mirror of
https://github.com/hwchase17/langchain
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0175906437
**Description:** This template utilizes Chroma and TGI (Text Generation Inference) to execute RAG on the Intel Xeon Scalable Processors. It serves as a demonstration for users, illustrating the deployment of the RAG service on the Intel Xeon Scalable Processors and showcasing the resulting performance enhancements. **Issue:** None **Dependencies:** The template contains the poetry project requirements to run this template. CPU TGI batching is WIP. **Twitter handle:** None --------- Signed-off-by: lvliang-intel <liang1.lv@intel.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
63 lines
1.2 KiB
Plaintext
63 lines
1.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "681a5d1e",
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"metadata": {},
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"source": [
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"## Connect to RAG App\n",
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"\n",
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"Assuming you are already running this server:\n",
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"```bash\n",
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"langserve start\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"\n",
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"gaudi_rag = RemoteRunnable(\"http://localhost:8000/intel-rag-xeon\")\n",
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"\n",
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"print(gaudi_rag.invoke(\"What was Nike's revenue in 2023?\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "07ae0005",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(gaudi_rag.invoke(\"How many employees work at Nike?\"))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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