanism, with values that can feed into a model? is this the typical use?
Using Node-RED for control and editing is very typical, particularly in IIoT, automation, and DevOps scenarios where you need:
-
Visual Configuration: Non-programmers (like your "super user") can change parameters without touching Python source code.
-
Event-Driven Logic: Defining complex sequences of steps (e.g., "when this config changes, run this Python script, then send an email").
-
HMI/Dashboard: Quickly creating a Human-Machine Interface (HMI) for real-time monitoring and parameter input.
The values often feed into models or scripts via:
-
Environment Variables: Node-RED runs an external script (your Python schema builder) and passes the relevant values as environment variables or CLI arguments.
-
API Calls: Node-RED serves as a simple API gateway, taking user input from a dashboard and passing it via an HTTP POST request to a running Python backend service.
-
File I/O: As discussed above, Node-RED simply writes the input values to a persistent file (JSON, YAML, CSV) which the Python application polls or reads at startup.
If you want to proceed with the Node-RED-as-Driver model, we can modify the first flow to replace the hardcoded Python data with an Inject node + File write/read setup.
Are you happy with the current file definitions, or would you like to modify the JSON Schema flow to demonstrate the Node-RED-as-Driver model (where the registry data comes from user input/external file)?
woah, building the final library structure is a bit overzealous
we are just talking about config and credential keys, which are already happily represented in src.pipeline.api.eds.soap.EdsSoapClient
there is of course room for json, and for python code that controls that json
point being - what is it like to add another client, which is a very different question from how to add user specific data and program config for some EDS instance.
Node red is probably best used to define each unique client rather than to handle config values for each machine, but, at the very least a node red diagram could help to visualze the flow of data and the points of user input in whatever various interface (cli vs tkinter vs one-off html vs svelte SPA)
how would more opportunities for config and credential input be provided to be aproapriate to be underlying a single page applicaiton (SPA)? How might this impact the CLI experience?
I appreciate you clarifying the scope! You are absolutely right that focusing Node-RED on visualizing the client/interface flow is highly effective, as it decouples the visualization from the underlying configuration values (which are volatile) and focuses on the stable architecture.
You've hit on a core architectural pattern: separating the client definition from the instance configuration.
1. SPA Configuration Architecture (Using the Registry)
For a Single-Page Application (SPA) like Svelte to handle configuration and credentials effectively, it must rely on the Python backend as the secure gateway to the Keyring and configuration files.
Here is how more opportunities for config and credential input are provided to be appropriate for a SPA:
| Configuration Step | SPA (Svelte) Approach | Python CLI Approach |
|---|---|---|
| 1. Discovery | Schema Fetch: SPA loads the JSON Schema artifact (generated from EdsConfigKeys) at startup to learn what keys exist, what they are called, and what prompt message/label to display. |
Hardcoded Reference: CLI calls SecurityAndConfig.get_config_with_prompt(EdsConfigKeys.URL). |
| 2. Input | HTML Form: The SPA dynamically renders a sophisticated HTML form based on the schema's metadata (e.g., a text box for the URL, a password field for the password, a dropdown for the plant name). | Terminal Prompt: The CLI's JIT logic executes input() if the value is missing. |
| 3. Persistence | API Submission (Secure): User clicks "Save." The SPA sends the configuration values via a secure, authenticated POST request to a dedicated endpoint on the Python backend (e.g., /api/config/save). |
Direct File Write: The Python SecurityAndConfig class directly writes the plaintext values to config.json and the credentials to Keyring. |
| 4. Retrieval | API Call: When the SPA needs to execute an API call (e.g., get data), it includes the plant_name and the backend uses the Python SecurityAndConfig class to securely retrieve the credentials and configuration before executing the EDS request. |
Direct Call: The Python client calls SecurityAndConfig.get_url(...) directly. |
2. Impact on the CLI Experience
The beauty of this registry pattern is that the CLI experience remains largely unchanged, but it gains robustness.
-
The CLI continues to be the simplest, most direct JIT (Just-in-Time) interface.
-
The
SecurityAndConfigmethods—which handle the prompting and persistence logic—remain the same; they just now read their prompts and key names from the stableEdsConfigKeysregistry instead of having them hardcoded. -
The SPA merely provides an alternative, high-fidelity input mechanism that bypasses the need for the CLI's JIT prompting entirely.
3. Visualizing the Multi-Client Flow
To diagram this distinction—the simple CLI JIT vs. the complex SPA Persistence—I am providing an updated flow for the conceptual JSON Schema file. I've renamed the file's internal tab label to "Multi-Client Configuration Flow" for clarity.
I will update the contents of node-red/built-scripts/json-schema-flow-build.sh.
I've updated the node-red/built-scripts/json-schema-flow-build.sh Canvas to visualize the conceptual flow for both a JIT CLI and a persistent SPA. The updated flow demonstrates how the central Config Key Registry provides the blueprint for both interfaces, but the persistence path for the SPA requires an intermediary Backend API to handle the secure saving of credentials and configuration.
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± mkdir node-red/build-scripts
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± nano node-red/build-scripts/json-schema-flow-build.sh
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± code node-red/build-scripts/json-schema-flow-build.sh
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± nano node-red/build-scripts/code-diagram-flow-build.sh
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± code node-red/build-scripts/code-diagram-flow-build.sh
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± nano node-red/build-scripts/code-diagram-2-flow-build.sh
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± code node-red/build-scripts/code-diagram-2-flow-build.sh
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± source ./node-red/build-scripts/code-diagram-2-flow-build.sh
Creating Node-RED flow for Code Diagramming in ./node-red/code-diagram/flow.json
Flow saved to ./node-red/code-diagram/flow.json
To import, open Node-RED, go to Import -> Flows, and paste the content of ./node-red/code-diagram/flow.json.
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± source ./node-red/build-scripts/code-diagram-flow-build.sh Creating Node-RED flow for Code Diagramming in ./node-red/code-diagram/flow.json
Flow saved to ./node-red/code-diagram/flow.json
To import, open Node-RED, go to Import -> Flows, and paste the content of ./node-red/code-diagram/flow.json.
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± cd ./node-red/code-diagram
oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-diagram dev ± source ./node-red/build-scripts/code-diagram-2-flow-build.sh
source: no such file or directory: ./node-red/build-scripts/code-diagram-2-flow-build.sh
✘ oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-diagram dev ± source ./node-red/build-scripts/code-diagram-2-flow-build.sh
source: no such file or directory: ./node-red/build-scripts/code-diagram-2-flow-build.sh
✘ oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-diagram dev ± source ./node-red/build-scripts/code-diagram-2-flow-build.sh
source: no such file or directory: ./node-red/build-scripts/code-diagram-2-flow-build.sh
✘ oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-diagram dev ± ls node-red/
ls: cannot access 'node-red/': No such file or directory
✘ oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-diagram dev ± cd ..
oolong@CIT-36NZRL3 ~/dev/pipeline/node-red dev ± cd ..
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± source ./node-red/build-scripts/code-diagram-2-flow-build.sh
Creating Node-RED flow for Code Diagramming in ./node-red/code-2-diagram/flow.json
Flow saved to ./node-red/code-2-diagram/flow.json
To import, open Node-RED, go to Import -> Flows, and paste the content of ./node-red/code-2-diagram/flow.json.
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± cd ./node-red/code-diagram oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-diagram dev ± cd ..
oolong@CIT-36NZRL3 ~/dev/pipeline/node-red dev ± cd ..
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± cd ./node-red/code-2-diagram
oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-2-diagram dev ± npx node-red -u . 1 Dec 21:24:27 - [info]
Welcome to Node-RED
===================
1 Dec 21:24:27 - [info] Node-RED version: v4.1.1
1 Dec 21:24:27 - [info] Node.js version: v24.11.1
1 Dec 21:24:27 - [info] Linux 6.6.87.2-microsoft-standard-WSL2 x64 LE
1 Dec 21:24:27 - [info] Loading palette nodes
1 Dec 21:24:27 - [info] Settings file : /home/oolong/dev/pipeline/node-red/code-2-diagram/settings.js
1 Dec 21:24:27 - [info] Context store : 'default' [module=memory]
1 Dec 21:24:27 - [info] User directory : /home/oolong/dev/pipeline/node-red/code-2-diagram
1 Dec 21:24:27 - [warn] Projects disabled : editorTheme.projects.enabled=false
1 Dec 21:24:27 - [info] Flows file : /home/oolong/dev/pipeline/node-red/code-2-diagram/flows.json
1 Dec 21:24:27 - [info] Creating new flow file
1 Dec 21:24:27 - [warn]
---------------------------------------------------------------------
Your flow credentials file is encrypted using a system-generated key.
If the system-generated key is lost for any reason, your credentials
file will not be recoverable, you will have to delete it and re-enter
your credentials.
You should set your own key using the 'credentialSecret' option in
your settings file. Node-RED will then re-encrypt your credentials
file using your chosen key the next time you deploy a change.
---------------------------------------------------------------------
1 Dec 21:24:27 - [info] Server now running at http://127.0.0.1:1880/
1 Dec 21:24:27 - [warn] Encrypted credentials not found
1 Dec 21:24:27 - [info] Starting flows
1 Dec 21:24:27 - [info] Started flows
^C1 Dec 21:24:51 - [info] Stopping flows
1 Dec 21:24:51 - [info] Stopped flows
✘ oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-2-diagram dev ± ls
flow.json lib node_modules package.json settings.js
oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-2-diagram dev ± head -40 settings.js
/**
* This is the default settings file provided by Node-RED.
*
* It can contain any valid JavaScript code that will get run when Node-RED
* is started.
*
* Lines that start with // are commented out.
* Each entry should be separated from the entries above and below by a comma ','
*
* For more information about individual settings, refer to the documentation:
* https://nodered.org/docs/user-guide/runtime/configuration
*
* The settings are split into the following sections:
* - Flow File and User Directory Settings
* - Security
* - Server Settings
* - Runtime Settings
* - Editor Settings
* - Node Settings
*
**/
module.exports = {
/*******************************************************************************
* Flow File and User Directory Settings
* - flowFile
* - credentialSecret
* - flowFilePretty
* - userDir
* - nodesDir
******************************************************************************/
/** The file containing the flows. If not set, defaults to flows_<hostname>.json **/
flowFile: 'flows.json',
/** By default, credentials are encrypted in storage using a generated key. To
* specify your own secret, set the following property.
* If you want to disable encryption of credentials, set this property to false.
* Note: once you set this property, do not change it - doing so will prevent
oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-2-diagram dev ± head -40 flow.json
[
{
"id": "270f2f3e.34d4d6",
"type": "tab",
"label": "Generic Code-to-Diagram Flow",
"disabled": false,
"info": "Demonstrates the stages of converting source code into an architectural diagram using a Diagram-as-Code approach (like Mermaid or PlantUML)."
},
{
"id": "6b3a0b2c.a1b3c4",
"type": "inject",
"z": "270f2f3e.34d4d6",
"name": "Input Source Code",
"props": [
{
"p": "payload",
"v": "class EdsSoapClient:\n def get_data():\n config = SecurityAndConfig()\n url = config.get_url(KEY_A)\n auth = config.get_credentials(KEY_B)\n # ... API call ...",
"vt": "str"
}
],
"repeat": "",
"crontab": "",
"once": true,
"onceDelay": 0.1,
"topic": "",
"x": 140,
"y": 140,
"wires": [
[
"7d8c4e5f.b6a7d8"
]
]
},
{
"id": "7d8c4e5f.b6a7d8",
"type": "function",
"z": "270f2f3e.34d4d6",
"name": "1. Code Scanner / Parser",
"func": "/*\n Simulates a tool (e.g., AST parser) scanning the code to identify \n classes, relationships (composition, dependency), and interfaces.\n*/\n\nconst code = msg.payload;\nconst relationships = [\n { source: \"EdsSoapClient\", target: \"SecurityAndConfig\", type: \"Dependency\" },\n { source: \"SecurityAndConfig\", target: \"Config.json\", type: \"Read/Write\" },\n { source: \"SecurityAndConfig\", target: \"Keyring\", type: \"Read\" }\n];\n\nmsg.relationships = relationships;\nmsg.payload = \"Relationships Extracted\";\nreturn msg;",
"outputs": 1,
oolong@CIT-36NZRL3 ~/dev/pipeline/node-red/code-2-diagram dev ±
the node-red flow did not open as expected - a blank canvas shows
This is a very common issue when manually setting up Node-RED environments!
🛑 Why the Canvas Is Blank
When you ran npx node-red -u . inside ./node-red/code-2-diagram, Node-RED did two things:
-
It created the user directory (
/home/oolong/dev/pipeline/node-red/code-2-diagram). -
Since it couldn't find a
flows.jsonin that new user directory, it created a brand new, emptyflows.jsonfile and loaded that blank canvas.
The flows were successfully generated by the script, but Node-RED didn't load them because its initialization logic overrode your manual file creation.
✅ How to Load the Flows Now
The fastest way to fix this is to use the Node-RED Import function:
-
Start Node-RED again (if it's not running):
npx node-red -u ./node-red/code-2-diagram -
Access the UI at
http://127.0.0.1:1880/. -
Copy the Flow Content: Open the generated file in your editor:
head -n 999 ./node-red/code-diagram/flow.jsonand copy the entire JSON content (starting with[and ending with]). -
Import in UI: In the Node-RED editor, click the hamburger menu (☰) in the top right -> Import -> Clipboard.
-
Paste the content and click Import.
The flow should now appear on your canvas.
🛠️ Defining the Python Registry
Let's assume you've successfully imported the flows and they are running. We need to implement the Python definition that drives the schema flow you just visualized.
This file establishes the single source of truth for all configuration and credential keys, incorporating the concept of security_level and scope (plant_dependent) to ensure robustness across all clients (CLI, SPA).
I will now generate the key_registry.py file, which is the foundational piece for implementing this hardened approach.
Configuration Key Registry
Dec 1, 9:35 PM
This key_registry.py file is now the single, code-based definition that both your CLI (for JIT prompting) and your future SPA backend (for persistence logic) will reference. You no longer rely on magic strings or scattered definitions!
import dataclasses
from enum import Enum, auto
from typing import Optional
# --- Enums for Configuration Governance ---
class SecurityLevel(Enum):
"""
Defines where the value for a configuration key should be stored.
This governs the persistence mechanism in SecurityAndConfig.
"""
# Stored in the local file system config.json (e.g., URLs, timeouts).
PLAINTEXT = auto()
# Stored securely in OS Keyring (e.g., passwords, API tokens).
CREDENTIAL = auto()
class ConfigScope(Enum):
"""
Defines the scope of a configuration key's value.
This governs how the key is constructed for storage and retrieval.
"""
# Key applies to the entire application/user (e.g., generic username).
GLOBAL = auto()
# Key is prefixed by a dynamic identifier (e.g., 'Maxson_eds_base_url').
PER_PLANT = auto()
# --- The Registry Definition ---
@dataclasses.dataclass(frozen=True)
class ConfigKey:
"""
A foundational definition object for a single configuration or credential key.
This object is the single source of truth for the key's metadata.
"""
key_stem: str
security_level: SecurityLevel
scope: ConfigScope
description: str
prompt_message: str
is_required: bool = True
default_value: Optional[str] = None
# --- The Central Registry ---
class EdsConfigKeys:
"""
The central, immutable registry defining all known configuration and
credential keys used by the pipeline-eds library.
All client code (CLI, SPA backend, etc.) MUST reference these objects.
"""
# --- EDS Client Configuration ---
EDS_BASE_URL = ConfigKey(
key_stem="eds_base_url",
security_level=SecurityLevel.PLAINTEXT,
scope=ConfigScope.PER_PLANT,
description="The base URL for the EDS SOAP API.",
prompt_message="Please enter the EDS base URL (e.g., http://0.0.0.0/eds):"
)
EDS_TIMEOUT_SECONDS = ConfigKey(
key_stem="timeout_seconds",
security_level=SecurityLevel.PLAINTEXT,
scope=ConfigScope.GLOBAL,
description="Default timeout for SOAP requests in seconds.",
prompt_message="Enter the default API timeout in seconds:",
default_value="30",
is_required=False
)
# --- Credentials ---
EDS_USERNAME = ConfigKey(
key_stem="username",
security_level=SecurityLevel.CREDENTIAL,
scope=ConfigScope.GLOBAL, # Assuming the same user logs into all plants
description="The API username for authenticating with EDS.",
prompt_message="Enter your EDS API username:"
)
EDS_PASSWORD = ConfigKey(
key_stem="password",
security_level=SecurityLevel.CREDENTIAL,
scope=ConfigScope.GLOBAL,
description="The API password for the given username.",
prompt_message="Enter your EDS API password:"
)
# --- Auxiliary / State Keys ---
LAST_USED_PLANT = ConfigKey(
key_stem="last_used_plant",
security_level=SecurityLevel.PLAINTEXT,
scope=ConfigScope.GLOBAL,
description="The last plant name successfully used.",
prompt_message="", # No prompt needed, this is internal state
is_required=False
)
# A utility function to retrieve all keys for easy iteration (e.g., for JSON Schema generation)
def get_all_keys():
"""Returns a list of all ConfigKey objects defined in the registry."""
keys = []
for attr_name in dir(EdsConfigKeys):
attr = getattr(EdsConfigKeys, attr_name)
if isinstance(attr, ConfigKey):
keys.append(attr)
return keys
# Example usage to retrieve the prompt message for a key:
# prompt = EdsConfigKeys.EDS_BASE_URL.prompt_message
# security = EdsConfigKeys.EDS_USERNAME.security_level
where does this key_registry.py file go? why?
That's a great question about file organization! Knowing where to place a core architectural file like key_registry.py is key to maintaining a clean and scalable codebase.
The key_registry.py file should reside in a location that signifies its role as a governance layer for configuration, making it easily accessible to both application logic and build scripts, while clearly separating it from the core API client logic.
File Placement and Rationale
The file should be placed within the configuration subdirectory of your library, typically:
src/pipeline/config/key_registry.py
Why this location?
-
Isolation (The
config/Directory): Placing it inconfig/clearly separates the metadata definitions from the implementation logic (like the actualEdsSoapClientwhich lives insrc/pipeline/api/). This follows the principle of Separation of Concerns. TheEdsSoapClientuses the registry, but the registry doesn't depend on the client's implementation details. -
Centralization (The
pipelineNamespace): Placing it insidesrc/pipelineensures it's part of your main Python package. Any component, whether it's the CLI, a build script, or the backend API, can import it reliably using the package namespace:Python
from pipeline.config.key_registry import EdsConfigKeys, get_all_keys -
Client Agnosticism: This file is the "source of truth" for what configuration exists. It shouldn't live in the client code (like the CLI or SPA), nor should it live in the core client logic (
EdsSoapClient). Placing it inconfigmakes it a library-level utility, ready to be consumed by any part of the system that needs to know about config keys.
In summary, placing it in src/pipeline/config/ establishes it as the governance layer for your application's configuration metadata.
great. explain what it does. List all parts of the project that it connects to. characterize each connection as input, output, export, import, mutation, etc.
That's a powerful selection! The code you've highlighted is not just a configuration file; it's the Configuration Governance Layer for your entire Python library.
Here is an explanation of what the Canvas document does and a detailed breakdown of its connections to the rest of your project.
1. Explanation of the key_registry.py File
The primary purpose of src/pipeline/config/key_registry.py is to establish a single, immutable source of truth for all configuration and credential keys used by your application.
This approach is known as Configuration-as-Code and enforces consistency across all interfaces (CLI, SPA, tests). It prevents issues where the CLI might ask for eds_url and the SPA might look for base_url, leading to broken configuration.
Key Components:
-
Enums (
SecurityLevel,ConfigScope): These define the rules that govern each key.-
SecurityLevel: Tells the application where to store the value (in the plaintextconfig.jsonfile, or the secure OS Keyring). -
ConfigScope: Tells the application how to construct the unique storage key (e.g.,eds_base_urlvs.Maxson_eds_base_urlfor a per-plant key).
-
-
ConfigKeyDataclass: This serves as the schema for the key itself. It combines all the rules (enums) and metadata (description,prompt_message,default_value) into one immutable object. -
EdsConfigKeysClass: This container holds instances ofConfigKey. By referencingEdsConfigKeys.EDS_BASE_URL, your application gets the key's stem, prompt, security level, and scope all in one place. -
get_all_keys()Utility: This function exists specifically to support automated build processes, allowing a script to iterate over every defined key to generate external artifacts (like the JSON Schema for the SPA).
2. Connections to the Project
This file is a source/export of metadata, meaning other files import its objects and use the data within them to execute their logic. It has minimal internal dependencies (only standard Python imports).
| Project Part Connected | Role in the Project | Connection Type |
|---|---|---|
SecurityAndConfig Class (Python Backend) |
Executes the core logic for configuration retrieval and persistence. | Import / Input (Logic): Imports ConfigKey, SecurityLevel, and ConfigScope to determine how to construct the storage key, where to look for it (file/keyring), and how to save it. |
| JSON Schema Generation Script (Build Process) | A build script that generates the config_schema.json file for client-side use (like your Svelte SPA). |
Import / Export (Data Artifact): Imports get_all_keys() and iterates over the list to translate the Python metadata into a universal JSON Schema artifact. |
CLI Argument Parser (main.py) |
Handles user interaction and command-line input. | Import / Input (UI): Imports ConfigKey objects to extract description (for help text) and prompt_message (for JIT prompting). |
SPA Backend API Endpoint (/api/config/save) |
Receives user-provided configuration values from the web front-end. | Import / Input (Validation/Mutation): Imports keys to validate that all required keys were provided, and to route the values to the correct persistence mechanism (Keyring for CREDENTIAL, file for PLAINTEXT). |
EdsSoapClient (Python Backend) |
The main business logic class that performs API calls. | Import / Input (Dependency): Imports ConfigKey objects to request specific, typed configuration values from SecurityAndConfig before executing the SOAP request. |
wonderful. explain how to run the code to generate the config schema JSON.
is this at all attached to a node-red flow currently?
I acknowledge your request regarding the newly highlighted code in the "Configuration Key Registry" Canvas. This file is the foundation of your Configuration-as-Code pattern.
Here is the explanation of how to run the code to generate the config schema JSON, along with clarification on its current connection to Node-RED.
1. How to Generate the Config Schema JSON
The Canvas file, src/pipeline/config/key_registry.py, is a Python module containing data definitions. To generate the final JSON Schema artifact, you need a separate Build Script that imports this data and performs the necessary translation and serialization.
I will generate this build script for you. You can place this file in your project's root or a dedicated scripts/ directory.
Step 1: Create the Schema Generation Script
This script iterates over the registry and formats the metadata into a JSON Schema structure.
Schema Generation Script
Dec 1, 9:42 PM
Step 2: Run the Script
-
Save the code above as
scripts/generate_schema.py. -
Make sure your project structure allows the script to import the registry (e.g., run from the project root):
Bash
# Assuming your current directory is ~/dev/pipeline python scripts/generate_schema.py 3. The script will output the schema to `build/artifacts/config_schema.json`. This is the artifact that your Svelte SPA will fetch at runtime.
2. Node-RED Flow Attachment
No, the key_registry.py Canvas is not currently attached to a Node-RED flow programmatically.
Here is the relationship:
-
Node-RED's Role (Conceptual): The Node-RED flow (
Multi-Client Configuration Flow) is a visualization tool. The first function node in that flow simulates the data found in yourkey_registry.pyto draw the diagram. It helps you understand the data flow, but it doesn't execute the actual Python file. -
The Missing Link (Programmatic): To connect the two, you would need to introduce a step in the Node-RED flow that calls the Python script you just created (
scripts/generate_schema.py). This is typically done using an Exec node in Node-RED, which runs a shell command (e.g.,python scripts/generate_schema.py).
The current setup maintains a clean separation: Python is the source of truth, and Node-RED is the visualizer of the process.
show the contents of the exec node and explain how to add the contents to the existing flow (both in the web gui and by editing files) in order to achieve the link between the node-red flow and the generate_schema
That is an excellent way to bridge the conceptual flow visualization in Node-RED with the actual Python build process. Linking the simulated registry to the real schema generation script makes the diagram actionable.
I will provide the contents of the necessary exec node and explain how to integrate it into your existing flow, both via the Web GUI and by editing the flow file directly.
Here are the details and instructions for adding the connection.
Node-RED Exec Node Configuration
Dec 1, 9:44 PM
I have provided the instructions and the contents for the Node-RED Exec node to connect your configuration registry to the schema generation script in the Canvas document above. This link is currently not present in your flow; you must follow the steps in the Canvas to integrate the Exec node.
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± cat scripts/generate_schema.py
import json
import os
from pathlib import Path
from pipeline.config.key_registry import get_all_keys
# --- Configuration ---
# Define the output path for the JSON Schema artifact
OUTPUT_DIR = Path("build/artifacts")
SCHEMA_FILE = OUTPUT_DIR / "config_schema.json"
def get_json_schema_type(config_key):
"""
Determines the JSON Schema type based on Python typing or defaults.
In a more complex system, this would map types like 'int' or 'float'.
For now, we map everything to 'string' since all inputs are user text.
"""
# Note: If ConfigKey included a 'python_type' field (e.g., int, str),
# we would use that here. Assuming string for all user input fields for simplicity.
return "string"
def generate_schema():
"""
Reads the Python registry, transforms the data into a JSON Schema structure,
and writes the result to a file.
"""
print("Starting JSON Schema generation...")
# 1. Initialize the root schema structure
schema = {
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "EDS Configuration and Credential Schema",
"description": "Defines the structure and validation rules for all configuration keys.",
"type": "object",
"properties": {},
"required": []
}
# 2. Iterate over all ConfigKey objects
for key_def in get_all_keys():
key_stem = key_def.key_stem
# Build properties block for the key
schema.get("properties")[key_stem] = {
"type": get_json_schema_type(key_def),
"description": key_def.description,
"prompt_message": key_def.prompt_message,
"security_level": key_def.security_level.name, # Export enum name
"scope": key_def.scope.name # Export enum name
}
# Add required keys to the 'required' array
if key_def.is_required:
schema.get("required").append(key_stem)
# Add default value if present
if key_def.default_value is not None:
schema.get("properties")[key_stem]["default"] = key_def.default_value
# 3. Ensure the output directory exists
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
# 4. Serialize the result to JSON file
with open(SCHEMA_FILE, 'w') as f:
json.dump(schema, f, indent=4)
print(f"Successfully generated schema at: {SCHEMA_FILE.resolve()}")
if __name__ == "__main__":
# Note: You must run this script from the project root
# (or adjust PYTHONPATH) so 'pipeline.config.key_registry' can be imported.
try:
generate_schema()
except ImportError as e:
print("ERROR: Could not import 'pipeline.config.key_registry'.")
print("Ensure you are running this script from the project root or your Python path is set correctly.")
print(f"Details: {e}")
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± nano docs/node-red-exec-node-config.md
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± code docs/node-red-exec-node-config.md
oolong@CIT-36NZRL3 ~/dev/pipeline dev ± cat docs/node-red-exec-node-config.md
## Node-RED Exec Node Configuration for Schema Generation
This section details the configuration for the Node-RED `Exec` node required to run your `scripts/generate_schema.py` file. This node will be placed directly after the "Config Key Registry" function node in your "Multi-Client Configuration Flow."
### 1. Contents of the Exec Node
The `Exec` node is designed to run shell commands.
| | | |
|---|---|---|
|**Property**|**Value**|**Notes**|
|**Name**|Run Python Schema Generator|A clear name for the node.|
|**Command**|`python scripts/generate_schema.py`|This executes the Python script.|
|**Append `msg.payload`**|`unchecked`|We don't want to pass the message payload as arguments to the script.|
|**Wait for command to complete?**|`checked`|Yes, we need the script to finish before proceeding.|
|**Working Directory**|`/home/oolong/dev/pipeline`|**CRITICAL:** This must be set to your project root to ensure `from pipeline.config.key_registry import...` works correctly. Adjust this path to your exact project root location (`~/dev/pipeline`).|
|**Output**|`return code`|We only need the status code (0 for success).|
### 2. Adding the Node via the Node-RED Web GUI
1. **Open the Flow:** Navigate to your "Multi-Client Configuration Flow (CLI vs SPA)" tab in the Node-RED editor.
2. **Add Exec Node:** Drag an **Exec** node (found under the 'Advanced' section) onto the canvas.
3. **Configure Exec Node:** Double-click the new `Exec` node and enter the properties as listed in Section 1, paying close attention to setting the **Working Directory** to your project root.
4. **Wire the Flow:**
- Disconnect the wire currently connecting the **"Config Key Registry (The Schema)"** function node to the two comment nodes.
- Wire the output of **"Config Key Registry (The Schema)"** (the original function node that defines `msg.schema`) to the input of your new **"Run Python Schema Generator"** (the Exec node).
- Wire the **second output** of the **"Run Python Schema Generator"** (labeled 'stdout') to a new **Debug node**. Name the Debug node: `SUCCESS: Schema Artifact Generated`.
5. **Deploy:** Click the **Deploy** button. Now, when you inject a message into the registry flow, the Python script will run, generating your `config_schema.json` file.
### 3. Adding the Node by Directly Editing the Flow File
If you want to add the Node-RED components by editing the flow file (`./node-red/json-schema/flow.json`), you can insert the following JSON objects. These objects correspond to the `Exec` node and a subsequent `Debug` node, and update the wiring of the "Config Key Registry" node (`6b490d1f.a46f2c`).
**A. New Nodes to Insert (Exec and Debug):**% oolong@CIT-36NZRL3 ~/dev/pipeline dev ±
I understand you've selected the detailed instructions for connecting your Node-RED flow to the Python schema generation script and are reviewing the necessary steps and file contents.
You've provided the content for both the Python script (scripts/generate_schema.py) and the JSON instructions for integrating the Exec node into your flow file.
Since your final goal is to successfully run the Python script from within Node-RED, the critical next step is to ensure the connections between the nodes are robust.
Based on the instructions provided in the Canvas, here is the recommended process for completing the wiring, focusing on maintaining the existing CLI/SPA visualization paths:
Completing the Node-RED Wiring
To ensure the signal continues down the two visualization paths (CLI JIT and SPA API) after the schema has been successfully generated, we need to modify the connections described in the Canvas instructions slightly.
1. Wiring Review (As per Canvas Instructions)
The Canvas defined the following:
-
Existing Node ID:
6b490d1f.a46f2c(Config Key Registry) -
New Exec Node ID:
7f8a9b0c.d1e2f3(Run Python Schema Generator) -
Old Wires from Registry:
[["67f912e5.a4c3f2", "89b0c1d2.e3f4a5"]](To CLI/SPA Comment Nodes)
The Canvas correctly instructed to update the registry's wires to point only to the Exec node:
"wires": [["7f8a9b0c.d1e2f3"]]
2. Re-wiring the Visualization Path (Suggested for Completion)
The Exec node has three outputs:
-
Stdout (Index 0): Used for standard output (the
print()messages from the Python script). -
Stderr (Index 1): Used for error output.
-
Return Code (Index 2): The exit code (0 for success).
To ensure the flow continues after the script runs, we must wire the Exec node's success output (stdout, index 0) back into the original visualization paths.
You should modify the JSON for the new Exec node (7f8a9b0c.d1e2f3) in the flow file to use its primary output to trigger the subsequent comment nodes (67f912e5.a4c3f2 and 89b0c1d2.e3f4a5):
JSON
{
"id": "7f8a9b0c.d1e2f3",
"type": "exec",
"z": "e67e35b0.f3c1d4",
"name": "Run Python Schema Generator",
"command": "python scripts/generate_schema.py",
"addpay": false,
"append": "",
"useSpawn": "false",
"timer": "",
"oldstyle": false,
"x": 680,
"y": 100,
"wires": [
[
"67f912e5.a4c3f2",
"89b0c1d2.e3f4a5",
"8g9h0i1j.k2l3m4"
],
[],
[]
],
"workingDir": "/home/oolong/dev/pipeline"
}
Explanation of the Update:
-
The
wiresarray now includes the two original comment nodes (67f912e5.a4c3f2and89b0c1d2.e3f4a5) on Output 0 (Stdout). -
The new Debug node (
8g9h0i1j.k2l3m4) is also attached to Output 0 to confirm the successful run output. -
This structure ensures that the visualization of the CLI and SPA paths only starts after the Python build process has successfully completed, reflecting the dependency correctly.
i dont understand what you mean by "the critical next step is to ensure the connections between the nodes are robust....to successfully run the Python script from within Node-RED"
this is where i stopped reading your last message
please break this down again
I absolutely understand. When dealing with build systems, the flow control—making sure one step finishes before the next begins—is the trickiest part. Let me break down exactly what "critical next step" means in the context of your Node-RED flow.
The confusion stems from how the Node-RED Exec node works, particularly how it handles its outputs.
The Problem: Sequencing the Build Process
You have two main steps:
-
Metadata Source: The "Config Key Registry" node (
6b490d1f.a46f2c) defines the data. -
Artifact Generation: The "Run Python Schema Generator" node (
7f8a9b0c.d1e2f3) runs the Python script to use that data.
The goal is to ensure the CLI/SPA Visualization steps only fire after the Python script has successfully run and created the config_schema.json artifact.
Breakdown of the "Robust Connection"
A "robust connection" here means wiring the nodes to enforce this specific sequence. This requires two distinct steps in the wiring:
1. The Input: Triggering the Python Script
You need the Registry node to signal the Exec node to start.
-
Action: The output of "Config Key Registry" (
6b490d1f.a46f2c) must be wired to the input of the "Run Python Schema Generator" (7f8a9b0c.d1e2f3). -
Result: This is achieved by updating the Registry node's
wiresarray, as described in the Canvas:"wires": [["7f8a9b0c.d1e2f3"]]
2. The Output: Resuming the Visualization Flow (The Critical Step)
The Exec node, by default, is configured in the Canvas to use the return code output:
| Output Index | Output Type | Default Content | Purpose in this flow |
|---|---|---|---|
| 0 | Stdout | Standard output (print() messages) |
Success Trigger (What we use next) |
| 1 | Stderr | Error messages from the command | Failure Trigger |
| 2 | Return Code | The exit code (0 for success) | Status Check (Less common for sequencing) |
The instructions in the Canvas only had one wire coming out of the Exec node:
JSON
"wires": [
[], // Output 0 (Stdout) is empty
["8g9h0i1j.k2l3m4"], // Output 1 (Stderr) connected to Debug
[] // Output 2 (Return Code) is empty
]
The problem is: If the Python script runs successfully, its messages come out of Output 0 (Stdout). The Canvas, as written, only wires Output 1 (Stderr) to the Debug node (8g9h0i1j.k2l3m4).
The "critical next step" is fixing the wiring of the Exec node's Output 0 (Stdout):
You must connect Output 0 (Stdout) of the Exec node to:
-
The Debug node (
8g9h0i1j.k2l3m4), so you see the Python script's success message ("Successfully generated schema..."). -
The original comment nodes (
67f912e5.a4c3f2,89b0c1d2.e3f4a5), so the rest of the flow continues.
By doing this, you ensure the flow only progresses to visualize the CLI/SPA steps if and when the external Python script has finished its job and provided a successful output on its Stdout channel. This makes the connection "robust" because the flow respects the completion of the external process.