es-to-pinecone-transfer 0.1.0

A pipeline to transfer documents from Elasticsearch to Pinecone with threading support

ES to Pinecone Transfer Pipeline

A Python package for transferring documents from Elasticsearch to Pinecone with threading support for faster operations.

Features

• Transfer documents from Elasticsearch to Pinecone with minimal configuration
• Support for multiple embedding providers (OpenAI, HuggingFace)
• Multi-threaded processing for faster operations
• Configurable batch sizes and field selection
• Progress tracking with tqdm
• Comprehensive error handling

Installation

pip install es-to-pinecone-transfer

Quick Start

1. Create a .env file with your configuration (see .env.example for reference)

2. Use the pipeline directly in your code:

from es_to_pinecone_transfer.pipeline import ElasticsearchToPineconePipeline

# Initialize and run the pipeline
pipeline = ElasticsearchToPineconePipeline()
pipeline.run()

3. Or use the command-line interface:

es-to-pinecone

Configuration

The pipeline can be configured using environment variables. Here are the key settings:

Elasticsearch Configuration

• ES_HOST: Elasticsearch host URL
• ES_USERNAME: Elasticsearch username (optional)
• ES_PASSWORD: Elasticsearch password (optional)
• ES_API_KEY: Elasticsearch API key (optional, preferred over username/password)
• ES_INDEX: Source index name

Embedding Configuration

• EMBEDDING_TYPE: Type of embedding to use (openai/huggingface/custom)
• OPENAI_API_KEY: OpenAI API key (if using OpenAI embeddings)
• OPENAI_MODEL: OpenAI model to use for embeddings
• HUGGINGFACE_MODEL: HuggingFace model to use (if using HuggingFace)

Pinecone Configuration

• PINECONE_API_KEY: Pinecone API key
• PINECONE_ENVIRONMENT: Pinecone environment
• PINECONE_INDEX_NAME: Target Pinecone index name

Pipeline Configuration

• BATCH_SIZE: Number of documents to process in each batch
• MAX_THREADS: Maximum number of threads to use
• FIELDS_TO_EMBED: Comma-separated list of fields to embed from ES documents
• METADATA_FIELDS: Comma-separated list of fields to include as metadata
• DEFAULT_NAMESPACE: Pinecone namespace to use

Advanced Usage

Custom Field Mapping

from es_to_pinecone_transfer.pipeline import ElasticsearchToPineconePipeline

pipeline = ElasticsearchToPineconePipeline()
pipeline.set_field_mapping({
    'title': 'headline',
    'content': 'body',
    'author': 'writer',
    'timestamp': 'date'
})
pipeline.run()

Progress Callbacks

def on_batch_complete(batch_number, total_batches):
    print(f"Completed batch {batch_number} of {total_batches}")

pipeline = ElasticsearchToPineconePipeline()
pipeline.set_progress_callback(on_batch_complete)
pipeline.run()

Error Handling

The pipeline provides comprehensive error handling:

try:
    pipeline = ElasticsearchToPineconePipeline()
    pipeline.run()
except ElasticsearchConnectionError as e:
    print(f"Elasticsearch connection failed: {e}")
except PineconeConnectionError as e:
    print(f"Pinecone connection failed: {e}")
except EmbeddingError as e:
    print(f"Embedding generation failed: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.