Merge abacc3768f into 1f87676d52

Co-authored-by: Garfield Dai <dai.hai@foxmail.com>

README.md

@@ -19,6 +19,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="join Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>

README_AR.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="join Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>

README_CN.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="join Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>

README_ES.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat en Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="join Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="seguir en X(Twitter)"></a>

README_FR.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat sur Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="join Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="suivre sur X(Twitter)"></a>

README_JA.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="Discordでチャット"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="X(Twitter)でフォロー"></a>

README_KL.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="Follow Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>

README_KR.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="Follow Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>

README_PT.md

@@ -19,6 +19,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="Follow Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
@@ -238,4 +241,4 @@ Para proteger sua privacidade, evite postar problemas de segurança no GitHub. E
 
 ## Licença
 
-Este repositório está disponível sob a [Licença de Código Aberto Dify](LICENSE), que é essencialmente Apache 2.0 com algumas restrições adicionais.
+Este repositório está disponível sob a [Licença de Código Aberto Dify](LICENSE), que é essencialmente Apache 2.0 com algumas restrições adicionais.

README_SI.md

@@ -0,0 +1,180 @@
+![cover-v5-optimized](https://github.com/langgenius/dify/assets/13230914/f9e19af5-61ba-4119-b926-d10c4c06ebab)
+
+<p align="center">
+  📌 <a href="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">Predstavljamo nalaganje datotek Dify Workflow: znova ustvarite Google NotebookLM Podcast</a>
+</p>
+
+<p align="center">
+  <a href="https://cloud.dify.ai">Dify Cloud</a> ·
+  <a href="https://docs.dify.ai/getting-started/install-self-hosted">Samostojno gostovanje</a> ·
+  <a href="https://docs.dify.ai">Dokumentacija</a> ·
+  <a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Povpraševanje za podjetja</a>
+</p>
+
+<p align="center">
+    <a href="https://dify.ai" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Product-F04438"></a>
+    <a href="https://dify.ai/pricing" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/free-pricing?logo=free&color=%20%23155EEF&label=pricing&labelColor=%20%23528bff"></a>
+    <a href="https://discord.gg/FngNHpbcY7" target="_blank">
+        <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
+            alt="chat on Discord"></a>
+    <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
+            alt="follow on X(Twitter)"></a>
+    <a href="https://hub.docker.com/u/langgenius" target="_blank">
+        <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
+    <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">
+        <img alt="Commits last month" src="https://img.shields.io/github/commit-activity/m/langgenius/dify?labelColor=%20%2332b583&color=%20%2312b76a"></a>
+    <a href="https://github.com/langgenius/dify/" target="_blank">
+        <img alt="Issues closed" src="https://img.shields.io/github/issues-search?query=repo%3Alanggenius%2Fdify%20is%3Aclosed&label=issues%20closed&labelColor=%20%237d89b0&color=%20%235d6b98"></a>
+    <a href="https://github.com/langgenius/dify/discussions/" target="_blank">
+        <img alt="Discussion posts" src="https://img.shields.io/github/discussions/langgenius/dify?labelColor=%20%239b8afb&color=%20%237a5af8"></a>
+</p>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
+  <a href="./README_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
+  <a href="./README_JA.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
+  <a href="./README_ES.md"><img alt="README en Español" src="https://img.shields.io/badge/Español-d9d9d9"></a>
+  <a href="./README_FR.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-d9d9d9"></a>
+  <a href="./README_KL.md"><img alt="README tlhIngan Hol" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
+  <a href="./README_KR.md"><img alt="README in Korean" src="https://img.shields.io/badge/한국어-d9d9d9"></a>
+  <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
+  <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
+  <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_SI.md"><img alt="README Slovenščina" src="https://img.shields.io/badge/Sloven%C5%A1%C4%8Dina-d9d9d9"></a>
+</p>
+
+
+Dify je odprtokodna platforma za razvoj aplikacij LLM. Njegov intuitivni vmesnik združuje agentski potek dela z umetno inteligenco, cevovod RAG, zmogljivosti agentov, upravljanje modelov, funkcije opazovanja in več, kar vam omogoča hiter prehod od prototipa do proizvodnje. 
+
+## Hitri začetek
+> Preden namestite Dify, se prepričajte, da vaša naprava izpolnjuje naslednje minimalne sistemske zahteve:
+> 
+>- CPU >= 2 Core
+>- RAM >= 4 GiB
+
+</br>
+
+Najlažji način za zagon strežnika Dify je prek docker compose . Preden zaženete Dify z naslednjimi ukazi, se prepričajte, da sta Docker in Docker Compose nameščena na vašem računalniku:
+
+```bash
+cd dify
+cd docker
+cp .env.example .env
+docker compose up -d
+```
+
+Po zagonu lahko dostopate do nadzorne plošče Dify v brskalniku na [http://localhost/install](http://localhost/install) in začnete postopek inicializacije.
+
+#### Iskanje pomoči
+Prosimo, glejte naša pogosta vprašanja [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) če naletite na težave pri nastavitvi Dify. Če imate še vedno težave, se obrnite na [skupnost ali nas](#community--contact).
+
+> Če želite prispevati k Difyju ali narediti dodaten razvoj, glejte naš vodnik za [uvajanje iz izvorne kode](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
+
+## Ključne značilnosti
+**1. Potek dela**: 
+  Zgradite in preizkusite zmogljive poteke dela AI na vizualnem platnu, pri čemer izkoristite vse naslednje funkcije in več.
+
+
+  https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa
+
+
+
+**2. Celovita podpora za modele**: 
+  Brezhibna integracija s stotinami lastniških/odprtokodnih LLM-jev ducatov ponudnikov sklepanja in samostojnih rešitev, ki pokrivajo GPT, Mistral, Llama3 in vse modele, združljive z API-jem OpenAI. Celoten seznam podprtih ponudnikov modelov najdete [tukaj](https://docs.dify.ai/getting-started/readme/model-providers).
+
+![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
+
+
+**3. Prompt IDE**: 
+  intuitivni vmesnik za ustvarjanje pozivov, primerjavo zmogljivosti modela in dodajanje dodatnih funkcij, kot je pretvorba besedila v govor, aplikaciji, ki temelji na klepetu. 
+
+**4. RAG Pipeline**: 
+  E Obsežne zmogljivosti RAG, ki pokrivajo vse od vnosa dokumenta do priklica, s podporo za ekstrakcijo besedila iz datotek PDF, PPT in drugih običajnih formatov dokumentov.
+
+**5. Agent capabilities**: 
+  definirate lahko agente, ki temeljijo na klicanju funkcij LLM ali ReAct, in dodate vnaprej izdelana orodja ali orodja po meri za agenta. Dify ponuja več kot 50 vgrajenih orodij za agente AI, kot so Google Search, DALL·E, Stable Diffusion in WolframAlpha.
+
+**6. LLMOps**: 
+  Spremljajte in analizirajte dnevnike aplikacij in učinkovitost skozi čas. Pozive, nabore podatkov in modele lahko nenehno izboljšujete na podlagi proizvodnih podatkov in opomb.
+
+**7. Backend-as-a-Service**: 
+  AVse ponudbe Difyja so opremljene z ustreznimi API-ji, tako da lahko Dify brez težav integrirate v svojo poslovno logiko.
+
+
+## Uporaba Dify
+
+- **Cloud </br>**
+Gostimo storitev Dify Cloud za vsakogar, ki jo lahko preizkusite brez nastavitev. Zagotavlja vse zmožnosti različice za samostojno namestitev in vključuje 200 brezplačnih klicev GPT-4 v načrtu peskovnika.
+
+- **Self-hosting Dify Community Edition</br>**
+Hitro zaženite Dify v svojem okolju s tem [začetnim vodnikom](#quick-start) . Za dodatne reference in podrobnejša navodila uporabite našo [dokumentacijo](https://docs.dify.ai) .
+
+
+- **Dify za podjetja/organizacije</br>**
+Ponujamo dodatne funkcije, osredotočene na podjetja. Zabeležite svoja vprašanja prek tega klepetalnega robota ali nam pošljite e-pošto, da se pogovorimo o potrebah podjetja. </br>
+  > Za novoustanovljena podjetja in mala podjetja, ki uporabljajo AWS, si oglejte Dify Premium na AWS Marketplace in ga z enim klikom uvedite v svoj AWS VPC. To je cenovno ugodna ponudba AMI z možnostjo ustvarjanja aplikacij z logotipom in blagovno znamko po meri.
+
+
+## Staying ahead
+
+Star Dify on GitHub and be instantly notified of new releases.
+
+![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
+
+
+## Napredne nastavitve
+
+Če morate prilagoditi konfiguracijo, si oglejte komentarje v naši datoteki .env.example in posodobite ustrezne vrednosti v svoji .env datoteki. Poleg tega boste morda morali prilagoditi docker-compose.yamlsamo datoteko, na primer spremeniti različice slike, preslikave vrat ali namestitve nosilca, glede na vaše specifično okolje in zahteve za uvajanje. Po kakršnih koli spremembah ponovno zaženite docker-compose up -d. Celoten seznam razpoložljivih spremenljivk okolja najdete tukaj .
+
+Če želite konfigurirati visoko razpoložljivo nastavitev, so na voljo Helm Charts in datoteke YAML, ki jih prispeva skupnost, ki omogočajo uvedbo Difyja v Kubernetes.
+
+- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
+- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
+- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
+
+#### Uporaba Terraform za uvajanje
+
+namestite Dify v Cloud Platform z enim klikom z uporabo [terraform](https://www.terraform.io/)
+
+##### Azure Global
+- [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)
+
+##### Google Cloud
+- [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
+
+## Prispevam
+
+Za tiste, ki bi radi prispevali kodo, si oglejte naš vodnik za prispevke . Hkrati vas prosimo, da podprete Dify tako, da ga delite na družbenih medijih ter na dogodkih in konferencah. 
+
+
+
+> Iščemo sodelavce za pomoč pri prevajanju Difyja v jezike, ki niso mandarinščina ali angleščina. Če želite pomagati, si oglejte i18n README za več informacij in nam pustite komentar v global-userskanalu našega strežnika skupnosti Discord .
+
+## Skupnost in stik
+
+* [Github Discussion](https://github.com/langgenius/dify/discussions). Najboljše za: izmenjavo povratnih informacij in postavljanje vprašanj.
+* [GitHub Issues](https://github.com/langgenius/dify/issues). Najboljše za: hrošče, na katere naletite pri uporabi Dify.AI, in predloge funkcij. Oglejte si naš [vodnik za prispevke](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [Discord](https://discord.gg/FngNHpbcY7). Najboljše za: deljenje vaših aplikacij in druženje s skupnostjo.
+* [X(Twitter)](https://twitter.com/dify_ai). Najboljše za: deljenje vaših aplikacij in druženje s skupnostjo.
+
+**Contributors**
+
+<a href="https://github.com/langgenius/dify/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langgenius/dify" />
+</a>
+
+## Star history
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
+
+
+## Varnostno razkritje
+
+Zaradi zaščite vaše zasebnosti se izogibajte objavljanju varnostnih vprašanj na GitHub. Namesto tega pošljite vprašanja na security@dify.ai in zagotovili vam bomo podrobnejši odgovor.
+
+## Licenca
+
+To skladišče je na voljo pod [odprtokodno licenco Dify](LICENSE) , ki je v bistvu Apache 2.0 z nekaj dodatnimi omejitvami.

README_TR.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="Discord'da sohbet et"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="Follow Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="X(Twitter)'da takip et"></a>

README_VI.md

@@ -15,6 +15,9 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="chat trên Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="Follow Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="theo dõi trên X(Twitter)"></a>
@@ -235,4 +238,4 @@ Triển khai Dify lên nền tảng đám mây với một cú nhấp chuột b
 
 ## Giấy phép
 
-Kho lưu trữ này có sẵn theo [Giấy phép Mã nguồn Mở Dify](LICENSE), về cơ bản là Apache 2.0 với một vài hạn chế bổ sung.
+Kho lưu trữ này có sẵn theo [Giấy phép Mã nguồn Mở Dify](LICENSE), về cơ bản là Apache 2.0 với một vài hạn chế bổ sung.

api/commands.py

@@ -589,7 +589,7 @@ def upgrade_db():
             click.echo(click.style("Database migration successful!", fg="green"))
 
         except Exception as e:
-            logging.exception(f"Database migration failed: {e}")
+            logging.exception("Failed to execute database migration")
         finally:
             lock.release()
     else:
@@ -633,7 +633,7 @@ where sites.id is null limit 1000"""
                 except Exception as e:
                     failed_app_ids.append(app_id)
                     click.echo(click.style("Failed to fix missing site for app {}".format(app_id), fg="red"))
-                    logging.exception(f"Fix app related site missing issue failed, error: {e}")
+                    logging.exception(f"Failed to fix app related site missing issue, app_id: {app_id}")
                     continue
 
             if not processed_count:

api/configs/app_config.py

@@ -27,7 +27,6 @@ class DifyConfig(
         # read from dotenv format config file
         env_file=".env",
         env_file_encoding="utf-8",
-        frozen=True,
         # ignore extra attributes
         extra="ignore",
     )

api/constants/languages.py

@@ -17,6 +17,7 @@ language_timezone_mapping = {
     "hi-IN": "Asia/Kolkata",
     "tr-TR": "Europe/Istanbul",
     "fa-IR": "Asia/Tehran",
+    "sl-SI": "Europe/Ljubljana",
 }
 
 languages = list(language_timezone_mapping.keys())

api/controllers/console/app/app.py

@@ -9,6 +9,7 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_resource_check,
+    enterprise_license_required,
     setup_required,
 )
 from core.ops.ops_trace_manager import OpsTraceManager
@@ -28,6 +29,7 @@ class AppListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         """Get app list"""
 
@@ -149,6 +151,7 @@ class AppApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     @get_app_model
     @marshal_with(app_detail_fields_with_site)
     def get(self, app_model):

api/controllers/console/app/audio.py

@@ -70,7 +70,7 @@ class ChatMessageAudioApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle post request to ChatMessageAudioApi")
             raise InternalServerError()
 
 
@@ -128,7 +128,7 @@ class ChatMessageTextApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle post request to ChatMessageTextApi")
             raise InternalServerError()
 
 
@@ -170,7 +170,7 @@ class TextModesApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle get request to TextModesApi")
             raise InternalServerError()
 
 

api/controllers/console/datasets/datasets.py

@@ -10,7 +10,7 @@ from controllers.console import api
 from controllers.console.apikey import api_key_fields, api_key_list
 from controllers.console.app.error import ProviderNotInitializeError
 from controllers.console.datasets.error import DatasetInUseError, DatasetNameDuplicateError, IndexingEstimateError
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.indexing_runner import IndexingRunner
 from core.model_runtime.entities.model_entities import ModelType
@@ -44,6 +44,7 @@ class DatasetListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         page = request.args.get("page", default=1, type=int)
         limit = request.args.get("limit", default=20, type=int)

api/controllers/console/datasets/datasets_document.py

@@ -948,7 +948,7 @@ class DocumentRetryApi(DocumentResource):
                     raise DocumentAlreadyFinishedError()
                 retry_documents.append(document)
             except Exception as e:
-                logging.exception(f"Document {document_id} retry failed: {str(e)}")
+                logging.exception(f"Failed to retry document, document id: {document_id}")
                 continue
         # retry document
         DocumentService.retry_document(dataset_id, retry_documents)

api/controllers/console/error.py

@@ -86,3 +86,9 @@ class NoFileUploadedError(BaseHTTPException):
     error_code = "no_file_uploaded"
     description = "Please upload your file."
     code = 400
+
+
+class UnauthorizedAndForceLogout(BaseHTTPException):
+    error_code = "unauthorized_and_force_logout"
+    description = "Unauthorized and force logout."
+    code = 401

api/controllers/console/workspace/account.py

@@ -14,7 +14,7 @@ from controllers.console.workspace.error import (
     InvalidInvitationCodeError,
     RepeatPasswordNotMatchError,
 )
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from extensions.ext_database import db
 from fields.member_fields import account_fields
 from libs.helper import TimestampField, timezone
@@ -79,6 +79,7 @@ class AccountProfileApi(Resource):
     @login_required
     @account_initialization_required
     @marshal_with(account_fields)
+    @enterprise_license_required
     def get(self):
         return current_user
 

api/controllers/console/workspace/models.py

@@ -72,7 +72,10 @@ class DefaultModelApi(Resource):
                     model=model_setting["model"],
                 )
             except Exception as ex:
-                logging.exception(f"{model_setting['model_type']} save error: {ex}")
+                logging.exception(
+                    f"Failed to update default model, model type: {model_setting['model_type']},"
+                    f" model:{model_setting.get('model')}"
+                )
                 raise ex
 
         return {"result": "success"}
@@ -156,7 +159,10 @@ class ModelProviderModelApi(Resource):
                         credentials=args["credentials"],
                     )
                 except CredentialsValidateFailedError as ex:
-                    logging.exception(f"save model credentials error: {ex}")
+                    logging.exception(
+                        f"Failed to save model credentials, tenant_id: {tenant_id},"
+                        f" model: {args.get('model')}, model_type: {args.get('model_type')}"
+                    )
                     raise ValueError(str(ex))
 
         return {"result": "success"}, 200

api/controllers/console/workspace/tool_providers.py

@@ -7,7 +7,7 @@ from werkzeug.exceptions import Forbidden
 
 from configs import dify_config
 from controllers.console import api
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from core.model_runtime.utils.encoders import jsonable_encoder
 from libs.helper import alphanumeric, uuid_value
 from libs.login import login_required
@@ -549,6 +549,7 @@ class ToolLabelsApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         return jsonable_encoder(ToolLabelsService.list_tool_labels())
 

api/controllers/console/wraps.py

@@ -8,10 +8,10 @@ from flask_login import current_user
 from configs import dify_config
 from controllers.console.workspace.error import AccountNotInitializedError
 from models.model import DifySetup
-from services.feature_service import FeatureService
+from services.feature_service import FeatureService, LicenseStatus
 from services.operation_service import OperationService
 
-from .error import NotInitValidateError, NotSetupError
+from .error import NotInitValidateError, NotSetupError, UnauthorizedAndForceLogout
 
 
 def account_initialization_required(view):
@@ -142,3 +142,15 @@ def setup_required(view):
         return view(*args, **kwargs)
 
     return decorated
+
+
+def enterprise_license_required(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        settings = FeatureService.get_system_features()
+        if settings.license.status in [LicenseStatus.INACTIVE, LicenseStatus.EXPIRED, LicenseStatus.LOST]:
+            raise UnauthorizedAndForceLogout("Your license is invalid. Please contact your administrator.")
+
+        return view(*args, **kwargs)
+
+    return decorated

api/controllers/web/audio.py

@@ -59,7 +59,7 @@ class AudioApi(WebApiResource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error: {str(e)}")
+            logging.exception("Failed to handle post request to AudioApi")
             raise InternalServerError()
 
 
@@ -117,7 +117,7 @@ class TextApi(WebApiResource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error: {str(e)}")
+            logging.exception("Failed to handle post request to TextApi")
             raise InternalServerError()
 
 

api/core/app/app_config/easy_ui_based_app/model_config/converter.py

@@ -11,7 +11,7 @@ from core.provider_manager import ProviderManager
 
 class ModelConfigConverter:
     @classmethod
-    def convert(cls, app_config: EasyUIBasedAppConfig, skip_check: bool = False) -> ModelConfigWithCredentialsEntity:
+    def convert(cls, app_config: EasyUIBasedAppConfig) -> ModelConfigWithCredentialsEntity:
         """
         Convert app model config dict to entity.
         :param app_config: app config
@@ -38,27 +38,23 @@ class ModelConfigConverter:
         )
 
         if model_credentials is None:
-            if not skip_check:
+            raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
-                raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
-            else:
-                model_credentials = {}
 
-        if not skip_check:
+        # check model
-            # check model
+        provider_model = provider_model_bundle.configuration.get_provider_model(
-            provider_model = provider_model_bundle.configuration.get_provider_model(
+            model=model_config.model, model_type=ModelType.LLM
-                model=model_config.model, model_type=ModelType.LLM
+        )
-            )
 
-            if provider_model is None:
+        if provider_model is None:
-                model_name = model_config.model
+            model_name = model_config.model
-                raise ValueError(f"Model {model_name} not exist.")
+            raise ValueError(f"Model {model_name} not exist.")
 
-            if provider_model.status == ModelStatus.NO_CONFIGURE:
+        if provider_model.status == ModelStatus.NO_CONFIGURE:
-                raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
+            raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
-            elif provider_model.status == ModelStatus.NO_PERMISSION:
+        elif provider_model.status == ModelStatus.NO_PERMISSION:
-                raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
+            raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
-            elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
+        elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
-                raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
+            raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
 
         # model config
         completion_params = model_config.parameters
@@ -76,7 +72,7 @@ class ModelConfigConverter:
 
         model_schema = model_type_instance.get_model_schema(model_config.model, model_credentials)
 
-        if not skip_check and not model_schema:
+        if not model_schema:
             raise ValueError(f"Model {model_name} not exist.")
 
         return ModelConfigWithCredentialsEntity(

api/core/app/apps/advanced_chat/app_generator.py

@@ -362,5 +362,5 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(f"Failed to process generate task pipeline, conversation_id: {conversation.id}")
                 raise e

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -242,7 +242,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                     start_listener_time = time.time()
                     yield MessageAudioStreamResponse(audio=audio_trunk.audio, task_id=task_id)
             except Exception as e:
-                logger.exception(e)
+                logger.exception(f"Failed to listen audio message, task_id: {task_id}")
                 break
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)

api/core/app/apps/base_app_generator.py

@@ -91,6 +91,9 @@ class BaseAppGenerator:
             )
 
         if variable_entity.type == VariableEntityType.NUMBER and isinstance(value, str):
+            # handle empty string case
+            if not value.strip():
+                return None
             # may raise ValueError if user_input_value is not a valid number
             try:
                 if "." in value:

api/core/app/apps/message_based_app_generator.py

@@ -80,7 +80,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(f"Failed to handle response, conversation_id: {conversation.id}")
                 raise e
 
     def _get_conversation_by_user(

api/core/app/apps/workflow/app_generator.py

@@ -298,5 +298,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(
+                    f"Fails to process generate task pipeline, task_id: {application_generate_entity.task_id}"
+                )
                 raise e

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -216,7 +216,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 else:
                     yield MessageAudioStreamResponse(audio=audio_trunk.audio, task_id=task_id)
             except Exception as e:
-                logger.exception(e)
+                logger.exception(f"Fails to get audio trunk, task_id: {task_id}")
                 break
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)

api/core/app/task_pipeline/message_cycle_manage.py

@@ -86,7 +86,7 @@ class MessageCycleManage:
                     conversation.name = name
                 except Exception as e:
                     if dify_config.DEBUG:
-                        logging.exception(f"generate conversation name failed: {e}")
+                        logging.exception(f"generate conversation name failed, conversation_id: {conversation_id}")
                     pass
 
                 db.session.merge(conversation)

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -217,6 +217,7 @@ class WorkflowCycleManage:
             ).total_seconds()
             db.session.commit()
 
+        db.session.add(workflow_run)
         db.session.refresh(workflow_run)
         db.session.close()
 

api/core/file/file_manager.py

@@ -74,6 +74,8 @@ def to_prompt_message_content(
                 data = _to_url(f)
             else:
                 data = _to_base64_data_string(f)
+            if f.extension is None:
+                raise ValueError("Missing file extension")
             return VideoPromptMessageContent(data=data, format=f.extension.lstrip("."))
         case _:
             raise ValueError("file type f.type is not supported")

api/core/helper/moderation.py

@@ -41,7 +41,7 @@ def check_moderation(model_config: ModelConfigWithCredentialsEntity, text: str)
                 if moderation_result is True:
                     return True
             except Exception as ex:
-                logger.exception(ex)
+                logger.exception(f"Fails to check moderation, provider_name: {provider_name}")
                 raise InvokeBadRequestError("Rate limit exceeded, please try again later.")
 
     return False

api/core/helper/module_import_helper.py

@@ -29,7 +29,7 @@ def import_module_from_source(*, module_name: str, py_file_path: AnyStr, use_laz
         spec.loader.exec_module(module)
         return module
     except Exception as e:
-        logging.exception(f"Failed to load module {module_name} from {py_file_path}: {str(e)}")
+        logging.exception(f"Failed to load module {module_name} from script file '{py_file_path}'")
         raise e
 
 

api/core/indexing_runner.py

@@ -554,7 +554,7 @@ class IndexingRunner:
                     qa_documents.append(qa_document)
                 format_documents.extend(qa_documents)
             except Exception as e:
-                logging.exception(e)
+                logging.exception("Failed to format qa document")
 
             all_qa_documents.extend(format_documents)
 

api/core/llm_generator/llm_generator.py

@@ -102,7 +102,7 @@ class LLMGenerator:
         except InvokeError:
             questions = []
         except Exception as e:
-            logging.exception(e)
+            logging.exception("Failed to generate suggested questions after answer")
             questions = []
 
         return questions
@@ -148,7 +148,7 @@ class LLMGenerator:
                 error = str(e)
                 error_step = "generate rule config"
             except Exception as e:
-                logging.exception(e)
+                logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
                 rule_config["error"] = str(e)
 
             rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -234,7 +234,7 @@ class LLMGenerator:
                 error_step = "generate conversation opener"
 
         except Exception as e:
-            logging.exception(e)
+            logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
             rule_config["error"] = str(e)
 
         rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -286,7 +286,9 @@ class LLMGenerator:
             error = str(e)
             return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
-            logging.exception(e)
+            logging.exception(
+                f"Failed to invoke LLM model, model: {model_config.get('name')}, language: {code_language}"
+            )
             return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
 
     @classmethod

api/core/memory/token_buffer_memory.py

@@ -1,3 +1,4 @@
+from collections.abc import Sequence
 from typing import Optional
 
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
@@ -27,7 +28,7 @@ class TokenBufferMemory:
 
     def get_history_prompt_messages(
         self, max_token_limit: int = 2000, message_limit: Optional[int] = None
-    ) -> list[PromptMessage]:
+    ) -> Sequence[PromptMessage]:
         """
         Get history prompt messages.
         :param max_token_limit: max token limit

api/core/model_manager.py

@@ -100,10 +100,10 @@ class ModelInstance:
 
     def invoke_llm(
         self,
-        prompt_messages: list[PromptMessage],
+        prompt_messages: Sequence[PromptMessage],
         model_parameters: Optional[dict] = None,
         tools: Sequence[PromptMessageTool] | None = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,

api/core/model_runtime/callbacks/base_callback.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from collections.abc import Sequence
 from typing import Optional
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
@@ -31,7 +32,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> None:
@@ -60,7 +61,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ):
@@ -90,7 +91,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> None:
@@ -120,7 +121,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> None:

api/core/model_runtime/entities/message_entities.py

@@ -1,4 +1,5 @@
 from abc import ABC
+from collections.abc import Sequence
 from enum import Enum
 from typing import Optional
 
@@ -57,6 +58,7 @@ class PromptMessageContentType(Enum):
     IMAGE = "image"
     AUDIO = "audio"
     VIDEO = "video"
+    DOCUMENT = "document"
 
 
 class PromptMessageContent(BaseModel):
@@ -107,7 +109,7 @@ class PromptMessage(ABC, BaseModel):
     """
 
     role: PromptMessageRole
-    content: Optional[str | list[PromptMessageContent]] = None
+    content: Optional[str | Sequence[PromptMessageContent]] = None
     name: Optional[str] = None
 
     def is_empty(self) -> bool:

api/core/model_runtime/entities/model_entities.py

@@ -87,6 +87,9 @@ class ModelFeature(Enum):
     AGENT_THOUGHT = "agent-thought"
     VISION = "vision"
     STREAM_TOOL_CALL = "stream-tool-call"
+    DOCUMENT = "document"
+    VIDEO = "video"
+    AUDIO = "audio"
 
 
 class DefaultParameterName(str, Enum):

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -2,7 +2,7 @@ import logging
 import re
 import time
 from abc import abstractmethod
-from collections.abc import Generator, Mapping
+from collections.abc import Generator, Mapping, Sequence
 from typing import Optional, Union
 
 from pydantic import ConfigDict
@@ -48,7 +48,7 @@ class LargeLanguageModel(AIModel):
         prompt_messages: list[PromptMessage],
         model_parameters: Optional[dict] = None,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -169,7 +169,7 @@ class LargeLanguageModel(AIModel):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -212,7 +212,7 @@ if you are not sure about the structure.
             )
 
         model_parameters.pop("response_format")
-        stop = stop or []
+        stop = list(stop) if stop is not None else []
         stop.extend(["\n```", "```\n"])
         block_prompts = block_prompts.replace("{{block}}", code_block)
 
@@ -408,7 +408,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -479,7 +479,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
@@ -601,7 +601,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -647,7 +647,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -694,7 +694,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -742,7 +742,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,

api/core/model_runtime/model_providers/azure_ai_studio/rerank/rerank.py

@@ -103,7 +103,7 @@ class AzureRerankModel(RerankModel):
             return RerankResult(model=model, docs=rerank_documents)
 
         except Exception as e:
-            logger.exception(f"Exception in Azure rerank: {e}")
+            logger.exception(f"Failed to invoke rerank model, model: {model}")
             raise
 
     def validate_credentials(self, model: str, credentials: dict) -> None:

api/core/model_runtime/model_providers/openai/llm/gpt-4o-audio-preview.yaml

@@ -8,6 +8,7 @@ features:
   - agent-thought
   - stream-tool-call
   - vision
+  - audio
 model_properties:
   mode: chat
   context_size: 128000

api/core/model_runtime/model_providers/sagemaker/rerank/rerank.py

@@ -113,7 +113,7 @@ class SageMakerRerankModel(RerankModel):
             return RerankResult(model=model, docs=rerank_documents)
 
         except Exception as e:
-            logger.exception(f"Exception {e}, line : {line}")
+            logger.exception(f"Failed to invoke rerank model, model: {model}")
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """

api/core/model_runtime/model_providers/sagemaker/speech2text/speech2text.py

@@ -78,7 +78,7 @@ class SageMakerSpeech2TextModel(Speech2TextModel):
             json_obj = json.loads(json_str)
             asr_text = json_obj["text"]
         except Exception as e:
-            logger.exception(f"failed to invoke speech2text model, {e}")
+            logger.exception(f"failed to invoke speech2text model, model: {model}")
             raise CredentialsValidateFailedError(str(e))
 
         return asr_text

api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py

@@ -117,7 +117,7 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
             return TextEmbeddingResult(embeddings=all_embeddings, usage=usage, model=model)
 
         except Exception as e:
-            logger.exception(f"Exception {e}, line : {line}")
+            logger.exception(f"Failed to invoke text embedding model, model: {model}, line: {line}")
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """

api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/common.py

@@ -1,5 +1,6 @@
 import json
 import random
+from collections import UserDict
 from datetime import datetime
 
 
@@ -10,9 +11,9 @@ class ChatRole:
     FUNCTION = "function"
 
 
-class _Dict(dict):
+class _Dict(UserDict):
-    __setattr__ = dict.__setitem__
+    __setattr__ = UserDict.__setitem__
-    __getattr__ = dict.__getitem__
+    __getattr__ = UserDict.__getitem__
 
     def __missing__(self, key):
         return None

api/core/moderation/output_moderation.py

@@ -126,6 +126,6 @@ class OutputModeration(BaseModel):
             result: ModerationOutputsResult = moderation_factory.moderation_for_outputs(moderation_buffer)
             return result
         except Exception as e:
-            logger.exception("Moderation Output error: %s", e)
+            logger.exception(f"Moderation Output error, app_id: {app_id}")
 
         return None

api/core/ops/ops_trace_manager.py

@@ -711,7 +711,7 @@ class TraceQueueManager:
                 trace_task.app_id = self.app_id
                 trace_manager_queue.put(trace_task)
         except Exception as e:
-            logging.exception(f"Error adding trace task: {e}")
+            logging.exception(f"Error adding trace task, trace_type {trace_task.trace_type}")
         finally:
             self.start_timer()
 
@@ -730,7 +730,7 @@ class TraceQueueManager:
             if tasks:
                 self.send_to_celery(tasks)
         except Exception as e:
-            logging.exception(f"Error processing trace tasks: {e}")
+            logging.exception("Error processing trace tasks")
 
     def start_timer(self):
         global trace_manager_timer

api/core/prompt/utils/prompt_message_util.py

@@ -1,3 +1,4 @@
+from collections.abc import Sequence
 from typing import cast
 
 from core.model_runtime.entities import (
@@ -14,7 +15,7 @@ from core.prompt.simple_prompt_transform import ModelMode
 
 class PromptMessageUtil:
     @staticmethod
-    def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: list[PromptMessage]) -> list[dict]:
+    def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: Sequence[PromptMessage]) -> list[dict]:
         """
         Prompt messages to prompt for saving.
         :param model_mode: model mode

api/core/rag/datasource/vdb/couchbase/couchbase_vector.py

@@ -242,7 +242,7 @@ class CouchbaseVector(BaseVector):
         try:
             self._cluster.query(query, named_parameters={"doc_ids": ids}).execute()
         except Exception as e:
-            logger.exception(e)
+            logger.exception(f"Failed to delete documents, ids: {ids}")
 
     def delete_by_document_id(self, document_id: str):
         query = f"""

api/core/rag/datasource/vdb/lindorm/lindorm_vector.py

@@ -79,7 +79,7 @@ class LindormVectorStore(BaseVector):
                 existing_docs = self._client.mget(index=self._collection_name, body={"ids": batch_ids}, _source=False)
                 return {doc["_id"] for doc in existing_docs["docs"] if doc["found"]}
             except Exception as e:
-                logger.exception(f"Error fetching batch {batch_ids}: {e}")
+                logger.exception(f"Error fetching batch {batch_ids}")
                 return set()
 
         @retry(stop=stop_after_attempt(3), wait=wait_fixed(60))
@@ -96,7 +96,7 @@ class LindormVectorStore(BaseVector):
                 )
                 return {doc["_id"] for doc in existing_docs["docs"] if doc["found"]}
             except Exception as e:
-                logger.exception(f"Error fetching batch {batch_ids}: {e}")
+                logger.exception(f"Error fetching batch ids: {batch_ids}")
                 return set()
 
         if ids is None:
@@ -177,7 +177,7 @@ class LindormVectorStore(BaseVector):
             else:
                 logger.warning(f"Index '{self._collection_name}' does not exist. No deletion performed.")
         except Exception as e:
-            logger.exception(f"Error occurred while deleting the index: {e}")
+            logger.exception(f"Error occurred while deleting the index: {self._collection_name}")
             raise e
 
     def text_exists(self, id: str) -> bool:
@@ -201,7 +201,7 @@ class LindormVectorStore(BaseVector):
         try:
             response = self._client.search(index=self._collection_name, body=query)
         except Exception as e:
-            logger.exception(f"Error executing search: {e}")
+            logger.exception(f"Error executing vector search, query: {query}")
             raise
 
         docs_and_scores = []

api/core/rag/datasource/vdb/myscale/myscale_vector.py

@@ -142,7 +142,7 @@ class MyScaleVector(BaseVector):
                 for r in self._client.query(sql).named_results()
             ]
         except Exception as e:
-            logging.exception(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
+            logging.exception(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")  # noqa:TRY401
             return []
 
     def delete(self) -> None:

api/core/rag/datasource/vdb/opensearch/opensearch_vector.py

@@ -158,7 +158,7 @@ class OpenSearchVector(BaseVector):
         try:
             response = self._client.search(index=self._collection_name.lower(), body=query)
         except Exception as e:
-            logger.exception(f"Error executing search: {e}")
+            logger.exception(f"Error executing vector search, query: {query}")
             raise
 
         docs = []

api/core/rag/embedding/cached_embedding.py

@@ -69,7 +69,7 @@ class CacheEmbedding(Embeddings):
                         except IntegrityError:
                             db.session.rollback()
                         except Exception as e:
-                            logging.exception("Failed transform embedding: %s", e)
+                            logging.exception("Failed transform embedding")
                 cache_embeddings = []
                 try:
                     for i, embedding in zip(embedding_queue_indices, embedding_queue_embeddings):
@@ -89,7 +89,7 @@ class CacheEmbedding(Embeddings):
                     db.session.rollback()
             except Exception as ex:
                 db.session.rollback()
-                logger.exception("Failed to embed documents: %s", ex)
+                logger.exception("Failed to embed documents: %s")
                 raise ex
 
         return text_embeddings
@@ -112,7 +112,7 @@ class CacheEmbedding(Embeddings):
             embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
         except Exception as ex:
             if dify_config.DEBUG:
-                logging.exception(f"Failed to embed query text: {ex}")
+                logging.exception(f"Failed to embed query text '{text[:10]}...({len(text)} chars)'")
             raise ex
 
         try:
@@ -126,7 +126,7 @@ class CacheEmbedding(Embeddings):
             redis_client.setex(embedding_cache_key, 600, encoded_str)
         except Exception as ex:
             if dify_config.DEBUG:
-                logging.exception("Failed to add embedding to redis %s", ex)
+                logging.exception(f"Failed to add embedding to redis for the text '{text[:10]}...({len(text)} chars)'")
             raise ex
 
         return embedding_results

api/core/rag/extractor/word_extractor.py

@@ -229,7 +229,7 @@ class WordExtractor(BaseExtractor):
                                 for i in url_pattern.findall(x.text):
                                     hyperlinks_url = str(i)
                     except Exception as e:
-                        logger.exception(e)
+                        logger.exception("Failed to parse HYPERLINK xml")
 
         def parse_paragraph(paragraph):
             paragraph_content = []

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -159,7 +159,7 @@ class QAIndexProcessor(BaseIndexProcessor):
                     qa_documents.append(qa_document)
                 format_documents.extend(qa_documents)
             except Exception as e:
-                logging.exception(e)
+                logging.exception("Failed to format qa document")
 
             all_qa_documents.extend(format_documents)
 

api/core/tools/provider/builtin/audio/tools/asr.py

@@ -57,13 +57,12 @@ class ASRTool(BuiltinTool):
                 name="model",
                 label=I18nObject(en_US="Model", zh_Hans="Model"),
                 human_description=I18nObject(
-                    en_US="All available ASR models",
+                    en_US="All available ASR models. You can config model in the Model Provider of Settings.",
-                    zh_Hans="所有可用的 ASR 模型",
+                    zh_Hans="所有可用的 ASR 模型。你可以在设置中的模型供应商里配置。",
                 ),
                 type=ToolParameter.ToolParameterType.SELECT,
                 form=ToolParameter.ToolParameterForm.FORM,
                 required=True,
-                default=options[0].value,
                 options=options,
             )
         )

api/core/tools/provider/builtin/audio/tools/tts.py

@@ -77,13 +77,12 @@ class TTSTool(BuiltinTool):
                 name="model",
                 label=I18nObject(en_US="Model", zh_Hans="Model"),
                 human_description=I18nObject(
-                    en_US="All available TTS models",
+                    en_US="All available TTS models. You can config model in the Model Provider of Settings.",
-                    zh_Hans="所有可用的 TTS 模型",
+                    zh_Hans="所有可用的 TTS 模型。你可以在设置中的模型供应商里配置。",
                 ),
                 type=ToolParameter.ToolParameterType.SELECT,
                 form=ToolParameter.ToolParameterForm.FORM,
                 required=True,
-                default=options[0].value,
                 options=options,
             ),
         )

api/core/tools/provider/builtin/email/tools/send.py

@@ -38,7 +38,7 @@ def send_mail(parmas: SendEmailToolParameters):
                 server.sendmail(parmas.email_account, parmas.sender_to, msg.as_string())
                 return True
         except Exception as e:
-            logging.exception("send email failed: %s", e)
+            logging.exception("send email failed")
             return False
     else:  # NONE or TLS
         try:
@@ -49,5 +49,5 @@ def send_mail(parmas: SendEmailToolParameters):
                 server.sendmail(parmas.email_account, parmas.sender_to, msg.as_string())
                 return True
         except Exception as e:
-            logging.exception("send email failed: %s", e)
+            logging.exception("send email failed")
             return False

api/core/tools/provider/builtin/fal/tools/wizper.py

@@ -0,0 +1,52 @@
+import io
+import os
+from typing import Any
+
+import fal_client
+
+from core.file.enums import FileAttribute, FileType
+from core.file.file_manager import download, get_attr
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class WizperTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        audio_file = tool_parameters.get("audio_file")
+        task = tool_parameters.get("task", "transcribe")
+        language = tool_parameters.get("language", "en")
+        chunk_level = tool_parameters.get("chunk_level", "segment")
+        version = tool_parameters.get("version", "3")
+
+        if audio_file.type != FileType.AUDIO:
+            return [self.create_text_message("Not a valid audio file.")]
+
+        api_key = self.runtime.credentials["fal_api_key"]
+
+        os.environ["FAL_KEY"] = api_key
+
+        audio_binary = io.BytesIO(download(audio_file))
+        mime_type = get_attr(file=audio_file, attr=FileAttribute.MIME_TYPE)
+        file_data = audio_binary.getvalue()
+
+        try:
+            audio_url = fal_client.upload(file_data, mime_type)
+
+        except Exception as e:
+            return [self.create_text_message(f"Error uploading audio file: {str(e)}")]
+
+        arguments = {
+            "audio_url": audio_url,
+            "task": task,
+            "language": language,
+            "chunk_level": chunk_level,
+            "version": version,
+        }
+
+        result = fal_client.subscribe(
+            "fal-ai/wizper",
+            arguments=arguments,
+            with_logs=False,
+        )
+
+        return self.create_json_message(result)

api/core/tools/provider/builtin/fal/tools/wizper.yaml

@@ -0,0 +1,489 @@
+identity:
+  name: wizper
+  author: Kalo Chin
+  label:
+    en_US: Wizper
+    zh_Hans: Wizper
+description:
+  human:
+    en_US: Transcribe an audio file using the Whisper model.
+    zh_Hans: 使用 Whisper 模型转录音频文件。
+  llm: Transcribe an audio file using the Whisper model.
+parameters:
+  - name: audio_file
+    type: file
+    required: true
+    label:
+      en_US: Audio File
+      zh_Hans: 音频文件
+    human_description:
+      en_US: "Upload an audio file to transcribe. Supports mp3, mp4, mpeg, mpga, m4a, wav, or webm formats."
+      zh_Hans: "上传要转录的音频文件。支持 mp3、mp4、mpeg、mpga、m4a、wav 或 webm 格式。"
+    llm_description: "Audio file to transcribe. Supported formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm."
+    form: llm
+  - name: task
+    type: select
+    required: true
+    label:
+      en_US: Task
+      zh_Hans: 任务
+    human_description:
+      en_US: "Choose whether to transcribe the audio in its original language or translate it to English"
+      zh_Hans: "选择是以原始语言转录音频还是将其翻译成英语"
+    llm_description: "Task to perform on the audio file. Either transcribe or translate. Default value: 'transcribe'. If 'translate' is selected as the task, the audio will be translated to English, regardless of the language selected."
+    form: form
+    default: transcribe
+    options:
+      - value: transcribe
+        label:
+          en_US: Transcribe
+          zh_Hans: 转录
+      - value: translate
+        label:
+          en_US: Translate
+          zh_Hans: 翻译
+  - name: language
+    type: select
+    required: true
+    label:
+      en_US: Language
+      zh_Hans: 语言
+    human_description:
+      en_US: "Select the primary language spoken in the audio file"
+      zh_Hans: "选择音频文件中使用的主要语言"
+    llm_description: "Language of the audio file."
+    form: form
+    default: en
+    options:
+      - value: af
+        label:
+          en_US: Afrikaans
+          zh_Hans: 南非语
+      - value: am
+        label:
+          en_US: Amharic
+          zh_Hans: 阿姆哈拉语
+      - value: ar
+        label:
+          en_US: Arabic
+          zh_Hans: 阿拉伯语
+      - value: as
+        label:
+          en_US: Assamese
+          zh_Hans: 阿萨姆语
+      - value: az
+        label:
+          en_US: Azerbaijani
+          zh_Hans: 阿塞拜疆语
+      - value: ba
+        label:
+          en_US: Bashkir
+          zh_Hans: 巴什基尔语
+      - value: be
+        label:
+          en_US: Belarusian
+          zh_Hans: 白俄罗斯语
+      - value: bg
+        label:
+          en_US: Bulgarian
+          zh_Hans: 保加利亚语
+      - value: bn
+        label:
+          en_US: Bengali
+          zh_Hans: 孟加拉语
+      - value: bo
+        label:
+          en_US: Tibetan
+          zh_Hans: 藏语
+      - value: br
+        label:
+          en_US: Breton
+          zh_Hans: 布列塔尼语
+      - value: bs
+        label:
+          en_US: Bosnian
+          zh_Hans: 波斯尼亚语
+      - value: ca
+        label:
+          en_US: Catalan
+          zh_Hans: 加泰罗尼亚语
+      - value: cs
+        label:
+          en_US: Czech
+          zh_Hans: 捷克语
+      - value: cy
+        label:
+          en_US: Welsh
+          zh_Hans: 威尔士语
+      - value: da
+        label:
+          en_US: Danish
+          zh_Hans: 丹麦语
+      - value: de
+        label:
+          en_US: German
+          zh_Hans: 德语
+      - value: el
+        label:
+          en_US: Greek
+          zh_Hans: 希腊语
+      - value: en
+        label:
+          en_US: English
+          zh_Hans: 英语
+      - value: es
+        label:
+          en_US: Spanish
+          zh_Hans: 西班牙语
+      - value: et
+        label:
+          en_US: Estonian
+          zh_Hans: 爱沙尼亚语
+      - value: eu
+        label:
+          en_US: Basque
+          zh_Hans: 巴斯克语
+      - value: fa
+        label:
+          en_US: Persian
+          zh_Hans: 波斯语
+      - value: fi
+        label:
+          en_US: Finnish
+          zh_Hans: 芬兰语
+      - value: fo
+        label:
+          en_US: Faroese
+          zh_Hans: 法罗语
+      - value: fr
+        label:
+          en_US: French
+          zh_Hans: 法语
+      - value: gl
+        label:
+          en_US: Galician
+          zh_Hans: 加利西亚语
+      - value: gu
+        label:
+          en_US: Gujarati
+          zh_Hans: 古吉拉特语
+      - value: ha
+        label:
+          en_US: Hausa
+          zh_Hans: 毫萨语
+      - value: haw
+        label:
+          en_US: Hawaiian
+          zh_Hans: 夏威夷语
+      - value: he
+        label:
+          en_US: Hebrew
+          zh_Hans: 希伯来语
+      - value: hi
+        label:
+          en_US: Hindi
+          zh_Hans: 印地语
+      - value: hr
+        label:
+          en_US: Croatian
+          zh_Hans: 克罗地亚语
+      - value: ht
+        label:
+          en_US: Haitian Creole
+          zh_Hans: 海地克里奥尔语
+      - value: hu
+        label:
+          en_US: Hungarian
+          zh_Hans: 匈牙利语
+      - value: hy
+        label:
+          en_US: Armenian
+          zh_Hans: 亚美尼亚语
+      - value: id
+        label:
+          en_US: Indonesian
+          zh_Hans: 印度尼西亚语
+      - value: is
+        label:
+          en_US: Icelandic
+          zh_Hans: 冰岛语
+      - value: it
+        label:
+          en_US: Italian
+          zh_Hans: 意大利语
+      - value: ja
+        label:
+          en_US: Japanese
+          zh_Hans: 日语
+      - value: jw
+        label:
+          en_US: Javanese
+          zh_Hans: 爪哇语
+      - value: ka
+        label:
+          en_US: Georgian
+          zh_Hans: 格鲁吉亚语
+      - value: kk
+        label:
+          en_US: Kazakh
+          zh_Hans: 哈萨克语
+      - value: km
+        label:
+          en_US: Khmer
+          zh_Hans: 高棉语
+      - value: kn
+        label:
+          en_US: Kannada
+          zh_Hans: 卡纳达语
+      - value: ko
+        label:
+          en_US: Korean
+          zh_Hans: 韩语
+      - value: la
+        label:
+          en_US: Latin
+          zh_Hans: 拉丁语
+      - value: lb
+        label:
+          en_US: Luxembourgish
+          zh_Hans: 卢森堡语
+      - value: ln
+        label:
+          en_US: Lingala
+          zh_Hans: 林加拉语
+      - value: lo
+        label:
+          en_US: Lao
+          zh_Hans: 老挝语
+      - value: lt
+        label:
+          en_US: Lithuanian
+          zh_Hans: 立陶宛语
+      - value: lv
+        label:
+          en_US: Latvian
+          zh_Hans: 拉脱维亚语
+      - value: mg
+        label:
+          en_US: Malagasy
+          zh_Hans: 马尔加什语
+      - value: mi
+        label:
+          en_US: Maori
+          zh_Hans: 毛利语
+      - value: mk
+        label:
+          en_US: Macedonian
+          zh_Hans: 马其顿语
+      - value: ml
+        label:
+          en_US: Malayalam
+          zh_Hans: 马拉雅拉姆语
+      - value: mn
+        label:
+          en_US: Mongolian
+          zh_Hans: 蒙古语
+      - value: mr
+        label:
+          en_US: Marathi
+          zh_Hans: 马拉地语
+      - value: ms
+        label:
+          en_US: Malay
+          zh_Hans: 马来语
+      - value: mt
+        label:
+          en_US: Maltese
+          zh_Hans: 马耳他语
+      - value: my
+        label:
+          en_US: Burmese
+          zh_Hans: 缅甸语
+      - value: ne
+        label:
+          en_US: Nepali
+          zh_Hans: 尼泊尔语
+      - value: nl
+        label:
+          en_US: Dutch
+          zh_Hans: 荷兰语
+      - value: nn
+        label:
+          en_US: Norwegian Nynorsk
+          zh_Hans: 新挪威语
+      - value: no
+        label:
+          en_US: Norwegian
+          zh_Hans: 挪威语
+      - value: oc
+        label:
+          en_US: Occitan
+          zh_Hans: 奥克语
+      - value: pa
+        label:
+          en_US: Punjabi
+          zh_Hans: 旁遮普语
+      - value: pl
+        label:
+          en_US: Polish
+          zh_Hans: 波兰语
+      - value: ps
+        label:
+          en_US: Pashto
+          zh_Hans: 普什图语
+      - value: pt
+        label:
+          en_US: Portuguese
+          zh_Hans: 葡萄牙语
+      - value: ro
+        label:
+          en_US: Romanian
+          zh_Hans: 罗马尼亚语
+      - value: ru
+        label:
+          en_US: Russian
+          zh_Hans: 俄语
+      - value: sa
+        label:
+          en_US: Sanskrit
+          zh_Hans: 梵语
+      - value: sd
+        label:
+          en_US: Sindhi
+          zh_Hans: 信德语
+      - value: si
+        label:
+          en_US: Sinhala
+          zh_Hans: 僧伽罗语
+      - value: sk
+        label:
+          en_US: Slovak
+          zh_Hans: 斯洛伐克语
+      - value: sl
+        label:
+          en_US: Slovenian
+          zh_Hans: 斯洛文尼亚语
+      - value: sn
+        label:
+          en_US: Shona
+          zh_Hans: 修纳语
+      - value: so
+        label:
+          en_US: Somali
+          zh_Hans: 索马里语
+      - value: sq
+        label:
+          en_US: Albanian
+          zh_Hans: 阿尔巴尼亚语
+      - value: sr
+        label:
+          en_US: Serbian
+          zh_Hans: 塞尔维亚语
+      - value: su
+        label:
+          en_US: Sundanese
+          zh_Hans: 巽他语
+      - value: sv
+        label:
+          en_US: Swedish
+          zh_Hans: 瑞典语
+      - value: sw
+        label:
+          en_US: Swahili
+          zh_Hans: 斯瓦希里语
+      - value: ta
+        label:
+          en_US: Tamil
+          zh_Hans: 泰米尔语
+      - value: te
+        label:
+          en_US: Telugu
+          zh_Hans: 泰卢固语
+      - value: tg
+        label:
+          en_US: Tajik
+          zh_Hans: 塔吉克语
+      - value: th
+        label:
+          en_US: Thai
+          zh_Hans: 泰语
+      - value: tk
+        label:
+          en_US: Turkmen
+          zh_Hans: 土库曼语
+      - value: tl
+        label:
+          en_US: Tagalog
+          zh_Hans: 他加禄语
+      - value: tr
+        label:
+          en_US: Turkish
+          zh_Hans: 土耳其语
+      - value: tt
+        label:
+          en_US: Tatar
+          zh_Hans: 鞑靼语
+      - value: uk
+        label:
+          en_US: Ukrainian
+          zh_Hans: 乌克兰语
+      - value: ur
+        label:
+          en_US: Urdu
+          zh_Hans: 乌尔都语
+      - value: uz
+        label:
+          en_US: Uzbek
+          zh_Hans: 乌兹别克语
+      - value: vi
+        label:
+          en_US: Vietnamese
+          zh_Hans: 越南语
+      - value: yi
+        label:
+          en_US: Yiddish
+          zh_Hans: 意第绪语
+      - value: yo
+        label:
+          en_US: Yoruba
+          zh_Hans: 约鲁巴语
+      - value: yue
+        label:
+          en_US: Cantonese
+          zh_Hans: 粤语
+      - value: zh
+        label:
+          en_US: Chinese
+          zh_Hans: 中文
+  - name: chunk_level
+    type: select
+    label:
+      en_US: Chunk Level
+      zh_Hans: 分块级别
+    human_description:
+      en_US: "Choose how the transcription should be divided into chunks"
+      zh_Hans: "选择如何将转录内容分成块"
+    llm_description: "Level of the chunks to return."
+    form: form
+    default: segment
+    options:
+      - value: segment
+        label:
+          en_US: Segment
+          zh_Hans: 段
+  - name: version
+    type: select
+    label:
+      en_US: Version
+      zh_Hans: 版本
+    human_description:
+      en_US: "Select which version of the Whisper large model to use"
+      zh_Hans: "选择要使用的 Whisper large 模型版本"
+    llm_description: "Version of the model to use. All of the models are the Whisper large variant."
+    form: form
+    default: "3"
+    options:
+      - value: "3"
+        label:
+          en_US: Version 3
+          zh_Hans: 版本 3

api/core/tools/tool/workflow_tool.py

@@ -175,7 +175,7 @@ class WorkflowTool(Tool):
 
                             files.append(file_dict)
                     except Exception as e:
-                        logger.exception(e)
+                        logger.exception(f"Failed to transform file {file}")
             else:
                 parameters_result[parameter.name] = tool_parameters.get(parameter.name)
 

api/core/tools/tool_file_manager.py

@@ -98,7 +98,7 @@ class ToolFileManager:
             response.raise_for_status()
             blob = response.content
         except Exception as e:
-            logger.exception(f"Failed to download file from {file_url}: {e}")
+            logger.exception(f"Failed to download file from {file_url}")
             raise
 
         mimetype = guess_type(file_url)[0] or "octet/stream"

api/core/tools/tool_manager.py

@@ -388,7 +388,7 @@ class ToolManager:
                     yield provider
 
                 except Exception as e:
-                    logger.exception(f"load builtin provider {provider} error: {e}")
+                    logger.exception(f"load builtin provider {provider}")
                     continue
         # set builtin providers loaded
         cls._builtin_providers_loaded = True

api/core/tools/utils/message_transformer.py

@@ -40,7 +40,7 @@ class ToolFileMessageTransformer:
                         )
                     )
                 except Exception as e:
-                    logger.exception(e)
+                    logger.exception(f"Failed to download image from {url}")
                     result.append(
                         ToolInvokeMessage(
                             type=ToolInvokeMessage.MessageType.TEXT,

api/core/variables/segments.py

@@ -118,11 +118,11 @@ class FileSegment(Segment):
 
     @property
     def log(self) -> str:
-        return str(self.value)
+        return ""
 
     @property
     def text(self) -> str:
-        return str(self.value)
+        return ""
 
 
 class ArrayAnySegment(ArraySegment):
@@ -155,3 +155,11 @@ class ArrayFileSegment(ArraySegment):
         for item in self.value:
             items.append(item.markdown)
         return "\n".join(items)
+
+    @property
+    def log(self) -> str:
+        return ""
+
+    @property
+    def text(self) -> str:
+        return ""

api/core/workflow/graph_engine/graph_engine.py

@@ -172,7 +172,7 @@ class GraphEngine:
                                 "answer"
                             ].strip()
                 except Exception as e:
-                    logger.exception(f"Graph run failed: {str(e)}")
+                    logger.exception("Graph run failed")
                     yield GraphRunFailedEvent(error=str(e))
                     return
 
@@ -692,7 +692,7 @@ class GraphEngine:
             )
             return
         except Exception as e:
-            logger.exception(f"Node {node_instance.node_data.title} run failed: {str(e)}")
+            logger.exception(f"Node {node_instance.node_data.title} run failed")
             raise e
         finally:
             db.session.close()

api/core/workflow/nodes/base/node.py

@@ -69,7 +69,7 @@ class BaseNode(Generic[GenericNodeData]):
         try:
             result = self._run()
         except Exception as e:
-            logger.exception(f"Node {self.node_id} failed to run: {e}")
+            logger.exception(f"Node {self.node_id} failed to run")
             result = NodeRunResult(
                 status=WorkflowNodeExecutionStatus.FAILED,
                 error=str(e),

api/core/workflow/nodes/llm/entities.py

@@ -39,7 +39,14 @@ class VisionConfig(BaseModel):
 
 
 class PromptConfig(BaseModel):
-    jinja2_variables: Optional[list[VariableSelector]] = None
+    jinja2_variables: Sequence[VariableSelector] = Field(default_factory=list)
+
+    @field_validator("jinja2_variables", mode="before")
+    @classmethod
+    def convert_none_jinja2_variables(cls, v: Any):
+        if v is None:
+            return []
+        return v
 
 
 class LLMNodeChatModelMessage(ChatModelMessage):
@@ -53,7 +60,14 @@ class LLMNodeCompletionModelPromptTemplate(CompletionModelPromptTemplate):
 class LLMNodeData(BaseNodeData):
     model: ModelConfig
     prompt_template: Sequence[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate
-    prompt_config: Optional[PromptConfig] = None
+    prompt_config: PromptConfig = Field(default_factory=PromptConfig)
     memory: Optional[MemoryConfig] = None
     context: ContextConfig
     vision: VisionConfig = Field(default_factory=VisionConfig)
+
+    @field_validator("prompt_config", mode="before")
+    @classmethod
+    def convert_none_prompt_config(cls, v: Any):
+        if v is None:
+            return PromptConfig()
+        return v

api/core/workflow/nodes/llm/exc.py

@@ -24,3 +24,11 @@ class LLMModeRequiredError(LLMNodeError):
 
 class NoPromptFoundError(LLMNodeError):
     """Raised when no prompt is found in the LLM configuration."""
+
+
+class NotSupportedPromptTypeError(LLMNodeError):
+    """Raised when the prompt type is not supported."""
+
+
+class MemoryRolePrefixRequiredError(LLMNodeError):
+    """Raised when memory role prefix is required for completion model."""

api/core/workflow/nodes/llm/node.py

@@ -1,4 +1,5 @@
 import json
+import logging
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Optional, cast
 
@@ -6,21 +7,26 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
 from core.entities.model_entities import ModelStatus
 from core.entities.provider_entities import QuotaUnit
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.file import FileType, file_manager
+from core.helper.code_executor import CodeExecutor, CodeLanguage
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities import (
-    AudioPromptMessageContent,
     ImagePromptMessageContent,
     PromptMessage,
     PromptMessageContentType,
     TextPromptMessageContent,
-    VideoPromptMessageContent,
 )
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
-from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessageRole,
+    SystemPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey, ModelType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
 from core.variables import (
@@ -32,8 +38,9 @@ from core.variables import (
     ObjectSegment,
     StringSegment,
 )
-from core.workflow.constants import SYSTEM_VARIABLE_NODE_ID
 from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.variable_entities import VariableSelector
+from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.event import InNodeEvent
 from core.workflow.nodes.base import BaseNode
@@ -62,14 +69,18 @@ from .exc import (
     InvalidVariableTypeError,
     LLMModeRequiredError,
     LLMNodeError,
+    MemoryRolePrefixRequiredError,
     ModelNotExistError,
     NoPromptFoundError,
+    NotSupportedPromptTypeError,
     VariableNotFoundError,
 )
 
 if TYPE_CHECKING:
     from core.file.models import File
 
+logger = logging.getLogger(__name__)
+
 
 class LLMNode(BaseNode[LLMNodeData]):
     _node_data_cls = LLMNodeData
@@ -123,17 +134,13 @@ class LLMNode(BaseNode[LLMNodeData]):
 
             # fetch prompt messages
             if self.node_data.memory:
-                query = self.graph_runtime_state.variable_pool.get((SYSTEM_VARIABLE_NODE_ID, SystemVariableKey.QUERY))
+                query = self.node_data.memory.query_prompt_template
-                if not query:
-                    raise VariableNotFoundError("Query not found")
-                query = query.text
             else:
                 query = None
 
             prompt_messages, stop = self._fetch_prompt_messages(
-                system_query=query,
+                user_query=query,
-                inputs=inputs,
+                user_files=files,
-                files=files,
                 context=context,
                 memory=memory,
                 model_config=model_config,
@@ -141,6 +148,8 @@ class LLMNode(BaseNode[LLMNodeData]):
                 memory_config=self.node_data.memory,
                 vision_enabled=self.node_data.vision.enabled,
                 vision_detail=self.node_data.vision.configs.detail,
+                variable_pool=self.graph_runtime_state.variable_pool,
+                jinja2_variables=self.node_data.prompt_config.jinja2_variables,
             )
 
             process_data = {
@@ -181,6 +190,17 @@ class LLMNode(BaseNode[LLMNodeData]):
                 )
             )
             return
+        except Exception as e:
+            logger.exception(f"Node {self.node_id} failed to run: {e}")
+            yield RunCompletedEvent(
+                run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    error=str(e),
+                    inputs=node_inputs,
+                    process_data=process_data,
+                )
+            )
+            return
 
         outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
 
@@ -203,8 +223,8 @@ class LLMNode(BaseNode[LLMNodeData]):
         self,
         node_data_model: ModelConfig,
         model_instance: ModelInstance,
-        prompt_messages: list[PromptMessage],
+        prompt_messages: Sequence[PromptMessage],
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
     ) -> Generator[NodeEvent, None, None]:
         db.session.close()
 
@@ -519,9 +539,8 @@ class LLMNode(BaseNode[LLMNodeData]):
     def _fetch_prompt_messages(
         self,
         *,
-        system_query: str | None = None,
+        user_query: str | None = None,
-        inputs: dict[str, str] | None = None,
+        user_files: Sequence["File"],
-        files: Sequence["File"],
         context: str | None = None,
         memory: TokenBufferMemory | None = None,
         model_config: ModelConfigWithCredentialsEntity,
@@ -529,58 +548,146 @@ class LLMNode(BaseNode[LLMNodeData]):
         memory_config: MemoryConfig | None = None,
         vision_enabled: bool = False,
         vision_detail: ImagePromptMessageContent.DETAIL,
-    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
+        variable_pool: VariablePool,
-        inputs = inputs or {}
+        jinja2_variables: Sequence[VariableSelector],
+    ) -> tuple[Sequence[PromptMessage], Optional[Sequence[str]]]:
+        prompt_messages = []
 
-        prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
+        if isinstance(prompt_template, list):
-        prompt_messages = prompt_transform.get_prompt(
+            # For chat model
-            prompt_template=prompt_template,
+            prompt_messages.extend(
-            inputs=inputs,
+                _handle_list_messages(
-            query=system_query or "",
+                    messages=prompt_template,
-            files=files,
+                    context=context,
-            context=context,
+                    jinja2_variables=jinja2_variables,
-            memory_config=memory_config,
+                    variable_pool=variable_pool,
-            memory=memory,
+                    vision_detail_config=vision_detail,
-            model_config=model_config,
+                )
-        )
+            )
-        stop = model_config.stop
+
+            # Get memory messages for chat mode
+            memory_messages = _handle_memory_chat_mode(
+                memory=memory,
+                memory_config=memory_config,
+                model_config=model_config,
+            )
+            # Extend prompt_messages with memory messages
+            prompt_messages.extend(memory_messages)
+
+            # Add current query to the prompt messages
+            if user_query:
+                message = LLMNodeChatModelMessage(
+                    text=user_query,
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                )
+                prompt_messages.extend(
+                    _handle_list_messages(
+                        messages=[message],
+                        context="",
+                        jinja2_variables=[],
+                        variable_pool=variable_pool,
+                        vision_detail_config=vision_detail,
+                    )
+                )
+
+        elif isinstance(prompt_template, LLMNodeCompletionModelPromptTemplate):
+            # For completion model
+            prompt_messages.extend(
+                _handle_completion_template(
+                    template=prompt_template,
+                    context=context,
+                    jinja2_variables=jinja2_variables,
+                    variable_pool=variable_pool,
+                )
+            )
+
+            # Get memory text for completion model
+            memory_text = _handle_memory_completion_mode(
+                memory=memory,
+                memory_config=memory_config,
+                model_config=model_config,
+            )
+            # Insert histories into the prompt
+            prompt_content = prompt_messages[0].content
+            if "#histories#" in prompt_content:
+                prompt_content = prompt_content.replace("#histories#", memory_text)
+            else:
+                prompt_content = memory_text + "\n" + prompt_content
+            prompt_messages[0].content = prompt_content
+
+            # Add current query to the prompt message
+            if user_query:
+                prompt_content = prompt_messages[0].content.replace("#sys.query#", user_query)
+                prompt_messages[0].content = prompt_content
+        else:
+            errmsg = f"Prompt type {type(prompt_template)} is not supported"
+            logger.warning(errmsg)
+            raise NotSupportedPromptTypeError(errmsg)
+
+        if vision_enabled and user_files:
+            file_prompts = []
+            for file in user_files:
+                file_prompt = file_manager.to_prompt_message_content(file, image_detail_config=vision_detail)
+                file_prompts.append(file_prompt)
+            if (
+                len(prompt_messages) > 0
+                and isinstance(prompt_messages[-1], UserPromptMessage)
+                and isinstance(prompt_messages[-1].content, list)
+            ):
+                prompt_messages[-1] = UserPromptMessage(content=prompt_messages[-1].content + file_prompts)
+            else:
+                prompt_messages.append(UserPromptMessage(content=file_prompts))
+
+        # Filter prompt messages
         filtered_prompt_messages = []
         for prompt_message in prompt_messages:
-            if prompt_message.is_empty():
+            if isinstance(prompt_message.content, list):
-                continue
-
-            if not isinstance(prompt_message.content, str):
                 prompt_message_content = []
-                for content_item in prompt_message.content or []:
+                for content_item in prompt_message.content:
-                    # Skip image if vision is disabled
+                    # Skip content if features are not defined
-                    if not vision_enabled and content_item.type == PromptMessageContentType.IMAGE:
+                    if not model_config.model_schema.features:
+                        if content_item.type != PromptMessageContentType.TEXT:
+                            continue
+                        prompt_message_content.append(content_item)
                         continue
 
-                    if isinstance(content_item, ImagePromptMessageContent):
+                    # Skip content if corresponding feature is not supported
-                        # Override vision config if LLM node has vision config,
+                    if (
-                        # cuz vision detail is related to the configuration from FileUpload feature.
+                        (
-                        content_item.detail = vision_detail
+                            content_item.type == PromptMessageContentType.IMAGE
-                        prompt_message_content.append(content_item)
+                            and ModelFeature.VISION not in model_config.model_schema.features
-                    elif isinstance(
+                        )
-                        content_item, TextPromptMessageContent | AudioPromptMessageContent | VideoPromptMessageContent
+                        or (
+                            content_item.type == PromptMessageContentType.DOCUMENT
+                            and ModelFeature.DOCUMENT not in model_config.model_schema.features
+                        )
+                        or (
+                            content_item.type == PromptMessageContentType.VIDEO
+                            and ModelFeature.VIDEO not in model_config.model_schema.features
+                        )
+                        or (
+                            content_item.type == PromptMessageContentType.AUDIO
+                            and ModelFeature.AUDIO not in model_config.model_schema.features
+                        )
                     ):
-                        prompt_message_content.append(content_item)
+                        continue
-
+                    prompt_message_content.append(content_item)
-                if len(prompt_message_content) > 1:
+                if len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT:
-                    prompt_message.content = prompt_message_content
-                elif (
-                    len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT
-                ):
                     prompt_message.content = prompt_message_content[0].data
-
+                else:
+                    prompt_message.content = prompt_message_content
+            if prompt_message.is_empty():
+                continue
             filtered_prompt_messages.append(prompt_message)
 
-        if not filtered_prompt_messages:
+        if len(filtered_prompt_messages) == 0:
             raise NoPromptFoundError(
                 "No prompt found in the LLM configuration. "
                 "Please ensure a prompt is properly configured before proceeding."
             )
 
+        stop = model_config.stop
         return filtered_prompt_messages, stop
 
     @classmethod
@@ -715,3 +822,198 @@ class LLMNode(BaseNode[LLMNodeData]):
                 }
             },
         }
+
+
+def _combine_text_message_with_role(*, text: str, role: PromptMessageRole):
+    match role:
+        case PromptMessageRole.USER:
+            return UserPromptMessage(content=[TextPromptMessageContent(data=text)])
+        case PromptMessageRole.ASSISTANT:
+            return AssistantPromptMessage(content=[TextPromptMessageContent(data=text)])
+        case PromptMessageRole.SYSTEM:
+            return SystemPromptMessage(content=[TextPromptMessageContent(data=text)])
+    raise NotImplementedError(f"Role {role} is not supported")
+
+
+def _render_jinja2_message(
+    *,
+    template: str,
+    jinjia2_variables: Sequence[VariableSelector],
+    variable_pool: VariablePool,
+):
+    if not template:
+        return ""
+
+    jinjia2_inputs = {}
+    for jinja2_variable in jinjia2_variables:
+        variable = variable_pool.get(jinja2_variable.value_selector)
+        jinjia2_inputs[jinja2_variable.variable] = variable.to_object() if variable else ""
+    code_execute_resp = CodeExecutor.execute_workflow_code_template(
+        language=CodeLanguage.JINJA2,
+        code=template,
+        inputs=jinjia2_inputs,
+    )
+    result_text = code_execute_resp["result"]
+    return result_text
+
+
+def _handle_list_messages(
+    *,
+    messages: Sequence[LLMNodeChatModelMessage],
+    context: Optional[str],
+    jinja2_variables: Sequence[VariableSelector],
+    variable_pool: VariablePool,
+    vision_detail_config: ImagePromptMessageContent.DETAIL,
+) -> Sequence[PromptMessage]:
+    prompt_messages = []
+    for message in messages:
+        if message.edition_type == "jinja2":
+            result_text = _render_jinja2_message(
+                template=message.jinja2_text or "",
+                jinjia2_variables=jinja2_variables,
+                variable_pool=variable_pool,
+            )
+            prompt_message = _combine_text_message_with_role(text=result_text, role=message.role)
+            prompt_messages.append(prompt_message)
+        else:
+            # Get segment group from basic message
+            if context:
+                template = message.text.replace("{#context#}", context)
+            else:
+                template = message.text
+            segment_group = variable_pool.convert_template(template)
+
+            # Process segments for images
+            file_contents = []
+            for segment in segment_group.value:
+                if isinstance(segment, ArrayFileSegment):
+                    for file in segment.value:
+                        if file.type in {FileType.IMAGE, FileType.VIDEO, FileType.AUDIO}:
+                            file_content = file_manager.to_prompt_message_content(
+                                file, image_detail_config=vision_detail_config
+                            )
+                            file_contents.append(file_content)
+                if isinstance(segment, FileSegment):
+                    file = segment.value
+                    if file.type in {FileType.IMAGE, FileType.VIDEO, FileType.AUDIO}:
+                        file_content = file_manager.to_prompt_message_content(
+                            file, image_detail_config=vision_detail_config
+                        )
+                        file_contents.append(file_content)
+
+            # Create message with text from all segments
+            plain_text = segment_group.text
+            if plain_text:
+                prompt_message = _combine_text_message_with_role(text=plain_text, role=message.role)
+                prompt_messages.append(prompt_message)
+
+            if file_contents:
+                # Create message with image contents
+                prompt_message = UserPromptMessage(content=file_contents)
+                prompt_messages.append(prompt_message)
+
+    return prompt_messages
+
+
+def _calculate_rest_token(
+    *, prompt_messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity
+) -> int:
+    rest_tokens = 2000
+
+    model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
+    if model_context_tokens:
+        model_instance = ModelInstance(
+            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
+        )
+
+        curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
+
+        max_tokens = 0
+        for parameter_rule in model_config.model_schema.parameter_rules:
+            if parameter_rule.name == "max_tokens" or (
+                parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+            ):
+                max_tokens = (
+                    model_config.parameters.get(parameter_rule.name)
+                    or model_config.parameters.get(str(parameter_rule.use_template))
+                    or 0
+                )
+
+        rest_tokens = model_context_tokens - max_tokens - curr_message_tokens
+        rest_tokens = max(rest_tokens, 0)
+
+    return rest_tokens
+
+
+def _handle_memory_chat_mode(
+    *,
+    memory: TokenBufferMemory | None,
+    memory_config: MemoryConfig | None,
+    model_config: ModelConfigWithCredentialsEntity,
+) -> Sequence[PromptMessage]:
+    memory_messages = []
+    # Get messages from memory for chat model
+    if memory and memory_config:
+        rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)
+        memory_messages = memory.get_history_prompt_messages(
+            max_token_limit=rest_tokens,
+            message_limit=memory_config.window.size if memory_config.window.enabled else None,
+        )
+    return memory_messages
+
+
+def _handle_memory_completion_mode(
+    *,
+    memory: TokenBufferMemory | None,
+    memory_config: MemoryConfig | None,
+    model_config: ModelConfigWithCredentialsEntity,
+) -> str:
+    memory_text = ""
+    # Get history text from memory for completion model
+    if memory and memory_config:
+        rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)
+        if not memory_config.role_prefix:
+            raise MemoryRolePrefixRequiredError("Memory role prefix is required for completion model.")
+        memory_text = memory.get_history_prompt_text(
+            max_token_limit=rest_tokens,
+            message_limit=memory_config.window.size if memory_config.window.enabled else None,
+            human_prefix=memory_config.role_prefix.user,
+            ai_prefix=memory_config.role_prefix.assistant,
+        )
+    return memory_text
+
+
+def _handle_completion_template(
+    *,
+    template: LLMNodeCompletionModelPromptTemplate,
+    context: Optional[str],
+    jinja2_variables: Sequence[VariableSelector],
+    variable_pool: VariablePool,
+) -> Sequence[PromptMessage]:
+    """Handle completion template processing outside of LLMNode class.
+
+    Args:
+        template: The completion model prompt template
+        context: Optional context string
+        jinja2_variables: Variables for jinja2 template rendering
+        variable_pool: Variable pool for template conversion
+
+    Returns:
+        Sequence of prompt messages
+    """
+    prompt_messages = []
+    if template.edition_type == "jinja2":
+        result_text = _render_jinja2_message(
+            template=template.jinja2_text or "",
+            jinjia2_variables=jinja2_variables,
+            variable_pool=variable_pool,
+        )
+    else:
+        if context:
+            template_text = template.text.replace("{#context#}", context)
+        else:
+            template_text = template.text
+        result_text = variable_pool.convert_template(template_text).text
+    prompt_message = _combine_text_message_with_role(text=result_text, role=PromptMessageRole.USER)
+    prompt_messages.append(prompt_message)
+    return prompt_messages

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -86,12 +86,14 @@ class QuestionClassifierNode(LLMNode):
         )
         prompt_messages, stop = self._fetch_prompt_messages(
             prompt_template=prompt_template,
-            system_query=query,
+            user_query=query,
             memory=memory,
             model_config=model_config,
-            files=files,
+            user_files=files,
             vision_enabled=node_data.vision.enabled,
             vision_detail=node_data.vision.configs.detail,
+            variable_pool=variable_pool,
+            jinja2_variables=[],
         )
 
         # handle invoke result

api/core/workflow/nodes/tool/tool_node.py

@@ -1,5 +1,4 @@
 from collections.abc import Mapping, Sequence
-from os import path
 from typing import Any
 
 from sqlalchemy import select
@@ -180,7 +179,6 @@ class ToolNode(BaseNode[ToolNodeData]):
         for response in tool_response:
             if response.type in {ToolInvokeMessage.MessageType.IMAGE_LINK, ToolInvokeMessage.MessageType.IMAGE}:
                 url = str(response.message) if response.message else None
-                ext = path.splitext(url)[1] if url else ".bin"
                 tool_file_id = str(url).split("/")[-1].split(".")[0]
                 transfer_method = response.meta.get("transfer_method", FileTransferMethod.TOOL_FILE)
 
@@ -202,7 +200,6 @@ class ToolNode(BaseNode[ToolNodeData]):
                 )
                 result.append(file)
             elif response.type == ToolInvokeMessage.MessageType.BLOB:
-                # get tool file id
                 tool_file_id = str(response.message).split("/")[-1].split(".")[0]
                 with Session(db.engine) as session:
                     stmt = select(ToolFile).where(ToolFile.id == tool_file_id)
@@ -211,7 +208,6 @@ class ToolNode(BaseNode[ToolNodeData]):
                         raise ValueError(f"tool file {tool_file_id} not exists")
                 mapping = {
                     "tool_file_id": tool_file_id,
-                    "type": FileType.IMAGE,
                     "transfer_method": FileTransferMethod.TOOL_FILE,
                 }
                 file = file_factory.build_from_mapping(
@@ -228,13 +224,8 @@ class ToolNode(BaseNode[ToolNodeData]):
                     tool_file = session.scalar(stmt)
                     if tool_file is None:
                         raise ToolFileError(f"Tool file {tool_file_id} does not exist")
-                if "." in url:
-                    extension = "." + url.split("/")[-1].split(".")[1]
-                else:
-                    extension = ".bin"
                 mapping = {
                     "tool_file_id": tool_file_id,
-                    "type": FileType.IMAGE,
                     "transfer_method": transfer_method,
                     "url": url,
                 }

api/extensions/ext_storage.py

@@ -70,7 +70,7 @@ class Storage:
         try:
             self.storage_runner.save(filename, data)
         except Exception as e:
-            logging.exception("Failed to save file: %s", e)
+            logging.exception(f"Failed to save file {filename}")
             raise e
 
     def load(self, filename: str, /, *, stream: bool = False) -> Union[bytes, Generator]:
@@ -80,42 +80,42 @@ class Storage:
             else:
                 return self.load_once(filename)
         except Exception as e:
-            logging.exception("Failed to load file: %s", e)
+            logging.exception(f"Failed to load file {filename}")
             raise e
 
     def load_once(self, filename: str) -> bytes:
         try:
             return self.storage_runner.load_once(filename)
         except Exception as e:
-            logging.exception("Failed to load_once file: %s", e)
+            logging.exception(f"Failed to load_once file {filename}")
             raise e
 
     def load_stream(self, filename: str) -> Generator:
         try:
             return self.storage_runner.load_stream(filename)
         except Exception as e:
-            logging.exception("Failed to load_stream file: %s", e)
+            logging.exception(f"Failed to load_stream file {filename}")
             raise e
 
     def download(self, filename, target_filepath):
         try:
             self.storage_runner.download(filename, target_filepath)
         except Exception as e:
-            logging.exception("Failed to download file: %s", e)
+            logging.exception(f"Failed to download file {filename}")
             raise e
 
     def exists(self, filename):
         try:
             return self.storage_runner.exists(filename)
         except Exception as e:
-            logging.exception("Failed to check file exists: %s", e)
+            logging.exception(f"Failed to check file exists {filename}")
             raise e
 
     def delete(self, filename):
         try:
             return self.storage_runner.delete(filename)
         except Exception as e:
-            logging.exception("Failed to delete file: %s", e)
+            logging.exception(f"Failed to delete file {filename}")
             raise e
 
 

api/factories/file_factory.py

@@ -180,6 +180,20 @@ def _get_remote_file_info(url: str):
     return mime_type, filename, file_size
 
 
+def _get_file_type_by_mimetype(mime_type: str) -> FileType:
+    if "image" in mime_type:
+        file_type = FileType.IMAGE
+    elif "video" in mime_type:
+        file_type = FileType.VIDEO
+    elif "audio" in mime_type:
+        file_type = FileType.AUDIO
+    elif "text" in mime_type or "pdf" in mime_type:
+        file_type = FileType.DOCUMENT
+    else:
+        file_type = FileType.CUSTOM
+    return file_type
+
+
 def _build_from_tool_file(
     *,
     mapping: Mapping[str, Any],
@@ -199,12 +213,13 @@ def _build_from_tool_file(
         raise ValueError(f"ToolFile {mapping.get('tool_file_id')} not found")
 
     extension = "." + tool_file.file_key.split(".")[-1] if "." in tool_file.file_key else ".bin"
+    file_type = mapping.get("type", _get_file_type_by_mimetype(tool_file.mimetype))
 
     return File(
         id=mapping.get("id"),
         tenant_id=tenant_id,
         filename=tool_file.name,
-        type=FileType.value_of(mapping.get("type")),
+        type=file_type,
         transfer_method=transfer_method,
         remote_url=tool_file.original_url,
         related_id=tool_file.id,

api/libs/smtp.py

@@ -39,13 +39,13 @@ class SMTPClient:
 
             smtp.sendmail(self._from, mail["to"], msg.as_string())
         except smtplib.SMTPException as e:
-            logging.exception(f"SMTP error occurred: {str(e)}")
+            logging.exception("SMTP error occurred")
             raise
         except TimeoutError as e:
-            logging.exception(f"Timeout occurred while sending email: {str(e)}")
+            logging.exception("Timeout occurred while sending email")
             raise
         except Exception as e:
-            logging.exception(f"Unexpected error occurred while sending email: {str(e)}")
+            logging.exception(f"Unexpected error occurred while sending email to {mail['to']}")
             raise
         finally:
             if smtp:

api/models/dataset.py

@@ -679,7 +679,7 @@ class DatasetKeywordTable(db.Model):
                     return json.loads(keyword_table_text.decode("utf-8"), cls=SetDecoder)
                 return None
             except Exception as e:
-                logging.exception(str(e))
+                logging.exception(f"Failed to load keyword table from file: {file_key}")
                 return None
 
 

api/poetry.lock

@@ -2411,6 +2411,41 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "faker"
+version = "32.1.0"
+description = "Faker is a Python package that generates fake data for you."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "Faker-32.1.0-py3-none-any.whl", hash = "sha256:c77522577863c264bdc9dad3a2a750ad3f7ee43ff8185072e482992288898814"},
+    {file = "faker-32.1.0.tar.gz", hash = "sha256:aac536ba04e6b7beb2332c67df78485fc29c1880ff723beac6d1efd45e2f10f5"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.4"
+typing-extensions = "*"
+
+[[package]]
+name = "fal-client"
+version = "0.5.6"
+description = "Python client for fal.ai"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fal_client-0.5.6-py3-none-any.whl", hash = "sha256:631fd857a3c44753ee46a2eea1e7276471453aca58faac9c3702f744c7c84050"},
+    {file = "fal_client-0.5.6.tar.gz", hash = "sha256:d3afc4b6250023d0ee8437ec504558231d3b106d7aabc12cda8c39883faddecb"},
+]
+
+[package.dependencies]
+httpx = ">=0.21.0,<1"
+httpx-sse = ">=0.4.0,<0.5"
+
+[package.extras]
+dev = ["fal-client[docs,test]"]
+docs = ["sphinx", "sphinx-autodoc-typehints", "sphinx-rtd-theme"]
+test = ["pillow", "pytest", "pytest-asyncio"]
+
 [[package]]
 name = "fastapi"
 version = "0.115.4"
@@ -4049,6 +4084,17 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "httpx-sse"
+version = "0.4.0"
+description = "Consume Server-Sent Event (SSE) messages with HTTPX."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
+    {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
+]
+
 [[package]]
 name = "huggingface-hub"
 version = "0.16.4"
@@ -8466,29 +8512,29 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.6.9"
+version = "0.7.3"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "ruff-0.7.3-py3-none-linux_armv6l.whl", hash = "sha256:34f2339dc22687ec7e7002792d1f50712bf84a13d5152e75712ac08be565d344"},
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+    {file = "ruff-0.7.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:fb397332a1879b9764a3455a0bb1087bda876c2db8aca3a3cbb67b3dbce8cda0"},
-    {file = "ruff-0.6.9-py3-none-macosx_11_0_arm64.whl", hash = "sha256:53fd8ca5e82bdee8da7f506d7b03a261f24cd43d090ea9db9a1dc59d9313914c"},
+    {file = "ruff-0.7.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:37d0b619546103274e7f62643d14e1adcbccb242efda4e4bdb9544d7764782e9"},
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+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5d59f0c3ee4d1a6787614e7135b72e21024875266101142a09a61439cb6e38a5"},
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+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:44eb93c2499a169d49fafd07bc62ac89b1bc800b197e50ff4633aed212569299"},
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+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c50f95a82b94421c964fae4c27c0242890a20fe67d203d127e84fbb8013855f5"},
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-    {file = "ruff-0.6.9-py3-none-win_arm64.whl", hash = "sha256:a9641e31476d601f83cd602608739a0840e348bda93fec9f1ee816f8b6798b93"},
+    {file = "ruff-0.7.3-py3-none-win_arm64.whl", hash = "sha256:1713e2c5545863cdbfe2cbce21f69ffaf37b813bfd1fb3b90dc9a6f1963f5a8c"},
-    {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
+    {file = "ruff-0.7.3.tar.gz", hash = "sha256:e1d1ba2e40b6e71a61b063354d04be669ab0d39c352461f3d789cac68b54a313"},
 ]
 
 [[package]]
@@ -11005,4 +11051,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "f20bd678044926913dbbc24bd0cf22503a75817aa55f59457ff7822032139b77"
+content-hash = "cf4e0467f622e58b51411ee1d784928962f52dbf877b8ee013c810909a1f07db"

api/pyproject.toml

@@ -35,6 +35,7 @@ select = [
     "S506", # unsafe-yaml-load
     "SIM", # flake8-simplify rules
     "TRY400", # error-instead-of-exception
+    "TRY401", # verbose-log-message
     "UP", # pyupgrade rules
     "W191", # tab-indentation
     "W605", # invalid-escape-sequence
@@ -122,6 +123,7 @@ celery = "~5.4.0"
 chardet = "~5.1.0"
 cohere = "~5.2.4"
 dashscope = { version = "~1.17.0", extras = ["tokenizer"] }
+fal-client = "0.5.6"
 flask = "~3.0.1"
 flask-compress = "~1.14"
 flask-cors = "~4.0.0"
@@ -265,6 +267,7 @@ weaviate-client = "~3.21.0"
 optional = true
 [tool.poetry.group.dev.dependencies]
 coverage = "~7.2.4"
+faker = "~32.1.0"
 pytest = "~8.3.2"
 pytest-benchmark = "~4.0.0"
 pytest-env = "~1.1.3"
@@ -278,4 +281,4 @@ pytest-mock = "~3.14.0"
 optional = true
 [tool.poetry.group.lint.dependencies]
 dotenv-linter = "~0.5.0"
-ruff = "~0.6.9"
+ruff = "~0.7.3"

api/services/account_service.py

@@ -779,7 +779,7 @@ class RegisterService:
             db.session.query(Tenant).delete()
             db.session.commit()
 
-            logging.exception(f"Setup failed: {e}")
+            logging.exception(f"Setup account failed, email: {email}, name: {name}")
             raise ValueError(f"Setup failed: {e}")
 
     @classmethod
@@ -821,7 +821,7 @@ class RegisterService:
             db.session.rollback()
         except Exception as e:
             db.session.rollback()
-            logging.exception(f"Register failed: {e}")
+            logging.exception("Register failed")
             raise AccountRegisterError(f"Registration failed: {e}") from e
 
         return account

api/services/app_service.py

@@ -88,7 +88,7 @@ class AppService:
             except (ProviderTokenNotInitError, LLMBadRequestError):
                 model_instance = None
             except Exception as e:
-                logging.exception(e)
+                logging.exception(f"Get default model instance failed, tenant_id: {tenant_id}")
                 model_instance = None
 
             if model_instance:

api/services/feature_service.py

@@ -1,3 +1,5 @@
+from enum import Enum
+
 from pydantic import BaseModel, ConfigDict
 
 from configs import dify_config
@@ -20,6 +22,20 @@ class LimitationModel(BaseModel):
     limit: int = 0
 
 
+class LicenseStatus(str, Enum):
+    NONE = "none"
+    INACTIVE = "inactive"
+    ACTIVE = "active"
+    EXPIRING = "expiring"
+    EXPIRED = "expired"
+    LOST = "lost"
+
+
+class LicenseModel(BaseModel):
+    status: LicenseStatus = LicenseStatus.NONE
+    expired_at: str = ""
+
+
 class FeatureModel(BaseModel):
     billing: BillingModel = BillingModel()
     members: LimitationModel = LimitationModel(size=0, limit=1)
@@ -47,6 +63,7 @@ class SystemFeatureModel(BaseModel):
     enable_social_oauth_login: bool = False
     is_allow_register: bool = False
     is_allow_create_workspace: bool = False
+    license: LicenseModel = LicenseModel()
 
 
 class FeatureService:
@@ -131,17 +148,31 @@ class FeatureService:
 
         if "sso_enforced_for_signin" in enterprise_info:
             features.sso_enforced_for_signin = enterprise_info["sso_enforced_for_signin"]
+
         if "sso_enforced_for_signin_protocol" in enterprise_info:
             features.sso_enforced_for_signin_protocol = enterprise_info["sso_enforced_for_signin_protocol"]
+
         if "sso_enforced_for_web" in enterprise_info:
             features.sso_enforced_for_web = enterprise_info["sso_enforced_for_web"]
+
         if "sso_enforced_for_web_protocol" in enterprise_info:
             features.sso_enforced_for_web_protocol = enterprise_info["sso_enforced_for_web_protocol"]
+
         if "enable_email_code_login" in enterprise_info:
             features.enable_email_code_login = enterprise_info["enable_email_code_login"]
+
         if "enable_email_password_login" in enterprise_info:
             features.enable_email_password_login = enterprise_info["enable_email_password_login"]
+
         if "is_allow_register" in enterprise_info:
             features.is_allow_register = enterprise_info["is_allow_register"]
+
         if "is_allow_create_workspace" in enterprise_info:
             features.is_allow_create_workspace = enterprise_info["is_allow_create_workspace"]
+
+        if "license" in enterprise_info:
+            if "status" in enterprise_info["license"]:
+                features.license.status = enterprise_info["license"]["status"]
+
+            if "expired_at" in enterprise_info["license"]:
+                features.license.expired_at = enterprise_info["license"]["expired_at"]

api/services/tools/api_tools_manage_service.py

@@ -195,7 +195,7 @@ class ApiToolManageService:
             # try to parse schema, avoid SSRF attack
             ApiToolManageService.parser_api_schema(schema)
         except Exception as e:
-            logger.exception(f"parse api schema error: {str(e)}")
+            logger.exception("parse api schema error")
             raise ValueError("invalid schema, please check the url you provided")
 
         return {"schema": schema}

api/services/tools/tools_transform_service.py

@@ -183,7 +183,7 @@ class ToolTransformService:
         try:
             username = db_provider.user.name
         except Exception as e:
-            logger.exception(f"failed to get user name for api provider {db_provider.id}: {str(e)}")
+            logger.exception(f"failed to get user name for api provider {db_provider.id}")
         # add provider into providers
         credentials = db_provider.credentials
         result = UserToolProvider(

api/tasks/annotation/delete_annotation_index_task.py

@@ -38,4 +38,4 @@ def delete_annotation_index_task(annotation_id: str, app_id: str, tenant_id: str
             click.style("App annotations index deleted : {} latency: {}".format(app_id, end_at - start_at), fg="green")
         )
     except Exception as e:
-        logging.exception("Annotation deleted index failed:{}".format(str(e)))
+        logging.exception("Annotation deleted index failed")

api/tasks/annotation/disable_annotation_reply_task.py

@@ -60,7 +60,7 @@ def disable_annotation_reply_task(job_id: str, app_id: str, tenant_id: str):
             click.style("App annotations index deleted : {} latency: {}".format(app_id, end_at - start_at), fg="green")
         )
     except Exception as e:
-        logging.exception("Annotation batch deleted index failed:{}".format(str(e)))
+        logging.exception("Annotation batch deleted index failed")
         redis_client.setex(disable_app_annotation_job_key, 600, "error")
         disable_app_annotation_error_key = "disable_app_annotation_error_{}".format(str(job_id))
         redis_client.setex(disable_app_annotation_error_key, 600, str(e))

api/tasks/annotation/enable_annotation_reply_task.py

@@ -93,7 +93,7 @@ def enable_annotation_reply_task(
             click.style("App annotations added to index: {} latency: {}".format(app_id, end_at - start_at), fg="green")
         )
     except Exception as e:
-        logging.exception("Annotation batch created index failed:{}".format(str(e)))
+        logging.exception("Annotation batch created index failed")
         redis_client.setex(enable_app_annotation_job_key, 600, "error")
         enable_app_annotation_error_key = "enable_app_annotation_error_{}".format(str(job_id))
         redis_client.setex(enable_app_annotation_error_key, 600, str(e))

api/tasks/batch_create_segment_to_index_task.py

@@ -103,5 +103,5 @@ def batch_create_segment_to_index_task(
             click.style("Segment batch created job: {} latency: {}".format(job_id, end_at - start_at), fg="green")
         )
     except Exception as e:
-        logging.exception("Segments batch created index failed:{}".format(str(e)))
+        logging.exception("Segments batch created index failed")
         redis_client.setex(indexing_cache_key, 600, "error")

api/tests/integration_tests/model_runtime/azure_ai_studio/test_llm.py

@@ -11,7 +11,6 @@ from core.model_runtime.entities.message_entities import (
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.azure_ai_studio.llm.llm import AzureAIStudioLargeLanguageModel
-from tests.integration_tests.model_runtime.__mock.azure_ai_studio import setup_azure_ai_studio_mock
 
 
 @pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)

api/tests/integration_tests/model_runtime/azure_ai_studio/test_rerank.py

@@ -4,29 +4,21 @@ import pytest
 
 from core.model_runtime.entities.rerank_entities import RerankResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.azure_ai_studio.rerank.rerank import AzureAIStudioRerankModel
+from core.model_runtime.model_providers.azure_ai_studio.rerank.rerank import AzureRerankModel
 
 
 def test_validate_credentials():
-    model = AzureAIStudioRerankModel()
+    model = AzureRerankModel()
 
     with pytest.raises(CredentialsValidateFailedError):
         model.validate_credentials(
             model="azure-ai-studio-rerank-v1",
             credentials={"api_key": "invalid_key", "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")},
-            query="What is the capital of the United States?",
-            docs=[
-                "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
-                "Census, Carson City had a population of 55,274.",
-                "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
-                "are a political division controlled by the United States. Its capital is Saipan.",
-            ],
-            score_threshold=0.8,
         )
 
 
 def test_invoke_model():
-    model = AzureAIStudioRerankModel()
+    model = AzureRerankModel()
 
     result = model.invoke(
         model="azure-ai-studio-rerank-v1",

api/tests/integration_tests/vdb/__mock/baiduvectordb.py

@@ -1,4 +1,5 @@
 import os
+from collections import UserDict
 from unittest.mock import MagicMock
 
 import pytest
@@ -11,7 +12,7 @@ from pymochow.model.table import Table
 from requests.adapters import HTTPAdapter
 
 
-class AttrDict(dict):
+class AttrDict(UserDict):
     def __getattr__(self, item):
         return self.get(item)
 

api/tests/integration_tests/vdb/__mock/upstashvectordb.py

@@ -1,4 +1,5 @@
 import os
+from collections import UserDict
 from typing import Optional
 
 import pytest
@@ -50,7 +51,7 @@ class MockIndex:
         return AttrDict({"dimension": 1024})
 
 
-class AttrDict(dict):
+class AttrDict(UserDict):
     def __getattr__(self, item):
         return self.get(item)
 

api/tests/unit_tests/core/workflow/nodes/llm/test_node.py

@@ -1,125 +1,484 @@
+from collections.abc import Sequence
+from typing import Optional
+
 import pytest
 
-from core.app.entities.app_invoke_entities import InvokeFrom
+from configs import dify_config
+from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
+from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
+from core.entities.provider_entities import CustomConfiguration, SystemConfiguration
 from core.file import File, FileTransferMethod, FileType
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    ImagePromptMessageContent,
+    PromptMessage,
+    PromptMessageRole,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelFeature, ModelType, ProviderModel
+from core.model_runtime.entities.provider_entities import ConfigurateMethod, ProviderEntity
+from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.variables import ArrayAnySegment, ArrayFileSegment, NoneSegment
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.graph_engine import Graph, GraphInitParams, GraphRuntimeState
 from core.workflow.nodes.answer import AnswerStreamGenerateRoute
 from core.workflow.nodes.end import EndStreamParam
-from core.workflow.nodes.llm.entities import ContextConfig, LLMNodeData, ModelConfig, VisionConfig, VisionConfigOptions
+from core.workflow.nodes.llm.entities import (
+    ContextConfig,
+    LLMNodeChatModelMessage,
+    LLMNodeData,
+    ModelConfig,
+    VisionConfig,
+    VisionConfigOptions,
+)
 from core.workflow.nodes.llm.node import LLMNode
 from models.enums import UserFrom
+from models.provider import ProviderType
 from models.workflow import WorkflowType
+from tests.unit_tests.core.workflow.nodes.llm.test_scenarios import LLMNodeTestScenario
 
 
-class TestLLMNode:
+class MockTokenBufferMemory:
-    @pytest.fixture
+    def __init__(self, history_messages=None):
-    def llm_node(self):
+        self.history_messages = history_messages or []
-        data = LLMNodeData(
-            title="Test LLM",
-            model=ModelConfig(provider="openai", name="gpt-3.5-turbo", mode="chat", completion_params={}),
-            prompt_template=[],
-            memory=None,
-            context=ContextConfig(enabled=False),
-            vision=VisionConfig(
-                enabled=True,
-                configs=VisionConfigOptions(
-                    variable_selector=["sys", "files"],
-                    detail=ImagePromptMessageContent.DETAIL.HIGH,
-                ),
-            ),
-        )
-        variable_pool = VariablePool(
-            system_variables={},
-            user_inputs={},
-        )
-        node = LLMNode(
-            id="1",
-            config={
-                "id": "1",
-                "data": data.model_dump(),
-            },
-            graph_init_params=GraphInitParams(
-                tenant_id="1",
-                app_id="1",
-                workflow_type=WorkflowType.WORKFLOW,
-                workflow_id="1",
-                graph_config={},
-                user_id="1",
-                user_from=UserFrom.ACCOUNT,
-                invoke_from=InvokeFrom.SERVICE_API,
-                call_depth=0,
-            ),
-            graph=Graph(
-                root_node_id="1",
-                answer_stream_generate_routes=AnswerStreamGenerateRoute(
-                    answer_dependencies={},
-                    answer_generate_route={},
-                ),
-                end_stream_param=EndStreamParam(
-                    end_dependencies={},
-                    end_stream_variable_selector_mapping={},
-                ),
-            ),
-            graph_runtime_state=GraphRuntimeState(
-                variable_pool=variable_pool,
-                start_at=0,
-            ),
-        )
-        return node
 
-    def test_fetch_files_with_file_segment(self, llm_node):
+    def get_history_prompt_messages(
-        file = File(
+        self, max_token_limit: int = 2000, message_limit: Optional[int] = None
+    ) -> Sequence[PromptMessage]:
+        if message_limit is not None:
+            return self.history_messages[-message_limit * 2 :]
+        return self.history_messages
+
+
+@pytest.fixture
+def llm_node():
+    data = LLMNodeData(
+        title="Test LLM",
+        model=ModelConfig(provider="openai", name="gpt-3.5-turbo", mode="chat", completion_params={}),
+        prompt_template=[],
+        memory=None,
+        context=ContextConfig(enabled=False),
+        vision=VisionConfig(
+            enabled=True,
+            configs=VisionConfigOptions(
+                variable_selector=["sys", "files"],
+                detail=ImagePromptMessageContent.DETAIL.HIGH,
+            ),
+        ),
+    )
+    variable_pool = VariablePool(
+        system_variables={},
+        user_inputs={},
+    )
+    node = LLMNode(
+        id="1",
+        config={
+            "id": "1",
+            "data": data.model_dump(),
+        },
+        graph_init_params=GraphInitParams(
+            tenant_id="1",
+            app_id="1",
+            workflow_type=WorkflowType.WORKFLOW,
+            workflow_id="1",
+            graph_config={},
+            user_id="1",
+            user_from=UserFrom.ACCOUNT,
+            invoke_from=InvokeFrom.SERVICE_API,
+            call_depth=0,
+        ),
+        graph=Graph(
+            root_node_id="1",
+            answer_stream_generate_routes=AnswerStreamGenerateRoute(
+                answer_dependencies={},
+                answer_generate_route={},
+            ),
+            end_stream_param=EndStreamParam(
+                end_dependencies={},
+                end_stream_variable_selector_mapping={},
+            ),
+        ),
+        graph_runtime_state=GraphRuntimeState(
+            variable_pool=variable_pool,
+            start_at=0,
+        ),
+    )
+    return node
+
+
+@pytest.fixture
+def model_config():
+    # Create actual provider and model type instances
+    model_provider_factory = ModelProviderFactory()
+    provider_instance = model_provider_factory.get_provider_instance("openai")
+    model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
+
+    # Create a ProviderModelBundle
+    provider_model_bundle = ProviderModelBundle(
+        configuration=ProviderConfiguration(
+            tenant_id="1",
+            provider=provider_instance.get_provider_schema(),
+            preferred_provider_type=ProviderType.CUSTOM,
+            using_provider_type=ProviderType.CUSTOM,
+            system_configuration=SystemConfiguration(enabled=False),
+            custom_configuration=CustomConfiguration(provider=None),
+            model_settings=[],
+        ),
+        provider_instance=provider_instance,
+        model_type_instance=model_type_instance,
+    )
+
+    # Create and return a ModelConfigWithCredentialsEntity
+    return ModelConfigWithCredentialsEntity(
+        provider="openai",
+        model="gpt-3.5-turbo",
+        model_schema=AIModelEntity(
+            model="gpt-3.5-turbo",
+            label=I18nObject(en_US="GPT-3.5 Turbo"),
+            model_type=ModelType.LLM,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={},
+        ),
+        mode="chat",
+        credentials={},
+        parameters={},
+        provider_model_bundle=provider_model_bundle,
+    )
+
+
+def test_fetch_files_with_file_segment(llm_node):
+    file = File(
+        id="1",
+        tenant_id="test",
+        type=FileType.IMAGE,
+        filename="test.jpg",
+        transfer_method=FileTransferMethod.LOCAL_FILE,
+        related_id="1",
+    )
+    llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], file)
+
+    result = llm_node._fetch_files(selector=["sys", "files"])
+    assert result == [file]
+
+
+def test_fetch_files_with_array_file_segment(llm_node):
+    files = [
+        File(
             id="1",
             tenant_id="test",
             type=FileType.IMAGE,
-            filename="test.jpg",
+            filename="test1.jpg",
             transfer_method=FileTransferMethod.LOCAL_FILE,
             related_id="1",
+        ),
+        File(
+            id="2",
+            tenant_id="test",
+            type=FileType.IMAGE,
+            filename="test2.jpg",
+            transfer_method=FileTransferMethod.LOCAL_FILE,
+            related_id="2",
+        ),
+    ]
+    llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], ArrayFileSegment(value=files))
+
+    result = llm_node._fetch_files(selector=["sys", "files"])
+    assert result == files
+
+
+def test_fetch_files_with_none_segment(llm_node):
+    llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], NoneSegment())
+
+    result = llm_node._fetch_files(selector=["sys", "files"])
+    assert result == []
+
+
+def test_fetch_files_with_array_any_segment(llm_node):
+    llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], ArrayAnySegment(value=[]))
+
+    result = llm_node._fetch_files(selector=["sys", "files"])
+    assert result == []
+
+
+def test_fetch_files_with_non_existent_variable(llm_node):
+    result = llm_node._fetch_files(selector=["sys", "files"])
+    assert result == []
+
+
+def test_fetch_prompt_messages__vison_disabled(faker, llm_node, model_config):
+    prompt_template = []
+    llm_node.node_data.prompt_template = prompt_template
+
+    fake_vision_detail = faker.random_element(
+        [ImagePromptMessageContent.DETAIL.HIGH, ImagePromptMessageContent.DETAIL.LOW]
+    )
+    fake_remote_url = faker.url()
+    files = [
+        File(
+            id="1",
+            tenant_id="test",
+            type=FileType.IMAGE,
+            filename="test1.jpg",
+            transfer_method=FileTransferMethod.REMOTE_URL,
+            remote_url=fake_remote_url,
         )
-        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], file)
+    ]
 
-        result = llm_node._fetch_files(selector=["sys", "files"])
+    fake_query = faker.sentence()
-        assert result == [file]
 
-    def test_fetch_files_with_array_file_segment(self, llm_node):
+    prompt_messages, _ = llm_node._fetch_prompt_messages(
-        files = [
+        user_query=fake_query,
-            File(
+        user_files=files,
-                id="1",
+        context=None,
-                tenant_id="test",
+        memory=None,
-                type=FileType.IMAGE,
+        model_config=model_config,
-                filename="test1.jpg",
+        prompt_template=prompt_template,
-                transfer_method=FileTransferMethod.LOCAL_FILE,
+        memory_config=None,
-                related_id="1",
+        vision_enabled=False,
-            ),
+        vision_detail=fake_vision_detail,
-            File(
+        variable_pool=llm_node.graph_runtime_state.variable_pool,
-                id="2",
+        jinja2_variables=[],
-                tenant_id="test",
+    )
-                type=FileType.IMAGE,
-                filename="test2.jpg",
-                transfer_method=FileTransferMethod.LOCAL_FILE,
-                related_id="2",
-            ),
-        ]
-        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], ArrayFileSegment(value=files))
 
-        result = llm_node._fetch_files(selector=["sys", "files"])
+    assert prompt_messages == [UserPromptMessage(content=fake_query)]
-        assert result == files
 
-    def test_fetch_files_with_none_segment(self, llm_node):
-        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], NoneSegment())
 
-        result = llm_node._fetch_files(selector=["sys", "files"])
+def test_fetch_prompt_messages__basic(faker, llm_node, model_config):
-        assert result == []
+    # Setup dify config
+    dify_config.MULTIMODAL_SEND_IMAGE_FORMAT = "url"
+    dify_config.MULTIMODAL_SEND_VIDEO_FORMAT = "url"
 
-    def test_fetch_files_with_array_any_segment(self, llm_node):
+    # Generate fake values for prompt template
-        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], ArrayAnySegment(value=[]))
+    fake_assistant_prompt = faker.sentence()
+    fake_query = faker.sentence()
+    fake_context = faker.sentence()
+    fake_window_size = faker.random_int(min=1, max=3)
+    fake_vision_detail = faker.random_element(
+        [ImagePromptMessageContent.DETAIL.HIGH, ImagePromptMessageContent.DETAIL.LOW]
+    )
+    fake_remote_url = faker.url()
 
-        result = llm_node._fetch_files(selector=["sys", "files"])
+    # Setup mock memory with history messages
-        assert result == []
+    mock_history = [
+        UserPromptMessage(content=faker.sentence()),
+        AssistantPromptMessage(content=faker.sentence()),
+        UserPromptMessage(content=faker.sentence()),
+        AssistantPromptMessage(content=faker.sentence()),
+        UserPromptMessage(content=faker.sentence()),
+        AssistantPromptMessage(content=faker.sentence()),
+    ]
 
-    def test_fetch_files_with_non_existent_variable(self, llm_node):
+    # Setup memory configuration
-        result = llm_node._fetch_files(selector=["sys", "files"])
+    memory_config = MemoryConfig(
-        assert result == []
+        role_prefix=MemoryConfig.RolePrefix(user="Human", assistant="Assistant"),
+        window=MemoryConfig.WindowConfig(enabled=True, size=fake_window_size),
+        query_prompt_template=None,
+    )
+
+    memory = MockTokenBufferMemory(history_messages=mock_history)
+
+    # Test scenarios covering different file input combinations
+    test_scenarios = [
+        LLMNodeTestScenario(
+            description="No files",
+            user_query=fake_query,
+            user_files=[],
+            features=[],
+            vision_enabled=False,
+            vision_detail=None,
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text=fake_context,
+                    role=PromptMessageRole.SYSTEM,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text="{#context#}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text=fake_assistant_prompt,
+                    role=PromptMessageRole.ASSISTANT,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=[
+                SystemPromptMessage(content=fake_context),
+                UserPromptMessage(content=fake_context),
+                AssistantPromptMessage(content=fake_assistant_prompt),
+            ]
+            + mock_history[fake_window_size * -2 :]
+            + [
+                UserPromptMessage(content=fake_query),
+            ],
+        ),
+        LLMNodeTestScenario(
+            description="User files",
+            user_query=fake_query,
+            user_files=[
+                File(
+                    tenant_id="test",
+                    type=FileType.IMAGE,
+                    filename="test1.jpg",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                )
+            ],
+            vision_enabled=True,
+            vision_detail=fake_vision_detail,
+            features=[ModelFeature.VISION],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text=fake_context,
+                    role=PromptMessageRole.SYSTEM,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text="{#context#}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text=fake_assistant_prompt,
+                    role=PromptMessageRole.ASSISTANT,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=[
+                SystemPromptMessage(content=fake_context),
+                UserPromptMessage(content=fake_context),
+                AssistantPromptMessage(content=fake_assistant_prompt),
+            ]
+            + mock_history[fake_window_size * -2 :]
+            + [
+                UserPromptMessage(
+                    content=[
+                        TextPromptMessageContent(data=fake_query),
+                        ImagePromptMessageContent(data=fake_remote_url, detail=fake_vision_detail),
+                    ]
+                ),
+            ],
+        ),
+        LLMNodeTestScenario(
+            description="Prompt template with variable selector of File",
+            user_query=fake_query,
+            user_files=[],
+            vision_enabled=False,
+            vision_detail=fake_vision_detail,
+            features=[ModelFeature.VISION],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text="{{#input.image#}}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=[
+                UserPromptMessage(
+                    content=[
+                        ImagePromptMessageContent(data=fake_remote_url, detail=fake_vision_detail),
+                    ]
+                ),
+            ]
+            + mock_history[fake_window_size * -2 :]
+            + [UserPromptMessage(content=fake_query)],
+            file_variables={
+                "input.image": File(
+                    tenant_id="test",
+                    type=FileType.IMAGE,
+                    filename="test1.jpg",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                )
+            },
+        ),
+        LLMNodeTestScenario(
+            description="Prompt template with variable selector of File without vision feature",
+            user_query=fake_query,
+            user_files=[],
+            vision_enabled=True,
+            vision_detail=fake_vision_detail,
+            features=[],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text="{{#input.image#}}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=mock_history[fake_window_size * -2 :] + [UserPromptMessage(content=fake_query)],
+            file_variables={
+                "input.image": File(
+                    tenant_id="test",
+                    type=FileType.IMAGE,
+                    filename="test1.jpg",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                )
+            },
+        ),
+        LLMNodeTestScenario(
+            description="Prompt template with variable selector of File with video file and vision feature",
+            user_query=fake_query,
+            user_files=[],
+            vision_enabled=True,
+            vision_detail=fake_vision_detail,
+            features=[ModelFeature.VISION],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text="{{#input.image#}}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=mock_history[fake_window_size * -2 :] + [UserPromptMessage(content=fake_query)],
+            file_variables={
+                "input.image": File(
+                    tenant_id="test",
+                    type=FileType.VIDEO,
+                    filename="test1.mp4",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                    extension="mp4",
+                )
+            },
+        ),
+    ]
+
+    for scenario in test_scenarios:
+        model_config.model_schema.features = scenario.features
+
+        for k, v in scenario.file_variables.items():
+            selector = k.split(".")
+            llm_node.graph_runtime_state.variable_pool.add(selector, v)
+
+        # Call the method under test
+        prompt_messages, _ = llm_node._fetch_prompt_messages(
+            user_query=scenario.user_query,
+            user_files=scenario.user_files,
+            context=fake_context,
+            memory=memory,
+            model_config=model_config,
+            prompt_template=scenario.prompt_template,
+            memory_config=memory_config,
+            vision_enabled=scenario.vision_enabled,
+            vision_detail=scenario.vision_detail,
+            variable_pool=llm_node.graph_runtime_state.variable_pool,
+            jinja2_variables=[],
+        )
+
+        # Verify the result
+        assert len(prompt_messages) == len(scenario.expected_messages), f"Scenario failed: {scenario.description}"
+        assert (
+            prompt_messages == scenario.expected_messages
+        ), f"Message content mismatch in scenario: {scenario.description}"

api/tests/unit_tests/core/workflow/nodes/llm/test_scenarios.py

@@ -0,0 +1,25 @@
+from collections.abc import Mapping, Sequence
+
+from pydantic import BaseModel, Field
+
+from core.file import File
+from core.model_runtime.entities.message_entities import PromptMessage
+from core.model_runtime.entities.model_entities import ModelFeature
+from core.workflow.nodes.llm.entities import LLMNodeChatModelMessage
+
+
+class LLMNodeTestScenario(BaseModel):
+    """Test scenario for LLM node testing."""
+
+    description: str = Field(..., description="Description of the test scenario")
+    user_query: str = Field(..., description="User query input")
+    user_files: Sequence[File] = Field(default_factory=list, description="List of user files")
+    vision_enabled: bool = Field(default=False, description="Whether vision is enabled")
+    vision_detail: str | None = Field(None, description="Vision detail level if vision is enabled")
+    features: Sequence[ModelFeature] = Field(default_factory=list, description="List of model features")
+    window_size: int = Field(..., description="Window size for memory")
+    prompt_template: Sequence[LLMNodeChatModelMessage] = Field(..., description="Template for prompt messages")
+    file_variables: Mapping[str, File | Sequence[File]] = Field(
+        default_factory=dict, description="List of file variables"
+    )
+    expected_messages: Sequence[PromptMessage] = Field(..., description="Expected messages after processing")

api/tests/unit_tests/oss/__mock/volcengine_tos.py

@@ -1,4 +1,5 @@
 import os
+from collections import UserDict
 from unittest.mock import MagicMock
 
 import pytest
@@ -14,7 +15,7 @@ from tests.unit_tests.oss.__mock.base import (
 )
 
 
-class AttrDict(dict):
+class AttrDict(UserDict):
     def __getattr__(self, item):
         return self.get(item)