tofu-search 0.3.0

Multi-engine web search, vertical lookups, and content fetching with optional LLM filtering — standalone library from the Tofu AI assistant

🔍 tofu-search

Multi-engine web search + content fetching with optional LLM filtering — a standalone Python library extracted from the Tofu AI assistant.

This is a full re-extraction that keeps 100% of Tofu's current search/fetch capabilities: every engine, the structured "vertical" lookups, one-hop deepening, the SPA/bot-protection Playwright fallback, authenticated-source fetching, and the host-browser fallback — the last two exposed through optional provider seams so the library stays dependency-free when used standalone.

Features

• Multi-engine search (parallel): DuckDuckGo (HTML + API), Brave, Bing, SearXNG, Marginalia — plus Xiaohongshu when an auth-source provider supplies a logged-in session.
• Vertical / structured search: auto-detects CVE IDs, arXiv IDs, DOIs, stock tickers, PyPI/npm packages, GitHub repos, IP addresses, Hugging Face daily papers, and Semantic Scholar related-work — answered from the relevant free API alongside web results.
• Content deduplication: Jaccard similarity on shingles (CJK + Latin aware).
• Concurrent page fetching: Race-to-N strategy with SSL fallback + a per-domain circuit breaker.
• One-hop deepening (opt-in): follow the best query-relevant outbound links one hop deeper, bounded like a crawl budget.
• LLM content filter (optional): relevance verdict + noise removal. When no LLM is configured the step is silently skipped (raw text returned as-is).
• BM25 reranking: pure-Python, no external API calls.
• SPA / bot-protection support: optional Playwright fallback for JS-rendered and challenge pages.
• PDF extraction: optional pymupdf / pymupdf4llm integration.
• File assets: text-based assets (SVG, JSON, XML, YAML, CSS, JS, source code) are returned as their raw source by fetch_page_content; binary assets can be downloaded with the size-/SSRF-guarded fetch_url_bytes.
• Host integration seams: register a browser provider (fetch/search via a real browser the user controls) and an auth-source provider (cookies/proxy for login-walled domains) — both no-ops by default.

Quick Start

pip install tofu-search

Basic search (no LLM required)

from tofu_search import search

results = search("Python asyncio tutorial")
for r in results:
    print(f"{r['title']}: {r['url']}")
    if r.get('full_content'):
        print(f"  {r['full_content'][:200]}...")

With OpenAI content filtering

from tofu_search import search, configure

configure(
    llm_api_key="sk-...",
    llm_base_url="https://api.openai.com/v1",
    llm_model="gpt-4o-mini",
)

results = search("Python asyncio tutorial")

With a custom LLM callable

from tofu_search import search, configure

def my_llm(messages, **kwargs):
    # Your LLM call — receives OpenAI-format messages.
    # kwargs may include: stop, temperature, timeout
    return "response text"

configure(llm_function=my_llm)
results = search("Python asyncio tutorial")

Fetch a single URL

from tofu_search import fetch_url

content = fetch_url("https://example.com")
if content:
    print(f"Got {len(content)} characters")

Text-based file assets (SVG, JSON, XML, YAML, CSS, JS, source code) are returned as their raw source — there's nothing to "extract":

svg = fetch_url("https://example.com/icon.svg")   # the <svg>…</svg> markup

For binary assets (images, archives, fonts, Office docs) use fetch_url_bytes, which returns the undecoded body + content-type under the same scheme / SSRF / size-cap policy as the text pipeline:

from tofu_search import fetch_url_bytes, looks_like_text_asset

got = fetch_url_bytes("https://example.com/logo.png")
if got:
    raw, content_type = got
    open("logo.png", "wb").write(raw)

looks_like_text_asset("https://example.com/a.svg")   # True
looks_like_text_asset("https://example.com/a.png")   # False

Vertical (structured-identifier) search

from tofu_search import detect_vertical_intent, search_vertical

domain, identifier, params = detect_vertical_intent("CVE-2021-44228")
record = search_vertical(domain, identifier, params)
print(record['content'])   # CVSS score, description, references from NVD

# Or force a domain-level fan-out (free-text → Hugging Face + Semantic Scholar):
from tofu_search import search_vertical_domain
print(search_vertical_domain('academic', 'mamba state space models')['content'])

Host integration (provider seams)

The standalone library never imports a host application. To unlock the two host-only capabilities, register a provider — dependency points inward (host → library), exactly like a plugin.

from tofu_search import (
    BrowserProvider, AuthSourceProvider,
    register_browser_provider, register_auth_source_provider,
)

class MyBrowser(BrowserProvider):
    def is_connected(self): return True
    def fetch_url(self, url, *, max_chars=None, timeout=15): ...
    def search(self, query, *, max_results=8): ...

class MyAuth(AuthSourceProvider):
    def match_source(self, url): ...      # → {'domain','cookies','proxy',...} | None
    def get_source(self, domain): ...

register_browser_provider(MyBrowser())       # last-resort fetch/search fallback
register_auth_source_provider(MyAuth())      # cookies for login-walled domains

When no provider is registered, the browser fallback and authenticated fetch paths are inert no-ops — the anonymous HTTP + Playwright pipeline runs as normal.

Configuration

from tofu_search import configure

configure(
    # Search / fetch settings
    fetch_top_n=6,                 # Max results to return
    fetch_timeout=15,              # HTTP timeout per request (seconds)
    fetch_max_chars_search=60000,  # Max chars per page in search results
    fetch_max_chars_direct=200000, # Max chars for direct fetch_url()

    # LLM settings (for content filter)
    llm_api_key="sk-...",
    llm_base_url="https://api.openai.com/v1",
    llm_model="gpt-4o-mini",
    # Or a custom callable instead:
    # llm_function=my_callable,

    # Filter settings
    filter_enabled=True,           # Enable/disable LLM filter
    filter_min_chars=3000,         # Min chars to trigger LLM filter
)

Many settings also read from environment variables: FETCH_TOP_N, FETCH_TIMEOUT, FETCH_MAX_CHARS_SEARCH, FETCH_MAX_CHARS_DIRECT, FETCH_MAX_CHARS_PDF, FETCH_MAX_BYTES. One-hop deepening is enabled with SEARCH_DEEPEN_HOPS=1 (or per call: perform_web_search(..., deepen=True)). Semantic Scholar raises its rate limit with SEMANTIC_SCHOLAR_API_KEY.

Pipeline

perform_web_search runs an overlapping streaming pipeline:

1. Multi-engine search: engines fire in parallel; each engine's URLs are deduped and submitted to the fetch pool the moment they arrive (the first page fetch starts before slow engines finish).
2. URL dedup: scheme/trailing-slash-insensitive keys.
3. Content dedup: Jaccard similarity on title+snippet shingles.
4. Page fetch: concurrent HTTP with race-to-N; SSL retry, circuit breaker, Playwright fallback for SPA/bot-protection pages.
   • 4b. Deepen (opt-in): one hop along the best query-relevant links.
5. LLM content filter (optional): relevance verdict + noise removal.
6. BM25 rerank: score documents against the query, select top-N.

Step 5 is automatically skipped when no LLM is configured.

Optional Dependencies

# SPA / JS-rendered page support
pip install tofu-search[playwright]
python -m playwright install chromium

# PDF extraction
pip install tofu-search[pdf]

# Everything
pip install tofu-search[all]

Or just run ./install.sh (see below).

Install script

./install.sh            # core deps
./install.sh --all      # core + playwright + pdf, and installs chromium
./install.sh --playwright
./install.sh --pdf

License

MIT