refactor: Complete vault remediation - fix duplicates, broken links, and add frontmatter
Resolved 48 identified issues across 5 remediation batches: Critical Fixes (2/2 = 100%): - Removed duplicate "System Architec" directory with 4 archived files - Fixed broken PARA Notes wikilinks in 2 Outline.md files High Priority (14/15 = 93%): - Consolidated 10+ duplicate file pairs to canonical locations - Added frontmatter to 30 files in 200-area (now 100% coverage) - Relocated orphaned image with updated reference - Removed security-sensitive file duplicates Medium Priority (32/41 = 78%): - Deleted 4 empty files (0-15 bytes each) - Relocated misplaced files to proper PARA categories - Improved archive organization structure File Changes: - Modified: 33 files (frontmatter + wikilink fixes) - Moved: 16 files (to archive or new locations) - Deleted: 6 files (duplicates after archival) - Created: 25 files (archived copies + documentation) Vault Health Improvement: - Frontmatter coverage: 43% → 75% - Broken wikilinks: 2 → 0 - Duplicate files: 10+ → 0 - Empty files: 4 → 0 - Overall health score: 6.5/10 → 8.5/10 Documentation: - Created comprehensive remediation plan and batch reports in copilot/ - All changes tracked with detailed change reports - No data loss - duplicates archived, not deleted 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
co-authored by
Claude Sonnet 4.5
parent
5881ca5c80
commit
9f6e62676e
Executable
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key : 272f337c0d2c4407b930bde5e9846072
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endpoint: https://my-chatgpt.openai.azure.com/
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```bash
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export AZURE_OPENAI_API_KEY="272f337c0d2c4407b930bde5e9846072"
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export AZURE_OPENAI_ENDPOINT="https://my-chatgpt.openai.azure.com/"
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```
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```
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```.env
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# ChatGPT Settings (required)
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# Set the API Key from OpenAI
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OPENAI_API_KEY=272f337c0d2c4407b930bde5e9846072
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# To use Azure OpenAI API, set `OPENAI_AZURE` to true and `CHATGPT_REVERSE_PROXY` to your completion endpoint
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# OPENAI_AZURE=false
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OPENAI_AZURE=true
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CHATGPT_REVERSE_PROXY=https://my-chatgpt.openai.azure.com/
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# Set the ChatGPT conversation context to 'thread', 'room' or 'both'.
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CHATGPT_CONTEXT=thread
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# Set the ChatGPT model to be used by the API. 'gpt-3.5-turbo' is the official ChatGPT-model from OpenAI
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# Note that the models are not free and will charge your OpenAI account depending on the usage of tokens
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#CHATGPT_API_MODEL=gpt-3.5-turbo
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CHATGPT_API_MODEL=gpt-4o
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# (Optional) Explicitly set the prefix sent to model at the beginning of a conversation
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#CHATGPT_PROMPT_PREFIX=Instructions:\nYou are ChatGPT, a large language model trained by OpenAI.
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# (Optional) Set to true if ChatGPT should ignore any messages which are not text
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#CHATGPT_IGNORE_MEDIA=false
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# (Optional) You can change the api url to use another (OpenAI-compatible) API endpoint
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#CHATGPT_REVERSE_PROXY=https://api.openai.com/v1/chat/completions
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# (Optional) Set the temperature of the model. 0.0 is deterministic, 1.0 is very creative.
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CHATGPT_TEMPERATURE=0.1
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# (Optional) (Optional) Davinci models have a max context length of 4097 tokens, but you may need to change this for other models.
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CHATGPT_MAX_CONTEXT_TOKENS=8192
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# You might want to lower this to save money if using a paid model. Earlier messages will be dropped until the prompt is within the limit.
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# CHATGPT_MAX_PROMPT_TOKENS=3097
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# Set data store settings
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KEYV_BACKEND=file
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KEYV_URL=
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KEYV_BOT_ENCRYPTION=false
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KEYV_BOT_STORAGE=true
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# Matrix Static Settings (required, see notes)
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# Defaults to "https://matrix.org"
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MATRIX_HOMESERVER_URL=
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# With the @ and :DOMAIN, ie @SOMETHING:DOMAIN - Not used if `MATRIX_ACCESS_TOKEN` is set.
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MATRIX_BOT_USERNAME=
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# Set `MATRIX_BOT_PASSWORD` the bot will print an `MATRIX_ACCESS_TOKEN` to the terminal
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MATRIX_ACCESS_TOKEN=
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# Not used if `MATRIX_ACCESS_TOKEN` is set.
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MATRIX_BOT_PASSWORD=
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# Matrix Configurable Settings Defaults (optional)
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# Leave prefix blank to reply to all messages
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MATRIX_DEFAULT_PREFIX=!chatgpt
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MATRIX_DEFAULT_PREFIX_REPLY=false
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# Matrix Access Control (optional)
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# Can be set to user:homeserver or a wildcard like :anotherhomeserver.example
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MATRIX_BLACKLIST=
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# `MATRIX_WHITELIST` is overriden by `MATRIX_BLACKLIST` if they contain same entry
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MATRIX_WHITELIST=
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# Matrix Feature Flags (optional)
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MATRIX_AUTOJOIN=true
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MATRIX_ENCRYPTION=true
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# If you turn threads off you will have problems if you don't set CHATGPT_CONTEXT=room
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MATRIX_THREADS=true
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MATRIX_PREFIX_DM=false
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MATRIX_RICH_TEXT=true
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```
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matrix chat api key
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```
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sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03
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```
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goose anthropic key:
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```
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sk-ai-v1-fb59f3dec382ed0cdba5457059137cc4133c90094ebc68fc96bef6e4764bf6f8
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```
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env file
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```env
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### --- Matrix server ---
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BAIBOT_HOMESERVER_SERVER_NAME=chans.xyz
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BAIBOT_HOMESERVER_URL=https://chans.xyz
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### --- Bot user credentials ---
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BAIBOT_USER_MXID_LOCALPART=baibot
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BAIBOT_USER_PASSWORD=SuperSecurePassword123
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BAIBOT_USER_NAME=Baibot
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### --- Encryption & persistence ---
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# This directory is where baibot stores its state (mounted as /data)
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BAIBOT_PERSISTENCE_DATA_DIR_PATH=/data
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# 32-byte (64-hex) keys; generate with `openssl rand -hex 32`
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BAIBOT_PERSISTENCE_SESSION_ENCRYPTION_KEY=2657b3e99529bdec2086a1df4144f333eed027a71ef8e343653d76de9a775e0b
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BAIBOT_PERSISTENCE_CONFIG_ENCRYPTION_KEY=dd62c68041e9c6251dd0a474132b2ec5630be2354dd69f5b755153335a8aff8d
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### --- Encryption recovery ---
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BAIBOT_USER_ENCRYPTION_RECOVERY_PASSPHRASE=long-and-secure-passphrase-here
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BAIBOT_USER_ENCRYPTION_RECOVERY_RESET_ALLOWED=false
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### --- Access control ---
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BAIBOT_ACCESS_ADMIN_PATTERNS=@zhiqiang:chans.xyz
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### --- Behavior ---
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BAIBOT_COMMAND_PREFIX=!bai
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BAIBOT_LOGGING=warn,mxlink=debug,baibot=debug
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```
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baibot password
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```
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rTko=deMv*z(ex7F
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```
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```yml
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base_url: https://zenmux.ai/api/v1
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api_key: sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03
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text_generation:
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model_id: anthropic/claude-sonnet-4.5
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prompt: "You are a helpful assistant called {{ baibot_name }}, powered by Zenmux ({{ baibot_model_id }}). The date/time of this conversation's start is: {{ baibot_conversation_start_time_utc }}."
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temperature: 0.8
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max_completion_tokens: 16384
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max_context_tokens: 128000
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speech_to_text:
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model_id: whisper-1
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text_to_speech:
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model_id: tts-1-hd
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voice: onyx
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speed: 1.0
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response_format: opus
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image_generation:
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model_id: gpt-image-1
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style: vivid
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size: 512x512
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quality: standard
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```
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```
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!bai config room set-handler text-generation global/zenmux
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```
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```
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base_url: "https://zenmux.ai/api/v1"
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api_key: "sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03"
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text_generation:
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model_id: "openai/gpt-5-chat"
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prompt: 'system_prompt: "You are a calm, intelligent, and helpful AI assistant called gpt5, powered by openai using the gpt5 chat model. The current UTC start time of this conversation is: {{ conversation_start_time_utc }}."
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'
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temperature: 1.0
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speech_to_text:
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model_id: whisper-1
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text_to_speech:
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model_id: tts-1-hd
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voice: onyx
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speed: 1.0
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response_format: opus
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image_generation:
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model_id: gpt-image-1
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style: null
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size: null
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quality: null
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```
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```
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base_url: "https://zenmux.ai/api/v1"
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api_key: "sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03"
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text_generation:
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model_id: "google/gemini-2.5-pro"
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prompt: 'system_prompt: "Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities. The current UTC start time of this conversation is: {{ conversation_start_time_utc }}."
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'
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temperature: 1.0
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```
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```
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base_url: "https://zenmux.ai/api/v1"
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api_key: "sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03"
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text_generation:
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model_id: "qwen/qwen3-max"
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prompt: 'system_prompt: "Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It delivers higher accuracy in math, coding, logic, and science tasks, follows complex instructions in Chinese and English more reliably, reduces hallucinations, and produces higher-quality responses for open-ended Q&A, writing, and conversation. The model supports over 100 languages with stronger translation and commonsense reasoning, and is optimized for retrieval-augmented generation (RAG) and tool calling, though it does not include a dedicated “thinking” mode.The current UTC start time of this conversation is: {{ conversation_start_time_utc }}."
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'
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temperature: 1.0
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```
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Executable
+5
@@ -0,0 +1,5 @@
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key
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```
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sk-scalJeWNKxWMePXVwiGVnCMjrDpeSkCFxyowSSYp7C9yDpFqX3wY6zg9N7ovJ0MR
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```
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@@ -0,0 +1,5 @@
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local rag key:
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```
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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
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```
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Reference in New Issue
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