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Copy file name to clipboardExpand all lines: pgml-cms/docs/open-source/korvus/guides/constructing-pipelines.md
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The `Pipeline` above generates embeddings and tsvectors for the `abstract` and splits and generates embeddings and tsvectors for the `text`.
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We can now perform search over both the `text` and `abstract` key of our documents. See the [guide for vector search](vector-search) for more information on how to do this.
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## Self-Hosting Specific Parameters
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**This section is only relevant for self hosted instances of PostgresML**. These parameters are never required for instances hosted by PostgresML.
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### Trust Remote Code
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Some HuggingFace models require the argument `trust_remote_code=true`. To enable this, pass it as a parameter in the pipeline construction:
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```python
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pipeline = Pipeline(
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"v0",
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{
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"text": {
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"semantic_search": {
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"model": "Alibaba-NLP/gte-base-en-v1.5",
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"parameters": {
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"trust_remote_code": True
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}
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}
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}
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}
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)
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```
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### HuggingFace authentication
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Pass your HuggingFace token into the pipeline to access gated repos:
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