PPLUS Trust Series GSC-2

AI-native vector embeddings for PPLUS Trust Series GSC-2 quarterly earnings calls

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Ticker

PYT

Coverage

2020–present

Embedding dims

768

Model

gemini-embedding-2-preview

About PYT Earnings Embeddings

VectorFin provides vector embeddings for every PPLUS Trust Series GSC-2 earnings call from 2020 through the present quarter. Each earnings call transcript is chunked into semantically coherent segments and vectorized using Google's gemini-embedding-2-preview model, producing 768-dimensional dense vectors optimized for cosine similarity search.

All data is bitemporal: every embedding chunk carries an effective_ts (when the earnings call occurred) and a knowledge_ts (when VectorFin ingested and vectorized the data). This enables point-in-time backtesting — query the data as it was known at any historical date.

Embedding data is updated within 24 hours of each earnings call. The full history is available via REST API (all plans) or as Apache Iceberg tables on GCS (Pro+ plans), queryable natively from Snowflake, BigQuery, or Databricks.

Common PYT use cases include quarter-over-quarter sentiment comparison, similarity search for analogous commentary at peers, and Retrieval-Augmented Generation (RAG) — grounding LLM answers about PPLUS Trust Series GSC-2 in citable transcript chunks.

Available fiscal periods

2024-Q4

2024-Q3

2024-Q2

2024-Q1

2023-Q4

2023-Q3

2023-Q2

2023-Q1

Showing recent 8 quarters. 2020–present available via API (beta).

Access via API

# Fetch PYT embeddings for the latest quarter
curl https://api.vectorfinancials.com/v1/embeddings/PYT \
  -H "X-API-Key: vf_sk_your_key_here" \
  -G \
  -d "fiscal_period=2024-Q4" \
  -d "limit=10"

# Response schema
{
  "data": [{
    "ticker": "PYT",
    "fiscal_period": "2024-Q4",
    "chunk_idx": 0,
    "text": "...",
    "embedding": [0.023, -0.091, ...],
    "effective_ts": "2025-01-30T00:00:00Z",
    "knowledge_ts": "2025-01-31T06:00:00Z",
    "model_version": "gemini-embedding-2-preview"
  }],
  "next_cursor": "..."
}

Related Concepts

Embedding →Retrieval-Augmented Generation (RAG) →Matryoshka (MRL) →Cosine Similarity →Bitemporal Data →Effective Timestamp →Knowledge Timestamp →Semantic Search →

Start using PYT embeddings today

Free tier includes top 100 tickers with 1,000 API calls/month.

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