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>
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wiki-vector-chat.py
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wiki-vector-chat.py
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#!/usr/bin/env python3
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"""Wiki VectorDB Chat — Multi-Provider AI Chat with RAG (KB + VectorDB).
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Serves at port 8770, proxied via nginx at /zportal/wiki/api/chat
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Uses wiki-api (:8097) for KB search and vector-db (:8099) for vector search.
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"""
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import asyncio
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import json
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import os
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import re
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import time
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import urllib.request
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from pathlib import Path
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PROVIDERS_FILE = Path("/opt/blog/wiki-chat-providers.json")
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CUSTOM_PROVIDERS_FILE = Path("/opt/blog/wiki-chat-providers.json")
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WIKI_API = "http://127.0.0.1:8097"
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VECTOR_DB = "http://127.0.0.1:8099"
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# Shared API token for wiki-api and vector-db
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_API_TOKEN = ""
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try:
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_API_TOKEN = Path("/opt/blog/.wiki-api-token").read_text().strip()
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except Exception:
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pass
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PRESETS = [
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{
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"id": "zai-coding",
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"name": "Z.ai Coding Plan",
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"base_url": "https://api.z.ai/api/coding/paas/v4",
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"model": "glm-4-plus",
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"format": "openai",
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"icon": "\u26a1",
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"description": "Official Z.ai coding plan API",
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},
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{
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"id": "openadapter",
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"name": "OpenAdapter",
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"base_url": "https://api.openadapter.com/v1",
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"model": "gpt-4o-mini",
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"format": "openai",
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"icon": "\u1f512",
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"description": "OpenAdapter unified API",
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},
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{
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"id": "openrouter",
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"name": "OpenRouter",
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"base_url": "https://openrouter.ai/api/v1",
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"model": "anthropic/claude-sonnet-4",
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"format": "openrouter",
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"icon": "\u1f6e3",
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"description": "Model router across providers",
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},
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{
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"id": "crofai",
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"name": "Crof.AI",
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"base_url": "https://api.crof.ai/v1",
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"model": "crof-4-plus",
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"format": "openai",
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"icon": "\u1f42a",
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"description": "Crof AI models",
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},
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{
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"id": "opencode-zen",
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"name": "Opencode Zen",
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"base_url": "https://api.zen.opencode.com/v1",
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"model": "glm-4-plus",
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"format": "openai",
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"icon": "\u1f9e0",
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"description": "Opencode Zen hosted models",
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},
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]
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def load_custom_providers():
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try:
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if CUSTOM_PROVIDERS_FILE.exists():
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return json.loads(CUSTOM_PROVIDERS_FILE.read_text())
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except Exception:
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pass
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return []
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def save_custom_providers(providers):
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CUSTOM_PROVIDERS_FILE.write_text(json.dumps(providers, indent=2))
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def get_all_providers():
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"""Return presets + custom providers."""
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custom = load_custom_providers()
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seen = {p["id"] for p in PRESETS}
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result = list(PRESETS)
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for p in custom:
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if p.get("id") not in seen:
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seen.add(p["id"])
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result.append(p)
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return result
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def detect_provider_format(base_url: str) -> str:
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from urllib.parse import urlparse
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host = urlparse(base_url).hostname.lower()
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if "ollama" in host or host in ("localhost", "127.0.0.1"):
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return "ollama"
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if "anthropic" in host:
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return "anthropic"
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if "openrouter" in host:
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return "openrouter"
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if "groq" in host:
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return "groq"
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return "openai"
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async def search_kb(query: str, limit: int = 3) -> str:
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"""Search wiki-kb.json via wiki-api."""
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try:
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url = f"{WIKI_API}/search?q={urllib.parse.quote(query)}&limit={limit}&token={_API_TOKEN}"
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req = urllib.request.Request(url)
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with urllib.request.urlopen(req, timeout=5) as resp:
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data = json.loads(resp.read())
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results = data.get("results", [])
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if not results:
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return ""
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lines = []
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for r in results[:limit]:
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q_text = r.get("q", "")
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a_text = r.get("a", "")
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topic = r.get("topic", "")
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score = r.get("_score", 0)
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lines.append(f"[{topic}] Q: {q_text}\nA: {a_text}")
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return "\n\n".join(lines)
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except Exception as e:
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return f"(KB search error: {e})"
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async def search_vector(query: str, top_k: int = 5) -> str:
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"""Search vector-db for related Discord/Reddit messages."""
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try:
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data = json.dumps({"query": query, "top_k": top_k}).encode()
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req = urllib.request.Request(
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f"{VECTOR_DB}/vector/search",
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data=data,
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headers={"Content-Type": "application/json", "x-api-key": _API_TOKEN},
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)
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with urllib.request.urlopen(req, timeout=8) as resp:
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result = json.loads(resp.read())
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hits = result.get("results", [])
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if not hits:
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return ""
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lines = []
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for h in hits[:top_k]:
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text = h.get("text", "")[:300]
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score = h.get("score", 0)
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source = h.get("source", "unknown")
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meta = h.get("metadata", {})
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author = meta.get("author", "")
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channel = meta.get("channel", "")
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preview = text.replace("\n", " ")[:200]
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lines.append(f"[{source}] @{author} in #{channel}: {preview} (score: {score:.2f})")
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return "\n\n".join(lines)
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except Exception as e:
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return f"(Vector search error: {e})"
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async def build_rag_context(user_message: str, rag_wiki: bool = True, rag_vector: bool = True) -> str:
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"""Build RAG context from KB + VectorDB searches."""
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kb_results = ""
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vec_results = ""
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tasks = []
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if rag_wiki:
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tasks.append(search_kb(user_message, 3))
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if rag_vector:
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tasks.append(search_vector(user_message, 5))
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if tasks:
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results = await asyncio.gather(*tasks)
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idx = 0
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if rag_wiki:
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kb_results = results[idx]; idx += 1
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if rag_vector:
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vec_results = results[idx]
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parts = [
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"You are Z.ai Wiki Assistant. Use ALL the knowledge sources below to answer the user's question.",
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"Draw from both the Wiki KB and Community Messages. Synthesize information even from partial matches.",
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"If the context mentions anything relevant, include it in your answer. Be specific — quote authors, channels, and details when available.",
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"Only say you don't have information if the sources are truly empty or completely unrelated.",
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"",
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]
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if rag_wiki:
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parts += ["=== Wiki Knowledge Base ===", kb_results or "(no KB results found)", ""]
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if rag_vector:
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parts += ["=== Related Community Messages (Discord/Reddit) ===", vec_results or "(no community messages found)"]
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if not rag_wiki and not rag_vector:
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parts.append("(RAG sources disabled for this session)")
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return "\n".join(parts)
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# ── LLM Provider Calls ──
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def format_messages_openai(system: str, messages: list, model: str) -> dict:
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"""Format for OpenAI-compatible /chat/completions endpoint."""
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return {"model": model, "messages": [{"role": "system", "content": system}] + messages,
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"temperature": 0.7, "max_tokens": 2048, "stream": True}
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def format_messages_anthropic(system: str, messages: list, model: str) -> dict:
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"""Convert OpenAI-format messages to Anthropic format."""
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anthropic_msgs = []
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for m in messages:
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role = "user" if m["role"] == "user" else "assistant"
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anthropic_msgs.append({"role": role, "content": m["content"]})
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return {"model": model, "system": system, "messages": anthropic_msgs,
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"max_tokens": 2048, "stream": True}
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def format_messages_ollama(system: str, messages: list, model: str) -> dict:
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"""Format for Ollama /api/chat endpoint."""
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ollama_msgs = []
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for m in messages:
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role = "user" if m["role"] == "user" else "assistant"
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ollama_msgs.append({"role": role, "content": m["content"]})
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return {"model": model, "messages": ollama_msgs, "stream": True}
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async def call_llm_stream(provider: dict, system: str, messages: list):
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"""Call LLM provider and yield SSE delta chunks."""
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base_url = provider["base_url"].rstrip("/")
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fmt = provider.get("format", detect_provider_format(base_url))
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api_key = provider.get("api_key", "")
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model = provider.get("model", "gpt-4o-mini")
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if fmt == "anthropic":
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payload = format_messages_anthropic(system, messages, model)
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url = f"{base_url}/v1/messages"
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headers = {"x-api-key": api_key, "Content-Type": "application/json",
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"anthropic-version": "2023-06-01"}
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elif fmt == "ollama":
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payload = format_messages_ollama(system, messages, model)
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url = f"{base_url}/api/chat"
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headers = {"Content-Type": "application/json"}
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else:
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# openai / openrouter / groq / default
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payload = format_messages_openai(system, messages, model)
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url = f"{base_url}/chat/completions"
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headers = {}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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if fmt == "openrouter":
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headers["HTTP-OpenRouter-AI-Model"] = model
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headers["Content-Type"] = "application/json"
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data = json.dumps(payload).encode()
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req = urllib.request.Request(url, data=data, headers=headers)
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try:
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with urllib.request.urlopen(req, timeout=60) as resp:
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reader = resp
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buf = b""
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while True:
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chunk = reader.read(4096)
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if not chunk:
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break
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buf += chunk
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while b"\n" in buf:
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line, buf = buf.split(b"\n", 1)
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line = line.decode("utf-8", errors="replace").strip()
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if not line:
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continue
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if line.startswith("data: "):
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data_str = line[5:].strip()
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if data_str == "[DONE]":
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yield {"type": "done"}
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return
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try:
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chunk_data = json.loads(data_str)
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except json.JSONDecodeError:
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yield {"delta": data_str, "type": "raw"}
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continue
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# Anthropic SSE format
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if fmt == "anthropic":
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evt_type = chunk_data.get("type", "")
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if evt_type == "content_block_delta":
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text = chunk_data.get("delta", {}).get("text", "")
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if text:
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yield {"delta": text, "type": "delta"}
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elif evt_type == "message_stop":
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yield {"type": "done"}
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return
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elif evt_type == "error":
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err_msg = chunk_data.get("error", {}).get("message", str(chunk_data))
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yield {"type": "error", "delta": err_msg}
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return
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continue
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# OpenAI-compatible SSE format
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deltas = chunk_data.get("choices", [{}])[0].get("delta", {})
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content = deltas.get("content", "")
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if content:
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yield {"delta": content, "type": "delta"}
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tool_calls = deltas.get("tool_calls")
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if tool_calls:
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names = [tc.get("function", {}).get("name", "?") for tc in tool_calls]
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yield {"delta": f"\n[Using tools: {', '.join(names)}]", "type": "tool"}
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finish = chunk_data.get("finish_reason")
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if finish:
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yield {"type": "done"}
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elif line.startswith("event:"):
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pass # SSE event name, skip
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except urllib.error.HTTPError as e:
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body = e.read().decode("utf-8", errors="replace")[:500]
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yield {"type": "error", "delta": f"HTTP {e.code}: {body}"}
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except Exception as e:
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yield {"type": "error", "delta": str(e)}
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# ── FastAPI App ──
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try:
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from fastapi import FastAPI
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from fastapi.responses import StreamingResponse, JSONResponse, Response
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from pydantic import BaseModel
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except ImportError:
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print("Installing fastapi...")
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os.system("pip install fastapi uvicorn httpx -q")
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from fastapi import FastAPI
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from fastapi.responses import StreamingResponse, JSONResponse, Response
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from pydantic import BaseModel
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app = FastAPI(title="Wiki VectorDB Chat")
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class ChatMessage(BaseModel):
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message: str
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provider_id: str = "zai-coding"
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history: list = []
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rag_wiki: bool = True
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rag_vector: bool = True
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mode: str = "chat"
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class ProviderSave(BaseModel):
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id: str
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name: str
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base_url: str
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model: str
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api_key: str = ""
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format: str = "openai"
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icon: str = "\u2b99"
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description: str = ""
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@app.get("/providers/presets")
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async def get_presets():
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return PRESETS
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@app.get("/providers")
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async def list_providers():
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return get_all_providers()
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@app.post("/providers/save")
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async def save_provider(p: ProviderSave):
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custom = load_custom_providers()
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p_dict = p.model_dump()
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# Update or append
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found = False
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for i, existing in enumerate(custom):
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if existing.get("id") == p.id:
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custom[i] = p_dict
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found = True
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break
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if not found:
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custom.append(p_dict)
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save_custom_providers(custom)
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return {"ok": True, "provider": p_dict}
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@app.delete("/providers/{provider_id}")
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async def delete_provider(provider_id: str):
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custom = load_custom_providers()
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custom = [p for p in custom if p.get("id") != provider_id]
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save_custom_providers(custom)
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return {"ok": True}
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@app.post("/chat/message")
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async def chat_message(msg: ChatMessage):
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async def generate():
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providers = get_all_providers()
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provider = next((p for p in providers if p.get("id") == msg.provider_id), None)
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if not provider:
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yield f"data: {json.dumps({'type':'error','delta':'Provider not found'})}\n\n"
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return
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# Build conversation history
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messages = []
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for h in msg.history[-10:]:
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messages.append(h)
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messages.append({"role": "user", "content": msg.message})
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# Build RAG context with per-session toggles
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rag_context = await build_rag_context(msg.message, msg.rag_wiki, msg.rag_vector)
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# Mode-specific system prompt additions
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mode_hints = {
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"chat": "",
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"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.",
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"brain": "\n\nMODE: Brainstorm. The user wants creative exploration. Think freely, offer multiple perspectives, suggest unconventional approaches. Be enthusiastic and expansive.",
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}
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system_prompt = rag_context + mode_hints.get(msg.mode, "")
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async for chunk in call_llm_stream(provider, system_prompt, messages):
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data = json.dumps(chunk, ensure_ascii=False)
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yield f"data: {data}\n\n"
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yield f"data: {json.dumps({'type':'done'})}\n\n"
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return StreamingResponse(generate(), media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
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@app.post("/chat/tunnel")
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async def chat_tunnel(msg: ChatMessage):
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"""Server-side token chat — uses ZAI_API_TOKEN env var if available."""
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async def generate():
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providers = get_all_providers()
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provider = next((p for p in providers if p.get("id") == msg.provider_id), None)
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if not provider:
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yield f"data: {json.dumps({'type':'error','delta':'Provider not found'})}\n\n"
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return
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# Use server-side token if available (for tunnel mode)
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token = os.environ.get("ZAI_API_TOKEN", "")
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if token and not provider.get("api_key"):
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provider = dict(provider)
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provider["api_key"] = token
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messages = []
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for h in msg.history[-10:]:
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messages.append(h)
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messages.append({"role": "user", "content": msg.message})
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rag_context = await build_rag_context(msg.message)
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async for chunk in call_llm_stream(provider, rag_context, messages):
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data = json.dumps(chunk, ensure_ascii=False)
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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()
|
||||
Reference in New Issue
Block a user