gwenlake 0.4.1

Gwenlake framework

Gwenlake Python Library

The Gwenlake Python library provides convenient access to the Gwenlake API from applications written in the Python language. It includes a pre-defined set of classes for API resources that initialize themselves dynamically from API responses which makes it compatible with a wide range of versions of the Gwenlake API.

Installation

If you just want to use the package, just run:

pip install -U git+https://github.com/gwenlake/gwenlake-python

Usage

The library needs to be configured with your account's secret key. Either set it as the GWENLAKE_API_KEY environment variable before using the library:

export GWENLAKE_API_KEY='sk-...'

Or set api_key to its value with the Client:

import gwenlake

client = gwenlake.Client(api_key = "sk-...")

Models

Use our inference platform to chat with models.

List models

r = client.models.list()
print(r)

Chat

messages = [
    {
        "role": "user",
        "content": "Anything about France?"
    }
]

r = client.chat.create(model="meta/llama-3.1-8b", messages=messages)
print(r)

Chat with streaming

The SDK also includes helpers to process streams and handle incoming events.

stream = client.chat.stream(model="meta/llama-3.1-8b", messages=messages)
for chunk in stream:
    if chunk["choices"][0]["delta"]["content"]:
        print(chunk["choices"][0]["delta"]["content"], end="")

Embeddings

Use our inference platform for embeddings using intfloat/e5-base-v2 or the multilingual intfloat/multilingual-e5-base model (supports 100 languages).

list_of_texts = [
    "Olympic Games will be in Paris in 2024",
    "Do Not Watch This Movie! Not funny at all",
    "Can you help me write an email to my best friend?",
]

response = client.embeddings.create(input=list_of_texts, model="e5-base-v2")
for item in response.data:
    print(item.embedding)

Prompts

Discover and share prompts in the Gwenlake Hub.

List prompts

response = client.prompts.list()
print(response)

Get a prompt

prompt = client.prompts.get("gwenlake/rag")
print(prompt)

Text Generation

Discover how to automatically combine prompts, datasets and models.

Retrieval Augmented Generation (RAG)

retriever = {
    "dataset": "gwenlake/csrd",
    "limit": 10
}

response = client.textgeneration.create(
    model="meta/llama-3.1-8b",
    prompt="gwenlake/rag",
    input={"query": "Explain CSRD"},
    retriever=retriever)
print(response["output"][0]["text"])

Files

Upload files on your private datasets.

import gwenlake

client = gwenlake.Client()

# upload a file into a dataset
r = client.files.upload("myteam/mydataset", file="test.pdf")
print(r)

# upload a file in a subdir
r = client.files.upload("myteam/mydataset/docs", file="test.pdf")
print(r)

# list files
r = client.files.list("myteam/mydataset")
print(r)

r = client.files.list("myteam/mydataset/docs")
print(r)

# get file
file = client.files.get("myteam/mydataset/test.pdf")