pgml.transform_stream()

pgml.transform_stream mirrors pgml.transform with two caveats:

• It returns a SETOF JSONB instead of JSONB.
• It only works with the text-generation task.

The pgml.transform_stream function is overloaded and can be used to chat with messages or complete text.

Chat

Use this for conversational AI applications or when you need to provide instructions and maintain context.

API

                content_copy
            
pgml.transform_stream(
    task JSONB,
    inputs ARRAY[]::JSONB,
    args JSONB
)

A simple example using meta-llama/Meta-Llama-3.1-8B-Instruct:

                content_copy
            
SELECT pgml.transform_stream(
    task => '{
        "task": "conversational",
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct"
    }'::JSONB,
    inputs => ARRAY[
        '{"role": "system", "content": "You are a friendly and helpful chatbot"}'::JSONB,
        '{"role": "user", "content": "Tell me about yourself."}'::JSONB
    ]
) AS answer;

Result

                content_copy
            
["I"]
["'m"]
[" so"]
[" glad"]
[" you"]
[" asked"]
["!"]
[" I"]
["'m"]
[" a"]
...

Results have been truncated for sanity.

Chat Parameters

We follow OpenAI's standard for model parameters:

• frequency_penalty - Penalizes the frequency of tokens
• logit_bias - Modify the likelihood of specified tokens
• logprobs - Return logprobs of the most likely token(s)
• top_logprobs - The number of most likely tokens to return at each token position
• max_tokens - The maximum number of tokens to generate
• n - The number of completions to build out
• presence_penalty - Control new token penalization
• response_format - The format of the response
• seed - The seed for randomness
• stop - An array of sequences to stop on
• temperature - The temperature for sampling
• top_p - An alternative sampling method

For more information on these parameters see OpenAI's docs.

An example with some common parameters:

                content_copy
            
SELECT pgml.transform_stream(
    task => '{
        "task": "conversational",
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct"
    }'::JSONB,
    inputs => ARRAY[
        '{"role": "system", "content": "You are a friendly and helpful chatbot"}'::JSONB,
        '{"role": "user", "content": "Tell me about yourself."}'::JSONB
    ],
    args => '{
        "max_tokens": 10,
        "temperature": 0.75,
        "seed": 10
    }'::JSONB
) AS answer;

Result

                content_copy
            
["I"]
["'m"]
[" so"]
[" glad"]
[" you"]
[" asked"]
["!"]
[" I"]
["'m"]
[" a"]

Completion

Use this for simpler text-generation tasks like completing sentences or generating content based on a prompt.

API

                content_copy
            
pgml.transform_stream(
    task JSONB,
    input text,
    args JSONB
)

A simple example using meta-llama/Meta-Llama-3.1-8B-Instruct:

                content_copy
            
SELECT pgml.transform_stream(
    task => '{
        "task": "text-generation",
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct"
    }'::JSONB,
    input => 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone'
) AS answer;

Result

                content_copy
            
[","]
[" Nine"]
[" for"]
[" Mort"]
["al"]
[" Men"]
[" doomed"]
[" to"]
[" die"]
[","]
[" One"]
[" for"]
[" the"]
[" Dark"]
[" Lord"]
[" on"]

Completion Parameters

We follow OpenAI's standard for model parameters:

• best_of - Generates "best_of" completions
• echo - Echo back the prompt
• frequency_penalty - Penalizes the frequency of tokens
• logit_bias - Modify the likelihood of specified tokens
• logprobs - Return logprobs of the most likely token(s)
• max_tokens - The maximum number of tokens to generate
• n - The number of completions to build out
• presence_penalty - Control new token penalization
• seed - The seed for randomness
• stop - An array of sequences to stop on
• temperature - The temperature for sampling
• top_p - An alternative sampling method

For more information on these parameters see OpenAI's docs.

An example with some common parameters:

                content_copy
            
SELECT pgml.transform_stream(
    task => '{
        "task": "text-generation",
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct"
    }'::JSONB,
    input => 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone',
    args => '{
        "max_tokens": 10,
        "temperature": 0.75,
        "seed": 10
    }'::JSONB
) AS answer;

Result

                content_copy
            
[","]
[" Nine"]
[" for"]
[" Mort"]
["al"]
[" Men"]
[" doomed"]
[" to"]
[" die"]
[","]