run-llama--llama_index/docs/examples/agent/bedrock_converse_agent.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "24103c51",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/bedrock_converse_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99cea58c-48bc-4af6-8358-df9695659983",
   "metadata": {},
   "source": [
    "# Function Calling AWS Bedrock Converse Agent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673df1fe-eb6c-46ea-9a73-a96e7ae7942e",
   "metadata": {},
   "source": [
    "This notebook shows you how to use our AWS Bedrock Converse agent, powered by function calling capabilities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b7bc2e-606f-411a-9490-fcfab9236dfc",
   "metadata": {},
   "source": [
    "## Initial Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e80e5b-aaee-4f23-b338-7ae62b08141f",
   "metadata": {},
   "source": [
    "Let's start by importing some simple building blocks.  \n",
    "\n",
    "The main thing we need is:\n",
    "1. AWS credentials with access to Bedrock and the Claude Haiku LLM\n",
    "2. a place to keep conversation history \n",
    "3. a definition for tools that our agent can use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41101795",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4985c578",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index\n",
    "%pip install llama-index-llms-bedrock-converse\n",
    "%pip install llama-index-embeddings-huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe08eb1-e638-4c00-9103-5c305bfacccf",
   "metadata": {},
   "source": [
    "Let's define some very simple calculator tools for our agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dd3c4a6-f3e0-46f9-ad3b-7ba57d1bc992",
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiply(a: int, b: int) -> int:\n",
    "    \"\"\"Multiple two integers and returns the result integer\"\"\"\n",
    "    return a * b\n",
    "\n",
    "\n",
    "def add(a: int, b: int) -> int:\n",
    "    \"\"\"Add two integers and returns the result integer\"\"\"\n",
    "    return a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeac7d4c-58fd-42a5-9da9-c258375c61a0",
   "metadata": {},
   "source": [
    "Make sure to set your AWS credentials, either the `profile_name` or the keys below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4becf171-6632-42e5-bdec-918a00934696",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.llms.bedrock_converse import BedrockConverse\n",
    "\n",
    "llm = BedrockConverse(\n",
    "    model=\"anthropic.claude-3-haiku-20240307-v1:0\",\n",
    "    # NOTE replace with your own AWS credentials\n",
    "    aws_access_key_id=\"AWS Access Key ID to use\",\n",
    "    aws_secret_access_key=\"AWS Secret Access Key to use\",\n",
    "    aws_session_token=\"AWS Session Token to use\",\n",
    "    region_name=\"AWS Region to use, eg. us-east-1\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "707d30b8-6405-4187-a9ed-6146dcc42167",
   "metadata": {},
   "source": [
    "## Initialize AWS Bedrock Converse Agent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798ca3fd-6711-4c0c-a853-d868dd14b484",
   "metadata": {},
   "source": [
    "Here we initialize a simple AWS Bedrock Converse agent with calculator functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38ab3938-1138-43ea-b085-f430b42f5377",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.agent.workflow import FunctionAgent\n",
    "\n",
    "agent = FunctionAgent(\n",
    "    tools=[multiply, add],\n",
    "    llm=llm,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500cbee4",
   "metadata": {},
   "source": [
    "### Chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9450401d-769f-46e8-8bab-0f27f7362f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = await agent.run(\"What is (121 + 2) * 5?\")\n",
    "print(str(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "538bf32f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# inspect sources\n",
    "print(response.tool_calls)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cabfdf01-8d63-43ff-b06e-a3059ede2ddf",
   "metadata": {},
   "source": [
    "## AWS Bedrock Converse Agent over RAG Pipeline\n",
    "\n",
    "Build an AWS Bedrock Converse agent over a simple 10K document. We use both HuggingFace embeddings and `BAAI/bge-small-en-v1.5` to construct the RAG pipeline, and pass it to the AWS Bedrock Converse agent as a tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48120dd4-7f50-426f-bc7e-a903e090d32e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p 'data/10k/'\n",
    "!curl -o 'data/10k/uber_2021.pdf' 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48c0cf98-3f10-4599-8437-d88dc89cefad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.tools import QueryEngineTool\n",
    "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n",
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "from llama_index.llms.bedrock_converse import BedrockConverse\n",
    "\n",
    "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
    "query_llm = BedrockConverse(\n",
    "    model=\"anthropic.claude-3-haiku-20240307-v1:0\",\n",
    "    # NOTE replace with your own AWS credentials\n",
    "    aws_access_key_id=\"AWS Access Key ID to use\",\n",
    "    aws_secret_access_key=\"AWS Secret Access Key to use\",\n",
    "    aws_session_token=\"AWS Session Token to use\",\n",
    "    region_name=\"AWS Region to use, eg. us-east-1\",\n",
    ")\n",
    "\n",
    "# load data\n",
    "uber_docs = SimpleDirectoryReader(\n",
    "    input_files=[\"./data/10k/uber_2021.pdf\"]\n",
    ").load_data()\n",
    "\n",
    "# build index\n",
    "uber_index = VectorStoreIndex.from_documents(\n",
    "    uber_docs, embed_model=embed_model\n",
    ")\n",
    "uber_engine = uber_index.as_query_engine(similarity_top_k=3, llm=query_llm)\n",
    "query_engine_tool = QueryEngineTool.from_defaults(\n",
    "    query_engine=uber_engine,\n",
    "    name=\"uber_10k\",\n",
    "    description=(\n",
    "        \"Provides information about Uber financials for year 2021. \"\n",
    "        \"Use a detailed plain text question as input to the tool.\"\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebfdaf80-e5e1-4c60-b556-20558da3d5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.agent.workflow import FunctionAgent\n",
    "\n",
    "agent = FunctionAgent(\n",
    "    tools=[query_engine_tool],\n",
    "    llm=llm,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58c53f2a-0a3f-4abe-b8b6-97a974ec7546",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = await agent.run(\n",
    "    \"Tell me both the risk factors and tailwinds for Uber? Do two parallel tool calls.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3b5bb7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(str(response))"
   ]
  }
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