This tool provides utilities for interacting with OpenAI APIs and storing the results in a SQLite database.

See Semantic search answers: Q&A against documentation with GPT3 + OpenAI embeddings for background on this project.

For a tutorial on using this for related content, see Storing and serving related documents with openai-to-sqlite and embeddings.

Install this tool using pip:

pip install openai-to-sqlite

You will need an OpenAI API key to use this tool.

You can create one at https://beta.openai.com/account/api-keys

You can then either set the API key as an environment variable:

export OPENAI_API_KEY=sk-...

Or pass it to each command using the --token sk-... option.

The openai-to-sqlite query command can be used to execute SQL queries that call OpenAI APIs.

Functions available are:

• chatgpt(prompt) - call the OpenAI Chat API using model gpt-3.5-turbo with the specified prompt.
• chatgpt(prompt, system) - call that API with the prompt and the specified system prompt.

More functions are planned in the future.

Here's how to use this command to run basic sentiment analysis against content in a table:

openai-to-sqlite query database.db "
  update messages set sentiment = chatgpt(
    'Sentiment analysis for this message: ' || message ||
    ' - ONLY return a lowercase string from: positive, negative, neutral, unknown'
  )
  where sentiment not in ('positive', 'negative', 'neutral', 'unknown')
    or sentiment is null
"

This updates the sentiment column in a table called messages. It populates it with the response from the specified prompt.

The command will display a progress bar indicating how many rows are being processed.

You can add an empty sentiment column to a table using sqlite-utils like this:

sqlite-utils add-column database.db messages sentiment

The embeddings command can be used to calculate and store OpenAI embeddings for strings of text.

Each embedding has a cost, so be sure to familiarize yourself with the pricing for the embedding model.

The command can accept data in four different ways:

• As a JSON file containing a list of objects
• As a CSV file
• As a TSV file
• By running queries against a SQLite database

For all of these formats there should be an id column, followed by one or more text columns.

The ID will be stored as the content ID. Any other columns will be concatenated together and used as the text to be embedded.

The embeddings from the API will then be saved as binary blobs in the embeddings table of the specified SQLite database - or another table, if you pass the -t/--table option.

Given a CSV file like this:

id,content
1,This is a test
2,This is another test

Embeddings can be stored like so:

openai-to-sqlite embeddings embeddings.db data.csv

The resulting schema looks like this:

CREATE TABLE [embeddings] (
   [id] TEXT PRIMARY KEY,
   [embedding] BLOB
);

The same data can be provided as TSV data:

id    content
1     This is a test
2     This is another test

Then imported like this:

openai-to-sqlite embeddings embeddings.db data.tsv

Or as JSON data:

[
  {"id": 1, "content": "This is a test"},
  {"id": 2, "content": "This is another test"}
]

Imported like this:

openai-to-sqlite embeddings embeddings.db data.json

In each of these cases the tool automatically detects the format of the data. It does this by inspecting the data itself - it does not consider the file extension.

If the automatic detection is not working, you can pass --format json, csv or tsv to explicitly specify a format:

openai-to-sqlite embeddings embeddings.db data.tsv --format tsv

You can use a filename of - to pipe data in to standard input:

cat data.tsv | openai-to-sqlite embeddings embeddings.db -

The --sql option can be used to read data to be embedded from the attached SQLite database. The query must return an id column and one or more text columns to be embedded.

openai-to-sqlite embeddings content.db \
  --sql "select id, title from documents"

This will create a embeddings table in the content.db database and populate it with embeddings calculated from the title column in that query.

You can also store embeddings in one database while reading data from another database, using the --attach alias filename.db option:

openai-to-sqlite embeddings embeddings.db \
  --attach documents documents.db \
  --sql "select id, title from documents.documents"

A progress bar will be displayed when using --sql that indicates how long the embeddings are likely to take to calculate.

The CSV/TSV/JSON options do not correctly display the progress bar. You can work around this by importing your data into SQLite first (e.g. using sqlite-utils) and then running the embeddings using --sql.

Embeddings will be sent to the OpenAI embeddings API in batches of 100. If you know that your data is short strings you can increase the batch size, up to 2048, using the --batch-size option:

openai-to-sqlite embeddings embeddings.db data.csv --batch-size 2048

The embedding column is a SQLite blob containing 1536 floating point numbers encoded as a sequence of 4 byte values.

You can extract them back to an array of floating point values in Python like this:

import struct

vector = struct.unpack(
    "f" * 1536, binary_embedding
)

Having saved the embeddings for content, you can run searches using the search command:

openai-to-sqlite search embeddings.db 'this is my search term'

The output will be a list of cosine similarity scores and content IDs:

openai-to-sqlite search blog.db 'cool datasette demo'

0.843 7849
0.830 8036
0.828 8195
0.826 8098
0.818 8086
0.817 8171
0.816 8121
0.815 7860
0.815 7872
0.814 8169

Add the -t/--table option if your embeddings are stored in a different table:

openai-to-sqlite search content.db 'this is my search term' -t documents

Add `--count 20` to retrieve 20 results (the default is 10).

Having saved the embeddings for content, you can search for similar content with the similar command:

oopenai-to-sqlite similar embeddings.db '<content identifier>'

The output will be a list of cosine similarity scores and content IDs:

openai-to-sqlite similar embeddings-bjcp-2021.db '23G Gose'

23G Gose
  1.000 23G Gose
  0.929 24A Witbier
  0.921 23A Berliner Weisse
  0.909 05B Kölsch
  0.907 01D American Wheat Beer
  0.906 27 Historical Beer: Lichtenhainer
  0.905 23D Lambic
  0.905 10A Weissbier
  0.904 04B Festbier
  0.904 01B American Lager

You can pass more than one IDs to see similarities calculated for each one:

openai-to-sqlite similar embeddings-bjcp-2021.db \
  '23G Gose' '01A American Light Lager'

Or pass --all to run similarity for every item in the database. This runs similarity calculations for the number of items squared so it can be quite a long running operation:

openai-to-sqlite similar embeddings-bjcp-2021.db --all

To save these calculations to a similarities table in the database, use the --save option:

openai-to-sqlite similar embeddings-bjcp-2021.db --all --save

The --save option disables output. You can re-enable output with --print:

openai-to-sqlite similar embeddings-bjcp-2021.db --all --save --print

To save to a database table with a name other than similarities, use --table:

openai-to-sqlite similar embeddings-bjcp-2021.db \
  --all --save --table my_similarities

Re-calculating similarities for every row in the database can be quite a lengthy operation.

If you know which rows have just been added, you can speed things up using --recalculate-for-matches.

This tells openai-to-sqlite similar to only re-calculate similarities for rows that are close matches to the specified rows.

This means you can add one or two additional records and then trigger an update of the saved similarity scores for just those new records plus for the twenty closest matches to those new records like this:

openai-to-sqlite similar embeddings-bjcp-2021.db \
  --save '23G Gose' '01A American Light Lager' \
  --recalculate-for-matches \
  --count 20 \
  --print

To contribute to this tool, first checkout the code. Then create a new virtual environment:

cd openai-to-sqlite
python -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

pip install -e '.[test]'

To run the tests:

pytest