Spiyweb

Graph-based retrieval for RAG, built on spreading activation — like a spider web.

Instead of cutting retrieval off at top-k, the query is injected into a vector graph as an energy seed and spreads outward with decay. Strongly related nodes light up first; weakly related but genuinely connected nodes light up later, through multiple hops. The answer is built from the whole web, not from one cluster of near-duplicates.

Status: Phase 1 — measured on three benchmarks, and the gate is not passed yet. The web beats both baselines on MuSiQue and 2WikiMultihopQA and loses to iterative retrieval on HotpotQA. The numbers are below, including the one that does not flatter the project. The design has been public from day one so the idea can be judged — and challenged — early.

Install and start

Python 3.11+ on Windows, macOS or Linux. One install, one word to start.

Windows (PowerShell)

pip install spiyweb          # or: uv tool install spiyweb
cd C:\path\to\your-project
spiyweb

macOS / Linux

pip3 install spiyweb         # or: uv tool install spiyweb   (pipx works too)
cd ~/path/to/your-project
spiyweb

spiyweb takes the window over: a welcome box with the spider, a transcript, a prompt, a status bar. Everything happens from there - type a question as a plain sentence, or a / command. The input box says what to type first until you have typed anything. Only closing the terminal ends it.

A first session, without leaving the screen:

/demo                    three recorded questions spread on a sample corpus
/doctor                  what is installed, what is missing, and the fix
/install index           the index-time extras and the spaCy model, in the background
/index                   pick a folder of .txt/.md, pick the options, build
who built the tower?     a plain sentence is a question for the active index
/show 2                  the second passage in full: text, source, how the energy got there
/compare who built it?   the same question as plain top-k, and what only the web found

/index runs in the background with its stage and elapsed time in the status line (and the terminal bell when a long build ends), so the screen never freezes; /kill stops it. The index it builds becomes the active one

• the status bar names it, /indexes switches between the ones nearby, and it is remembered next time. /tune damping 0.8 seed 6 changes the spread for the session and asks the last question again; /config holds the lasting knobs (profile, warm follow-ups, ring map, speed, glyphs, colour, mouse). /save writes the query on screen to a Markdown file. /help lists every command, ? the shortcuts, tab completes commands and folders, and up/down remembers earlier sessions.

Your own application can be watched too: start it in a second terminal - or from the prompt with ! python app.py - and every query it makes through spiyweb.open_index(...) plays on screen as it happens.

pip install spiyweb on its own pulls in nothing - the core is pure Python. Building an index needs the index-time extras once (/install index in the monitor, or pip install "spiyweb[index]" plus python -m spacy download xx_ent_wiki_sm).

If the console cannot draw braille or box lines (an old conhost, a plain TERM=dumb), SPIYWEB_ASCII=1 spiyweb uses the plain glyph set; a pipe or a CI job gets the usage text and exit 2 instead of a prompt.

Why

Classic RAG retrieval has two structural weaknesses:

1. A hard cutoff has no notion of indirect relevance. When an answer is distributed across several documents, no single one of them may be among the most similar chunks — so top-k never sees it.
2. Repetition is treated as content. Ten near-identical chunks fill ten context slots and tell the model one thing, badly.

How it works

Formally: spreading activation over a sparse multi-layer graph, mathematically equivalent to Personalized PageRank with a damping factor. It reduces to repeated sparse matrix–vector products — cheap and numerically stable.

HOP 0   Q = 10.0  ->  first contact (cosine):   A = 5.60    C = 4.40
HOP 1   A -> B 2.24, D 1.12      C -> D 1.76, E 0.88 (dies)
HOP 2   D = 1.12 + 1.76 = 2.88   <- converging evidence
HOP 3   energy below threshold, the web stops on its own

RESULT  A 5.60 | C 4.40 | D 2.88 | B 2.24 | F 1.73

D is never the single most similar node to the query, yet it ranks third — because two independent weak paths converged on it. That promotion is the entire value proposition.

Try it

What the monitor shows: the ranking bars growing in hop by hop on the left, the ring map on the right, the ledger line under both, the numbered top passages under that - and, only when the run found them, the things a ranking hides: contradictions kept on both sides, claims the corpus disputes, themes with no bridge between them. No socket, no server, no code in your application: the monitor leaves a marker in .spiyweb/, the library appends its records there while the marker is fresh, the monitor tails them. /find locates the file that imports spiyweb and the index it opens; /lint reads the corpus's shape; /replay plays a recorded query again; examples/hello_web.py is a three-question app to watch first.

The same verbs work without the screen, for scripts and pipes:

pip install "spiyweb[index]"
python -m spacy download xx_ent_wiki_sm

spiyweb index docs/ my-index          # a directory of .txt/.md -> an index
spiyweb query my-index "what happened afterwards"
spiyweb lint my-index                 # what is wrong with the CORPUS

spiyweb index docs/ my-index --propositions adds the second node layer: a local LLM (Ollama, llama3.1:8b by default) splits every passage into short, self-contained facts, and those join the graph beside the passages. It costs one LLM call per passage - about three seconds each on a laptop GPU - and it is the measured difference that matters: on MuSiQue the web over a passage-only graph beat plain top-k by +.005 (inside the noise), and over the same corpus with propositions by +.047, CI [+.029, +.067]. An interrupted run resumes where it stopped; --llm-model, --llm-url and --llm-key-env point it at another model or any OpenAI-compatible API, the last one naming the environment variable that holds the key rather than the key.

lint is the diagnostic that needs no query: it reads the graph's shape and reports islands nothing bridges, hubs that grind arriving energy into dust, near-identical passages that will compete for one seed slot, and the sources that contradict the rest of the corpus.

spiyweb version says which extras are installed and prints the pip install line for the ones that are not — it works on a bare install, which is exactly when you need it to.

The core itself is pure Python with no dependencies at all. Similarities come from the caller; core/ never computes them.

git clone https://github.com/Yigtwxx/spiyweb && cd spiyweb
uv sync --group dev && uv run pytest

from spiyweb import Graph, propagate

graph = Graph.from_edges(
    [
        ("A", "A_dup", 0.0),  # near duplicate, edge suppressed by dedup
        ("A", "B", 0.8),
        ("A", "D", 0.4),
        ("C", "D", 0.6),
        ("C", "E", 0.3),
        ("D", "F", 0.5),
    ]
)
result = propagate(graph, seeds={"A": 0.9, "C": 0.7})

[(node, round(energy, 3)) for node, energy in result.ranked()]
# [('A', 5.625), ('C', 4.375), ('D', 2.875), ('B', 2.25), ('F', 1.725)]
result.activations["D"].contributors  # ('A', 'C') — converging evidence
result.stop_reason  # 'threshold' — the web stopped itself

E is missing because 0.875 of energy reached it against a floor of 1.5, and A_dup is missing because its edge was suppressed and its share redistributed. Neither outcome came from a result-count parameter.

Bring your own corpus

pip install "spiyweb[index]"

import spiyweb
from spiyweb.indexing import DocumentInput, TextUnit, build_index
from spiyweb.indexing import SentenceTransformerEmbedder, load_spacy_pipeline

docs = [
    DocumentInput(source_id="handbook", units=(TextUnit(text=part) for part in parts))
    for parts in my_documents
]
build_index(
    docs,
    "data/mydocs",
    embedder=SentenceTransformerEmbedder(),
    entity_pipeline=load_spacy_pipeline(),
)

with spiyweb.open_index("data/mydocs") as index:
    answer = index.retrieve("who signed off on the change?", profile="precise")

    for passage in answer.passages:
        print(f"{passage.energy:5.2f}  {passage.votes} votes  {passage.text[:70]}")

    print(answer.profile)  # which profile ran; "explore" when you named none
    print(answer.confidence)  # total energy, node count, hop depth
    print(answer.dedup_mode)  # which duplicate rules actually ran
    for path in answer.paths():  # how the energy reached each node
        print(path)

There is no k. The web stops when its energy falls below the threshold - that self-termination is the argument against top-k, so a k= parameter here would quietly reintroduce the thing being argued against. Slice answer.passages if you want fewer.

profile is optional. Name none and pass no config, and the library runs explore (spiyweb.DEFAULT_PROFILE) - the bare RetrievalConfig() carries the canonical worked example above and cannot spread past five seeds, so it is what you get only when you build it yourself, and retrieve() warns if what you built cannot leave hop 0.

open_index wires duplicate suppression correctly, which is not a detail: the mechanism needs a config AND a similarity backend, and this project's own measurement campaign ran with it silently off for want of the second half. answer.dedup_mode is the receipt.

Public API

import spiyweb is the query-time contract: everything in spiyweb.__all__ and nothing else. spiyweb.indexing is the index-time contract, on the same terms; it imports with nothing installed, and only the FAISS-bound names ask for pip install "spiyweb[store]". Anything reached through a submodule path (spiyweb.core.*, spiyweb.evaluation.*) is internal and may change without notice. Both surfaces are snapshot-tested, and changes to them are written down in CHANGELOG.md.

What is different

Spreading activation over graphs is not new (HippoRAG, GraphRAG, LightRAG, RAPTOR). Spiyweb's claimed differentiators are elsewhere:

• Redundancy becomes a vote, not noise. When a near-duplicate is found during propagation, its edge is severed and its energy share redistributed — and the surviving idea's vote count goes up. Repetition turns into corpus-support evidence instead of burning context slots. To our knowledge this dynamic dedup-to-vote conversion has no published equivalent.
• Honesty outputs. Every retrieval returns a confidence score (total energy, node count, hop depth), corpus-gap warnings (two dense clusters with no bridge), contradiction records with a ready-made, LLM-free question for the user, and activation paths as explanations the LLM can cite. The retriever can say "I don't know, and here is why."
• Coloured multi-seed bridging. A decomposed query injects differently coloured seeds; a node where two colours meet is a bridge — exactly where a multi-hop answer lives.
• The web stops itself. Termination is a relative energy threshold, not a "return N results" parameter.

Traces

Every query an open index answers is recorded as a self-contained trace: the activated subgraph, the passages' text, the energy ledger and the settings the call ran with. Nothing else is needed to read it back - no index, no store, no dependency.

Traces are held in memory (the last 200) and cost no disk unless asked:

index = spiyweb.open_index("my-index", trace=spiyweb.TraceConfig(directory="traces"))

That writes JSONL, and a machine that holds no index can read it back:

from spiyweb import load_traces

for record in load_traces("traces/traces.jsonl"):
    print(record.query, record.profile, record.ledger)

The layout of a recorded call - hop rings, layers, a side-by-side against plain top-k - lives in spiyweb.scene (numpy only, spiyweb[view]), and the hop-ring rule itself in spiyweb.rings (nothing at all), so one query produces one picture no matter what draws it. The terminal monitor is what draws it now; the browser face that used to was removed after 0.1.2.

Phase 1 plan

Phase 1 also succeeds if it fails clearly: a reliable negative number is a better outcome than a polished library built on an unmeasured assumption.

Results so far

S@5 = 0.65 · support recall + 0.35 · Novelty@5. 1000 questions per run, paired bootstrap intervals, winning configuration applied unchanged to every dataset after the first.

RESULTS.md is the complete record — one table with every number Phase 1 produced: the gate, the metric decomposition, the per-hop breakdown, all five failed rescue rounds, every mechanism ablation, the contradiction-detection measurements, and the seven limits that go with them.

The gate asks for both baselines, so HotpotQA is a failure, not a footnote. The diagnosis is that the advantage is depth-dependent: 74.5% of HotpotQA questions already have all their gold in the dense top-5, where Novelty@5 is 0 by construction and spreading can only displace.

Five pre-registered attempts to close that gap were all recorded as negative, and Phase 1 closed on that. Four worked in the ranking layer (a confidence gate, blending, novelty-free slots); the fifth moved inside propagation, letting the decomposition's colour count pick the query profile. It won +.0025 CI [+.0005,+.0050] on the tuning set, was not confirmed on the seed-123 set, and changed HotpotQA by exactly zero — not one question's window moved. One earlier attempt helped HotpotQA and hurt the deeper sets, which is exactly the trade the gate is meant to refuse.

So the honest headline is a result with a condition attached: the advantage over both baselines is real on the deeper sets and does not transfer to a benchmark that is entirely 2-hop.

Cost: ~2.3 LLM calls per question at query time, against roughly 4 for the iterative baseline.

Documentation

• CLAUDE.md — condensed engineering ground truth: the settled invariants, the architecture boundaries, and the rules a change must not break. The full design specification and the decision log (every choice with its rationale and its rejected alternatives) are kept privately.

The website

site/ holds the project's landing page: a static Astro site, no UI framework, deployed to Vercel with site as the project root. The engravings are public-domain spider plates, halftoned at build time from site/images.manifest.json; the outputs in site/public/images are committed, so a build never fetches anything.

cd site
npm install
npm run dev      # local preview
npm run build    # static output in site/dist
npm run images   # re-halftone after editing images.manifest.json
npm run favicon  # re-render the favicon set from scripts/favicon.source.svg

Contributing

Design feedback is welcome right now — especially prior art for the dedup-to-vote mechanism. See CONTRIBUTING.md.

License

Apache-2.0 © 2026 Yigit Erdogan