core/rag/db.py, vector_store.py, tool.py, bm25.py, and reranker.py
all run only in the FastAPI parent process. Switch their loggers
from Python stdlib to studio's structlog get_logger so their output
shows up in the same JSON stream as the rest of the backend (the
request_completed / RAG search lines).
embeddings.py and ingestion.py stay on stdlib because they execute
inside the mp.spawn ingestion subprocess, which doesn't inherit the
parent's structlog configuration.
asg017/sqlite-vec is Apache-2.0 and OSI-approved. Replaces
qdrant-client (~30 MB) with a small SQLite extension loaded into a
dedicated rag.db file. Single file holds RAG vectors; bm25s indexes
and chat-side studio.db are unaffected.
- New core/rag/db.py owns the rag.db connection and sqlite-vec load.
Extension load runs once at first open. Process-wide singleton
protected by a lock; check_same_thread=False + WAL handles the
FastAPI thread pool.
- core/rag/vector_store.py keeps the same public API
(ensure_collection / upsert_chunks / search / collection_exists /
delete_scope / delete_document) so callers in routes/rag.py,
core/rag/ingestion.py, core/rag/tool.py, and core/rag/retrieval.py
don't change. ensure_collection is now a no-op; collection_exists
returns True iff the scope has at least one indexed vector.
- search uses sqlite-vec's vec_distance_cosine and converts distance
to similarity in [0, 1] so the per-scope min_score threshold
semantics stay identical.
- Mixed-dim scopes coexist behind WHERE scope = ? — the per-scope
embedder resolver guarantees one embedder per scope.
- requirements/rag.txt swaps qdrant-client for sqlite-vec.
- utils/paths/storage_roots.py drops rag_vectordb_root() (the old
qdrant directory); rag.db lives directly under rag_root().
- Rewritten tests/python/test_rag_vector_store.py for the new
semantics (collection_exists tracks populated scopes; new tests
for filtered search and upsert conflict resolution).
Python build requirement: connection.enable_load_extension(True)
must be available. install.sh creates the venv via uv-managed
python-build-standalone, which is compiled with
--enable-loadable-sqlite-extensions, so this works on standard
installs. core/rag/db.py raises an actionable error on the rare
custom-interpreter case.
The query was always going through the default text embedder
(bge-small, 384-d) regardless of how the scope was ingested. A
multimodal thread indexed by Qwen3-VL (2048-d) crashed at cosine
similarity with 'shapes (104,2048) and (384,) not aligned'.
Resolve the scope's embedder at search time:
- kb_<id> -> rag_knowledge_bases.embedding_model column
- thread_<id> -> thread settings (with fallback to defaults +
RAG_EMBEDDER_MATRIX matrix lookup)
Pass it through retrieve_hybrid/retrieve_dense to embeddings.encode
so the query lands in the same vector space as the docs. Both the
/api/rag/search route and the search_knowledge_base tool use the
same resolver.
Qwen3-VL-Embedding-2B's modules.json references
sentence_transformers.base.modules, introduced after 5.2.0.
Unlike BGE-VL, Qwen3-VL has no fragile custom forward subclass to
break against the newer ST internals, so the bump should land
cleanly for the multimodal path.
Built on Qwen3 (not CLIP), so the 77-token text cap that bit
BGE-VL-base is gone — long chunks embed losslessly. Loads via
vanilla SentenceTransformer with trust_remote_code, no custom
adapter needed. 2048-d shared text+image space.
Adds qwen-vl-utils>=0.0.14 to rag.txt for image preprocessing,
required by the Qwen3-VL embedder family.
BGE-VL inherits CLIP's 77-token text positional embedding table —
longer chunks crash inside the text model with a shape mismatch.
Pre-tokenize with truncation=True, max_length=77 and call
get_text_features directly so the high-level encode() (which does
not truncate) is bypassed. Log when truncation happens — text
chunks beyond the cap are silently cut, so multimodal mode is
lossy on the text channel. Image channel is unaffected.
BGE-VL's sentence-transformers shim (bge_vl_clip_transformer.py)
is coupled to specific ST internals and broke across both the 5.2
and 5.3 pin attempts. The canonical load path documented at
bge-model.com is transformers.AutoModel with trust_remote_code +
model.set_processor() + model.encode(text=/images=).
- Wrap that path in _BGEVLAdapter exposing the slice of
SentenceTransformer API the ingester uses (encode for text or PIL
images, get_sentence_embedding_dimension, best-effort tokenize).
- Restore BAAI/BGE-VL-base in RAG_EMBEDDER_MATRIX.
- Revert sentence_transformers pin to 5.2.0 — no longer relevant
since the multimodal path no longer touches ST.
BGE-VL-base ships a custom Transformer subclass that's tightly
coupled to a specific sentence-transformers internal API — it
imports a module path missing in older ST and overrides forward()
expecting a return shape that changed in newer ST. We can't pin ST
to BGE-VL's exact version without risking the training/inference
paths that use the same library, so swap the multimodal default to
the canonical clip-ViT-B-32 which sentence-transformers wraps
natively (no trust_remote_code, no shim, ST-version-independent).
Same 512-d shared text+image space.
BGE-VL's custom modeling code imports
sentence_transformers.base.modules.transformer, which doesn't exist
in the 5.2.0 pin. Bump to >=5.3.0,<7 so the new Module package is
available.
BGE-VL (multimodal) and nomic-embed-text-v1.5 (late chunking) both
ship custom modeling code in their model repos; sentence-transformers
refuses to import the referenced module (e.g. bge_vl_clip_transformer)
without trust_remote_code=True. Safe to enable because the embedder
matrix is config-pinned — users don't supply arbitrary names.
client.search() was removed/renamed to query_points() in
qdrant-client 1.10+. Use the new API when available and fall back
to search() for older pinned versions.
- chat-adapter pre-fetches retrieval on every turn when RAG is on,
regardless of provider. Users no longer have to phrase queries as
'the document I attached' for retrieval to fire. Local tool models
still get search_knowledge_base registered as a refinement path.
- New per-thread ragMinScore slider (Min relevance, 0..1) gates
retrieved hits by dense cosine similarity. Hits below the floor
(and BM25-only hits with no dense signal) are dropped server-side
so unrelated docs don't get injected when the user's query is
off-topic from what's indexed.
- Backend logs at search start (scope, top_k, min_score, query
preview), after retrieval (retrieved vs met_threshold counts),
and on return (final hit count) for both /api/rag/search and the
search_knowledge_base tool path.
- System-prompt nudge prepended when pre-fetch returns hits so the
model knows to cite [1], [2] rather than paraphrase silently.
no-torch-runtime.txt is only consumed in NO_TORCH (Intel Mac
GGUF-only) mode, so qdrant-client / bm25s / pymupdf etc. were never
installed in the normal install path. Split them into rag.txt and
add a step to install_python_stack.py that installs it after studio
deps, skipped only when NO_TORCH is set.
When ragToolEnabled is on, the in-thread composer's + button now
opens a doc-only picker and uploads selected files to the per-thread
RAG pipeline (matching SharedComposer). Pending chips show
Uploading -> Indexing -> Ready status; Send is blocked while any
doc is in-flight. Previously these files were base64'd inline as
native attachments, which bypassed RAG entirely.
The Phase 4 RAG button only existed in shared-composer; the
assistant-ui in-thread composer renders its own pill set, so RAG
was missing once a thread had messages.
Fresh [] literals from useRagStore selectors triggered Zustand's
Object.is snapshot check on every render, causing React #185 on the
chat page when documentsByScope[key] was unpopulated.
Two useEffects added in Phase 2C and Phase 4 followed the anti-pattern
of calling a state setter inside the effect and listing the setter's
output in the dep array. Both could re-fire indefinitely when the
selected slice changed shape on each render — which the chat page hit
on first load.
chat-settings-sheet.tsx (thread settings loader)
- Before: `useEffect(load, [..., threadSettings, ...])` with
`if (!threadSettings) load()` inside. After load, the Zustand
selector returned a freshly-constructed slice, dep changed,
effect re-ran. If anything in between caused threadSettings to
briefly flicker undefined (e.g. a race during initial hydration
or a fast subsequent thread switch), the load fired again and the
cycle repeated.
- After: ref-guarded by activeThreadId — `threadSettingsLoadedRef`
tracks which threadId has been loaded; the effect deps shrink to
`[ragSource.kind, activeThreadId, loadThreadSettings]`, all
stable per-thread, removing the feedback loop.
ingestion-toast-stack.tsx (terminal-job auto-dismiss)
- Before: `useEffect(..., [jobs, dismissedJobs])` with
`setDismissedJobs(prev => new Set(prev).add(jobId))` inside the
scheduled setTimeout. Each setter creates a new Set reference;
the dep change re-triggers the effect, which clears and
reschedules timers. Under fast SSE event arrival or a strict-mode
double-mount, the scheduler runs faster than its cleanup and
React caps the depth.
- After: dismissedJobs is read via a ref (kept in sync at the top
of the component); the effect only depends on `[jobs]`. A
`scheduledJobsRef` prevents duplicate timer scheduling for the
same job across multiple effect runs, and the setDismissedJobs
updater no-ops when the job is already dismissed.
No behavior change for the happy path — toasts still auto-dismiss
after DISMISS_DELAY_MS; thread settings still load on first sight
of a thread.
Two errors surfaced by the frontend build (`tsc -b`) after the Phase 4
commit c74fc13eb landed:
src/features/chat/chat-settings-sheet.tsx(449,9): TS2451 — cannot
redeclare block-scoped 'activeThreadId'.
src/features/rag/stores/rag-store.ts(326,9): TS2774 — condition will
always return true since this function is always defined.
Fixes
- chat-settings-sheet.tsx: an `activeThreadId` declaration already
existed near the code-exec section (line 636). Phase 2B's RAG
retrieval-section block introduced a second declaration at line 449.
The earlier one is needed for the Retrieval block; drop the later
redeclaration — downstream code still resolves it via lexical scope.
- rag-store.ts subscribeJob: `get().jobUnsubscribers[jobId]` indexes a
`Record<string, () => void>`. Without `noUncheckedIndexedAccess` TS
infers the result as the function type (never undefined), so
`if (existing)` is always-truthy. Replace with `if (jobId in
get().jobUnsubscribers) return;` — same semantics, satisfies TS.
Promotes RAG to a first-class composer toggle alongside Think / Web
Search / Code, with tool-use semantics on local models that support
tools and a pre-fetch fallback on external providers. The model
decides when to call `search_knowledge_base` on local inference; on
external providers retrieval still fires before each message (the
existing pre-fetch path), gated on the same button.
Backend
- core/rag/tool.py (new): search_knowledge_base handler + JSON-schema
tool spec. Resolves scope (kb_id wins over thread_id) from the
request's rag_scope, runs retrieve_hybrid + optional rerank, then
hydrates filename / page_number / text from sqlite and formats as
numbered Markdown citations ('[1] file.pdf (page 5): ...') for the
LLM to cite. Empty scope returns a user-facing hint; empty results
return a clear no-match message instead of an empty string.
- core/inference/tools.py: SEARCH_KNOWLEDGE_BASE_TOOL added to
ALL_TOOLS (lazy import keeps tools.py importable on inference
paths that never touch RAG). execute_tool() gains a tool_context
parameter that carries per-request extras the LLM doesn't see
(currently just rag_scope). The new 'search_knowledge_base' branch
dispatches to the handler with scope unpacked from tool_context.
- core/inference/llama_cpp.py + safetensors_agentic.py +
orchestrator.py: thread tool_context through generate_chat_completion_
with_tools / run_safetensors_tool_loop / execute_tool. Both local
backends (GGUF llama-server and safetensors agentic) carry the same
context object.
- models/inference.py: ChatCompletionRequest gains optional
rag_scope: dict ({kb_id?, thread_id?, enable_rerank?, default_top_k?,
reranker_model?}). Ignored unless 'search_knowledge_base' is in
enabled_tools.
- routes/inference.py: both the GGUF and safetensors call sites for
generate_chat_completion_with_tools forward payload.rag_scope into
tool_context.
Frontend
- chat-runtime-store.ts: global ragToolEnabled boolean + setter +
CHAT_RAG_TOOL_ENABLED_KEY localStorage, mirroring toolsEnabled /
codeToolsEnabled. Settings-hydration migration auto-flips
ragToolEnabled=true for pre-Phase-4 users who already had ragSource
set, so existing RAG users don't silently lose retrieval on upgrade.
- shared-composer.tsx: new 'RAG' pill button after Images (uses
lucide BookOpenIcon, composer-pill-btn style, data-active toggle).
Disabled when no model is loaded. Toggling on from ragSource='off'
auto-flips source to 'thread' so the sidebar lands ready-to-go.
- chat-adapter.ts:
* The existing pre-fetch block is now gated on ragToolEnabled AND
only fires when the tool path isn't viable (external provider OR
local model without tool-use support). Tool-capable local models
skip pre-fetch and let the LLM decide.
* The local-model body assembly adds 'search_knowledge_base' to
enabled_tools and packs ragSource + enableRerank + ragTopK into a
rag_scope object the backend tool handler consumes.
- chat-settings-sheet.tsx: entire Retrieval CollapsibleSection is
wrapped in {ragToolEnabled && ...} so it hides when the button is
off — the button is now the single on/off control. The 'Off'
option is removed from the Source dropdown (the button handles
that). Default open when shown so settings are one click away.
Tests
- test_rag_tool_handler.py: handler covers empty query, missing
scope, kb_id > thread_id precedence, thread-only path, citation
formatting (numbered + page numbers + unknown source); tool spec
shape (function/name/required); execute_tool dispatch with and
without tool_context; ALL_TOOLS includes the new spec without
dropping the existing ones.
Verification scope
- Local GGUF with tools: toggle button on, upload doc, ask about
doc content → assistant emits a search_knowledge_base tool call
card (rendered by the existing ToolFallback component since no
custom UI exists yet — that's a v2 nice-to-have).
- External provider (Anthropic / OpenAI / etc.): same button, same
UX, but uses the pre-fetch path under the hood.
- Migration: pre-existing ragSource != off → button initializes ON
so retrieval keeps working.
Threads can now opt into late chunking or multimodal mode independently
of the KBs they reference. Per-thread settings persist in chat_settings
under thread:<id>:rag and fall back to the app-level defaults when the
thread hasn't set anything explicitly.
Backend (routes/rag.py)
- ThreadRagSettings / UpdateThreadRagSettingsRequest Pydantic models.
- GET/PUT /api/rag/threads/{thread_id}/settings backed by
chat_settings (upsert_chat_settings_merge). Same (multimodal, late)
constraint enforcement as the create + defaults endpoints.
- POST /api/rag/threads/{thread_id}/reingest now accepts the same
body shape — if any field is set, the new settings are persisted
via set_thread_rag_settings BEFORE the reingest, so subsequent
uploads pick up the change too.
- upload_thread_document reads the per-thread settings and passes
them through to _start_ingestion, replacing the previous hard-coded
('standard', 'text', RAG_EMBEDDING_MODEL) defaults.
Frontend
- rag-api.ts: ThreadRagSettings type + getThreadRagSettings /
setThreadRagSettings wrappers. reingestThreadDocuments now accepts
optional UpdateThreadRagSettingsRequest opts.
- rag-store.ts: threadSettings map keyed by threadId, plus
loadThreadSettings / updateThreadSettings actions. reingestThread
refreshes the local settings copy when opts were supplied.
- chat-settings-sheet.tsx Retrieval section: when source = thread,
shows side-by-side Mode + Chunking selects above the documents
list. Selecting a different value:
- persists immediately if the thread has no docs
- prompts "Re-index N documents?" if docs exist; on Yes calls
reingestThread with the new opts, on No reverts the select
The (multimodal, late) constraint is enforced via per-option
disabled + tooltip, matching the KB create dialog.
Power users can now set their preferred chunking strategy / mode /
embedder once in Settings → Knowledge Bases and have new KBs use those
values by default, instead of toggling on every create.
Backend
- routes/rag.py:
- GET /api/rag/defaults returns the stored RagDefaults (or sensible
fallbacks when nothing is set: standard / text / null embedder).
- PUT /api/rag/defaults is PATCH-style — only fields present in the
body overwrite. The (multimodal, late) constraint is enforced
here too, so users can't poison the defaults with a combination
the create path would reject.
- Persistence reuses the existing chat_settings store via
upsert_chat_settings_merge; the values live under a single
rag.defaults key as a nested JSON dict.
Frontend
- rag-api.ts: getRagDefaults / setRagDefaults wrappers + RagDefaults
+ UpdateRagDefaultsRequest types.
- rag-store.ts: defaults state, loadDefaults / updateDefaults
actions. loadDefaults swallows errors so a missing endpoint just
leaves defaults null.
- rag-defaults-section.tsx (new): self-contained mode + strategy +
embedding-model controls, persists on change. Used in the Settings
KB tab below the ThreadIndexList section.
- knowledge-bases-tab.tsx: mounts RagDefaultsSection below thread
indexes with a separator.
- kb-create-dialog.tsx: loads defaults on open and prefills the form
with them (falls back to hard-coded standard / text when defaults
haven't loaded yet). reset() returns to the latest defaults rather
than the hard-coded ones.
Adds a floating progress-card stack mounted at the app root that
watches useRagStore.jobs and renders one card per in-flight ingestion
job, regardless of which page the user is on. Wraps the existing
IngestionProgress component so the progress UI stays consistent with
the per-doc chips in the KB detail panel and chat sidebar.
- Terminal cards (complete/error) linger for 4s then auto-dismiss.
- A manual dismiss button is always available.
- Reduced-motion preference is respected (no slide animation).
- Positioned bottom-right, z-50, pointer-events-none container so
clicks pass through to the page underneath.
Mounted in app/routes/__root.tsx next to the existing SettingsDialog
so it's visible across every authenticated route. Closes the last
deferred item from Phase 2.
Closes the upgrade-path gap from Phase 3: a KB or thread whose chunks
were ingested under one strategy can now be rebuilt under a different
one without losing the uploaded files.
Backend
- routes/rag.py:
- POST /api/rag/knowledge-bases/{kb_id}/reingest takes optional
chunking_strategy / mode / embedding_model in the body. Validates
the (multimodal, late) constraint via _validate_mode_combo, updates
the rag_knowledge_bases row, wipes scope artifacts (sqlite chunks
via cascade, Qdrant collection, bm25), and re-INSERTs a fresh
rag_documents row + ingestion job per stored file. Returns the new
job IDs so callers can stream progress via the existing SSE.
- POST /api/rag/threads/{thread_id}/reingest is the simpler thread
variant — no body, rebuilds with current defaults.
- Shared _reingest_scope helper strips the UUID upload prefix when
re-naming docs so users see the original filenames again.
Frontend
- rag-api.ts: reingestKnowledgeBase(kbId, opts) and
reingestThreadDocuments(threadId) wrappers + ReingestResponse type.
- rag-store.ts: reingestKB / reingestThread actions refresh the KB +
doc lists and subscribe to every returned job so the existing
IngestionProgress chips render without further wiring.
- kb-reconfigure-dialog.tsx (new): mirrors KBCreateDialog but
pre-fills with the KB's current strategy / mode / embedder, enforces
the same (multimodal + late) constraint with disabled options, and
confirms before submitting. Submit label flips between "Re-index"
(no settings change) and "Reconfigure & re-index".
- kb-detail-panel.tsx: header gains the chunking + mode summary and
a "Reconfigure…" button that opens the dialog. Button is disabled
when the KB has no documents.
- chat-settings-sheet.tsx Retrieval section: "Re-index" button beside
the existing "Clear thread index" when the thread has documents.
Tests
- test_rag_reingest.py: ReingestKBRequest accepts optional fields,
rejects unknown enum values via Pydantic, and the shared mode-combo
guard still bites on the reingest path.
When a KB has mode = 'multimodal', ingestion extracts images alongside
text and embeds both into a shared 512-d vector space via BGE-VL-base.
Image hits become first-class search results — useful for slides,
reports, and diagrams where text-only retrieval loses ~30-50% of the
content.
Backend
- embeddings.py: new encode_images(image_bytes_list) — opens bytes via
PIL and routes to the SentenceTransformer (BGE-VL accepts PIL images
in the same encode call as text).
- ingestion.py: _subprocess_worker gains document_id arg and a new
_stream_image_chunks() helper. For multimodal KBs the standard text
chunking runs first, then images are saved to
rag_uploads_root() / 'images' / <document_id> / img-NNNN.<ext> and
embedded; for each image with an adjacent caption, both an
'image'-kind chunk (vector = encoded image) and a 'caption'-kind
chunk (vector = encoded caption text) are streamed back with a
shared pair_group field.
- ingestion.py parent: _insert_chunks_and_collect_for_bm25 now reads
kind / image_path / pair_group from the subprocess message,
populates the new rag_chunks columns, and runs a second pass that
sets linked_chunk_id for each image ↔ caption pair. BM25 indexes
text + caption chunks only — image chunks have no tokenisable body.
- retrieval.py: Hit gains a `kind` field plumbed through bm25, dense,
RRF, and rerank paths.
- reranker.py: image-kind hits skip CrossEncoder rerank (text-only
model) but are appended back in their original relative position
rather than dropped.
- routes/rag.py: new GET /api/rag/images/{document_id}/{filename}
static-file route with realpath containment check. SearchHit gains
`kind` and `image_url` fields so the chat UI can render image
thumbnails alongside text hits. KB-doc upload threads kind/mode
through to ingestion.
Frontend
- rag-api.ts: SearchHit gains optional `kind` and `image_url`.
- kb-create-dialog.tsx: new Mode select (Text / Multimodal) alongside
the existing Chunking strategy select. The forbidden
(multimodal + late) combo is enforced in the UI — each side
disables the conflicting option on the other side with a tooltip
explaining why. Embedding-model placeholder cycles through the
three valid defaults (bge-small / nomic / BGE-VL).
- kb-list.tsx + chat-settings-sheet.tsx: 🖼️ MM badge alongside the
⚡ Late one so multimodal KBs are obvious at a glance.
Tests
- test_rag_multimodal.py: parser returns images when want_images=True
and skips them when False; _validate_mode_combo rejects the
forbidden (multimodal, late) pair with 400; RAG_EMBEDDER_MATRIX
contains the three valid combos and excludes the forbidden one;
image URL construction shape is verified. A server-marked test
loads BGE-VL-base end-to-end and confirms image + text vectors
share the same dimension.
Phase 3 of the plan is now feature-complete on the backend; the
remaining items (re-ingest UX for changing strategy on existing KBs)
are tracked under "Backfill UX" and can land separately.
When a KB has chunking_strategy = 'late', ingestion takes a separate
code path that embeds the full document in a single forward pass and
mean-pools token embeddings per chunk span. Each chunk vector carries
full-document context via the encoder's bidirectional attention —
Jina's published technique, ~+6.5 nDCG@10 on long docs.
Backend
- chunking.py: new chunk_pages_with_spans() that joins all pages into a
single full_doc, runs the existing recursive splitter, and returns
per-chunk (char_start, char_end) offsets. Page-number metadata is
recovered by overlap with the original page ranges so PDF citations
still work. Existing chunk_pages() unchanged.
- embeddings.py: new late_chunk_encode(doc_text, char_spans). Tokenizes
the doc with return_offsets_mapping, runs the underlying transformer
to get per-token last_hidden_state, then mean-pools per chunk span.
When the doc exceeds the embedder's context, falls back to windowed
late chunking with a 512-token overlap so cross-window context is
partially preserved.
- ingestion.py _subprocess_worker: branches on chunking_strategy.
'late' path: chunk_pages_with_spans -> late_chunk_encode -> one big
chunks_batch message. 'standard' path unchanged. Both reuse the same
parent-side pump.
- ingestion.enqueue_ingestion: new chunking_strategy + mode kwargs;
defaults to 'standard' / 'text' for legacy callers. embedder model
resolved via resolve_embedder() from the (mode, strategy) matrix.
- routes/rag.py: KB-doc upload reads chunking_strategy + mode from the
KB row (defensive .get for pre-Phase-3 schemas) and threads them
through _start_ingestion.
Frontend
- kb-create-dialog.tsx: new "Chunking strategy" select with Standard /
Late options. Embedding-model placeholder switches to nomic when
Late is picked. createKB request now carries chunking_strategy.
- kb-list.tsx + chat-settings-sheet.tsx: small "⚡ Late" badge next to
late-chunking KB names in the settings KB list and the chat sidebar
dropdown so users see the mode at a glance.
Tests
- test_rag_late_chunking.py: pure-python tests for chunk_pages_with_spans
(chunks index back into full_doc; page numbers inherited by overlap;
pages joined with blank line). A server-marked test loads
all-MiniLM-L6-v2 to exercise late_chunk_encode end-to-end.
No multimodal yet; that's Phase 3B-multimodal (next PR).
Phase 3 lays two orthogonal per-KB knobs in the data and API layers so
follow-up commits (Phase 3B-late, Phase 3B-multimodal) only need to add
their code path and UI selector, not schema or types.
Schema (studio/backend/storage/studio_db.py)
- rag_knowledge_bases gains chunking_strategy ('standard'|'late', default
'standard') and mode ('text'|'multimodal', default 'text'). Both are
immutable after KB creation — changing either invalidates existing
chunks because they were ingested through a specific pipeline.
- rag_chunks gains kind ('text'|'image'|'caption', default 'text'),
image_path (NULLABLE), linked_chunk_id (NULLABLE) — used by Phase
3B-multimodal to pair image chunks with their captions.
- Idempotent ALTER TABLE additions for existing installs (mirrors the
chat_threads display_name / *_code_exec_container_id pattern earlier
in the file).
API (studio/backend/routes/rag.py)
- ChunkingStrategy + KBMode Literal aliases.
- CreateKBRequest accepts both fields with backward-compat defaults.
- KBResponse exposes both.
- _validate_mode_combo rejects (multimodal, late) with 400 — no public
open-weight embedder supports both at once. Surface the constraint
early rather than failing silently during ingestion.
Config (studio/backend/utils/rag/config.py)
- RAG_EMBEDDER_MATRIX dict keyed by (mode, strategy) → embedder name.
- resolve_embedder() helper falls back to RAG_EMBEDDING_MODEL for legacy
KBs that pre-date the columns.
- (multimodal, late) intentionally absent.
Frontend (studio/frontend/src/features/rag/)
- api/rag-api.ts: ChunkingStrategy + KBMode types; KnowledgeBase
interface and createKnowledgeBase request type updated.
- stores/rag-store.ts: createKB signature uses the shared request type.
No user-visible UI changes yet — the only currently-usable combination
is (text, standard), so adding one-option selectors would be UX noise.
Phase 3B-late and Phase 3B-multimodal each add the relevant selector
option as part of shipping the code path.
Replace bare-pypdf/python-docx/BeautifulSoup extraction with Markdown-
preserving parsers so the chunker can split on real heading boundaries
instead of running paragraphs together.
Parsers
- pdf.py: pymupdf + pymupdf4llm.to_markdown() per page; pypdf kept as
fallback when pymupdf can't open the file.
- docx.py: mammoth.convert_to_html() + markdownify, with an explicit
style_map so Title/Heading 1..6 become h1..h6 in the output.
- html.py: BeautifulSoup pre-scrub (drop script/style) then markdownify
so <h*>, <table>, <ul> convert faithfully.
- text.py: signature update only; TXT/MD pass through unchanged.
- parsers/__init__.py: new ParsedImage + ParseResult dataclass; parse()
signature is now parse(path, *, want_images=False) -> ParseResult.
ParseResult is iterable over .pages for backward compat.
Chunker
- chunking.py: prepend Markdown heading separators ("\n# " .. "\n#### ")
to the priority list so heading-aware splits happen for free once the
parsers emit Markdown.
Ingestion
- ingestion.py: single call site updated to consume ParseResult.pages.
Deps (no-torch-runtime.txt)
+ pymupdf>=1.24, pymupdf4llm>=0.0.17, mammoth>=1.7, markdownify>=0.13
- pypdf kept as a fallback path.
Tests
- test_rag_parsers.py asserts Markdown headings survive PDF/DOCX/HTML
extraction; also exercises ParseResult iteration backward-compat.
- test_rag_chunking.py: new case verifying chunks start at Markdown
heading boundaries when the input is Markdown.
Foundation for Phase 3B-late (heading-aware spans for late chunking)
and Phase 3B-multimodal (want_images=True enables image extraction in
the same parser layer). No schema or opt-in flags in this commit.
Bundles three independent CI regressions hitting the maintainer PR
backlog. Each one is verified end-to-end on a staging fork against
real Ubuntu / macOS / Windows GitHub-hosted runners before this
lands.
1. Windows --no-torch install: pydantic + pydantic-core drift to
incompatible versions under `uv pip install --no-deps -r
no-torch-runtime.txt` because pip resolves each independently
from latest. pydantic.VERSION 2.13.4 pins pydantic-core==2.46.4
but pydantic-core 2.47.0 was the freshest published wheel, so
`import pydantic` raised
`SystemError: pydantic-core 2.47.0 is incompatible with the
current pydantic version`. Resolve pydantic WITH deps in a
focused pip call (install.sh, install.ps1,
install_python_stack.py) before the --no-deps no-torch-runtime
pass so pip pins pydantic-core to the version pydantic declares.
pydantic's transitive deps (annotated-types, pydantic-core,
typing-extensions, typing-inspection) are torch-free. Drop the
redundant `Patch Studio venv with full typer / pydantic dep
trees` workaround from the four Windows smoke YAMLs.
Supersedes #5733 + #5734.
2. Linux Studio Update CI: upstream llama.cpp b9261+ split each
binary's entry code into a paired `libllama-<binary>-impl.so`
shared library. `llama-server` and `llama-quantize` NEEDED-link
against `libllama-server-impl.so` / `libllama-quantize-impl.so`
with RUNPATH `$ORIGIN`, so the prebuilt overlay must copy those
alongside the binaries. Without that, ldd reports them missing,
preflight rejects, the installer falls back to source build, and
studio-update-smoke annotates `setup.sh idempotency regressed`.
Add `libllama-*-impl.so*` to the Linux runtime patterns and lock
the pattern in test_rocm_support.TestRuntimePatterns.
3. Mac Studio UI Chat: change-password submit clicked while
disabled. The disable gate only checked new + confirm password
length, but Playwright's first click landed before the
current-password field's React state had committed, so the form
was simultaneously logically-invalid (current_password empty) and
the button was disabled. Tighten the gate to require
`currentPassword.length >= 8` and mirror the same check in the
submit handler so Enter / autofill cannot bypass.
Supersedes #5738.
The pill wired the request end of the loop but the response was lost
on the client: the backend emits a `tool_end` _toolEvent carrying the
base64 PNG on `image_b64` / `image_mime`, but the chat-adapter only
read the `result` string and the generic ToolFallback printed the
prompt as JSON args with an empty Result block -- the "I see no
image" symptom in the chat.
- chat-adapter: when the closing `tool_end` is for `image_generation`,
repackage `image_b64` + `image_mime` (+ size/quality/background)
into a structured result object instead of dropping them.
- New `ImageGenerationToolUI` reads that result and renders the image
inline via `<img src="data:image/...;base64,...">` with the prompt
as a caption. Falls back to a spinner while the request is still
running.
- Register the component under `image_generation` in thread.tsx's
tools.by_name map so it preempts ToolFallback for this tool only.
#5685 wired the backend to honor `prompt_cache_ttl` on the request,
but there was no UI to actually pick it -- every Studio chat ended up
on Anthropic's default 5 minute pool. This adds a Cache TTL selector
to the chat settings sheet's Provider section, visible only when the
provider supports the choice (Anthropic today) and Prompt caching is
on.
- New `promptCacheTtl?: "5m" | "1h"` on `ExternalProviderConfig`.
Normalizer drops the field on providers that don't support the
choice so localStorage stays clean across provider swaps.
- `supportsProviderPromptCacheTtl` + `isPromptCacheTtl` helpers so
the picker, normalizer, and adapter all agree on which values are
valid.
- Settings sheet renders a small Select (5 minutes / 1 hour) right
under the Prompt caching switch when the toggle is on; flipping
it persists on the provider config like the other per-provider
knobs.
- chat-adapter passes `prompt_cache_ttl` on outbound requests when
the value is valid; omitted otherwise so the backend keeps
inheriting Anthropic's 5m default.
The backend already wires OpenAI's Responses-API image_generation
server tool: when `enabled_tools` carries "image_generation" on an
OpenAI cloud request, _stream_openai_responses appends
`{type: "image_generation"}` to the request's tools array and emits
`image_generation_call` output items back to the assistant stream
(see backend/core/inference/external_provider.py and
backend/tests/test_openai_image_generation.py for the round-trip).
This wires the frontend half so a user can actually opt into it from
the composer next to the Search and Code pills, instead of the tool
sitting dormant.
- `providerSupportsBuiltinImageGeneration` gates on OpenAI cloud
(`api.openai.com`) + a Responses-API model prefix (gpt-5.x, o3).
Mirror of the backend's `is_openai_cloud` guard so the pill is hidden
on custom OpenAI-compat backends (ollama / llama.cpp / vLLM) that
report `provider_type="openai"` but would 400 on the tool.
- New `imageToolsEnabled` flag in chat-runtime-store, persisted under
`unsloth_chat_image_tools_enabled` and reset on model change in
chat-page exactly like `codeToolsEnabled`.
- `chat-adapter` appends "image_generation" to `enabled_tools` and
flips `enable_tools: true` when the pill is on, so the existing
backend dispatch picks it up.
- Composer renders an Images pill (lucide `ImageIcon`) immediately
after the Code pill, only when the active model advertises the
capability. The in-thread composer (assistant-ui/thread.tsx) gets
the matching `ImagesToggle` for parity.
The first pass only wired the localStorage mirror into `setCheckpoint`,
but the main chat-page picker actually selects an external model by
calling `setParams({ ...store.params, checkpoint: value })`. That path
never hit `setCheckpoint`, so the persisted slot stayed empty and a
refresh fell back to whatever `/api/inference/status.active_model`
returned -- the previously loaded local model (Qwen3.5 etc) or null
("Select model") when nothing was loaded locally.
Mirror the persistence in `setParams` whenever the checkpoint changes
so every entry point converges on the same behavior. `setCheckpoint`
still does it directly so the load path (compare, GGUF auto-load,
gemma fallback in chat-adapter) keeps working.
* Add Anthropic prompt guards for disabled tools
* fix: merge Anthropic tool guard into structured system prompts
* fix: scope Anthropic disabled-tool guard wording
* chore: adjust claude guard prompt
* chore: add openai to list of prompt guarded providers
* Studio: include web_fetch in the per-turn disabled-tool guard
Add webFetchEnabledForThisTurn alongside webSearchEnabledForThisTurn
and codeExecEnabledForThisTurn. Use it in the enabled_tools payload
so web_fetch follows the Search pill the same way web_search does,
and mention "web fetch" in the disabled-tool guard prose on providers
that ship the tool (Anthropic today; other providers stay inert via
providerSupportsBuiltinWebFetch).
---------
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Selecting a connected external provider (Anthropic, OpenAI, Google, etc.)
and refreshing the page reverted the picker back to no selection. Root
cause is that `PersistedInferenceParams` in `chat-settings-api.ts`
excludes `checkpoint` from the server-side settings payload by design.
Local model selections survive refresh because the backend re-derives
them from `/api/inference/status.active_model`, but external selections
have no backend mirror, so they were lost.
Fix: persist `external::*` checkpoints to a small dedicated
`localStorage` key (`unsloth_chat_last_external_checkpoint`) and hydrate
from it on store init. Local checkpoints continue to come from the
backend status as before; only external ids are mirrored client-side.
`setCheckpoint` writes the key when an external id is selected and
clears it when switching back to a local id, and `clearCheckpoint`
clears it so the picker does not snap back after an explicit reset.
Deleting a connection in one browser left the same connection stuck in
every other browser/tab. The user could not delete or edit it from there
because the local state never caught up with the server, and clicks
either no-op'd or threw on a missing-row backend response.
Two pieces caused the bug:
1. `ChatProvidersSettings` ran its backend sync once on mount and then
silently kept localStorage providers whenever `listProviderConfigs`
returned an empty array, on the assumption that an empty server
response had to be a transient glitch. That assumption is wrong when
another browser removed the last connection. With the guard gone,
trust any successful API response, including an empty list. A focus /
visibilitychange listener now triggers a silent re-sync so the dialog
does not need to be closed and reopened to pick up remote deletes.
2. `deleteProviderConfig` threw on HTTP 404, so once Browser A deleted a
connection, Browser B's "Delete" click failed and the local row stuck
around. Treat 404 as success: the server's job is already done and
the local cache only needs to be pruned.
* Studio: PDF / document attachments for Anthropic + OpenAI
Studio's local-GGUF chat already supports image attachments via the
`image_url` content part shape. PDFs and other documents had no
plumbing for the external-provider path: there was no normalised
content type the frontend could send that translated to Anthropic's
native `document` block or OpenAI's `input_file`.
Add a Studio-side `input_document` content part on assistant /
user messages with three shapes:
{type: "input_document",
file_data: "data:application/pdf;base64,<DATA>",
filename?: "name.pdf",
media_type?: "application/pdf"}
{type: "input_document",
file_url: "https://example.com/doc.pdf",
filename?: "doc.pdf"}
Translation:
- Anthropic Messages API: emits a `document` block with
`{source: {type:"base64", media_type, data}}` or
`{source: {type:"url", url}}`, plus an optional `title` from
`filename`. PDFs are extracted server-side by Anthropic per their
vision/document docs and counted toward input tokens.
- OpenAI Responses API: emits `{type:"input_file", file_data |
file_url, filename?}`. PDFs are extracted server-side.
Empty / unparseable `input_document` parts are silently dropped so
a malformed frontend payload can't blow up the request.
Tests:
- New `test_multimodal_document.py` with 6 cases pinning the
outbound body shape for base64 + URL inputs on both providers,
and the empty-part drop behavior on both.
- The Anthropic assertions strip the prompt-cache wrapper
(`cache_control:{type:ephemeral}` that the tail-message caching
layer adds) before comparing the document core fields, so this
test stays focused on the translation, not the caching layer.
Live verified end-to-end against both providers: a 363-byte
single-page "HELLO" PDF, base64-encoded, attached as a `document`
block to Opus 4.7 and as an `input_file` to gpt-5.5. Both models
correctly extracted the word "HELLO" from the PDF.
Follow-up (out of scope):
- Pydantic schema entry on ChatMessage.content for `input_document`
(today it rides through because ChatCompletionRequest uses
extra=allow). Will tighten when the frontend attach button lands.
- Frontend file-picker UX for non-image attachments on the external
provider path.
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* Address review: gate empty-content msg + skip empty data-URI payload
Gemini High + Codex P2 on PR #5689:
1. Anthropic translation appended an empty `anthropic_parts` array
when every part was dropped (e.g. user sent only an unparseable
input_document). Anthropic 400s on "messages.N.content: at least
one block is required". Skip the whole-message append when no
parts survived. The OpenAI Responses path already had the
equivalent guard, so this brings the two providers into parity.
2. `data:application/pdf;base64,` with no payload (or whitespace-only)
parses to an empty `source.data` string. Anthropic rejects that
with 400 as well. Skip the document block before constructing it.
Plus 2 new test cases pinning both behaviors:
- `test_anthropic_empty_only_document_drops_whole_message`: confirms
a turn whose only content is an unparseable input_document does
NOT make it onto the outbound `messages` array.
- `test_anthropic_empty_data_uri_payload_is_dropped`: confirms an
empty-payload data-URI is filtered out at translation time.
(Note re: gemini's other High note about adding `input_document` to
the Pydantic ContentPart union -- ChatCompletionRequest is configured
with `extra=allow` so the part rides through today. Tightening the
union belongs with the frontend attach-button PR that surfaces the
field; called out as follow-up in the PR description.)
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* Address review: register input_document in ContentPart + builder
Reviewer caught that the translation code on the external_provider
side was unreachable from a real ChatCompletionRequest:
- ContentPart is a discriminated Union of (text, image_url) only, so
any `{"type": "input_document", ...}` part was rejected by Pydantic
at request parsing with a discriminator error before the helper
could see it.
- _build_external_messages in routes/inference.py only walked text
and image_url parts, so even with a permissive schema the document
parts would have been silently dropped instead of forwarded to
the per-provider translator.
Fixes:
- Add InputDocumentContentPart with optional file_data / file_url /
filename / media_type and Tag("input_document") on the Union.
- Extend _build_external_messages to pass input_document through as
a plain dict for vision-capable providers (so external_provider's
existing Anthropic `document` and OpenAI Responses `input_file`
mappers actually run) and strip them on non-vision providers.
Tests added: schema accepts input_document, builder passes it to
vision providers, builder strips it on non-vision providers.
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* Address review: validate file_data before preferring over file_url
Codex P2 caught that the OpenAI input_document translator treats any
truthy file_data as valid and never falls back to file_url. That
means a malformed `data:application/pdf;base64,` (empty payload) or
a whitespace-only data URI gets forwarded as `file_data=""` and
400s the whole turn, AND silently discards a perfectly recoverable
file_url on the same part.
Mirror the Anthropic-side guard onto the OpenAI Responses path:
treat any "data:" URI with no actual base64 payload as missing and
fall through to file_url. Standalone-empty data URIs (no fallback)
are dropped entirely instead of being sent to the wire.
Tests added: empty data URI + valid file_url -> file_url wins,
whitespace-only data URI + valid file_url -> file_url wins,
empty data URI without fallback -> part is dropped.
* Address review: Anthropic side also falls back to file_url on empty data URI
Codex P2 follow-up to my earlier fix: I added the empty-data-URI ->
file_url fallback to the OpenAI Responses translator but missed
the Anthropic translator, which still `continue`d on empty payloads
and discarded an otherwise valid file_url on the same part. Result:
when the frontend supplied both file_data (placeholder / broken)
AND a working file_url, Anthropic silently lost the attachment;
when the message contained only that part, the whole message could
be dropped before reaching the wire.
Mirrored the OpenAI guard: any "data:" URI with no actual base64
payload (`data:application/pdf;base64,` or whitespace-only) is
treated as missing, and the file_url branch takes over. The
all-parts-dropped guard further down already handles the
no-fallback case.
Tests added: empty data URI + valid file_url -> URL source on the
wire with the filename preserved; whitespace-only data URI + valid
file_url -> URL source on the wire.
* Address review: gate input_document passthrough to anthropic + openai
Codex P1: only `_stream_anthropic` and `_stream_openai_responses`
have explicit translation logic for input_document parts (the former
maps to {type:"document", source:...}, the latter to
{type:"input_file", file_data|file_url}). Every other provider
(gemini / mistral / kimi / openrouter / deepseek / qwen / custom)
goes through the generic /chat/completions passthrough that forwards
`messages` verbatim, so any input_document part on a non-vision
route on those providers would 400 with an unknown content_part
type.
Added `_INPUT_DOCUMENT_PROVIDERS = frozenset({"anthropic", "openai"})`
constant and gated the pass-through branch on `provider_type in
_INPUT_DOCUMENT_PROVIDERS`. Every other provider strips the part
(text content survives). Threaded provider_type through from
_proxy_to_external_provider's call site.
Tests updated: vision + provider in {anthropic, openai} still
forwards; six unmapped providers (gemini/mistral/kimi/openrouter/
deepseek/qwen) strip the part; missing provider_type strips
defensively. The existing non-vision drop test still passes.
* Fix stale web_fetch tool-version assertion after merging main
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: wire OpenAI Responses server-side context compaction
The OpenAI Responses API accepts a `context_management` field that
enables server-side compaction. When the rendered prompt crosses the
configured threshold, the API runs a server-side compaction step and
the request continues against the compacted prefix. No beta header
and no dated version pin are required, per the docs.
Changes:
- Add `compaction_threshold: Optional[int]` (ge=1_000, le=2_000_000)
to ChatCompletionRequest. Thread through `routes/inference.py` ->
`stream_chat_completion` -> `_stream_openai_responses`.
- In `_stream_openai_responses`, when threshold is set AND the base
URL points at cloud OpenAI (api.openai.com), attach
`context_management: [{type:"compaction", compact_threshold:N}]`
to the outbound body. Non-cloud bases (ollama, llama.cpp, "custom"
presets) silently drop the field so we don't 400 those servers.
- Add `test_openai_compaction.py` with 4 cases: cloud OpenAI sets
the field verbatim, low-threshold probe passes through (we don't
clamp on the OpenAI side because the API accepts whatever),
non-cloud base drops the field, omitted threshold leaves body
untouched.
Live verified against the real OpenAI API on gpt-5.5:
`context_management:[{type:"compaction", compact_threshold:200000}]`
returns 200 with no error.
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* Address review: accept Azure OpenAI base URLs + raise compaction floor
Two reviewer follow-ups on the OpenAI compaction PR:
1. The `is_openai_cloud = "api.openai.com" in self.base_url` check
excluded Azure OpenAI Foundry, even though Azure exposes the
same /v1/responses extensions (context_management,
prompt_cache_retention, container shell). Users on Azure saw
their compaction toggle silently no-op. Broadened the check to
also match `*.openai.azure.com` and made it case-insensitive so
URLs copy-pasted from the Azure portal still resolve. Non-cloud
OpenAI-compatible servers (ollama / llama.cpp / vLLM / "custom"
preset) still fall outside the gate.
2. The schema floor on compaction_threshold was ge=1_000, which is
well below the upstream Responses API's effective minimum
(vercel/ai#12486, langchain-ai/langchain#35464 report
`compact_threshold is not enabled` 400s on Azure at 100k; cloud
uses 200k as the canonical example). Raised the floor to 10k
so obvious typos surface as a clean 422 from FastAPI rather than
an opaque upstream 400 the user has to debug from the SSE
stream.
Tests added: Azure base URL carries both context_management and
prompt_cache_retention; mixed-case Azure URLs match; schema rejects
9_999 and accepts 10_000.
* Address review: drop schema-level compaction floor (cross-provider regression)
Codex P2 follow-up on the previous floor bump: ge=10_000 was
enforced globally at the ChatCompletionRequest layer, but the field
is documented as a no-op on every non-cloud OpenAI base and every
non-OpenAI provider. With the global floor, an Anthropic / ollama
/ llama.cpp / custom request that happens to carry compaction_threshold
below 10k was rejected with 422 at request validation time instead
of being silently ignored as the description promised.
Reverted the schema floor to ge=1 (any positive int) and rewrote
the description to call out per-provider routing: OpenAI cloud's
effective floor is around 200k and surfaces upstream 400s below
that; _stream_anthropic clamps sub-50k values up. Per-provider
helpers stay the single source of truth on the floor.
Test updated to pin: zero is still rejected, but every positive
value (1, 5_000, 9_999, 10_000, 200_000) passes schema validation.
* Address CodeQL: hostname-anchored OpenAI cloud detection
CodeQL py/incomplete-url-substring-sanitization fired on
`".openai.azure.com" in _base`. An attacker who controls the
configured base_url could slip cloud-only request body fields
(prompt_cache_retention, context_management compaction, container
shell) to an arbitrary server with:
https://evil.com/api.openai.com/v1https://api.openai.com.attacker.com/v1https://attacker.com/.openai.azure.com/v1https://my-resource.openai.azure.com.attacker.com/openai/v1
Replaced the substring check with a `_is_openai_family_cloud`
helper that runs urllib.parse.urlparse on the URL and matches the
lowercased hostname exactly (`api.openai.com`) or via `endswith`
on the leading-dot suffix (`.openai.azure.com`). Both halves are
host-anchored so path / fake-subdomain bypasses fail.
Test added: every attacker-controlled bypass shape above must NOT
carry context_management OR prompt_cache_retention on the wire.
Existing Azure and openai.com tests still pass.
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* Address review: scope compaction_threshold description to OpenAI on this branch
Codex P2: the field description on this PR mentioned Anthropic
compaction behavior, but the Anthropic wiring lives on PR 5686
(separate branch). On feat/openai-compaction alone, _stream_anthropic
has no compaction_threshold parameter, so the field is silently
ignored for Anthropic requests and the doc claim was misleading.
Trimmed the description to OpenAI cloud + Azure Foundry only on
this branch. PR 5686 already re-adds the Anthropic clause via its
own change, so the rebase / merge order on main will land the
combined description naturally once both PRs ship.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: wire Anthropic server-side context compaction
Anthropic ships server-side context compaction as a beta
(`compact-2026-01-12`). When the rendered prompt crosses the
configured input-token threshold, Anthropic runs an extra LLM pass
that summarises older turns and the request continues against the
compacted prefix. The response carries the original top-level fields
plus a new `context_management` block (with `applied_edits`) and
`usage.iterations[]` accounting per pass.
Per the docs the feature is currently supported on Opus 4.6, Opus 4.7,
Sonnet 4.6, and Mythos preview. The minimum threshold is 50k tokens;
under-50k requests 400.
Changes:
- Add prefix gate + helper `_anthropic_supports_compaction` plus
constants `_ANTHROPIC_COMPACTION_PREFIXES`, `_ANTHROPIC_COMPACTION_BETA`,
`_ANTHROPIC_COMPACTION_TYPE`, `_ANTHROPIC_COMPACTION_MIN`.
- Add `compaction_threshold: Optional[int]` to ChatCompletionRequest
(50k ge bound, 2M le bound). Thread through `routes/inference.py`
-> `stream_chat_completion` -> `_stream_anthropic`.
- In `_stream_anthropic`, when threshold is set AND the model
accepts compaction, attach `context_management.edits[{type:
"compact_20260112", trigger:{type:"input_tokens", value:N}}]` to
the outbound body. Sub-50k values are clamped up to 50k to keep
the request well-formed.
- Refactor the anthropic-beta header builder to merge any combination
of `code-execution-2025-08-25` + `compact-2026-01-12` flags into
one header value. Unrelated betas added at the registry level still
pass through.
- Add `test_anthropic_compaction.py` with 16 cases: gate matrix
(every doc-listed model), correct body shape, threshold clamping,
beta header merge with code execution, silent no-op on unsupported
models, omitted-threshold pass-through.
Live verified end-to-end against the real Anthropic API:
`compact_20260112` accepted on Opus 4.7, response carries
`context_management.applied_edits` + `usage.iterations[]` as
documented. (The first WebFetch-summarised version of these docs
suggested `compact_20260120`; the actual API only accepts
`compact_20260112`, matching the beta-header date. Worth pinning
behind a test so a future doc update can't drift back.)
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* Address review: drop ge=50_000 clamp + parse usage.iterations[]
Two reviewer follow-ups on the compaction PR:
1. Pydantic ge=50_000 on compaction_threshold was dead code.
FastAPI rejected sub-50k threshold values with a 422 before the
`max(int(...), _ANTHROPIC_COMPACTION_MIN)` clamp in
_stream_anthropic could ever fire. Relaxed the floor to ge=1 so
the in-helper clamp actually does its job; the schema comment
now explains why this is intentional. Added a regression test
that posts a value of 1 and 49_999 through the real request
schema.
2. Anthropic publishes per-iteration token counts in
`usage.iterations[]` whenever a fresh compaction has run, and
the top-level input_tokens / output_tokens cover only the
`message` iteration -- billing must add the compaction
iterations on top. Aggregate compaction iteration tokens into
`last_usage["compaction_input_tokens" / "compaction_output_tokens"]`
so the cost surface (PR 5690) can read them without re-walking
the array, and surface both figures in the closing stream
summary log. Added two tests: one that pins the aggregation on a
compacted turn and one that pins `None` when no fresh
iterations land (so re-applied compaction blocks don't double-bill).
Sourcing: https://platform.claude.com/docs/en/build-with-claude/compaction
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* Address review: round-trip Anthropic compaction blocks across turns
Codex P1: once context_management is enabled and Anthropic runs
server-side compaction mid-stream, the response carries a
`{type:"compaction", content:"<summary>"}` content block on the
assistant message. The translator only handled text_delta and
input_json_delta on content_block_delta, so the compaction block
was silently dropped. Worse, the request schema's ContentPart
discriminated Union didn't accept `type:"compaction"`, and
_build_external_messages didn't pass it through, so even a
hand-crafted assistant message carrying the block would 422 at
parse time. Net result: Anthropic re-compacted from scratch on
every subsequent turn, wasting input tokens and reasoning budget.
End-to-end backend wiring of the round-trip:
1. SSE translator. _stream_anthropic now tracks a `current_compaction`
state slot. content_block_start with type=="compaction" seeds it
(Anthropic may include the summary on the start event AND/OR
stream it via text_delta events on the same block index --
handle both). text_delta inside a compaction block routes into
the compaction buffer instead of the user-visible content
stream, since the summary is opaque internal state, not
assistant prose. content_block_stop emits a `compaction_block`
tool_event carrying the full summary so the chat-adapter can
persist it. compaction_blocks_seen is surfaced in the closing
summary log.
2. Pydantic schema. Added CompactionContentPart with Tag("compaction")
on the ContentPart Union so requests carrying the block parse
cleanly. Required `content` field with a docstring pointing at
the Anthropic docs.
3. Message builder. _build_external_messages forwards compaction
parts on both vision and non-vision paths; the per-provider
stream helper decides whether to forward to the wire (Anthropic
does; other providers ignore the part). When a non-vision route
ends up with a single text part, collapse back to a string
so providers that don't accept content arrays still get the
expected shape.
4. _stream_anthropic outbound translator. {type:"compaction"} parts
on an assistant message land on the wire verbatim. Empty/missing
`content` is skipped so a malformed stored block can't 400
Anthropic.
Tests added (5): stream emits compaction_block tool event with the
summary intact; user-visible content stream does NOT carry the
summary text; outbound body forwards compaction parts verbatim on
the next turn; Pydantic schema accepts the part; builder passes
it through on both vision and non-vision provider routes.
Frontend follow-up: the chat-adapter needs to persist the
compaction_block tool_event onto the stored assistant message so
turn N+1 includes it in payload.messages. Pinned in the PR
description.
Sourcing: https://platform.claude.com/docs/en/build-with-claude/compaction
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* Address review: gate compaction-part passthrough to Anthropic only
Codex P1: my previous round-trip change preserved {type:"compaction"}
parts on every provider route in _build_external_messages. That
meant a chat history with prior compaction state silently leaked
the Anthropic-specific block to OpenAI/DeepSeek/Mistral/Gemini/
Kimi/OpenRouter on a provider switch, where generic
/chat/completions passthrough hands the unknown content type to
the upstream API and 400s the whole turn.
Added a `provider_type` kwarg to _build_external_messages and
gated the compaction forwarder on `provider_type == "anthropic"`.
Every other value (including the legacy None for callers that
don't pass it yet) strips the part. The Anthropic stream helper
still maps it to a native `compaction` block on the wire.
Threaded provider_type through from _proxy_to_external_provider's
call site.
Tests updated: vision + provider="anthropic" still forwards; six
non-anthropic providers strip the part; missing provider_type
strips defensively; non-vision + anthropic still forwards; non-vision
+ non-anthropic collapses back to a text string.
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* feat: Persist chat history in backend storage
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* Address chat tombstone batching review
* fix: update desktop auth routes stub
* chat db settings storage
* chat db settings routes
* chat db settings client
* chat db settings store
* chat db settings wiring
* chat db history storage
* chat db settings migration
* chat db settings fallback
* chat db container metadata
* chat db legacy migration fixes
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* chat ci auth background reads
* chat auth storage fixes
* chat migration final fixes
* chat export batch message lookup
* chat history review fixes
* chat prune sync fix
* chat settings hydration retry
* gate settings persistence
* Scope chat-history rows by subject; fix hijack, clear-confirm, hydrate race
Backend storage and routes:
- chat_threads / chat_messages / chat_settings carry a NOT NULL subject
column with composite PRIMARY KEY (id, subject). Two authenticated
identities can no longer see or wipe each other's data.
- Pre-existing rows on an existing studio.db migrate under sentinel
subject __legacy_unscoped__ via rename + rebuild + copy; single-user
installs see no behavior change.
- ON CONFLICT(id, subject) DO UPDATE ... WHERE chat_messages.thread_id =
excluded.thread_id refuses cross-thread re-parenting via upsert.
upsert_chat_message + sync_chat_messages now raise
ChatMessageThreadMismatch which the routes map to HTTP 409.
- replace_thread_messages rejects body messages whose threadId does not
match the URL thread (HTTP 400) instead of silently rewriting them.
- DELETE /api/chat requires ?confirm=true, returns row count, logs the
subject and count.
- upsert_chat_settings_merge does read + deep-merge + write inside a
single BEGIN IMMEDIATE so concurrent writers no longer drop each
other's updates. The route delegates to this helper.
- New POST /api/chat/messages:batch returns {thread_id -> messages[]}
for many threads in one HTTP call. Subject-scoped. Unknown ids return
empty lists instead of 404 so the sidebar/search caller can rebuild
atomically.
Frontend:
- chat-runtime-store: hydrate-failure catch sets settingsHydrated:true
so a transient backend blip no longer permanently disables
persistence. setParams bumps inferenceParamMutationVersions
unconditionally so a slow hydration response cannot clobber a
pre-hydrate user edit. saveSettingsPatch replaces the serial chain
with a debounced pendingPatch + deep merge; flush on beforeunload.
- chat-history-storage: clearStoredChats returns ClearStoredChatsResult
distinguishing backend / legacy / both outcomes.
listStoredChatThreadsWithMessages uses the batched fetch (one HTTP
call) instead of Promise.all per-thread; legacy Dexie fallback only
fires when the batch result is empty.
- chat-api: batchListChatMessages with graceful 404 / 405 fallback to
per-thread listChatMessages for older servers.
- chat-thread-tombstones: store {id, deletedAt} tuples with 90-day GC
and a 5000-entry cap so localStorage stays bounded. Back-compat reads
pre-fix plain strings. Adds removeChatThreadTombstones (rollback) and
clearAllChatThreadTombstones (post-legacy-purge clean-up).
- use-chat-sidebar-items: deleteChatItem tombstones synchronously
BEFORE the backend round-trip and rolls back on failure (restores
pre-PR optimistic UX). 300 ms trailing debounce on
CHAT_HISTORY_UPDATED_EVENT plus requestSeq guard so stream-time event
bursts produce at most one fetch per quiet window.
Tests:
- studio/backend/tests/pr5272_sim/ adds 64 regression tests covering
schema migration from pre-fix shape, subject scoping, cross-thread
hijack, bulk-replace mismatch, clear-confirm, concurrent settings,
unicode + 2MB content + SQL-injection-safe binding, chunking
boundary at 900 and 901 ids, batched endpoint (multi-subject + 1200
ids + per-thread order), and grep contracts for the frontend patches.
test_chat_history_storage.py updated to pass subject.
Verified locally on Linux + macOS + Windows GitHub Actions runners
(staging fork): 64 pass + 2 from the PR's own backend test on all
three OSes.
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* Drop subject scoping and clear-confirm gate (Studio is single-user)
Per maintainer feedback: subject scoping, cross-thread message hijack
guard, and DELETE /api/chat ?confirm=true gate are unnecessary because
Studio is intentionally single-user (the client already shows a confirm
dialog before clear-all).
This commit reverts those backend changes and keeps only the
non-multi-user pieces from the earlier fix commit:
- studio_db.py: restored to pre-fix shape; adds upsert_chat_settings_merge
which does atomic read + deep-merge + write under BEGIN IMMEDIATE so
two concurrent slider drags cannot drop one another's updates.
- routes/chat_history.py: restored; put_settings now calls the atomic
merge instead of doing the read-merge-write across three separate
connections. Adds POST /api/chat/messages:batch to collapse the
sidebar/search rebuild from N round-trips to 1.
- frontend/api/chat-api.ts: align batchListChatMessages request and
response keys with the backend (threadIds / messagesByThreadId).
- tests/test_chat_history_storage.py: add atomic-merge concurrency test,
deep-merge nested-key test, and 901-id chunking-boundary test.
- Drop the pr5272_sim test directory (those tests covered the reverted
subject-scoping/hijack/confirm behavior).
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* Fix sidebar delete crash, keepalive on settings beforeunload flush, search rebuild race
Two correctness bugs and one perf race surfaced by a fresh code review of
the prior fix commit:
- chat-api.ts: notifyChatHistoryUpdated was declared as a non-exported
function, but use-chat-sidebar-items.ts imports it. The import would
fail tsc with TS2305 and at runtime the optimistic-delete and
delete-failure rollback paths would both throw.
- chat-runtime-store.ts + chat-settings-api.ts + chat-settings-storage.ts:
the beforeunload settings flush is now actually keepalive. Without it
the browser cancels the in-flight PUT on tab close, so the last slider
drag is silently dropped (which is exactly the case the
debounce+beforeunload combination was meant to protect against).
- use-chat-search-index.ts: rebuilds now coalesce with a 300ms trailing
debounce and discard out-of-order responses via a requestSeq guard.
Matches the sibling pattern in use-chat-sidebar-items.ts so two rapid
CHAT_HISTORY_UPDATED_EVENTs (run-start + run-end save during a turn)
cannot land with stale data winning.
- chat-thread-tombstones.ts: drop dead clearAllChatThreadTombstones with
no call sites; Dexie is never wiped so the function has no use.
* fix(studio): protect chat persistence writes
* fix(studio): align chat history clear semantics
* fix(studio): show partial chat clear feedback
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* fix(studio): preserve chat persistence fallbacks
* fix(studio): harden chat thread persistence checks
* Preserve chat message timestamps
* Gate chat stream on history save
* Make chat thread backfill best effort
* Avoid chat message 404 probe
* Tighten chat legacy fallbacks
* chat: server-side ledger so legacy Dexie import is recoverable
The boolean localStorage sentinel
(unsloth_chat_legacy_imported_to_studio_db) made importLegacyChatsIfNeeded
non-recoverable: deleting studio.db while the browser keeps the flag
silently hides every legacy Dexie thread from the sidebar (verified by
the 3-GPU validation probe; matches the third review comment on PR
#5272). Same trap fires for browser-profile sync to a fresh machine
and any other path that wipes studio.db while keeping IndexedDB.
Source of truth moves into studio.db itself via a new
chat_legacy_import_log table keyed by legacy thread id. The ledger
disappears together with studio.db, so the next launch re-runs the
import from whatever Dexie still holds. localStorage stays as a
per-session perf hint only.
Performance, all bounded by the three new fast-paths before any
backend work:
A) localStorage hint says "imported earlier in this session" -- 0
network, ~0 ms. Covers the warm sidebar mount.
B) indexedDB.databases() reports no "unsloth-chat" DB -- 0 network,
~1 ms. Covers every new user who never had the old browser-only
Studio (the common case after launch).
C) db.threads.count() + db.messages.count() are both 0 -- 0 network,
~5 ms. Covers returning users who migrated long ago and Dexie was
never repopulated.
Only when all three miss does the code talk to the backend
(GET /api/chat/import-ledger -> diff vs Dexie -> existing import path
-> POST /api/chat/import-ledger to record what was just imported).
Per-thread tracking is enough because Dexie is read-only after this
PR; a thread's message set does not grow.
Backend deployments that predate the import-ledger routes are
handled transparently: the client treats 404/405 as an empty ledger
and re-runs the (idempotent via UPSERT) import on next launch.
Changes:
- storage/studio_db.py: new chat_legacy_import_log table (WITHOUT
ROWID, PK on legacy_thread_id) + list_chat_legacy_import_log() +
record_chat_legacy_import_log() (idempotent batch UPSERT).
- routes/chat_history.py: GET + POST /api/chat/import-ledger with the
obvious request/response models.
- frontend api/chat-api.ts: listChatImportLedger() (returns a Set for
O(1) diff) + recordChatImportLedger(), both with 404/405 fallback.
- frontend utils/chat-history-storage.ts: importLegacyChatsIfNeeded
gains three fast-paths, ledger fetch on the slow path, and writes
the ledger after a successful import. The localStorage helper is
unchanged on the surface; it just stops being authoritative.
- tests: 5 new test_legacy_import_log_* cases (empty default, record
+ list round-trip, idempotency, input dedup, empty/null ignore).
All 9 pre-existing tests still pass.
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* Make the legacy-import recovery actually recoverable
The previous commit added a server-side ledger to make Dexie -> studio.db
import recoverable after a studio.db wipe, but the localStorage perf hint
still short-circuited the import gate before the ledger was ever consulted.
After a wipe, the hint stayed "true" and the bulk re-import never ran -- the
ledger sat empty and only the per-thread lazy materialize-on-continue path
restored data.
Changes:
- Remove the localStorage short-circuit from importLegacyChatsIfNeeded so
the ledger is checked on every fresh tab. legacyChatImportPromise keeps
the per-session cache; the hint now only matters for the listing paths.
- Batch the slow path: one db.messages.where().anyOf().toArray() and one
batchListChatMessages() instead of 2N round-trips. At 1k threads this
drops a multi-second blocking import to a single request pair.
- recordChatImportLedger returns {accepted, inserted, supported}. The
localStorage hint is only flipped when supported is true, so old
backends (404 / 405 / 501) no longer permanently poison recovery.
- Ledger backfill: threads already present in chat_threads but missing
from the ledger now get added too, so old-FE-then-new-FE deployments
don't redo the diff every launch.
- Backend response field renamed recorded -> {accepted, inserted}.
accepted is the deduped non-empty input count; inserted is the rows
actually new (via INSERT ... RETURNING). Bounded by Field(max_length=
10_000) on the request payload.
- Storage helpers renamed: chat_legacy_import_log -> chat_legacy_imports,
record_* -> upsert_* to match the existing noun/verb conventions.
- DEXIE_DB_NAME exported from db.ts; duplicate constant in
chat-history-storage.ts removed.
- 3 new route-level tests for /api/chat/import-ledger covering the
round-trip, the (accepted, inserted) split, and the 10k payload cap.
All 18 chat-history tests pass.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: wire Anthropic web_fetch server-side tool
Studio's Anthropic passthrough only forwarded web_search and
code_execution when enabled_tools was set. Asking Claude through Studio
to fetch a URL produced no fetch (the tool was not in the outbound
tools array), so users had to fall back to web_search even when they
already had the exact URL they wanted.
This change opts in web_fetch_20250910 when enabled_tools contains
"web_fetch". The new tool entry is appended alongside any existing
web_search / code_execution entries:
{"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 5}
No anthropic-beta header is required (web_fetch is GA); the existing
code-execution-2025-08-25 flag continues to merge cleanly when both
tools are enabled in the same turn.
SSE translation mirrors the web_search path. A `server_tool_use` block
with name="web_fetch" emits a `tool_start` _toolEvent carrying the
URL the model asked to fetch; the matching `web_fetch_tool_result`
block emits a `tool_end` _toolEvent whose result string follows the
Title / URL / Snippet shape parseSourcesFromResult on the frontend
already expects, so the source pill renders identically. Error blocks
(`web_fetch_tool_error`) are surfaced as "Error: <error_code>" matching
the code_execution error path.
The final "Anthropic stream complete" log line picks up web_fetch_
requested / web_fetch_invocations / web_fetch_urls so support reports
of "the model did not fetch anything" can be triaged from the log.
Verified end to end against claude-haiku-4-5 with
`enabled_tools=["web_fetch"]`: the model emitted tool_start with
url=https://example.com and tool_end with the page Title + URL +
Snippet, plus the assistant message correctly read back "Example
Domain" as the title.
Tests:
- 5 new unit tests in test_anthropic_web_fetch.py covering tool
registration, the combined web_search + web_fetch + code_execution
request body, the pill-off case, and SSE translation for both
success and error paths.
- All 242 existing Anthropic + OpenAI provider tests still pass.
The enabled_tools field description in models/inference.py is updated
so OpenAPI consumers see the new option.
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* web_fetch: title fallback to URL, log parse failures, drop dead checks
Three review nits on the previous commit:
1. `_format_web_fetch_result` left `title` empty when Anthropic omitted
`document.title`. The frontend `parseSourcesFromResult` only emits
a source pill when both `Title:` and `URL:` lines are present, so
fetches against pages without an HTML title tag silently lost
their citation in the UI. Fall back to `title = title or url`,
matching the web_search formatter.
2. The broad `except Exception` around `json.loads(buffer)` for the
web_fetch input swallowed the failure with no trace. Log at debug
so a malformed partial_json buffer can be triaged from the server
log without changing behavior.
3. `inner` was already sanitised to a dict at the matching
content_block_start and `_format_web_fetch_result` always returns
a non-empty string (defaulting to "(fetch complete)"), so the
`isinstance(inner, dict) else {}` guard and the
`result_text or "(fetch complete)"` fallback at the emit site
were dead code. Removed.
Added a test exercising the titleless path so the fallback stays
covered.
* chat-adapter: emit source pills for web_fetch tool calls
`parseSourcesFromResult` was only wired up for tool calls where
`toolName === "web_search"`, so the Title / URL / Snippet block the
backend formatter emits for `web_fetch_tool_result` never reached the
source-pill renderer. Users saw the raw tool result in the tool card
but the dedicated source-pill row at the message tail stayed empty.
Both web_search and web_fetch ship the same text shape today, so the
fix is to broaden the gate.
* Address review: wire web_fetch from Search pill + fix pause_turn truncation
Two reviewer follow-ups on the Anthropic web_fetch PR:
1. The backend tool wiring landed but the frontend chat-adapter
never put `web_fetch` in `enabled_tools`, so toggling the Search
pill only ever attached `web_search` -- web_fetch was unreachable
from the UI. Added providerSupportsBuiltinWebFetch() (Anthropic
today) and paired the entry with the existing Search pill, since
the canonical workflow is "search returns URLs, fetch reads
them" and there is no separate UI toggle yet.
2. `pause_turn` from Anthropic's stop_reason vocabulary fell through
the finish_reason map's "stop" default, which the OpenAI-format
client renders as end-of-message and truncates the answer. Per
the docs pause_turn means "Claude paused a long server-tool
turn (web_search / web_fetch) and will resume". Mapped to None
and skipped the chunk emission so the SSE stream still ends with
[DONE] on message_stop but no terminal finish_reason lands on
the client. While there: added explicit mappings for `tool_use`
(-> tool_calls) and `refusal` (-> content_filter) which were
also falling through to "stop".
Tests added: pause_turn emits no finish_reason, end_turn still
emits "stop", refusal maps to "content_filter".
Sourcing: https://platform.claude.com/docs/en/api/messages#response-stop-reason
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* Studio: per-session cost calculator + /api/providers/pricing endpoint
Neither the Anthropic Messages API nor the OpenAI Responses API
reports a `cost` field on the response. Both expose detailed token
counts (input, output, cache hits, server-tool invocations); pricing
multipliers live in the provider docs. The frontend's "cost so far"
display was impossible without scraping the server log.
Land the math + a snapshot endpoint so the cost calculator can run
client-side from the existing usage chunk plumbing. The actual UI
hookup belongs in a frontend follow-up (and is gated on PR #5670's
usage-chunk emission landing so the frontend sees the usage block
in the first place).
Changes:
- New `core/inference/pricing.py` with:
- Per-MTok base pricing tables for every active Anthropic and
gpt-5.x family member. Dated snapshots inherit the canonical-id
price via prefix match so future snapshots cost the same as the
canonical id until pricing changes.
- Shared multipliers for Anthropic cache writes (5m: 1.25x, 1h: 2x)
and reads (0.1x); OpenAI cache reads (0.1x); Anthropic server
tool surcharges ($10 / 1k web_search, $0.05 / hour code_exec
beyond the 50-hour daily free tier).
- `calculate_cost(provider, model, usage)` returns a per-turn USD
breakdown plus billable token counts, with priced=False for
unknown models so the UI can still render token counts.
- `pricing_snapshot()` returns the whole table for the frontend
so it doesn't re-implement the multipliers.
- New `GET /api/providers/pricing` returning the snapshot, scoped
behind the existing auth dependency.
- New `backend/tests/test_pricing.py` with 12 cases pinning the
math against documented values: base input/output multiplication,
5m / 1h / read multipliers, default-to-5m fallback when the
breakdown is absent, web_search per-1k pricing, code_execution
per-hour pricing, dated-snapshot fallback, OpenAI cache-read
discount accounting (cached tokens subtracted from full-price
bucket and re-billed at 0.1x), unknown model graceful-degrade,
and the snapshot endpoint shape.
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* Studio: verified OpenAI pricing + fix billable input double-count
Address the cost-calculator review:
- OpenAI prices were 2-6x under the actual published rates.
Cross-checked the live developers.openai.com/api/docs/pricing page
and replaced every entry. gpt-5.5 is 5/30, gpt-5.5-pro is 30/180,
gpt-5.4 is 2.5/15, gpt-5.4-mini 0.75/4.5, gpt-5.4-nano 0.20/1.25,
gpt-5.3-codex 1.75/14. Added chat-latest alias to the canonical
chat-snapshot rate. Dropped o3 / o4 / gpt-4.5 rows that are no
longer listed on the page; calculator returns priced=False instead
of silently billing at zero.
- billable_input_tokens was double-counting cached tokens for
OpenAI. Anthropic excludes cache_* buckets from input_tokens so
we add them; OpenAI folds cache_read_input_tokens into
input_tokens already, so the tooltip read 1.8M for a 1.0M bill.
Branched the math by provider and added a regression test.
Sourcing notes in the module docstring updated.
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* Address review: canonical 4.5 ids, long-context tier, OpenAI tool fees
Three Codex P1 follow-ups on the cost calculator:
1. Canonical Anthropic 4.5 ids missing from ANTHROPIC_PRICING.
claude-opus-4-5 / claude-sonnet-4-5 / claude-haiku-4-5 (no date
suffix) are the ids used by backend defaults
(PROVIDER_REGISTRY['anthropic'].default_models), but the table
only had the dated forms. _lookup's prefix fallback doesn't help
because the canonical id is SHORTER than the dated key, so
str.startswith goes the wrong way and the calculator returned
priced=False + zero cost. Added the canonical aliases for
opus-4-5, sonnet-4-5, haiku-4-5, and opus-4-1.
2. OpenAI long-context tier. gpt-5.5 and gpt-5.4 cross over at
272k input tokens to a 2x input / 1.5x output rate (gpt-5.5:
$5/$30 -> $10/$45; gpt-5.4: $2.50/$15 -> $5/$22.50). Turns past
the threshold were systematically undercounted at headline
rates. Added long_context_threshold / long_context_input_per_mtok /
long_context_output_per_mtok columns and a tier-selection step
in calculate_cost; model_priced gains a "(long-context >272000)"
suffix when the higher tier applies so the tooltip can show
which rate was used. gpt-5.5-pro / gpt-5.4-pro / mini / nano /
codex have no published long-context tier today, so they keep a
single rate.
3. OpenAI server-tool surcharges. web_search is $10/1000 calls and
the hosted shell container is $0.03 per 20-minute session on the
default 1g tier (~$0.09/hr). server_tools_usd was previously
stuck at 0.0 for OpenAI even when web_search and shell tools
fired, so sessions with tool use understated cost. Added
OPENAI_WEB_SEARCH_USD_PER_1K and OPENAI_CONTAINER_USD_PER_HOUR
constants plus a parallel of the Anthropic surcharge block that
reads counts from usage["openai_tool_use"]. The SSE translator
wires the counts in a follow-up commit; the calculator is now
ready for them. pricing_snapshot also exposes both constants so
the frontend tooltip can render the per-call rate.
Existing tests updated to stay in the short-context tier where they
were testing base rates; new tests pin canonical 4.5 lookups,
long-context crossover on gpt-5.5/gpt-5.4, the absence of crossover
on mini/nano/codex, and OpenAI tool surcharges (web_search,
container hours, combined total).
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* Studio: wire OpenAI image_generation tool
OpenAI's Responses API exposes server-side image generation as a
tool entry (`{type: "image_generation"}`); the result comes back as
an `image_generation_call` output item with the base64 image on
`result`, the actual prompt used on `revised_prompt`, plus `size`,
`quality`, `output_format`, `background`. The model decides when to
call the tool based on the user's request; rendering uses one of
the gpt-image-* backbones server-side.
Available on every gpt-5.x family member plus gpt-4.1, gpt-4o, o3,
o4-mini per the docs.
Changes:
- Append `{type:"image_generation"}` to the Responses request tools
array when `enabled_tools` carries `image_generation` AND the base
URL points at cloud OpenAI. Non-cloud bases (ollama, llama.cpp,
"custom" presets that collapse to provider="openai") silently drop
the tool to avoid 400s.
- Mirror the same logic in `_build_body` (the post-expiry retry
builder) so retries carry the same tool set as the original
attempt.
- Handle `image_generation_call` items in
`response.output_item.done`: emit `tool_start` with
`arguments:{kind:"image", prompt:<revised_prompt>}` and `tool_end`
with `image_b64`, `image_mime`, `size`, `quality`, `background`
so the chat adapter can render an inline preview. Image bytes go
on the tool_end chunk; no extra fields on the chat-completions
envelope so the OpenAI SDK shape stays clean.
- Add `import time` (used for synthesised tool_call_id fallback).
- Add `test_openai_image_generation.py` with 5 cases: tool entry on
cloud OpenAI, combined with web_search + code_execution
(verifies all three coexist), non-cloud drop, omitted pill leaves
body untouched, output item translation produces the expected
tool_start + tool_end chunks.
Live verified end-to-end: `gpt-5.4-mini` with `image_generation`
tool returned an `image_generation_call` carrying ~1MB of base64
PNG plus the gpt-image backbone's revised prompt.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use time.time_ns() for synthesised image_generation tool_call_id
Gemini medium on PR #5688: `int(time.time() * 1000)` has 1ms
resolution; two image generations resolving in the same millisecond
would collide on the synthesised id. Bump to nanoseconds.
(In practice the upstream `image_generation_call` item always carries
its own `id`; the synthesised fallback only fires when OpenAI omits
it -- rare, but cheap to harden.)
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: support Anthropic 1h cache TTL via prompt_cache_ttl field
Anthropic exposes two ephemeral cache pools per request: the default
5-minute pool, and a 1-hour pool selected by attaching `ttl:"1h"` to
the `cache_control` marker. 1h writes are billed at 2x base input vs
1.25x for 5m, but reads stay at 0.1x for both, so a single extra read
landing more than 5 minutes after the write pays off the premium.
Studio hardcoded the 5m pool via `cache_control: {type:"ephemeral"}`
on both breakpoints. For chats with multi-minute idle gaps (people
juggling tabs, long-running tool calls between turns), the cache
expires before the next turn and every read becomes a cache_creation,
not a cache_read -- exactly the case where the 1h pool wins.
Changes:
- Add `prompt_cache_ttl: Optional[Literal["5m", "1h"]]` to
ChatCompletionRequest. Default (None) preserves today's 5m behavior.
- Thread through `routes/inference.py` ->
`stream_chat_completion` -> `_stream_anthropic`.
- Build a shared `cache_marker` dict in `_stream_anthropic`; attach
`ttl` only when the request asks for one of the two valid values.
Unknown TTL strings are silently dropped to avoid sending malformed
markers (the upstream API would 400).
- Apply the same marker to both existing breakpoints (system block at
line 1175 and the latest-message tail at line 1198 / 1213) so the
pool selection is consistent across the whole prefix.
- Add `test_anthropic_cache_ttl.py` with 11 parametrized cases
pinning the outbound body shape: omitted -> default marker;
explicit `5m`/`1h` -> ttl field set; unknown values dropped;
caching off -> no markers at all.
Verified upstream that `cache_control: {type:"ephemeral", ttl:"1h"}`
is accepted by the Anthropic API today; no beta header required.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Relax prompt_cache_ttl to Optional[str] (Codex P1)
Declaring `prompt_cache_ttl` as `Optional[Literal["5m", "1h"]]` made
FastAPI/Pydantic 422 the request before _stream_anthropic could even
see the field. The whole point of the downstream drop-unknown-values
behaviour was to keep a stale frontend from crashing the request;
the strict Literal at the request layer defeated that.
Loosen the schema to Optional[str]; the existing in-helper guard
already restricts forwarded values to {"5m", "1h"} (everything else
is silently dropped). Test suite stays unchanged -- the bogus-value
cases in test_anthropic_cache_ttl.py already pass arbitrary strings
through and assert they are dropped before the wire.
* Address review: confirm extended-cache-ttl beta header is GA
Reviewer asked whether the 1h cache TTL still requires the
`extended-cache-ttl-2025-04-11` anthropic-beta header. Investigated:
- Live-tested api.anthropic.com on claude-opus-4-7 (2026-05-22)
with cache_control={type:"ephemeral", ttl:"1h"} and NO beta
header. Got status 200 and ephemeral_1h_input_tokens populated
on the create turn, plus cache_read_input_tokens populated on
the reuse turn.
- Cross-checked the current prompt-caching docs: no mention of
any beta header on the 1h TTL path.
Conclusion: the gate has been promoted to GA. The code already
does not send the beta header (the cache_marker dict only carries
`type`/`ttl`), so no wire change is needed. Pinned the contract
with two regression tests that assert the header is NOT on the
outbound request, and added a docstring note explaining the
investigation outcome so a future reader does not re-add it.
---------
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* Studio: per-model Anthropic server-side tool versions
Anthropic ships date-pinned tool versions per model family. Studio
currently hard-codes `web_search_20250305`, `web_fetch_20250910`, and
`code_execution_20250825` for every model, which means Opus 4.6/4.7,
Sonnet 4.6 and the Opus/Sonnet 4.5 family never get the newer
`_20260209` / `_20260120` variants. Those newer variants add dynamic
filtering (Claude writes code to rank/filter web results before they
enter context) and REPL state persistence + programmatic tool calling
inside the sandbox, which is what the user-facing pills are supposed
to expose.
Hardcoding the legacy versions also breaks if a future model family
drops the legacy types: the request 400s instead of falling back.
Changes:
- Add `_anthropic_web_search_version`, `_anthropic_web_fetch_version`,
`_anthropic_code_execution_version` helpers that pick the newest
variant the model accepts and fall back to the GA versions for
everything else.
- Add `_ANTHROPIC_CODE_EXECUTION_BETA` constant since the beta header
(`code-execution-2025-08-25`) is shared across both code-execution
date variants per the upstream docs.
- Wire the helpers into `_stream_anthropic` so the outbound body
carries the right pinned version per request.
- Add parametrized dispatch tests in
`test_anthropic_tool_versions.py` covering Opus 4.7/4.6/4.5,
Sonnet 4.6/4.5, Haiku 4.5, Opus 4.1/4.0, Sonnet 4.0, 3.5 Sonnet,
plus streaming integration tests that verify the outbound body
uses the right versions on Opus 4.7 (new web_search + new
code_execution), Haiku 4.5 (legacy both), and Sonnet 4.5 (legacy
web_search + new code_execution).
- Update existing `test_anthropic_code_execution.py` cases that
pinned the old version on Opus 4.7 to expect the new ones.
Verified end-to-end against the live Anthropic API: Opus 4.7 with
both pills enabled accepts the newer-pinned tools without a 400, and
Haiku 4.5 still works on the legacy fallback path.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
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Roland Tannous