voice-ai-integration

Shengwang Integration

Workflow

Step 0: Ensure doc index exists (MANDATORY)

⚠️ Execute this BEFORE any routing or code generation.

Check if references/docs.txt already exists. If it does, skip this step entirely. If it does not exist, download it:

bash skills/voice-ai-integration/scripts/fetch-docs.sh

This downloads a static doc index from doc.shengwang.cn — no user data is sent. If download fails, proceed with local reference docs and fallback URLs.

Step 1: Route to the correct product module

Progressive disclosure rule: Only read the file that the current step points to. Do not scan the references/ directory or pre-read files that have not been linked from the current step. Each product module's README.md will tell you which files to read next and when. If a file is not referenced by the document you are currently reading, do not open it.

Match the user's request to a product module using the route table. If the match is clear, route directly — do not ask extra questions.

Route Table

User intent	Route to
Credentials, AppID, REST auth	general
Generate Token, token server, AccessToken2	token-server
ConvoAI voice agent work	conversational-ai
RTC SDK integration	rtc
RTM messaging / signaling	rtm
Cloud Recording	cloud-recording
Download SDK, sample project, GitHub repo	Route to the relevant product module above

Product Recognition Aid

When the user describes a use case without naming a product, use this to infer the match:

Product	What it does	Typical user says
ConvoAI	AI voice agent (ASR→LLM→TTS over RTC)	"AI语音", "voice bot", "对话式AI", "AI agent", "AI 客服"
RTC SDK	Real-time audio/video between humans	"视频通话", "直播", "video call", "live streaming"
RTM	Real-time messaging / signaling	"聊天", "消息", "chat", "signaling"
Cloud Recording	Record RTC sessions server-side	"录制", "recording", "存档", "回看"
Token generation	Generate RTC / RTM tokens	"token", "鉴权", "token server"

Common Combinations

Use case	Products needed
AI voice assistant	ConvoAI (primary) + RTC SDK (client)
AI voice assistant + chat history	ConvoAI + RTC SDK + RTM
1v1 / group video call	RTC SDK
Video call + chat	RTC SDK + RTM
Live streaming with recording	RTC SDK + Cloud Recording
Record AI conversations	ConvoAI + RTC SDK + Cloud Recording
Chat / messaging only	RTM

Routing Rules

• Infer obvious context — do not ask if the answer is already clear
• Do not ask product-specific configuration questions (providers, SDK versions, project structure) at this level; let the product module handle those
• If the product is clear but the request mode is ambiguous (quickstart vs debugging vs feature), let the product module decide internally
• If multiple products are needed, route to the primary product first, then address supporting products in order
• ConvoAI has the most detailed internal routing (see its README.md); always delegate ConvoAI-specific decisions to its module

When the product is still unclear

If the route table and recognition aid above are not enough to determine the product:

1. Ask only for the missing detail that would change the routing decision
2. Ask at most one question at a time
3. Prefer natural wording over an interview script
4. Once the product is clear, produce a short routing recap and continue:

ZH:

已了解的信息
─────────────────────────────
场景：          [use case]
主要产品：      [primary product]
配套产品：      [supporting products / 无]
─────────────────────────────

EN:

What I have so far
─────────────────────────────
Use case:       [use case]
Primary:        [primary product]
Supporting:     [supporting products / none]
─────────────────────────────

Do not stop for a separate confirmation step — continue to the product module automatically.

Step 2: Let the product module drive implementation

Each product module follows its own workflow. Do not duplicate implementation logic here.

Common pattern across modules:

1. Use local reference docs in references/ first
2. Fetch remote docs via doc-fetching.md only when local references are insufficient
3. Fallback to web search only after doc fetching has been attempted

Runtime Requirements

• bash and curl for local doc-fetch helper scripts
• git for sample-repo inspection when the sample-aligned path is chosen
• Network access to doc.shengwang.cn, doc-mcp.shengwang.cn, and gitee.com

Network behavior:

• fetch-docs.sh downloads a static file from doc.shengwang.cn/llms.txt — no user data is sent
• fetch-doc-content.sh fetches a single doc page by URI from doc-mcp.shengwang.cn — only the doc URI is sent, no user context
• git clone is used only for sample repo inspection from gitee.com — only the repo URL is sent

Credential and service-activation requirements vary by product — see each product module and general/credentials-and-auth.md for details. Never hardcode credentials.

Safety & Consent Rules

• Do not clone external repos into the user's main workspace by default — prefer a temporary path
• Do not modify an existing user project until the user explicitly asks for code generation
• Do not write secrets into project files — prefer env vars and example placeholders
• Before performing network fetches or repo clones, state what will be downloaded
• If a required dependency or credential is missing, stop and explain the blocker

Download Rules

• Use git clone --depth 1 <url> with HTTPS repo root URLs only
• On any download failure: report the error, provide the URL for manual download, never silently skip

Links

• Console: https://console.shengwang.cn/
• Docs (CN): https://doc.shengwang.cn/
• GitHub: https://github.com/Shengwang-Community