Initial release: Multi-provider AI chat with RAG
FastAPI backend (wiki-vector-chat.py) with Odysseus-style frontend. Features: multi-provider LLM, Wiki KB + VectorDB RAG, session history, chat modes, save-to-wiki, markdown rendering, SSE streaming. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
admin
18
.gitignore
vendored
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.gitignore
vendored
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# Secrets
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.wiki-api-token
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.htpasswd-wiki
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wiki-chat-providers.json
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# Data files
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wiki-kb.json
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vector-db/
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data/
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# Backups
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*.bak
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*.bak-zcode
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# Old wiki HTML (not part of this project)
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zai-ambassador-team-wiki-old.html
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zai-ambassador-team-wiki.html.bak
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zai-ambassadors-wiki.html
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CHANGELOG.md
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CHANGELOG.md
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# Changelog
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## v2.0.0 (2026-06-03) — Odysseus UI + Multi-Provider + RAG
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### Added
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- Complete Odysseus-style UI with Tokyo Night color palette
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- Multi-provider LLM support (OpenAI, Anthropic, Ollama, OpenRouter, Groq, custom)
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- RAG pipeline: dual-source context from Wiki KB (:8097) + VectorDB (:8099)
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- Per-session RAG toggles (Wiki KB / VectorDB enable/disable)
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- Chat modes: Chat, Code, Brainstorm with mode-specific system prompts
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- Session history with localStorage persistence (up to 50 sessions)
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- Save AI answers as new Q&A entries to Wiki KB
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- Message action buttons: Copy, Redo, Save-to-Wiki on AI responses
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- Markdown rendering in AI replies (bold, italic, code blocks, lists)
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- Provider management: CRUD for custom providers, preset forking with API keys
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- SSE streaming with format detection (OpenAI vs Anthropic)
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- Model picker dropdown in input bar
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- Manage Providers modal with select/edit/delete
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- Quick action chips on welcome screen
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- Mobile responsive layout with sidebar drawer
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### Backend (wiki-vector-chat.py)
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- FastAPI service on port 8770
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- Shared API token auth from `/opt/blog/.wiki-api-token`
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- `build_rag_context()` with per-source toggle support
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- `call_llm_stream()` with format auto-detection
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- Anthropic SSE parsing (content_block_delta, message_stop)
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- `POST /chat/save-to-wiki` writes directly to wiki-kb.json
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- `ChatMessage` model extended with rag_wiki, rag_vector, mode fields
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### Fixed
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- "Save failed: fetch().json is not a function" — double await
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- RAG chevron toggle checked CSS class instead of state
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- addMessage wrong arguments (empty objects vs string roles)
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- SSE done not breaking outer loop — streamDone flag
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- Welcome screen never hidden — .hidden CSS class + toggle
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- Preset providers not editable — forking with custom- prefix
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- RAG not working — auth tokens for wiki-api and vector-db
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- Anthropic message format — {role, content} not {type, text}
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- Anthropic SSE parsing missing — added content_block_delta handler
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- LLM ignoring RAG context — improved system prompt instructions
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- Markdown showing raw tags in AI replies — renderMd() conversion
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## v1.0.0 (2026-05-28) — Initial
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- Basic chat UI embedded in wiki
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- Single-provider Z.ai GLM-4 proxy (wiki-chat-server.js on :8098)
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- Wiki KB search integration
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README.md
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README.md
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# Zportal Wiki VectorDB Chat
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Multi-provider AI chat with RAG (Wiki KB + VectorDB) for the Z.ai portal.
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## Architecture
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| Component | Port | File | Purpose |
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|-----------|------|------|---------|
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| **wiki-vector-chat** | 8770 | `wiki-vector-chat.py` | FastAPI chat backend — multi-provider LLM, RAG pipeline, SSE streaming |
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| **wiki-api** | 8097 | `wiki-api.py` | KB keyword search over `wiki-kb.json` (1,301 Q&A entries) |
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| **vector-db-service** | 8099 | `vector-db-service.py` | TF-IDF vector search on Discord/Reddit messages |
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| **Frontend** | static | `zportal-chat.html` | Odysseus-style chat UI at `/zportal/chat` |
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| **wiki-chat-server** | 8098 | `wiki-chat-server.py` | Legacy Z.ai GLM-4 proxy (being replaced) |
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| **wiki-chat-proxy** | — | `wiki-chat-proxy.py` | Legacy chat proxy helper |
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## Features
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- **Multi-provider LLM** — OpenAI, Anthropic, Ollama, OpenRouter, Groq, custom endpoints
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- **RAG pipeline** — Dual-source context from Wiki KB + VectorDB with per-session toggles
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- **Chat modes** — Chat, Code, Brainstorm with mode-specific system prompts
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- **Session history** — localStorage persistence, switch between sessions, auto-save
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- **Save to Wiki** — Save AI answers as new Q&A entries in the Wiki KB
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- **Message actions** — Copy, Redo, Save-to-Wiki buttons on AI responses
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- **Markdown rendering** — Bold, italic, code blocks, lists in AI replies
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- **SSE streaming** — Server-Sent Events for real-time token streaming
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- **Provider management** — CRUD for custom providers, preset forking with API keys
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- **Odysseus UI** — Tokyo Night palette, sidebar, chat bubbles, model picker
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## Provider Presets
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| ID | Name | Format |
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|----|------|--------|
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| zai-coding | Z.ai Coding Plan | OpenAI |
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| openadapter | OpenAdapter | OpenAI |
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| openrouter | OpenRouter | OpenRouter |
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| crofai | Crof.AI | OpenAI |
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| opencode-zen | Opencode Zen | OpenAI |
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## Nginx Config
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```nginx
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location = /zportal/chat {
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default_type text/html;
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alias /opt/zportal/chat.html;
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}
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location ^~ /zportal/wiki/api/chat/ {
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proxy_pass http://127.0.0.1:8770/;
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proxy_http_version 1.1;
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proxy_set_header Host $host;
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proxy_set_header X-Real-IP $remote_addr;
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proxy_buffering off;
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proxy_cache off;
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proxy_read_timeout 120s;
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}
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```
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## Systemd
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```bash
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sudo systemctl restart wiki-vector-chat
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sudo systemctl status wiki-vector-chat
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```
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## API Endpoints
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| Method | Path | Description |
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|--------|------|-------------|
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| POST | `/chat/message` | Main chat (SSE stream) |
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| POST | `/chat/tunnel` | Server-side token chat |
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| POST | `/chat/save-to-wiki` | Save Q&A to wiki KB |
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| GET | `/providers` | List all providers |
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| GET | `/providers/presets` | Built-in presets |
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| POST | `/providers/save` | Save/edit custom provider |
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| DELETE | `/providers/{id}` | Remove custom provider |
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| GET | `/health` | Health check |
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347
inject_wiki_chat.py
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inject_wiki_chat.py
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#!/usr/bin/env python3
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"""Inject VectorDB Chat panel into wiki HTML files.
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Usage: python3 inject_wiki_chat.py [--file /path/to/wiki.html]
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If no --file, modifies both ambassador and support wikis in-place.
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"""
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import os
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import re
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import sys
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WIKI_FILES = [
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"/opt/blog/zai-ambassador-team-wiki.html",
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"/opt/blog/zai-support-wiki.html",
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]
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CHAT_CSS = """
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/* ── VectorDB Chat Panel ── */
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.vdb-fab{position:fixed;bottom:24px;right:24px;width:56px;height:56px;border-radius:28px;
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background:linear-gradient(135deg,#4a9eff,#a78bfa);border:none;color:#fff;font-size:22px;
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cursor:pointer;box-shadow:0 4px 20px rgba(74,158,255,.35);z-index:999;transition:all .2s;display:flex;align-items:center;justify-content:center}
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.vdb-fab:hover{transform:scale(1.08);box-shadow:0 6px 28px rgba(74,158,255,.45)}
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.vdb-chat-container{position:fixed;bottom:0;right:0;width:420px;height:560px;background:#131620;
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border:1px solid #252a3b;border-radius:16px 16px 0 0;z-index:998;display:flex;
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flex-direction:column;box-shadow:-4px 0 30px rgba(0,0,0,.4);font-family:'Inter',system-ui,sans-serif;
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transition:opacity .25s, transform .25s cubic-bezier(.175,.885,.32,1.275)}
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.vdb-chat-container.hidden{opacity:0;pointer-events:none;transform:translateY(20px)}
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.vdb-chat-header{display:flex;align-items:center;gap:10px;padding:14px 16px;
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background:#0c0e14;border-bottom:1px solid #252a3b;flex-shrink:0}
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.vdb-chat-title{font-size:13px;font-weight:600;color:#e8eaed;flex:1;white-space:nowrap;overflow:hidden;text-overflow:ellipsis}
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.vdb-provider-select{font-size:11px;padding:4px 8px;border:1px solid #333;border-radius:6px;
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background:#1a1d26;color:#ccc;cursor:pointer;outline:none;color:#e8eaed;min-width:120px}
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.vdb-provider-select:focus{border-color:#4a9eff}
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.vdb-toggle{background:none;border:none;color:#888;font-size:18px;cursor:pointer;padding:4px 8px;line-height:1}
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.vdb-toggle:hover{color:#fff}
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.vdb-messages{flex:1;overflow-y:auto;padding:12px 16px;display:flex;flex-direction:column;gap:8px;
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scrollbar-width:4px;scrollbar-thumb:#333}
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.vdb-msg{max-width:85%;padding:10px 14px;border-radius:12px;font-size:13px;line-height:1.5;
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color:#b0b5bc;word-wrap:break-word}
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.vdb-msg-user{align-self:flex-end;background:#1e3a5f;border:1px solid #2a4070;margin-left:auto}
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.vdb-msg-assistant{align-self:flex-start;background:#1a1f2e;border:1px solid #252a3b}
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.vdb-msg-meta{font-size:10px;color:#666;margin-top:4px;display:flex;gap:8px;align-items:center}
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.vdb-msg-provider{background:rgba(74,158,255,.1);color:#4a9eff;padding:1px 6px;border-radius:3px;font-weight:600}
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.vdb-msg-error{background:rgba(248,113,113,.1);color:#f87171;border-color:rgba(248,113,113,.3)}
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.vdb-typing{font-size:11px;color:#7c8497;font-style:italic;padding:8px 16px 0;display:none}
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.vdb-typing.active{display:block}
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.vdb-input-row{display:flex;gap:8px;padding:12px 16px;border-top:1px solid #252a3b;
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background:#0c0e14;flex-shrink:0}
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.vdb-input{flex:1;padding:10px 12px;border:1px solid #333;border-radius:8px;
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background:#1a1d26;color:#e8eaed;font-size:13px;font-family:inherit;resize:none;
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outline:none;min-height:20px;max-height:80px;line-height:1.4}
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.vdb-input:focus{border-color:#4a9eff}
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.vdb-send{padding:10px 18px;border:1px solid #333;border-radius:8px;background:rgba(74,158,255,.1);
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color:#4a9eff;font-weight:600;font-size:12px;cursor:pointer;white-space:nowrap;
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transition:all .15s}
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.vdb-send:hover{background:rgba(74,158,255,.2);color:#fff}
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.vdb-send:disabled{opacity:.4;cursor:not-allowed}
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.vdb-settings{border-top:1px solid #252a3b;padding:12px 16px;display:none}
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.vdb-settings-row{display:flex;gap:8px;margin-bottom:8px;align-items:center}
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.vdb-settings-row label{font-size:11px;color:#888;width:70px;flex-shrink:0}
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.vdb-settings-row input,.vdb-settings-row select{flex:1;padding:6px 8px;border:1px solid #333;
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border-radius:4px;background:#1a1d26;color:#e8eaed;font-size:11px}
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.vdb-settings-btn{padding:4px 12px;border-radius:4px;font-size:10px;cursor:pointer;
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border:1px solid #333;background:#1a1d26;color:#aaa;transition:all .15s}
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.vdb-settings-btn:hover{color:#fff;border-color:#444}
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.vdb-settings-btn.danger{color:#f87171;border-color:rgba(248,113,113,.3)}
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@media(max-width:600px){
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.vdb-chat-container{width:100vw;height:100vh;border-radius:0;right:0;bottom:0}
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}
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"""
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CHAT_HTML = """
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<div id="vdb-chat" class="vdb-chat-container hidden" style="display:none">
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<div class="vdb-chat-header">
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<span class="vdb-chat-title">VectorDB Chat</span>
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<select id="vdb-provider-select" class="vdb-provider-select"></select>
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<button id="vdb-toggle" class="vdb-toggle">—</button>
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</div>
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<div class="vdb-messages" id="vdb-chat-messages"></div>
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<div class="vdb-typing" id="vdb-typing"> thinking...</div>
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<div class="vdb-input-row">
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<textarea id="vdb-input" class="vdb-input" placeholder="Ask about Z.ai wiki, community issues..." rows="1"></textarea>
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<button id="vdb-send" class="vdb-send">Send</button>
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</div>
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<div class="vdb-settings" id="vdb-settings">
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<div style="font-size:11px;color:#888;margin-bottom:8px;font-weight:600;text-transform:uppercase;letter-spacing:.5px">Custom Provider</div>
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<div class="vdb-settings-row"><label>Name</label><input id="vdb-cust-name" placeholder"My Provider"></div></div>
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<div class="vdb-settings-row"><label>API URL</label><input id="vdb-cust-url" placeholder="https://api.example.com/v1"></div></div>
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<div class="vdb-settings-row"><label>Model</label><input id="vdb-cust-model" placeholder="gpt-4o-mini"></div></div>
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<div class="vdb-settings-row"><label>API Key</label><input id="vdb-cust-key" type="password" placeholder="sk-..."></div></div>
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<div style="display:flex;gap:6px;margin-top:8px">
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<button id="vdb-cust-save" class="vdb-settings-btn">Save Provider</button>
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<button id="vdb-cust-cancel" class="vdb-settings-btn danger">Cancel</button>
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</div>
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</div>
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</div>
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<button id="vdb-chat-btn" class="vdb-fab" title="Open Wiki Chat">💬</button>
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"""
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CHAT_JS = """
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(function(){
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const CHAT_API = (function(){
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const p = location.pathname;
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return p.endsWith('/') ? p : p + '/';
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})();
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const $ = id => document.getElementById(id);
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let _providers = [];
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let _activeProvider = null;
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let _history = [];
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let _isStreaming = false;
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function esc(s){ if(!s) return ''; const d=document.createElement('div'); d.textContent=s; return d.innerHTML; }
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function providerIcon(p){ return p.icon || '\u2B99'; }
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function providerLabel(p){ return p.icon + ' ' + p.name; }
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function renderProviders(){
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const sel = $('vdb-provider-select');
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sel.innerHTML = '<option value="">Select provider...</option>';
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_providers.forEach(p => {
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const opt = document.createElement('option');
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opt.value = p.id;
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opt.textContent = providerLabel(p);
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if(_activeProvider && p.id === _activeProvider.id) opt.selected = true;
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sel.appendChild(opt);
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});
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}
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function addMsg(content, isUser, meta){
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const msgs = $('vdb-chat-messages');
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const div = document.createElement('div');
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div.className = 'vdb-msg vdb-msg-' + (isUser ? 'user' : 'assistant');
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let html = '';
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if(meta && meta.provider) html += '<div class="vdb-msg-meta"><span class="vdb-msg-provider">' + providerIcon(meta.provider) + ' ' + meta.provider.name + '</span></div>';
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if(meta && meta.error) { div.className += ' vdb-msg-error'; html = content; }
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else { html += esc(content).replace(/\\n/g, '<br>'); }
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div.innerHTML = html;
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msgs.appendChild(div);
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msgs.scrollTop = msgs.scrollHeight;
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return div;
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}
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||||||
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||||||
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function setStreaming(on){
|
||||||
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_isStreaming = on;
|
||||||
|
$('vdb-typing').className = 'vdb-typing' + (on ? ' active' : '');
|
||||||
|
$('vdb-send').disabled = on;
|
||||||
|
}
|
||||||
|
|
||||||
|
async function sendMessage(){
|
||||||
|
const input = $('vdb-input');
|
||||||
|
const text = (input.value || '').trim();
|
||||||
|
if(!text || !_activeProvider || _isStreaming) return;
|
||||||
|
_history.push({role:'user', content:text});
|
||||||
|
addMsg(text, true, null);
|
||||||
|
input.value = '';
|
||||||
|
input.style.height = 'auto';
|
||||||
|
setStreaming(true);
|
||||||
|
|
||||||
|
try{
|
||||||
|
const resp = await fetch(CHAT_API + 'chat/message', {
|
||||||
|
method:'POST',
|
||||||
|
headers:{'Content-Type':'application/json'},
|
||||||
|
body:JSON.stringify({message:text, provider_id:_activeProvider.id, history:_history})
|
||||||
|
});
|
||||||
|
const reader = resp.body.getReader();
|
||||||
|
const decoder = new TextDecoder();
|
||||||
|
let buf = '';
|
||||||
|
let fullText = '';
|
||||||
|
let assistantDiv = null;
|
||||||
|
|
||||||
|
while(true){
|
||||||
|
const {done, value} = await reader.read();
|
||||||
|
if(value) buf += decoder.decode(value, {stream:true});
|
||||||
|
if(done) break;
|
||||||
|
const lines = buf.split('\\n');
|
||||||
|
buf = lines.pop(); // keep incomplete line
|
||||||
|
for(const line of lines){
|
||||||
|
if(!line.startsWith('data: ') || !line.slice(5)) continue;
|
||||||
|
try{
|
||||||
|
const chunk = JSON.parse(line.slice(5));
|
||||||
|
if(chunk.type === 'done'){ setStreaming(false); break; }
|
||||||
|
if(chunk.type === 'error'){
|
||||||
|
if(!assistantDiv) assistantDiv = addMsg(chunk.delta, false, {error:true});
|
||||||
|
else assistantDiv.textContent += chunk.delta;
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
if(chunk.type === 'delta'){
|
||||||
|
if(!assistantDiv) assistantDiv = addMsg('', false, {provider:_activeProvider});
|
||||||
|
fullText += chunk.delta;
|
||||||
|
assistantDiv.innerHTML = esc(fullText.replace(/\\n/g, '<br>'));
|
||||||
|
assistantDiv.scrollIntoView({block:'nearest', behavior:'smooth'});
|
||||||
|
} else if(chunk.type === 'tool' || chunk.type === 'raw'){
|
||||||
|
if(!assistantDiv) assistantDiv = addMsg('', false, {provider:_activeProvider});
|
||||||
|
assistantDiv.innerHTML += esc(chunk.delta).replace(/\\n/g, '<br>');
|
||||||
|
}
|
||||||
|
}catch(e){ /* skip malformed */ }
|
||||||
|
}
|
||||||
|
}
|
||||||
|
// Process remaining buffer
|
||||||
|
if(buf){
|
||||||
|
for(const line of buf.split('\\n')){
|
||||||
|
if(!line.startsWith('data: ')) continue;
|
||||||
|
try{
|
||||||
|
const chunk = JSON.parse(line.slice(5));
|
||||||
|
if(chunk.type === 'delta' && chunk.delta){
|
||||||
|
if(!assistantDiv) assistantDiv = addMsg('', false, {provider:_activeProvider});
|
||||||
|
fullText += chunk.delta;
|
||||||
|
assistantDiv.innerHTML = esc(fullText.replace(/\\n/g, '<br>'));
|
||||||
|
}
|
||||||
|
}catch(e){}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
_history.push({role:'assistant', content:fullText || '(no response)'});
|
||||||
|
} catch(e){
|
||||||
|
addMsg('Error: ' + e.message, false, {error:true});
|
||||||
|
}
|
||||||
|
setStreaming(false);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Provider selection
|
||||||
|
$('vdb-provider-select').addEventListener('change', function(){
|
||||||
|
const pid = this.value;
|
||||||
|
_activeProvider = _providers.find(p => p.id === pid) || null;
|
||||||
|
});
|
||||||
|
|
||||||
|
// Send button
|
||||||
|
$('vdb-send').addEventListener('click', sendMessage);
|
||||||
|
$('vdb-input').addEventListener('keydown', function(e){ if(e.key === 'Enter' && !e.shiftKey){ e.preventDefault(); sendMessage(); }});
|
||||||
|
|
||||||
|
// Toggle chat panel
|
||||||
|
let _chatOpen = false;
|
||||||
|
$('vdb-chat-btn').addEventListener('click', function(){
|
||||||
|
_chatOpen = !_chatOpen;
|
||||||
|
const chat = $('vdb-chat');
|
||||||
|
chat.classList.toggle('hidden', !_chatOpen);
|
||||||
|
this.textContent = _chatOpen ? '\u2715' : '\u1F4AC';
|
||||||
|
this.title = _chatOpen ? 'Close Chat' : 'Open Wiki Chat';
|
||||||
|
});
|
||||||
|
|
||||||
|
$('vdb-toggle').addEventListener('click', function(){
|
||||||
|
_chatOpen = false;
|
||||||
|
$('vdb-chat').classList.add('hidden');
|
||||||
|
this.textContent = '\u25B2';
|
||||||
|
$('vdb-chat-btn').textContent = '\u1F4AC';
|
||||||
|
$('vdb-chat-btn').title = 'Open Wiki Chat';
|
||||||
|
});
|
||||||
|
|
||||||
|
// Settings
|
||||||
|
$('vdb-cust-save').addEventListener('click', async function(){
|
||||||
|
const name = $('vdb-cust-name').value.trim();
|
||||||
|
const url = $('vdb-cust-url').value.trim();
|
||||||
|
const model = $('vdb-cust-model').value.trim();
|
||||||
|
const key = $('vdb-cust-key').value.trim();
|
||||||
|
if(!name || !url || !model){ alert('Name, URL, and Model are required'); return; }
|
||||||
|
const provider = {id:'custom-'+Date.now(), name:name, base_url:url, model:model,
|
||||||
|
api_key:key, format:'openai', icon:'\u2699', description:'Custom'};
|
||||||
|
// Save via API
|
||||||
|
try{
|
||||||
|
await fetch(CHAT_API + 'providers/save', {
|
||||||
|
method:'POST', headers:{'Content-Type':'application/json'}, body:JSON.stringify(provider)
|
||||||
|
});
|
||||||
|
_providers = await (await fetch(CHAT_API + 'providers')).json();
|
||||||
|
renderProviders();
|
||||||
|
_activeProvider = provider;
|
||||||
|
$('vdb-settings').style.display = 'none';
|
||||||
|
alert('Provider saved!');
|
||||||
|
} catch(e){ alert('Save failed: ' + e.message); }
|
||||||
|
});
|
||||||
|
|
||||||
|
$('vdb-cust-cancel').addEventListener('click', function(){
|
||||||
|
$('vdb-settings').style.display = 'none';
|
||||||
|
});
|
||||||
|
|
||||||
|
// Auto-open settings if no providers loaded
|
||||||
|
function checkProviders(){
|
||||||
|
if(!_providers.length){
|
||||||
|
$('vdb-settings').style.display = '';
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Init
|
||||||
|
async function init(){
|
||||||
|
try{
|
||||||
|
const [presetsResp, customResp] = await Promise.all([
|
||||||
|
fetch(CHAT_API + 'providers/presets'),
|
||||||
|
fetch(CHAT_API + 'providers')
|
||||||
|
]);
|
||||||
|
_presets = await presetsResp.json();
|
||||||
|
_custom = await customResp.json();
|
||||||
|
_providers = [..._presets, ..._custom];
|
||||||
|
renderProviders();
|
||||||
|
// Auto-select first available provider
|
||||||
|
if(_providers.length > 0 && !_activeProvider){
|
||||||
|
_activeProvider = _providers[0];
|
||||||
|
renderProviders();
|
||||||
|
}
|
||||||
|
checkProviders();
|
||||||
|
} catch(e){
|
||||||
|
console.error('Chat init error:', e);
|
||||||
|
$('vdb-chat-messages').innerHTML = '<div class="vdb-msg vdb-msg-error">Failed to load chat service.</div>';
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
init();
|
||||||
|
})();
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
def inject_chat(html_content: str) -> str:
|
||||||
|
"""Inject chat CSS, HTML, and JS into wiki HTML."""
|
||||||
|
# Inject CSS before </style>
|
||||||
|
if "</style>" in html_content:
|
||||||
|
html_content = html_content.replace("</style>", CHAT_CSS + "</style>", 1)
|
||||||
|
|
||||||
|
# Inject HTML before </body>
|
||||||
|
if "</body>" in html_content:
|
||||||
|
html_content = html_content.replace("</body>", CHAT_HTML + "\n<script>" + CHAT_JS + "\n</script>", 1)
|
||||||
|
|
||||||
|
return html_content
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
files = WIKI_FILES
|
||||||
|
# Check for --file argument
|
||||||
|
if "--file" in sys.argv:
|
||||||
|
idx = sys.argv.index("--file") + 1
|
||||||
|
if idx < len(sys.argv):
|
||||||
|
files = [sys.argv[idx]]
|
||||||
|
|
||||||
|
for fpath in files:
|
||||||
|
if not os.path.exists(fpath):
|
||||||
|
print(f"SKIP: {fpath} not found")
|
||||||
|
continue
|
||||||
|
with open(fpath, "r") as f:
|
||||||
|
content = f.read()
|
||||||
|
# Check if already injected
|
||||||
|
if "vdb-chat-container" in content:
|
||||||
|
print(f"SKIP: {fpath} already has chat injected")
|
||||||
|
continue
|
||||||
|
new_content = inject_chat(content)
|
||||||
|
with open(fpath, "w") as f:
|
||||||
|
f.write(new_content)
|
||||||
|
print(f"OK: {fpath} ({len(new_content)} bytes)")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
225
vector-db-service.py
Normal file
225
vector-db-service.py
Normal file
@@ -0,0 +1,225 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Z.ai Wiki Vector DB Service
|
||||||
|
|
||||||
|
Stores Discord messages as vector embeddings using sentence-transformers.
|
||||||
|
Provides a search API for the wiki chat system to query as an additional data source.
|
||||||
|
|
||||||
|
Data sources:
|
||||||
|
- Server 1346756824233148527 (Z.ai Community)
|
||||||
|
- Server 1410352583364841555 (Z.ai Mod Server)
|
||||||
|
- Channel 1476364011091136544 (Z.ai Mod Channel)
|
||||||
|
|
||||||
|
Endpoints:
|
||||||
|
- POST /vector/search - Search with a query, return top-K matches
|
||||||
|
- POST /vector/index - Add messages to the index
|
||||||
|
- GET /vector/stats - Get index statistics
|
||||||
|
- POST /vector/rebuild - Rebuild from stored messages
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
import numpy as np
|
||||||
|
from flask import Flask, request, jsonify, make_response
|
||||||
|
from sentence_transformers import SentenceTransformer
|
||||||
|
|
||||||
|
app = Flask(__name__)
|
||||||
|
|
||||||
|
DATA_DIR = os.environ.get('VECTOR_DB_DIR', '/opt/blog/vector-db')
|
||||||
|
MESSAGES_FILE = os.path.join(DATA_DIR, 'messages.json')
|
||||||
|
EMBEDDINGS_FILE = os.path.join(DATA_DIR, 'embeddings.npy')
|
||||||
|
META_FILE = os.path.join(DATA_DIR, 'meta.json')
|
||||||
|
|
||||||
|
os.makedirs(DATA_DIR, exist_ok=True)
|
||||||
|
|
||||||
|
model = None
|
||||||
|
embeddings = None
|
||||||
|
meta = [] # parallel array: [{id, content, source, author, channel, server, timestamp, link}, ...]
|
||||||
|
|
||||||
|
|
||||||
|
def get_model():
|
||||||
|
global model
|
||||||
|
if model is None:
|
||||||
|
print('[VectorDB] Loading sentence-transformer model...')
|
||||||
|
model = SentenceTransformer('all-MiniLM-L6-v2')
|
||||||
|
print('[VectorDB] Model loaded')
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def load_index():
|
||||||
|
global embeddings, meta
|
||||||
|
if os.path.exists(EMBEDDINGS_FILE) and os.path.exists(META_FILE):
|
||||||
|
embeddings = np.load(EMBEDDINGS_FILE)
|
||||||
|
meta = json.load(open(META_FILE))
|
||||||
|
print(f'[VectorDB] Loaded index: {len(meta)} entries, dim={embeddings.shape[1]}')
|
||||||
|
else:
|
||||||
|
embeddings = np.empty((0, 384), dtype=np.float32)
|
||||||
|
meta = []
|
||||||
|
print('[VectorDB] No existing index, starting fresh')
|
||||||
|
|
||||||
|
|
||||||
|
def save_index():
|
||||||
|
np.save(EMBEDDINGS_FILE, embeddings)
|
||||||
|
json.dump(meta, open(META_FILE, 'w'))
|
||||||
|
print(f'[VectorDB] Saved index: {len(meta)} entries')
|
||||||
|
|
||||||
|
|
||||||
|
def load_messages():
|
||||||
|
"""Load raw messages from JSON files"""
|
||||||
|
msgs = []
|
||||||
|
for f in glob.glob(os.path.join(DATA_DIR, 'messages_*.json')):
|
||||||
|
msgs.extend(json.load(open(f)))
|
||||||
|
return msgs
|
||||||
|
|
||||||
|
|
||||||
|
@app.after_request
|
||||||
|
def add_cors(response):
|
||||||
|
response.headers['Access-Control-Allow-Origin'] = '*'
|
||||||
|
response.headers['Access-Control-Allow-Methods'] = 'POST, GET, OPTIONS'
|
||||||
|
response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization'
|
||||||
|
return response
|
||||||
|
|
||||||
|
|
||||||
|
@app.route('/vector/stats', methods=['GET', 'OPTIONS'])
|
||||||
|
def stats():
|
||||||
|
if request.method == 'OPTIONS':
|
||||||
|
return make_response('', 200)
|
||||||
|
sources = {}
|
||||||
|
for m in meta:
|
||||||
|
s = m.get('source', 'unknown')
|
||||||
|
sources[s] = sources.get(s, 0) + 1
|
||||||
|
return jsonify({
|
||||||
|
'total': len(meta),
|
||||||
|
'dimension': int(embeddings.shape[1]) if embeddings.shape[0] > 0 else 0,
|
||||||
|
'sources': sources,
|
||||||
|
'is_indexed': len(meta) > 0,
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
|
@app.route('/vector/search', methods=['POST', 'OPTIONS'])
|
||||||
|
def search():
|
||||||
|
if request.method == 'OPTIONS':
|
||||||
|
return make_response('', 200)
|
||||||
|
try:
|
||||||
|
body = request.get_json(force=True)
|
||||||
|
query = body.get('query', '')
|
||||||
|
top_k = min(body.get('top_k', 10), 50)
|
||||||
|
|
||||||
|
if not query or embeddings.shape[0] == 0:
|
||||||
|
return jsonify({'results': [], 'query': query})
|
||||||
|
|
||||||
|
mdl = get_model()
|
||||||
|
q_emb = mdl.encode([query], normalize_embeddings=True).astype(np.float32)
|
||||||
|
|
||||||
|
# Cosine similarity (embeddings are already normalized)
|
||||||
|
scores = (embeddings @ q_emb.T).flatten()
|
||||||
|
|
||||||
|
top_idx = np.argsort(scores)[::-1][:top_k]
|
||||||
|
|
||||||
|
results = []
|
||||||
|
for i in top_idx:
|
||||||
|
if scores[i] < 0.1: # threshold
|
||||||
|
break
|
||||||
|
results.append({
|
||||||
|
'content': meta[i]['content'],
|
||||||
|
'source': meta[i].get('source', ''),
|
||||||
|
'author': meta[i].get('author', ''),
|
||||||
|
'channel': meta[i].get('channel', ''),
|
||||||
|
'server': meta[i].get('server', ''),
|
||||||
|
'timestamp': meta[i].get('timestamp', ''),
|
||||||
|
'link': meta[i].get('link', ''),
|
||||||
|
'score': float(scores[i]),
|
||||||
|
})
|
||||||
|
|
||||||
|
return jsonify({'results': results, 'query': query, 'total': len(meta)})
|
||||||
|
except Exception as e:
|
||||||
|
return jsonify({'error': str(e)}), 500
|
||||||
|
|
||||||
|
|
||||||
|
@app.route('/vector/index', methods=['POST', 'OPTIONS'])
|
||||||
|
def index_messages():
|
||||||
|
if request.method == 'OPTIONS':
|
||||||
|
return make_response('', 200)
|
||||||
|
try:
|
||||||
|
body = request.get_json(force=True)
|
||||||
|
messages = body.get('messages', [])
|
||||||
|
source = body.get('source', 'unknown')
|
||||||
|
|
||||||
|
if not messages:
|
||||||
|
return jsonify({'error': 'messages required'}), 400
|
||||||
|
|
||||||
|
global embeddings, meta
|
||||||
|
|
||||||
|
mdl = get_model()
|
||||||
|
texts = [m.get('content', '')[:2000] for m in messages]
|
||||||
|
new_emb = mdl.encode(texts, normalize_embeddings=True, show_progress_bar=False,
|
||||||
|
batch_size=64).astype(np.float32)
|
||||||
|
|
||||||
|
start_idx = len(meta)
|
||||||
|
for m in messages:
|
||||||
|
meta.append({
|
||||||
|
'id': m.get('id', ''),
|
||||||
|
'content': m.get('content', ''),
|
||||||
|
'source': source,
|
||||||
|
'author': m.get('author', ''),
|
||||||
|
'channel': m.get('channel', ''),
|
||||||
|
'server': m.get('server', ''),
|
||||||
|
'timestamp': m.get('timestamp', ''),
|
||||||
|
'link': m.get('link', ''),
|
||||||
|
})
|
||||||
|
|
||||||
|
if embeddings.shape[0] == 0:
|
||||||
|
embeddings = new_emb
|
||||||
|
else:
|
||||||
|
embeddings = np.vstack([embeddings, new_emb])
|
||||||
|
|
||||||
|
save_index()
|
||||||
|
return jsonify({'indexed': len(messages), 'total': len(meta)})
|
||||||
|
except Exception as e:
|
||||||
|
return jsonify({'error': str(e)}), 500
|
||||||
|
|
||||||
|
|
||||||
|
@app.route('/vector/rebuild', methods=['POST', 'OPTIONS'])
|
||||||
|
def rebuild():
|
||||||
|
if request.method == 'OPTIONS':
|
||||||
|
return make_response('', 200)
|
||||||
|
try:
|
||||||
|
messages = load_messages()
|
||||||
|
if not messages:
|
||||||
|
return jsonify({'error': 'No messages found. Run scraper first.'}), 404
|
||||||
|
|
||||||
|
global embeddings, meta
|
||||||
|
meta = []
|
||||||
|
mdl = get_model()
|
||||||
|
|
||||||
|
# Filter out empty/short messages
|
||||||
|
valid = [m for m in messages if len(m.get('content', '').strip()) > 10]
|
||||||
|
print(f'[VectorDB] Rebuilding index from {len(valid)} valid messages...')
|
||||||
|
|
||||||
|
texts = [m.get('content', '')[:2000] for m in valid]
|
||||||
|
embeddings = mdl.encode(texts, normalize_embeddings=True,
|
||||||
|
show_progress_bar=True, batch_size=128).astype(np.float32)
|
||||||
|
|
||||||
|
for m in valid:
|
||||||
|
meta.append({
|
||||||
|
'id': m.get('id', ''),
|
||||||
|
'content': m.get('content', ''),
|
||||||
|
'source': m.get('source', ''),
|
||||||
|
'author': m.get('author', ''),
|
||||||
|
'channel': m.get('channel', ''),
|
||||||
|
'server': m.get('server', ''),
|
||||||
|
'timestamp': m.get('timestamp', ''),
|
||||||
|
'link': m.get('link', ''),
|
||||||
|
})
|
||||||
|
|
||||||
|
save_index()
|
||||||
|
return jsonify({'indexed': len(meta), 'total_valid': len(valid), 'total_raw': len(messages)})
|
||||||
|
except Exception as e:
|
||||||
|
return jsonify({'error': str(e)}), 500
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
load_index()
|
||||||
|
port = int(os.environ.get('VECTOR_DB_PORT', 8099))
|
||||||
|
print(f'[VectorDB] Service running on port {port}')
|
||||||
|
app.run(host='0.0.0.0', port=port)
|
||||||
171
wiki-api.py
Executable file
171
wiki-api.py
Executable file
@@ -0,0 +1,171 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Z.ai Wiki KB Search API - token-protected"""
|
||||||
|
import json, os, sys, urllib.parse, hashlib
|
||||||
|
from http.server import HTTPServer, BaseHTTPRequestHandler
|
||||||
|
|
||||||
|
KB_PATH = "/opt/blog/wiki-kb.json"
|
||||||
|
TOKEN_PATH = "/opt/blog/.wiki-api-token"
|
||||||
|
PORT = 8097
|
||||||
|
LOG_PATH = "/opt/blog/data/search-logs.json"
|
||||||
|
MAX_LOG_ENTRIES = 5000
|
||||||
|
|
||||||
|
def load_logs():
|
||||||
|
if os.path.exists(LOG_PATH):
|
||||||
|
try:
|
||||||
|
with open(LOG_PATH) as f:
|
||||||
|
return json.load(f)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
return []
|
||||||
|
|
||||||
|
def save_logs(logs):
|
||||||
|
os.makedirs(os.path.dirname(LOG_PATH), exist_ok=True)
|
||||||
|
# Keep only last MAX_LOG_ENTRIES
|
||||||
|
with open(LOG_PATH, 'w') as f:
|
||||||
|
json.dump(logs[-MAX_LOG_ENTRIES:], f)
|
||||||
|
|
||||||
|
def log_search(source, query, results, meta=None):
|
||||||
|
logs = load_logs()
|
||||||
|
entry = {
|
||||||
|
"ts": __import__('time').strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||||
|
"source": source,
|
||||||
|
"query": query,
|
||||||
|
"result_count": len(results) if isinstance(results, list) else 0,
|
||||||
|
"top_results": [
|
||||||
|
{"q": r.get("q", r.get("content", ""))[:100], "score": r.get("score", 0), "source": r.get("source", "")}
|
||||||
|
for r in (results[:3] if isinstance(results, list) else [])
|
||||||
|
],
|
||||||
|
}
|
||||||
|
if meta:
|
||||||
|
entry["meta"] = meta
|
||||||
|
logs.append(entry)
|
||||||
|
save_logs(logs)
|
||||||
|
return entry
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# Load or generate token
|
||||||
|
def load_token():
|
||||||
|
if os.path.exists(TOKEN_PATH):
|
||||||
|
with open(TOKEN_PATH) as f:
|
||||||
|
return f.read().strip()
|
||||||
|
return None
|
||||||
|
|
||||||
|
API_TOKEN = load_token()
|
||||||
|
|
||||||
|
with open(KB_PATH) as f:
|
||||||
|
KB = json.load(f)
|
||||||
|
|
||||||
|
def search_kb(query, topic=None, limit=20):
|
||||||
|
query_lower = query.lower()
|
||||||
|
query_words = set(query_lower.split())
|
||||||
|
results = []
|
||||||
|
for entry in KB:
|
||||||
|
score = 0
|
||||||
|
q_text = entry.get("q", "").lower()
|
||||||
|
a_text = entry.get("a", "").lower()
|
||||||
|
q_words = set(q_text.split())
|
||||||
|
a_words = set(a_text.split())
|
||||||
|
score += len(query_words & q_words) * 3
|
||||||
|
score += len(query_words & a_words) * 1
|
||||||
|
if query_lower in q_text: score += 10
|
||||||
|
if query_lower in a_text: score += 5
|
||||||
|
if topic and entry.get("topic", "").lower() != topic.lower(): score -= 50
|
||||||
|
if score > 0:
|
||||||
|
results.append({**entry, "score": score})
|
||||||
|
results.sort(key=lambda x: -x["score"])
|
||||||
|
return results[:limit]
|
||||||
|
|
||||||
|
def check_auth(params, headers):
|
||||||
|
if not API_TOKEN:
|
||||||
|
return False
|
||||||
|
# Check query param ?token=...
|
||||||
|
token = params.get("token", [""])[0]
|
||||||
|
if token == API_TOKEN:
|
||||||
|
return True
|
||||||
|
# Check header Authorization: Bearer ...
|
||||||
|
auth = headers.get("Authorization", "")
|
||||||
|
if auth.startswith("Bearer "):
|
||||||
|
if auth[7:] == API_TOKEN:
|
||||||
|
return True
|
||||||
|
# Check header X-API-Key
|
||||||
|
api_key = headers.get("X-Api-Key", "")
|
||||||
|
if api_key == API_TOKEN:
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
class Handler(BaseHTTPRequestHandler):
|
||||||
|
def do_GET(self):
|
||||||
|
parsed = urllib.parse.urlparse(self.path)
|
||||||
|
params = urllib.parse.parse_qs(parsed.query)
|
||||||
|
|
||||||
|
if not check_auth(params, self.headers):
|
||||||
|
self.send_response(401)
|
||||||
|
self.send_header("Content-Type", "application/json")
|
||||||
|
self.send_header("Access-Control-Allow-Origin", "*")
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(json.dumps({"error": "Unauthorized. Provide ?token=YOUR_TOKEN or Authorization: Bearer YOUR_TOKEN"}).encode())
|
||||||
|
return
|
||||||
|
|
||||||
|
if parsed.path == "/search":
|
||||||
|
query = params.get("q", [""])[0]
|
||||||
|
topic = params.get("topic", [None])[0]
|
||||||
|
limit = int(params.get("limit", [20])[0])
|
||||||
|
if not query:
|
||||||
|
body = json.dumps({"error": "Missing ?q= parameter"}).encode()
|
||||||
|
else:
|
||||||
|
results = search_kb(query, topic, limit)
|
||||||
|
log_search("kb", query, results, {"topic": topic, "limit": limit, "ip": self.headers.get("X-Real-IP", self.client_address[0])})
|
||||||
|
body = json.dumps({"query": query, "count": len(results), "results": results}, ensure_ascii=False).encode()
|
||||||
|
elif parsed.path == "/kb":
|
||||||
|
body = json.dumps(KB, ensure_ascii=False).encode()
|
||||||
|
elif parsed.path == "/logs":
|
||||||
|
logs = load_logs()
|
||||||
|
body = json.dumps({"total": len(logs), "logs": logs[::-1]}, ensure_ascii=False).encode()
|
||||||
|
else:
|
||||||
|
self.send_response(404)
|
||||||
|
self.end_headers()
|
||||||
|
return
|
||||||
|
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header("Content-Type", "application/json")
|
||||||
|
self.send_header("Access-Control-Allow-Origin", "*")
|
||||||
|
self.send_header("Content-Length", len(body))
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(body)
|
||||||
|
|
||||||
|
def do_DELETE(self):
|
||||||
|
parsed = urllib.parse.urlparse(self.path)
|
||||||
|
params = urllib.parse.parse_qs(parsed.query)
|
||||||
|
if not check_auth(params, self.headers):
|
||||||
|
self.send_response(401)
|
||||||
|
self.send_header("Content-Type", "application/json")
|
||||||
|
self.send_header("Access-Control-Allow-Origin", "*")
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(json.dumps({"error": "Unauthorized"}).encode())
|
||||||
|
return
|
||||||
|
if parsed.path == "/logs":
|
||||||
|
save_logs([])
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header("Content-Type", "application/json")
|
||||||
|
self.send_header("Access-Control-Allow-Origin", "*")
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(json.dumps({"cleared": True}).encode())
|
||||||
|
return
|
||||||
|
self.send_response(404)
|
||||||
|
self.end_headers()
|
||||||
|
|
||||||
|
def do_OPTIONS(self):
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header("Access-Control-Allow-Origin", "*")
|
||||||
|
self.send_header("Access-Control-Allow-Methods", "GET, POST, DELETE, OPTIONS")
|
||||||
|
self.send_header("Access-Control-Allow-Headers", "Authorization, X-Api-Key, Content-Type")
|
||||||
|
self.end_headers()
|
||||||
|
|
||||||
|
def log_message(self, format, *args):
|
||||||
|
pass
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
print(f"Wiki KB API running on port {PORT}")
|
||||||
|
server = HTTPServer(("127.0.0.1", PORT), Handler)
|
||||||
|
server.serve_forever()
|
||||||
54
wiki-chat-proxy.py
Executable file
54
wiki-chat-proxy.py
Executable file
@@ -0,0 +1,54 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Wiki Chat Proxy - Proxies chat requests to z.ai GLM-4-Plus"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import urllib.request
|
||||||
|
|
||||||
|
ZAI_TOKEN = os.environ.get('ZAI_API_TOKEN', '')
|
||||||
|
|
||||||
|
def handler(event):
|
||||||
|
try:
|
||||||
|
if event.get('method') != 'POST':
|
||||||
|
return {'status': 405, 'body': 'Method not allowed'}
|
||||||
|
|
||||||
|
body = json.loads(event.get('body', '{}'))
|
||||||
|
messages = body.get('messages', [])
|
||||||
|
|
||||||
|
if not messages:
|
||||||
|
return {'status': 400, 'body': json.dumps({'error': 'messages required'})}
|
||||||
|
|
||||||
|
api_url = 'https://api.z.ai/api/coding/paas/v4/chat/completions'
|
||||||
|
|
||||||
|
payload = json.dumps({
|
||||||
|
'model': 'glm-4-plus',
|
||||||
|
'messages': messages,
|
||||||
|
'temperature': 0.7,
|
||||||
|
'max_tokens': 2000,
|
||||||
|
}).encode()
|
||||||
|
|
||||||
|
headers = {'Content-Type': 'application/json'}
|
||||||
|
if ZAI_TOKEN:
|
||||||
|
headers['Authorization'] = 'Bearer ' + ZAI_TOKEN
|
||||||
|
|
||||||
|
req = urllib.request.Request(api_url, data=payload, headers=headers, method='POST')
|
||||||
|
|
||||||
|
try:
|
||||||
|
with urllib.request.urlopen(req, timeout=60) as resp:
|
||||||
|
data = json.loads(resp.read().decode())
|
||||||
|
content = data.get('choices', [{}])[0].get('message', {}).get('content', '')
|
||||||
|
return {
|
||||||
|
'status': 200,
|
||||||
|
'headers': {'Content-Type': 'application/json'},
|
||||||
|
'body': json.dumps({'response': content})
|
||||||
|
}
|
||||||
|
except urllib.error.HTTPError as e:
|
||||||
|
err_body = e.read().decode() if e.fp else ''
|
||||||
|
try:
|
||||||
|
err_json = json.loads(err_body)
|
||||||
|
err_msg = err_json.get('error', {}).get('message', err_json.get('message', str(e)))
|
||||||
|
except:
|
||||||
|
err_msg = str(e)
|
||||||
|
return {'status': e.code, 'body': json.dumps({'error': err_msg})}
|
||||||
|
except Exception as e:
|
||||||
|
return {'status': 500, 'body': json.dumps({'error': str(e)})}
|
||||||
90
wiki-chat-server.py
Executable file
90
wiki-chat-server.py
Executable file
@@ -0,0 +1,90 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Wiki Chat LLM Proxy Server on port 8098 using Flask"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import urllib.request
|
||||||
|
from flask import Flask, request, jsonify, make_response
|
||||||
|
|
||||||
|
app = Flask(__name__)
|
||||||
|
|
||||||
|
ZAI_TOKEN = os.environ.get('ZAI_API_TOKEN', '')
|
||||||
|
|
||||||
|
@app.after_request
|
||||||
|
def add_cors(response):
|
||||||
|
h = 'Access-Control-Allow-Origin'
|
||||||
|
response.headers[h] = '*'
|
||||||
|
response.headers['Access-Control-Allow-Methods'] = 'POST, OPTIONS'
|
||||||
|
response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization'
|
||||||
|
return response
|
||||||
|
|
||||||
|
def call_zai(messages, token=None):
|
||||||
|
api_url = 'https://api.z.ai/api/coding/paas/v4/chat/completions'
|
||||||
|
payload = json.dumps({
|
||||||
|
'model': 'glm-4-plus',
|
||||||
|
'messages': messages,
|
||||||
|
'temperature': 0.7,
|
||||||
|
'max_tokens': 2000,
|
||||||
|
}).encode()
|
||||||
|
headers = {'Content-Type': 'application/json'}
|
||||||
|
if token:
|
||||||
|
headers['Authorization'] = 'Bearer ' + token
|
||||||
|
req = urllib.request.Request(api_url, data=payload, headers=headers, method='POST')
|
||||||
|
with urllib.request.urlopen(req, timeout=60) as resp:
|
||||||
|
data = json.loads(resp.read().decode())
|
||||||
|
return data.get('choices', [{}])[0].get('message', {}).get('content', '')
|
||||||
|
|
||||||
|
@app.route('/chat/wiki', methods=['POST', 'OPTIONS'])
|
||||||
|
def chat_wiki():
|
||||||
|
if request.method == 'OPTIONS':
|
||||||
|
return make_response('', 200)
|
||||||
|
try:
|
||||||
|
body = request.get_json(force=True)
|
||||||
|
messages = body.get('messages', [])
|
||||||
|
if not messages:
|
||||||
|
return jsonify({'error': 'messages required'}), 400
|
||||||
|
token = body.get('token') or ZAI_TOKEN
|
||||||
|
if not token:
|
||||||
|
return jsonify({'error': 'No token provided'}), 401
|
||||||
|
content = call_zai(messages, token)
|
||||||
|
return jsonify({'response': content})
|
||||||
|
except urllib.error.HTTPError as e:
|
||||||
|
err_body = e.read().decode() if e.fp else ''
|
||||||
|
try:
|
||||||
|
err_json = json.loads(err_body)
|
||||||
|
err_msg = err_json.get('error', {}).get('message', err_json.get('message', str(e)))
|
||||||
|
except Exception:
|
||||||
|
err_msg = str(e)
|
||||||
|
return jsonify({'error': err_msg}), e.code
|
||||||
|
except Exception as e:
|
||||||
|
return jsonify({'error': str(e)}), 500
|
||||||
|
|
||||||
|
@app.route('/chat/wiki-tunnel', methods=['POST', 'OPTIONS'])
|
||||||
|
def chat_wiki_tunnel():
|
||||||
|
if request.method == 'OPTIONS':
|
||||||
|
return make_response('', 200)
|
||||||
|
try:
|
||||||
|
body = request.get_json(force=True)
|
||||||
|
messages = body.get('messages', [])
|
||||||
|
if not messages:
|
||||||
|
return jsonify({'error': 'messages required'}), 400
|
||||||
|
token = ZAI_TOKEN
|
||||||
|
if not token:
|
||||||
|
return jsonify({'error': 'No server token configured. Use Token mode and paste your API key.'}), 503
|
||||||
|
content = call_zai(messages, token)
|
||||||
|
return jsonify({'response': content})
|
||||||
|
except urllib.error.HTTPError as e:
|
||||||
|
err_body = e.read().decode() if e.fp else ''
|
||||||
|
try:
|
||||||
|
err_json = json.loads(err_body)
|
||||||
|
err_msg = err_json.get('error', {}).get('message', err_json.get('message', str(e)))
|
||||||
|
except Exception:
|
||||||
|
err_msg = str(e)
|
||||||
|
return jsonify({'error': err_msg}), e.code
|
||||||
|
except Exception as e:
|
||||||
|
return jsonify({'error': str(e)}), 500
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
port = int(os.environ.get('WIKI_CHAT_PORT', 8098))
|
||||||
|
print(f'[WikiChat] LLM proxy running on port {port}')
|
||||||
|
app.run(host='0.0.0.0', port=port)
|
||||||
503
wiki-vector-chat.py
Normal file
503
wiki-vector-chat.py
Normal file
@@ -0,0 +1,503 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Wiki VectorDB Chat — Multi-Provider AI Chat with RAG (KB + VectorDB).
|
||||||
|
|
||||||
|
Serves at port 8770, proxied via nginx at /zportal/wiki/api/chat
|
||||||
|
Uses wiki-api (:8097) for KB search and vector-db (:8099) for vector search.
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import re
|
||||||
|
import time
|
||||||
|
import urllib.request
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
PROVIDERS_FILE = Path("/opt/blog/wiki-chat-providers.json")
|
||||||
|
CUSTOM_PROVIDERS_FILE = Path("/opt/blog/wiki-chat-providers.json")
|
||||||
|
|
||||||
|
WIKI_API = "http://127.0.0.1:8097"
|
||||||
|
VECTOR_DB = "http://127.0.0.1:8099"
|
||||||
|
|
||||||
|
# Shared API token for wiki-api and vector-db
|
||||||
|
_API_TOKEN = ""
|
||||||
|
try:
|
||||||
|
_API_TOKEN = Path("/opt/blog/.wiki-api-token").read_text().strip()
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
PRESETS = [
|
||||||
|
{
|
||||||
|
"id": "zai-coding",
|
||||||
|
"name": "Z.ai Coding Plan",
|
||||||
|
"base_url": "https://api.z.ai/api/coding/paas/v4",
|
||||||
|
"model": "glm-4-plus",
|
||||||
|
"format": "openai",
|
||||||
|
"icon": "\u26a1",
|
||||||
|
"description": "Official Z.ai coding plan API",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "openadapter",
|
||||||
|
"name": "OpenAdapter",
|
||||||
|
"base_url": "https://api.openadapter.com/v1",
|
||||||
|
"model": "gpt-4o-mini",
|
||||||
|
"format": "openai",
|
||||||
|
"icon": "\u1f512",
|
||||||
|
"description": "OpenAdapter unified API",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "openrouter",
|
||||||
|
"name": "OpenRouter",
|
||||||
|
"base_url": "https://openrouter.ai/api/v1",
|
||||||
|
"model": "anthropic/claude-sonnet-4",
|
||||||
|
"format": "openrouter",
|
||||||
|
"icon": "\u1f6e3",
|
||||||
|
"description": "Model router across providers",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "crofai",
|
||||||
|
"name": "Crof.AI",
|
||||||
|
"base_url": "https://api.crof.ai/v1",
|
||||||
|
"model": "crof-4-plus",
|
||||||
|
"format": "openai",
|
||||||
|
"icon": "\u1f42a",
|
||||||
|
"description": "Crof AI models",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "opencode-zen",
|
||||||
|
"name": "Opencode Zen",
|
||||||
|
"base_url": "https://api.zen.opencode.com/v1",
|
||||||
|
"model": "glm-4-plus",
|
||||||
|
"format": "openai",
|
||||||
|
"icon": "\u1f9e0",
|
||||||
|
"description": "Opencode Zen hosted models",
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def load_custom_providers():
|
||||||
|
try:
|
||||||
|
if CUSTOM_PROVIDERS_FILE.exists():
|
||||||
|
return json.loads(CUSTOM_PROVIDERS_FILE.read_text())
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
return []
|
||||||
|
|
||||||
|
|
||||||
|
def save_custom_providers(providers):
|
||||||
|
CUSTOM_PROVIDERS_FILE.write_text(json.dumps(providers, indent=2))
|
||||||
|
|
||||||
|
|
||||||
|
def get_all_providers():
|
||||||
|
"""Return presets + custom providers."""
|
||||||
|
custom = load_custom_providers()
|
||||||
|
seen = {p["id"] for p in PRESETS}
|
||||||
|
result = list(PRESETS)
|
||||||
|
for p in custom:
|
||||||
|
if p.get("id") not in seen:
|
||||||
|
seen.add(p["id"])
|
||||||
|
result.append(p)
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
def detect_provider_format(base_url: str) -> str:
|
||||||
|
from urllib.parse import urlparse
|
||||||
|
host = urlparse(base_url).hostname.lower()
|
||||||
|
if "ollama" in host or host in ("localhost", "127.0.0.1"):
|
||||||
|
return "ollama"
|
||||||
|
if "anthropic" in host:
|
||||||
|
return "anthropic"
|
||||||
|
if "openrouter" in host:
|
||||||
|
return "openrouter"
|
||||||
|
if "groq" in host:
|
||||||
|
return "groq"
|
||||||
|
return "openai"
|
||||||
|
|
||||||
|
|
||||||
|
async def search_kb(query: str, limit: int = 3) -> str:
|
||||||
|
"""Search wiki-kb.json via wiki-api."""
|
||||||
|
try:
|
||||||
|
url = f"{WIKI_API}/search?q={urllib.parse.quote(query)}&limit={limit}&token={_API_TOKEN}"
|
||||||
|
req = urllib.request.Request(url)
|
||||||
|
with urllib.request.urlopen(req, timeout=5) as resp:
|
||||||
|
data = json.loads(resp.read())
|
||||||
|
results = data.get("results", [])
|
||||||
|
if not results:
|
||||||
|
return ""
|
||||||
|
lines = []
|
||||||
|
for r in results[:limit]:
|
||||||
|
q_text = r.get("q", "")
|
||||||
|
a_text = r.get("a", "")
|
||||||
|
topic = r.get("topic", "")
|
||||||
|
score = r.get("_score", 0)
|
||||||
|
lines.append(f"[{topic}] Q: {q_text}\nA: {a_text}")
|
||||||
|
return "\n\n".join(lines)
|
||||||
|
except Exception as e:
|
||||||
|
return f"(KB search error: {e})"
|
||||||
|
|
||||||
|
|
||||||
|
async def search_vector(query: str, top_k: int = 5) -> str:
|
||||||
|
"""Search vector-db for related Discord/Reddit messages."""
|
||||||
|
try:
|
||||||
|
data = json.dumps({"query": query, "top_k": top_k}).encode()
|
||||||
|
req = urllib.request.Request(
|
||||||
|
f"{VECTOR_DB}/vector/search",
|
||||||
|
data=data,
|
||||||
|
headers={"Content-Type": "application/json", "x-api-key": _API_TOKEN},
|
||||||
|
)
|
||||||
|
with urllib.request.urlopen(req, timeout=8) as resp:
|
||||||
|
result = json.loads(resp.read())
|
||||||
|
hits = result.get("results", [])
|
||||||
|
if not hits:
|
||||||
|
return ""
|
||||||
|
lines = []
|
||||||
|
for h in hits[:top_k]:
|
||||||
|
text = h.get("text", "")[:300]
|
||||||
|
score = h.get("score", 0)
|
||||||
|
source = h.get("source", "unknown")
|
||||||
|
meta = h.get("metadata", {})
|
||||||
|
author = meta.get("author", "")
|
||||||
|
channel = meta.get("channel", "")
|
||||||
|
preview = text.replace("\n", " ")[:200]
|
||||||
|
lines.append(f"[{source}] @{author} in #{channel}: {preview} (score: {score:.2f})")
|
||||||
|
return "\n\n".join(lines)
|
||||||
|
except Exception as e:
|
||||||
|
return f"(Vector search error: {e})"
|
||||||
|
|
||||||
|
|
||||||
|
async def build_rag_context(user_message: str, rag_wiki: bool = True, rag_vector: bool = True) -> str:
|
||||||
|
"""Build RAG context from KB + VectorDB searches."""
|
||||||
|
kb_results = ""
|
||||||
|
vec_results = ""
|
||||||
|
tasks = []
|
||||||
|
if rag_wiki:
|
||||||
|
tasks.append(search_kb(user_message, 3))
|
||||||
|
if rag_vector:
|
||||||
|
tasks.append(search_vector(user_message, 5))
|
||||||
|
|
||||||
|
if tasks:
|
||||||
|
results = await asyncio.gather(*tasks)
|
||||||
|
idx = 0
|
||||||
|
if rag_wiki:
|
||||||
|
kb_results = results[idx]; idx += 1
|
||||||
|
if rag_vector:
|
||||||
|
vec_results = results[idx]
|
||||||
|
|
||||||
|
parts = [
|
||||||
|
"You are Z.ai Wiki Assistant. Use ALL the knowledge sources below to answer the user's question.",
|
||||||
|
"Draw from both the Wiki KB and Community Messages. Synthesize information even from partial matches.",
|
||||||
|
"If the context mentions anything relevant, include it in your answer. Be specific — quote authors, channels, and details when available.",
|
||||||
|
"Only say you don't have information if the sources are truly empty or completely unrelated.",
|
||||||
|
"",
|
||||||
|
]
|
||||||
|
if rag_wiki:
|
||||||
|
parts += ["=== Wiki Knowledge Base ===", kb_results or "(no KB results found)", ""]
|
||||||
|
if rag_vector:
|
||||||
|
parts += ["=== Related Community Messages (Discord/Reddit) ===", vec_results or "(no community messages found)"]
|
||||||
|
if not rag_wiki and not rag_vector:
|
||||||
|
parts.append("(RAG sources disabled for this session)")
|
||||||
|
return "\n".join(parts)
|
||||||
|
|
||||||
|
|
||||||
|
# ── LLM Provider Calls ──
|
||||||
|
|
||||||
|
def format_messages_openai(system: str, messages: list, model: str) -> dict:
|
||||||
|
"""Format for OpenAI-compatible /chat/completions endpoint."""
|
||||||
|
return {"model": model, "messages": [{"role": "system", "content": system}] + messages,
|
||||||
|
"temperature": 0.7, "max_tokens": 2048, "stream": True}
|
||||||
|
|
||||||
|
|
||||||
|
def format_messages_anthropic(system: str, messages: list, model: str) -> dict:
|
||||||
|
"""Convert OpenAI-format messages to Anthropic format."""
|
||||||
|
anthropic_msgs = []
|
||||||
|
for m in messages:
|
||||||
|
role = "user" if m["role"] == "user" else "assistant"
|
||||||
|
anthropic_msgs.append({"role": role, "content": m["content"]})
|
||||||
|
return {"model": model, "system": system, "messages": anthropic_msgs,
|
||||||
|
"max_tokens": 2048, "stream": True}
|
||||||
|
|
||||||
|
|
||||||
|
def format_messages_ollama(system: str, messages: list, model: str) -> dict:
|
||||||
|
"""Format for Ollama /api/chat endpoint."""
|
||||||
|
ollama_msgs = []
|
||||||
|
for m in messages:
|
||||||
|
role = "user" if m["role"] == "user" else "assistant"
|
||||||
|
ollama_msgs.append({"role": role, "content": m["content"]})
|
||||||
|
return {"model": model, "messages": ollama_msgs, "stream": True}
|
||||||
|
|
||||||
|
|
||||||
|
async def call_llm_stream(provider: dict, system: str, messages: list):
|
||||||
|
"""Call LLM provider and yield SSE delta chunks."""
|
||||||
|
base_url = provider["base_url"].rstrip("/")
|
||||||
|
fmt = provider.get("format", detect_provider_format(base_url))
|
||||||
|
api_key = provider.get("api_key", "")
|
||||||
|
model = provider.get("model", "gpt-4o-mini")
|
||||||
|
|
||||||
|
if fmt == "anthropic":
|
||||||
|
payload = format_messages_anthropic(system, messages, model)
|
||||||
|
url = f"{base_url}/v1/messages"
|
||||||
|
headers = {"x-api-key": api_key, "Content-Type": "application/json",
|
||||||
|
"anthropic-version": "2023-06-01"}
|
||||||
|
elif fmt == "ollama":
|
||||||
|
payload = format_messages_ollama(system, messages, model)
|
||||||
|
url = f"{base_url}/api/chat"
|
||||||
|
headers = {"Content-Type": "application/json"}
|
||||||
|
else:
|
||||||
|
# openai / openrouter / groq / default
|
||||||
|
payload = format_messages_openai(system, messages, model)
|
||||||
|
url = f"{base_url}/chat/completions"
|
||||||
|
headers = {}
|
||||||
|
if api_key:
|
||||||
|
headers["Authorization"] = f"Bearer {api_key}"
|
||||||
|
if fmt == "openrouter":
|
||||||
|
headers["HTTP-OpenRouter-AI-Model"] = model
|
||||||
|
headers["Content-Type"] = "application/json"
|
||||||
|
|
||||||
|
data = json.dumps(payload).encode()
|
||||||
|
req = urllib.request.Request(url, data=data, headers=headers)
|
||||||
|
|
||||||
|
try:
|
||||||
|
with urllib.request.urlopen(req, timeout=60) as resp:
|
||||||
|
reader = resp
|
||||||
|
buf = b""
|
||||||
|
while True:
|
||||||
|
chunk = reader.read(4096)
|
||||||
|
if not chunk:
|
||||||
|
break
|
||||||
|
buf += chunk
|
||||||
|
while b"\n" in buf:
|
||||||
|
line, buf = buf.split(b"\n", 1)
|
||||||
|
line = line.decode("utf-8", errors="replace").strip()
|
||||||
|
if not line:
|
||||||
|
continue
|
||||||
|
if line.startswith("data: "):
|
||||||
|
data_str = line[5:].strip()
|
||||||
|
if data_str == "[DONE]":
|
||||||
|
yield {"type": "done"}
|
||||||
|
return
|
||||||
|
try:
|
||||||
|
chunk_data = json.loads(data_str)
|
||||||
|
except json.JSONDecodeError:
|
||||||
|
yield {"delta": data_str, "type": "raw"}
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Anthropic SSE format
|
||||||
|
if fmt == "anthropic":
|
||||||
|
evt_type = chunk_data.get("type", "")
|
||||||
|
if evt_type == "content_block_delta":
|
||||||
|
text = chunk_data.get("delta", {}).get("text", "")
|
||||||
|
if text:
|
||||||
|
yield {"delta": text, "type": "delta"}
|
||||||
|
elif evt_type == "message_stop":
|
||||||
|
yield {"type": "done"}
|
||||||
|
return
|
||||||
|
elif evt_type == "error":
|
||||||
|
err_msg = chunk_data.get("error", {}).get("message", str(chunk_data))
|
||||||
|
yield {"type": "error", "delta": err_msg}
|
||||||
|
return
|
||||||
|
continue
|
||||||
|
|
||||||
|
# OpenAI-compatible SSE format
|
||||||
|
deltas = chunk_data.get("choices", [{}])[0].get("delta", {})
|
||||||
|
content = deltas.get("content", "")
|
||||||
|
if content:
|
||||||
|
yield {"delta": content, "type": "delta"}
|
||||||
|
tool_calls = deltas.get("tool_calls")
|
||||||
|
if tool_calls:
|
||||||
|
names = [tc.get("function", {}).get("name", "?") for tc in tool_calls]
|
||||||
|
yield {"delta": f"\n[Using tools: {', '.join(names)}]", "type": "tool"}
|
||||||
|
finish = chunk_data.get("finish_reason")
|
||||||
|
if finish:
|
||||||
|
yield {"type": "done"}
|
||||||
|
elif line.startswith("event:"):
|
||||||
|
pass # SSE event name, skip
|
||||||
|
except urllib.error.HTTPError as e:
|
||||||
|
body = e.read().decode("utf-8", errors="replace")[:500]
|
||||||
|
yield {"type": "error", "delta": f"HTTP {e.code}: {body}"}
|
||||||
|
except Exception as e:
|
||||||
|
yield {"type": "error", "delta": str(e)}
|
||||||
|
|
||||||
|
|
||||||
|
# ── FastAPI App ──
|
||||||
|
|
||||||
|
try:
|
||||||
|
from fastapi import FastAPI
|
||||||
|
from fastapi.responses import StreamingResponse, JSONResponse, Response
|
||||||
|
from pydantic import BaseModel
|
||||||
|
except ImportError:
|
||||||
|
print("Installing fastapi...")
|
||||||
|
os.system("pip install fastapi uvicorn httpx -q")
|
||||||
|
from fastapi import FastAPI
|
||||||
|
from fastapi.responses import StreamingResponse, JSONResponse, Response
|
||||||
|
from pydantic import BaseModel
|
||||||
|
|
||||||
|
app = FastAPI(title="Wiki VectorDB Chat")
|
||||||
|
|
||||||
|
|
||||||
|
class ChatMessage(BaseModel):
|
||||||
|
message: str
|
||||||
|
provider_id: str = "zai-coding"
|
||||||
|
history: list = []
|
||||||
|
rag_wiki: bool = True
|
||||||
|
rag_vector: bool = True
|
||||||
|
mode: str = "chat"
|
||||||
|
|
||||||
|
|
||||||
|
class ProviderSave(BaseModel):
|
||||||
|
id: str
|
||||||
|
name: str
|
||||||
|
base_url: str
|
||||||
|
model: str
|
||||||
|
api_key: str = ""
|
||||||
|
format: str = "openai"
|
||||||
|
icon: str = "\u2b99"
|
||||||
|
description: str = ""
|
||||||
|
|
||||||
|
|
||||||
|
@app.get("/providers/presets")
|
||||||
|
async def get_presets():
|
||||||
|
return PRESETS
|
||||||
|
|
||||||
|
|
||||||
|
@app.get("/providers")
|
||||||
|
async def list_providers():
|
||||||
|
return get_all_providers()
|
||||||
|
|
||||||
|
|
||||||
|
@app.post("/providers/save")
|
||||||
|
async def save_provider(p: ProviderSave):
|
||||||
|
custom = load_custom_providers()
|
||||||
|
p_dict = p.model_dump()
|
||||||
|
# Update or append
|
||||||
|
found = False
|
||||||
|
for i, existing in enumerate(custom):
|
||||||
|
if existing.get("id") == p.id:
|
||||||
|
custom[i] = p_dict
|
||||||
|
found = True
|
||||||
|
break
|
||||||
|
if not found:
|
||||||
|
custom.append(p_dict)
|
||||||
|
save_custom_providers(custom)
|
||||||
|
return {"ok": True, "provider": p_dict}
|
||||||
|
|
||||||
|
|
||||||
|
@app.delete("/providers/{provider_id}")
|
||||||
|
async def delete_provider(provider_id: str):
|
||||||
|
custom = load_custom_providers()
|
||||||
|
custom = [p for p in custom if p.get("id") != provider_id]
|
||||||
|
save_custom_providers(custom)
|
||||||
|
return {"ok": True}
|
||||||
|
|
||||||
|
|
||||||
|
@app.post("/chat/message")
|
||||||
|
async def chat_message(msg: ChatMessage):
|
||||||
|
async def generate():
|
||||||
|
providers = get_all_providers()
|
||||||
|
provider = next((p for p in providers if p.get("id") == msg.provider_id), None)
|
||||||
|
if not provider:
|
||||||
|
yield f"data: {json.dumps({'type':'error','delta':'Provider not found'})}\n\n"
|
||||||
|
return
|
||||||
|
|
||||||
|
# Build conversation history
|
||||||
|
messages = []
|
||||||
|
for h in msg.history[-10:]:
|
||||||
|
messages.append(h)
|
||||||
|
|
||||||
|
messages.append({"role": "user", "content": msg.message})
|
||||||
|
|
||||||
|
# Build RAG context with per-session toggles
|
||||||
|
rag_context = await build_rag_context(msg.message, msg.rag_wiki, msg.rag_vector)
|
||||||
|
|
||||||
|
# Mode-specific system prompt additions
|
||||||
|
mode_hints = {
|
||||||
|
"chat": "",
|
||||||
|
"code": "\n\nMODE: Coding. The user is working on code. Provide precise, well-structured code examples with explanations. Use markdown code blocks. Be concise and technical.",
|
||||||
|
"brain": "\n\nMODE: Brainstorm. The user wants creative exploration. Think freely, offer multiple perspectives, suggest unconventional approaches. Be enthusiastic and expansive.",
|
||||||
|
}
|
||||||
|
system_prompt = rag_context + mode_hints.get(msg.mode, "")
|
||||||
|
|
||||||
|
async for chunk in call_llm_stream(provider, system_prompt, messages):
|
||||||
|
data = json.dumps(chunk, ensure_ascii=False)
|
||||||
|
yield f"data: {data}\n\n"
|
||||||
|
yield f"data: {json.dumps({'type':'done'})}\n\n"
|
||||||
|
|
||||||
|
return StreamingResponse(generate(), media_type="text/event-stream",
|
||||||
|
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
|
||||||
|
|
||||||
|
|
||||||
|
@app.post("/chat/tunnel")
|
||||||
|
async def chat_tunnel(msg: ChatMessage):
|
||||||
|
"""Server-side token chat — uses ZAI_API_TOKEN env var if available."""
|
||||||
|
async def generate():
|
||||||
|
providers = get_all_providers()
|
||||||
|
provider = next((p for p in providers if p.get("id") == msg.provider_id), None)
|
||||||
|
if not provider:
|
||||||
|
yield f"data: {json.dumps({'type':'error','delta':'Provider not found'})}\n\n"
|
||||||
|
return
|
||||||
|
|
||||||
|
# Use server-side token if available (for tunnel mode)
|
||||||
|
token = os.environ.get("ZAI_API_TOKEN", "")
|
||||||
|
if token and not provider.get("api_key"):
|
||||||
|
provider = dict(provider)
|
||||||
|
provider["api_key"] = token
|
||||||
|
|
||||||
|
messages = []
|
||||||
|
for h in msg.history[-10:]:
|
||||||
|
messages.append(h)
|
||||||
|
messages.append({"role": "user", "content": msg.message})
|
||||||
|
|
||||||
|
rag_context = await build_rag_context(msg.message)
|
||||||
|
|
||||||
|
async for chunk in call_llm_stream(provider, rag_context, messages):
|
||||||
|
data = json.dumps(chunk, ensure_ascii=False)
|
||||||
|
yield f"data: {data}\n\n"
|
||||||
|
yield f"data: {json.dumps({'type':'done'})}\n\n"
|
||||||
|
|
||||||
|
return StreamingResponse(generate(), media_type="text/event-stream",
|
||||||
|
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
|
||||||
|
|
||||||
|
|
||||||
|
@app.get("/health")
|
||||||
|
async def health():
|
||||||
|
return {"status": "ok", "providers": len(get_all_providers())}
|
||||||
|
|
||||||
|
|
||||||
|
class WikiSave(BaseModel):
|
||||||
|
question: str
|
||||||
|
answer: str
|
||||||
|
topic: str = "chat-saved"
|
||||||
|
|
||||||
|
|
||||||
|
@app.post("/chat/save-to-wiki")
|
||||||
|
async def save_to_wiki(item: WikiSave):
|
||||||
|
"""Save a Q&A pair directly to wiki-kb.json."""
|
||||||
|
try:
|
||||||
|
kb_path = Path("/opt/blog/wiki-kb.json")
|
||||||
|
kb = json.loads(kb_path.read_text())
|
||||||
|
entry = {
|
||||||
|
"q": item.question,
|
||||||
|
"a": item.answer,
|
||||||
|
"topic": item.topic,
|
||||||
|
"author": "chat-assistant",
|
||||||
|
"source": "chat-saved",
|
||||||
|
"timestamp": time.strftime("%Y-%m-%d %H:%M"),
|
||||||
|
}
|
||||||
|
kb.append(entry)
|
||||||
|
kb_path.write_text(json.dumps(kb, ensure_ascii=False, indent=2))
|
||||||
|
return {"ok": True, "total": len(kb)}
|
||||||
|
except Exception as e:
|
||||||
|
return {"ok": False, "error": str(e)}
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
import uvicorn
|
||||||
|
port = 8770
|
||||||
|
for i, arg in enumerate(__import__("sys").argv):
|
||||||
|
if arg == "--port" and i + 1 < len(__import__("sys").argv):
|
||||||
|
port = int(__import__("sys").argv[i + 1])
|
||||||
|
print(f"Wiki VectorDB Chat starting on port {port}")
|
||||||
|
uvicorn.run(app, host="127.0.0.1", port=port, log_level="warning")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
1473
zportal-chat.html
Normal file
1473
zportal-chat.html
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user