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Cognition integration provider #302
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…-ai/refinery-gateway into cognition-integration-provider
Checked further on folder permission for a external user ([email protected]) Items in Folder without permission: Not sure how easy it would be to collect an email from that but would potentially be needed for the user access to double check i did the same with my private email with the same resulting in the same uuid (so not every user gets a new uuid) so i'm assuming there are some steps involved in the process to get the emails also i never got an actual invite link via email but that is a sharepoint issue not ours
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…ode-kern-ai/refinery-gateway into cognition-integration-provider
Something is problematic when i start unexcluded. I'm not sure what but in the docker compose file i always get some kind of database error during execution: Error 1
Error 2
The PGRES_TUPLES_OK error seems to be related to the process pool working wrongly with the session object (at least according to chatgpt :D ) GPT AnswerThe root of what you’re seeing is actually two different but related problems: 1. “number of values in row (20) differ from number of column processors (9)”This comes straight out of SQLAlchemy’s row‐mapping machinery: your ORM class definition for How to verify and fix
After you’ve aligned model ←→ table, that 20-vs‑9 error will go away. 2. “psycopg2.DatabaseError: error with status PGRES_TUPLES_OK and no message from the libpq” in child processesThis one is a classic side‑effect of using a forked worker (via The cure is: make sure each worker process creates its own engine/session, and never inherits an open pool from the parent. Two straightforward strategies:
Either of those approaches will eliminate the “PGRES_TUPLES_OK” commit‐without‐message error. Putting it all together
Once those are both addressed, your sharepoint extraction should proceed cleanly:
Let me know if you hit any snags while reconciling your schema or refactoring your multiprocessing setup! I'll work on this (jwittmeyer).
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I think currently we can remove language form the calculated attributes since it's calculating from the summary which we force to be in the language of the tokenizer
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Filter merging with access management doesn't work (filter e.g. extension + ANNOTATOR user) i'll look into it
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This is the main PR.
Related PRs
Notes
New repository
Important
Retrieve:
Tenant and Client IDs in Azure Portal (@kern.ai) => Key Vaults => dev-krn-accompio => Objects -> Secrets => O365-TENANT-ID and O365-UNATTENDED-CLIENT-ID
Certificate .pfx file in Azure Portal (@kern.ai) => Key Vaults => dev-krn-admin => Objects -> Certificates => dev-accompio-certificate =>
Download in PFX/PEM format
. The passphrase is an empty string. Use" "
(space) as an environment variable value (reference).Document Library ID:
b!zzhsLojhuEaDy3fQIjUZxLC67xk1l9lFpzlKVAQ1-uDYfIQ6DMGYQrkemTRS4V0Q
Use dev-setup@cognition-integration-provider to run cognition-integration-provider (
bash start -a -b cognition-integration-provider
)Tests
Tests were not developed for this container due to long running extraction and transformation tasks
Affected areas
dev-setup
,deployment-cognition
,deployment-managed-cognition
refinery-submodule-model
cognition-task-master
admin-dashboard
cognition-ui
refinery-ui
refinery-gateway
cognition-gateway
cognition-integration-provider
Performance
MP - multiprocessing (# workers)

SP - singleprocessing