API Features · Python

Build RAG with Embeddings

Build Retrieval-Augmented Generation pipelines with XALEN embedding models. Search classical texts and astrology knowledge bases.

1. Install

pip install xalen

2. Code

# Generate embeddings
result = client.embeddings.create(
    model=#A6E3A1;">"e5-large-v2",
    input=[#A6E3A1;">"What is Gajakesari Yoga?", "Jupiter Moon conjunction effects"]
)

vectors = [item.embedding #89B4FA;">for item in result.data]
#89B4FA;">print(f"Dimensions: {len(vectors[0])}")  # 1024

# Use with your vector DB (Pinecone, pgvector, etc.)
# for semantic search over classical texts

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Last updated: 2026-05-21