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notebooks/hybrid_rag_pipeline_with_breakpoints.ipynb

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@@ -286,7 +286,7 @@
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"source": [
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"## Resuming from a break point\n",
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"\n",
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"We can then resume a pipeline from a `saved_state` by passing it to the `Pipeline.run()` method. This will run the pipeline to the end."
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"We can then resume a pipeline from its saved `pipeline_snapshot` by passing it to the Pipeline.run() method. This will run the pipeline to the end."
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}
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],
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"source": [
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" # Load the saved state and continue execution\n",
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" # Load the pipeline_snapshot and continue execution\n",
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"from haystack.core.pipeline.breakpoint import load_pipeline_snapshot\n",
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"\n",
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"snapshot = load_pipeline_snapshot(\"snapshots/query_embedder_2025_07_26_12_58_26.json\")\n",
@@ -335,7 +335,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To demonstrate the use case stated in point 1. we reuse the same query pipeline with a new question. First, we run the pipeline with the prompt set. Then, we define a breakpoint at the `prompt_builder` to try an alternative prompt. This allows us to compare the results generated by different prompts without running the whole pipeline again."
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"To demonstrate the use case stated in point 1, we reuse the same query pipeline with a new question. First, we run the pipeline with the prompt that we originally passed to the prompt_builder. Then, we define a breakpoint at the prompt_builder to try an alternative prompt. This allows us to compare the results generated by different prompts without running the whole pipeline again."
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we just load the state file and resume the pipeline with the altered state."
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"Now we just load the snapshot file and resume the pipeline with the updated snapshot."
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]
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},
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{

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