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searxng/searx/plugins/ai_summary.py
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jasonwitty b47fd08cb7 [feat] plugin: AI summary of search results via local Ollama server
Add an optional, disabled-by-default plugin that shows an AI generated
summary at the top of the result page, generated by a (local) Ollama
server:

- async: the result page is never delayed; a client plugin streams the
  answer (NDJSON over a new /ai_summary endpoint) into a placeholder
  answer with a typing indicator, collapsed behind a More button, with
  an inline follow-up chat
- trigger: first page of general searches only, skipped when an infobox
  or instant answer already answers the query
- grounding (per-user preference): send the top result snippets as
  context, the model answers from them instead of its own knowledge
- configuration: new AI Summary preferences tab (server URL, model,
  grounding) with instance defaults in a new ai_summary: settings
  section; all three preferences can be locked for public instances

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-28 10:47:18 -07:00

303 lines
12 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-or-later
"""Plugin that displays an AI generated summary of the search query at the top
of the result page. The summary is generated by a (local) `Ollama`_ server.
The Ollama server URL and the model are configured by the user in the *AI
Summary* tab of the preferences (``ai_summary_server``, ``ai_summary_model``);
the administrator can configure instance wide defaults in the ``ai_summary:``
section and lock the preferences via :ref:`settings preferences`.
.. attention::
A user configurable server URL allows any user of the instance to make the
SearXNG server send requests to a URL of their choice (`SSRF`_), and each
summary is real LLM work. This plugin is intended for private instances --
on a public instance, lock the ``ai_summary_server``, ``ai_summary_model``
and ``ai_summary_grounding`` preferences and configure the ``ai_summary:``
section instead.
The result page is never delayed by this plugin: it only places an empty
placeholder (:py:obj:`searx.result_types.AiSummary`) in the answer area, which
is filled asynchronously by the client (``client/simple/src/js/plugin/
AiSummary.ts``) from the ``/ai_summary`` endpoint (registered in
:py:obj:`searx.webapp`). The endpoint streams the tokens from Ollama's
``/api/chat`` to the client as `NDJSON`_.
A summary is only generated on the first page of a *general* search and only
if no engine has contributed an infobox (e.g. wikipedia / wikidata) or an
instant answer (e.g. ddg definitions) -- in these cases the query is most
likely a lookup of a well known term that is already answered.
The requests to the Ollama server are sent directly (not via
:py:obj:`searx.network`), an outgoing proxy configuration is deliberately not
applied to reach an Ollama server in the local network.
Configuration of the defaults (:py:obj:`searx.ai_summary.SettingsAISummary`):
.. code:: yaml
ai_summary:
base_url: "http://127.0.0.1:11434"
model: "llama3.2:3b"
.. code:: yaml
plugins:
searx.plugins.ai_summary.SXNGPlugin:
active: false
.. _Ollama: https://ollama.com/
.. _NDJSON: https://github.com/ndjson/ndjson-spec
.. _SSRF: https://owasp.org/www-community/attacks/Server_Side_Request_Forgery
"""
import typing as t
import json
import logging
import re
import time
from urllib.parse import urlparse
import flask
import httpx
from flask_babel import gettext
from searx import get_setting
from searx.ai_summary import SettingsAISummary, build_ollama_messages
from searx.extended_types import sxng_request
from searx.result_types import EngineResults
import searx.ai_summary
from . import Plugin, PluginInfo
if t.TYPE_CHECKING:
from searx.search import SearchWithPlugins
from searx.extended_types import SXNG_Request
from . import PluginCfg
VALID_ROLES = ("user", "assistant")
MODEL_NAME_REGEXP = re.compile(r"[A-Za-z0-9._:/-]{1,128}")
def _get_client(base_url: str, cfg: SettingsAISummary) -> httpx.Client:
"""HTTP client for one request to the Ollama server at ``base_url``."""
return httpx.Client(
base_url=base_url,
timeout=httpx.Timeout(connect=cfg.connect_timeout, read=cfg.read_timeout, write=10.0, pool=10.0),
)
def _valid_server(url: str) -> bool:
try:
parsed = urlparse(url)
except ValueError:
return False
return parsed.scheme in ("http", "https") and bool(parsed.netloc) and len(url) <= 256
def _user_server(request: "SXNG_Request", cfg: SettingsAISummary) -> str:
"""The Ollama server URL for this request: the user's ``ai_summary_server``
preference, or the administrator's default."""
return str(request.preferences.get_value("ai_summary_server") or "").strip() or cfg.base_url
class SXNGPlugin(Plugin):
"""Plugin that adds the AI summary placeholder to the result page, the
``/ai_summary`` endpoint itself is registered in :py:obj:`searx.webapp`."""
id = "ai_summary"
def __init__(self, plg_cfg: "PluginCfg"):
super().__init__(plg_cfg)
self.info = PluginInfo(
id=self.id,
name=gettext("AI summary"),
description=gettext(
"Show an AI generated summary of the search query on top of the"
" result page (uses an Ollama server, see the settings below)."
),
preference_section="ai",
)
def init(self, app: "flask.Flask") -> bool:
cfg: SettingsAISummary = get_setting("ai_summary")
if cfg.base_url:
searx.ai_summary.MODELS = list(cfg.models) or self._probe_models(cfg)
if not cfg.model and searx.ai_summary.MODELS:
cfg.model = searx.ai_summary.MODELS[0]
if cfg.model and cfg.model not in searx.ai_summary.MODELS:
searx.ai_summary.MODELS.insert(0, cfg.model)
return True
def _probe_models(self, cfg: SettingsAISummary) -> list[str]:
"""Request the list of models from the Ollama server (``GET
/api/tags``). The server might not be up when SearXNG starts, a
failing probe only leaves the model suggestion list empty."""
try:
with _get_client(cfg.base_url, cfg) as client:
resp = client.get("/api/tags")
resp.raise_for_status()
models = [model["name"] for model in resp.json().get("models", [])]
except (httpx.HTTPError, ValueError, KeyError) as exc:
self.log.warning("can't request model list from %s: %s", cfg.base_url, exc)
models = []
return models or ([cfg.model] if cfg.model else [])
def post_search(self, request: "SXNG_Request", search: "SearchWithPlugins") -> EngineResults | None:
results = EngineResults()
sq = search.search_query
cfg: SettingsAISummary = get_setting("ai_summary")
skip = (
sq.pageno > 1
# post_search is also called for the json, csv and rss formats,
# the placeholder is only useful on the HTML result page
or request.form.get("format", "html") != "html"
or "general" not in sq.categories
# an infobox (e.g. wikipedia / wikidata) or an instant answer
# (e.g. ddg definitions) most likely already answers the query
or bool(search.result_container.infoboxes)
or bool(search.result_container.answers)
or not sq.query.strip()
# without an Ollama server (user preference or admin default)
# there is nothing to show
or not _user_server(request, cfg)
)
if skip:
return None
grounding = bool(request.preferences.get_value("ai_summary_grounding"))
results.add(results.types.AiSummary(query=sq.query, grounding=grounding))
return results
def _bad_request(msg: str) -> flask.Response:
return flask.Response(json.dumps({"error": msg}), status=400, mimetype="application/json")
def _validate_messages(messages: t.Any, cfg: SettingsAISummary) -> list[dict[str, str]]:
if not isinstance(messages, list) or not messages or len(messages) > cfg.max_history_messages:
raise ValueError("invalid messages")
for msg in messages:
if not isinstance(msg, dict) or msg.keys() != {"role", "content"}:
raise ValueError("invalid message")
if msg["role"] not in VALID_ROLES or not isinstance(msg["content"], str):
raise ValueError("invalid message")
if not msg["content"].strip() or len(msg["content"]) > cfg.max_message_length:
raise ValueError("invalid message")
if messages[-1]["role"] != "user":
raise ValueError("last message is not a user message")
return messages
def _validate_context(context: t.Any, cfg: SettingsAISummary) -> list[dict[str, str]]:
if not isinstance(context, list) or len(context) > cfg.max_context_items:
raise ValueError("invalid context")
for item in context:
if not isinstance(item, dict) or not item.keys() <= {"title", "url", "snippet"}:
raise ValueError("invalid context item")
for val in item.values():
if not isinstance(val, str) or len(val) > cfg.max_message_length:
raise ValueError("invalid context item")
return context
def _validate_payload(payload: t.Any, cfg: SettingsAISummary) -> tuple[list[dict[str, str]], list[dict[str, str]]]:
"""Validate the request body of the ``/ai_summary`` endpoint and return
the ``messages`` and ``context`` lists. Raises a :py:obj:`ValueError` for
any malformed payload."""
if not isinstance(payload, dict):
raise ValueError("payload is not an object")
return _validate_messages(payload.get("messages"), cfg), _validate_context(payload.get("context", []), cfg)
def ai_summary_view() -> flask.Response:
"""Stream an AI generated answer for the messages in the request body,
response is NDJSON: ``{"delta": ..}`` lines followed by one final
``{"done": true, ..}`` line."""
cfg: SettingsAISummary = get_setting("ai_summary")
if SXNGPlugin.id not in sxng_request.user_plugins:
return flask.Response(json.dumps({"error": "plugin is not enabled"}), status=403, mimetype="application/json")
try:
messages, context = _validate_payload(sxng_request.get_json(force=True, silent=True), cfg)
except ValueError as exc:
return _bad_request(str(exc))
server = _user_server(sxng_request, cfg)
if not _valid_server(server):
return _bad_request("no valid Ollama server configured")
model = str(sxng_request.preferences.get_value("ai_summary_model") or "").strip() or cfg.model
if not MODEL_NAME_REGEXP.fullmatch(model):
return _bad_request("no valid model configured")
ollama_payload = {
"model": model,
"messages": build_ollama_messages(cfg, messages, context),
"stream": True,
"keep_alive": cfg.keep_alive,
}
# open the upstream connection before streaming, a connection error is
# reported as HTTP 502 instead of a line in an already started stream
client = _get_client(server, cfg)
stream_ctx = client.stream("POST", "/api/chat", json=ollama_payload)
upstream = None
try:
upstream = stream_ctx.__enter__() # pylint: disable=unnecessary-dunder-call
if upstream.status_code != 200:
stream_ctx.__exit__(None, None, None)
upstream = None
except httpx.HTTPError:
upstream = None
if upstream is None:
client.close()
return flask.Response(json.dumps({"error": "upstream error"}), status=502, mimetype="application/json")
# from here on nothing must be read from the request context, the
# generator runs after the request context has been torn down
log = logging.getLogger("searx.plugins.ai_summary")
def ndjson(obj: dict[str, t.Any]) -> bytes:
# the generator bypasses flask's response encoding (direct_passthrough)
return (json.dumps(obj) + "\n").encode()
def generate():
start = time.monotonic()
try:
for line in upstream.iter_lines():
if time.monotonic() - start > cfg.stream_timeout:
yield ndjson({"done": True, "error": "timeout"})
return
if not line.strip():
continue
data = json.loads(line)
if data.get("done"):
yield ndjson({"done": True, "model": model})
return
delta = data.get("message", {}).get("content", "")
if delta:
yield ndjson({"delta": delta})
yield ndjson({"done": True, "model": model})
except (httpx.HTTPError, ValueError) as exc:
log.warning("error while streaming from Ollama: %s", exc)
yield ndjson({"done": True, "error": "upstream error"})
finally:
stream_ctx.__exit__(None, None, None)
client.close()
return flask.Response(
generate(),
mimetype="application/x-ndjson",
headers={"Cache-Control": "no-store", "X-Accel-Buffering": "no"},
direct_passthrough=True,
)