Text Search and Hybrid Retrieval¶

KGLite has two ranked-retrieval lanes, and they find different things.

The lexical lane — a BM25 index built by build_text_index(), queried with the Cypher scalar text_bm25() — ranks documents by word overlap, weighting each word by how rare it is in the corpus. It finds the exact term: a product code, a surname, an error string, a rare noun. It needs no model, no vectors and no GPU, and it answers 'ORA-01555' correctly the first time it sees it.

The semantic lane — embeddings plus vector_score() / search_text(), see Semantic Search — ranks by meaning. It finds the paraphrase that shares no word with the query, and it does not care that the user typed “car” and the document says “vehicle”.

Neither is a superset of the other, and the failure modes are opposite: an embedding blurs away the rare token that is the query, and a keyword index cannot see a synonym. That is why the interesting configuration is both, in one query — which in KGLite is one Cypher statement rather than two systems and a merge step, because score_fuse() combines the lanes per row while ordinary MATCH / WHERE does the graph part around them.

You want…	Lane
Exact terms, codes, names, identifiers	lexical (`text_bm25`)
Paraphrase, synonymy, “something like this”	semantic (`vector_score`)
A short query typed by a human	both, fused
No embedding model in the deployment	lexical only
Non-text similarity (images, feature vectors)	semantic only

This guide is the Python-side walk-through. The exact Cypher semantics of text_bm25() and score_fuse() — argument forms, null rules, weights — live in the Cypher reference under Lexical search and Fusing the lexical and semantic lanes.

Build the index¶

The index is opt-in and explicit, like create_index() and build_vector_index(): nothing builds one for you, and a graph that never calls build_text_index() never pays for one.

One index covers one (node type, property) pair.

import kglite
import pandas as pd

articles = pd.DataFrame({
    "id": [1, 2, 3, 4],
    "title": [
        "Low-light photosynthesis",
        "Rust memory model",
        "Shade tolerance in ferns",
        "Chlorophyll assays",
    ],
    "body": [
        "Photosynthesis in low light depends on chlorophyll density.",
        "Ownership and borrowing govern memory in Rust.",
        "Ferns tolerate deep shade and low light for long periods.",
        "A chlorophyll assay measures pigment concentration.",
    ],
})

graph = kglite.KnowledgeGraph()
graph.add_nodes(articles, "Article", "id", "title")

report = graph.build_text_index("Article", "body")
print(report)
# {'indexed': 4, 'skipped': 0, 'terms': 26}

indexed is the number of documents in the corpus, skipped the nodes passed over, and terms the size of the resulting vocabulary.

What counts as a document. Every node of the type whose property holds a string — including the empty string, which indexes as a document with no terms and still counts in the corpus statistics. A node whose property is absent or holds a number, a list, or anything else non-string is skipped and counted in skipped: BM25 indexes text, and a stringified number is not text.

What counts as a word. Runs of alphanumeric characters are terms and everything else separates them; terms are lowercased per character. The rule is unicode-aware — Tromsø is one token, not two — and it is the same rule text_normalize() exposes, applied identically at build time and at query time so the two cannot drift.

There is no stemming and no stopword list. That is deliberate rather than unfinished: BM25’s IDF term already discounts a word that appears in nearly every document, statistically, without a per-language list to maintain or to be wrong about. 'the' in a query contributes almost nothing to the ranking because almost every document has it. There is also no CJK segmentation — a run of Han characters with no separator is one term.

Memory. Roughly 26 bytes per token on a near-worst-case synthetic corpus (2,000 documents of 40 tokens drawn from a 5,000-term vocabulary, so almost every token is a distinct term — 2.08 MB for 80,000 tokens, measured). Real prose repeats words inside a document, which collapses two postings into one with a higher frequency, so it lands lower. Multiply your token count by 26 bytes for a ceiling: a corpus of 100,000 documents averaging 200 tokens is 20 million tokens, so budget half a gigabyte and expect to use less.

Storage modes. The default (in-memory) and 'mapped' backends build the index. The 'disk' backend refuses, loudly:

build_text_index('Article', 'body') is not supported on a disk-backed graph:
the BM25 index is heap-resident, and building one over a graph sized for the
disk backend is the memory cliff that backend exists to avoid. Use the default
(in-memory) or 'mapped' storage mode.

That is the point of the disk backend — it exists so a graph larger than RAM stays queryable, and a heap-resident inverted index over it would undo that silently.

has_text_index(node_type, property) reports whether one is built; drop_text_index(node_type, property) removes it and returns whether there was one.

Rank with `text_bm25()`¶

text_bm25(n, 'property', 'query text') is an ordinary Cypher scalar returning that row’s BM25 relevance, so it composes with everything else in a query:

rows = graph.cypher("""
    MATCH (a:Article)
    RETURN a.title AS title,
           text_bm25(a, 'body', $q) AS score
    ORDER BY score DESC
    LIMIT 10
""", params={"q": "low light chlorophyll"})

print(rows.to_df())
#                       title     score
# 0  Low-light photosynthesis  2.052358
# 1  Shade tolerance in ferns  1.239125
# 2        Chlorophyll assays  0.763694
# 3         Rust memory model  0.000000

Two results are deliberately different answers:

0.0 — the document was searched and shares no word with the query.
null — the index holds no document for that row: it was created after the build and has not been caught up yet, or its property is absent or not a string.

Collapsing them would make an index that is quietly behind the graph look like a corpus with no matches. Ties break by node id, so an unchanged corpus returns the same order every time.

Calling text_bm25() on a (type, property) with no index is an error naming build_text_index — never a silent column of nulls.

The shape that gets the fast path¶

This exact shape —

RETURN … text_bm25(n, prop, q) AS score · ORDER BY score DESC · LIMIT k, over a MATCH that binds the whole indexed node type

— plans as a single operator that asks the index’s posting lists for their own best k documents instead of scoring every row. Measured on a 100,000-document synthetic corpus (release build, Apple Silicon, 2026-08-25, p50 over 200 rounds):

Query	Per-row scoring	Postings top-k
Two terms, the rarer in ~30 documents	30.8 ms	7.5 ms
Opens with a near-stopword (nothing to prune)	25.2 ms	21.4 ms

So a search over a six-figure corpus is single-digit to low-tens of milliseconds, and the cost now follows the query’s selectivity instead of being flat. Both paths are exact: candidates are scored through the same kernel in the same summation order, so the rows and their order are identical either way.

The operator hands the query back to ordinary per-row scoring whenever the index cannot answer it alone — a WHERE that makes the rows a subset of the corpus, ORDER BY … ASC, a per-row property or query argument, an index that has fallen behind, or fewer matching documents than the LIMIT asks for. Those queries answer exactly the same, just without the shortcut:

rows = graph.cypher("""
    MATCH (a:Article)
    WHERE text_bm25(a, 'body', $q) > 0
    RETURN a.title AS title, text_bm25(a, 'body', $q) AS score
    ORDER BY score DESC
    LIMIT 5
""", params={"q": "chlorophyll"})

print(rows.to_df())
#                       title     score
# 0        Chlorophyll assays  0.763694
# 1  Low-light photosynthesis  0.684119

Filter first when the filter is what makes the query fast (a year, an author, a one-hop traversal); leave the WHERE off and let the postings do the work when you are ranking the whole corpus.

The freshness contract¶

A text index does not follow writes. It records them and folds them in when a query next reads it. That one sentence has three consequences worth stating plainly, because they are the whole contract.

Writes are never slowed. Recording a creation is a comparison of one node slot against a high-water mark — O(1) per bulk operation, nothing per row — so add_nodes() into an indexed graph runs at the speed it would without one. Edges never touch a text index at all. A graph with no text index pays a single branch. This is not a tuning claim; it is why the design is watermark-based, and there are committed benchmark cells that fail if bulk ingest into an indexed graph diverges from the unindexed control.

Queries catch up, up to a limit. When a query reads the index, an outstanding delta at or under that index’s auto_refresh_limit (default 1,000 documents) is folded in first, inline, and the query sees fresh scores without anyone calling anything. The limit is set to 1 below only so the next example can cross it; leave it at the default in real code:

small = kglite.KnowledgeGraph()
small.add_nodes(articles, "Article", "id", "title")
small.build_text_index("Article", "body", auto_refresh_limit=1)

small.cypher("CREATE (a:Article {id: 5, title: 'Moss', body: 'low light moss beds'})")
print(small.cypher("SHOW INDEXES").to_df()[["name", "type", "stale", "delta"]])
#            name      type  stale  delta
# 0  Article.body  FULLTEXT   True      1

rows = small.cypher("""
    MATCH (a:Article) RETURN a.title AS title, text_bm25(a, 'body', 'low light') AS score
    ORDER BY score DESC LIMIT 3
""")
print(rows.to_df())
#                       title     score
# 0                      Moss  1.307173
# 1  Low-light photosynthesis  1.018472
# 2  Shade tolerance in ferns  0.917187

print(rows.warnings)
# []
print(small.cypher("SHOW INDEXES").to_df()[["stale", "delta"]].to_dict("records"))
# [{'stale': False, 'delta': 0}]

The new node scored, nothing was rebuilt by hand, and the index came back clean.

auto_refresh_limit is a document count, not a time budget — and it is worth knowing why, because it is the one place the honest number is not the flattering one. Folding one document in splices into the posting list of every term that document uses, and those lists grow with the corpus, so the per-document cost rises with index size (measured: 0.08 ms per document over a 20,000-document corpus, 0.4 ms over a 100,000-document one). Past roughly 1,500 documents, folding costs more than rebuilding the index outright — so the catch-up rebuilds instead. A refresh therefore costs the cheaper of the two and never more than one rebuild, whatever you set the limit to; raising the limit far above that crossover buys rebuilds, not an ever-slower fold.

Over the limit, the index says so. It serves what it has, scores the rows it has no document for null, and attaches a warning naming the delta and the call that fixes it:

for i in range(6, 9):
    small.cypher(f"CREATE (a:Article {{id: {i}, title: 'Extra {i}', body: 'low light algae'}})")

rows = small.cypher("""
    MATCH (a:Article) RETURN a.title AS title, text_bm25(a, 'body', 'low light') AS score
    ORDER BY score DESC LIMIT 4
""")
print(rows.to_df()["score"].isna().sum())
# 3
for w in rows.warnings:
    print(w)
# text index 'Article.body' is stale: up to 3 documents are unindexed, over its
# auto_refresh_limit of 1 — those rows score null. Rebuild with
# build_text_index('Article', 'body').

A read-only graph gets the same treatment with the reason named, since a query may not write to it:

small.read_only(True)
rows = small.cypher("MATCH (a:Article) RETURN text_bm25(a, 'body', 'low light') AS score")
print(rows.warnings[0])
# text index 'Article.body' is stale: up to 3 documents are unindexed, and this
# graph is read-only, so a query cannot catch it up — those rows score null.
# Rebuild with build_text_index('Article', 'body').
small.read_only(False)

The point is that you never have to guess: a query that reads a behind-the-graph index says so, in the result, with the number.

Checking and repairing freshness¶

SHOW INDEXES reports stale and delta for every opt-in index. delta is an upper bound on the documents a catch-up would re-read; both columns are null on index kinds that are maintained on every write and have nothing to report. graph.schema() lists which indexes exist ('Article.body [text]') without the freshness columns — use SHOW INDEXES when the question is how far behind.

Calling build_text_index() again rebuilds the index wholesale — the route back from any delta, at any time. Omitting auto_refresh_limit on that call keeps whatever the existing index used, so a rebuild does not quietly restore the default.

Two things are not staleness:

Deletes. Deleting a node prunes its document immediately, at the delete. It has to: the freed node slot is handed to the next node created, and an orphaned document would be inherited by it and score as a ghost.
vacuum(). It renumbers every node, so it drops text indexes wholesale. Rebuild after vacuuming — the same rule HNSW indexes have always had. Auto-refreshing across a vacuum would be a hidden full rebuild inside whatever query happened to run next.

A text index is saved with the graph. save() writes it into the .kgl as its own self-describing section carrying its resolved column, its refresh ceiling and its staleness, and kglite.load() restores all of it — a reloaded index that was stale is still stale by the same delta. The section is a rebuildable cache, not a format break: a graph with no text index writes byte-identical files to before, older files load unchanged, and a section a build cannot read is skipped rather than refused (rebuild it in that case).

Both lanes in one query¶

This is the reason the lexical lane exists here rather than in a separate package. score_fuse() combines several ranked lanes into one number per row, so keyword relevance and semantic similarity rank the same query in a single statement — with the graph filters and traversals of ordinary Cypher around them.

graph.set_embeddings("Article", "body", {
    1: [1.0, 0.0],
    2: [0.0, 1.0],
    3: [0.9, 0.4],
    4: [0.8, 0.2],
})

hits = graph.cypher("""
    MATCH (a:Article)
    RETURN a.title AS title,
           score_fuse(text_bm25(a, 'body', $q),
                      vector_score(a, 'body_emb', $qv)) AS score
    ORDER BY score DESC
    LIMIT 3
""", params={"q": "low light", "qv": [1.0, 0.1]})

print(hits.to_df())
#                       title     score
# 0  Low-light photosynthesis  1.181638
# 1  Shade tolerance in ferns  1.094407
# 2        Chlorophyll assays  0.494731

(In a real deployment the vectors come from embed_texts() and an embedder — see Semantic Search. Two-dimensional literals are used here so the example runs without a model.)

The lanes weigh equally by default. A trailing list weights them, in argument order — relative, so [3, 1] and [0.75, 0.25] rank identically:

hits = graph.cypher("""
    MATCH (a:Article)
    RETURN a.title AS title,
           score_fuse(text_bm25(a, 'body', $q),
                      vector_score(a, 'body_emb', $qv), [0.7, 0.3]) AS score
    ORDER BY score DESC
    LIMIT 3
""", params={"q": "low light", "qv": [1.0, 0.1]})
print(hits.to_df().head(1))
#                       title     score
# 0  Low-light photosynthesis  1.256278

An absent lane leaves the average; it does not score zero. A lane returns null for a row it could not see — a document the text index has not caught up with, a node with no stored embedding — and that row keeps the score of the lanes that did run, with the absent lane’s weight leaving the denominator too. Zero would mean “this lane looked and found nothing”, which would rank a document one lane could not see below a document both lanes actively disliked. The result is null only when every lane is absent. This is the rule that lets hybrid retrieval work on a half-embedded corpus.

A wrong-length weights list, a negative weight and a non-numeric score are all errors rather than a quietly different ranking.

Reciprocal Rank Fusion¶

There is no rrf() scalar, because RRF works on each lane’s rank across the whole result set and a per-row scalar sees one row. It is two lines of what already exists — window rank() in a WITH, then fuse the reciprocals:

hits = graph.cypher("""
    MATCH (a:Article)
    WITH a, rank() OVER (ORDER BY text_bm25(a, 'body', $q) DESC) AS lex_rank,
            rank() OVER (ORDER BY vector_score(a, 'body_emb', $qv) DESC) AS vec_rank
    RETURN a.title AS title,
           score_fuse(1.0 / (60 + lex_rank), 1.0 / (60 + vec_rank)) AS score
    ORDER BY score DESC
    LIMIT 3
""", params={"q": "low light", "qv": [1.0, 0.1]})

print(hits.to_df().head(2))
#                       title     score
# 0  Low-light photosynthesis  0.016393
# 1  Shade tolerance in ferns  0.016001

Reach for RRF when the lanes’ scores are on incomparable scales — BM25 is unbounded, cosine is not — since ranks discard the magnitudes. Fuse the scores directly when the magnitudes carry information you want. The Cypher reference carries the same recipe alongside the score_fuse semantics.

The vector lane catches up the same way¶

Freshness is one shared mechanism, so an HNSW vector index behaves like a text index where it can. Writing vectors after build_vector_index() no longer drops the index: the outstanding vectors are recorded and folded in at query entry while the delta is at or under auto_refresh_limit, a larger delta is served by the exact scan (correct, and slower) until you rebuild or call refresh_vector_index(), and both facts show up in SHOW INDEXES.

The one difference is what a query is allowed to do for you:

Catch-up never embeds. A node with no vector is not part of the delta — it is counted in SHOW INDEXES’ unembedded column and stays invisible to vector search until you embed it. No query turns into an embedding run behind your back; run embed_texts() when you mean to.

vec = kglite.KnowledgeGraph()
vec.add_nodes(articles, "Article", "id", "title")
vec.set_embeddings("Article", "body", {1: [1.0, 0.0], 2: [0.0, 1.0], 3: [0.9, 0.4], 4: [0.8, 0.2]})
vec.build_vector_index("Article", "body")

vec.cypher("CREATE (a:Article {id: 9, title: 'New', body: 'low light lichen'})")
print(vec.cypher("SHOW INDEXES").to_df()[["type", "stale", "delta", "unembedded"]].to_dict("records"))
# [{'type': 'VECTOR', 'stale': False, 'delta': 0, 'unembedded': 1}]

vec.add_embeddings("Article", "body", {9: [0.6, 0.5]})
print(vec.cypher("SHOW INDEXES").to_df()[["stale", "delta", "unembedded"]].to_dict("records"))
# [{'stale': True, 'delta': 1, 'unembedded': 0}]

print(vec.refresh_vector_index("Article", "body"))
# 1

What still drops a vector index is a change to the slot layout it addresses: deleting an embedded node, rolling that delete back, and vacuum(). Rebuild after those.