tantivy-py/docs/tutorials.md

Tutorials

Building an index and populating it

import tempfile
import pathlib
import tantivy

# Declaring our schema.
schema_builder = tantivy.SchemaBuilder()
schema_builder.add_text_field("title", stored=True)
schema_builder.add_text_field("body", stored=True)
schema_builder.add_integer_field("doc_id",stored=True)
schema = schema_builder.build()

# Creating our index (in memory)
index = tantivy.Index(schema)

To have a persistent index, use the path parameter to store the index on the disk, e.g:

tmpdir = tempfile.TemporaryDirectory()
index_path = pathlib.Path(tmpdir.name) / "index"
index_path.mkdir()
persistent_index = tantivy.Index(schema, path=str(index_path))

By default, tantivy offers the following tokenizers which can be used in tantivy-py:

• default default is the tokenizer that will be used if you do not assign a specific tokenizer to your text field. It will chop your text on punctuation and whitespaces, removes tokens that are longer than 40 chars, and lowercase your text.

• raw Does not actual tokenizer your text. It keeps it entirely unprocessed. It can be useful to index uuids, or urls for instance.

• en_stem

In addition to what default does, the en_stem tokenizer also apply stemming to your tokens. Stemming consists in trimming words to remove their inflection. This tokenizer is slower than the default one, but is recommended to improve recall.

to use the above tokenizers, simply provide them as a parameter to add_text_field. e.g.

schema_builder_tok = tantivy.SchemaBuilder()
schema_builder_tok.add_text_field("body",  stored=True,  tokenizer_name='en_stem')

Adding one document.

writer = index.writer()
writer.add_document(tantivy.Document(
	doc_id=1,
    title=["The Old Man and the Sea"],
    body=["""He was an old man who fished alone in a skiff in the Gulf Stream and he had gone eighty-four days now without taking a fish."""],
))
# ... and committing
writer.commit()
writer.wait_merging_threads()

Note that wait_merging_threads() must come at the end, because the writer object will not be usable after this call.

Building and Executing Queries

First you need to get a searcher for the index

# Reload the index to ensure it points to the last commit.
index.reload()
searcher = index.searcher()

Then you need to get a valid query object by parsing your query on the index.

query = index.parse_query("fish days", ["title", "body"])
(best_score, best_doc_address) = searcher.search(query, 3).hits[0]
best_doc = searcher.doc(best_doc_address)
assert best_doc["title"] == ["The Old Man and the Sea"]

Using the snippet generator

hit_text = best_doc["body"][0]
print(f"{hit_text=}")
assert hit_text == (
    "He was an old man who fished alone in a skiff in the "
    "Gulf Stream and he had gone eighty-four days now "
    "without taking a fish."
)

from tantivy import SnippetGenerator
snippet_generator = SnippetGenerator.create(
    searcher, query, schema, "body"
)
snippet = snippet_generator.snippet_from_doc(best_doc)

The snippet object provides the hit ranges. These are the marker offsets in the text that match the query.

highlights = snippet.highlighted()
first_highlight = highlights[0]
assert first_highlight.start == 93
assert first_highlight.end == 97
assert hit_text[first_highlight.start:first_highlight.end] == "days"

The snippet object can also generate a marked-up HTML snippet:

html_snippet = snippet.to_html()
assert html_snippet == (
    "He was an old man who fished alone in a skiff in the "
    "Gulf Stream and he had gone eighty-four <b>days</b> now "
    "without taking a <b>fish</b>"
)