Content Clustering Pipeline

import requests

from mixpeek import Mixpeek

client = Mixpeek(api_key="YOUR_API_KEY", namespace="clusters")
API = "https://api.mixpeek.com/v1"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY", "X-Namespace": "clusters"}

# 1. A bucket for articles, and a collection that embeds each one
bucket = client.buckets.create(
    bucket_name="articles",
    bucket_schema={"properties": {"article": {"type": "text"}}},
)
collection = client.collections.create(
    collection_name="articles",
    source={"type": "bucket", "bucket_ids": [bucket["bucket_id"]]},
    feature_extractor={
        "feature_extractor_name": "text_extractor",
        "version": "v1",
    },
)
client.buckets.upload(
    bucket["bucket_id"],
    blobs=[{"property": "article", "type": "text", "data": "s3://your-bucket/articles/cloud-costs.md"}],
)
client.collections.trigger(collection["collection_id"])

# 2. A k-means cluster over the embeddings, with LLM-written labels. The SDK has no
#    clusters resource, so this is REST.
cluster = requests.post(API + "/clusters", headers=HEADERS, json={
    "cluster_name": "article-themes",
    "collection_ids": [collection["collection_id"]],
    "cluster_type": "vector",
    "vector_config": {
        "feature_uris": ["mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1"],
        "clustering_method": "kmeans",
        "algorithm_params": {"n_clusters": 20},
    },
    "llm_labeling": {"enabled": True, "provider": "openai", "model_name": "gpt-4o-mini-2024-07-18"},
}).json()
requests.post(API + "/clusters/" + cluster["cluster_id"] + "/execute", headers=HEADERS, json={})

# 3. Once the execution finishes, read the groups
groups = requests.get(API + "/clusters/" + cluster["cluster_id"] + "/groups", headers=HEADERS).json()
for group in groups["groups"]:
    print(group)