langchain/docs/extras/integrations/llms/bedrock.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bedrock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.llms.bedrock import Bedrock\n",
    "\n",
    "llm = Bedrock(\n",
    "    credentials_profile_name=\"bedrock-admin\",\n",
    "    model_id=\"amazon.titan-tg1-large\",\n",
    "    endpoint_url=\"custom_endpoint_url\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using in a conversation chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import ConversationChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "conversation = ConversationChain(\n",
    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
    ")\n",
    "\n",
    "conversation.predict(input=\"Hi there!\")"
   ]
  }
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