import numpy as np # NOTE you may need to `pip install numpy`.
from isaacus import Isaacus
# Create an Isaacus API client.
# NOTE see https://docs.isaacus.com/quickstart to learn how to get an API key.
client = Isaacus(api_key="PASTE_YOUR_API_KEY_HERE")
# Download the GitHub terms of service as an example.
tos = client.get(path="https://examples.isaacus.com/github-tos.md", cast_to=str)
# Embed the terms of service.
document_response = client.embeddings.create(
model="kanon-2-embedder",
texts=tos, # You can pass a single text or a list of texts here.
task="retrieval/document",
)
# Embed our search query.
query_responses = client.embeddings.create(
model="kanon-2-embedder",
texts=[
"What are GitHub's billing policies?", # This is a relevant query.
"What are Microsoft's billing policies?", # This is an irrelevant query.
],
task="retrieval/query",
)
# Unpack the embeddings.
document_embedding = document_response.embeddings[0].embedding
query_embeddings = query_responses.embeddings
relevant_query_embedding = query_embeddings[0].embedding
irrelevant_query_embedding = query_embeddings[1].embedding
# Compute the similarity between the queries and the document.
relevant_similarity = np.dot(relevant_query_embedding, document_embedding)
irrelevant_similarity = np.dot(irrelevant_query_embedding, document_embedding)
# Log the results.
print(f"Similarity of relevant query to the document: {relevant_similarity * 100:.2f}")
print(f"Similarity of irrelevant query to the document: {irrelevant_similarity * 100:.2f}")
