lucivy 2.0.0

Fast BM25 full-text search with substring matching, fuzzy search, and regex — powered by Rust

lucivy

Fast BM25 full-text search for Python — with substring matching, fuzzy search, regex, and highlights. Powered by Rust.

Install

pip install lucivy

Quick start

import lucivy

index = lucivy.Index.create("/tmp/my_index", fields=[
    {"name": "title", "type": "text", "stored": True},
    {"name": "body", "type": "text", "stored": True},
])

index.add(1, title="Rust Programming", body="Systems programming with memory safety")
index.add(2, title="Python Guide", body="Data science and web development")
index.commit()

results = index.search("programming", highlights=True)
for r in results:
    print(r.doc_id, r.score, r.highlights)

API

Create / open

# Create a new index
index = lucivy.Index.create("/tmp/my_index", fields=[
    {"name": "title", "type": "text", "stored": True},
    {"name": "body",  "type": "text", "stored": True},
    {"name": "score", "type": "f64", "fast": True},
])

# Create a sharded index (4 shards)
index = lucivy.Index.create("/tmp/my_index", fields=[...], shards=4)

# Open an existing index
index = lucivy.Index.open("/tmp/my_index")

Field types: "text" (full-text, tokenized), "u64", "i64", "f64", "bool", "date".

Add / update / delete

# Fields are passed as keyword arguments
index.add(1, title="Hello", body="World", score=3.14)

index.add_many([
    {"doc_id": 1, "title": "Hello", "body": "World"},
    {"doc_id": 2, "title": "Foo", "body": "Bar"},
])

index.update(1, title="Updated title", body="Updated body")
index.delete(2)
index.commit()

Search

# String query — each word is searched across all text fields (contains_split)
results = index.search("rust async programming")

# Options
results = index.search("rust", limit=20, highlights=True, allowed_ids=[1, 3, 5])

# Retrieve stored field values with results
results = index.search("rust", fields=True)
for r in results:
    print(r.doc_id, r.fields['title'], r.fields['body'])

contains — substring, fuzzy, regex (cross-token)

All substring queries are cross-token: they match across token boundaries.

# Substring — matches "programming", "programmer", "getProgramHandle", etc.
index.search({"type": "contains", "field": "body", "value": "program"})

# Fuzzy substring (Levenshtein distance)
index.search({"type": "contains", "field": "body", "value": "mutx", "distance": 1})

# Regex substring — cross-token regex matching
index.search({"type": "contains", "field": "body", "value": "lock.*mutex", "regex": True})

# Prefix / startsWith — match must start at token boundary (SI=0)
index.search({"type": "startsWith", "field": "body", "value": "prog"})

# Exact whole-token match
index.search({"type": "term", "field": "body", "value": "lock"})

# Phrase — adjacent tokens in order
index.search({"type": "phrase", "field": "body", "value": "mutex lock"})

contains_split — multi-word search

Split on whitespace, each word becomes a contains query, combined with boolean OR.

index.search({"type": "contains_split", "field": "body", "value": "rust safety"})

# With fuzzy distance — each word gets fuzzy tolerance
index.search({"type": "contains_split", "field": "body", "value": "memry safty", "distance": 1})

boolean — combine queries with must / should / must_not

index.search({
    "type": "boolean",
    "must": [
        {"type": "contains", "field": "body", "value": "rust"},
    ],
    "should": [
        {"type": "contains", "field": "title", "value": "guide"},
    ],
    "must_not": [
        {"type": "contains", "field": "body", "value": "deprecated"},
    ],
})

Filtering

Filter on non-text fields (combined with AND):

index.search({
    "type": "contains", "field": "body", "value": "lock",
    "filters": [
        {"field": "category", "op": "eq", "value": "kernel"},
        {"field": "score", "op": "gte", "value": 0.5},
        {"field": "status", "op": "in", "value": ["active", "review"]},
    ]
})

Filter ops: eq, ne, lt, lte, gt, gte, in, not_in, between, starts_with, contains.

Pre-filter by document ID (fast, bitmap-based):

index.search({"type": "contains", "field": "body", "value": "lock"}, allowed_ids=[1, 2, 3])

Snapshots (export / import)

# Export index to a .luce file
index.export_snapshot_to("./backup.luce")

# Export as bytes
blob = index.export_snapshot()

# Import from .luce file
restored = lucivy.Index.import_snapshot_from("./backup.luce", dest_path="./restored_index")

# Import from bytes
with open("./backup.luce", "rb") as f:
    restored = lucivy.Index.import_snapshot(f.read(), dest_path="./restored_index")

Delta sync (incremental)

Sync only the segments that changed since the client's last version.

# Get current shard versions
versions = index.shard_versions()

# Export delta (only changed segments)
delta = index.export_sharded_delta(client_versions)

# Apply delta on the client side
client_index.apply_sharded_delta(delta)

Distributed search

Run BM25 search across multiple machines with correct IDF.

import lucivy

query = {"type": "contains", "field": "body", "value": "mutex"}

# 1. Each node exports its local BM25 stats
stats_a = node_a.export_stats(query)  # JSON string
stats_b = node_b.export_stats(query)  # JSON string

# 2. Coordinator merges stats from all nodes
merged = lucivy.merge_stats([stats_a, stats_b])

# 3. Each node searches with global stats (correct IDF across all nodes)
results_a = node_a.search_with_global_stats(query, merged, limit=10)
results_b = node_b.search_with_global_stats(query, merged, limit=10)

# 4. Coordinator merges top-K results by score
all_results = sorted(results_a + results_b, key=lambda r: r.score, reverse=True)[:10]

Properties

index.num_docs    # number of documents (property, no parentheses)
index.num_shards  # number of shards (property)
index.path        # index directory path (property)
index.schema      # list of {"name": "...", "type": "..."} dicts (property)
index.close()     # flush + release writer lock

License

MIT