{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 3 Building an Agent Reasoning Loop](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/4/building-an-agent-reasoning-loop) to using Llama 3. It shows how to define a complete agent reasoning loop to reason over tools and multiple steps on a complex question the user asks about a single document while maintaining memory.\n",
    "\n",
    "You should take the course before or after going through this notebook to have a deeper understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eiJsOa29ej7G",
    "outputId": "71cbfc5e-4073-4bb7-e7e0-8af67bc76fb7"
   },
   "outputs": [],
   "source": [
    "!pip install llama-index\n",
    "!pip install llama-index-embeddings-huggingface\n",
    "!pip install llama-index-llms-groq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sG25Zlzsfg3K"
   },
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CrQQkzV8dOJg",
    "outputId": "5ef72923-d717-424b-9088-a1d35952967b"
   },
   "outputs": [],
   "source": [
    "!wget \"https://openreview.net/pdf?id=VtmBAGCN7o\" -O metagpt.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup the Query Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, SummaryIndex\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.tools import FunctionTool, QueryEngineTool\n",
    "from llama_index.core.vector_stores import MetadataFilters, FilterCondition\n",
    "from typing import List, Optional\n",
    "\n",
    "def get_doc_tools(\n",
    "    file_path: str,\n",
    "    name: str,\n",
    ") -> str:\n",
    "    \"\"\"Get vector query and summary query tools from a document.\"\"\"\n",
    "\n",
    "    # load documents\n",
    "    documents = SimpleDirectoryReader(input_files=[file_path]).load_data()\n",
    "    splitter = SentenceSplitter(chunk_size=1024)\n",
    "    nodes = splitter.get_nodes_from_documents(documents)\n",
    "    vector_index = VectorStoreIndex(nodes)\n",
    "\n",
    "    def vector_query(\n",
    "        query: str,\n",
    "        page_numbers: Optional[List[str]] = None\n",
    "    ) -> str:\n",
    "        \"\"\"Use to answer questions over the MetaGPT paper.\n",
    "\n",
    "        Useful if you have specific questions over the MetaGPT paper.\n",
    "        Always leave page_numbers as None UNLESS there is a specific page you want to search for.\n",
    "\n",
    "        Args:\n",
    "            query (str): the string query to be embedded.\n",
    "            page_numbers (Optional[List[str]]): Filter by set of pages. Leave as NONE\n",
    "                if we want to perform a vector search\n",
    "                over all pages. Otherwise, filter by the set of specified pages.\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        page_numbers = page_numbers or []\n",
    "        metadata_dicts = [\n",
    "            {\"key\": \"page_label\", \"value\": p} for p in page_numbers\n",
    "        ]\n",
    "\n",
    "        query_engine = vector_index.as_query_engine(\n",
    "            similarity_top_k=2,\n",
    "            filters=MetadataFilters.from_dicts(\n",
    "                metadata_dicts,\n",
    "                condition=FilterCondition.OR\n",
    "            )\n",
    "        )\n",
    "        response = query_engine.query(query)\n",
    "        return response\n",
    "\n",
    "\n",
    "    vector_query_tool = FunctionTool.from_defaults(\n",
    "        name=f\"vector_tool_{name}\",\n",
    "        fn=vector_query\n",
    "    )\n",
    "\n",
    "    summary_index = SummaryIndex(nodes)\n",
    "    summary_query_engine = summary_index.as_query_engine(\n",
    "        response_mode=\"tree_summarize\",\n",
    "        use_async=True,\n",
    "    )\n",
    "    summary_tool = QueryEngineTool.from_defaults(\n",
    "        name=f\"summary_tool_{name}\",\n",
    "        query_engine=summary_query_engine,\n",
    "        description=(\n",
    "            \"Use ONLY IF you want to get a holistic summary of MetaGPT. \"\n",
    "            \"Do NOT use if you have specific questions over MetaGPT.\"\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    return vector_query_tool, summary_tool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Llama and Agent\n",
    "\n",
    "Note: The LlamaIndex's FunctionCallingAgentWorker API doesn't work correctly with Groq Llama, so we use ReActAgent here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "\n",
    "os.environ['GROQ_API_KEY'] = 'xxx' # get a free key at https://console.groq.com/keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.llms.groq import Groq\n",
    "\n",
    "from llama_index.core import Settings, VectorStoreIndex\n",
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "\n",
    "llm = Groq(model=\"llama3-70b-8192\", temperature=0)\n",
    "Settings.llm = llm\n",
    "\n",
    "Settings.embed_model = HuggingFaceEmbedding(\n",
    "    model_name=\"BAAI/bge-small-en-v1.5\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_tool, summary_tool = get_doc_tools(\"metagpt.pdf\", \"metagpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QkaALpnIQ01b"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.agent import ReActAgent\n",
    "\n",
    "query_engine_tools = [vector_tool, summary_tool]\n",
    "\n",
    "agent = ReActAgent.from_tools(\n",
    "    query_engine_tools,\n",
    "    llm=llm,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d9wVsIt586s8",
    "outputId": "aed1974d-5724-4543-d010-ca9997f95973"
   },
   "outputs": [],
   "source": [
    "response = agent.query(\n",
    "    \"Tell me about the agent roles in MetaGPT, and how they communicate with each other.\"\n",
    ")\n",
    "print(str(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_vrBO-S1-HjF",
    "outputId": "b8e8d35a-25f0-48d9-f992-3e5c3f573efe"
   },
   "outputs": [],
   "source": [
    "print(response.source_nodes[0].get_content(metadata_mode=\"all\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HQ0dzdD1AZn7",
    "outputId": "632142ae-891b-4dfc-cae8-5153b8b58bc6"
   },
   "outputs": [],
   "source": [
    "response = agent.query(\n",
    "    \"Tell me about the evaluation datasets used.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7gg99LMhA_vq",
    "outputId": "44c5844f-8aec-4ad7-e38c-f62ac0298b35"
   },
   "outputs": [],
   "source": [
    "print(response.source_nodes[0].get_content(metadata_mode=\"all\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9FplhNMmBO7o",
    "outputId": "1eab921a-4670-48a1-c2a0-0c3f4142ed33"
   },
   "outputs": [],
   "source": [
    "# use agent.chat instead of agent.query to pass conversational history automatically to answer follow up questions\n",
    "response = agent.chat(\"Tell me the results over one of the above datasets.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "38YcfpmdB2_L",
    "outputId": "cb71adc1-876f-4c41-def6-b2c638ec66f1"
   },
   "outputs": [],
   "source": [
    "# use agent.chat instead of agent.query to pass conversational history automatically to answer follow up questions\n",
    "response = agent.chat(\"Tell me more.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C-FsTWo0CnRE"
   },
   "source": [
    "## Lower-Level: Debuggability and Control\n",
    "\n",
    "Note: The LlamaIndex's FunctionCallingAgentWorker API doesn't work correctly with Groq Llama, so we use ReActAgent here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = ReActAgent.from_tools(\n",
    "    query_engine_tools,\n",
    "    llm=llm,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rCBVOv5MFnhY"
   },
   "outputs": [],
   "source": [
    "task = agent.create_task(\n",
    "    \"Tell me about the agent roles in MetaGPT, \"\n",
    "    \"and then how they communicate with each other.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ovokdaUpFoUx",
    "outputId": "f9cb5cc4-a2cf-4800-8bf2-b12f595cef4a"
   },
   "outputs": [],
   "source": [
    "step_output = agent.run_step(task.task_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iNQCyED2FqeP",
    "outputId": "bcd1a12c-38f3-49ea-f1d6-821696be644c"
   },
   "outputs": [],
   "source": [
    "completed_steps = agent.get_completed_steps(task.task_id)\n",
    "print(f\"Num completed for task {task.task_id}: {len(completed_steps)}\")\n",
    "print(completed_steps[0].output.sources[0].raw_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DwnKrC63FuKw",
    "outputId": "71151f4a-f540-4f3f-98a9-62d858e5dcb9"
   },
   "outputs": [],
   "source": [
    "upcoming_steps = agent.get_upcoming_steps(task.task_id)\n",
    "print(f\"Num upcoming steps for task {task.task_id}: {len(upcoming_steps)}\")\n",
    "upcoming_steps[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JXlVID-NFwL6",
    "outputId": "25e723c5-f4e3-4645-b45f-30046a1d95df"
   },
   "outputs": [],
   "source": [
    "step_output = agent.run_step(\n",
    "    task.task_id, input=\"What about how agents share information?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m8cM1jA7GZHX",
    "outputId": "f5e75a19-0fb6-4334-c1cd-0be31f6bc82b"
   },
   "outputs": [],
   "source": [
    "step_output = agent.run_step(task.task_id)\n",
    "print(step_output.is_last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DH3b_t9mGeP8"
   },
   "outputs": [],
   "source": [
    "response = agent.finalize_response(task.task_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zgaq4yqZGhRm",
    "outputId": "c1434fcc-003b-4e26-ff47-58f477127a52"
   },
   "outputs": [],
   "source": [
    "print(str(response))"
   ]
  }
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