Table: openai_completion - Query OpenAI Completions using SQL

OpenAI Completions is a service within the OpenAI API that allows you to generate text. It provides a way to interact with the API, enabling users to input a prompt and receive a generated text completion in response. OpenAI Completions is typically used for tasks such as drafting emails, writing code, answering questions, creating conversational agents, tutoring, translating languages, simulating characters for video games, and more.

Table Usage Guide

The openai_completion table provides insights into text completions within OpenAI. As a data scientist or AI developer, explore completion-specific details through this table, including generated text, model used, and other related information. Utilize it to understand the performance of different models, analyze the generated text, and gain insights into the behavior of the OpenAI API.

Important Notes

A prompt or settings -> 'prompt' where qualifier is required for all queries.

Examples

Basic example with default settings

Discover the segments that yield completion results for a specific prompt in an OpenAI model. This is particularly useful for generating creative content, such as taglines for businesses.

select
  completion
from
  openai_completion
where
  prompt = 'Write a tagline for an ice cream shop.';

select
  completion
from
  openai_completion
where
  prompt = 'Write a tagline for an ice cream shop.';

Completion with specific settings

Explore the completion of a specific task within the OpenAI platform by specifying certain settings. This can be useful to understand the output of a particular model under defined parameters, providing insights into its performance and usefulness for specific tasks. settings is a JSONB object that accepts any of the completion API request parameters.

select
  completion
from
  openai_completion
where
  settings = '{
    "model": "text-davinci-003",
    "max_tokens": 60,
    "temperature": 0.7,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 1
  }'
  and prompt = 'A neutron star is the collapsed core of a massive supergiant star,
which had a total mass of between 10 and 25 solar masses, possibly more if the
star was especially metal-rich.[1] Neutron stars are the smallest and densest
stellar objects, excluding black holes and hypothetical white holes, quark
stars, and strange stars.[2] Neutron stars have a radius on the order of 10
kilometres (6.2 mi) and a mass of about 1.4 solar masses.[3] They result from
the supernova explosion of a massive star, combined with gravitational
collapse, that compresses the core past white dwarf star density to that of
atomic nuclei.

TL;DR

';

select
  completion
from
  openai_completion
where
  settings = '{
    "model": "text-davinci-003",
    "max_tokens": 60,
    "temperature": 0.7,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 1
  }'
  and prompt = 'A neutron star is the collapsed core of a massive supergiant star,
which had a total mass of between 10 and 25 solar masses, possibly more if the
star was especially metal-rich.[1] Neutron stars are the smallest and densest
stellar objects, excluding black holes and hypothetical white holes, quark
stars, and strange stars.[2] Neutron stars have a radius on the order of 10
kilometres (6.2 mi) and a mass of about 1.4 solar masses.[3] They result from
the supernova explosion of a massive star, combined with gravitational
collapse, that compresses the core past white dwarf star density to that of
atomic nuclei.

TL;DR

';

Prompt through settings

Analyze the settings to understand the completion status of specific tasks in OpenAI. This is particularly useful when you want to monitor the progress of tasks such as writing a tagline for an ice cream shop. The prompt column takes precedence, but you can also provide prompt text through settings if easier.

select
  completion
from
  openai_completion
where
  settings = '{"prompt": "Write a tagline for an ice cream shop."}';

select
  completion
from
  openai_completion
where
  settings = '{"prompt": "Write a tagline for an ice cream shop."}';

Code analysis with change of model and a stop sequence

Explore the quality of code snippets by analyzing their completion status. This query is useful to understand the efficiency of different code models and identify potential areas of improvement.

select
  completion
from
  openai_completion
where
  settings = '{
    "model": "code-davinci-002",
    "max_tokens": 64,
    "temperature": 0,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 1,
    "stop": ["\"\"\""]
  }'
  and prompt = '
class Log:
    def __init__(self, path):
        dirname = os.path.dirname(path)
        os.makedirs(dirname, exist_ok=True)
        f = open(path, "a+")

        # Check that the file is newline-terminated
        size = os.path.getsize(path)
        if size > 0:
            f.seek(size - 1)
            end = f.read(1)
            if end != "\n":
                f.write("\n")
        self.f = f
        self.path = path

    def log(self, event):
        event["_event_id"] = str(uuid.uuid4())
        json.dump(event, self.f)
        self.f.write("\n")

    def state(self):
        state = {"complete": set(), "last": None}
        for line in open(self.path):
            event = json.loads(line)
            if event["type"] == "submit" and event["success"]:
                state["complete"].add(event["id"])
                state["last"] = event
        return state

"""
Here''s what the above class is doing:
1.';

select
  completion
from
  openai_completion
where
  settings = '{
    "model": "code-davinci-002",
    "max_tokens": 64,
    "temperature": 0,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 1,
    "stop": ["\"\"\""]
  }'
  and prompt = '
class Log:
    def __init__(self, path):
        dirname = os.path.dirname(path)
        os.makedirs(dirname, exist_ok=True)
        f = open(path, "a+")

        # Check that the file is newline-terminated
        size = os.path.getsize(path)
        if size > 0:
            f.seek(size - 1)
            end = f.read(1)
            if end != "\n":
                f.write("\n")
        self.f = f
        self.path = path

    def log(self, event):
        event["_event_id"] = str(uuid.uuid4())
        json.dump(event, self.f)
        self.f.write("\n")

    def state(self):
        state = {"complete": set(), "last": None}
        for line in open(self.path):
            event = json.loads(line)
            if event["type"] == "submit" and event["success"]:
                state["complete"].add(event["id"])
                state["last"] = event
        return state

"""
Here''s what the above class is doing:
1.';

Schema for openai_completion

Name	Type	Operators	Description
_ctx	jsonb		Steampipe context in JSON form, e.g. connection_name.
completion	text		Completions for a given text prompt.
finish_reason	text		The reason for the execution to be terminated.
index	bigint		The index location of the result.
log_probs	jsonb		Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens.
prompt	text	=	The prompt to generate completions for, encoded as a string.
settings	jsonb	=	Settings is a JSONB object that accepts any of the completion API request parameters.

Export

This table is available as a standalone Exporter CLI. Steampipe exporters are stand-alone binaries that allow you to extract data using Steampipe plugins without a database.

You can download the tarball for your platform from the Releases page, but it is simplest to install them with the steampipe_export_installer.sh script:

/bin/sh -c "$(curl -fsSL https://steampipe.io/install/export.sh)" -- openai

You can pass the configuration to the command with the --config argument:

steampipe_export_openai --config '<your_config>' openai_completion