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https://github.com/Unstructured-IO/unstructured.git
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bc791d53f4
Adds OpenSearch as a source and destination. Since OpenSearch is a fork of Elasticsearch, these connectors rely heavily on inheriting the Elasticsearch connectors whenever possible. - Adds OpenSearch source connector to be able to ingest documents from OpenSearch. - Adds OpenSearch destination connector to be able to ingest documents from any supported source, embed them and write the embeddings / documents into OpenSearch. - Defines an example unstructured elements schema for users to be able to setup their unstructured OpenSearch indexes easily. --------- Co-authored-by: potter-potter <david.potter@gmail.com>
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
import os
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from unstructured.ingest.connector.elasticsearch import (
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ElasticsearchWriteConfig,
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)
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from unstructured.ingest.connector.local import SimpleLocalConfig
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from unstructured.ingest.connector.opensearch import (
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OpenSearchAccessConfig,
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SimpleOpenSearchConfig,
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)
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from unstructured.ingest.interfaces import (
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ChunkingConfig,
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EmbeddingConfig,
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PartitionConfig,
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ProcessorConfig,
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ReadConfig,
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)
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from unstructured.ingest.runner import LocalRunner
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from unstructured.ingest.runner.writers.base_writer import Writer
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from unstructured.ingest.runner.writers.opensearch import (
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OpenSearchWriter,
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)
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def get_writer() -> Writer:
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return OpenSearchWriter(
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connector_config=SimpleOpenSearchConfig(
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access_config=OpenSearchAccessConfig(
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hosts=os.getenv("OPENSEARCH_HOSTS"),
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username=os.getenv("OPENSEARCH_USERNAME"),
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password=os.getenv("OPENSEARCH_PASSWORD"),
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),
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index_name=os.getenv("OPENSEARCH_INDEX_NAME"),
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),
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write_config=ElasticsearchWriteConfig(
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batch_size_bytes=15_000_000,
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num_processes=2,
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),
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)
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if __name__ == "__main__":
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writer = get_writer()
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runner = LocalRunner(
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processor_config=ProcessorConfig(
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verbose=True,
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output_dir="local-output-to-opensearch",
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num_processes=2,
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),
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connector_config=SimpleLocalConfig(
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input_path="example-docs/book-war-and-peace-1225p.txt",
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),
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read_config=ReadConfig(),
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partition_config=PartitionConfig(),
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chunking_config=ChunkingConfig(chunk_elements=True),
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embedding_config=EmbeddingConfig(
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provider="langchain-huggingface",
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),
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writer=writer,
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writer_kwargs={},
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)
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runner.run()
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