---

name: embed description: Generate text embeddings using Qwen3 models via HuggingFace TEI. Use this skill to embed texts, configure the embedding service, or batch process documents. Invoke with /embed.

Text Embedding

This skill manages text embedding generation using Qwen3 models via HuggingFace Text Embeddings Inference (TEI).

Architecture

• Remote Service: HuggingFace TEI running on GPU instance
• Client: HTTP client with exponential backoff and batching
• Output: 1536-dimensional vectors
• Storage: Hive/Parquet tables or numpy arrays

Start TEI Service

On a GPU instance:

Copy# Using Docker
docker run --gpus all -p 8080:80 \
  ghcr.io/huggingface/text-embeddings-inference:latest \
  --model-id Alibaba-NLP/gte-Qwen2-1.5B-instruct

# Or with specific model
docker run --gpus all -p 8080:80 \
  ghcr.io/huggingface/text-embeddings-inference:latest \
  --model-id Qwen/Qwen3-Embedding-0.6B

Python Client Usage

Copyfrom wc_simd.embed import EmbedServiceClient

# Initialize client
client = EmbedServiceClient(endpoint="http://gpu-host:8080/embed")

# Embed single text
vector = client.embed(["Hello world"])[0]

# Embed batch
vectors = client.embed(["text1", "text2", "text3"])

PySpark Integration

Copyfrom wc_simd.embed import create_embed_udf

# Create UDF for Spark
embed_udf = create_embed_udf(endpoint="http://172.19.0.1:8080/embed")

# Apply to DataFrame
df_with_embeddings = df.withColumn("embedding", embed_udf("text_column"))

Text Chunking

For long texts, use the chunker before embedding:

Copyfrom wc_simd.embed import TextChunker

chunker = TextChunker(chunk_size=1000, overlap=200)
chunks = chunker.split(long_text)

# Embed chunks
embeddings = client.embed(chunks)

Elasticsearch Indexing

Copyfrom elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk

es = Elasticsearch(["http://localhost:9200"])

# Index with dense vector
actions = [
    {
        "_index": "text_embeddings",
        "_source": {
            "text": chunk,
            "embedding": embedding.tolist(),
            "work_id": work_id
        }
    }
    for chunk, embedding in zip(chunks, embeddings)
]

bulk(es, actions)

Configuration

Models

Troubleshooting

Connection Errors

Ensure TEI service is running and accessible. From Docker Spark, use 172.19.0.1 (gateway IP).

Rate Limiting

Increase batch size or add delays between requests.

OOM on GPU

Reduce batch size or use a smaller model.