hubmesh

# hubmesh

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<!-- mcp-name: io.github.DemigodDSK/hubmesh -->

**Centrality-aware GraphRAG retrieval planner. Drop-in layer over any vector DB.**

`hubmesh` is a Python library that improves multi-hop RAG quality on top of an existing
vector database. You don't replace your infrastructure — you add a smart planner between
your vector DB and your LLM.

## What problem this solves

Naive vector retrieval ("embed query, get top-k by cosine similarity") fails on multi-hop
questions like *"Where was the founder of the company that acquired Slack born?"* The
correct answer requires retrieving entities along a reasoning path, not the single most
similar item.

GraphRAG and HippoRAG showed that running a small Personalized PageRank over a knowledge
graph at query time can substantially improve multi-hop retrieval. `hubmesh` extends
that line with two contributions:

1. **Multi-component seed selection.** Instead of picking PPR seeds by raw query
   similarity (which picks wrong-community seeds at high feature overlap), seeds are
   chosen by a multi-component score combining query relevance, structural fit, and
   coverage diversity.
2. **Budget-aware context packing.** Once relevant entities are scored, pack them into
   the LLM's context window with explicit coverage and redundancy control rather than
   just truncating top-k.

The multi-component scoring pattern is adapted from the NNSI framework
(Naidu Dsk, ICOMP'25 — to appear) for SDN topology
optimization, repurposed here for retrieval planning.

## Quickstart

### In-memory (testing, small corpora)

```python
from hubmesh import Planner
from hubmesh.adapters import InMemoryStore

embed = ...   # callable: text -> np.ndarray
docs = [...]  # list of Document or strings or dicts

store = InMemoryStore.from_documents(docs, embed=embed)
planner = Planner(store=store, embed=embed)
result = planner.retrieve(query="...", top_k=10, budget_tokens=4000)
```

### Qdrant adapter (production)

```python
from hubmesh import Planner
from hubmesh.adapters import QdrantStore

store = QdrantStore.from_documents(docs)                          # in-memory
store = QdrantStore.from_documents(docs, path="./qdrant_data")    # on-disk
store = QdrantStore.from_documents(docs, url="http://localhost:6333")  # remote

planner = Planner(store=store, embed=embed)
result = planner.retrieve(query="...", top_k=10)
```

### Chroma adapter

```python
from hubmesh.adapters import ChromaStore

store = ChromaStore.from_documents(docs)                          # ephemeral
store = ChromaStore.from_documents(docs, persist_directory="./chroma_data")
store = ChromaStore.from_documents(docs, host="localhost", port=8000)
```

### Multi-hop / KG mode

```python
from hubmesh.kg import build_entity_kg
import spacy

nlp = spacy.load("en_core_web_sm")
kg = build_entity_kg(docs, nlp=nlp)

planner = Planner(store=store, kg=kg, nlp=nlp)
result = planner.retrieve(query="Where was the founder of the company that bought Slack born?",
                          top_k=10, budget_tokens=4000)

# RetrievalResult includes reasoning paths showing why each doc was returned
for path in result.reasoning:
    print(f"  score={path.score:.3f}  {' → '.join(path.node_ids)}")
```

### LLM-extracted KG (richer than spaCy)

```python
from hubmesh.kg_llm import build_entity_kg_llm
from hubmesh.entity_linker import EmbeddingLinker, make_st_embedder

def llm(prompt):  # provider-agnostic — bring your own
    return your_llm_call(prompt)

kg = build_entity_kg_llm(docs, llm=llm, cache_path="kg_cache.json")

# optional: cross-document entity dedup — same Linker protocol as the spaCy path
kg = build_entity_kg_llm(docs, llm=llm, cache_path="kg_cache.json",
                         linker=EmbeddingLinker(embed=make_st_embedder()))

planner = Planner(store=store, kg=kg)
```

### Better entity linking

```python
from hubmesh.kg import build_entity_kg
from hubmesh.entity_linker import EmbeddingLinker, make_st_embedder

# Cluster surface variations: "United States" / "U.S." / "USA" → one entity
linker = EmbeddingLinker(embed=make_st_embedder(), threshold=0.82)
kg = build_entity_kg(docs, linker=linker)
```

### Iterative multi-hop: let your agent drive

```python
r1 = planner.retrieve(query=question, top_k=5)

# your agent reads r1, spots the bridge entity, then aims hop 2 at it:
r2 = planner.retrieve(
    query=question, top_k=5,
    seed_entities=["Nimbus Analytics"],           # merged with the query's own seeds
    exclude_docs=[s.doc.id for s in r1.sources],  # don't re-retrieve consumed docs
)
```

Seed mentions resolve through the alias index, so free-text entity names
work. The query path stays deterministic and LLM-free — the planning
intelligence lives in the caller.

### MCP server: plug hubmesh into any agent

```bash
pip install "hubmesh[mcp]"
python -m spacy download en_core_web_sm
```

```json
{"mcpServers": {"hubmesh": {"command": "hubmesh-mcp"}}}
```

Exposes the planner as deterministic operator tools over stdio —
`index_corpus`, `retrieve` (seed-steerable, as above), `resolve_entities`,
`entity_neighbors`, `path_between`, `get_document`, `graph_stats`,
`list_corpora`. Your agent is the solver: it decomposes the question,
reads each hop, and aims the next one; the server answers in
milliseconds with zero LLM calls. Corpora persist as plain JSON/NPZ
under `~/.hubmesh/corpora`.

The server warms up models and persisted corpora in the background at
launch (~5-10s on first run), so tool calls stay fast from the start —
relevant for strict-timeout connector clients (Perplexity, etc.).

For web-based connector clients, serve SSE natively — no gateway
process needed:

```bash
hubmesh-mcp --transport sse --port 8000 --allow-tunnel
ngrok http 8000     # paste https://<your-url>/sse into the connector
```

Tunnel field notes (from a live Perplexity integration): **ngrok works**
(free tier included); **cloudflared quick tunnels buffer SSE bodies**
and hang tool calls; **supergateway is unnecessary** here and crashes
on reconnect. `--allow-tunnel` accepts the tunnel's forwarded Host
header — without it, proxied requests get 421 Misdirected Request.

Full field report — setup, error decoder, a 9/9 test battery run
through Perplexity, and two findings about reasoning-model behaviour —
in [docs/perplexity.md](docs/perplexity.md).

### Chunking long documents

```python
from hubmesh import chunk_by_sentences, chunk_documents

chunks = chunk_documents(
    [{"id": "doc1", "text": long_text}, ...],
    strategy="sentences", target_tokens=200,
)
# Then embed chunks and index normally
```

## Installation

```bash
pip install hubmesh                   # core
pip install "hubmesh[qdrant]"         # Qdrant adapter
pip install "hubmesh[chroma]"         # Chroma adapter
pip install "hubmesh[kg]"             # entity-linked KG (spaCy)
pip install "hubmesh[linker]"         # embedding-based entity linker
pip install "hubmesh[all]"            # everything
python -m spacy download en_core_web_sm   # required for KG mode
```

## Design

```
query → first-pass ANN  → induced subgraph → multi-component scoring
                              ↓                        ↓
                       community anchoring → Personalized PageRank
                              ↓                        ↓
                              └─────→ ranking → budget-aware packing → context
```

Each layer is independently testable and replaceable. Adapters wrap your existing vector
DB so you don't have to migrate.

## Benchmarks

**Headline:** on multi-hop QA, hubmesh's KG mode beats both naive cosine
retrieval and a HippoRAG-style PPR-only ablation that uses the same KG,
at every hop depth.

| Benchmark | Setting | recall@10 vs naive |
|---|---|---:|
| **HotpotQA** dev, **N=7405** (full) | KG mode | **+5.90 pts** |
| HotpotQA dev, N=500 | KG mode | **+5.0 pts** |
| MuSiQue dev, N=300, 2-hop | KG mode | **+6.0 pts** |
| MuSiQue dev, N=300, 3-hop | KG mode | +3.2 pts |
| MuSiQue dev, N=300, 4-hop | KG mode | **+5.0 pts** |

All rows measured with v0.4.0 defaults (alias-indexed seeds + NNSI-KG
convergence; ablation JSONs committed in `benchmarks/`). Disclosed:
convergence trades top-rank precision for depth recall — recall@2 is
**−0.75 pts vs naive on full dev** (dips ≤0.5 at smaller n); if you
retrieve with `top_k=2`, set `use_convergence=False`. Multi-seed
queries cost ~1.5–1.8× (still zero LLM tokens, deterministic).

vs PPR-only ablation on the same KG: **+29.8 pts** on HotpotQA at N=500
(measured on v0.2.0) — the multi-component scoring is doing the work,
not just "having a graph."

On the full N=7405 HotpotQA dev: hubmesh hits **75.2% supporting-fact
recall@10** vs naive cosine's **69.3%** (+4.21 pts at recall@5;
recall@2 −0.75, disclosed above).

Latency: **~22 ms** mean / 26 ms p95 per query on a 7K-node KG (after PPR
matrix caching); ~3 s/query at the 66K-paragraph full-dev scale with
v0.4 convergence on.

See [BENCHMARKS.md](BENCHMARKS.md) for the full methodology, ablations,
per-hop breakdown, and notes on what this proves and doesn't.

Reproduce:
```bash
python benchmarks/run_hotpotqa.py --n 500 --kg
python benchmarks/run_musique.py  --n 300 --kg
python benchmarks/profile_query.py        # latency profile
```

## Status

Pre-alpha (v0.4.0). Core algorithms implemented and validated; adapters for
in-memory, Qdrant, and Chroma; entity-linked KG with both spaCy NER and
LLM-based extraction (both linker-aware); alias-indexed entity resolution;