pypi_packages: sqlite-comprehend
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| sqlite-comprehend | Tools for running data in a SQLite database through AWS Comprehend | [] | # sqlite-comprehend [](https://pypi.org/project/sqlite-comprehend/) [](https://github.com/simonw/sqlite-comprehend/releases) [](https://github.com/simonw/sqlite-comprehend/actions?query=workflow%3ATest) [](https://github.com/simonw/sqlite-comprehend/blob/master/LICENSE) Tools for running data in a SQLite database through [AWS Comprehend](https://aws.amazon.com/comprehend/) See [sqlite-comprehend: run AWS entity extraction against content in a SQLite database](https://simonwillison.net/2022/Jul/11/sqlite-comprehend/) for background on this project. ## Installation Install this tool using `pip`: pip install sqlite-comprehend ## Demo You can see examples of tables generated using this command here: - [comprehend_entities](https://datasette.simonwillison.net/simonwillisonblog/comprehend_entities) - the extracted entities, classified by type - [blog_entry_comprehend_entities](https://datasette.simonwillison.net/simonwillisonblog/blog_entry_comprehend_entities) - a table relating entities to the entries that they appear in - [comprehend_entity_types](https://datasette.simonwillison.net/simonwillisonblog/comprehend_entity_types) - a small lookup table of entity types ## Configuration You will need AWS credentials with the `comprehend:BatchDetectEntities` [IAM permission](https://docs.aws.amazon.com/comprehend/latest/dg/access-control-managing-permissions.html). You can configure credentials [using these instructions](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html). You can also save them to a JSON or INI configuration file and pass them to the command using `-a credentials.ini`, or pass them using the `--access-key` and `--secret-key` options. ## Entity extraction The `sqlite-comprehend entities` command runs entity extraction against every row in the specified table and saves the results to your database. Specify the database, the table and one or more columns containing text in that table. The following runs against the `text` column in the `pages` table of the `sfms.db` SQLite database: sqlite-comprehend sfms.db pages text Results will be written into a `pages_comprehend_entities` table. Change the name of the output table by passing `-o other_table_name`. You can run against a subset of rows by adding a `--where` clause: sqlite-comprehend sfms.db pages text --where 'id < 10' You can also used named parameters in your `--where` clause: sqlite-comprehend sfms.db pages text --where 'id < :maxid' -p maxid 10 Only the first 5,000 characters for each row will be considered. Be sure to review [Comprehend's pricing](https://aws.amazon.com/comprehend/pricing/) - which starts at $0.0001 per hundred characters. If your context includes HTML tags, you can strip them out before extracting entities by adding `--strip-tags`: sqlite-comprehend sfms.db pages text --strip-tags Rows that have been processed are recorded in the `pages_comprehend_entities_done` table. If you run the command more than once it will only process rows that have been newly added. You can delete records from that `_done` table to run them again. ### sqlite-comprehend entities --help <!-- [[[cog from click.testing import CliRunner from sqlite_comprehend import cli runner = CliRunner() result = runner.invoke(cli.cli, ["entities", "--help"]) help = result.output.replace("Usage: cli", "Usage: sqlite-comprehend") cog.out( "```\n{}\n```".format(help) ) ]]] --> ``` Usage: sqlite-comprehend entities [OPTIONS] DATABASE TABLE COLUMNS... Detect entities in columns in a table To extract entities from columns text1 and text2 in mytable: sqlite-comprehend entities my.db mytable text1 text2 To run against just a subset of the rows in the table, add: --where "id < :max_id" -p max_id 50 Results will be written to a table called mytable_comprehend_entities To specify a different output table, use -o custom_table_name Options: --where TEXT WHERE clause to filter table -p, --param <TEXT TEXT>... Named :parameters for SQL query -o, --output TEXT Custom output table -r, --reset Start from scratch, deleting previous results --strip-tags Strip HTML tags before extracting entities --access-key TEXT AWS access key ID --secret-key TEXT AWS secret access key --session-token TEXT AWS session token --endpoint-url TEXT Custom endpoint URL -a, --auth FILENAME Path to JSON/INI file containing credentials --help Show this message and exit. ``` <!-- [[[end]]] --> ## Schema Assuming an input table called `pages` the tables created by this tool will have the following schema: <!-- [[[cog import cog, json from sqlite_comprehend import cli from unittest.mock import patch from click.testing import CliRunner import sqlite_utils import tempfile, pathlib tmpdir = pathlib.Path(tempfile.mkdtemp()) db_path = str(tmpdir / "data.db") db = sqlite_utils.Database(db_path) db["pages"].insert_all( [ { "id": 1, "text": "John Bob", }, { "id": 2, "text": "Sandra X", }, ], pk="id", ) with patch('boto3.client') as client: client.return_value.batch_detect_entities.return_value = { "ResultList": [ { "Index": 0, "Entities": [ { "Score": 0.8, "Type": "PERSON", "Text": "John Bob", "BeginOffset": 0, "EndOffset": 5, }, ], }, { "Index": 1, "Entities": [ { "Score": 0.8, "Type": "PERSON", "Text": "Sandra X", "BeginOffset": 0, "EndOffset": 5, }, ], }, ], "ErrorList": [], } runner = CliRunner() result = runner.invoke(cli.cli, [ "entities", db_path, "pages", "text" ]) cog.out("```sql\n") cog.out(db.schema) cog.out("\n```") ]]] --> ```sql CREATE TABLE [pages] ( [id] INTEGER PRIMARY KEY, [text] TEXT ); CREATE TABLE [comprehend_entity_types] ( [id] INTEGER PRIMARY KEY, [value] TEXT ); CREATE TABLE [comprehend_entities] ( [id] INTEGER PRIMARY KEY, [name] TEXT, [type] INTEGER REFERENCES [comprehend_entity_types]([id]) ); CREATE TABLE [pages_comprehend_entities] ( [id] INTEGER REFERENCES [pages]([id]), [score] FLOAT, [entity] INTEGER REFERENCES [comprehend_entities]([id]), [begin_offset] INTEGER, [end_offset] INTEGER ); CREATE UNIQUE INDEX [idx_comprehend_entity_types_value] ON [comprehend_entity_types] ([value]); CREATE UNIQUE INDEX [idx_comprehend_entities_type_name] ON [comprehend_entities] ([type], [name]); CREATE TABLE [pages_comprehend_entities_done] ( [id] INTEGER PRIMARY KEY REFERENCES [pages]([id]) ); ``` <!-- [[[end]]] --> ## Development To contribute to this tool, first checkout the code. Then create a new virtual environment: cd sqlite-comprehend python -m venv venv source venv/bin/activate Now install the dependencies and test dependencies: pip install -e '.[test]' To run the tests: pytest | Simon Willison | text/markdown | https://github.com/simonw/sqlite-comprehend | Apache License, Version 2.0 | https://pypi.org/project/sqlite-comprehend/ | https://pypi.org/project/sqlite-comprehend/ | {"CI": "https://github.com/simonw/sqlite-comprehend/actions", "Changelog": "https://github.com/simonw/sqlite-comprehend/releases", "Homepage": "https://github.com/simonw/sqlite-comprehend", "Issues": "https://github.com/simonw/sqlite-comprehend/issues"} | https://pypi.org/project/sqlite-comprehend/0.2.2/ | ["click", "boto3", "sqlite-utils", "pytest ; extra == 'test'", "pytest-mock ; extra == 'test'", "cogapp ; extra == 'test'"] | >=3.7 | 0.2.2 | 0 |