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Installation

You can install the Prem Python or JavaScript SDKs directly from PyPI or npm.

pip install premai

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Usage

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Getting Started

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

from premai import Prem

client = Prem(
    api_key=YOUR_API_KEY
)

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Chat completion

The chat.completions module is set to use default launchpad parameters unless otherwise specified. Optional parameters can also be defined as needed.

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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)

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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="")

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Optional parameters

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

  • session_id: A unique identifier to maintain session context, useful for tracking conversations or data across multiple requests.
  • repositories: Options for Retrieval Augmented Generation (RAG). Will override launched model settings. (More about that on next section)
  • 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.
  • max_tokens: The maximum number of tokens to generate for completion. If omitted, the default launchpad max tokens will be used.
  • stream: If set, partial message deltas will be sent, like in ChatGPT.
  • temperature: The temperature to use for completion. If omitted, the default launchpad temperature 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)

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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="..."), ...]

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Repositories

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

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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.
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