Sempra
AI-native vector embeddings for Sempra quarterly earnings calls
Ticker
SREA
Coverage
2020–present
Embedding dims
768
Model
gemini-embedding-2-preview
About SREA Earnings Embeddings
VectorFin provides vector embeddings for every Sempra 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 SREA use cases include quarter-over-quarter sentiment comparison, similarity search for analogous commentary at peers, and Retrieval-Augmented Generation (RAG) — grounding LLM answers about Sempra in citable transcript chunks.
Available fiscal periods
Showing recent 8 quarters. 2020–present available via API (beta).
Access via API
# Fetch SREA embeddings for the latest quarter
curl https://api.vectorfinancials.com/v1/embeddings/SREA \
-H "X-API-Key: vf_sk_your_key_here" \
-G \
-d "fiscal_period=2024-Q4" \
-d "limit=10"
# Response schema
{
"data": [{
"ticker": "SREA",
"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": "..."
}Start using SREA embeddings today
Free tier includes top 100 tickers with 1,000 API calls/month.