llm/docs/logging.md

(logging)=

Logging to SQLite

llm defaults to logging all prompts and responses to a SQLite database.

You can find the location of that database using the llm logs path command:

llm logs path

On my Mac that outputs:

/Users/simon/Library/Application Support/io.datasette.llm/logs.db

This will differ for other operating systems.

To avoid logging an individual prompt, pass --no-log or -n to the command:

llm 'Ten names for cheesecakes' -n

To turn logging by default off:

llm logs off

If you've turned off logging you can still log an individual prompt and response by adding --log:

llm 'Five ambitious names for a pet pterodactyl' --log

To turn logging by default back on again:

llm logs on

To see the status of the logs database, run this:

llm logs status

Example output:

Logging is ON for all prompts
Found log database at /Users/simon/Library/Application Support/io.datasette.llm/logs.db
Number of conversations logged: 33
Number of responses logged:     48
Database file size:             19.96MB

(viewing-logs)=

Viewing the logs

You can view the logs using the llm logs command:

llm logs

This will output the three most recent logged items in Markdown format, showing both the prompt and the response formatted using Markdown.

To get back just the most recent prompt response as plain text, add -r/--response:

llm logs -r

Use -x/--extract to extract and return the first fenced code block from the selected log entries:

llm logs --extract

Or --xl/--extract-last for the last fenced code block:

llm logs --extract-last

Add --json to get the log messages in JSON instead:

llm logs --json

Add -n 10 to see the ten most recent items:

llm logs -n 10

Or -n 0 to see everything that has ever been logged:

llm logs -n 0

You can truncate the display of the prompts and responses using the -t/--truncate option. This can help make the JSON output more readable:

llm logs -n 5 -t --json

(logs-conversation)=

Logs for a conversation

To view the logs for the most recent {ref}conversation <conversation> you have had with a model, use -c:

llm logs -c

To see logs for a specific conversation based on its ID, use --cid ID or --conversation ID:

llm logs --cid 01h82n0q9crqtnzmf13gkyxawg

Searching the logs

You can search the logs for a search term in the prompt or the response columns.

llm logs -q 'cheesecake'

The most relevant terms will be shown at the bottom of the output.

Filtering by model

You can filter to logs just for a specific model (or model alias) using -m/--model:

llm logs -m chatgpt

Browsing logs using Datasette

You can also use Datasette to browse your logs like this:

datasette "$(llm logs path)"

SQL schema

Here's the SQL schema used by the logs.db database:

CREATE TABLE [conversations] (
  [id] TEXT PRIMARY KEY,
  [name] TEXT,
  [model] TEXT
);
CREATE TABLE [responses] (
  [id] TEXT PRIMARY KEY,
  [model] TEXT,
  [prompt] TEXT,
  [system] TEXT,
  [prompt_json] TEXT,
  [options_json] TEXT,
  [response] TEXT,
  [response_json] TEXT,
  [conversation_id] TEXT REFERENCES [conversations]([id]),
  [duration_ms] INTEGER,
  [datetime_utc] TEXT,
  [input_tokens] INTEGER,
  [output_tokens] INTEGER,
  [token_details] TEXT
);
CREATE VIRTUAL TABLE [responses_fts] USING FTS5 (
  [prompt],
  [response],
  content=[responses]
);
CREATE TABLE [attachments] (
  [id] TEXT PRIMARY KEY,
  [type] TEXT,
  [path] TEXT,
  [url] TEXT,
  [content] BLOB
);
CREATE TABLE [prompt_attachments] (
  [response_id] TEXT REFERENCES [responses]([id]),
  [attachment_id] TEXT REFERENCES [attachments]([id]),
  [order] INTEGER,
  PRIMARY KEY ([response_id],
  [attachment_id])
);

responses_fts configures SQLite full-text search against the prompt and response columns in the responses table.