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enh: embedding_batch_size for local embedding engine
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@@ -795,7 +795,9 @@ def get_embedding_function(
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return await asyncio.to_thread(
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(
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lambda query, prefix=None: embedding_function.encode(
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query, **({"prompt": prefix} if prefix else {})
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query,
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batch_size=int(embedding_batch_size),
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**({"prompt": prefix} if prefix else {}),
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).tolist()
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),
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query,
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@@ -950,24 +950,24 @@
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</div>
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</div>
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{#if RAG_EMBEDDING_ENGINE === 'ollama' || RAG_EMBEDDING_ENGINE === 'openai' || RAG_EMBEDDING_ENGINE === 'azure_openai'}
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<div class=" mb-2.5 flex w-full justify-between">
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<div class=" self-center text-xs font-medium">
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{$i18n.t('Embedding Batch Size')}
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</div>
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<div class="">
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<input
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bind:value={RAG_EMBEDDING_BATCH_SIZE}
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type="number"
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class=" bg-transparent text-center w-14 outline-none"
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min="-2"
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max="16000"
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step="1"
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/>
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</div>
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<div class=" mb-2.5 flex w-full justify-between">
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<div class=" self-center text-xs font-medium">
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{$i18n.t('Embedding Batch Size')}
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</div>
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<div class="">
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<input
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bind:value={RAG_EMBEDDING_BATCH_SIZE}
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type="number"
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class=" bg-transparent text-center w-14 outline-none"
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min="-2"
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max="16000"
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step="1"
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/>
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</div>
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</div>
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{#if RAG_EMBEDDING_ENGINE === 'ollama' || RAG_EMBEDDING_ENGINE === 'openai' || RAG_EMBEDDING_ENGINE === 'azure_openai'}
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<div class=" mb-2.5 flex w-full justify-between">
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<div class="self-center text-xs font-medium">
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<Tooltip
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