# Description of Changes
## What & why
Customers using ADFS (or any generic OIDC provider that doesn't emit
`email`) hit `Attribute value for 'email' cannot be null` during OAuth2
login with no visibility into what claims the provider actually sent.
The only available remedy was guessing at
`security.oauth2.useAsUsername` until something worked.
This PR adds a new opt-in `security.oauth2.debugLogging` flag (default
`false`). When enabled, `CustomOAuth2UserService` logs:
- All ID token claims (sorted, with values)
- All UserInfo endpoint claims (if any)
- The merged attribute key set Spring exposes to `getAttribute()`
- The value the configured `useAsUsername` actually resolved to
- A **`Hint:`** line listing the claim keys present in the token that
map to a valid `UsernameAttribute` enum value — i.e. exactly what the
operator could put in `useAsUsername` to make login work
Logged at `INFO` on the success path and `ERROR` on failure (inside the
existing `catch (IllegalArgumentException)` block that throws
`OAuth2AuthenticationException`). The block is wrapped with a `[OAUTH2
DEBUG] ... [/OAUTH2 DEBUG]` banner and ends with a PII warning so
operators don't leave it on in production.
Default off → zero observable change for anyone not actively
troubleshooting.
## Files changed
| File | Why |
|---|---|
| `app/common/.../ApplicationProperties.java` | New `debugLogging` field
on the `OAUTH2` config class with javadoc warning about PII |
| `app/core/src/main/resources/settings.yml.template` | Documents
`oauth2.debugLogging` so it appears on next startup |
| `app/proprietary/.../security/service/CustomOAuth2UserService.java` |
Emits the claim dump + suggestion hint when the flag is on |
|
`app/proprietary/.../security/service/CustomOAuth2UserServiceDebugLoggingTest.java`
(new) | Unit test: mocks the OIDC delegate, asserts off-path is silent
and on-path emits the dump with the right Hint contents |
## End-to-end verification
Ran the bundled `testing/compose/docker-compose-keycloak-oauth.yml`
Keycloak realm, configured `security.oauth2.useAsUsername: mail`
(Keycloak emits `email`, not `mail`) and `provider: demarest` (matches
the original customer bug report). Triggered the OAuth flow at
`http://localhost:8080/oauth2/authorization/demarest` and confirmed:
- The ERROR-level dump fires with the full 19-claim ID token decoded
- `-- Value at 'mail' : <NULL — this is why login fails>` correctly
identifies the missing claim
- `-- Hint:` correctly suggests `[email, family_name, given_name,
preferred_username]` (the four keys present that map to valid
`UsernameAttribute` values)
- Auth still fails with the original `OAuth2AuthenticationException` —
no change to control flow, just added diagnostic logging
Unit test (`CustomOAuth2UserServiceDebugLoggingTest`) covers both
branches.
## Reviewer notes
- **No new public APIs.** The flag is config-only; no servlet endpoints
exposed.
- **PII is logged when the flag is on.** This is the whole point —
operators need to see the claims to fix their config — but it's gated,
defaults off, and the dump self-documents with a `WARNING: ... Set
security.oauth2.debugLogging=false once troubleshooting is complete.`
footer.
- **Why log everything, not just sub/email?** Because the operator
doesn't know in advance which claim they actually want. ADFS uses `upn`
in some configs and `preferred_username` in others; Azure AD uses `oid`;
the customer here had neither. Dumping the full set is the only way to
make the diagnostic self-service.
- **Out of scope for this PR (follow-ups):**
- The `UsernameAttribute` enum doesn't include `upn` / `unique_name`
(common ADFS claims). If the customer's token only has `upn`, the Hint
will be empty even though the operator can see `upn` in the dump. Worth
a separate PR to extend the enum.
- The known-provider validator in `Provider.java` (rejects e.g.
`useAsUsername: mail` for `provider: keycloak` at startup) bypasses our
diagnostic for those provider names. ADFS customers using `provider:
<name>` fall into the `default` branch so are not affected — but it's a
sharp edge worth documenting.
---
## Checklist
### General
- [x] I have read the [Contribution
Guidelines](https://github.com/Stirling-Tools/Stirling-PDF/blob/main/CONTRIBUTING.md)
- [x] I have read the [Stirling-PDF Developer
Guide](https://github.com/Stirling-Tools/Stirling-PDF/blob/main/DeveloperGuide.md)
(if applicable)
- [ ] I have read the [How to add new languages to
Stirling-PDF](https://github.com/Stirling-Tools/Stirling-PDF/blob/main/devGuide/HowToAddNewLanguage.md)
(if applicable) — N/A, backend-only change
- [x] I have performed a self-review of my own code
- [x] My changes generate no new warnings
### Documentation
- [ ] Doc-repo update (if functionality has heavily changed) —
diagnostic flag is self-documenting via the `settings.yml.template`
comment and the in-log warning; happy to add a doc-repo entry if
reviewers want one
- [ ] Translation tags — N/A
### UI Changes (if applicable)
- [ ] N/A — backend-only
### Testing (if applicable)
- [x] Unit test added (`CustomOAuth2UserServiceDebugLoggingTest`)
covering on/off paths and Hint correctness
- [x] End-to-end verified locally against bundled Keycloak compose with
intentionally misconfigured `useAsUsername`
- [x] Full `:proprietary:test` suite passes
# Description of Changes
Adds storage in the database for full document content alongside the RAG
content (and changes the service to `DocumentService` instead of
`RagService`). Then adds a generic capability that should be usable by
any agent (currently just used by the Question Agent) which allows the
agent to pull out the full contents of the doc, chunks it into various
sections that will fit in the context window, and then processes them in
parallel to create an intermediate result, and then processes the
intermediate result into a final answer. It will re-chunk as many times
as necessary to get the content small enough for the actual answer to be
analysed (I've tested on PDFs ~3500 pages long, which is well above the
context limit and requires maybe 3 rounds of compression to get an
answer).
The new full doc analysis stuff is heavier than the RAG lookup so both
remain. The agents should use RAG for targeted info and the chunked
reasoner for info that requires reading the full doc.
# Description of Changes
Hooks up the (alpha) PDF Editor backend to the AI engine Edit Agent via
an intermediary API which is easier for the agent to call. It suffers
from all the same issues that the PDF Editor does in actually editing
the text, but should also benefit from any fixes to that.
It also adds protection against the underlying tools misbehaving by
hanging, and fixes a hanging bug in the PDF Editor.
---------
Co-authored-by: EthanHealy01 <[email protected]>
# Description of Changes
Have the Java send a list of enabled endpoints to the AI engine so it
can intelligently respond to the user that the tool does exist but is
disabled on the server so it can't acutally run the operation, instead
of the current behaviour where it sends the API call back and then 503
errors because the execution fails when the URL is disabled.
<img width="380" height="208" alt="image"
src="https://github.com/user-attachments/assets/5842fb2e-2e55-45a5-8205-25515636daae"
/>
---------
Co-authored-by: EthanHealy01 <[email protected]>
# Description of Changes
Flesh out the RAG system and connect it to the PDF Question Agent so it
can respond to questions about PDFs of an extremely large size.
I'd expect lots more work will need to be done to finish off the RAG
system to really be what we need, but this should be a reasonable start
which will let us connect it to tools and have the ingestion mostly
handled automatically. I'm leaving file deletion and proper file ID
management to be done in a future PR. We also need to consider whether
all tools should retrieve content exclusively via RAG, or whether it's
beneficial to have tools sometimes fetch the direct content and other
times fetch it from RAG.
A diagram of the expected interaction is as follows:
```mermaid
sequenceDiagram
autonumber
actor U as User
participant FE as Frontend<br/>(ChatPanel)
participant J as Java<br/>(AiWorkflowService)
participant O as Engine:<br/>OrchestratorAgent
participant QA as Engine:<br/>PdfQuestionAgent
participant RAG as Engine:<br/>RagService + SqliteVecStore
participant V as VoyageAI<br/>(embeddings)
participant L as LLM<br/>(Claude / etc.)
U->>FE: types "Summarise this PDF"<br/>(PDF already uploaded)
FE->>J: POST /api/v1/ai/orchestrate/stream<br/>multipart: fileInputs[], userMessage
Note over J: ByteHashFileIdStrategy<br/>id = sha256(bytes)[:16]
J->>O: POST /api/v1/orchestrator<br/>{ files:[{id,name}], userMessage }
O->>L: route via fast model
L-->>O: delegate_pdf_question
O->>QA: PdfQuestionRequest
loop for each file
QA->>RAG: has_collection(file.id)
RAG-->>QA: false
end
QA-->>O: NeedIngestResponse(files_to_ingest)
O-->>J: { outcome:"need_ingest", filesToIngest:[...] }
Note over J: onNeedIngest
loop per file
J->>J: PDFBox: extract page text
J->>O: POST /api/v1/rag/documents<br/>(long-running timeout)
O->>RAG: chunk + stage documents
O->>V: embed_documents (batches of 256)
V-->>O: embeddings
O->>RAG: add_documents
O-->>J: { chunks_indexed: N }
end
Note over J: retry with resumeWith=pdf_question
J->>O: POST /api/v1/orchestrator
Note over O: fast-path to PdfQuestionAgent
O->>QA: PdfQuestionRequest
Note over QA: build RagCapability<br/>pinned to file IDs
QA->>L: run(prompt) with search_knowledge tool
loop up to max_searches
L->>QA: search_knowledge(query)
QA->>V: embed_query
V-->>QA: query vector
QA->>RAG: search(vector, collections=[file.id])
RAG-->>QA: top-k chunks
QA-->>L: formatted chunks
end
Note over QA: once budget spent,<br/>prepare() hides the tool
L-->>QA: PdfQuestionAnswerResponse
QA-->>O: answer
O-->>J: { outcome:"answer", answer, evidence }
J-->>FE: SSE "result"
FE->>U: assistant bubble
```
# Description of Changes
Add an extra parameter to every agent to receive the conversation
history in addition to the current message. This will make it possible
to answer followup questions from the AI without needing to give full
context in your message.
# Description of Changes
Redesign AI engine so that it autogenerates the `tool_models.py` file
from the OpenAPI spec so the Python has access to the Java API
parameters and the full list of Java tools that it can run. CI ensures
that whenever someone modifies a tool endpoint that the AI enigne tool
models get updated as well (the dev gets told to run `task
engine:tool-models`).
There's loads of advantages to having the Java be the one that actually
executes the tools, rather than the frontend as it was previously set up
to theoretically use:
- The AI gets much better descriptions of the params from the API docs
- It'll be usable headless in the future so a Java daemon could run to
execute ops on files in a folder without the need for the UI to run
- The Java already has all the logic it needs to execute the tools
- We don't need to parse the TypeScript to find the API (which is hard
because the TS wasn't designed to be computer-read to extract the API)
I've also hooked up the prototype frontend to ensure it's working
properly, and have built it in a way that all the tool names can be
translated properly, which was always an issue with previous prototypes
of this.
---------
Co-authored-by: Anthony Stirling <[email protected]>
Co-authored-by: EthanHealy01 <[email protected]>
# Description of Changes
Adds a streaming endpoint to the Java AI orchestrator
(`/api/v1/ai/orchestrate/stream` in addition to the existing
`/api/v1/ai/orchestrate`). This allows the caller to get updates of what
stage of orchestration is being run at the time so UIs can give the user
feedback.
Also contains some dubious Gradle changes to suppress errors coming from
Spotless, when it crashes in Google stuff. I'm not sure if that's
appropriate to add, feel free to ask for changes in review.
# Description of Changes
Add Java orchestration layer which can connect and go back and forth
with the AI engine to get results for the user. It's expected that the
AI engine will not be publicly available and this Java layer will always
be in front of it, to manage sessions and auth etc.
## PR: Certificate Pre-Validation for Document Signing
### Problem
When a participant uploaded a certificate to sign a document, there was
no validation at submission time. If the certificate had the wrong
password, was expired, or was incompatible with the signing algorithm,
the error only surfaced during **finalization** — potentially days
later, after all other participants had signed. At that point the
session is stuck with no way to recover.
Additionally, `buildKeystore` in the finalization service only
recognised `"P12"` as a cert type, causing a `400 Invalid certificate
type: PKCS12` error when the **owner** signed using the standard
`PKCS12` identifier.
---
### What this PR does
#### Backend — Certificate pre-validation service
Adds `CertificateSubmissionValidator`, which validates a keystore before
it is stored by:
1. Loading the keystore with the provided password (catches wrong
password / corrupt file)
2. Checking the certificate's validity dates (catches expired and
not-yet-valid certs)
3. Test-signing a blank PDF using the same `PdfSigningService` code path
as finalization (catches algorithm incompatibilities)
This runs on both the participant submission endpoint
(`WorkflowParticipantController`) and the owner signing endpoint
(`SigningSessionController`), so both flows are protected.
#### Backend — Bug fix
`SigningFinalizationService.buildKeystore` now accepts `"PKCS12"` and
`"PFX"` as aliases for `"P12"`, consistent with how the validator
already handles them. This fixes a `400` error when the owner signed
using the `PKCS12` cert type.
#### Frontend — Real-time validation feedback
`ParticipantView` gains a debounced validation call (600ms) triggered
whenever the cert file or password changes. The UI shows:
- A spinner while validating
- Green "Certificate valid until [date] · [subject name]" on success
- Red error message on failure (wrong password, expired, not yet valid)
- The submit button is disabled while validation is in flight
#### Tests — Three layers
| Layer | File | Coverage |
|---|---|---|
| Service unit | `CertificateSubmissionValidatorTest` | 11 tests — valid
P12/JKS, wrong password, corrupt bytes, expired, not-yet-valid, signing
failure, cert type aliases |
| Controller unit | `WorkflowParticipantValidateCertificateTest` | 4
tests — valid cert, invalid cert, missing file, invalid token |
| Controller integration | `CertificateValidationIntegrationTest` | 6
tests — real `.p12`/`.jks` files through the full controller → validator
stack |
| Frontend E2E | `CertificateValidationE2E.spec.ts` | 7 Playwright tests
— all feedback states, button behaviour, SERVER type bypass |
#### CI
- **PR**: Playwright runs on chromium when frontend files change (~2-3
min)
- **Nightly / on-demand**: All three browsers (chromium, firefox,
webkit) at 2 AM UTC, also manually triggerable via `workflow_dispatch`
# Description of Changes
Change the SAML support for SSO to understand when a request is coming
from the desktop app, and use the alternate auth flow that the desktop
app requires.
Anthony StirlingandGitHub
ConnorYohandGitHub
dependabot[bot]andGitHub
PandaManandGitHub
