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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "681a5d1e",
"metadata": {},
"source": [
"## Run Template\n",
"\n",
"In `server.py`, set -\n",
"```\n",
"from fastapi import FastAPI\n",
"from langserve import add_routes\n",
"from propositional_retrieval import chain\n",
"\n",
"app = FastAPI(\n",
" title=\"LangChain Server\",\n",
" version=\"1.0\",\n",
" description=\"Retriever and Generator for RAG Chroma Dense Retrieval\",\n",
")\n",
"\n",
"add_routes(app, chain, path=\"/propositional-retrieval\")\n",
"\n",
"if __name__ == \"__main__\":\n",
" import uvicorn\n",
"\n",
" uvicorn.run(app, host=\"localhost\", port=8000)\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d774be2a",
"metadata": {},
"outputs": [],
"source": [
"from langserve.client import RemoteRunnable\n",
"\n",
"rag_app = RemoteRunnable(\"http://localhost:8001/propositional-retrieval\")\n",
"rag_app.invoke(\"How are transformers related to convolutional neural networks?\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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