Embedding Pipelines

embedding-pipelines

Purpose

This skill manages embedding pipelines for AI/ML models, enabling creation, optimization, and deployment of pipelines that handle vector embeddings for tasks like NLP or recommendation systems. It integrates with frameworks like Hugging Face or TensorFlow to streamline workflows.

When to Use

Use this skill when you need to generate, fine-tune, or deploy embedding models, such as transforming text into vectors for similarity searches. Apply it in scenarios involving large datasets, model optimization for inference speed, or integrating embeddings into production ML pipelines.

Key Capabilities

• Create embedding pipelines with custom models (e.g., BERT, Word2Vec) and data sources.
• Optimize pipelines for performance, including dimensionality reduction via PCA or quantization.
• Deploy pipelines to cloud environments like AWS Sagemaker or local servers.
• Monitor pipeline metrics such as embedding quality and latency.
• Support for batch and real-time processing with configurable input formats (e.g., JSON, CSV).

Usage Patterns

Always initialize with authentication via environment variable $EMBEDDING_API_KEY. Use CLI for quick tasks or API for programmatic integration. Start by defining a pipeline configuration file (YAML or JSON), then execute commands to build and deploy. For loops or scripts, wrap API calls in error-checked functions. Example pattern: Load config, create pipeline, optimize, then deploy.

Common Commands/API

Use the OpenClaw CLI with the embedding-pipelines subcommand. Authentication requires setting $EMBEDDING_API_KEY before running commands.

• Create a pipeline:

openclaw embedding-pipelines create --config pipeline.yaml --model bert (Config file example: {"model": "bert", "data_path": "data.csv"})

• Optimize a pipeline:

openclaw embedding-pipelines optimize --pipeline-id 123 --method pca --dimensions 128 (API endpoint: POST /api/embedding-pipelines/123/optimize with body: {"method": "pca", "dimensions": 128})

• Deploy a pipeline:

openclaw embedding-pipelines deploy --pipeline-id 123 --endpoint http://my-server:8080 (Code snippet: import requests response = requests.post('http://api.openclaw.com/api/embedding-pipelines/deploy', json={"id": 123, "endpoint": "http://my-server:8080"}, headers={"Authorization": f"Bearer {os.environ['EMBEDDING_API_KEY']}"} )

• List pipelines:

openclaw embedding-pipelines list --filter active (API: GET /api/embedding-pipelines?filter=active)

Config format is JSON or YAML, e.g.: { "model": "bert", "input_type": "text", "output_dim": 768 }

Integration Notes

Integrate by setting $EMBEDDING_API_KEY in your environment. For Python scripts, use the OpenClaw SDK: install via pip install openclaw-sdk, then import and authenticate. Example: from openclaw import EmbeddingPipelines; client = EmbeddingPipelines(api_key=os.environ['EMBEDDING_API_KEY']). Ensure your application handles asynchronous responses for long-running tasks. For Kubernetes, mount config files as secrets and reference them in deployment YAML.

Error Handling

Check CLI exit codes (e.g., non-zero for failures) and API response status codes (e.g., 400 for bad requests, 401 for auth errors). Handle specific errors like invalid config by parsing response JSON (e.g., {"error": "Invalid model type"}). In code, use try-except blocks: try: response = client.create_pipeline(config) except Exception as e: if "Invalid config" in str(e): print("Fix config and retry")

Log errors with details like pipeline ID for debugging. Retry transient errors (e.g., network issues) with exponential backoff.

Usage Examples

1. Create and optimize a simple embedding pipeline for text data: First, create a config file pipeline.yaml with: {"model": "bert", "data_path": "text_data.csv"}. Then run: export EMBEDDING_API_KEY=your_key_here openclaw embedding-pipelines create --config pipeline.yaml Follow with: openclaw embedding-pipelines optimize --pipeline-id 456 --method quantization

2. Deploy an optimized pipeline to a cloud endpoint: After optimization, deploy with: openclaw embedding-pipelines deploy --pipeline-id 456 --endpoint https://sagemaker-endpoint.aws.com In a script: client = EmbeddingPipelines(api_key=os.environ['EMBEDDING_API_KEY']) client.deploy(456, "https://sagemaker-endpoint.aws.com")

Graph Relationships

• Relates to: "model-training" (for feeding optimized embeddings into training loops)
• Depends on: "data-preprocessing" (for handling input data cleaning)
• Integrates with: "inference-serving" (for deploying pipelines to production servers)
• Conflicts with: None directly, but avoid concurrent use with "vector-search" if pipelines overlap