langchain/templates/rag-chroma-private/rag_chroma_private.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "232fd40d-cf6a-402d-bcb8-414184a8e924",
   "metadata": {},
   "source": [
    "## Run Template\n",
    "\n",
    "In `server.py`, set -\n",
    "```\n",
    "add_routes(app, chain_private, path=\"/rag_chroma_private\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "888494ca-0509-4070-b36f-600a042f352c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Based on the given context, the answer to the question \"How does agent memory work?\" can be inferred as follows:\\n\\nAgent memory refers to the long-term memory module of an autonomous agent system, which records a comprehensive list of agents\\' experiences in natural language. Each element is an observation or event directly provided by the agent, and inter-agent communication can trigger new natural language statements. The retrieval model surfaces the context to inform the agent\\'s behavior according to relevance, recency, and importance.\\n\\nIn other words, the agent memory is a component of the autonomous agent system that stores and manages the agent\\'s experiences and observations in a long-term memory module, which is based on natural language processing and generation capabilities of a large language model (LLM). The memory is used to inform the agent\\'s behavior and decision-making, and it can be triggered by inter-agent communication.'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langserve.client import RemoteRunnable\n",
    "rag_app = RemoteRunnable('http://0.0.0.0:8001/rag_chroma_private/')\n",
    "rag_app.invoke(\"How does agent memory work?\")"
   ]
  }
 ],
 "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}