Resolves the issues surfaced by a self-review of the adaptive chunking
work. Tests covering each regression were added first to prove the
fix.
Critical
- Heading splitter no longer prepends breadcrumb to Content. The
breadcrumb now lives on Chunk.ContextHeader; EmbeddingContent()
combines header + content for the embedder. This restores the
End-Start == len(Content) invariant relied on by knowledge.go's
document-reconstruction path (summary generation, UI highlight) and
also eliminates the duplicate-heading regression where the section
heading appeared twice in a chunk's body.
- splitBySeparators is now genuinely recursive: when one separator
leaves a piece > ChunkSize, the next-priority separator is applied
inside that piece. Mirrors the Python reference. Without this, a
document with one paragraph break followed by a long run of
newline-separated lines could emit a single ~1950-rune chunk for
ChunkSize=300.
Medium
- SplitParentChild forces children onto the recursive tier, skipping
the per-parent profile pass (was N extra O(N) scans).
- Heuristic splitter snaps overlap start to the nearest semantic
boundary or newline instead of slicing mid-line / mid-word.
- Strategy.Split returns the legacy tier's output directly when the
whole chain rejects rather than running SplitText a second time.
Low
- ensureDefaults caps ChunkOverlap at ChunkSize/2 so pathological
configs (TokenLimit clamping ChunkSize tiny while Overlap stays
high) cannot produce ~97% overlap chunks.
- DefaultChunkOverlap docstring spells out the migration story for
KBs that stored ChunkOverlap=0.
ContextHeader plumbing
- chunker.Chunk and types.ParsedChunk gain ContextHeader plus an
EmbeddingContent() helper.
- types.Chunk gets a transient (gorm:"-") ContextHeader field so the
knowledge service can build embedding inputs as
titlePrefix + chunk.EmbeddingContent() without a DB schema change.
https://claude.ai/code/session_01XADhx6mtu2ZYW3DE9Lun6k
Reciprocal Rank Fusion previously hard-coded k=60 and weights 0.7/0.3.
Both are now sourced from RetrievalConfig (RRFK, RRFVectorWeight,
RRFKeywordWeight) with the same numbers as defaults via three new
GetEffective accessors. fuseOrDeduplicate / fuseWithRRF accept the
config; HybridSearch reads it from tenantInfo and forwards.
Lets corpus owners tune fusion behavior — large corpora benefit from
larger k, keyword-heavy domains from a higher keyword weight, etc. —
without changing application code.
https://claude.ai/code/session_01XADhx6mtu2ZYW3DE9Lun6k
batchEmbedWithBackoff replaces the previous fixed 100ms × 5 retry loop
with proper exponential backoff (200/400/800/1600/3200 ms) and respects
context cancellation between attempts. sanitizeForEmbedding caps any
single embedding input at 20k runes — well above realistic chunk
budgets, so it only triggers on pathologically large inputs and logs
a warning when it does.
https://claude.ai/code/session_01XADhx6mtu2ZYW3DE9Lun6k
The Go splitter previously combined all separators into one regex, losing
the priority-based recursive behavior of the Python reference. This pass
walks the separator list in order: the first separator that yields >1
piece wins, mirroring docreader/splitter/splitter.py. Larger semantic
boundaries (\n\n) now win over smaller ones (\n).
Additional cross-cutting changes:
- ChunkingConfig gains Strategy / TokenLimit / Languages fields so the
per-KB config can pick a tier and pin embedding token budgets.
- buildSplitterConfig in the knowledge service uses the new
chunker.DefaultChunkSize / DefaultChunkOverlap constants (overlap
consolidated to 80 ≈ 15%) instead of the previous hardcoded 50.
- ensureDefaults clamps ChunkSize to a per-language character budget
derived from TokenLimit so chunks stay safe under embedding model
token caps. Chinese (~1.7 chars/tok) gets a tighter clamp than
English (~4 chars/tok).
https://claude.ai/code/session_01XADhx6mtu2ZYW3DE9Lun6k
Adds the building blocks for tiered chunking strategies — multilingual
patterns, token approximation, document profiling, validation, and a
strategy resolver that today still falls through to the legacy
SplitText. Default Strategy is empty/legacy so existing knowledge bases
continue to chunk identically.
- tokens.go: char/token approximation per language (en/de/zh/mixed)
and a coarse DetectLanguage heuristic
- patterns.go: source-of-truth regex bibliothek for headings, numbered
sections, multilingual chapter markers, visual separators, etc.
- profiler.go: single-pass document scan + SelectStrategy chain
- validator.go: rejects obviously broken chunk-sets to drive fallbacks
- strategy.go: public Split / SplitParentChild entry points; resolves
StrategyAuto via the profiler, anything else maps to a fixed tier
- splitter.go: SplitterConfig gains Strategy / TokenLimit / Languages
fields and DefaultChunkSize / DefaultChunkOverlap constants (overlap
raised to 80 ≈ 15%)
- knowledge.go: switches both call sites to chunker.Split /
SplitParentChild — behavior unchanged for empty Strategy
https://claude.ai/code/session_01XADhx6mtu2ZYW3DE9Lun6k
knowledgeService.buildStorageConfig had hardcoded switches in two places that
only handled local/minio/cos. Knowledge bases configured for tos, s3, oss, or
ks3 reached the docreader with a DocParserStorageConfig containing only the
Provider tag — bucket, endpoint, and credentials were silently dropped, and
extraction stalled or fetched from the wrong location.
- Tenant-merge switch: add tos/s3/oss/ks3 cases mapping
StorageEngineConfig.{TOS,S3,OSS,KS3} fields onto DocParserStorageConfig.
Same shape as the existing minio/cos branches; AccessKey -> AccessKeyID,
SecretKey -> SecretAccessKey, no AppID.
- Legacy compat switch: list tos/s3/oss/ks3 alongside local with hasKBFull=false
so fall-through to the tenant-merge path is intentional rather than an
unrecognised provider sliding past the switch. The legacy `cos_config` column
predates these providers and they should never resolve via that path.
- Doc drift: types/custom_agent.go ImageStorageProvider comment and
handler/system.go StorageEngineStatusItem.Name comment now list all 7
providers (matched against types.StorageEngineConfig and ParseProviderScheme).
Tests (knowledge_storage_config_test.go, +268 LOC, 17 cases):
- TestBuildStorageConfig_TenantMergeAllProviders pins per-provider field
population for all 7 providers; the tos/s3/oss/ks3 sub-tests are the
regression guard for #1117.
- TestBuildStorageConfig_LegacyPathOnlyForCOSAndMinIO pins that legacy
StorageConfig data is only consumed for cos/minio and skipped for the
others (intentional fall-through).
- TestBuildStorageConfig_NoTenantFallsThroughToEmpty pins the no-tenant case.
Out of scope (called out in the linked issue):
- The systemic refactor (single source-of-truth provider registry) — left as
a follow-up; this PR keeps changes local to the buggy code path.
- initialization.go validateStorageEngineConfig multimodal validation gap —
the InitializationRequest struct does not carry tos/s3/oss/ks3 fields, so
fixing it requires API surface additions; can ship separately.
Closes#1117 (the buildStorageConfig switches; remaining items deferred).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
internal/application/service/knowledge.go had grown to 9883 lines /
149 functions spanning CRUD, document processing, summary/question
generation, clone/move, FAQ (CRUD+import+index+export), wiki cleanup,
multimodal image, and file utilities. Navigating it, or diffing
changes in it, was increasingly painful.
Purely mechanical split into 8 files in the same package. No
signatures, behaviors, or types were modified. knowledgeService
struct, NewKnowledgeService, and package-level errors remain in
knowledge.go unchanged. Per-file imports were trimmed by goimports.
knowledge.go 497 struct + constructor + errs + CRUD reads + Search
knowledge_create.go 1144 CreateFrom{File,URL,Passage,Manual} + helpers
knowledge_delete.go 686 Delete + wiki cleanup + ProcessKnowledgeListDelete
knowledge_process.go 2407 ProcessDocument/Summary/Question + Reparse + UpdateImageInfo
knowledge_clone_move.go 1049 Clone* + Move* + progress persistence
knowledge_faq.go 1932 FAQ CRUD + Search + Export
knowledge_faq_import.go 1939 FAQ import/validate/index + runningFAQImportInfo
knowledge_util.go 352 file-type/url/hash + VLM+Storage config + resolveFileService
Verification: go build, go vet, gofmt on the 8 files, and
golangci-lint all clean (22 issues post-split matches 22 pre-split
in knowledge.go — no new issues introduced). The pre-existing
unused warnings on getVLMConfig / buildStorageConfig were left as-is
rather than removing dead code beyond the scope of this task.
Addresses two pre-merge review notes:
1. Extract the sentinel-returning threshold check from getSummary into
a small package-private helper (checkSufficientSummaryContent) and
add a unit test for it. The new test pins the behavior the caller
in ProcessSummaryGeneration relies on: the sentinel is returned for
empty / image-only / sub-threshold / scanned-PDF-without-OCR inputs,
and nil is returned for legitimate short notes and scanned PDFs
where VLM OCR succeeded.
The downstream "errors.Is(err, errInsufficientSummaryContent) →
SummaryStatusFailed + skip summary chunk" branch is mechanical (no
branching beyond the errors.Is check); covering it end-to-end would
require mocking KnowledgeRepository / ChunkRepository / chat.Chat,
for which there is no precedent in this package.
2. Switch minTextContentRunes from const to var so tests can override
the threshold and so future config plumbing can adjust it at
runtime without a rebuild. The new test exercises the override.
Drive-by comment fix: extractEntitiesAndConceptsNoUpsert still claimed
summary slugs are code-generated from the document title. Since the
previous commit they are derived from the knowledge ID instead.
Pure documentation change, no behavior impact.
Self-review of the prior commit surfaced one critical regression and
several smaller issues. This change addresses them.
Critical: the old stripImageMarkup stripped the WHOLE <image>...</image>
block, but searchutil.EnrichContentWithImageInfo wraps successful VLM OCR
output in exactly that shape — so a scanned PDF where OCR worked was
being rejected by hasSufficientTextContent and routed to the same
"failed" path as the OCR-empty case. The fix splits stripping into:
* <image_original>...</image_original> blocks removed entirely
(they only carry the redundant Markdown ref already stripped above)
* <image>, <images>, <image_caption>, <image_ocr> tags removed at
the tag level, INNER TEXT PRESERVED, so OCR / caption text reaches
the threshold check and the LLM call.
A regression test pins this down with realistic enriched-image XML that
the previous code failed silently on.
Other fixes:
* Threshold lowered from 50 to 10 runes — short legitimate documents
(brief memos, single-line notes) must still pass.
* Summary chunk content no longer prefixes "# Document\n<filename>";
only the LLM-generated summary is indexed, so filenames cannot
leak back into RAG retrieval context.
* Wiki summary slug is now derived from the knowledge ID rather than
the upload filename, eliminating filename-based slugs like
"summary/mx5280-pdf" that downstream wiki-edit prompts can read.
* Citation sourceRef likewise drops the "|<docTitle>" suffix.
* Dead docTitle parameter removed from extractCandidateSlugs,
extractEntitiesAndConceptsNoUpsert, classifyChunkCitations.
* The redundant stripImageMarkup pass in the warning log is gone;
realTextRuneCount caches the count once and uses
utf8.RuneCountInString to avoid the rune-slice allocation.
Wiki-page display titles still flow through DocTitle (filename) into
later wiki-edit / index-rebuild prompts. That is a larger refactor of
the wiki pipeline and is intentionally left for a separate PR.
Scanned PDFs without a successful OCR pass (e.g. when no VLM is configured
on the KB, or when the VLM returned a "no text content" reply that the
sanitizer discarded) reach the summary and wiki-ingest pipelines with
nothing but markdown image references for content. The downstream LLM
prompts used to receive the filename, file type, and title alongside that
empty content, and the generate_summary template explicitly forbade the
model from refusing — so the model would invent a topic from the filename
alone, producing e.g. a "Canon scanner manual" summary for an MX5280.pdf
that is actually a scanned legal letter.
Two changes shut this down:
1. Drop filename / title / file-type from every LLM prompt input. The
wiki summary, knowledge extract, candidate slug, and chunk citation
prompts now see only the document content. The knowledge summary
path no longer prepends the "Document Type: X / File Name: Y" intro.
2. Add a content-sufficiency guard in front of the LLM call. A new
helper strips markdown image references and HTML image tags and
refuses to invoke the model when fewer than 50 real-text runes
remain. getSummary returns a sentinel error in that case and the
caller marks the knowledge as SummaryStatusFailed instead of
surfacing the first chunk's bare image reference. Wiki ingest skips
LLM extraction with a clear log message.
The generate_summary.yaml prompt is also updated: the "NEVER output
refusal phrases" clause is removed and replaced with an explicit
empty-content rule that returns a deterministic placeholder line.
Real OCR (Tesseract / PaddleOCR) and filename-pattern sanitisation are
intentionally left for separate PRs.
Add Kingsoft Cloud KS3 as a supported storage provider, including
backend service implementation, storage config UI, and provider
scheme recognition throughout the codebase.
AgentFinalAnswer may carry Done:true more than once (e.g. finish chunk
plus EOF). Guard AddEpisode with sync.Once so memory persists once per
turn, consistent with qa.go completion handling.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Apply the session-list additions to the SQLite init migration (Lite build)
and make QueryPaged dialect-aware: LOWER(..) LIKE on SQLite, ILIKE on
Postgres, drop NULLS LAST on SQLite. Escape LIKE wildcards in the keyword
via the existing escapeLikeKeyword helper.
- SetPinned now returns rowsAffected so the handler can respond 404 on
unknown / unauthorized session IDs instead of a misleading 200.
- GET /sessions always returns the enriched shape (pin state + IM origin
fields) so the frontend never needs a second roundtrip; the dual-path
legacy branch in the handler is gone.
- Register pin routes with the wildcard name that matches each verb's
existing radix tree (POST :session_id, DELETE :id) and accept either
param name in the handler; avoids gin's "wildcard conflicts" panic.
- Drop the redundant [platform] prefix from IM session titles now that
the list renders a platform icon alongside the title; add unit tests
for shortID / buildUserSessionTitle / buildThreadSessionTitle.
- Frontend: remove the submenu search input and its i18n/keyword wiring
(search lives elsewhere in the app); pin icon uses the TDesign `pin`
glyph and inherits color so the active session turns green; optimistic
pin moves the item to the top of the list so it shows up at the top
of the Pinned group; IM text badge replaced by the platform SVG icons
from assets/img/im, desaturated by default and full-color on hover
or when the session is active.
Sessions today are a flat list ordered by updated_at. Two gaps showed up in
practice:
- Users cannot find specific chats as the list grows beyond a screen.
- IM-created sessions (WeCom/Feishu/Slack/...) are indistinguishable:
every title was "IM-<platform>" or "IM-<platform>-<username>" and the
list API hid the underlying im_channel_sessions mapping, so admins had
no way to tell which Feishu group a session came from.
Backend
- Migration 000039 adds sessions.user_id (owner), is_pinned, pinned_at
plus a composite index for the list query. Existing rows keep user_id
NULL and stay visible at the tenant level for backward compatibility.
- CreateSession now writes the caller's user_id from auth context.
- GET /sessions accepts keyword / source / agent_id. When any filter is
set, the response switches to enriched items that LEFT JOIN
im_channel_sessions and expose im_platform / im_chat_id / im_thread_id
/ im_user_id / im_agent_id / im_channel_id. No filters => legacy shape,
existing clients unaffected.
- Ordering: is_pinned DESC, pinned_at DESC NULLS LAST, updated_at DESC.
- POST/DELETE /sessions/:id/pin for user-scoped pin/unpin.
- IM session titles: "[platform] <user|chat|thread>" with short ID
suffixes so group/DM/thread sessions are visually distinct without
needing a round-trip to fetch a display name from the IM adapter.
Frontend
- Search input debounced at 300ms drives the keyword filter.
- Pinned chats render in a dedicated group above the time-based groups,
with a pin icon and a pin/unpin entry in the per-chat dropdown.
- IM chats get a short [platform] badge in the list.
- Pin toggle is optimistic and guards against double-clicks.
- zh/en/ko/ru i18n keys added for the new strings.
Reverts the tenant-ID-from-context change in da5fcd3. The storage path
encodes the resource owner's tenant, and the presigned-URL verifier
uses that ID to look up the owning tenant's StorageEngineConfig. Using
the caller's tenant ID from context would break cross-tenant shared
resources — e.g. when tenant Y reads an image from a KB shared by
tenant X, signing with Y would cause the verifier to open Y's storage
backend and 404 on X's file.
Keep:
- presignDefaultTTL shortened from 24h to 2h (independent improvement).
- godoc note on ParseTenantIDFromStoragePath flagging the ambiguity
for cloud paths with numeric bucket/region names.
- Unit tests for path-based tenant extraction and the no-external-URL
backward-compat path.
Addresses review feedback on the IM storage URL rewrite:
- localFileService.GetFileURL now reads tenant ID from request context
first, falling back to ParseTenantIDFromStoragePath only when context
is absent. Fixes ambiguity for cloud providers whose paths embed
numeric bucket/region names before the tenant segment, which could
mint presigned URLs bound to the wrong tenant ID.
- Shorten presigned URL default TTL from 24h to 2h. A leaked HMAC key
authorizes cross-tenant file reads, so URLs should expire quickly;
IM clients fetch referenced images within seconds anyway.
- Document ParseTenantIDFromStoragePath as a best-effort fallback.
- Add unit tests covering context-first, path-fallback, and the
no-external-URL backward-compat path.
IM platforms (Feishu, Slack, Telegram, DingTalk, Mattermost, WeCom) cannot
render provider:// URLs (local://, minio://, s3://, etc.) that appear in
LLM answers containing knowledge base images. The web frontend handles
these via the authenticated /files endpoint, but IM clients need publicly
resolvable HTTP URLs.
Changes:
- Add HMAC-SHA256 presigned URL utility (internal/utils/presign.go) for
generating time-limited, signature-verified file access URLs
- Add GET /api/v1/files/presigned endpoint that serves files without
session auth, verified by HMAC signature and expiry
- Update localFileService.GetFileURL() to return presigned HTTP URLs
when APP_EXTERNAL_URL is configured (cloud backends already return
presigned URLs via their SDKs)
- Add IM content rewriting pipeline: strip <image> XML tags, strip
citation tags, rewrite storage URLs to HTTP — applied at all IM
output points (streaming flush, non-streaming reply, fallback)
- Add holdback buffer in streaming flush to prevent URL/tag splitting
across chunk boundaries
Closes#1058
CreateTenant and UpdateAPIKey both overwrote tenant.APIKey with the
AES-encrypted ciphertext before returning, causing the UI to display
"enc:v1:..." right after generating or resetting an API key. Save the
plaintext in a local variable, encrypt only the value handed to
UpdateTenant, and return the plaintext to callers.
Subsequent reads via GetTenantByID continue to rely on the AfterFind
hook for transparent decryption, so this only affects the initial
write-back path.
When users ask broad queries like "请整理知识库中的数据" in RAG mode,
vector/keyword search returns nothing because the query has no specific
content to match. The user needs to see what documents exist, not search
results.
- Add buildKBDocumentListing() to fetch document titles/filenames from
the knowledge base and inject them into the fallback prompt via
{{kb_documents}} placeholder
- Update fallback prompt templates (model_fallback, default_fallback_prompt)
to include document listing context so the LLM can guide users
- Improve rewrite prompt: tighten summarize vs kb_search classification,
expand kb_search to cover browse/organize/list operations, add examples
Closes#959
- Add per-KB sync.Map lock in Lite mode to mirror Redis SetNX concurrency.
- Requeue failed wiki ops in Lite by enqueueing asynq tasks with delay/tracing.
- Teach SyncTaskExecutor to honor ProcessIn delay and MaxRetry like Redis asynq.
- Introduced a mechanism to track and requeue failed operations during the wiki ingest process.
- Added a new `requeueFailedOps` function to append failed operations back to the Redis pending list for retry in subsequent batches.
- Updated the `ProcessWikiIngest` method to collect failed operations and ensure they are retried after trimming the pending list.
This change improves the robustness of the ingest process by preventing data loss from transient failures.
Two critical fixes to the peekPendingList function:
1. Add error logging for JSON unmarshal failures (line 303)
- Previously: Malformed JSON items were silently skipped with no indication
- Now: Each failure is logged with the error and raw item content (first 100 chars)
- This makes data loss visible and debuggable
2. Fix trim count mismatch (line 325)
- Previously: Returned len(result) - the count of raw Redis items peeked
- Now: Returns len(ops) - the count of successfully parsed items
- Impact: Malformed items now stay in Redis for retry instead of being discarded
- This prevents silent data loss when JSON encoding issues occur
Root cause of the reported issue:
- When 8 files are uploaded in a batch, if one has encoding issues that cause
JSON.Unmarshal to fail, it was silently dropped from processing
- The file was trimmed from Redis without being processed
- Result: Only 7 of 8 files appeared in the Wiki
Fix approach:
- Make unmarshal failures visible in logs
- Retry malformed items in the next batch instead of discarding them
- Admin can see the errors and take corrective action
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Previously the Langfuse integration only traced in-process HTTP requests
(chat / search / eval), so file uploads and every downstream asynq task
(document parse, chunk embedding, OCR/VLM, summary / question gen, wiki
ingest, datasource sync, etc.) produced either disconnected shallow
traces or no observation at all.
This change threads one trace end-to-end:
- tracer: add SPAN observation type and StartSpan; add ResumeTrace so a
worker can attach to an upstream trace without emitting a duplicate
trace-create; StartGeneration now auto-picks parentObservationId from
ctx so nested trace -> span -> generation trees render correctly.
- types.TracingContext + LangfuseTracingCarrier: embed on all 17 asynq
payloads so trace_id / parent_obs_id / user_id / session_id serialise
into every job.
- langfuse.InjectTracing: injected at 28 enqueue sites before json.Marshal
so the HTTP-layer trace survives the Redis hop.
- langfuse.AsynqMiddleware: mux.Use hook that peeks the payload, either
resumes the upstream trace or opens a standalone asynq.<type> trace
for scheduled jobs, and wraps the handler in a SPAN with task metadata
(id / queue / retry / payload_bytes) plus ERROR level on failure.
- GinMiddleware.shouldTrace: whitelist ingestion / knowledge-mutation /
FAQ / wiki / datasource endpoints so the root trace actually starts.
- Tests: tracer_test.go covers span nesting, error status, and
ResumeTrace no-trace-create guarantee; asynq_test.go covers
InjectTracing round-trip, middleware resume path, and standalone
trace fallback.
- Docs: docs/Langfuse\u96c6\u6210.md now lists the covered task types
and documents the cross-process propagation model.
No behavioural change when Langfuse is disabled (all new code paths are
no-ops and carriers serialise to empty strings with omitempty).
Closes#620#497. Add opt-in Langfuse observability covering all five
model types (chat, embedding, rerank, VLM, ASR) with HTTP-request-scoped
traces and Docker Compose support (both cloud and self-hosted).
Core package internal/tracing/langfuse:
- HTTP client with batched async ingestion (non-blocking in request path)
- Sampling, environment / release tagging, and graceful fallback when
LANGFUSE_* env vars are absent (wrappers become no-ops)
- Gin middleware opens one trace per traced request and finishes it after
the handler chain returns, attaching method / path / user / session
- Trace context is stored under a typed key exported from internal/types
so logger.CloneContext can preserve it across handler / goroutine
boundaries (otherwise each LLM call auto-created an orphan trace,
fragmenting one request into many)
Per-model generation wrappers (opt-in via NewChat/NewEmbedder/...):
- chat: captures prompt, streaming output, token usage + TTFT
- embedding: approximates tokens when the provider omits usage
- rerank: previews query/docs, summarizes results to keep payload small
- vlm: records image count and total bytes, never uploads raw pixels
- asr: records file size and audio duration, never uploads audio bytes
Async title generation (GenerateTitleAsync) now forwards the trace key
into the goroutine so title calls appear under the parent chat trace.
Docker Compose:
- LANGFUSE_* env passthrough on the `app` service for cloud deployments
- Optional `langfuse` profile spins up a self-hosted Langfuse stack that
reuses WeKnora's existing PostgreSQL (separate database via an idempotent
init container that fixes ICU collation drift) and Redis (separate DB
number), adding only ClickHouse, MinIO, web and worker containers
- web/worker entrypoints URL-encode DB_PASSWORD / REDIS_PASSWORD at start
to avoid Prisma P1013 when passwords contain @ / # / etc.
Docs: docs/Langfuse集成.md covers cloud vs self-hosted, per-model usage
strategy, code map, and resource footprint.
The share-time validation forced every agent bound to a knowledge base to
have a rerank model configured, which blocked Wiki-type agents
(`wiki_search` / `wiki_read_page` / …) from being shared even though they
never invoke the reranker at runtime.
Align the check with the runtime logic in `session_agent_qa.go`: only
require a rerank model when the agent's allowed_tools include
`knowledge_search` (or rely on the default tool set, which contains it).
Wiki-only agents can now be shared without a rerank model; hybrid / RAG
agents still enforce the requirement.
Also update the handler error message to reflect the new rule.
Introduce per-model `custom_headers` config (similar to OpenAI Python SDK's
`extra_headers`) so users can inject gateway auth tokens, trace IDs, etc.
into every outbound API request for chat / embedding / rerank / VLLM / ASR
models. Reserved headers like Authorization / Content-Type are always
preserved to avoid breaking auth or signing flows.
Along the way, unify production and "test connection" paths onto a single
`ConfigFromModel(*types.Model, appID, appSecret)` constructor in each
model package. Both `service.modelService.GetXxxModel` and the four
`handler.initialization.Check*Model` / `TestEmbeddingModel` endpoints now
go through the same field mapping, so new parameters only need to be
added in one place.
Backend:
- Add `CustomHeaders map[string]string` to `types.ModelParameters` with
JSON/YAML `omitempty` tags for forward compatibility.
- New `internal/utils/extraheaders.go` providing `ApplyCustomHeaders`
(for hand-rolled HTTP paths) and `WrapHTTPClientWithHeaders` /
`CustomHeadersRoundTripper` (for SDK paths like go-openai). Reserved
headers (Authorization, api-key, Content-Type, Accept, Host,
Content-Length, User-Agent) are filtered out.
- Inject headers in every remote model path: chat (SDK + raw HTTP),
embedding (openai/aliyun/jina/volcengine/nvidia/azure_openai), rerank
(remote_api/aliyun/jina/nvidia/zhipu — via shared `customHeaderSetter`
interface), VLM and ASR (via http.Client wrapping).
- Add `ConfigFromModel` to each of chat/embedding/rerank/vlm/asr,
consolidating field mapping (ExtraConfig, CustomHeaders, InterfaceType
defaulting, WeKnoraCloud credentials, etc.) and cover it with
dedicated unit tests per package.
- Refactor `service/model.go`: replace ~100 lines of hand-written Config
literals with one-line `ConfigFromModel` calls; drop the now-unused
`stringMapToAnyMap` helper.
- Refactor `handler/initialization.go` test-connection endpoints: merge
four ad-hoc request structs into one `ModelTestRequest`, extract
`buildTestModel` and `resolveTenantWeKnoraCloudCreds` helpers, and
route all four endpoints through `ConfigFromModel` + `NewXxx` so the
test path is now behaviorally identical to production.
Frontend:
- Add `custom_headers` to the `ModelConfig` API type.
- `ModelEditorDialog.vue`: new Key-Value header editor (add/remove rows),
auto-converts map <-> array when loading/saving, and passes the
current header set to the `Test Connection` button so the preview
reflects exactly what production will send.
- `ModelSettings.vue`: serialize the header array back into a map for
the backend (drops empty rows).
- i18n: add custom-header labels / descriptions / placeholders to
zh-CN, en-US, ru-RU, ko-KR.
Tests:
- `internal/utils/extraheaders_test.go` covers reserved-header filtering
and round-tripper wrapping (including nil-client fallback).
- New `config_from_model_test.go` in chat / embedding / rerank / vlm /
asr verifies all fields (CustomHeaders, ExtraConfig, InterfaceType
defaulting, AppID/AppSecret) are propagated end-to-end.
- Introduced a unified capability requirements system for agent tools, ensuring consistent filtering of knowledge bases based on their capabilities.
- Implemented derived `kb_filter` logic to streamline compatibility checks between agent tools and knowledge bases, reducing redundancy in configuration.
- Updated frontend components to reflect new filtering logic, including improved handling of empty states and user feedback when no compatible knowledge bases are available.
- Enhanced internationalization support by adding relevant messages for tool compatibility issues across multiple languages.
- Introduced UpdateAutoLinkedContent method to persist changes from machine-only link decorators without incrementing the version number, ensuring user-facing revisions remain consistent.
- Updated existing methods to utilize the new auto-linked content update logic, enhancing the handling of cross-link injection and dead-link cleanup.
- Enhanced the KnowledgeBase and WikiBrowser components to improve user experience during wiki operations, including status tracking and indexing indicators.
- Added scheduling for wiki status probes to provide timely feedback on indexing processes after user-triggered actions.
- Updated the scrubWikiPendingIngest method to accept a reason parameter, improving logging clarity during wiki cleanup and reparse operations.
- Introduced prepareWikiForReparse to maintain the integrity of the pending ingest queue during reparse events, preventing stale operations from executing.
- Enhanced the mapping logic in the wiki ingest process to differentiate between stale and reparse updates, ensuring accurate handling of knowledge contributions.
- Improved logging to provide better insights into the number of reparse and stale slugs processed during wiki ingestion.
- Enhanced the UpdatePage and UpdateMeta methods to differentiate between user-visible content changes and bookkeeping updates, ensuring the version number is only incremented for actual content modifications.
- Updated the WikiPage struct and associated interfaces to reflect the new versioning policy, improving clarity on when the version is modified.
- Improved documentation for methods to clarify their intended use and behavior regarding versioning and metadata updates.
- Changed `kb_selection_mode` in agent type presets from "selected" to "all" to allow agents to access all knowledge bases by default.
- Revised prompt templates to clarify the handling of bound knowledge bases, replacing the `{{knowledge_bases}}` placeholder with a reference to the `<bound_knowledge_bases>` block in the user message's `<runtime_context>`.
- Enhanced internal logic to ensure that only searchable knowledge bases are considered during retrieval, improving the efficiency of knowledge searches.
- Added new functions for better formatting and handling of knowledge base metadata in the runtime context.
- Updated documentation and comments for clarity on the changes made to knowledge base interactions.
- Introduced extraction granularity settings in the wiki configuration, allowing users to control the level of detail in entity and concept extraction (focused, standard, exhaustive).
- Updated the UI to reflect these new options, including tooltips for better user guidance.
- Refactored the knowledge base editor to support the new extraction granularity feature, ensuring a seamless user experience.
- Improved the backend logic for wiki ingest tasks to utilize the new granularity settings, enhancing the accuracy of extracted content.
- Added tests to validate the new extraction granularity functionality and ensure robust performance across different configurations.
- Updated the knowledge base service to ensure WikiConfig is created when wiki indexing is enabled, enhancing backward compatibility.
- Removed legacy syncing of WikiConfig.Enabled to IndexingStrategy.WikiEnabled, streamlining the logic for wiki enablement.
- Introduced a custom retry delay function for asynq tasks to handle wiki ingest lock conflicts more effectively, improving task management.
- Cleaned up the container file by removing unnecessary whitespace, contributing to better code readability.
- Deleted the RebuildKnowledgeBaseIndex function from the knowledge service, which was responsible for reprocessing all documents in a knowledge base.
- Removed the RebuildIndex handler from the KnowledgeBaseHandler, eliminating the endpoint that triggered the rebuild process.
- Updated the router to remove the route for rebuilding the index, streamlining the knowledge base management functionality.
- Updated the DeleteKnowledge and DeleteKnowledgeList functions to skip vector store cleanup for knowledge entries without an embedding model, preventing unnecessary errors.
- Enhanced logging to provide clear information when skipping cleanup for knowledge without embeddings, improving overall robustness and clarity in the deletion process.
These changes ensure that the deletion process is more efficient and user-friendly, particularly for knowledge bases that do not utilize embeddings.
- Added a check to skip vector and keyword indexing for knowledge bases that do not require an embedding model, preventing errors during the indexing process.
- Ensured that chunks are marked as indexed even when skipping the indexing step, maintaining a consistent state for downstream processes.
- Enhanced logging to provide clear information on the indexing status and any issues encountered while updating chunk statuses.
These changes improve the robustness of the indexing process in the ImageMultimodalService, ensuring better handling of knowledge bases without embedding capabilities.
- Updated the agent system prompt to emphasize the use of regex for chunk searches, improving retrieval accuracy and efficiency.
- Introduced a new `Capabilities` field in the `KnowledgeBaseInfo` struct to define the retrieval surfaces available for each knowledge base, guiding the agent's strategy selection.
- Enhanced the `grep_chunks` tool to support regex queries, allowing for more flexible and powerful text pattern matching in knowledge base chunks.
- Improved JSON handling in the `RepairJSON` function to address invalid escape sequences, particularly for regex patterns, ensuring robust parsing and error handling.
- Updated documentation and comments across various files to clarify usage and expectations for regex and knowledge base capabilities.
These changes significantly enhance the agent's ability to retrieve relevant information while ensuring the integrity of input data handling.
- Added a new configuration file `agent_type_presets.yaml` to define various agent type presets, including RAG Q&A, Wiki Q&A, Hybrid (Wiki + RAG), and Data Analysis.
- Updated the `builtin_agents.yaml` to associate built-in agents with the new agent type presets, enhancing their configuration options.
- Enhanced the agent system prompt templates to reflect the new agent types and their functionalities.
- Implemented API endpoints to retrieve agent type presets, allowing for dynamic loading in the frontend.
- Updated the frontend to support agent type selection in the agent editor modal, improving user experience by auto-filling relevant configurations based on selected presets.
These changes significantly enhance the flexibility and usability of the agent system, allowing users to easily configure agents based on predefined types.
- Updated the SuggestedQuestion interface to include 'wiki' as a new source type, allowing for more diverse question origins.
- Implemented ListRecentForSuggestions method in the wikiPageRepository to retrieve recent wiki pages for fallback suggestions, improving the agent's ability to generate relevant questions when other sources are unavailable.
- Enhanced the customAgentService to utilize the new wiki page suggestions, ensuring a broader range of questions can be generated from wiki content.
These changes significantly improve the agent's suggestion capabilities by incorporating wiki content, enhancing user interaction and information retrieval.
- Enhanced the `cleanupWikiOnKnowledgeDelete` function to pass the full knowledge object, allowing for more accurate title and summary retrieval during wiki cleanup.
- Introduced a tombstone mechanism in Redis to mark recently deleted knowledge, enabling in-flight wiki ingest tasks to skip processing for deleted documents.
- Updated the `isKnowledgeGone` function to check for deleted knowledge using the tombstone, improving efficiency in handling concurrent delete and ingest operations.
- Added logic in the `wikiIngestService` to filter out updates for deleted knowledge, ensuring that no ghost references are created during the ingest process.
- Implemented lint checks for stale source references in the `WikiLintService`, allowing for automatic cleanup of pages referencing deleted knowledge.
These changes significantly enhance the robustness of the wiki system, ensuring consistency and accuracy in handling knowledge deletions and related wiki pages.
- Updated the agent system prompt to include new auxiliary tools and improved synthesis and issue flagging steps, enhancing the clarity of the workflow.
- Revised constraints to emphasize the importance of retrieving information from the wiki first and using skills when applicable.
- Expanded localization files for English, Korean, Russian, and Chinese to include new wiki tool descriptions and statuses, ensuring consistency across languages.
- Improved the agent editor modal to provide a clearer overview of tool statuses and retrieval preferences, enhancing user experience.
These changes significantly improve the functionality and usability of the agent system, particularly in relation to wiki interactions and tool management.
- Added the WikiScope struct to define the retrieval scope for wiki knowledge bases, allowing for optional filtering by specific document IDs.
- Implemented NewWikiScopesFromKBIDs constructor for easier creation of WikiScope instances.
- Enhanced the wikiReadPageTool and wikiSearchTool to utilize WikiScope, improving the handling of knowledge base IDs and document filtering during page retrieval.
- Updated the agent service to carry document whitelists into the wiki scopes, ensuring that only relevant pages are surfaced based on user mentions.
These changes significantly improve the flexibility and accuracy of wiki page retrieval, enhancing the overall user experience when interacting with wiki content.
- Introduced multiple new documentation files, including a complete documentation index, quick reference guides, and comprehensive analyses of the knowledge base and wiki settings.
- The new files provide detailed insights into the architecture, data models, API endpoints, and implementation strategies for the knowledge base and wiki functionalities.
- Enhanced navigation and usability for developers and team members by summarizing key components, configurations, and data flows related to the knowledge base system.
These additions significantly improve the clarity and accessibility of documentation, supporting better onboarding and reference for current and future team members.
- Introduced `enrichChunkImageInfo` function to populate image information for text chunks by retrieving data from child chunks, ensuring that image-related content is not lost during processing.
- Updated `reconstructContent` to `reconstructEnrichedContent`, allowing for the inclusion of OCR text and captions from images, improving the quality of content sent to the LLM.
- Added `writeDedupItemXML` and `xmlEscape` functions to format entity and concept entries as structured XML, enhancing clarity and preventing nonsensical merges in deduplication prompts.
- Refined the `deduplicateExtractedBatch` method to utilize the new XML formatting for better output consistency.
These changes significantly improve the handling of image data and the overall clarity of deduplication prompts, contributing to a more effective wiki content management system.
Claude