34061a4ac1
- 10 MCP tools via thin proxy on MacBook - Backend REST API + Dashboard on CT 145 Docker - Services: YaCy crawler, OpenSearch index, Qdrant vectors, Ollama LLM - All 4 services healthy and verified
469 lines
24 KiB
Python
469 lines
24 KiB
Python
#!/usr/bin/env python3
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"""
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AI Research Engine — Backend API
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Runs on CT 145, handles all heavy lifting.
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Exposes REST API consumed by the MCP proxy (MacBook) and dashboard.
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"""
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import os
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import json
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import httpx
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from fastapi import FastAPI, Query, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import HTMLResponse
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import uvicorn
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app = FastAPI(title="AI Research Engine Backend")
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
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# ── Config ──────────────────────────────────────────────────
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YACY_URL = os.getenv("YACY_URL", "http://localhost:8090")
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OPENSEARCH_URL = os.getenv("OPENSEARCH_URL", "http://localhost:9200")
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QDRANT_URL = os.getenv("QDRANT_URL", "http://10.30.20.68:6333")
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OLLAMA_URL = os.getenv("OLLAMA_URL", "http://10.30.20.186:11434")
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OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "ornith:latest")
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INDEX_NAME = os.getenv("INDEX_NAME", "research_docs")
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client = httpx.Client(timeout=30.0)
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ollama = httpx.Client(timeout=120.0, base_url=OLLAMA_URL)
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# ── Helpers ──────────────────────────────────────────────────
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def _ensure_index():
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try:
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client.get(f"{OPENSEARCH_URL}/{INDEX_NAME}")
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except Exception:
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try:
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client.put(f"{OPENSEARCH_URL}/{INDEX_NAME}", json={
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"settings": {"number_of_shards": 1, "number_of_replicas": 0},
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"mappings": {"properties": {
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"url": {"type": "keyword"}, "title": {"type": "text"},
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"content": {"type": "text"}, "excerpt": {"type": "text"},
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"category": {"type": "keyword"}, "source_domain": {"type": "keyword"},
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"crawled_at": {"type": "date"}, "indexed_at": {"type": "date"},
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"metadata": {"type": "object"},
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}}
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})
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except Exception:
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pass
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def _ensure_qdrant():
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try:
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client.get(f"{QDRANT_URL}/collections/{INDEX_NAME}")
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except Exception:
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try:
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client.put(f"{QDRANT_URL}/collections/{INDEX_NAME}", json={
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"vectors": {"size": 768, "distance": "Cosine"}
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})
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except Exception:
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pass
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def _ai_chat(prompt: str, system: str = "You are a research assistant. Be concise and factual.") -> str:
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r = ollama.post("/api/chat", json={
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"model": OLLAMA_MODEL, "messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": prompt},
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], "stream": False,
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"options": {"temperature": 0.3, "num_predict": 2048},
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})
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body = r.json()
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return body.get("message", {}).get("content", "") or body.get("thinking", "") or ""
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def _get_embedding(text: str) -> list:
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try:
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r = ollama.post("/api/embeddings", json={"model": "nomic-embed-text-v2-moe:latest", "prompt": text[:2048]})
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return r.json().get("embedding", [])
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except Exception:
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return []
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# ── Status ────────────────────────────────────────────────────
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@app.get("/health")
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def health():
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return {"status": "ok"}
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@app.get("/api/status")
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def status():
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svc = {}
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try:
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r = client.get(f"{OPENSEARCH_URL}/_cluster/health")
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cnt = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_count").json() if r.status_code == 200 else {"count": 0}
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svc["opensearch"] = {"status": r.json().get("status", "down"), "documents": cnt.get("count", 0)}
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except Exception:
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svc["opensearch"] = {"status": "down"}
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try:
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r = client.get(f"{QDRANT_URL}/healthz")
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col = client.get(f"{QDRANT_URL}/collections/{INDEX_NAME}").json()
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svc["qdrant"] = {"status": "ok", "points": col.get("result", {}).get("points_count", 0)}
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except Exception:
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svc["qdrant"] = {"status": "down"}
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try:
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r = client.get(f"{YACY_URL}/yacysearch.json", params={"query": "test", "maximumRecords": 1})
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svc["yacy"] = {"status": "ok" if r.status_code == 200 else "down"}
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except Exception:
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svc["yacy"] = {"status": "down"}
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try:
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r = ollama.get("/api/tags")
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svc["ollama"] = {"status": "ok", "models": len(r.json().get("models", []))}
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except Exception:
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svc["ollama"] = {"status": "down"}
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return {"services": svc}
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# ── Search ────────────────────────────────────────────────────
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@app.get("/api/search")
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def search_web(q: str = Query(...), category: str = "", limit: int = 10):
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_ensure_index()
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body = {
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"size": limit,
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"query": {"bool": {"must": [{"multi_match": {"query": q, "fields": ["title^3", "content", "excerpt"]}}]}},
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"highlight": {"fields": {"content": {"fragment_size": 200, "number_of_fragments": 2}}},
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}
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if category:
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body["query"]["bool"]["filter"] = [{"term": {"category": category}}]
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try:
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r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json=body, params={"refresh": "true"})
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result = r.json()
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hits = []
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for h in result.get("hits", {}).get("hits", []):
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src = h["_source"]
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hits.append({"url": src.get("url"), "title": src.get("title"),
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"excerpt": src.get("excerpt") or (h.get("highlight", {}).get("content", [""])[0]),
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"category": src.get("category"), "crawled_at": src.get("crawled_at"), "score": h["_score"]})
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return {"query": q, "total": result.get("hits", {}).get("total", {}).get("value", 0), "hits": hits}
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except Exception as e:
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return {"query": q, "total": 0, "hits": [], "note": f"Index may be empty. {e}"}
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@app.get("/api/semantic-search")
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def semantic_search(q: str = Query(...), limit: int = 10):
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_ensure_qdrant()
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emb = _get_embedding(q)
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if not emb:
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return {"hits": [], "error": "Embedding model not available"}
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try:
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r = client.post(f"{QDRANT_URL}/collections/{INDEX_NAME}/points/search", json={
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"vector": emb, "limit": limit, "with_payload": True, "with_vector": False})
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hits = [{"url": p.get("payload", {}).get("url"), "title": p.get("payload", {}).get("title"),
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"excerpt": str(p.get("payload", {}).get("excerpt", ""))[:300], "score": p.get("score")}
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for p in r.json().get("result", [])]
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return {"query": q, "total": len(hits), "hits": hits}
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except Exception as e:
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return {"hits": [], "error": str(e)}
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# ── Crawl ─────────────────────────────────────────────────────
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@app.get("/api/crawl")
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def crawl_url(url: str = Query(...), depth: int = 1):
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try:
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r = client.get(f"{YACY_URL}/Crawler_p.json", params={
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"crawlingDomMaxPages": 50, "crawlingDepth": depth,
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"crawlingStart": url, "crawlingQ": "on",
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"bookmarkTitle": "research", "bookmarkFolder": "/research",
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"indexText": "on", "indexMedia": "on",
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"crawlingMode": "url", "cachePolicy": "iffresh",
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})
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return {"status": "crawl_started", "url": url, "depth": depth}
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except Exception as e:
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return {"status": "error", "url": url, "error": str(e)}
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@app.get("/api/crawl-topic")
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def crawl_topic(topic: str = Query(...), max_urls: int = 20):
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discovered = []
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try:
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r = client.get(f"{YACY_URL}/yacysearch.json", params={"query": topic, "maximumRecords": max_urls, "resource": "global"})
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for ch in r.json().get("channels", []):
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for item in ch.get("items", []):
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if item.get("link"):
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discovered.append({"url": item["link"], "title": item.get("title", ""), "snippet": item.get("description", "")})
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except Exception as e:
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return {"topic": topic, "error": f"Discovery failed: {e}", "urls_discovered": 0, "urls_crawled": 0}
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crawled = 0
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for u in discovered[:max_urls]:
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try:
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client.get(f"{YACY_URL}/Crawler_p.json", params={
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"crawlingDomMaxPages": 10, "crawlingDepth": 0,
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"crawlingStart": u["url"], "crawlingQ": "on",
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"indexText": "on", "indexMedia": "on",
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"crawlingMode": "url", "cachePolicy": "iffresh",
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}, timeout=5.0)
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crawled += 1
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except Exception:
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pass
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return {"topic": topic, "urls_discovered": len(discovered), "urls_crawled": crawled}
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# ── Document Retrieval ────────────────────────────────────────
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@app.get("/api/document")
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def retrieve_document(url: str = Query(...)):
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try:
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r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={
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"size": 1, "query": {"term": {"url": url}}})
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hits = r.json().get("hits", {}).get("hits", [])
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if not hits:
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return {"error": "Not found", "url": url}
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src = hits[0]["_source"]
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return {"url": src.get("url"), "title": src.get("title"),
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"content": src.get("content", "")[:10000], "excerpt": src.get("excerpt"),
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"category": src.get("category"), "crawled_at": src.get("crawled_at")}
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except Exception as e:
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return {"error": str(e), "url": url}
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# ── AI Synthesis ──────────────────────────────────────────────
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@app.get("/api/summarize")
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def summarize_sources(urls: str = Query(...), instruction: str = "Summarize key points"):
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url_list = [u.strip() for u in urls.split(",") if u.strip()]
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combined = ""
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for url in url_list[:5]:
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try:
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r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={"size": 1, "query": {"term": {"url": url}}})
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hits = r.json().get("hits", {}).get("hits", [])
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if hits:
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src = hits[0]["_source"]
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combined += f"\n\n--- {url} ---\n{src.get('title','')}\n{src.get('content', src.get('excerpt',''))[:2000]}"
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except Exception:
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pass
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if not combined.strip():
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return {"error": "No content retrieved"}
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summary = _ai_chat(f"Instruction: {instruction}\n\nSources:{combined}\n\nProvide a structured summary.")
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return {"instruction": instruction, "sources": len(url_list), "summary": summary}
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@app.get("/api/extract")
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def extract_information(url: str = Query(...), schema: str = Query("company names, products, prices")):
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try:
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r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={"size": 1, "query": {"term": {"url": url}}})
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hits = r.json().get("hits", {}).get("hits", [])
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if not hits:
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return {"error": "Not found", "url": url}
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content = hits[0]["_source"].get("content", "")[:8000]
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prompt = f"Extract: {schema}\n\nDocument:\n{content}\n\nReturn ONLY valid JSON."
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result = _ai_chat(prompt, system="Extract structured data. Return ONLY valid JSON. No explanation.")
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return {"url": url, "schema": schema, "extracted": result}
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except Exception as e:
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return {"error": str(e)}
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@app.get("/api/report")
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def create_report(topic: str = Query(...), sources: str = ""):
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if sources:
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urls = [u.strip() for u in sources.split(",") if u.strip()]
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else:
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try:
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r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={
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"size": 8, "query": {"multi_match": {"query": topic, "fields": ["title^3", "content"]}}})
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urls = [h["_source"]["url"] for h in r.json().get("hits", {}).get("hits", [])]
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except Exception:
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urls = []
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gathered = ""
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for url in urls[:8]:
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try:
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r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={"size": 1, "query": {"term": {"url": url}}})
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hits = r.json().get("hits", {}).get("hits", [])
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if hits:
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src = hits[0]["_source"]
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gathered += f"\n\n### {src.get('title','Source')}\nURL: {url}\n{src.get('content',src.get('excerpt',''))[:1500]}"
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except Exception:
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pass
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prompt = f"""Research topic: {topic}\nSources:{gathered if gathered else ' No sources found.'}
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Generate a comprehensive report:
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1. Executive Summary
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2. Key Findings (numbered)
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3. Source Analysis
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4. Knowledge Gaps
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5. Recommendations
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Be thorough, use markdown, cite sources."""
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report = _ai_chat(prompt, system="You are a senior research analyst. Produce thorough, structured reports.")
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return {"topic": topic, "sources_used": len(urls), "report": report}
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# ── Research Pipeline ─────────────────────────────────────────
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@app.get("/api/research")
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def research_topic(topic: str = Query(...)):
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steps = []
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kw_result = {"hits": []}
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sem_result = {"hits": []}
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try:
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r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={
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"size": 5, "query": {"multi_match": {"query": topic, "fields": ["title^3", "content", "excerpt"]}},
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"highlight": {"fields": {"content": {"fragment_size": 200, "number_of_fragments": 1}}},
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})
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hits = [{"title": h["_source"].get("title"), "excerpt": h.get("highlight", {}).get("content", [h["_source"].get("excerpt", "")])[0]}
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for h in r.json().get("hits", {}).get("hits", [])]
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kw_result = {"total": len(hits), "hits": hits}
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steps.append("keyword_search_done")
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except Exception:
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steps.append("keyword_search_skipped")
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try:
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emb = _get_embedding(topic)
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if emb:
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r = client.post(f"{QDRANT_URL}/collections/{INDEX_NAME}/points/search", json={
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"vector": emb, "limit": 5, "with_payload": True})
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hits2 = [{"title": p.get("payload", {}).get("title", ""), "excerpt": str(p.get("payload", {}).get("excerpt", ""))[:200],
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"score": p.get("score")} for p in r.json().get("result", [])]
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sem_result = {"total": len(hits2), "hits": hits2}
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steps.append("semantic_search_done")
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except Exception:
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steps.append("semantic_search_skipped")
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try:
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r = client.get(f"{YACY_URL}/yacysearch.json", params={"query": topic, "maximumRecords": 10, "resource": "global"})
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discovered = [item.get("link") for ch in r.json().get("channels", []) for item in ch.get("items", []) if item.get("link")]
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for url in discovered[:10]:
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try:
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client.get(f"{YACY_URL}/Crawler_p.json", params={
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"crawlingDomMaxPages": 10, "crawlingDepth": 0, "crawlingStart": url,
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"crawlingQ": "on", "indexText": "on", "indexMedia": "on",
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"crawlingMode": "url", "cachePolicy": "iffresh"}, timeout=5.0)
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except Exception:
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pass
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steps.append(f"crawl_dispatched_{len(discovered[:10])}")
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except Exception:
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steps.append("crawl_skipped")
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all_sources = ""
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for h in kw_result.get("hits", [])[:3] + sem_result.get("hits", [])[:3]:
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all_sources += f"- {h.get('title', 'Unknown')}: {h.get('excerpt', '')[:200]}\n"
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summary = ""
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if all_sources:
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summary = _ai_chat(
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f"Research topic: {topic}\n\nSources:\n{all_sources}\n\nConcise research summary (3-5 paragraphs): "
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"key findings, important sources, knowledge gaps, next steps.")
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return {"topic": topic, "steps": steps,
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"keyword_results": kw_result, "semantic_results": sem_result,
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"ai_summary": summary}
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# ── Dashboard ─────────────────────────────────────────────────
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@app.get("/", response_class=HTMLResponse)
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def dashboard():
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return HTMLResponse("""
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<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,initial-scale=1.0">
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<title>AI Research Engine</title><link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap" rel="stylesheet">
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<style>
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:root{--bg:#09090b;--surface:#18181b;--border:#27272a;--accent:#7c3aed;--accent2:#a855f7;--text:#f4f4f5;--muted:#a1a1aa;--green:#22c55e;--red:#ef4444;--amber:#f59e0b}
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*{box-sizing:border-box;margin:0;padding:0}
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body{font-family:'Inter',system-ui,sans-serif;background:var(--bg);color:var(--text);min-height:100vh}
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.header{background:var(--surface);border-bottom:1px solid var(--border);padding:20px 32px;display:flex;justify-content:space-between;align-items:center;position:sticky;top:0;z-index:10}
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.header h1{font-size:1.4rem;font-weight:800;background:linear-gradient(135deg,var(--accent),var(--accent2),#ec4899);-webkit-background-clip:text;-webkit-text-fill-color:transparent;letter-spacing:-0.02em}
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.header .badge{font-size:0.75rem;color:var(--muted);background:var(--border);padding:6px 12px;border-radius:999px}
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.container{max-width:1400px;margin:0 auto;padding:32px 24px}
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.hero{text-align:center;padding:48px 0 32px}
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.hero h2{font-size:2.2rem;font-weight:800;letter-spacing:-0.03em;margin-bottom:12px}
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.hero p{color:var(--muted);font-size:1.1rem;max-width:600px;margin:0 auto}
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.search-box{display:flex;gap:12px;max-width:800px;margin:0 auto 40px}
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.search-box input{flex:1;padding:16px 24px;border-radius:16px;border:1px solid var(--border);background:var(--surface);color:var(--text);font-size:1rem;outline:none;transition:border-color .2s}
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.search-box input:focus{border-color:var(--accent)}
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.search-box button{padding:16px 32px;border-radius:16px;border:none;font-weight:600;font-size:1rem;cursor:pointer;transition:all .2s}
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.btn-primary{background:var(--accent);color:white}
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.btn-primary:hover{background:var(--accent2)}
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.btn-secondary{background:var(--surface);color:var(--text);border:1px solid var(--border)}
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.btn-secondary:hover{border-color:var(--accent)}
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.grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(260px,1fr));gap:16px;margin-bottom:32px}
|
|
.card{background:var(--surface);border:1px solid var(--border);border-radius:16px;padding:24px;transition:border-color .2s}
|
|
.card:hover{border-color:var(--accent)}
|
|
.card .label{color:var(--muted);font-size:0.8rem;text-transform:uppercase;letter-spacing:0.05em;margin-bottom:8px}
|
|
.card .value{font-size:1.8rem;font-weight:700}
|
|
.card .sub{color:var(--muted);font-size:0.85rem;margin-top:4px}
|
|
.ok{color:var(--green)}.down{color:var(--red)}.warn{color:var(--amber)}
|
|
.action-bar{display:flex;gap:10px;margin-bottom:28px;flex-wrap:wrap}
|
|
.action-bar button{padding:10px 20px;border-radius:10px;border:1px solid var(--border);background:var(--surface);color:var(--text);cursor:pointer;font-size:0.9rem;transition:all .15s}
|
|
.action-bar button:hover{border-color:var(--accent);background:#1f1f23}
|
|
.results{display:flex;flex-direction:column;gap:12px}
|
|
.result-card{background:var(--surface);border:1px solid var(--border);border-radius:14px;padding:20px;transition:border-color .15s}
|
|
.result-card:hover{border-color:var(--accent)}
|
|
.result-card h3{margin-bottom:6px;font-size:1.1rem}
|
|
.result-card h3 a{color:var(--accent2);text-decoration:none}
|
|
.result-card h3 a:hover{text-decoration:underline}
|
|
.result-card .excerpt{color:var(--muted);font-size:0.9rem;line-height:1.5}
|
|
.result-card .meta{color:#71717a;font-size:0.78rem;margin-top:10px;display:flex;gap:16px}
|
|
.loading{text-align:center;padding:48px;color:var(--muted)}
|
|
.hidden{display:none}
|
|
pre{background:var(--surface);border:1px solid var(--border);border-radius:12px;padding:20px;overflow-x:auto;font-size:0.85rem;line-height:1.6;white-space:pre-wrap}
|
|
.report{background:var(--surface);border:1px solid var(--border);border-radius:14px;padding:24px;line-height:1.7}
|
|
.report h1,.report h2,.report h3{color:var(--accent2);margin:16px 0 8px}
|
|
.report ul,.report ol{padding-left:20px;margin:8px 0}
|
|
.report li{margin:4px 0}
|
|
</style></head><body>
|
|
<div class="header"><h1>🧠 AI Research Engine</h1><div class="badge">Self-hosted · Private</div></div>
|
|
<div class="container">
|
|
<div class="hero"><h2>Your Private Research Cloud</h2><p>Discover, index, and synthesize knowledge — all on your own infrastructure.</p></div>
|
|
<div class="search-box">
|
|
<input type="text" id="query" placeholder="Research anything..." onkeydown="if(event.key==='Enter')search()">
|
|
<button class="btn-primary" onclick="search()">🔍 Search</button>
|
|
<button class="btn-secondary" onclick="research()" style="background:var(--accent);color:white;">🧪 Deep Research</button>
|
|
</div>
|
|
<div class="action-bar">
|
|
<button onclick="loadStatus()">📡 System Status</button>
|
|
<button onclick="toggleCrawl()">🕷️ Crawl URL</button>
|
|
<button onclick="loadReport()">📄 Generate Report</button>
|
|
</div>
|
|
<div id="crawl-box" class="hidden" style="margin-bottom:20px;display:flex;gap:10px;">
|
|
<input type="text" id="crawl-input" placeholder="https://example.com" style="flex:1;padding:12px 16px;border-radius:12px;border:1px solid var(--border);background:var(--surface);color:var(--text);">
|
|
<button class="btn-primary" onclick="crawl()">Crawl</button>
|
|
</div>
|
|
<div class="grid" id="stats"></div>
|
|
<div id="output"></div>
|
|
</div>
|
|
<script>
|
|
async function api(p){const r=await fetch(p);return r.json()}
|
|
async function loadStatus(){
|
|
try{const d=await api('/api/status');let h='';for(const[n,i]of Object.entries(d.services||{})){
|
|
const c=i.status==='ok'||i.status==='green'?'ok':'down';
|
|
let x='';if(i.documents!==undefined)x=i.documents+' docs';if(i.points!==undefined)x=i.points+' vectors';if(i.models!==undefined)x=i.models+' models';
|
|
h+=`<div class="card"><div class="label">${n}</div><div class="value ${c}">${i.status||'down'}</div><div class="sub">${x||'—'}</div></div>`}
|
|
document.getElementById('stats').innerHTML=h}catch(e){}
|
|
}
|
|
async function search(){
|
|
const q=document.getElementById('query').value;if(!q)return;
|
|
document.getElementById('output').innerHTML='<div class="loading">Searching...</div>';
|
|
const d=await api('/api/search?q='+encodeURIComponent(q));
|
|
let h=`<p style="color:var(--muted);margin-bottom:16px">${d.total||0} results for "${d.query||q}"</p><div class="results">`;
|
|
for(const r of(d.hits||[]))h+=`<div class="result-card"><h3><a href="${r.url||'#'}" target="_blank">${r.title||'Untitled'}</a></h3><div class="excerpt">${r.excerpt||''}</div><div class="meta"><span>⭐ ${(r.score||0).toFixed(1)}</span>${r.category?`<span>📁 ${r.category}</span>`:''}<span>${r.crawled_at||''}</span></div></div>`;
|
|
h+='</div>';document.getElementById('output').innerHTML=h}
|
|
async function research(){
|
|
const q=document.getElementById('query').value;if(!q)return;
|
|
document.getElementById('output').innerHTML='<div class="loading">Deep researching... this may take 30-60 seconds</div>';
|
|
const d=await api('/api/research?topic='+encodeURIComponent(q));
|
|
let h=`<div class="card" style="margin-bottom:16px"><div class="label">Research Complete</div><div class="value" style="font-size:1.2rem">${d.topic}</div><div class="sub">Steps: ${(d.steps||[]).join(' → ')}</div></div>`;
|
|
if(d.ai_summary)h+=`<div class="report"><h2>AI Summary</h2>${d.ai_summary.replace(/\\n/g,'<br>')}</div>`;
|
|
h+=`<div style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-top:16px">`;
|
|
h+=`<div class="card"><div class="label">Keyword Results</div><div class="sub">${d.keyword_results?.total||0} hits</div></div>`;
|
|
h+=`<div class="card"><div class="label">Semantic Results</div><div class="sub">${d.semantic_results?.total||0} hits</div></div></div>`;
|
|
document.getElementById('output').innerHTML=h}
|
|
function toggleCrawl(){document.getElementById('crawl-box').classList.toggle('hidden')}
|
|
async function crawl(){
|
|
const u=document.getElementById('crawl-input').value;if(!u)return;
|
|
document.getElementById('output').innerHTML='<div class="loading">Dispatching crawl...</div>';
|
|
const d=await api('/api/crawl?url='+encodeURIComponent(u));
|
|
document.getElementById('output').innerHTML='<pre>'+JSON.stringify(d,null,2)+'</pre>'}
|
|
async function loadReport(){
|
|
const q=document.getElementById('query').value||'AI research';
|
|
document.getElementById('output').innerHTML='<div class="loading">Generating report...</div>';
|
|
const d=await api('/api/report?topic='+encodeURIComponent(q));
|
|
document.getElementById('output').innerHTML=`<div class="report"><h2>📊 Research Report: ${d.topic}</h2>${(d.report||'').replace(/\\n/g,'<br>')}<p style="color:var(--muted);margin-top:16px">Sources: ${d.sources_used||0}</p></div>`}
|
|
loadStatus()
|
|
</script></body></html>""")
|
|
|
|
if __name__ == "__main__":
|
|
uvicorn.run(app, host="0.0.0.0", port=8000)
|