premai 0.3.49

A client library for accessing Prem APIs

Prem Python SDK

Installation

You can install the Prem Python SDK directly from npm.

pip install premai

Usage

Getting Started

To use the Prem Python SDK, you need to obtain an API key from the Prem platform. You can then create a Prem instance to make requests to the API.

from premai import Prem

client = Prem(
    api_key=YOUR_API_KEY
)

Chat completion

The chat.completions module allows you to generate completions based on user input. Here's an example:

messages = [
    {"role": "user", "content": "Who won the world series in 2020?"},
]
project_id = PROJECT_ID

# Create completion
response = client.chat.completions.create(
    project_id=project_id,
    messages=messages,
)

print(response.choices)

Chat completion with stream

You can also create a completion with a stream to receive the response in chunks by passing the stream parameter as true (default is false).

# Create completion with stream
response = client.chat.completions.create(
    project_id=project_id,
    messages=messages,
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta["content"]:
        print(chunk.choices[0].delta["content"], end="")

Optional parameters

By default, the chat.completions module uses the default launchpad parameters. You can also specify the following optional parameters:

• model: The model to use for completion. If omitted, the default launchpad model will be used.
• system_prompt: The system prompt to use for completion. If omitted, the default launchpad system prompt will be used.
• session_id: A unique identifier to maintain session context, useful for tracking conversations or data across multiple requests.
• temperature: The temperature to use for completion. If omitted, the default launchpad temperature will be used.
• max_tokens: The maximum number of tokens to generate for completion. If omitted, the default launchpad max tokens will be used.
• top_p: The nucleus sampling probability to use for completion. If omitted, the default launchpad top p will be used.
• frequency_penalty: The frequency penalty to use for completion. If omitted, the default launchpad frequency penalty will be used.
• presence_penalty: The presence penalty to use for completion. If omitted, the default launchpad presence penalty will be used.

Example:

model = "gpt-3.5-turbo"
system_prompt = "You are a helpful assistant."
session_id = "my-session"
temperature = 0.7
messages = [
    { "role": "user", "content": "Who won the world series in 2020?" },
]

response = client.chat.completions.create(
    project_id=project_id,
    messages=messages,
    model=model,
    system_prompt=system_prompt,
    session_id=session_id,
    temperature=temperature
)

print(response)

Enhanced Chat Completion with Retrieval Augmented Generation (RAG)

Enhance your chat completions by leveraging contextual data from specified repositories. A repository is a collection of documents, each containing information that can be utilized by the RAG system to provide enriched and context-aware responses.

If you've linked your repositories in the launchpad, relax—you're all set for effortless chat completions! The system automatically uses those parameters by default, ensuring a seamless and easy experience. However, if you wish to customize the process, you can specify the repositories parameter to fit your exact needs. Just define:

• ids: Your selected repository IDs.
• similarity_threshold: The least similarity score for content relevance.
• limit: The number of content pieces to include.

For guidance on managing repositories, see the Repositories section.

messages = [
    { "role": "user", "content": "Which is Jack's pet name?" },
]

repositories = dict(
  ids=[REPOSITORY_ID, ...],
  similarity_threshold=0.65,
  limit=3
)

# Create completion
response = client.chat.completions.create(
  project_id=PROJECT_ID,
  messages=messages,
  repositories=repositories,
  stream=False
)

print(response.choices[0].message.content)
# "Jack's pet name is Sparky."

print(response.document_chunks)
# E.g., [DocumentChunks(repository_id=4, document_id=14, chunk_id=15, document_name="pets_and_their_owners.txt", similarity_score=0.67, content="..."), ...]

Repositories

Repositories act as storage for documents, organized to facilitate efficient information retrieval. Manipulating repository content is straightforward.

Document creation

To add a document to a repository, you can use the create method provided by the document API. Here's an example of how to create and upload a document:

FILE_CONTENT = "My friend Jack has a beautiful pet, he gave it the name Sparky, [...]"

response = client.repository.document.create(
	repository_id=REPOSITORY_ID,
	name="pets_and_their_owners.txt",
	content=FILE_CONTENT,
	document_type="text",
)

print(response)
# E.g., DocumentOutput(repository_id=4, document_id=14, name="pets_and_their_owners.txt", type="text", status="UPLOADED", chunk_count=0, error=None)

After uploading, the document state is reflected in fields such as:

• status: Shows UPLOADED initially, changes once processed (e.g., PROCESSING).
• chunk_count: Number of data chunks; starts at 0 and increases post-processing.
• error: Non-null if an error arose during processing.