import json, os, logging, base64, time, hashlib, re, http.client, ssl, concurrent.futures, threading, math
from collections import Counter
from urllib.parse import urlparse
try:
from searx.network import get_network
except ImportError:
get_network = None
from flask import request, abort, jsonify
from searx.plugins import Plugin, PluginInfo
from searx.result_types import EngineResults
from searx import settings
from flask_babel import gettext
from markupsafe import Markup
logger = logging.getLogger(__name__)
try:
import valkey as _valkey_mod
_VALKEY_AVAILABLE = True
except ImportError:
_VALKEY_AVAILABLE = False
_valkey_mod = None
logger.warning("AI Answers: valkey package not found. Streaming via Valkey unavailable.")
TOKEN_EXPIRY_SEC = 3600
STREAM_TIMEOUT_SEC = 60
CONV_TTL = 1800
def _get_streaming_connection(url: str, verify_ssl: bool = True):
parsed = urlparse(url)
host = parsed.hostname
port = parsed.port or (443 if parsed.scheme == 'https' else 80)
path = parsed.path + ('?' + parsed.query if parsed.query else '')
if verify_ssl and get_network is not None:
try:
net = get_network()
verify_ssl = getattr(net, 'verify', True)
except Exception:
pass
if parsed.scheme == 'https':
if not verify_ssl:
ctx = ssl._create_unverified_context()
else:
try:
import certifi
ctx = ssl.create_default_context(cafile=certifi.where())
except ImportError:
ctx = ssl.create_default_context()
conn = http.client.HTTPSConnection(host, port, timeout=STREAM_TIMEOUT_SEC, context=ctx)
else:
conn = http.client.HTTPConnection(host, port, timeout=STREAM_TIMEOUT_SEC)
return conn, path
def _tokenize(text: str) -> list:
text = text.lower()
text = re.sub(r'[^\w\s]', ' ', text)
return [t for t in text.split() if len(t) > 2]
def _tfidf_score(query_tokens: list, doc_tokens: list) -> float:
if not doc_tokens or not query_tokens:
return 0.0
doc_len = len(doc_tokens)
doc_counter = Counter(doc_tokens)
k1 = 1.5
b = 0.75
avg_len = 150
score = 0.0
for qt in query_tokens:
tf = doc_counter.get(qt, 0) / doc_len
idf = 1.0 / (1.0 + doc_counter.get(qt, 0) / max(doc_len, 1))
tf_bm25 = (tf * (k1 + 1)) / (tf + k1 * (1 - b + b * doc_len / avg_len))
score += tf_bm25 * math.log(1 + idf)
return score
def _chunk_text(text: str, chunk_size: int = 512, overlap: int = 64) -> list:
tokens = _tokenize(text)
if len(tokens) <= chunk_size:
return [text]
chunks = []
start = 0
while start < len(tokens):
end = min(start + chunk_size, len(tokens))
chunks.append(' '.join(tokens[start:end]))
if end >= len(tokens):
break
start += chunk_size - overlap
return chunks
_VALKEY_POOL = None
def _get_valkey_pool():
global _VALKEY_POOL
if _VALKEY_POOL is None:
assert _valkey_mod is not None
_VALKEY_POOL = _valkey_mod.ConnectionPool(
host=os.getenv('VALKEY_HOST', 'searxng-valkey'),
port=int(os.getenv('VALKEY_PORT', 6379)),
db=0,
decode_responses=True,
)
return _VALKEY_POOL
def _get_valkey():
if not _VALKEY_AVAILABLE or _valkey_mod is None:
raise RuntimeError("valkey package not installed")
return _valkey_mod.Valkey(connection_pool=_get_valkey_pool())
def _load_conversation(session_id: str) -> list:
try:
v = _get_valkey()
raw = v.get(f"ai:conv:{session_id}")
if raw:
return json.loads(raw)
except Exception as e:
logger.debug(f"{PLUGIN_NAME}: conv load failed: {e}")
return []
def _save_conversation(session_id: str, turns: list) -> None:
try:
v = _get_valkey()
turns = turns[-20:]
v.setex(f"ai:conv:{session_id}", CONV_TTL, json.dumps(turns))
except Exception as e:
logger.debug(f"{PLUGIN_NAME}: conv save failed: {e}")
def stream_to_valkey(job_id: str, payload: str, headers: dict, endpoint_url: str, model: str):
chunks_key = f"ai:job:{job_id}:chunks"
status_key = f"ai:job:{job_id}:status"
conn = None
try:
vk = _get_valkey()
url = endpoint_url
res = None
for _ in range(3):
conn, path = _get_streaming_connection(url)
conn.request("POST", path, body=payload.encode('utf-8'), headers=headers)
res = conn.getresponse()
if res.status in (301, 302, 307, 308):
location = res.getheader('Location', '')
res.read()
conn.close()
conn = None
if not location:
raise RuntimeError(f"Redirect {res.status} with no Location")
url = location if location.startswith('http') else \
f"{urlparse(url).scheme}://{urlparse(url).netloc}{location}"
continue
break
else:
raise RuntimeError("Too many redirects to Ollama endpoint")
if res.status != 200:
body = res.read(1024).decode('utf-8', errors='replace')
raise RuntimeError(f"Ollama error {res.status}: {body[:200]}")
think_depth = 0
pending = ''
chunk_count = 0
while True:
raw_line = res.readline()
if not raw_line:
break
line = raw_line.decode('utf-8', errors='replace').rstrip('\r\n')
if not line or not line.startswith('data: '):
continue
data_str = line[6:]
if data_str == '[DONE]':
break
try:
obj = json.loads(data_str)
except (json.JSONDecodeError, ValueError):
continue
choices = obj.get('choices', [])
if not choices:
continue
delta = choices[0].get('delta', {})
text = delta.get('content') or ''
reasoning = delta.get('reasoning') or ''
chunk = text if text else reasoning
if not chunk:
continue
pending += chunk
# Filter ... blocks, push clean content immediately
while True:
if think_depth == 0:
think_start = pending.find('')
if think_start == -1:
if pending:
vk.rpush(chunks_key, pending)
vk.expire(chunks_key, 120)
chunk_count += 1
pending = ''
break
else:
before = pending[:think_start]
if before:
vk.rpush(chunks_key, before)
vk.expire(chunks_key, 120)
chunk_count += 1
pending = pending[think_start + 7:]
think_depth = 1
else:
think_end = pending.find('')
if think_end == -1:
break
else:
pending = pending[think_end + 8:]
think_depth = 0
if think_depth == 0 and pending:
vk.rpush(chunks_key, pending)
vk.expire(chunks_key, 120)
chunk_count += 1
logger.debug(f"AI Answers: stream complete, wrote {chunk_count} chunks")
vk.rpush(chunks_key, '__DONE__')
vk.expire(chunks_key, 120)
vk.set(status_key, 'done', ex=120)
except Exception as e:
logger.error(f"AI Answers: stream_to_valkey error for job {job_id}: {e}", exc_info=True)
try:
vk2 = _get_valkey()
vk2.rpush(chunks_key, f"__ERROR__{e}")
vk2.expire(chunks_key, 120)
vk2.set(status_key, 'error', ex=120)
except Exception:
pass
finally:
if conn:
try:
conn.close()
except Exception:
pass
PLUGIN_NAME = "AI Answers"
DEFAULT_TABS = "general,science,it,news"
# UI assets
INTERACTIVE_CSS = '''
@keyframes sxng-fade-in-up {
0% { opacity: 0; transform: translateY(10px); }
100% { opacity: 1; transform: translateY(0); }
}
.sxng-footer {
display: flex;
align-items: center;
gap: 0.5rem;
margin-top: 1rem;
opacity: 0;
animation: sxng-fade-in-up 0.5s ease-out forwards;
}
.sxng-btn {
display: inline-flex;
align-items: center;
justify-content: center;
width: 32px;
height: 32px;
padding: 0;
border: 1px solid var(--color-result-border, rgba(0,0,0,0.1));
border-radius: 4px;
background: var(--color-base-background-hover, rgba(0,0,0,0.06));
color: var(--color-base-font, inherit);
cursor: pointer;
vertical-align: middle;
line-height: 1.4;
}
.sxng-btn:hover {
background: var(--color-result-border, rgba(0,0,0,0.15));
color: var(--color-base-font, inherit);
}
.sxng-btn svg { width: 18px; height: 18px; fill: currentColor; }
.sxng-input-wrapper {
flex-grow: 1;
display: flex;
height: 32px;
align-items: center;
margin: 0 0.5rem;
position: relative;
}
.sxng-input {
width: 100%;
height: -webkit-fill-available;
background: var(--color-base-background-hover, rgba(0,0,0,0.06));
border: 1px solid var(--color-result-border, rgba(0,0,0,0.15));
color: var(--color-base-font, inherit);
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
font-size: 0.78em;
padding: 3px 8px;
border-radius: 4px;
line-height: 1.4;
vertical-align: middle;
}
.sxng-input:focus { outline: none; }
.sxng-input::placeholder { color: var(--color-base-font, inherit); opacity: 0.4; }
.sxng-input-line {
position: absolute;
bottom: 0;
left: 0;
width: 0;
height: 1px;
background: var(--color-result-link, #5e81ac);
transition: width 0.3s ease;
}
.sxng-input:focus + .sxng-input-line { width: 100%; }
.sxng-user-msg {
display: block;
width: fit-content;
max-width: 80%;
margin: 0.75rem 0 0.75rem auto;
padding: 0.25rem 0.6rem 0.25rem 0;
border-right: 2px solid var(--color-result-link, #5e81ac);
text-align: right;
font-size: 0.85rem;
line-height: 1.4;
opacity: 0.55;
animation: sxng-fade-in-up 0.3s ease-out forwards;
}
.sxng-input-wrapper:focus-within {
opacity: 1;
color: var(--color-result-link, #5e81ac);
background: var(--color-base-background-hover, rgba(0,0,0,0.05)) !important;
}
.sxng-model-select {
appearance: none;
-webkit-appearance: none;
background: url("data:image/svg+xml;charset=UTF-8,%3C%3Fxml%20version%3D%221.0%22%20encoding%3D%22UTF-8%22%3F%3E%0A%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20viewBox%3D%220%200%20512%20512%22%3E%0A%3Cg%20fill%3D%22%23aaa%22%3E%0A%3Cpolygon%20points%3D%22128%2C192%20256%2C320%20384%2C192%22%2F%3E%3C%2Fg%3E%0A%3C%2Fsvg%3E") calc(100% + 2rem) / 1rem no-repeat content-box border-box;
background-color: var(--color-base-background-hover, rgba(0,0,0,0.06));
text-overflow: ellipsis;
border: 0px solid var(--color-result-border, rgba(0,0,0,0.1));
border-right-width: 2rem;
border-right-color: transparent;
border-radius: 5px;
outline: none;
height: 25px;
color: var(--color-base-font, inherit);
font-size: .9rem;
padding: 1px 10px 1px 10px !important;
margin: 0;
cursor: pointer;
display: none;
max-width: 8rem;
vertical-align: middle;
}
.sxng-model-select:hover {
background-color: var(--color-result-border, rgba(0,0,0,0.15));
}
.sxng-reasoning {
margin: 0.5rem 0; padding: 0.5rem;
border-left: 2px solid var(--color-result-link, #5e81ac);
background: var(--color-base-background-hover, rgba(0,0,0,0.03));
font-size: 0.85rem; opacity: 0.7; transition: opacity 0.2s;
}
.sxng-reasoning:hover { opacity: 1; }
.sxng-reasoning summary { cursor: pointer; font-weight: bold; color: var(--color-result-link, #5e81ac); }
.sxng-thought-content { margin-top: 0.5rem; white-space: pre-wrap; font-family: monospace; }
.sxng-citation-footer {
margin-top: 0.75rem;
padding-top: 0.5rem;
border-top: 1px solid var(--color-sidebar-bg, #424247);
display: flex;
flex-wrap: wrap;
gap: 0.4rem 0.75rem;
}
.sxng-citation-item a {
font-size: 0.75em;
color: var(--color-result-link, #5e81ac);
text-decoration: none;
opacity: 1;
font-weight: 600;
}
.sxng-citation-item a:hover {
opacity: 1;
text-decoration: underline;
}
.sxng-prior-history {
margin-bottom: 0.75rem;
padding: 0.5rem;
border-left: 2px solid var(--color-result-link, #5e81ac);
opacity: 0.85;
font-size: 0.85em;
}
.sxng-prior-history summary {
cursor: pointer;
color: var(--color-result-link, #5e81ac);
font-weight: 700;
}
.sxng-prior-answer {
margin: 0.25rem 0;
padding-left: 0.5rem;
color: var(--color-base-font, inherit);
}
.sxng-md-content {
line-height: 1.6;
}
.sxng-md-content ul, .sxng-md-content ol {
margin: 0.5rem 0;
padding-left: 1.5rem;
}
.sxng-md-content li {
margin: 0.2rem 0;
}
.sxng-md-content p {
margin: 0.4rem 0;
}
.sxng-md-content code {
font-family: monospace;
}
'''
INTERACTIVE_HTML = '''
'''
CITATION_HELPER_JS = r'''
function parseMarkdown(text) {
text = text.replace(/\*\*(.*?)\*\*/g, '$1');
text = text.replace(/__(.*?)__/g, '$1');
text = text.replace(/(?$1');
text = text.replace(/(?$1');
text = text.replace(/`([^`]+)`/g, '$1');
text = text.replace(/((?:^|\n)[*\-+] .+)+/g, (match) => {
const items = match.trim().split('\n').map(line => {
const content = line.replace(/^[*\-+] /, '').trim();
return `${content}`;
}).join('');
return ``;
});
text = text.replace(/((?:^|\n)\d+\. .+)+/g, (match) => {
const items = match.trim().split('\n').map(line => {
const content = line.replace(/^\d+\. /, '').trim();
return `${content}`;
}).join('');
return `${items}
`;
});
text = text.replace(/^### (.+)$/gm, '$1
');
text = text.replace(/^## (.+)$/gm, '$1
');
text = text.replace(/^# (.+)$/gm, '$1
');
text = text.replace(/^---+$/gm, '
');
text = text.replace(/\n\n/g, '');
text = text.replace(/\n(?!<)/g, '
');
return text;
}
function linkCitationsInElement(el, urls) {
const walker = document.createTreeWalker(
el, NodeFilter.SHOW_TEXT, null
);
const textNodes = [];
let node;
while (node = walker.nextNode()) {
textNodes.push(node);
}
textNodes.forEach(textNode => {
const text = textNode.textContent;
if (!/\[\d/.test(text)) return;
const span = document.createElement('span');
span.innerHTML = text.replace(/\[(\d{1,2}(?:,\s*\d{1,2})*)\]/g, (match, nums) => {
return nums.split(/\s*,\s*/).map(n => {
const idx = parseInt(n.trim());
const url = urls[idx - 1];
if (url) {
return `[${n.trim()}]`;
}
return match;
}).join('');
});
textNode.parentNode.replaceChild(span, textNode);
});
}
function renderCitations(text, urls) {
const fragment = document.createDocumentFragment();
const re = /\[(\d{1,2}(?:\s*,\s*\d{1,2})*)\]/g;
let lastIdx = 0;
const matches = [...text.matchAll(re)];
matches.forEach(match => {
if (match.index > lastIdx) {
const s = document.createElement('span');
s.className = 'sxng-chunk';
s.textContent = text.substring(lastIdx, match.index);
fragment.appendChild(s);
}
match[1].split(/\s*,\s*/).forEach(n => {
const idx = parseInt(n.trim());
if (idx >= 1 && idx <= urls.length) {
const url = urls[idx-1];
if (url) {
const a = document.createElement('a');
a.href = url;
a.target = '_blank';
a.style.cssText = 'text-decoration:none;color:var(--color-result-link);font-weight:bold;';
a.textContent = `[${n.trim()}]`;
a.className = 'sxng-chunk';
fragment.appendChild(a);
} else {
const s = document.createElement('span');
s.className = 'sxng-chunk';
s.textContent = `[${n.trim()}]`;
fragment.appendChild(s);
}
} else {
const s = document.createElement('span');
s.className = 'sxng-chunk';
s.textContent = `[${n.trim()}]`;
fragment.appendChild(s);
}
});
lastIdx = match.index + match[0].length;
});
if (lastIdx < text.length) {
const s = document.createElement('span');
s.className = 'sxng-chunk';
// Preserve whitespace by not trimming
s.textContent = text.substring(lastIdx);
fragment.appendChild(s);
}
return fragment;
}
function renderCitationFooter(textContent, urls, container) {
const re = /\[(\d{1,2}(?:\s*,\s*\d{1,2})*)\]/g;
const usedIndices = new Set();
let m;
while ((m = re.exec(textContent)) !== null) {
m[1].split(/\s*,\s*/).forEach(n => {
const idx = parseInt(n.trim());
if (idx >= 1 && idx <= urls.length && urls[idx - 1]) {
usedIndices.add(idx);
}
});
}
if (usedIndices.size === 0) return;
const sorted = [...usedIndices].sort((a, b) => a - b);
const footer = document.createElement('div');
footer.className = 'sxng-citation-footer';
sorted.forEach(n => {
const url = urls[n - 1];
if (!url) return;
let domain;
try { domain = new URL(url).hostname.replace('www.', ''); } catch(e) { domain = url; }
const item = document.createElement('span');
item.className = 'sxng-citation-item';
const a = document.createElement('a');
a.href = url;
a.target = '_blank';
a.textContent = `[${n}] ${domain}`;
item.appendChild(a);
footer.appendChild(item);
});
container.appendChild(footer);
}
'''
INTERACTIVE_JS = r'''
const footer = document.getElementById('sxng-footer');
const input = document.getElementById('sxng-action-input');
if (typeof model_init !== 'undefined' && model_init) {
const _ms = document.getElementById('sxng-model-select');
if (_ms) {
const _o = document.createElement('option');
_o.value = model_init;
_o.textContent = model_init;
_o.selected = true;
_ms.appendChild(_o);
}
}
// conversation saved as base64 URL fragment.
const updateState = () => {
try {
let state = {
t: conversation.turns.map(t => ({
r: t.role === 'user' ? 'u' : 'a',
c: t.content.replace(/\s+/g, ' ').trim()
})),
u: urls
};
const encodeB64 = (obj) => {
const u8 = new TextEncoder().encode(JSON.stringify(obj));
let bin = '';
// Use a loop to avoid RangeError: Maximum call stack size exceeded
for (let i = 0; i < u8.byteLength; i++) {
bin += String.fromCharCode(u8[i]);
}
return btoa(bin);
};
let b64 = encodeB64(state);
while (b64.length > 2000 && state.t.length > 2) {
state.t.splice(1, 2); // Delete in Q&A pairs
b64 = encodeB64(state);
}
history.replaceState(null, null, '#ai=' + b64);
} catch(e) {}
};
if (location.hash.includes('ai=')) {
try {
const b64 = location.hash.split('ai=')[1];
const uint8 = new Uint8Array(atob(b64).split('').map(c => c.charCodeAt(0)));
const json = new TextDecoder().decode(uint8);
const state = JSON.parse(json);
if (state.t && state.t.length > 0) {
// Restore URLs for citation indexing
if (state.u && Array.isArray(state.u)) {
urls = state.u;
}
conversation.turns = state.t.map(t => ({
role: t.r === 'u' ? 'user' : 'assistant',
content: t.c.trim(),
ts: 0
}));
const injectCitations = (text) => {
return renderCitations(text, urls);
};
data.innerHTML = '';
conversation.turns.forEach((turn, i) => {
if (turn.role === 'user') {
if (turn.content !== conversation.originalQuery) {
const u = document.createElement('span');
u.className = 'sxng-user-msg';
u.textContent = turn.content;
data.appendChild(u);
const clr = document.createElement('div');
clr.style.clear = 'both';
data.appendChild(clr);
}
} else {
data.appendChild(injectCitations(turn.content));
}
});
box.style.display = 'block';
if(footer && is_interactive) footer.style.display = 'flex';
restored = true;
}
} catch(e) { console.warn('Restore failed', e); }
}
document.getElementById('btn-copy').onclick = async (e) => {
const btn = e.currentTarget;
const originalContent = btn.innerHTML;
const text = Array.from(data.childNodes)
.filter(n => n.nodeType === 3 || n.tagName === 'SPAN')
.map(n => n.textContent)
.join('');
await navigator.clipboard.writeText(text);
btn.innerHTML = '';
setTimeout(() => btn.innerHTML = originalContent, 2000);
};
document.getElementById('btn-regen').onclick = async () => {
// Remove only the last assistant response and its citation footer
const lastMd = [...data.querySelectorAll('.sxng-md-content')].pop();
if (lastMd) {
const nextSib = lastMd.nextElementSibling;
if (nextSib && nextSib.classList.contains('sxng-citation-footer')) nextSib.remove();
lastMd.remove();
}
const existingCursor = data.querySelector('.sxng-cursor');
if (existingCursor) existingCursor.remove();
const regenCursor = document.createElement('span');
regenCursor.className = 'sxng-cursor';
data.appendChild(regenCursor);
footer.style.display = 'none';
if (conversation.turns.length > 0 && conversation.turns[conversation.turns.length - 1].role === 'assistant') {
conversation.turns.pop();
}
updateState();
if (conversation.turns.length <= 1) {
await startStream();
} else {
const val = conversation.turns[conversation.turns.length - 1].content;
const currentText = conversation.turns.slice(0, -1).slice(-6)
.map(t => (t.role === 'user' ? 'Q' : 'A') + ': ' + t.content)
.join('\\n\\n');
await startStream(val, currentText);
}
updateState();
};
const btnClearHistory = document.getElementById('btn-clear-history');
if (btnClearHistory) {
btnClearHistory.onclick = async () => {
if (!session_id_init) return;
try {
await fetch(`${script_root}/ai-conversation`, {
method: 'DELETE',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({tk: tk_init, session_id: session_id_init})
});
} catch(e) {}
conversation.turns = [{role: 'user', content: q_init, ts: Date.now()}];
location.reload();
};
}
const handleAction = async (e) => {
if (e) e.preventDefault();
const val = input.value.trim();
conversation.turns.push({role: 'user', content: val, ts: Date.now()});
updateState();
const currentText = conversation.turns.slice(0, -1).slice(-6)
.map(t => (t.role === 'user' ? 'Q' : 'A') + ': ' + t.content)
.join('\\n\\n');
input.value = '';
input.blur();
footer.style.display = 'none';
if (val) {
const cursor = data.querySelector('.sxng-cursor');
if (cursor) cursor.remove();
const userMsg = document.createElement('span');
userMsg.className = 'sxng-user-msg';
userMsg.textContent = val;
data.appendChild(userMsg);
const clr = document.createElement('div');
clr.style.clear = 'both';
data.appendChild(clr);
const newCursor = document.createElement('span');
newCursor.className = 'sxng-cursor';
data.appendChild(newCursor);
const synthesized = synthesizeQuery(q_init, val);
let auxContext = null;
try {
const auxData = await fetch(script_root + '/ai-auxiliary-search', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({query: synthesized, lang: lang_init, offset: urls.length, tk: tk_init, session_id: session_id_init})
}).then(r => r.json());
if (auxData.context) {
const originalBackground = conversation.originalContext.substring(0, 1500);
auxContext = `FRESH SOURCES (most relevant):\\n${auxData.context}\\n\\nBACKGROUND (for reference):\\n${originalBackground}`;
if (auxData.new_urls && Array.isArray(auxData.new_urls)) {
urls = urls.concat(auxData.new_urls);
}
}
} catch (err) {}
await startStream(val, currentText, auxContext);
updateState();
} else {
const cursor = data.querySelector('.sxng-cursor');
if (cursor) cursor.remove();
data.appendChild(document.createElement('br'));
data.appendChild(document.createElement('br'));
const newCursor = document.createElement('span');
newCursor.className = 'sxng-cursor';
data.appendChild(newCursor);
await startStream("Continue", currentText);
updateState();
}
};
document.getElementById('sxng-action-form').onsubmit = handleAction;
input.onfocus = () => {
setTimeout(() => {
input.scrollIntoView({behavior: 'smooth', block: 'center'});
}, 300);
};
(function fetchModels() {
const _msel2 = document.getElementById('sxng-model-select');
if (!_msel2) return;
const _modelsUrl = script_root + '/ai-models?tk=' + encodeURIComponent(tk_init);
console.log('[AI Answers] Fetching models from', _modelsUrl);
fetch(_modelsUrl)
.then(r => r.ok ? r.json() : Promise.reject('HTTP ' + r.status))
.then(d => {
const models = (d && d.models && d.models.length > 0) ? d.models : [model_init];
const _cur = _msel2.value || model_init;
_msel2.innerHTML = '';
models.forEach(m => {
const o = document.createElement('option');
o.value = m; o.textContent = m;
if (m === _cur) o.selected = true;
_msel2.appendChild(o);
});
_msel2.style.display = 'inline-block';
})
.catch(() => {
if (model_init) {
const o = document.createElement('option');
o.value = model_init; o.textContent = model_init;
o.selected = true;
_msel2.appendChild(o);
_msel2.style.display = 'inline-block';
}
});
})();
'''
FRONTEND_JS_TEMPLATE = r"""
(async () => {
const is_interactive = __IS_INTERACTIVE__;
const q_init = __JS_Q__;
const lang_init = __JS_LANG__;
let urls = __JS_URLS__;
const b64_init = __B64_CONTEXT__;
const tk_init = __TK__;
const script_root = __SCRIPT_ROOT__;
const model_init = __MODEL_INIT__;
const session_id_init = __SESSION_ID__;
const intent_init = __INTENT__;
if (session_id_init && !document.cookie.includes('sxng_ai_session')) {
document.cookie = `sxng_ai_session=${session_id_init}; path=/; max-age=1800; SameSite=Lax`;
}
const conversation = {
originalQuery: q_init,
originalContext: new TextDecoder().decode(Uint8Array.from(atob(b64_init), c => c.charCodeAt(0))),
turns: [{role: 'user', content: q_init, ts: Date.now()}]
};
const box = document.getElementById('sxng-stream-box');
const data = document.getElementById('sxng-stream-data');
(function applyTheme() {
try {
const root = document.documentElement;
const s = getComputedStyle(root);
const get = (v, fallback) => s.getPropertyValue(v).trim() || fallback;
const theme = {
'--color-answer-background': get('--color-answer-background', '#313338'),
'--color-answer-font': get('--color-answer-font', '#fff'),
'--color-result-link': get('--color-result-link', '#8aacf7'),
'--color-base-font': get('--color-base-font', '#cdd6f4'),
'--color-sidebar-bg': get('--color-sidebar-bg', '#424247'),
'--color-result-hover': get('--color-result-hover', '#303033'),
'--color-base-background': get('--color-base-background', '#2a2a2e'),
'--color-search-font': get('--color-search-font', '#bbb'),
'--color-result-border': get('--color-result-border', '#4c566a'),
'--color-result-description':get('--color-result-description', '#d8dee9'),
'--color-toolkit-select-background': get('--color-toolkit-select-background', '#313338'),
};
// Apply to box and any ai-answers container
const targets = [box, document.getElementById('ai-answers')].filter(Boolean);
targets.forEach(el => {
Object.entries(theme).forEach(([k, v]) => {
if (v) el.style.setProperty(k, v);
});
});
} catch(e) {}
})();
// Move AI Overview outside #answers, place it before #results
(function relocateBox() {
const answersDiv = document.getElementById('answers');
if (!box || !answersDiv) return;
// Create our own container
const aiContainer = document.createElement('div');
aiContainer.id = 'ai-answers';
const rootStyle = getComputedStyle(document.documentElement);
const getVar = (v, fb) => rootStyle.getPropertyValue(v).trim() || fb;
const bg = getVar('--color-answer-background', '');
const answerFont = getVar('--color-answer-font', '');
// Detect light mode by checking if answer font is dark
const isLight = answerFont && (answerFont.includes('0,0,0') ||
answerFont.includes('#000') || answerFont.includes('#333') ||
answerFont.includes('#444') || answerFont.includes('rgb(0') ||
answerFont.includes('rgb(3') || answerFont.includes('rgb(4') ||
answerFont.includes('rgb(5') || answerFont.includes('rgb(6'));
const containerBg = isLight
? 'rgba(0,0,0,0.06)'
: (bg || 'var(--color-answer-background, #313338)');
aiContainer.style.cssText = [
`background: ${containerBg}`,
'padding: 1rem',
'margin: 0 0 1rem 0',
`color: ${getVar('--color-answer-font', 'var(--color-answer-font, #fff)')}`,
'border-radius: 8px',
'box-sizing: border-box',
'width: 100%'
].join('; ');
// Move our box into the new container
aiContainer.appendChild(box);
const resultsGrid = document.getElementById('results');
if (resultsGrid) {
// Insert as first child of #results grid so grid-area:answers applies
resultsGrid.insertBefore(aiContainer, resultsGrid.firstChild);
} else {
answersDiv.parentNode.insertBefore(aiContainer, answersDiv);
}
// Hide #answers entirely since our box is now elsewhere
answersDiv.style.display = 'none';
})();
let restored = false;
let isStreaming = false;
__CITATION_HELPER_JS__
(function applyIntentBadge() {
const intentEmoji = {factual:'📖',howto:'🔧',technical:'⌨️',comparison:'⚖️',opinion:'💬',current:'📰',local:'📍'}[intent_init] || '';
if (intentEmoji) {
const label = box ? box.querySelector('.sxng-ai-label') : null;
if (label) label.innerHTML += ` ${intentEmoji}`;
}
})();
__INTERACTIVE_JS_INIT__
async function loadPriorConversation() {
if (!session_id_init) return;
try {
const res = await fetch(
`${script_root}/ai-conversation?tk=${encodeURIComponent(tk_init)}&session_id=${session_id_init}`
);
if (!res.ok) return;
const d = await res.json();
const turns = d.turns || [];
if (turns.length === 0) return;
const historyDiv = document.createElement('details');
historyDiv.className = 'sxng-prior-history';
historyDiv.innerHTML = 'Prior conversation';
turns.slice(-6).forEach(turn => {
const el = document.createElement('div');
el.className = turn.role === 'user' ? 'sxng-user-msg' : 'sxng-prior-answer';
el.textContent = turn.content;
historyDiv.appendChild(el);
});
data.insertBefore(historyDiv, data.firstChild);
} catch(e) {
console.debug('[AI Answers] Could not load prior conversation:', e);
}
}
async function saveConversationTurn() {
if (!session_id_init) return;
try {
const turns = conversation.turns.slice(-20);
await fetch(`${script_root}/ai-conversation`, {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({tk: tk_init, session_id: session_id_init, turns: turns})
});
} catch(e) {
console.debug('[AI Answers] Could not save conversation:', e);
}
}
function synthesizeQuery(original, followup) {
const cleanOrig = original.replace(/^(what|how|why|when|where|who|which|is|are|can|does|do)(\s+(is|are|do|does|can|to|a|an|the))?\s+/i, '');
const origWords = cleanOrig.split(' ').slice(0, 12);
return `${origWords.join(' ')} ${followup}`.trim();
}
__STREAM_FN_SIG__ {
if (isStreaming) {
console.warn('[AI Answers] Stream already in progress, ignoring duplicate call');
return;
}
isStreaming = true;
try {
const ctx = auxContext || conversation.originalContext;
box.style.display = 'block';
const controller = new AbortController();
let timeoutId = setTimeout(() => controller.abort(), 90000);
const finalQ = __STREAM_Q__;
const _selMdl = (document.getElementById('sxng-model-select') || {value: ''}).value;
const bodyObj = { q: finalQ, lang: lang_init, context: ctx, tk: tk_init, model: _selMdl, session_id: session_id_init__STREAM_BODY__ };
const res = await fetch(script_root + '/ai-stream', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(bodyObj),
signal: controller.signal
});
clearTimeout(timeoutId);
if (!res.ok) {
const errSpan = document.createElement('span');
errSpan.style.color = '#bf616a';
errSpan.textContent = "Error: " + res.statusText;
data.appendChild(errSpan);
return;
}
const respJson = await res.json();
if (respJson.error) {
const cursorErr = data.querySelector('.sxng-cursor');
if (cursorErr) cursorErr.remove();
const errSpan = document.createElement('span');
errSpan.style.color = '#bf616a';
errSpan.textContent = "⚠️ " + respJson.error;
data.appendChild(errSpan);
return;
}
const jobId = respJson.job_id;
if (!jobId) {
const cursorErr = data.querySelector('.sxng-cursor');
if (cursorErr) cursorErr.remove();
const errSpan = document.createElement('span');
errSpan.style.color = '#bf616a';
errSpan.textContent = 'No job ID returned. Check server logs.';
data.appendChild(errSpan);
return;
}
let cursor = data.querySelector('.sxng-cursor');
if (!cursor) {
cursor = document.createElement('span');
cursor.className = 'sxng-cursor';
data.appendChild(cursor);
}
const streamContainer = document.createElement('div');
streamContainer.className = 'sxng-stream-container';
if (cursor) cursor.before(streamContainer);
else data.appendChild(streamContainer);
let buffer = '';
let fullText = '';
const flushBuffer = (force = false) => {
if (!buffer) return;
if (force) {
const fragment = renderCitations(buffer, urls);
streamContainer.appendChild(fragment);
buffer = '';
return;
}
while (true) {
const match = buffer.match(/(\[\d+(?:,\s*\d+)*\])/);
if (!match) break;
const preText = buffer.substring(0, match.index);
if (preText) {
const s = document.createElement('span');
s.className = 'sxng-chunk';
s.textContent = preText;
streamContainer.appendChild(s);
}
const citationText = match[0];
const fragment = renderCitations(citationText, urls);
streamContainer.appendChild(fragment);
buffer = buffer.substring(match.index + match[0].length);
}
const openIdx = buffer.lastIndexOf('[');
if (openIdx === -1) {
if (buffer) {
const s = document.createElement('span');
s.className = 'sxng-chunk';
s.textContent = buffer;
streamContainer.appendChild(s);
buffer = '';
}
} else {
const safeChunk = buffer.substring(0, openIdx);
if (safeChunk) {
const s = document.createElement('span');
s.className = 'sxng-chunk';
s.textContent = safeChunk;
streamContainer.appendChild(s);
}
buffer = buffer.substring(openIdx);
if (buffer.length > 50) {
const s = document.createElement('span');
s.className = 'sxng-chunk';
s.textContent = buffer[0];
streamContainer.appendChild(s);
buffer = buffer.substring(1);
}
}
};
let offset = 0;
const maxPolls = 600;
let polls = 0;
while (polls < maxPolls) {
polls++;
await new Promise(r => setTimeout(r, 150));
let statusRes;
try {
statusRes = await fetch(
`${script_root}/ai-status/${jobId}?tk=${encodeURIComponent(tk_init)}&offset=${offset}`,
{ signal: controller.signal }
);
} catch (fetchErr) {
if (fetchErr.name === 'AbortError') throw fetchErr;
continue;
}
if (statusRes.status === 404) {
const cursorE = data.querySelector('.sxng-cursor');
if (cursorE) cursorE.remove();
const expiredSpan = document.createElement('span');
expiredSpan.style.color = '#bf616a';
expiredSpan.textContent = 'Response expired. Please search again.';
data.appendChild(expiredSpan);
return;
}
if (!statusRes.ok) continue;
const statusData = await statusRes.json();
if (statusData.error) {
const cursorE = data.querySelector('.sxng-cursor');
if (cursorE) cursorE.remove();
const errSpan2 = document.createElement('span');
errSpan2.style.color = '#bf616a';
errSpan2.textContent = '⚠️ ' + statusData.error;
data.appendChild(errSpan2);
return;
}
for (const chunk of (statusData.chunks || [])) {
fullText += chunk;
buffer += chunk;
flushBuffer(false);
}
offset += (statusData.chunks || []).length;
if (statusData.done) {
flushBuffer(true);
break;
}
}
streamContainer.remove();
if (cursor) cursor.remove();
const rendered = parseMarkdown(fullText.trim());
const mdDiv = document.createElement('div');
mdDiv.className = 'sxng-md-content';
mdDiv.innerHTML = rendered;
linkCitationsInElement(mdDiv, urls);
data.appendChild(mdDiv);
renderCitationFooter(fullText, urls, data);
const collectedResponse = fullText;
__INTERACTIVE_JS_COMPLETE__
if (collectedResponse) {
conversation.turns.push({role: 'assistant', content: collectedResponse.trim(), ts: Date.now()});
await saveConversationTurn();
}
if (arguments.length === 0 && typeof updateState === 'function') {
updateState();
}
} catch (e) {
console.error('[AI Answers] Fatal stream exception:', e);
const errSpan = document.createElement('span');
errSpan.style.cssText = 'color: #bf616a; font-weight: bold; display: block; margin-top: 0.5rem;';
if (e.name === 'AbortError') {
errSpan.textContent = "⚠️ Connection to AI provider timed out.";
} else {
errSpan.textContent = "⚠️ AI Widget encountered a fatal error. Check browser console.";
}
if (data) {
const cursor = data.querySelector('.sxng-cursor');
if (cursor) cursor.remove();
data.appendChild(errSpan);
}
} finally {
isStreaming = false;
}
}
await loadPriorConversation();
if (!restored) startStream();
})();
"""
def _detect_intent(query: str) -> str:
q = query.lower().strip()
if any(w in q for w in ['news', 'latest', 'recent', 'today', 'yesterday',
'this week', 'breaking', '2025', '2026', 'update']):
return 'current'
if any(q.startswith(p) for p in ['how to', 'how do i', 'how can i',
'how do you', 'steps to', 'guide to',
'tutorial', 'how does']):
return 'howto'
if any(w in q for w in ['install', 'configure', 'setup', 'set up',
'enable', 'disable', 'fix', 'repair']):
return 'howto'
if any(w in q for w in ['error', 'exception', 'traceback', 'debug',
'code', 'function', 'script', 'api', 'command',
'terminal', 'bash', 'python', 'javascript',
'docker', 'linux', 'git', 'sql', 'regex']):
return 'technical'
if ' vs ' in q or ' versus ' in q or 'difference between' in q or \
'compare ' in q or (' or ' in q and len(q.split()) < 8):
return 'comparison'
if any(q.startswith(p) for p in ['best ', 'top ', 'worst ', 'should i',
'is it worth', 'recommend']):
return 'opinion'
if any(w in q for w in ['worth it', 'better than', 'best way',
'recommend', 'suggestion', 'advice']):
return 'opinion'
if any(w in q for w in ['near me', 'nearby', 'local', 'in my area',
'closest', 'directions to']):
return 'local'
if any(q.startswith(p) for p in ['what is', 'what are', 'who is',
'who was', 'when did', 'when was',
'where is', 'where was', 'why is',
'why does', 'define ', 'what does']):
return 'factual'
return 'general'
INTENT_CONFIGS = {
'factual': {
'system_suffix': (
"This is a factual question. Provide a direct, accurate definition "
"or explanation. Lead with the core fact. Cite your primary source. "
"2-3 sentences maximum."
),
'task': "DEFINE: State the fact or definition directly. No preamble.",
'format': "Plain prose. No lists. Cite the most authoritative source first."
},
'howto': {
'system_suffix': (
"This is a how-to question. Provide clear, actionable steps. "
"Be specific and practical. Number the key steps if there are more than 2."
),
'task': "INSTRUCT: Give the key steps or method directly. Be actionable.",
'format': "Numbered steps if 3+, otherwise prose. Cite sources for each step."
},
'technical': {
'system_suffix': (
"This is a technical question. Be precise and specific. "
"Include exact commands, syntax, or error explanations where relevant. "
"Prioritize official documentation and technical sources."
),
'task': "TECHNICAL: Provide the precise technical answer. Include specifics.",
'format': "Exact terminology. Commands in backticks if applicable. Cite docs."
},
'comparison': {
'system_suffix': (
"This is a comparison question. Objectively compare the options. "
"Highlight key differences. Avoid picking a winner unless sources clearly support it."
),
'task': "COMPARE: State the key differences between the options directly.",
'format': "Brief parallel structure. Cite a source for each side if available."
},
'opinion': {
'system_suffix': (
"This is a recommendation or opinion question. Synthesize what sources say. "
"Present the consensus view if one exists. Note disagreement if present. "
"Do not present personal opinions as fact."
),
'task': "SYNTHESIZE: State what sources recommend or what consensus says.",
'format': "Lead with the consensus. Note any caveats. Cite sources."
},
'current': {
'system_suffix': (
"This is a current events question. Prioritize the most recent sources. "
"Note the date of information if relevant. Be clear about what is known vs uncertain."
),
'task': "REPORT: State the latest known information directly. Note recency.",
'format': "Lead with most recent fact. Include dates where available. Cite news sources."
},
'local': {
'system_suffix': (
"This is a local or location-based question. "
"Provide relevant location-specific information from sources. "
"Note if information may vary by location."
),
'task': "LOCAL: Provide location-relevant information from sources.",
'format': "Be specific to the location context. Cite local sources."
},
'general': {
'system_suffix': (
"Provide a concise, accurate overview that directly answers the query."
),
'task': "ANSWER FIRST: Lead with the direct answer. No preamble.",
'format': "2-4 sentences. Cite most relevant sources."
},
}
import typing
if typing.TYPE_CHECKING:
from searx.search import SearchWithPlugins
from searx.extended_types import SXNG_Request
from . import PluginCfg
class SXNGPlugin(Plugin):
id = "ai_answers"
def __init__(self, plg_cfg: "PluginCfg"):
super().__init__(plg_cfg)
self.info = PluginInfo(
id=self.id,
name=gettext(f"{PLUGIN_NAME} Plugin"),
description=gettext("Live AI search answers using LLM providers."),
preference_section="general",
)
self._load_config()
def _load_config(self):
self.interactive = os.getenv('LLM_INTERACTIVE', 'true').lower().strip() in ('true', '1', 'yes', 'on')
self.question_mark_required = os.getenv('LLM_QUESTION_MARK_REQUIRED', 'false').lower().strip() in ('true', '1', 'yes', 'on')
raw_url = os.getenv('LLM_URL', 'http://ollama:11434/v1/chat/completions').strip()
if not raw_url.startswith(('http://', 'https://')):
raw_url = f"http://{raw_url}"
self.endpoint_url = raw_url
self.api_key = 'ollama'
self.model = os.getenv('LLM_MODEL', 'qwen3.5:9b').strip()
try:
self.max_tokens = max(1, int(os.getenv('LLM_MAX_TOKENS', 200)))
except ValueError:
logger.warning(f"{PLUGIN_NAME}: Invalid LLM_MAX_TOKENS value. Enforcing default (200).")
self.max_tokens = 200
try:
self.temperature = float(os.getenv('LLM_TEMPERATURE', 0.2))
except ValueError:
logger.warning(f"{PLUGIN_NAME}: Invalid LLM_TEMPERATURE value. Enforcing default (0.2).")
self.temperature = 0.2
try:
self.context_deep_count = max(0, int(os.getenv('LLM_CONTEXT_DEEP_COUNT', 5)))
except ValueError:
logger.warning(f"{PLUGIN_NAME}: Invalid LLM_CONTEXT_DEEP_COUNT value. Enforcing default (5).")
self.context_deep_count = 5
try:
self.context_shallow_count = max(0, int(os.getenv('LLM_CONTEXT_SHALLOW_COUNT', 15)))
except ValueError:
logger.warning(f"{PLUGIN_NAME}: Invalid LLM_CONTEXT_SHALLOW_COUNT value. Enforcing default (15).")
self.context_shallow_count = 15
self.allowed_tabs = set(t.strip() for t in os.getenv('LLM_TABS', DEFAULT_TABS).split(','))
server_secret = settings.get('server', {}).get('secret_key', '')
self.secret = hashlib.sha256(f"ai_answers_{server_secret}".encode()).hexdigest()
self.system_prompt = os.getenv('LLM_SYSTEM_PROMPT', '').strip()
def _parse_aux_results(self, raw_results, raw_infoboxes, raw_answers):
results = []
limit = self.context_deep_count + self.context_shallow_count
for r in raw_results[:limit]:
# MainResult (attribute access) and LegacyResult (dict access)
if hasattr(r, 'title'):
results.append({
'title': getattr(r, 'title', ''),
'content': getattr(r, 'content', ''),
'url': getattr(r, 'url', ''),
'publishedDate': getattr(r, 'publishedDate', '')
})
else:
# Legacy dictionary-style access
results.append({
'title': r.get('title', ''),
'content': r.get('content', ''),
'url': r.get('url', ''),
'publishedDate': r.get('publishedDate', '')
})
# SearXNG already merges infoboxes by ID, use first
infoboxes = []
for ib in raw_infoboxes[:1]:
infoboxes.append({
'name': ib.get('infobox', '') or ib.get('title', ''),
'content': str(ib.get('content') or '')[:2000],
'attributes': ib.get('attributes', [])
})
answers = []
for a in list(raw_answers)[:2]:
ans_text = ""
if hasattr(a, 'answer') and isinstance(getattr(a, 'answer', None), str):
ans_text = a.answer
elif isinstance(a, dict) and a.get('answer'):
ans_text = str(a['answer'])
if ans_text and 'id="sxng-stream-box"' not in ans_text and not ans_text.strip().startswith('<'):
answers.append(ans_text)
return results, infoboxes, answers
def init(self, app):
@app.route('/ai-auxiliary-search', methods=['POST'])
def ai_auxiliary_search():
if not self.api_key:
abort(403)
data = request.json or {}
token = data.get('tk', '')
# Token access control
try:
ts, sig = token.rsplit('.', 1)
expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
abort(403)
except (ValueError, KeyError, AttributeError):
abort(403)
aux_ip = request.headers.get('X-Real-IP') or request.headers.get('X-Forwarded-For')
if aux_ip:
logger.debug(f"{PLUGIN_NAME}: /ai-auxiliary-search from proxied IP {aux_ip}")
query = data.get('query', '').strip()
lang = data.get('lang', 'all')
categories = data.get('categories', 'general')
offset = data.get('offset', 0)
if not query:
return jsonify({'results': []})
try:
from searx.search import SearchWithPlugins
from searx.search.models import SearchQuery
from searx.query import RawTextQuery
from searx.webadapter import get_engineref_from_category_list
preferences = getattr(request, 'preferences', None)
disabled_engines = preferences.engines.get_disabled() if preferences else []
rtq = RawTextQuery(query, disabled_engines)
if isinstance(categories, str):
category_list = [c.strip() for c in categories.split(',') if c.strip()]
else:
category_list = categories or ['general']
enginerefs = get_engineref_from_category_list(category_list, disabled_engines)
sq = SearchQuery(
query=rtq.getQuery(),
engineref_list=enginerefs,
lang=lang,
pageno=1,
)
search_obj = SearchWithPlugins(sq, request, user_plugins=[])
result_container = search_obj.search()
raw_results = result_container.get_ordered_results()
raw_infoboxes = getattr(result_container, 'infoboxes', [])
raw_answers = getattr(result_container, 'answers', [])
results, infoboxes, answers = self._parse_aux_results(raw_results, raw_infoboxes, raw_answers)
context_str, new_urls = self._assemble_context(results, infoboxes, answers, offset)
return jsonify({
'context': context_str,
'new_urls': new_urls,
'results': results,
'infoboxes': infoboxes,
'answers': answers,
'query': query
})
except Exception as e:
logger.error(f"{PLUGIN_NAME}: Aux search failed: {e}")
return jsonify({'results': [], 'error': 'Search failed'}), 500
@app.route('/ai-models', methods=['GET'])
def ai_models():
token = request.args.get('tk', '')
models_ip = request.headers.get('X-Real-IP') or request.headers.get('X-Forwarded-For')
if models_ip:
logger.debug(f"{PLUGIN_NAME}: /ai-models from proxied IP {models_ip}")
try:
ts, sig = token.rsplit('.', 1)
expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
abort(403)
except (ValueError, KeyError, AttributeError):
abort(403)
auth_headers = {"Authorization": f"Bearer {self.api_key}"}
p = urlparse(self.endpoint_url)
base = f"{p.scheme}://{p.netloc}"
def fetch_get(start_url):
url = start_url
for _ in range(5):
conn, path = _get_streaming_connection(url)
conn.request("GET", path, headers=auth_headers)
res = conn.getresponse()
if res.status in (301, 302, 307, 308):
location = res.getheader('Location', '')
res.read(); conn.close()
if not location:
return None
url = location if location.startswith('http') else f"{urlparse(url).scheme}://{urlparse(url).netloc}{location}"
continue
return res
return None
for models_url, parse_fn in [
(f"{base}/v1/models", lambda d: [m['id'] for m in d.get('data', [])]),
(f"{base}/api/tags", lambda d: [m['name'] for m in d.get('models', [])]),
]:
try:
res = fetch_get(models_url)
if res and res.status == 200:
models = parse_fn(json.loads(res.read().decode('utf-8', errors='replace')))
if models:
return jsonify({'models': models})
elif res:
res.read()
except Exception as e:
logger.debug(f"{PLUGIN_NAME}: /ai-models attempt {models_url} failed: {e}")
return jsonify({'models': [self.model] if self.model else []})
@app.route('/ai-conversation', methods=['GET', 'POST', 'DELETE'])
def ai_conversation():
if request.method == 'GET':
token = request.args.get('tk', '')
else:
body = request.json or {}
token = body.get('tk', '')
try:
ts, sig = token.rsplit('.', 1)
expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
abort(403)
except (ValueError, KeyError, AttributeError):
abort(403)
if request.method == 'GET':
session_id = request.args.get('session_id', '')
if not session_id:
return jsonify({'turns': []})
turns = _load_conversation(session_id)
return jsonify({'turns': turns[-10:]})
elif request.method == 'POST':
body = request.json or {}
session_id = body.get('session_id', '')
turns = body.get('turns', [])
if session_id:
_save_conversation(session_id, turns)
return jsonify({'ok': True})
else: # DELETE
body = request.json or {}
session_id = body.get('session_id', '')
if session_id:
try:
v = _get_valkey()
v.delete(f"ai:conv:{session_id}")
except Exception as e:
logger.debug(f"{PLUGIN_NAME}: conv delete failed: {e}")
return jsonify({'ok': True})
@app.route('/ai-stream', methods=['POST'])
def handle_ai_stream():
data = request.json or {}
token = data.get('tk', '')
q = data.get('q', '')
lang = data.get('lang', 'all')
try:
ts, sig = token.rsplit('.', 1)
expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
abort(403)
except (ValueError, KeyError, AttributeError):
abort(403)
session_id = data.get('session_id', '')
prior_conv = _load_conversation(session_id) if session_id else []
if prior_conv:
history_lines = []
for turn in prior_conv[-6:]:
role = 'User' if turn.get('role') == 'user' else 'Assistant'
content = turn.get('content', '')[:500]
history_lines.append(f"{role}: {content}")
cross_search_history = '\n'.join(history_lines)
else:
cross_search_history = ''
context_text = data.get('context', '')
prev_answer = (data.get('prev_answer') or '')[-4000:]
req_model = (data.get('model') or '').strip()
effective_model = req_model or self.model
client_ip = request.headers.get('X-Real-IP') or request.headers.get('X-Forwarded-For')
if client_ip:
logger.debug(f"{PLUGIN_NAME}: /ai-stream from proxied IP {client_ip}")
if not self.api_key:
return Response("Missing API key or query", status=400)
intent = _detect_intent(q)
intent_cfg = INTENT_CONFIGS.get(intent, INTENT_CONFIGS['general'])
logger.debug(f"{PLUGIN_NAME}: detected intent '{intent}' for query: {q[:50]}")
today = time.strftime("%Y-%m-%d")
lang_instruction = f" Respond in {lang}." if lang not in ('all', 'auto') else ""
base_sys = self.system_prompt if self.system_prompt else \
"You are a direct, citation-accurate search synthesis engine."
SYSTEM = (
f"{base_sys} Today is {today}.{lang_instruction} "
"Output only your final answer. Do not output your thinking process, "
"reasoning steps, or internal monologue. Begin your response with the "
f"direct answer immediately. {intent_cfg['system_suffix']}"
)
max_source_idx = 0
if context_text:
indices = re.findall(r'\[(\d+)\]', context_text)
if indices:
max_source_idx = max(map(int, indices))
CORE_RULES = [
"Answer the question directly using the provided context.",
"MUST CITE SOURCES by tailing a sentence with [n] or [n,n] etc. If citing general knowledge, use [*].",
"Never explain your process. The user expects a direct response.",
"Use markdown formatting where it improves clarity: **bold** for key terms, bullet lists for enumerations, numbered lists for steps. Keep formatting minimal and purposeful.",
intent_cfg['format'],
"If sources and general knowledge are insufficient, respond with 'Insufficient information to answer.'"
]
if q == "Continue":
task = "CONTINUE: Pick up exactly where previous answer stopped. No repetition. Seamless flow."
elif prev_answer:
task = "FOLLOW-UP: Address the new question using prior context. Prioritize the new query."
else:
task = intent_cfg['task']
grounding = "GROUNDING: KNOWLEDGE GRAPH > DEEP > SHALLOW." if context_text else "GROUNDING: No sources available. Use general knowledge and cite as [*] which means based on general knowledge."
history_rule = "HISTORY: Refer to prior exchange for context. Ideally, do not repeat any claims." if prev_answer else None
instructions = [task] + CORE_RULES + [grounding]
if history_rule:
instructions.append(history_rule)
numbered_instructions = "\n".join(f"{i+1}. {r}" for i, r in enumerate(instructions))
prompt = f"""{SYSTEM}
{context_text or 'None.'}
{prev_answer or 'None.'}
{cross_search_history or 'None.'}
{q}
{numbered_instructions}
"""
job_id = hashlib.sha256(f"{time.time()}{q}".encode()).hexdigest()[:16]
payload_dict = {
"model": effective_model,
"messages": [
{"role": "system", "content": SYSTEM},
{"role": "user", "content": prompt},
{"role": "assistant", "content": ""},
],
"stream": True,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
}
stream_payload = json.dumps(payload_dict)
stream_headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.api_key}",
}
try:
vk = _get_valkey()
vk.set(f"ai:job:{job_id}:status", "running", ex=120)
except Exception as e:
logger.error(f"{PLUGIN_NAME}: Valkey unavailable: {e}", exc_info=True)
return jsonify({"error": "Streaming service unavailable (Valkey connection failed)."}), 503
t = threading.Thread(
target=stream_to_valkey,
args=(job_id, stream_payload, stream_headers, self.endpoint_url, effective_model),
daemon=True,
)
t.start()
if session_id:
turns = _load_conversation(session_id)
turns.append({'role': 'user', 'content': q, 'ts': int(time.time())})
_save_conversation(session_id, turns)
return jsonify({"job_id": job_id})
@app.route('/ai-status/', methods=['GET'])
def ai_status(job_id):
token = request.args.get('tk', '')
try:
ts, sig = token.rsplit('.', 1)
expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
abort(403)
except (ValueError, KeyError, AttributeError):
abort(403)
offset = max(0, int(request.args.get('offset', 0)))
chunks_key = f"ai:job:{job_id}:chunks"
status_key = f"ai:job:{job_id}:status"
try:
vk = _get_valkey()
status = vk.get(status_key)
if status is None:
return jsonify({"error": "Job not found or expired"}), 404
raw_chunks = vk.lrange(chunks_key, offset, -1)
except Exception as e:
logger.error(f"{PLUGIN_NAME}: Valkey error in /ai-status: {e}", exc_info=True)
return jsonify({"error": "Stream service temporarily unavailable"}), 503
done = False
error = None
chunks = []
for chunk in raw_chunks:
if chunk == '__DONE__':
done = True
break
elif chunk.startswith('__ERROR__'):
error = chunk[9:]
done = True
break
else:
chunks.append(chunk)
return jsonify({"chunks": chunks, "done": done, "error": error})
return True
def _fetch_page_text(self, url: str, timeout: int = 5) -> str:
SKIP_DOMAINS = ('youtube.com', 'twitter.com', 'x.com', 'instagram.com', 'facebook.com', 'reddit.com')
try:
if url.endswith('.pdf'):
return ''
if any(d in url for d in SKIP_DOMAINS):
return ''
current_url = url
for _ in range(3): # initial request + up to 2 redirects
parsed = urlparse(current_url)
host = parsed.hostname or ''
if not host:
return ''
port = parsed.port or (443 if parsed.scheme == 'https' else 80)
path = (parsed.path or '/') + ('?' + parsed.query if parsed.query else '')
if parsed.scheme == 'https':
try:
import certifi
ctx = ssl.create_default_context(cafile=certifi.where())
except ImportError:
ctx = ssl.create_default_context()
conn = http.client.HTTPSConnection(host, port, timeout=timeout, context=ctx)
else:
conn = http.client.HTTPConnection(host, port, timeout=timeout)
try:
conn.request('GET', path, headers={'User-Agent': 'Mozilla/5.0 (compatible; SearXNG-AI/1.0)'})
res = conn.getresponse()
if res.status in (301, 302, 303, 307, 308):
location = res.getheader('Location', '')
res.read()
if not location:
return ''
current_url = location if location.startswith('http') else f"{parsed.scheme}://{parsed.netloc}{location}"
continue
if res.status != 200:
return ''
html = res.read(256 * 1024).decode('utf-8', errors='replace')
finally:
conn.close()
html = re.sub(r']*>.*?', '', html, flags=re.DOTALL | re.IGNORECASE)
html = re.sub(r']*>.*?', '', html, flags=re.DOTALL | re.IGNORECASE)
html = re.sub(r']*>.*?', '', html, flags=re.DOTALL | re.IGNORECASE)
html = re.sub(r']*>.*?', '', html, flags=re.DOTALL | re.IGNORECASE)
html = re.sub(r']*>.*?', '', html, flags=re.DOTALL | re.IGNORECASE)
text = re.sub(r'<[^>]+>', '', html)
text = (text.replace('&', '&').replace('<', '<').replace('>', '>')
.replace('"', '"').replace(''', "'").replace(' ', ' '))
text = re.sub(r'\s+', ' ', text).strip()
logger.debug(f"{PLUGIN_NAME}: fetched {len(text)} chars from {url}")
return text[:6000]
return ''
except Exception:
return ''
def _enrich_results(self, clean_results: list, query: str) -> list:
query_tokens = _tokenize(query)
enrich_count = min(5, self.context_deep_count + 2)
for r in clean_results:
r['fetched_content'] = ''
r['relevance_score'] = 0.0
futures_map: dict = {}
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
for r in clean_results[:enrich_count]:
futures_map[executor.submit(self._fetch_page_text, r.get('url', ''))] = r
for future, r in futures_map.items():
try:
text = future.result(timeout=4)
if not text or len(text) < 100:
snippet = r.get('content', '')
if snippet:
r['relevance_score'] = _tfidf_score(query_tokens, _tokenize(snippet))
continue
chunks = _chunk_text(text, chunk_size=512, overlap=64)
best_chunk = ''
best_score = -1.0
for chunk in chunks:
score = _tfidf_score(query_tokens, _tokenize(chunk))
if score > best_score:
best_score = score
best_chunk = chunk
r['fetched_content'] = best_chunk[:800]
r['relevance_score'] = best_score
logger.debug(
f"{PLUGIN_NAME}: [{r.get('url', '')}] "
f"score={best_score:.4f} chunks={len(chunks)}"
)
except Exception as e:
logger.debug(f"{PLUGIN_NAME}: enrich failed for {r.get('url', '')}: {e}")
enriched = [r for r in clean_results[:enrich_count] if r.get('relevance_score', 0) > 0]
not_enriched = clean_results[enrich_count:]
enriched.sort(key=lambda r: r['relevance_score'], reverse=True)
reranked = enriched + not_enriched
seen_urls = {r.get('url') for r in reranked}
for r in clean_results:
if r.get('url') not in seen_urls:
reranked.append(r)
seen_urls.add(r.get('url'))
if enriched:
logger.debug(
f"{PLUGIN_NAME}: reranked {len(enriched)} results, "
f"top score={enriched[0]['relevance_score']:.4f}"
)
return reranked
def _assemble_context(self, clean_results, infoboxes, answers, offset=0) -> tuple[str, list]:
"""Builds context string from normalized search data. Returns (context_str, urls)."""
context_parts = []
result_urls = []
knowledge_graph_lines = []
for ib in infoboxes:
ib_name = ib.get('name', '') or ib.get('infobox', '') or ib.get('title', '')
ib_content = str(ib.get('content', '')).replace('\n', ' ').strip()
if ib_name:
parts = [f"INFOBOX [{ib_name}]:"]
if ib_content:
parts.append(ib_content)
for attr in ib.get('attributes', []):
attr_label = attr.get('label', '')
attr_value = attr.get('value', '')
if attr_label and attr_value:
parts.append(f" {attr_label}: {attr_value}")
knowledge_graph_lines.append(" ".join(parts) if len(parts) == 2 else "\n".join(parts))
for ans_text in answers:
if ans_text and not str(ans_text).startswith('<'):
knowledge_graph_lines.append(f"ANSWER: {str(ans_text)[:300]}")
if knowledge_graph_lines:
context_parts.append("KNOWLEDGE GRAPH:\n" + "\n".join(knowledge_graph_lines))
deep_lines = []
for i, r in enumerate(clean_results[:self.context_deep_count]):
url = r.get('url', '')
result_urls.append(url)
domain = urlparse(url).netloc.replace('www.', '')
date_str = f" ({r.get('publishedDate')})" if r.get('publishedDate') else ""
title = r.get('title', '').replace('\n', ' ').strip()
idx = i + 1 + offset
fetched_content = r.get('fetched_content', '')
if fetched_content:
deep_lines.append(f"[{idx}] {domain}{date_str}: {title}: {fetched_content}")
else:
logger.debug(f"{PLUGIN_NAME}: falling back to snippet for [{idx}] {domain}")
content = str(r.get('content', '')).replace('\n', ' ').strip()[:800]
deep_lines.append(f"[{idx}] {domain}{date_str}: {title}: {content}")
if deep_lines:
context_parts.append("DEEP SOURCES:\n" + "\n".join(deep_lines))
if self.context_shallow_count > 0:
shallow_lines = []
start_idx = self.context_deep_count
end_idx = self.context_deep_count + self.context_shallow_count
for i, r in enumerate(clean_results[start_idx:end_idx]):
url = r.get('url', '')
result_urls.append(url)
domain = urlparse(url).netloc.replace('www.', '')
title = r.get('title', '').replace('\n', ' ').strip()[:60]
idx = i + 1 + start_idx + offset
shallow_lines.append(f"[{idx}] {domain}: {title}")
if shallow_lines:
context_parts.append("SHALLOW SOURCES (headlines):\n" + "\n".join(shallow_lines))
return "\n\n".join(context_parts), result_urls
def post_search(self, request: "SXNG_Request", search: "SearchWithPlugins") -> EngineResults:
results = EngineResults()
try:
if request and hasattr(request, 'headers') and request.headers.get('X-AI-Auxiliary'):
return results
if request and request.form.get('format', 'html') != 'html':
return results
if self.question_mark_required and '?' not in search.search_query.query:
return results
current_tabs = set(search.search_query.categories)
if not current_tabs: current_tabs = {'general'}
if not self.active or not self.api_key or search.search_query.pageno > 1 or not self.allowed_tabs.intersection(current_tabs):
return results
raw_results = search.result_container.get_ordered_results()
raw_infoboxes = getattr(search.result_container, 'infoboxes', [])
raw_answers = getattr(search.result_container, 'answers', [])
q_clean = search.search_query.query.strip()
clean_results, infoboxes, answers = self._parse_aux_results(raw_results, raw_infoboxes, raw_answers)
clean_results = self._enrich_results(clean_results, q_clean)
context_str, _ = self._assemble_context(clean_results, infoboxes, answers)
ts = str(int(time.time()))
lang = search.search_query.lang
sig = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
tk = f"{ts}.{sig}"
# XSS blocking
safe_json = lambda x: json.dumps(x).replace('<', '\\u003c').replace('>', '\\u003e').replace('&', '\\u0026')
session_id = request.cookies.get('sxng_ai_session')
if not session_id:
session_id = hashlib.sha256(
f"{time.time()}{os.urandom(16).hex()}".encode()
).hexdigest()[:24]
js_session_id = safe_json(session_id)
detected_intent = _detect_intent(q_clean)
js_intent = safe_json(detected_intent)
b64_context = base64.b64encode(context_str.encode('utf-8')).decode('utf-8')
total_context_count = self.context_deep_count + self.context_shallow_count
raw_urls = [r.get('url', '') for r in clean_results[:total_context_count]]
js_q = safe_json(q_clean)
js_lang = safe_json(lang)
js_urls = safe_json(raw_urls)
js_b64_context = safe_json(b64_context)
js_tk = safe_json(tk)
js_script_root = safe_json((request.script_root if request else '').rstrip('/'))
js_model_init = safe_json(self.model)
is_interactive = self.interactive
interactive_css = INTERACTIVE_CSS if is_interactive else ''
interactive_html = INTERACTIVE_HTML if is_interactive else ''
interactive_js_init = INTERACTIVE_JS if is_interactive else ''
if is_interactive:
interactive_js_complete = "footer.style.display = 'flex';"
else:
interactive_js_complete = ''
stream_fn_sig = 'async function startStream(overrideQ = null, prevAnswer = null, auxContext = null)'
stream_q = 'overrideQ || q_init' if is_interactive else 'q_init'
stream_body = 'prev_answer: prevAnswer' if is_interactive else ''
js_code = FRONTEND_JS_TEMPLATE \
.replace("__IS_INTERACTIVE__", 'true' if is_interactive else 'false') \
.replace("__TK__", js_tk) \
.replace("__SCRIPT_ROOT__", js_script_root) \
.replace("__MODEL_INIT__", js_model_init) \
.replace("__SESSION_ID__", js_session_id) \
.replace("__INTENT__", js_intent) \
.replace("__CITATION_HELPER_JS__", CITATION_HELPER_JS) \
.replace("__INTERACTIVE_JS_INIT__", interactive_js_init) \
.replace("__STREAM_FN_SIG__", stream_fn_sig) \
.replace("__STREAM_Q__", stream_q) \
.replace("__STREAM_BODY__", ', ' + stream_body if stream_body else '') \
.replace("__INTERACTIVE_JS_COMPLETE__", interactive_js_complete) \
.replace("__JS_LANG__", js_lang) \
.replace("__JS_URLS__", js_urls) \
.replace("__B64_CONTEXT__", js_b64_context) \
.replace("__JS_Q__", js_q)
html_payload = f'''
{interactive_html}
'''
search.result_container.answers.add(results.types.Answer(answer=Markup(html_payload)))
except Exception as e:
logger.error(f"{PLUGIN_NAME}: {e}")
return results