Enhanced

Metadata Enrichment Pipeline

Automatically enrich your data with extracted metadata: entities, topics, sentiment, language, and custom attributes. Transform raw content into structured, queryable data.

text

image

video

audio

Single Tier

4.7K runs

Run in Builder

from mixpeek import Mixpeek

client = Mixpeek(api_key="YOUR_API_KEY")

namespace = client.namespaces.create(name="enriched-data")
collection = client.collections.create(
    namespace_id=namespace.id,
    name="customer-feedback",
    extractors=[
        "entity-extraction",
        "topic-classification",
        "sentiment-analysis",
        "language-detection"
    ]
)

# Upload content - metadata extracted automatically
client.buckets.upload(
    collection_id=collection.id,
    url="s3://your-bucket/feedback/"
)

# Query enriched data
positive_feedback = client.documents.search(
    namespace_id=namespace.id,
    filters={
        "sentiment": "positive",
        "topic": "product-quality"
    }
)

Feature Extractors

Retriever Stages

Use Cases Using This Recipe

Intermediate

6 min read

Contextual Page Signals for Ad Placement

One article URL in, five structured signals out: IAB category, entities with salience scores, sentiment, brand safety, keywords.

Five, from a single pass

Signals per URL

advertising

ip safety

Who It's For

Contextual advertising and ad-targeting teams who decide, per article, which creative renders beside it, and who need that decision to be defensible to a brand.

View Details

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