# Description of Changes
Vite currently warns that when it's bundling our code that the chunk
size is way too high because most of the imports are static so it can't
split them into smaller chunks. This PR changes a few key areas to use
lazy imports to try and make the chunks as small as possible with
minimal code changes.
Vite's warnings kick in at minified chunks being >500kB, and we've got a
little way to go still to reach that, but we can keep chipping away at
this and I'd rather get the biggest wins done now. I've also included
Lighthouse scores because there's been discussion about improving ours
recently. It's not the aim of this PR to improve it, but it's nice that
it makes it a little better.
## Current main chunks
Build split into 12 chunks. Largest chunk in build is:
```
[frontend:build] dist/assets/index-B6JiWDxZ.js 5,175.51 kB │ gzip: 1,495.85 kB
```
<img width="1442" height="775" alt="image"
src="https://github.com/user-attachments/assets/b0e8a3fa-4ef3-4ccd-8c1d-bfed2d99bd27"
/>
Lighthouse score:
<img width="423" height="146" alt="before"
src="https://github.com/user-attachments/assets/c62056e8-2e77-49a6-a1ae-f08ec8021fb3"
/>
## This PR's chunks
Build split into 176 chunks. Largest chunk in build is:
```
[frontend:build] dist/assets/index-qCgeCY4B.js 2,878.54 kB │ gzip: 861.03 kB
```
<img width="1447" height="776" alt="image"
src="https://github.com/user-attachments/assets/8d0c3cf0-cc25-41c3-b114-4940d3e99349"
/>
Lighthouse score:
<img width="402" height="145" alt="after"
src="https://github.com/user-attachments/assets/99a26eb3-bd15-4b92-bf22-82b58b458f52"
/>
---------
Co-authored-by: EthanHealy01 <80844253+EthanHealy01@users.noreply.github.com>
# 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 <80844253+EthanHealy01@users.noreply.github.com>
# 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
```
