Platform
Use AI and machine learning models in a few click.
A set of models, endpoints and tools ready to use!
Embedding
State of the art embeddings in more than 100 languages.
Chat Completion
GPT-3.5 Turbo, GPT-4 and Open Source LLMs.
Pipelines
Chain prompts and models.
Vectorization and RAG
Read, store and query your documents for RAG.
Start using our models with a simple API call.
# Example using Gwenlake API for documents embedding
import gwenlake
documents = [
"Do Not Watch This Movie!",
"This movie is SOOOO funny!!! The acting is WONDERFUL.",
"Everything about this movie is horrible, from the acting to the editing.",
]
api_key = "sk-XXX" # get in touch to get your API Key
client = gwenlake.Client(api_key)
response = client.embeddings.create(input=list_of_texts, model="e5-base-v2")
for item in response.data:
print(pd.DataFrame(item.embedding))
