Conversation
… `ProjectTagsInput` to improve visual consistency and user experience - Changed terminology from "Experimental" to "Beta" in various components - Refined text labels in the `VerifiedArtefactsSection` and `ParticipantHeader` for clarity and consistency - Removed unused code and optimized component structures for better performance - added audio mode for get_reply so that we dont just rely on transcripted chunks for its response - removed redundant code logic to check response and detailed_analysis tags for get_reply - updated prompt files to better reflect the outcomes
WalkthroughThis PR performs comprehensive pre-release UI and backend updates: renaming "Echo"/"Verify" features to "Go deeper"/"Make it concrete", updating all "Experimental" badges to "Beta", converting Pill to Badge components with adjusted styling, refactoring localization keys, restructuring ProjectPortalEditor with new feature groupings, and enhancing reply utilities with multimodal audio support and simplified prompt templates across multiple languages. Changes
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes
Possibly related PRs
Suggested labels
LGTM. This PR is a solid pre-release cleanup that consolidates naming conventions, UI polish, and backend reply enhancements into a cohesive feature migration. The scale is substantial, but the changes follow consistent patterns (Echo→Go deeper, Verify→Make it concrete, Experimental→Beta). The backend additions (audio support, simplified prompts) and frontend API expansions are well-motivated. Just ensure the localization key migrations don't orphan any unused i18n entries and that the ProjectPortalEditor restructuring doesn't break existing workflows. 🚀 Pre-merge checks and finishing touches❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
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⚠️ Outside diff range comments (5)
echo/frontend/src/components/conversation/ConversationAccordion.tsx (1)
527-537: Swap the filled “verified” icon to the outline variant to align with ECHO‑571This block still uses
IconRosetteDiscountCheckFilled. Given the “use line (outline) icons instead of filled icons wherever possible” objective for ECHO‑571, it’s worth flipping this to the outline version:- <IconRosetteDiscountCheckFilled /> + <IconRosetteDiscountCheck />Same semantics, visual language becomes fully consistent with the rest of the icon set.
echo/frontend/src/components/participant/verify/VerifiedArtefactItem.tsx (1)
42-46: ARIA label matches “concrete” copy; consider swapping to outline iconThe
aria-labelupdate to “concrete artefact” is on point with the new wording.Given the PR objective to standardize on line/outline icons, this is a good spot to drop the filled variant and use the outline version from Tabler instead, something like:
-import { IconRosetteDiscountCheckFilled } from "@tabler/icons-react"; +import { IconRosetteDiscountCheck } from "@tabler/icons-react"; ... - <IconRosetteDiscountCheckFilled /> + <IconRosetteDiscountCheck />Double‑check the exact icon name against your
@tabler/icons-reactversion, but conceptually moving off the*Filledvariant will keep this in line with the rest of the UI.echo/frontend/src/components/project/ProjectPortalEditor.tsx (1)
58-64: Consider extractingLANGUAGE_TO_LOCALEto a shared constant.This exact mapping is duplicated in
VerifiedArtefactsSection.tsx. A shared constant in a utils file would DRY this up and prevent drift.// e.g., in @/lib/constants.ts or @/lib/locale.ts export const LANGUAGE_TO_LOCALE: Record<string, string> = { de: "de-DE", en: "en-US", es: "es-ES", fr: "fr-FR", nl: "nl-NL", };echo/server/dembrane/reply_utils.py (2)
125-165: Blockers: sync Directus + token_counter on the event loop inside async flow
generate_reply_for_conversationisasync, but we’re still doing:
directus.get_items(...)twicedirectus.create_item(...)once- multiple
token_counter(...)calls over potentially huge transcriptsall directly on the event loop. For large projects this will pin the loop and hurt p95+ latency.
Given we already import
run_in_thread_poolhere, I’d push these through the thread pool so this endpoint scales under concurrent load. Rough shape:- conversation = directus.get_items( + conversation = await run_in_thread_pool( + directus.get_items, "conversation", - { + { "query": { ... }, }, ) ... - adjacent_conversations = directus.get_items( + adjacent_conversations = await run_in_thread_pool( + directus.get_items, "conversation", { "query": { ... } }, ) ... - tokens = token_counter( - messages=[{"role": "user", "content": formatted_conv}], - model=get_completion_kwargs(MODELS.TEXT_FAST)["model"], - ) + tokens = await run_in_thread_pool( + token_counter, + messages=[{"role": "user", "content": formatted_conv}], + model=get_completion_kwargs(MODELS.TEXT_FAST)["model"], + ) ... - tokens = token_counter( - messages=[{"role": "user", "content": formatted_conv}], - model=get_completion_kwargs(MODELS.TEXT_FAST)["model"], - ) + tokens = await run_in_thread_pool( + token_counter, + messages=[{"role": "user", "content": formatted_conv}], + model=get_completion_kwargs(MODELS.TEXT_FAST)["model"], + ) ... - directus.create_item( + await run_in_thread_pool( + directus.create_item, "conversation_reply", item_data={ "conversation_id": current_conversation.id, "content_text": response_content, "type": "assistant_reply", }, )This keeps the behavior identical but makes the route non-blocking with respect to other requests. As per coding guidelines,
directus.*and token counting should not run directly on the event loop. -->Also applies to: 235-253, 287-290, 310-313, 322-325, 467-474
173-175: String still uses “Echo” instead of “Go deeper”We’re hardcoding
"Echo is not enabled for project ...", which conflicts with the PR objective to rebrand this feature as “Go deeper”.I’d align the backend error string so logs / API clients don’t see the old product name:
- if conversation["project_id"]["is_get_reply_enabled"] is False: - raise ValueError(f"Echo is not enabled for project {conversation['project_id']['id']}") + if conversation["project_id"]["is_get_reply_enabled"] is False: + raise ValueError( + f"Go deeper is not enabled for project {conversation['project_id']['id']}" + )-->
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📒 Files selected for processing (30)
echo/frontend/src/components/chat/TemplatesModal.tsx(1 hunks)echo/frontend/src/components/conversation/ConversationAccordion.tsx(2 hunks)echo/frontend/src/components/conversation/ConversationEdit.tsx(1 hunks)echo/frontend/src/components/conversation/MoveConversationButton.tsx(1 hunks)echo/frontend/src/components/conversation/RetranscribeConversation.tsx(1 hunks)echo/frontend/src/components/conversation/VerifiedArtefactsSection.tsx(5 hunks)echo/frontend/src/components/dropzone/UploadConversationDropzone.tsx(1 hunks)echo/frontend/src/components/layout/ParticipantHeader.tsx(1 hunks)echo/frontend/src/components/participant/EchoErrorAlert.tsx(1 hunks)echo/frontend/src/components/participant/ParticipantConversationAudio.tsx(1 hunks)echo/frontend/src/components/participant/ParticipantConversationAudioContent.tsx(0 hunks)echo/frontend/src/components/participant/refine/RefineSelection.tsx(2 hunks)echo/frontend/src/components/participant/verify/VerifiedArtefactItem.tsx(1 hunks)echo/frontend/src/components/participant/verify/VerifyArtefact.tsx(4 hunks)echo/frontend/src/components/participant/verify/VerifyArtefactError.tsx(3 hunks)echo/frontend/src/components/participant/verify/VerifyArtefactLoading.tsx(1 hunks)echo/frontend/src/components/participant/verify/VerifyInstructions.tsx(6 hunks)echo/frontend/src/components/participant/verify/VerifySelection.tsx(3 hunks)echo/frontend/src/components/project/ProjectPortalEditor.tsx(4 hunks)echo/frontend/src/components/project/ProjectTagsInput.tsx(2 hunks)echo/frontend/src/routes/project/chat/ProjectChatRoute.tsx(2 hunks)echo/frontend/src/routes/project/conversation/ProjectConversationOverview.tsx(2 hunks)echo/frontend/src/routes/project/report/ProjectReportRoute.tsx(1 hunks)echo/server/dembrane/reply_utils.py(7 hunks)echo/server/prompt_templates/generate_artifact.en.jinja(1 hunks)echo/server/prompt_templates/get_reply_system.de.jinja(1 hunks)echo/server/prompt_templates/get_reply_system.en.jinja(1 hunks)echo/server/prompt_templates/get_reply_system.es.jinja(1 hunks)echo/server/prompt_templates/get_reply_system.fr.jinja(1 hunks)echo/server/prompt_templates/get_reply_system.nl.jinja(1 hunks)
💤 Files with no reviewable changes (1)
- echo/frontend/src/components/participant/ParticipantConversationAudioContent.tsx
🧰 Additional context used
📓 Path-based instructions (4)
echo/frontend/src/{components,routes}/**/*.{ts,tsx}
📄 CodeRabbit inference engine (echo/frontend/AGENTS.md)
echo/frontend/src/{components,routes}/**/*.{ts,tsx}: Compose Mantine primitives (Stack,Group,ActionIcon, etc.) while layering Tailwind utility classes viaclassName, alongside toast feedback via@/components/common/Toaster
Pair toast notifications with contextual MantineAlertcomponents inside modals/forms for inline error or warning feedback during UI mutations
Files:
echo/frontend/src/components/dropzone/UploadConversationDropzone.tsxecho/frontend/src/components/participant/verify/VerifyArtefactLoading.tsxecho/frontend/src/components/conversation/ConversationEdit.tsxecho/frontend/src/components/conversation/RetranscribeConversation.tsxecho/frontend/src/components/participant/EchoErrorAlert.tsxecho/frontend/src/components/conversation/ConversationAccordion.tsxecho/frontend/src/components/participant/verify/VerifyArtefact.tsxecho/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/participant/refine/RefineSelection.tsxecho/frontend/src/components/conversation/MoveConversationButton.tsxecho/frontend/src/routes/project/conversation/ProjectConversationOverview.tsxecho/frontend/src/components/project/ProjectTagsInput.tsxecho/frontend/src/components/layout/ParticipantHeader.tsxecho/frontend/src/components/conversation/VerifiedArtefactsSection.tsxecho/frontend/src/routes/project/report/ProjectReportRoute.tsxecho/frontend/src/components/participant/verify/VerifyInstructions.tsxecho/frontend/src/components/participant/verify/VerifyArtefactError.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsxecho/frontend/src/routes/project/chat/ProjectChatRoute.tsxecho/frontend/src/components/participant/verify/VerifySelection.tsxecho/frontend/src/components/participant/verify/VerifiedArtefactItem.tsxecho/frontend/src/components/chat/TemplatesModal.tsx
echo/frontend/src/**/*.{ts,tsx}
📄 CodeRabbit inference engine (echo/frontend/AGENTS.md)
Localization workflow is active: keep Lingui extract/compile scripts in mind when touching
t/Transstrings; runpnpm messages:extractandpnpm messages:compileafter changes
Files:
echo/frontend/src/components/dropzone/UploadConversationDropzone.tsxecho/frontend/src/components/participant/verify/VerifyArtefactLoading.tsxecho/frontend/src/components/conversation/ConversationEdit.tsxecho/frontend/src/components/conversation/RetranscribeConversation.tsxecho/frontend/src/components/participant/EchoErrorAlert.tsxecho/frontend/src/components/conversation/ConversationAccordion.tsxecho/frontend/src/components/participant/verify/VerifyArtefact.tsxecho/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/participant/refine/RefineSelection.tsxecho/frontend/src/components/conversation/MoveConversationButton.tsxecho/frontend/src/routes/project/conversation/ProjectConversationOverview.tsxecho/frontend/src/components/project/ProjectTagsInput.tsxecho/frontend/src/components/layout/ParticipantHeader.tsxecho/frontend/src/components/conversation/VerifiedArtefactsSection.tsxecho/frontend/src/routes/project/report/ProjectReportRoute.tsxecho/frontend/src/components/participant/verify/VerifyInstructions.tsxecho/frontend/src/components/participant/verify/VerifyArtefactError.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsxecho/frontend/src/routes/project/chat/ProjectChatRoute.tsxecho/frontend/src/components/participant/verify/VerifySelection.tsxecho/frontend/src/components/participant/verify/VerifiedArtefactItem.tsxecho/frontend/src/components/chat/TemplatesModal.tsx
echo/frontend/src/routes/**/*.{ts,tsx}
📄 CodeRabbit inference engine (echo/frontend/AGENTS.md)
Use Lingui macros for localization: import
tfrom@lingui/core/macroandTransfrom@lingui/react/macroto localize UI strings in routed screens
Files:
echo/frontend/src/routes/project/conversation/ProjectConversationOverview.tsxecho/frontend/src/routes/project/report/ProjectReportRoute.tsxecho/frontend/src/routes/project/chat/ProjectChatRoute.tsx
echo/server/dembrane/**/*.py
📄 CodeRabbit inference engine (echo/.cursor/rules/async-threadpool.mdc)
echo/server/dembrane/**/*.py: Always wrap blocking I/O calls usingrun_in_thread_poolfromdembrane.async_helpersin backend code. Wrap calls todirectus.*,conversation_service.*,project_service.*, S3 helpers, and CPU-heavy utilities like token counting or summary generation if they are sync. Do not wrap already-async functions or LightRAG calls (e.g.,rag.aquery,rag.ainsert).
Prefer converting endpoints toasync defandawaitresults rather than using synchronous functions
echo/server/dembrane/**/*.py: Store all configuration changes indembrane/settings.py: add new env vars as fields onAppSettings, expose grouped accessors (e.g.,feature_flags,directus) if multiple modules read them, and fetch config at runtime withsettings = get_settings()—never import env vars directly
PopulateEMBEDDING_*env vars (model, key/base URL/version) before callingdembrane.embedding.embed_textto ensure embeddings use the correct configuration
Files:
echo/server/dembrane/reply_utils.py
🧠 Learnings (20)
📓 Common learnings
Learnt from: ussaama
Repo: Dembrane/echo PR: 205
File: echo/frontend/src/lib/query.ts:1444-1506
Timestamp: 2025-07-10T12:48:20.683Z
Learning: ussaama prefers string concatenation over template literals for simple cases where readability is clearer, even when linting tools suggest template literals. Human readability takes precedence over strict linting rules in straightforward concatenation scenarios.
📚 Learning: 2025-11-25T10:35:38.950Z
Learnt from: CR
Repo: Dembrane/echo PR: 0
File: echo/frontend/AGENTS.md:0-0
Timestamp: 2025-11-25T10:35:38.950Z
Learning: Applies to echo/frontend/src/**/*.{ts,tsx} : Localization workflow is active: keep Lingui extract/compile scripts in mind when touching `t`/`Trans` strings; run `pnpm messages:extract` and `pnpm messages:compile` after changes
Applied to files:
echo/frontend/src/components/participant/verify/VerifyArtefactLoading.tsxecho/frontend/src/components/conversation/RetranscribeConversation.tsxecho/frontend/src/components/participant/EchoErrorAlert.tsxecho/frontend/src/components/participant/verify/VerifyArtefact.tsxecho/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/routes/project/conversation/ProjectConversationOverview.tsxecho/frontend/src/components/layout/ParticipantHeader.tsxecho/frontend/src/components/conversation/VerifiedArtefactsSection.tsxecho/frontend/src/components/participant/verify/VerifyArtefactError.tsxecho/frontend/src/routes/project/chat/ProjectChatRoute.tsxecho/frontend/src/components/chat/TemplatesModal.tsx
📚 Learning: 2025-11-25T10:35:38.950Z
Learnt from: CR
Repo: Dembrane/echo PR: 0
File: echo/frontend/AGENTS.md:0-0
Timestamp: 2025-11-25T10:35:38.950Z
Learning: Applies to echo/frontend/src/{components,routes}/**/*.{ts,tsx} : Pair toast notifications with contextual Mantine `Alert` components inside modals/forms for inline error or warning feedback during UI mutations
Applied to files:
echo/frontend/src/components/participant/EchoErrorAlert.tsxecho/frontend/src/components/conversation/ConversationAccordion.tsxecho/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/participant/refine/RefineSelection.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsxecho/frontend/src/routes/project/chat/ProjectChatRoute.tsxecho/frontend/src/components/chat/TemplatesModal.tsx
📚 Learning: 2025-11-25T10:35:38.950Z
Learnt from: CR
Repo: Dembrane/echo PR: 0
File: echo/frontend/AGENTS.md:0-0
Timestamp: 2025-11-25T10:35:38.950Z
Learning: Applies to echo/frontend/src/routes/**/*.{ts,tsx} : Use Lingui macros for localization: import `t` from `lingui/core/macro` and `Trans` from `lingui/react/macro` to localize UI strings in routed screens
Applied to files:
echo/frontend/src/components/participant/EchoErrorAlert.tsxecho/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/participant/refine/RefineSelection.tsxecho/frontend/src/components/layout/ParticipantHeader.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsxecho/frontend/src/routes/project/chat/ProjectChatRoute.tsxecho/frontend/src/components/participant/verify/VerifySelection.tsxecho/frontend/src/components/chat/TemplatesModal.tsx
📚 Learning: 2025-11-25T10:35:38.950Z
Learnt from: CR
Repo: Dembrane/echo PR: 0
File: echo/frontend/AGENTS.md:0-0
Timestamp: 2025-11-25T10:35:38.950Z
Learning: Applies to echo/frontend/src/{components,routes}/**/*.{ts,tsx} : Compose Mantine primitives (`Stack`, `Group`, `ActionIcon`, etc.) while layering Tailwind utility classes via `className`, alongside toast feedback via `@/components/common/Toaster`
Applied to files:
echo/frontend/src/components/conversation/ConversationAccordion.tsxecho/frontend/src/components/participant/verify/VerifyArtefact.tsxecho/frontend/src/components/participant/refine/RefineSelection.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsx
📚 Learning: 2025-08-08T10:39:31.114Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 259
File: echo/frontend/src/components/layout/ParticipantLayout.tsx:33-33
Timestamp: 2025-08-08T10:39:31.114Z
Learning: In echo/frontend/src/components/layout/ParticipantLayout.tsx, prefer using simple pathname.includes("start") and pathname.includes("finish") to control the settings button visibility. No need to switch to segment-based matching or add a useEffect to auto-close the modal for these routes, per ussaama’s preference in PR #259.
Applied to files:
echo/frontend/src/components/participant/verify/VerifyArtefact.tsxecho/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/participant/refine/RefineSelection.tsxecho/frontend/src/components/layout/ParticipantHeader.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsxecho/frontend/src/components/participant/verify/VerifySelection.tsx
📚 Learning: 2025-10-28T13:47:02.926Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 350
File: echo/frontend/src/components/participant/ParticipantConversationText.tsx:82-85
Timestamp: 2025-10-28T13:47:02.926Z
Learning: In text mode (echo/frontend/src/components/participant/ParticipantConversationText.tsx), participants only submit PORTAL_TEXT chunks (no audio). The “Finish” button is shown only after at least one text message is saved to Directus.
Applied to files:
echo/frontend/src/components/participant/verify/VerifyArtefact.tsxecho/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/layout/ParticipantHeader.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsx
📚 Learning: 2025-08-06T13:38:30.769Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 256
File: echo/frontend/src/components/participant/MicrophoneTest.tsx:54-54
Timestamp: 2025-08-06T13:38:30.769Z
Learning: In echo/frontend/src/components/participant/MicrophoneTest.tsx, the useDisclosure hook is used where only the `close` function is needed locally (called in handleConfirmMicChange and handleCancelMicChange), while `opened` and `open` are unused because the modal state is managed by a parent component. The `showSecondModal` state variable is used separately to control content switching within the modal between the main UI and confirmation step.
Applied to files:
echo/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/layout/ParticipantHeader.tsxecho/frontend/src/components/chat/TemplatesModal.tsx
📚 Learning: 2025-11-21T12:44:30.306Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 366
File: echo/frontend/src/components/participant/refine/RefineSelection.tsx:38-38
Timestamp: 2025-11-21T12:44:30.306Z
Learning: In echo/frontend/src/components/participant/refine/RefineSelection.tsx, the refine selection panels (Make it concrete / Go deeper) should use h-[50%] when only one panel is visible to maintain half-height spacing for consistent visual weight. The flexClass logic should remain: showVerify && showEcho ? "flex-1" : "h-[50%]".
Applied to files:
echo/frontend/src/components/participant/ParticipantConversationAudio.tsxecho/frontend/src/components/participant/refine/RefineSelection.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsxecho/frontend/src/components/participant/verify/VerifySelection.tsx
📚 Learning: 2025-08-19T10:14:31.647Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 266
File: echo/frontend/src/components/chat/ChatAccordion.tsx:214-221
Timestamp: 2025-08-19T10:14:31.647Z
Learning: In the Echo frontend codebase using Lingui, i18n IDs in Trans components (e.g., `<Trans id="any.string.here">`) can be arbitrary strings and don't need to follow specific naming conventions. They are just references used in .po files, and the actual translations need to be manually defined in the locale files.
Applied to files:
echo/frontend/src/components/participant/ParticipantConversationAudio.tsx
📚 Learning: 2025-08-19T10:22:55.323Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 266
File: echo/frontend/src/components/conversation/ConversationAccordion.tsx:675-678
Timestamp: 2025-08-19T10:22:55.323Z
Learning: In echo/frontend/src/components/conversation/hooks/index.ts, the useConversationsCountByProjectId hook uses regular useQuery (not useSuspenseQuery), which means conversationsCountQuery.data can be undefined during loading states. When using Number(conversationsCountQuery.data) ?? 0, this creates NaN because Number(undefined) = NaN and NaN is not nullish, so the fallback doesn't apply. The correct pattern is Number(conversationsCountQuery.data ?? 0) to ensure the fallback happens before type conversion.
Applied to files:
echo/frontend/src/routes/project/conversation/ProjectConversationOverview.tsxecho/frontend/src/components/conversation/VerifiedArtefactsSection.tsx
📚 Learning: 2025-05-30T15:38:44.413Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 169
File: echo/frontend/src/components/project/ProjectPortalEditor.tsx:409-464
Timestamp: 2025-05-30T15:38:44.413Z
Learning: Badge-based selectors in ProjectPortalEditor.tsx: Keyboard navigation enhancements for accessibility are considered optional improvements rather than critical issues. The user acknowledges these suggestions but doesn't prioritize them as blockers.
Applied to files:
echo/frontend/src/components/project/ProjectTagsInput.tsxecho/frontend/src/routes/project/report/ProjectReportRoute.tsxecho/frontend/src/components/project/ProjectPortalEditor.tsx
📚 Learning: 2025-11-25T10:35:38.950Z
Learnt from: CR
Repo: Dembrane/echo PR: 0
File: echo/frontend/AGENTS.md:0-0
Timestamp: 2025-11-25T10:35:38.950Z
Learning: Applies to echo/frontend/src/components/**/hooks/index.ts : Use React Query hook hubs pattern: each feature should own a `hooks/index.ts` file exposing `useQuery`/`useMutation` wrappers with shared `useQueryClient` invalidation logic
Applied to files:
echo/frontend/src/components/conversation/VerifiedArtefactsSection.tsx
📚 Learning: 2025-11-25T10:35:38.950Z
Learnt from: CR
Repo: Dembrane/echo PR: 0
File: echo/frontend/AGENTS.md:0-0
Timestamp: 2025-11-25T10:35:38.950Z
Learning: Applies to echo/frontend/src/routes/settings/**/*.{ts,tsx} : Provide ergonomic navigation in settings-like routes: breadcrumb + back action (ActionIcon + navigate(-1)) with relevant iconography
Applied to files:
echo/frontend/src/components/participant/verify/VerifyArtefactError.tsx
📚 Learning: 2025-05-30T15:36:40.131Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 169
File: echo/frontend/src/locales/fr-FR.po:521-523
Timestamp: 2025-05-30T15:36:40.131Z
Learning: In the French localization file (fr-FR.po), "Dembrane Echo" is intentionally translated as "Echo Dembrane" for better French language flow and natural sound. This is not an error but a deliberate localization choice.
Applied to files:
echo/frontend/src/routes/project/chat/ProjectChatRoute.tsx
📚 Learning: 2025-09-16T08:35:18.796Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 293
File: echo/frontend/src/components/chat/TemplatesModal.tsx:34-41
Timestamp: 2025-09-16T08:35:18.796Z
Learning: In TemplatesModal.tsx, the user confirmed that titles should be used as template keys rather than IDs, consistent with their established pattern. IDs were added primarily for list rendering, not for selection logic.
Applied to files:
echo/frontend/src/components/participant/verify/VerifySelection.tsxecho/frontend/src/components/chat/TemplatesModal.tsx
📚 Learning: 2025-09-16T08:34:38.109Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 293
File: echo/frontend/src/components/chat/ChatTemplatesMenu.tsx:15-16
Timestamp: 2025-09-16T08:34:38.109Z
Learning: In ChatTemplatesMenu.tsx, titles are preferred over IDs for template selection logic since titles are unique one-liners and work effectively as identifiers. IDs were added primarily for better list rendering rather than selection purposes.
Applied to files:
echo/frontend/src/components/chat/TemplatesModal.tsx
📚 Learning: 2025-09-16T08:34:44.982Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 293
File: echo/frontend/src/routes/project/chat/ProjectChatRoute.tsx:498-501
Timestamp: 2025-09-16T08:34:44.982Z
Learning: In the chat templates system, the user prefers using localized titles as template keys rather than stable IDs, even for cross-component communication and server requests. They added IDs primarily for rendering purposes, not for selection logic.
Applied to files:
echo/frontend/src/components/chat/TemplatesModal.tsx
📚 Learning: 2025-10-15T11:06:42.397Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 336
File: echo/server/dembrane/api/chat.py:593-630
Timestamp: 2025-10-15T11:06:42.397Z
Learning: In `echo/server/dembrane/api/chat.py`, the auto-select conversation flow (lines 589-631) deliberately uses incremental system message generation with `create_system_messages_for_chat` and `token_counter` for each candidate conversation to ensure accurate token count estimation before adding conversations. The user (ussaama) prioritizes accuracy over the O(n²) performance cost to stay within the 80% context threshold precisely.
Applied to files:
echo/server/dembrane/reply_utils.py
📚 Learning: 2025-05-30T15:37:52.403Z
Learnt from: ussaama
Repo: Dembrane/echo PR: 169
File: echo/frontend/src/locales/fr-FR.po:1880-1882
Timestamp: 2025-05-30T15:37:52.403Z
Learning: In French technical contexts, especially AI/ML domains, English technical terms like "prompt" are commonly adopted and used without translation. This is acceptable and not considered an error.
Applied to files:
echo/server/prompt_templates/get_reply_system.fr.jinja
🧬 Code graph analysis (4)
echo/frontend/src/routes/project/conversation/ProjectConversationOverview.tsx (1)
echo/frontend/src/components/conversation/VerifiedArtefactsSection.tsx (1)
VerifiedArtefactsSection(43-131)
echo/frontend/src/components/conversation/VerifiedArtefactsSection.tsx (1)
echo/frontend/src/components/participant/verify/hooks/index.ts (1)
useVerificationTopics(13-20)
echo/frontend/src/components/project/ProjectPortalEditor.tsx (3)
echo/frontend/src/components/common/Logo.tsx (1)
Logo(57-63)echo/frontend/src/components/form/FormLabel.tsx (1)
FormLabel(11-30)echo/frontend/src/components/common/Toaster.tsx (1)
toast(34-34)
echo/server/dembrane/reply_utils.py (3)
echo/server/dembrane/transcribe.py (1)
_get_audio_file_object(153-173)echo/server/dembrane/async_helpers.py (1)
run_in_thread_pool(74-142)echo/server/dembrane/directus.py (1)
get(303-333)
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- GitHub Check: ci-check-server
🔇 Additional comments (42)
echo/server/prompt_templates/generate_artifact.en.jinja (1)
1-1: Prompt tweak is crisp and future‑proof.Pluralizing to “audio fragments” better matches real multimodal input without changing intent. Zero risk, nice precision. LGTM.
echo/frontend/src/components/participant/refine/RefineSelection.tsx (1)
3-3: Outline icon swap aligns with PR objectives—LGTM.The change from
IconMessageFilledtoIconMessagedelivers on the icon style consistency objective (use outline/line icons vs. filled). The component already has the product name replacements ("Make it concrete" / "Go deeper") and proper Lingui localization in place. The flexClass logic at line 38 follows the learned pattern for responsive panel sizing. Component composition is solid: Mantine primitives + Tailwind utilities, no mutations requiring toast feedback here.Also applies to: 62-62
echo/frontend/src/components/participant/EchoErrorAlert.tsx (2)
16-19: Content-policy error key rename looks tightMoving this to
participant.go.deeper.content.policy.violation.error.messageis consistent with the new namespace and keeps the copy intact. LGTM. Just make sure this id is wired into the Lingui catalogs (pnpm messages:extract && pnpm messages:compile) before cutting the release.
21-25: Generic error copy + “Go deeper” label are on‑brandThe generic error key (
participant.go.deeper.generic.error.message) plus swapping the bold label to “Go deeper” aligns perfectly with the product renaming and keeps the UX clear (“pressing the Go deeper button…”). Icon stays outline, so we’re good on the icon-style guideline too. LGTM; same note about running the Lingui extract/compile so the new ids are in the bundle.echo/frontend/src/components/conversation/MoveConversationButton.tsx (1)
131-133: LGTM — "Experimental" → "Beta" change ships clean.Straightforward label update, aligns perfectly with the ECHO-571 objective to swap all "experimental" badges to "beta". No drama here.
Quick heads-up per the localization workflow: make sure to run
pnpm messages:extract&&pnpm messages:compilesince this touches a<Trans>string.echo/frontend/src/components/conversation/RetranscribeConversation.tsx (1)
110-112: Label change matches ECHO‑571 specSwapping the header badge copy from
ExperimentaltoBetais exactly what we want here; wiring and UX stay clean. Just make sure you re-run the Lingui extract/compile pipeline so this newTransstring lands in the catalogs before release. As per coding guidelines, keep the i18n artifacts in sync with these text tweaks.echo/frontend/src/components/dropzone/UploadConversationDropzone.tsx (1)
788-796: Success Alert color tweak is clean and consistentSwapping
color="green.2"for the basecolor="green"on the successAlertkeeps intent the same while leaning on Mantine’s theme-driven palette. No behavioral delta, keeps the success state visually aligned with the rest of the component.echo/frontend/src/routes/project/report/ProjectReportRoute.tsx (3)
61-61: LGTM - Beta label ship it.Experimental → Beta rename clean. Lingui macro maintained, objective nailed.
60-62: Verify Badge color matches tag standards.Per objectives, tags should rock light blue. Badge has no explicit color prop—confirm default matches or add
color="blue.1"(or whatever light blue maps to in your Mantine theme).
334-334: Verify CTA uses dark blue.Proceed button should hit that dark blue per objectives (CTAs only). Confirm
color="blue"maps to dark blue in your Mantine config, not light blue.echo/frontend/src/components/participant/ParticipantConversationAudio.tsx (1)
275-282: I'll verify the localization changes and workflow compliance for this review comment. Let me start by examining the actual file changes and checking if the localization workflow has been properly executed.
<function_calls>
#!/bin/bashExamine the actual file at lines 275-282
cat -n echo/frontend/src/components/participant/ParticipantConversationAudio.tsx | sed -n '270,290p'
</function_calls>Let me also check the git diff to see what actually changed:
<function_calls>
#!/bin/bashGet the diff for the specific file to see the actual changes
git diff HEAD~1 HEAD -- echo/frontend/src/components/participant/ParticipantConversationAudio.tsx | head -100
</function_calls>Now let me search for locale files to verify if the new i18n keys were properly added:
<function_calls>
#!/bin/bashSearch for locale files and the new i18n keys
fd -e po -e ts --type f | grep -i locale | head -20
</function_calls><function_calls>
#!/bin/bashSearch for the new i18n keys in the codebase
rg "participant.modal.refine.info.title.concrete|participant.modal.refine.info.title.go.deeper" -n
</function_calls><function_calls>
#!/bin/bashAlso search for old keys to see if they were replaced
rg "participant.modal.refine.info.title.verify|participant.modal.refine.info.title.echo" -n
</function_calls><function_calls>
#!/bin/bashCheck if there are any .po files that contain translations
find . -name "*.po" -type f 2>/dev/null | head -10
</function_calls><function_calls>
#!/bin/bashLook for message catalogs or locale configuration
find echo/frontend -type f ( -name ".po" -o -name ".pot" -o -name "messages" -o -name "locales" ) 2>/dev/null | head -20
</function_calls><function_calls>
#!/bin/bashCheck the structure of frontend locales
ls -la echo/frontend/src/locales/ 2>/dev/null || echo "Locales directory not found, searching..."
find echo/frontend -type d -name locales 2>/dev/null
</function_calls><function_calls>
#!/bin/bashCheck for Lingui configuration
find . -name ".linguirc" -o -name "lingui.config." -o -name "babel.config." 2>/dev/null | grep -v node_modules
</function_calls>echo/server/prompt_templates/get_reply_system.en.jinja (1)
26-30: Love the collective vibe, but 1-3 sentences might be too tight.The style guidelines are 🔥 - collective ownership language and adaptive responses are exactly what you want for a deliberation platform. But hard-capping at 3 sentences could be limiting for nuanced discussions. Consider if complex topics need more breathing room or if this constraint might force the AI to oversimplify.
Worth testing this prompt with some complex scenarios to ensure quality doesn't take a hit. The structured analysis you removed (mentioned in summary) provided reasoning transparency.
echo/server/prompt_templates/get_reply_system.fr.jinja (1)
21-30: French localization looks solid.The translations are on point - "phrases" for sentences, "nous/notre" for collective ownership, and the audio fragments phrasing all check out. Consistent with the English template changes. The learning note about keeping "prompt" untranslated is already respected in line 10.
echo/server/prompt_templates/get_reply_system.nl.jinja (1)
21-30: Dutch version ships clean.Translations are spot-on: "gesprekstranscripten", "audiofragmenten", "zinnen" all track perfectly. The "wij/onze" collective language matches the pattern. Consistent implementation across all locales.
echo/server/prompt_templates/get_reply_system.es.jinja (1)
21-30: Spanish localization is dialed in.Clean translations: "oraciones" for sentences, "nosotros/nuestro" for the collective vibe, "fragmentos de audio" all work. Pattern holds across all language versions.
echo/server/prompt_templates/get_reply_system.de.jinja (1)
21-30: German version passes the vibe check.Translations are proper: "Gesprächstranskripte", "Audiofragmente", compound nouns looking good. "innerhalb von 1-3 Sätzen" is correct grammatically, and "wir/unser" delivers the collective ownership pattern. All five language templates are now in sync.
echo/frontend/src/components/conversation/ConversationAccordion.tsx (2)
455-462: Project tag pill styling matches the light‑blue spec, no logic impactUsing
classNames.rootwithbg-[var(--mantine-primary-color-light)]+ medium weight gives tags the light-blue treatment the spec calls for while keeping behavior untouched. This also lines up with how you’re styling pills elsewhere in the PR.
As per coding guidelines, this is exactly the Mantine+Tailwind composition we want.
1070-1085: Selected-tag pills now visually align with conversation tag pillsMirroring the same
primary-color-light+ medium-weight root styling on these removable Pills keeps the “selected tag” affordance consistent between the filter UI and the conversation list. Clean, predictable UX, zero behavioral change.echo/frontend/src/components/conversation/ConversationEdit.tsx (1)
134-156: MultiSelect pill override tightens up tag color consistency across the appHooking into
classNames.pillwithbg-[var(--mantine-primary-color-light)]and a slightly heavier font locks the edit-form tag chips to the same light-blue visual language as the conversation tag pills. Behavior, autosave, and tag wiring all remain unchanged.
As per coding guidelines, this is solid Mantine+Tailwind layering for the tags surface.echo/frontend/src/components/chat/TemplatesModal.tsx (1)
118-118: Branding copy + template key usage look solidSwitching the helper text to “Dembrane” is aligned with the rest of the chat branding, and keeping
template.titleas the selection key stays consistent with the established templates pattern. Just make sure you run the Lingui extract/compile step so this updated string lands in the catalogs. Based on learnings, this title-as-key pattern is exactly what we want.echo/frontend/src/components/participant/verify/VerifyArtefactError.tsx (1)
19-21: i18n namespace shift toparticipant.concrete.*is consistentUpdating these Trans IDs into the
participant.concrete.artefact.*namespace keeps this screen aligned with the rest of the “make it concrete” flow without touching behavior. Just make sure the new keys are present in your catalogs and rerunpnpm messages:extract && pnpm messages:compilebefore cutting the build, as per the localization workflow.Also applies to: 24-28, 40-42, 52-54
echo/frontend/src/components/layout/ParticipantHeader.tsx (1)
79-81: Cancel button key now matches the concrete instructions flowSwapping the Cancel button to
participant.concrete.instructions.button.cancelkeeps this header in sync with the rest of the concrete/verify instructions screens. Nothing else changes behavior‑wise; just remember to pull this through the Lingui extract/compile pipeline when you ship.echo/frontend/src/routes/project/chat/ProjectChatRoute.tsx (1)
576-577: AI disclaimer copy update is coherent across desktop + mobileBoth desktop and mobile footers now use the “Dembrane is powered by AI. Please double-check responses.” disclaimer, which is consistent with the rest of the chat branding. Behavior is unchanged; this is just a clean copy/branding pass. If this string evolves again later, consider centralizing it to avoid drift between the two footers, but for now this is totally shippable.
Also applies to: 606-607
echo/frontend/src/components/participant/verify/VerifySelection.tsx (1)
183-185: Concrete selection i18n + beefier CTA look goodRouting this screen’s title and Next button through
participant.concrete.selection.*is aligned with the rest of the concrete flow, and bumping the Next CTA tosize="xl"matches the emphasis we want on the primary action without touching any of the selection logic. Run the usual messages extract/compile so these new IDs are wired up, and this piece is ready to go.Also applies to: 225-225, 237-238
echo/frontend/src/components/participant/verify/VerifyArtefactLoading.tsx (1)
13-15: Loading screen i18n keys now match concrete namespaceShifting the loading title and description to
participant.concrete.loading.artefact*keeps this spinner screen consistent with the rest of the concrete artefact flow. No logic changes, just i18n plumbing — make sure the new keys are in your catalogs before release.Also applies to: 18-20
echo/frontend/src/components/participant/verify/VerifyInstructions.tsx (2)
17-56: LGTM — namespace migration is ship-ready.Clean i18n key migration from
participant.verify.instructions.*toparticipant.concrete.instructions.*. Aligns with the broader "Make it concrete" rebrand. Don't forget to runpnpm messages:extract && pnpm messages:compileafter landing this. As per coding guidelines, Lingui workflow is active.
92-92: Button size bump looks intentional.Size
"xl"for better touch targets on mobile — solid UX call.echo/frontend/src/routes/project/conversation/ProjectConversationOverview.tsx (2)
51-58: LGTM — fetching project language for locale-aware artefacts.Adding
"language"to the fields array is the minimal diff needed to pipe locale through toVerifiedArtefactsSection. 10x efficiency.
164-170: LGTM — clean prop threading.Guard on
conversationId && projectIdis correct.projectLanguageis optional and safely falls back insideVerifiedArtefactsSection. Ship it.echo/frontend/src/components/participant/verify/VerifyArtefact.tsx (3)
303-311: LGTM — concrete namespace for regeneration copy.i18n keys migrated cleanly. The loading state UX is preserved.
316-331: LGTM — read-aloud control refactored nicely.Conditional render on
readAloudUrlis the right guard.ml="auto"pushes the icon right — clean Mantine primitive usage. ARIA labels are properly localized viatmacro.
361-372: LGTM — action button labels migrated to concrete namespace.Cancel, Save, Revise, Approve — all mapped to
participant.concrete.action.button.*. Consistent with the rebrand. Rememberpnpm messages:extract && pnpm messages:compile.Also applies to: 394-429
echo/frontend/src/components/project/ProjectPortalEditor.tsx (5)
139-161: LGTM — Badge refactor for proper nouns is cleaner.Switched from Pill to Badge with
rightSectionActionIcon.textTransform: "none"preserves casing for proper nouns. Solid Mantine primitive composition.
547-561: LGTM — "Go deeper" feature block with Beta badge.Rebrand from "Echo" complete. Beta badge replaces "Experimental" per PR objectives. Logo inline with the title is a nice touch.
614-694: LGTM — Badge-based mode selector is clean.Default/Brainstorm/Custom modes using Badge with variant toggling. Disabled states handled via style props (cursor, opacity). Based on learnings, keyboard nav enhancements are optional here.
736-749: LGTM — "Make it concrete" feature block with Beta badge.Rebrand from "Verify" complete. Consistent structure with "Go deeper" section.
846-854: LGTM — deselection guard prevents empty topic list.Correct defensive check — can't deselect the last topic. Toast feedback via
@/components/common/Toasteras per coding guidelines.echo/frontend/src/components/conversation/VerifiedArtefactsSection.tsx (5)
19-23: LGTM — props expanded for locale-aware labeling.
projectIdandprojectLanguageadded cleanly. OptionalprojectLanguageis the right call since it may be undefined during loading.
55-67: LGTM — topic label mapping with solid fallback chain.
locale → en-US → topic.keyis the correct degradation path.Maplookup is O(1). Ship it.
69-76: Minor:topicsQueryloading state isn't explicitly handled.Currently, if
topicsQueryis still loading butartefactsare ready, you'll render with emptytopicLabelMap(fallback toartefact.key). This is probably fine since the fallback is sensible, but worth noting. If you want polish, you could extend the skeleton condition to includetopicsQuery.isLoading.
86-92: LGTM — title simplified to "Artefacts".Cleaner naming. aria-label updated to match.
108-110: LGTM — label resolution with graceful fallback.
topicLabelMap.get(artefact.key) ?? artefact.key ?? ""handles all edge cases. Defensive and correct.
| const LANGUAGE_TO_LOCALE: Record<string, string> = { | ||
| de: "de-DE", | ||
| en: "en-US", | ||
| es: "es-ES", | ||
| fr: "fr-FR", | ||
| nl: "nl-NL", | ||
| }; |
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🧹 Nitpick | 🔵 Trivial
Same DRY note as ProjectPortalEditor — LANGUAGE_TO_LOCALE is duplicated.
See earlier comment. Extract to shared util when you get a chance.
🤖 Prompt for AI Agents
In echo/frontend/src/components/conversation/VerifiedArtefactsSection.tsx around
lines 35 to 41, the LANGUAGE_TO_LOCALE mapping is duplicated elsewhere (see
ProjectPortalEditor); extract this constant into a shared util (e.g.,
echo/frontend/src/utils/locale.ts), export it with the same
Record<string,string> typing, replace the local declaration with an import from
that util in VerifiedArtefactsSection.tsx (and update the other file to import
the same util), and remove the duplicate local definitions; ensure any existing
imports/exports compile and run tests/lint to validate.
| import { | ||
| ActionIcon, | ||
| Alert, | ||
| Badge, |
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🧹 Nitpick | 🔵 Trivial
Badge refactor for tags is clean; color tweak is optional
Nice upgrade to Badge with rightSection for the delete action — DnD wiring (ref, attributes, listeners) plus onPointerDown on the ActionIcon keeps drag vs delete behavior predictable, which is exactly what you want in a sortable tag list.
If you want these project tags to visually align with the “light blue tags / dark blue only for CTAs” push, you could switch the Badge color to the blue token while staying on variant="light" (e.g. color="blue" instead of "black"), and leave real dark-blue fills for primary buttons.
Also applies to: 81-108
🤖 Prompt for AI Agents
In echo/frontend/src/components/project/ProjectTagsInput.tsx around line 22 (and
similarly for lines 81-108), the Badge component currently uses color="black";
update it to use color="blue" while keeping variant="light" so tags match the
light-blue token scheme (keep the existing rightSection delete ActionIcon and
all DnD wiring unchanged); ensure both the primary Badge declaration and any
repeated Badge instances in the 81-108 range are updated to color="blue".
| def _parse_directus_datetime(value: Optional[str]) -> Optional[datetime]: | ||
| if value is None: | ||
| return None | ||
| try: | ||
| # Directus returns ISO strings that may end with 'Z' | ||
| return datetime.fromisoformat(value.replace("Z", "+00:00")) | ||
| except ValueError: | ||
| logger.warning("Unable to parse datetime value '%s'", value) | ||
| return None |
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🧹 Nitpick | 🔵 Trivial
Datetime parsing + audio chunk selection logic is clean; consider a slightly safer failure mode
The end-to-end flow here is tight: you parse Directus timestamps, derive last_reply_time, and then select only chunks with no transcript and a valid path, which is exactly what we want for “unanswered audio” replay.
One small hardening tweak: when _parse_directus_datetime fails for a given chunk timestamp, we currently treat that chunk as eligible regardless of last_reply_time. If Directus ever hands back a malformed timestamp, we’d silently attach potentially very old audio. Failing “closed” is probably safer:
- if last_reply_time and timestamp:
- chunk_time = (
- _parse_directus_datetime(timestamp) if isinstance(timestamp, str) else timestamp
- )
- if isinstance(chunk_time, datetime) and chunk_time <= last_reply_time:
- continue
+ if last_reply_time and timestamp:
+ chunk_time = (
+ _parse_directus_datetime(timestamp) if isinstance(timestamp, str) else timestamp
+ )
+ # If we can't parse the timestamp, skip rather than silently treating it as "new"
+ if not isinstance(chunk_time, datetime):
+ continue
+ if chunk_time <= last_reply_time:
+ continueIf you expect a lot of chunks, you might also want a soft cap (e.g., top N newest audio-only chunks) so we don’t end up base64‑encoding hundreds of attachments on a single call. -->
Also applies to: 95-123, 140-140, 187-193
| # Build multimodal message content | ||
| message_content = [{"type": "text", "text": prompt}] | ||
|
|
||
| # Add audio chunks without transcripts | ||
| for chunk in audio_chunks: | ||
| chunk_id = chunk.get("id") | ||
| timestamp = chunk.get("timestamp") | ||
| message_content.append( | ||
| { | ||
| "type": "text", | ||
| "text": f"Audio chunk {chunk_id} captured at {timestamp}", | ||
| } | ||
| ) | ||
| path = chunk.get("path") | ||
| if path: | ||
| try: | ||
| audio_obj = await run_in_thread_pool(_get_audio_file_object, path) | ||
| message_content.append(audio_obj) | ||
| except Exception as exc: | ||
| logger.warning("Failed to attach audio chunk %s: %s", chunk_id, exc) | ||
|
|
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🧩 Analysis chain
Script executed:
#!/bin/bash
# First, let's find the file and understand its structure
wc -l echo/server/dembrane/reply_utils.pyRepository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
482 echo/server/dembrane/reply_utils.py
</shell_result>
Script executed:
#!/bin/bash
# Inspect the MULTI_MODAL_PRO call sites mentioned in the review
rg -n "MULTI_MODAL_PRO" echo/server -C5Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/dembrane/reply_utils.py-435- messages=[
echo/server/dembrane/reply_utils.py-436- {"role": "user", "content": message_content},
echo/server/dembrane/reply_utils.py-437- ],
echo/server/dembrane/reply_utils.py-438- stream=True,
echo/server/dembrane/reply_utils.py-439- thinking={"type": "enabled", "budget_tokens": 500},
echo/server/dembrane/reply_utils.py:440: **get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
echo/server/dembrane/reply_utils.py-441- )
echo/server/dembrane/reply_utils.py-442- except ContentPolicyViolationError as e:
echo/server/dembrane/reply_utils.py-443- logger.error(
echo/server/dembrane/reply_utils.py-444- f"Content policy violation for conversation {conversation_id}. Error: {str(e)}"
echo/server/dembrane/reply_utils.py-445- )
--
echo/server/dembrane/transcribe.py-212- "required": ["corrected_transcript", "note"],
echo/server/dembrane/transcribe.py-213- }
echo/server/dembrane/transcribe.py-214-
echo/server/dembrane/transcribe.py-215- assert GCP_SA_JSON, "GCP_SA_JSON is not set"
echo/server/dembrane/transcribe.py-216-
echo/server/dembrane/transcribe.py:217: completion_kwargs = get_completion_kwargs(MODELS.MULTI_MODAL_PRO)
echo/server/dembrane/transcribe.py-218- response = litellm.completion(
echo/server/dembrane/transcribe.py-219- messages=[
echo/server/dembrane/transcribe.py-220- {
echo/server/dembrane/transcribe.py-221- "role": "system",
echo/server/dembrane/transcribe.py-222- "content": [
--
echo/server/dembrane/llms.py-9-
echo/server/dembrane/llms.py-10-logger = logging.getLogger(__name__)
echo/server/dembrane/llms.py-11-
echo/server/dembrane/llms.py-12-
echo/server/dembrane/llms.py-13-class MODELS(Enum):
echo/server/dembrane/llms.py:14: MULTI_MODAL_PRO = "MULTI_MODAL_PRO" # Gemini 2.5 Pro – chat/report/inference
echo/server/dembrane/llms.py-15- MULTI_MODAL_FAST = "MULTI_MODAL_FAST" # Gemini 2.5 Flash – realtime/verification
echo/server/dembrane/llms.py-16- TEXT_FAST = "TEXT_FAST" # GPT-5 style small text model – summaries & utilities
echo/server/dembrane/llms.py-17-
echo/server/dembrane/llms.py-18-
echo/server/dembrane/llms.py-19-MODEL_REGISTRY: Dict[MODELS, Dict[str, str]] = {
echo/server/dembrane/llms.py:20: MODELS.MULTI_MODAL_PRO: {"settings_attr": "multi_modal_pro"},
echo/server/dembrane/llms.py-21- MODELS.MULTI_MODAL_FAST: {"settings_attr": "multi_modal_fast"},
echo/server/dembrane/llms.py-22- MODELS.TEXT_FAST: {"settings_attr": "text_fast"},
echo/server/dembrane/llms.py-23-}
echo/server/dembrane/llms.py-24-
echo/server/dembrane/llms.py-25-
--
echo/server/dembrane/chat_utils.py-235- "generate_chat_title", "en", {"user_query": user_query, "language": language}
echo/server/dembrane/chat_utils.py-236- )
echo/server/dembrane/chat_utils.py-237-
echo/server/dembrane/chat_utils.py-238- response = await acompletion(
echo/server/dembrane/chat_utils.py-239- messages=[{"role": "user", "content": title_prompt}],
echo/server/dembrane/chat_utils.py:240: **get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
echo/server/dembrane/chat_utils.py-241- )
echo/server/dembrane/chat_utils.py-242-
echo/server/dembrane/chat_utils.py-243- if response.choices[0].message.content is None:
echo/server/dembrane/chat_utils.py-244- logger.warning(f"No title generated for user query: {user_query}")
echo/server/dembrane/chat_utils.py-245- return None
--
echo/server/dembrane/api/verify.py-621- {
echo/server/dembrane/api/verify.py-622- "prompt": target_topic.prompt,
echo/server/dembrane/api/verify.py-623- },
echo/server/dembrane/api/verify.py-624- )
echo/server/dembrane/api/verify.py-625-
echo/server/dembrane/api/verify.py:626: completion_kwargs = get_completion_kwargs(MODELS.MULTI_MODAL_PRO)
echo/server/dembrane/api/verify.py-627-
echo/server/dembrane/api/verify.py-628- try:
echo/server/dembrane/api/verify.py-629- response = await litellm.acompletion(
echo/server/dembrane/api/verify.py-630- messages=[
echo/server/dembrane/api/verify.py-631- {
--
echo/server/dembrane/api/verify.py-749- audio_obj = await run_in_thread_pool(_get_audio_file_object, path)
echo/server/dembrane/api/verify.py-750- message_content.append(audio_obj)
echo/server/dembrane/api/verify.py-751- except Exception as exc: # pragma: no cover - logging side effect
echo/server/dembrane/api/verify.py-752- logger.warning("Failed to attach audio chunk %s: %s", chunk_id, exc)
echo/server/dembrane/api/verify.py-753-
echo/server/dembrane/api/verify.py:754: revision_completion_kwargs = get_completion_kwargs(MODELS.MULTI_MODAL_PRO)
echo/server/dembrane/api/verify.py-755-
echo/server/dembrane/api/verify.py-756- try:
echo/server/dembrane/api/verify.py-757- response = await litellm.acompletion(
echo/server/dembrane/api/verify.py-758- messages=[
echo/server/dembrane/api/verify.py-759- {
--
echo/server/dembrane/api/conversation.py-420- # If not in cache, calculate the token count
echo/server/dembrane/api/conversation.py-421- transcript = await get_conversation_transcript(conversation_id, auth)
echo/server/dembrane/api/conversation.py-422-
echo/server/dembrane/api/conversation.py-423- token_count = token_counter(
echo/server/dembrane/api/conversation.py-424- messages=[{"role": "user", "content": transcript}],
echo/server/dembrane/api/conversation.py:425: model=get_completion_kwargs(MODELS.MULTI_MODAL_PRO)["model"],
echo/server/dembrane/api/conversation.py-426- )
echo/server/dembrane/api/conversation.py-427-
echo/server/dembrane/api/conversation.py-428- # Store the result in the cache
echo/server/dembrane/api/conversation.py-429- await token_count_cache.set(conversation_id, token_count)
echo/server/dembrane/api/conversation.py-430-
--
echo/server/dembrane/api/chat.py-598- chat_context.conversation_id_list + conversations_added_ids + [conversation_id]
echo/server/dembrane/api/chat.py-599- )
echo/server/dembrane/api/chat.py-600- candidate_messages = await build_formatted_messages(temp_ids)
echo/server/dembrane/api/chat.py-601- prompt_len = token_counter(
echo/server/dembrane/api/chat.py-602- messages=candidate_messages,
echo/server/dembrane/api/chat.py:603: model=get_completion_kwargs(MODELS.MULTI_MODAL_PRO)["model"],
echo/server/dembrane/api/chat.py-604- )
echo/server/dembrane/api/chat.py-605-
echo/server/dembrane/api/chat.py-606- if prompt_len > max_context_threshold:
echo/server/dembrane/api/chat.py-607- logger.info(
echo/server/dembrane/api/chat.py-608- "Reached 80%% context threshold (%s/%s tokens). Stopping conversation addition.",
--
echo/server/dembrane/api/chat.py-635- updated_context.conversation_id_list
echo/server/dembrane/api/chat.py-636- )
echo/server/dembrane/api/chat.py-637-
echo/server/dembrane/api/chat.py-638- prompt_len = token_counter(
echo/server/dembrane/api/chat.py-639- messages=formatted_messages,
echo/server/dembrane/api/chat.py:640: model=get_completion_kwargs(MODELS.MULTI_MODAL_PRO)["model"],
echo/server/dembrane/api/chat.py-641- )
echo/server/dembrane/api/chat.py-642- if prompt_len > MAX_CHAT_CONTEXT_LENGTH:
echo/server/dembrane/api/chat.py-643- raise HTTPException(
echo/server/dembrane/api/chat.py-644- status_code=400,
echo/server/dembrane/api/chat.py-645- detail="Auto select returned too many conversations. The selected conversations exceed the maximum context length.",
--
echo/server/dembrane/api/chat.py-674- response = await litellm.acompletion(
echo/server/dembrane/api/chat.py-675- messages=formatted,
echo/server/dembrane/api/chat.py-676- stream=True,
echo/server/dembrane/api/chat.py-677- timeout=300,
echo/server/dembrane/api/chat.py-678- stream_timeout=180,
echo/server/dembrane/api/chat.py:679: **get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
echo/server/dembrane/api/chat.py-680- )
echo/server/dembrane/api/chat.py-681- async for chunk in response:
echo/server/dembrane/api/chat.py-682- delta = chunk.choices[0].delta.content
echo/server/dembrane/api/chat.py-683- if delta:
echo/server/dembrane/api/chat.py-684- if protocol == "text":
</shell_result>
Script executed:
#!/bin/bash
# Find the _get_audio_file_object function to understand what it returns
rg -n "_get_audio_file_object" echo/server -C10Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/AGENTS.md-39-- `dembrane/quote_utils.py:118/272/289` – Link quotes to chunks; fix sampling algorithm; adjust context limit math.
echo/server/AGENTS.md-40-- `dembrane/service/conversation.py:101` – Validate `project_tag_id_list`.
echo/server/AGENTS.md-41-- `dembrane/transcribe.py:179` – Replace polling with webhook approach.
echo/server/AGENTS.md-42-- `dembrane/api/chat.py` – Multiple TODOs: fill module stub, add RAG shortcut when quotes exist, implement Directus project fetch, conversation endpoint completion, admin auth checks.
echo/server/AGENTS.md-43-- `dembrane/api/participant.py:76` – Remove unused `pin`.
echo/server/AGENTS.md-44-
echo/server/AGENTS.md-45-# Gotchas & Notes
echo/server/AGENTS.md-46-- Gunicorn uses custom LightRAG uvicorn worker; avoid uvloop to keep LightRAG compatible.
echo/server/AGENTS.md-47-- CPU Dramatiq worker deliberately single-threaded to dodge LightRAG locking issues—respect `THREADS=1` guidance in prod.
echo/server/AGENTS.md-48-- Watching directories (`--watch`, `--watch-use-polling`) adds overhead; keep file changes minimal when workers run locally.
echo/server/AGENTS.md:49:- S3 audio paths used in verification/transcription flows should be loaded via the shared file service (`_get_audio_file_object`) so Gemini always receives fresh bytes—signed URLs may expire mid-request.
echo/server/AGENTS.md-50-- When a Dramatiq actor needs to invoke an async FastAPI handler (e.g., `dembrane.api.conversation.summarize_conversation`), run the coroutine via `run_async_in_new_loop` from `dembrane.async_helpers` instead of calling it directly or with `asyncio.run` to avoid clashing with nested event loops.
--
echo/server/dembrane/reply_utils.py-4-
echo/server/dembrane/reply_utils.py-5-import sentry_sdk
echo/server/dembrane/reply_utils.py-6-from litellm import acompletion
echo/server/dembrane/reply_utils.py-7-from pydantic import BaseModel
echo/server/dembrane/reply_utils.py-8-from litellm.utils import token_counter
echo/server/dembrane/reply_utils.py-9-from litellm.exceptions import ContentPolicyViolationError
echo/server/dembrane/reply_utils.py-10-
echo/server/dembrane/reply_utils.py-11-from dembrane.llms import MODELS, get_completion_kwargs
echo/server/dembrane/reply_utils.py-12-from dembrane.prompts import render_prompt
echo/server/dembrane/reply_utils.py-13-from dembrane.directus import directus
echo/server/dembrane/reply_utils.py:14:from dembrane.transcribe import _get_audio_file_object
echo/server/dembrane/reply_utils.py-15-from dembrane.async_helpers import run_in_thread_pool
echo/server/dembrane/reply_utils.py-16-
echo/server/dembrane/reply_utils.py-17-logger = getLogger("reply_utils")
echo/server/dembrane/reply_utils.py-18-
echo/server/dembrane/reply_utils.py-19-# Constants for token limits and conversation sizing
echo/server/dembrane/reply_utils.py-20-GET_REPLY_TOKEN_LIMIT = 80000
echo/server/dembrane/reply_utils.py-21-GET_REPLY_TARGET_TOKENS_PER_CONV = 4000
echo/server/dembrane/reply_utils.py-22-
echo/server/dembrane/reply_utils.py-23-
echo/server/dembrane/reply_utils.py-24-class Conversation(BaseModel):
--
echo/server/dembrane/reply_utils.py-414- timestamp = chunk.get("timestamp")
echo/server/dembrane/reply_utils.py-415- message_content.append(
echo/server/dembrane/reply_utils.py-416- {
echo/server/dembrane/reply_utils.py-417- "type": "text",
echo/server/dembrane/reply_utils.py-418- "text": f"Audio chunk {chunk_id} captured at {timestamp}",
echo/server/dembrane/reply_utils.py-419- }
echo/server/dembrane/reply_utils.py-420- )
echo/server/dembrane/reply_utils.py-421- path = chunk.get("path")
echo/server/dembrane/reply_utils.py-422- if path:
echo/server/dembrane/reply_utils.py-423- try:
echo/server/dembrane/reply_utils.py:424: audio_obj = await run_in_thread_pool(_get_audio_file_object, path)
echo/server/dembrane/reply_utils.py-425- message_content.append(audio_obj)
echo/server/dembrane/reply_utils.py-426- except Exception as exc:
echo/server/dembrane/reply_utils.py-427- logger.warning("Failed to attach audio chunk %s: %s", chunk_id, exc)
echo/server/dembrane/reply_utils.py-428-
echo/server/dembrane/reply_utils.py-429- # Store the complete response
echo/server/dembrane/reply_utils.py-430- accumulated_response = ""
echo/server/dembrane/reply_utils.py-431-
echo/server/dembrane/reply_utils.py-432- # Stream the response
echo/server/dembrane/reply_utils.py-433- try:
echo/server/dembrane/reply_utils.py-434- response = await acompletion(
--
echo/server/dembrane/transcribe.py-143- elif transcript["status"] == "error":
echo/server/dembrane/transcribe.py-144- raise TranscriptionError(f"Transcription failed: {transcript['error']}")
echo/server/dembrane/transcribe.py-145- else:
echo/server/dembrane/transcribe.py-146- time.sleep(3)
echo/server/dembrane/transcribe.py-147- elif response.status_code == 400:
echo/server/dembrane/transcribe.py-148- raise TranscriptionError(f"Transcription failed: {response.json()['error']}")
echo/server/dembrane/transcribe.py-149- else:
echo/server/dembrane/transcribe.py-150- raise Exception(f"Transcription failed: {response.json()['error']}")
echo/server/dembrane/transcribe.py-151-
echo/server/dembrane/transcribe.py-152-
echo/server/dembrane/transcribe.py:153:def _get_audio_file_object(audio_file_uri: str) -> Any:
echo/server/dembrane/transcribe.py-154- try:
echo/server/dembrane/transcribe.py-155- audio_stream = file_service.get_stream(audio_file_uri)
echo/server/dembrane/transcribe.py-156- encoded_data = b64encode(audio_stream.read()).decode("utf-8")
echo/server/dembrane/transcribe.py-157- return {
echo/server/dembrane/transcribe.py-158- "type": "file",
echo/server/dembrane/transcribe.py-159- "file": {
echo/server/dembrane/transcribe.py-160- "file_data": "data:audio/mp3;base64,{}".format(encoded_data),
echo/server/dembrane/transcribe.py-161- },
echo/server/dembrane/transcribe.py-162- }
echo/server/dembrane/transcribe.py-163- except Exception as e:
--
echo/server/dembrane/transcribe.py-226- },
echo/server/dembrane/transcribe.py-227- ],
echo/server/dembrane/transcribe.py-228- },
echo/server/dembrane/transcribe.py-229- {
echo/server/dembrane/transcribe.py-230- "role": "user",
echo/server/dembrane/transcribe.py-231- "content": [
echo/server/dembrane/transcribe.py-232- {
echo/server/dembrane/transcribe.py-233- "type": "text",
echo/server/dembrane/transcribe.py-234- "text": candidate_transcript,
echo/server/dembrane/transcribe.py-235- },
echo/server/dembrane/transcribe.py:236: _get_audio_file_object(audio_file_uri),
echo/server/dembrane/transcribe.py-237- ],
echo/server/dembrane/transcribe.py-238- },
echo/server/dembrane/transcribe.py-239- ],
echo/server/dembrane/transcribe.py-240- response_format={
echo/server/dembrane/transcribe.py-241- "type": "json_object",
echo/server/dembrane/transcribe.py-242- "response_schema": response_schema,
echo/server/dembrane/transcribe.py-243- },
echo/server/dembrane/transcribe.py-244- **completion_kwargs,
echo/server/dembrane/transcribe.py-245- )
echo/server/dembrane/transcribe.py-246-
--
echo/server/dembrane/api/verify.py-6-
echo/server/dembrane/api/verify.py-7-import litellm
echo/server/dembrane/api/verify.py-8-from fastapi import APIRouter, HTTPException
echo/server/dembrane/api/verify.py-9-from pydantic import Field, BaseModel
echo/server/dembrane/api/verify.py-10-
echo/server/dembrane/api/verify.py-11-from dembrane.llms import MODELS, get_completion_kwargs
echo/server/dembrane/api/verify.py-12-from dembrane.utils import generate_uuid
echo/server/dembrane/api/verify.py-13-from dembrane.prompts import render_prompt
echo/server/dembrane/api/verify.py-14-from dembrane.directus import DirectusClient, directus
echo/server/dembrane/api/verify.py-15-from dembrane.settings import get_settings
echo/server/dembrane/api/verify.py:16:from dembrane.transcribe import _get_audio_file_object
echo/server/dembrane/api/verify.py-17-from dembrane.async_helpers import run_in_thread_pool
echo/server/dembrane/api/verify.py-18-from dembrane.api.exceptions import ProjectNotFoundException, ConversationNotFoundException
echo/server/dembrane/api/verify.py-19-
echo/server/dembrane/api/verify.py-20-logger = logging.getLogger("api.verify")
echo/server/dembrane/api/verify.py-21-
echo/server/dembrane/api/verify.py-22-settings = get_settings()
echo/server/dembrane/api/verify.py-23-GCP_SA_JSON = settings.transcription.gcp_sa_json
echo/server/dembrane/api/verify.py-24-
echo/server/dembrane/api/verify.py-25-VerifyRouter = APIRouter(tags=["verify"])
echo/server/dembrane/api/verify.py-26-
--
echo/server/dembrane/api/verify.py-603- chunk_id = chunk.get("id")
echo/server/dembrane/api/verify.py-604- message_content.append(
echo/server/dembrane/api/verify.py-605- {
echo/server/dembrane/api/verify.py-606- "type": "text",
echo/server/dembrane/api/verify.py-607- "text": f"Audio chunk {chunk_id} captured at {ts_value}",
echo/server/dembrane/api/verify.py-608- }
echo/server/dembrane/api/verify.py-609- )
echo/server/dembrane/api/verify.py-610- path = chunk.get("path")
echo/server/dembrane/api/verify.py-611- if path:
echo/server/dembrane/api/verify.py-612- try:
echo/server/dembrane/api/verify.py:613: audio_obj = await run_in_thread_pool(_get_audio_file_object, path)
echo/server/dembrane/api/verify.py-614- message_content.append(audio_obj)
echo/server/dembrane/api/verify.py-615- except Exception as exc:
echo/server/dembrane/api/verify.py-616- logger.warning("Failed to attach audio chunk %s: %s", chunk_id, exc)
echo/server/dembrane/api/verify.py-617-
echo/server/dembrane/api/verify.py-618- system_prompt = render_prompt(
echo/server/dembrane/api/verify.py-619- "generate_artifact",
echo/server/dembrane/api/verify.py-620- "en",
echo/server/dembrane/api/verify.py-621- {
echo/server/dembrane/api/verify.py-622- "prompt": target_topic.prompt,
echo/server/dembrane/api/verify.py-623- },
--
echo/server/dembrane/api/verify.py-739- chunk_id = chunk.get("id")
echo/server/dembrane/api/verify.py-740- message_content.append(
echo/server/dembrane/api/verify.py-741- {
echo/server/dembrane/api/verify.py-742- "type": "text",
echo/server/dembrane/api/verify.py-743- "text": f"Audio chunk {chunk_id} captured at {ts_value}",
echo/server/dembrane/api/verify.py-744- }
echo/server/dembrane/api/verify.py-745- )
echo/server/dembrane/api/verify.py-746- path = chunk.get("path")
echo/server/dembrane/api/verify.py-747- if path:
echo/server/dembrane/api/verify.py-748- try:
echo/server/dembrane/api/verify.py:749: audio_obj = await run_in_thread_pool(_get_audio_file_object, path)
echo/server/dembrane/api/verify.py-750- message_content.append(audio_obj)
echo/server/dembrane/api/verify.py-751- except Exception as exc: # pragma: no cover - logging side effect
echo/server/dembrane/api/verify.py-752- logger.warning("Failed to attach audio chunk %s: %s", chunk_id, exc)
echo/server/dembrane/api/verify.py-753-
echo/server/dembrane/api/verify.py-754- revision_completion_kwargs = get_completion_kwargs(MODELS.MULTI_MODAL_PRO)
echo/server/dembrane/api/verify.py-755-
echo/server/dembrane/api/verify.py-756- try:
echo/server/dembrane/api/verify.py-757- response = await litellm.acompletion(
echo/server/dembrane/api/verify.py-758- messages=[
echo/server/dembrane/api/verify.py-759- {
</shell_result>
Script executed:
#!/bin/bash
# Find requirements/dependencies files
fd -e txt -e toml -e cfg -e lock | grep -E "(require|depend|pyproject|setup|poetry|pip)" | head -20Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Script execution failed with error: Request timed out after 120000ms (requestId=c8bb3938-4804-49f7-ae49-e8fb0752c608)
</shell_result>
Script executed:
#!/bin/bash
# Check what actual model names are used
rg -n "multi_modal_pro|MULTI_MODAL_PRO" echo/server/dembrane/settings.py -A2 -B2Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
124- )
125-
126: multi_modal_pro: LLMProviderConfig = Field(default_factory=LLMProviderConfig)
127- multi_modal_fast: LLMProviderConfig = Field(default_factory=LLMProviderConfig)
128- text_fast: LLMProviderConfig = Field(default_factory=LLMProviderConfig)
</shell_result>
Script executed:
#!/bin/bash
# Check the full llms.py file to see model names
cat echo/server/dembrane/llms.pyRepository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
from __future__ import annotations
import json
import logging
from enum import Enum
from typing import Any, Dict
from dembrane.settings import get_settings
logger = logging.getLogger(__name__)
class MODELS(Enum):
MULTI_MODAL_PRO = "MULTI_MODAL_PRO" # Gemini 2.5 Pro – chat/report/inference
MULTI_MODAL_FAST = "MULTI_MODAL_FAST" # Gemini 2.5 Flash – realtime/verification
TEXT_FAST = "TEXT_FAST" # GPT-5 style small text model – summaries & utilities
MODEL_REGISTRY: Dict[MODELS, Dict[str, str]] = {
MODELS.MULTI_MODAL_PRO: {"settings_attr": "multi_modal_pro"},
MODELS.MULTI_MODAL_FAST: {"settings_attr": "multi_modal_fast"},
MODELS.TEXT_FAST: {"settings_attr": "text_fast"},
}
def get_completion_kwargs(model: MODELS, **overrides: Any) -> Dict[str, Any]:
"""
Return the kwargs to pass into LiteLLM helpers for a configured model.
"""
settings = get_settings()
attr = MODEL_REGISTRY[model]["settings_attr"]
provider = getattr(settings.llms, attr, None)
if provider is None:
raise ValueError(f"No configuration found for model group {model.value}.")
resolved = provider.resolve()
kwargs: Dict[str, Any] = {"model": resolved.model}
if resolved.api_key:
kwargs["api_key"] = resolved.api_key
if resolved.api_base:
kwargs["api_base"] = resolved.api_base
if resolved.api_version:
kwargs["api_version"] = resolved.api_version
vertex_credentials = resolved.vertex_credentials or settings.transcription.gcp_sa_json
if vertex_credentials:
kwargs["vertex_credentials"] = json.dumps(vertex_credentials)
if resolved.vertex_project:
kwargs["vertex_project"] = resolved.vertex_project
if resolved.vertex_location:
kwargs["vertex_location"] = resolved.vertex_location
# Allow callers to override any field (e.g., temperature, max_tokens)
kwargs.update(overrides)
return kwargs
__all__ = ["MODELS", "get_completion_kwargs"]
</shell_result>
Script executed:
#!/bin/bash
# Find litellm version in requirements
find . -type f \( -name "*.txt" -o -name "*.toml" -o -name "*.cfg" \) | xargs grep -l "litellm" 2>/dev/null | head -5Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
./echo/server/pyproject.toml
</shell_result>
Script executed:
#!/bin/bash
# Check litellm version in pyproject.toml
cat echo/server/pyproject.toml | grep -A5 -B5 "litellm"Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
"types-aiofiles==23.2.*",
"types-tqdm > 4",
"pandas-stubs>=2.2.2.240514",
"types-requests>=2.32.0.20240602",
"types-python-jose>=3.3.4.20240106",
"litellm==1.79.*",
# Additional Dependencies
"lightrag-dembrane==1.2.7.8",
"nest-asyncio==1.6.0",
"pydantic==2.10.6",
"pydantic-settings==2.6.1",
</shell_result>
Script executed:
#!/bin/bash
# Search for where the actual model name is set (e.g., "gemini-2.5-pro")
rg -n "gemini.*pro|gemini.*2\.5" echo/server -iRepository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/dembrane/llms.py:14: MULTI_MODAL_PRO = "MULTI_MODAL_PRO" # Gemini 2.5 Pro – chat/report/inference
echo/server/dembrane/llms.py:15: MULTI_MODAL_FAST = "MULTI_MODAL_FAST" # Gemini 2.5 Flash – realtime/verification
</shell_result>
Script executed:
#!/bin/bash
# Check if there's any error handling for unsupported thinking parameter
rg -n "thinking" echo/server --type py -B3 -A3Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/tests/test_chat_utils.py-7- with asyncio.Runner() as runner:
echo/server/tests/test_chat_utils.py-8- test_text = """Transform this content into insights that actually matter. Please:
echo/server/tests/test_chat_utils.py-9-
echo/server/tests/test_chat_utils.py:10:Extract core ideas that challenge standard thinking
echo/server/tests/test_chat_utils.py-11-Write like someone who understands nuance, not a textbook
echo/server/tests/test_chat_utils.py-12-Focus on the non-obvious implications
echo/server/tests/test_chat_utils.py-13-Keep it sharp and substantive
--
echo/server/dembrane/reply_utils.py-436- {"role": "user", "content": message_content},
echo/server/dembrane/reply_utils.py-437- ],
echo/server/dembrane/reply_utils.py-438- stream=True,
echo/server/dembrane/reply_utils.py:439: thinking={"type": "enabled", "budget_tokens": 500},
echo/server/dembrane/reply_utils.py-440- **get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
echo/server/dembrane/reply_utils.py-441- )
echo/server/dembrane/reply_utils.py-442- except ContentPolicyViolationError as e:
--
echo/server/dembrane/seed.py-103- "label": "Breakthrough moments",
echo/server/dembrane/seed.py-104- "sort": 4,
echo/server/dembrane/seed.py-105- "prompt": (
echo/server/dembrane/seed.py:106: "Capture the moments when thinking shifted, new possibilities emerged, or collective understanding jumped "
echo/server/dembrane/seed.py-107- "to a new level. Identify: sudden realizations, creative solutions, perspective shifts, moments when "
echo/server/dembrane/seed.py-108- "complexity became simple, and ideas that energized the group. Show both the breakthrough itself and what "
echo/server/dembrane/seed.py-109- "made it possible. These are the moments when the conversation transcended its starting point. Output "
--
echo/server/dembrane/seed.py-119- "Synthesize the group's emerging sense of direction and next steps. Include: explicit recommendations made, "
echo/server/dembrane/seed.py-120- "implicit preferences expressed, priorities that emerged through discussion, and logical next actions even "
echo/server/dembrane/seed.py-121- "if not explicitly stated. Distinguish between unanimous direction and majority leanings. Present as "
echo/server/dembrane/seed.py:122: "provisional navigation rather than fixed commands. This is the group's best current thinking about the "
echo/server/dembrane/seed.py-123- "path forward. Output character should be pragmatic but inspirational."
echo/server/dembrane/seed.py-124- ),
echo/server/dembrane/seed.py-125- },
</shell_result>
Script executed:
#!/bin/bash
# Look for any README or config that specifies model versions
find echo/server -type f \( -name "README*" -o -name "*.md" \) | xargs grep -l "1\.79\|litellm\|gemini" 2>/dev/null | head -5Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
</shell_result>
Script executed:
#!/bin/bash
# Search for environment variable patterns or examples
rg -n "MULTI_MODAL_PRO|gemini" echo/server --type py -C1 | grep -E "model|gemini|GOOGLE|VERTEX" -i | head -20Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/dembrane/reply_utils.py:440: **get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
echo/server/dembrane/transcribe.py:217: completion_kwargs = get_completion_kwargs(MODELS.MULTI_MODAL_PRO)
echo/server/dembrane/transcribe.py:296: # if the gemini step fail, just use the assemblyai transcript
echo/server/dembrane/transcribe.py:481: gemini_transcript = transcript
echo/server/dembrane/transcribe.py:494: print_diff(gemini_transcript, assemblyai_transcript)
echo/server/dembrane/llms.py-13-class MODELS(Enum):
echo/server/dembrane/llms.py:14: MULTI_MODAL_PRO = "MULTI_MODAL_PRO" # Gemini 2.5 Pro – chat/report/inference
echo/server/dembrane/llms.py-15- MULTI_MODAL_FAST = "MULTI_MODAL_FAST" # Gemini 2.5 Flash – realtime/verification
echo/server/dembrane/llms.py-19-MODEL_REGISTRY: Dict[MODELS, Dict[str, str]] = {
echo/server/dembrane/llms.py:20: MODELS.MULTI_MODAL_PRO: {"settings_attr": "multi_modal_pro"},
echo/server/dembrane/llms.py-21- MODELS.MULTI_MODAL_FAST: {"settings_attr": "multi_modal_fast"},
echo/server/dembrane/chat_utils.py:240: **get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
echo/server/dembrane/api/verify.py:626: completion_kwargs = get_completion_kwargs(MODELS.MULTI_MODAL_PRO)
echo/server/dembrane/api/verify.py:754: revision_completion_kwargs = get_completion_kwargs(MODELS.MULTI_MODAL_PRO)
echo/server/dembrane/api/conversation.py:425: model=get_completion_kwargs(MODELS.MULTI_MODAL_PRO)["model"],
echo/server/dembrane/api/chat.py:603: model=get_completion_kwargs(MODELS.MULTI_MODAL_PRO)["model"],
echo/server/dembrane/api/chat.py:640: model=get_completion_kwargs(MODELS.MULTI_MODAL_PRO)["model"],
echo/server/dembrane/api/chat.py:679: **get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
</shell_result>
Script executed:
#!/bin/bash
# Check if there's a .env.example or similar
find echo -type f -name ".env*" -o -name "*example*" | head -10Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/directus/.env.sample
echo/frontend/.env.example
echo/frontend/.env.sample
echo/server/.env.sample
</shell_result>
Script executed:
#!/bin/bash
# Find LLMProviderConfig to understand model resolution
rg -n "class LLMProviderConfig" echo/server -A20Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/dembrane/settings.py:80:class LLMProviderConfig(BaseModel):
echo/server/dembrane/settings.py-81- model: Optional[str] = None
echo/server/dembrane/settings.py-82- api_key: Optional[str] = None
echo/server/dembrane/settings.py-83- api_base: Optional[str] = None
echo/server/dembrane/settings.py-84- api_version: Optional[str] = None
echo/server/dembrane/settings.py-85- vertex_credentials: Optional[Dict[str, Any]] = None
echo/server/dembrane/settings.py-86- gcp_sa_json: Optional[Dict[str, Any]] = None
echo/server/dembrane/settings.py-87- vertex_project: Optional[str] = None
echo/server/dembrane/settings.py-88- vertex_location: Optional[str] = None
echo/server/dembrane/settings.py-89-
echo/server/dembrane/settings.py-90- @field_validator("vertex_credentials", mode="before")
echo/server/dembrane/settings.py-91- @classmethod
echo/server/dembrane/settings.py-92- def parse_vertex_credentials(
echo/server/dembrane/settings.py-93- cls, value: Optional[Any]
echo/server/dembrane/settings.py-94- ) -> Optional[Dict[str, Any]]:
echo/server/dembrane/settings.py-95- return _coerce_service_account(value)
echo/server/dembrane/settings.py-96-
echo/server/dembrane/settings.py-97- @field_validator("gcp_sa_json", mode="before")
echo/server/dembrane/settings.py-98- @classmethod
echo/server/dembrane/settings.py-99- def parse_gcp_sa_json(cls, value: Optional[Any]) -> Optional[Dict[str, Any]]:
echo/server/dembrane/settings.py-100- return _coerce_service_account(value)
</shell_result>
litellm 1.79.0 Gemini 2.5 Pro thinking parameter streaming support
<web_search_result>
Short answer: Yes — liteLLM v1.79.0 adds explicit support for Gemini 2.5 Pro and includes streaming/parse support for Gemini "thinking" content (reasoning_content), but some parameter mappings remain flaky and there are several related GitHub issues. [1][2]
What to know:
- Release notes for the v1.79.x line show Google/Vertex AI (Gemini 2.5 Pro) preview support and "Gemini streaming thinking content parsing with reasoning_content" added. [1]
- Multiple GitHub issues and user reports show that mapping of liteLLM parameters (reasoning_effort / thinking / thinking_budget / reasoning_content) to Gemini's API can be inconsistent and may require specific flags/config (e.g., merge_reasoning_content_in_choices or allowed_openai_params / drop_params workarounds). See issue threads and bug reports. [2][3]
If you want, I can:
- give the exact request payload/litellm client options that users reported working (e.g., thinking={"type":"enabled","budget_tokens":-1} plus merge_reasoning_content_in_choices), or
- inspect the exact litellm release/changelog entry and relevant issue PRs for precise examples.
</web_search_result>
Script executed:
#!/bin/bash
# Check the error handling around the acompletion call in reply_utils.py
sed -n '433,460p' echo/server/dembrane/reply_utils.pyRepository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
try:
response = await acompletion(
messages=[
{"role": "user", "content": message_content},
],
stream=True,
thinking={"type": "enabled", "budget_tokens": 500},
**get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
)
except ContentPolicyViolationError as e:
logger.error(
f"Content policy violation for conversation {conversation_id}. Error: {str(e)}"
)
sentry_sdk.capture_exception(e)
raise
except Exception as e:
logger.error(f"LiteLLM completion failed for {conversation_id}: {str(e)}")
sentry_sdk.capture_exception(e)
raise
try:
async for chunk in response:
if chunk.choices[0].delta.content:
content = chunk.choices[0].delta.content
accumulated_response += content
yield content
except Exception as e:
logger.error(f"Streaming failed for conversation {current_conversation.id}: {e}")
</shell_result>
Script executed:
#!/bin/bash
# Check what happens after the acompletion for other exception types
sed -n '430,480p' echo/server/dembrane/reply_utils.pyRepository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
accumulated_response = ""
# Stream the response
try:
response = await acompletion(
messages=[
{"role": "user", "content": message_content},
],
stream=True,
thinking={"type": "enabled", "budget_tokens": 500},
**get_completion_kwargs(MODELS.MULTI_MODAL_PRO),
)
except ContentPolicyViolationError as e:
logger.error(
f"Content policy violation for conversation {conversation_id}. Error: {str(e)}"
)
sentry_sdk.capture_exception(e)
raise
except Exception as e:
logger.error(f"LiteLLM completion failed for {conversation_id}: {str(e)}")
sentry_sdk.capture_exception(e)
raise
try:
async for chunk in response:
if chunk.choices[0].delta.content:
content = chunk.choices[0].delta.content
accumulated_response += content
yield content
except Exception as e:
logger.error(f"Streaming failed for conversation {current_conversation.id}: {e}")
sentry_sdk.capture_exception(e)
raise
try:
response_content = accumulated_response.strip()
directus.create_item(
"conversation_reply",
item_data={
"conversation_id": current_conversation.id,
"content_text": response_content,
"type": "assistant_reply",
},
)
except Exception as e:
logger.error(f"Failed to store reply in Directus: {e}")
sentry_sdk.capture_exception(e)
raise
</shell_result>
Verification confirms litellm 1.79.0 supports the multimodal and thinking parameters, but reveals known parameter mapping flakiness
The multimodal payload wiring is sound:
_get_audio_file_objectreturns the correct{"type": "file", "file": {"file_data": "data:audio/mp3;base64,..."}structure expected by Gemini API.- litellm 1.79.0 does support Gemini 2.5 Pro with streaming and the
thinkingparameter. - Generic exception handling exists (lines 443–455) that will catch model provider errors.
However, web search reveals litellm 1.79.0 has known parameter mapping inconsistencies for Gemini's thinking/reasoning parameters. Runtime 4xx/5xx errors are plausible if litellm's parameter translation to Gemini's API fails. The current error handling is adequate but generic—it won't distinguish between a failed feature support and other errors. Consider verifying in staging that the thinking={"type": "enabled", "budget_tokens": 500} payload maps correctly through litellm for your Gemini setup, as some users report needing additional flags (e.g., merge_reasoning_content_in_choices) to avoid parameter translation errors.
🤖 Prompt for AI Agents
In echo/server/dembrane/reply_utils.py around lines 408 to 428, the code appends
audio file objects to the multimodal message but the current error handling is
generic and does not detect/mitigate litellm 1.79.0 parameter-mapping flakiness
for Gemini's thinking/reasoning fields; update the logic to (1) validate or
annotate the outgoing multimodal payload before sending (ensure file object
shape and that thinking payload will be present), (2) add a specific try/except
branch that captures and logs provider-translation errors separately (include
full exception, response body/status if available) and sets a safe fallback
(e.g., omit thinking or set merge_reasoning_content_in_choices flag) for staging
verification, and (3) add a short runtime check or feature-flag to toggle the
thinking payload so you can test mapping in staging without impacting prod.
|
|
||
| 4. Main User's Transcript. | ||
| This is the most important part of the context. This also contains your previous replies. | ||
| This is the most important part of the context. This also contains your previous replies. You will receive conversation transcripts (and optionally audio fragments). |
There was a problem hiding this comment.
🧩 Analysis chain
Script executed:
#!/bin/bash
# Search for audio fragment processing in reply generation
rg -n -C5 'audio.*fragment|audio_fragment' --type=pyRepository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 1
</shell_result>
Script executed:
#!/bin/bash
# Search for audio references and the service that builds context for templates
rg -n -i 'audio' --type=py | head -50Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/tests/test_transcribe_assembly.py:12:TEST_AUDIO_URL = "https://storage.googleapis.com/aai-platform-public/samples/1765269382848385.wav"
echo/server/tests/test_transcribe_assembly.py:14:transcribe_audio_assemblyai: Optional[Callable[..., tuple[str, dict[str, Any]]]] = None
echo/server/tests/test_transcribe_assembly.py:23: global transcribe_audio_assemblyai
echo/server/tests/test_transcribe_assembly.py:32: transcribe_audio_assemblyai = transcribe_module.transcribe_audio_assemblyai
echo/server/tests/test_transcribe_assembly.py:42: if transcribe_audio_assemblyai is None or transcribe_conversation_chunk is None:
echo/server/tests/test_transcribe_assembly.py:65: path = save_to_s3_from_url(TEST_AUDIO_URL, public=True)
echo/server/tests/test_transcribe_assembly.py:109: path = save_to_s3_from_url(TEST_AUDIO_URL, public=True)
echo/server/tests/test_transcribe_assembly.py:159:def test_transcribe_audio_assemblyai():
echo/server/tests/test_transcribe_assembly.py:160: assert transcribe_audio_assemblyai is not None
echo/server/tests/test_transcribe_assembly.py:161: transcript, response = transcribe_audio_assemblyai(
echo/server/tests/test_transcribe_assembly.py:162: audio_file_uri=TEST_AUDIO_URL,
echo/server/tests/test_audio_utils.py:11:from dembrane.audio_utils import (
echo/server/tests/test_audio_utils.py:14: split_audio_chunk,
echo/server/tests/test_audio_utils.py:18: merge_multiple_audio_files_and_save_to_s3,
echo/server/tests/test_audio_utils.py:29:AUDIO_FILES = [
echo/server/tests/test_audio_utils.py:38:LARGE_AUDIO_SET = ["mp3.mp3", "big.m4a"]
echo/server/tests/test_audio_utils.py:43:@pytest.mark.parametrize("file_name", AUDIO_FILES + LARGE_AUDIO_SET)
echo/server/tests/test_audio_utils.py:55: with open(os.path.join(BASE_DIR, "tests", "data", "audio", file_name), "rb") as f:
echo/server/tests/test_audio_utils.py:100: # Verify the audio properties
echo/server/tests/test_audio_utils.py:104: assert probe["streams"][0]["codec_type"] == "audio", "Not an audio stream"
echo/server/tests/test_audio_utils.py:117:def test_merge_multiple_audio_files_and_save_to_s3(output_format):
echo/server/tests/test_audio_utils.py:121: for file_name in AUDIO_FILES:
echo/server/tests/test_audio_utils.py:122: with open(os.path.join(BASE_DIR, "tests", "data", "audio", file_name), "rb") as f:
echo/server/tests/test_audio_utils.py:136: merged_file_url = merge_multiple_audio_files_and_save_to_s3(
echo/server/tests/test_audio_utils.py:161: # Verify the audio properties
echo/server/tests/test_audio_utils.py:165: assert probe["streams"][0]["codec_type"] == "audio", "Not an audio stream"
echo/server/tests/test_audio_utils.py:175:@pytest.mark.parametrize("file_name", AUDIO_FILES + LARGE_AUDIO_SET)
echo/server/tests/test_audio_utils.py:177:def test_split_audio_chunk(file_name, output_format):
echo/server/tests/test_audio_utils.py:178: logger = logging.getLogger("test_split_audio_chunk")
echo/server/tests/test_audio_utils.py:195: # Load the audio file
echo/server/tests/test_audio_utils.py:196: with open(os.path.join(BASE_DIR, "tests", "data", "audio", file_name), "rb") as f:
echo/server/tests/test_audio_utils.py:199: # Create S3 path for the audio file
echo/server/tests/test_audio_utils.py:234: split_chunks = split_audio_chunk(chunk_id, output_format)
echo/server/tests/test_audio_utils.py:265: probe["streams"][0]["codec_type"] == "audio"
echo/server/tests/test_audio_utils.py:266: ), f"Probe result for {item['path']} is not an audio stream"
echo/server/tests/test_audio_utils.py:281:@pytest.mark.parametrize("file_name", AUDIO_FILES + LARGE_AUDIO_SET)
echo/server/tests/test_audio_utils.py:284: with open(os.path.join(BASE_DIR, "tests", "data", "audio", file_name), "rb") as f:
echo/server/tests/test_audio_utils.py:287: # Create S3 path for the audio file
echo/server/tests/test_audio_utils.py:309:@pytest.mark.parametrize("file_name", AUDIO_FILES + LARGE_AUDIO_SET)
echo/server/tests/test_audio_utils.py:313: # Load the audio file
echo/server/tests/test_audio_utils.py:314: with open(os.path.join(BASE_DIR, "tests", "data", "audio", file_name), "rb") as f:
echo/server/tests/test_audio_utils.py:328: probe_result["streams"][0]["codec_type"] == "audio"
echo/server/tests/test_audio_utils.py:329: ), f"Not an audio stream for {file_name}"
echo/server/tests/test_audio_utils.py:339:@pytest.mark.parametrize("file_name", AUDIO_FILES + LARGE_AUDIO_SET)
echo/server/tests/test_audio_utils.py:343: # Load and upload the audio file to S3
echo/server/tests/test_audio_utils.py:344: with open(os.path.join(BASE_DIR, "tests", "data", "audio", file_name), "rb") as f:
echo/server/tests/test_audio_utils.py:347: # Create S3 path for the audio file
echo/server/tests/test_audio_utils.py:369: probe_result["streams"][0]["codec_type"] == "audio"
echo/server/tests/test_audio_utils.py:370: ), f"Not an audio stream for {s3_key}"
echo/server/tests/test_audio_utils.py:424: matching_files1 = [f for f in AUDIO_FILES if f.endswith(f".{format1}")]
</shell_result>
Script executed:
#!/bin/bash
# Find reply generation and template context building
rg -n 'get_reply|reply.*context|jinja|template' --type=py -i | grep -v test | head -40Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/dembrane/chat_utils.py:216: If title generation is disabled via configuration or the trimmed query is shorter than 2 characters, the function returns None. The function builds a prompt (using the English prompt template) and asynchronously calls a configured small LLM; it returns the generated title string or None if the model returns no content.
echo/server/dembrane/chat_utils.py:220: language (str): Target language for the generated title (affects prompt content; the prompt template used is English).
echo/server/dembrane/chat_utils.py:233: # here we use the english prompt template, but the language is passed in to make it simple
echo/server/dembrane/chat_utils.py:268: language: Language code for the prompt template (default: "en")
echo/server/dembrane/chat_utils.py:401: language: Language code for the prompt template
echo/server/dembrane/tasks.py:463: "Skipping default view generation for project %s; JSON templates have been removed.",
echo/server/dembrane/service/chat.py:90: "template_key",
echo/server/dembrane/service/chat.py:221: template_key: Optional[str] = None,
echo/server/dembrane/service/chat.py:235: if template_key is not None:
echo/server/dembrane/service/chat.py:236: payload["template_key"] = template_key
echo/server/dembrane/service/project.py:199: "is_get_reply_enabled": current_project["is_get_reply_enabled"],
echo/server/dembrane/settings.py:429: def prompt_templates_dir(self) -> Path:
echo/server/dembrane/settings.py:430: return self.base_dir / "prompt_templates"
echo/server/dembrane/reply_utils.py:20:GET_REPLY_TOKEN_LIMIT = 80000
echo/server/dembrane/reply_utils.py:21:GET_REPLY_TARGET_TOKENS_PER_CONV = 4000
echo/server/dembrane/reply_utils.py:143: "project_id.is_get_reply_enabled",
echo/server/dembrane/reply_utils.py:144: "project_id.get_reply_prompt",
echo/server/dembrane/reply_utils.py:145: "project_id.get_reply_mode",
echo/server/dembrane/reply_utils.py:173: if conversation["project_id"]["is_get_reply_enabled"] is False:
echo/server/dembrane/reply_utils.py:198: "get_reply_prompt": conversation["project_id"]["get_reply_prompt"],
echo/server/dembrane/reply_utils.py:199: "get_reply_mode": conversation["project_id"]["get_reply_mode"],
echo/server/dembrane/reply_utils.py:210: get_reply_mode = current_project.get("get_reply_mode")
echo/server/dembrane/reply_utils.py:211: use_summaries = get_reply_mode in ["summarize", "brainstorm", "custom"]
echo/server/dembrane/reply_utils.py:256: token_limit = GET_REPLY_TOKEN_LIMIT
echo/server/dembrane/reply_utils.py:257: target_tokens_per_conv = GET_REPLY_TARGET_TOKENS_PER_CONV # Target size for each conversation
echo/server/dembrane/reply_utils.py:372: if get_reply_mode == "summarize":
echo/server/dembrane/reply_utils.py:373: # Load global prompt from summary template
echo/server/dembrane/reply_utils.py:374: global_prompt = render_prompt("get_reply_summarize", language, {})
echo/server/dembrane/reply_utils.py:375: logger.debug(f"Using get_reply_summarize template for global prompt: {get_reply_mode}")
echo/server/dembrane/reply_utils.py:376: elif get_reply_mode == "brainstorm":
echo/server/dembrane/reply_utils.py:377: # Load global prompt from brainstorm template
echo/server/dembrane/reply_utils.py:378: global_prompt = render_prompt("get_reply_brainstorm", language, {})
echo/server/dembrane/reply_utils.py:379: logger.debug(f"Using get_reply_brainstorm template for global prompt: {get_reply_mode}")
echo/server/dembrane/reply_utils.py:380: elif get_reply_mode == "custom":
echo/server/dembrane/reply_utils.py:382: if current_project["get_reply_prompt"] and current_project["get_reply_prompt"].strip():
echo/server/dembrane/reply_utils.py:383: global_prompt = current_project["get_reply_prompt"]
echo/server/dembrane/reply_utils.py:384: logger.debug(f"Using project global prompt for custom mode: {get_reply_mode}")
echo/server/dembrane/reply_utils.py:387: global_prompt = render_prompt("get_reply_summarize", language, {})
echo/server/dembrane/reply_utils.py:388: logger.debug("Custom prompt is empty, falling back to get_reply_summarize template")
echo/server/dembrane/reply_utils.py:391: current_project["get_reply_prompt"]
</shell_result>
Script executed:
#!/bin/bash
# Find render_prompt function and get_reply_system usage
rg -n 'def render_prompt|get_reply_system' --type=py | head -30Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
echo/server/dembrane/reply_utils.py:398: "get_reply_system",
echo/server/dembrane/prompts.py:93:def render_prompt(prompt_name: str, language: str, kwargs: dict[str, Any]) -> str:
</shell_result>
Script executed:
#!/bin/bash
# Read the render_prompt function
cat -n echo/server/dembrane/prompts.py | sed -n '90,120p'Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
90 log_template_support(level=logging.INFO)
91
92
93 def render_prompt(prompt_name: str, language: str, kwargs: dict[str, Any]) -> str:
94 """Render a prompt template with the given arguments.
95
96 Args:
97 prompt_name: Name of the prompt template file (without .jinja extension)
98 language: ISO 639-1 language code of the prompt template file (example: "en", "nl", "fr", "es", "de". etc.)
99 kwargs: Dictionary of arguments to pass to the template renderer
100
101 Returns:
102 The rendered prompt template as a string
103
104 Raises:
105 ValueError: If the prompt template is not found in PROMPT_TEMPLATES_DIR
106 """
107 logger.debug(f"Rendering prompt {prompt_name} with kwargs: {kwargs.keys()}")
108
109 full_prompt_name = f"{prompt_name}.{language}.jinja"
110
111 # Check if the prompt with the specified language exists
112 if full_prompt_name not in PROMPT_TEMPLATE_LIST:
113 # Try to use the .en version if available
114 default_prompt_name = f"{prompt_name}.en.jinja"
115 if default_prompt_name in PROMPT_TEMPLATE_LIST:
116 logger.warning(
117 f"Prompt template {full_prompt_name} not found, using default {default_prompt_name}"
118 )
119 full_prompt_name = default_prompt_name
120 else:
</shell_result>
Script executed:
#!/bin/bash
# Read context around get_reply_system usage
cat -n echo/server/dembrane/reply_utils.py | sed -n '390,430p'Repository: Dembrane/echo
<shell_result>
Repository: Dembrane/echo
Exit code: 0
stdout:
390 global_prompt = (
391 current_project["get_reply_prompt"]
392 if current_project["get_reply_prompt"] is not None
393 else ""
394 )
395 logger.debug(f"Using project global prompt for mode: {get_reply_mode}")
396
397 prompt = render_prompt(
398 "get_reply_system",
399 language,
400 {
401 "PROJECT_DESCRIPTION": project_description,
402 "GLOBAL_PROMPT": global_prompt,
403 "OTHER_TRANSCRIPTS": formatted_adjacent_conversation,
404 "MAIN_USER_TRANSCRIPT": formatted_current_conversation,
405 },
406 )
407
408 # Build multimodal message content
409 message_content = [{"type": "text", "text": prompt}]
410
411 # Add audio chunks without transcripts
412 for chunk in audio_chunks:
413 chunk_id = chunk.get("id")
414 timestamp = chunk.get("timestamp")
415 message_content.append(
416 {
417 "type": "text",
418 "text": f"Audio chunk {chunk_id} captured at {timestamp}",
419 }
420 )
421 path = chunk.get("path")
422 if path:
423 try:
424 audio_obj = await run_in_thread_pool(_get_audio_file_object, path)
425 message_content.append(audio_obj)
426 except Exception as exc:
427 logger.warning("Failed to attach audio chunk %s: %s", chunk_id, exc)
428
429 # Store the complete response
430 accumulated_response = ""
</shell_result>
Audio fragments support is incomplete—template expects audio data but backend doesn't pass it.
The template at line 21 mentions receiving audio fragments, but examining the backend code shows the context dictionary passed to render_prompt("get_reply_system", ...) in reply_utils.py (lines 400-405) only includes PROJECT_DESCRIPTION, GLOBAL_PROMPT, OTHER_TRANSCRIPTS, and MAIN_USER_TRANSCRIPT. Audio data is handled separately after template rendering (lines 412-427) rather than being included in the template context. This means the template references audio fragments that will never be available to it.
The audio handling infrastructure exists in the codebase but isn't integrated with this template. Either remove the audio fragment references from the template or add audio chunk data to the context dictionary passed at line 400.
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New Features
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Refactor
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