{
 "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_L4_Building_a_Multi-Document_Agent.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 4 Building a Multi-Document Agent](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/5/building-a-multi-document-agent) to using Llama 3. It shows how to use an agent to handle multiple documents and increasing degrees of complexity.\n",
    "\n",
    "You should take the course before or after going through this notebook to have a deeper understanding.\n",
    "\n",
    "Note: Unlike Lessons 1 and 3 where we use Llama 3 70b on [Groq](https://groq.com/), this lesson uses Llama 3 on [Fireworks.ai](https://fireworks.ai/) to overcome the rate limit issue with Groq on some summary tool calling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eiJsOa29ej7G",
    "outputId": "4f524ff3-a0c1-49a2-8adb-15b96d5a7b63"
   },
   "outputs": [],
   "source": [
    "!pip install llama-index\n",
    "!pip install llama-index-embeddings-huggingface\n",
    "!pip install llama-index-llms-fireworks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sG25Zlzsfg3K"
   },
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0z66nY0gKzR6"
   },
   "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": "code",
   "execution_count": null,
   "metadata": {
    "id": "NGVdQc2FHejT"
   },
   "outputs": [],
   "source": [
    "urls = [\n",
    "    \"https://openreview.net/pdf?id=VtmBAGCN7o\",\n",
    "    \"https://openreview.net/pdf?id=6PmJoRfdaK\",\n",
    "    \"https://openreview.net/pdf?id=hSyW5go0v8\",\n",
    "]\n",
    "\n",
    "papers = [\n",
    "    \"metagpt.pdf\",\n",
    "    \"longlora.pdf\",\n",
    "    \"selfrag.pdf\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xju9QpOCK-vw",
    "outputId": "4d9ad762-cfa4-4a46-c2be-6197e554d822"
   },
   "outputs": [],
   "source": [
    "for url, paper in zip(urls, papers):\n",
    "  !wget \"{url}\" -O \"{paper}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EXLxN6f1LUDu",
    "outputId": "67b0801b-a033-44bb-953a-5ca769e91c4d"
   },
   "outputs": [],
   "source": [
    "import os \n",
    "\n",
    "os.environ['FIREWORKS_API_KEY'] = 'xxx' # get a free key at https://fireworks.ai/api-keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.llms.fireworks import Fireworks\n",
    "\n",
    "# Llama 3 8b on Fireworks.ai also works in some cases, but 70b works better overall\n",
    "#llm = Fireworks(model=\"accounts/fireworks/models/llama-v3-8b-instruct\", temperature=0)\n",
    "llm = Fireworks(model=\"accounts/fireworks/models/llama-v3-70b-instruct\", temperature=0)\n",
    "\n",
    "# a quick sanity test\n",
    "#llm.complete(\"Who wrote the  book godfather? \").text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    },
    "id": "SHZbbMdQLWZg",
    "outputId": "72e5ca72-f19b-4684-b873-7d9387640bf3"
   },
   "outputs": [],
   "source": [
    "from llama_index.core import Settings, VectorStoreIndex\n",
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "\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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NnZdKJKVdOFs",
    "outputId": "bea8307e-927f-44f6-f78c-f602f46a963f"
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "paper_to_tools_dict = {}\n",
    "for paper in papers:\n",
    "    print(f\"Getting tools for paper: {paper}\")\n",
    "    vector_tool, summary_tool = get_doc_tools(paper, Path(paper).stem)\n",
    "    paper_to_tools_dict[paper] = [vector_tool, summary_tool]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FktfixgiLsMm"
   },
   "outputs": [],
   "source": [
    "initial_tools = [t for paper in papers for t in paper_to_tools_dict[paper]]"
   ]
  },
  {
   "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",
    "    initial_tools,\n",
    "    llm=llm,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d9wVsIt586s8",
    "outputId": "de9a4d02-605b-4215-c234-2ab47482b48f"
   },
   "outputs": [],
   "source": [
    "response = agent.query(\n",
    "    \"Tell me about the evaluation dataset used in LongLoRA, \"\n",
    "    \"and then tell me about the evaluation results\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 602
    },
    "id": "_vrBO-S1-HjF",
    "outputId": "d7890da1-adc8-46d8-be3a-aeca37f52dca"
   },
   "outputs": [],
   "source": [
    "response = agent.query(\"Give me a summary of both Self-RAG and LongLoRA\")\n",
    "print(str(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iyhvuua7GjVD"
   },
   "outputs": [],
   "source": [
    "urls = [\n",
    "    \"https://openreview.net/pdf?id=VtmBAGCN7o\",\n",
    "    \"https://openreview.net/pdf?id=6PmJoRfdaK\",\n",
    "    \"https://openreview.net/pdf?id=LzPWWPAdY4\",\n",
    "    \"https://openreview.net/pdf?id=VTF8yNQM66\",\n",
    "    \"https://openreview.net/pdf?id=hSyW5go0v8\",\n",
    "    \"https://openreview.net/pdf?id=9WD9KwssyT\",\n",
    "    \"https://openreview.net/pdf?id=yV6fD7LYkF\",\n",
    "    \"https://openreview.net/pdf?id=hnrB5YHoYu\",\n",
    "    \"https://openreview.net/pdf?id=WbWtOYIzIK\",\n",
    "    \"https://openreview.net/pdf?id=c5pwL0Soay\",\n",
    "    \"https://openreview.net/pdf?id=TpD2aG1h0D\"\n",
    "]\n",
    "\n",
    "papers = [\n",
    "    \"metagpt.pdf\",\n",
    "    \"longlora.pdf\",\n",
    "    \"loftq.pdf\",\n",
    "    \"swebench.pdf\",\n",
    "    \"selfrag.pdf\",\n",
    "    \"zipformer.pdf\",\n",
    "    \"values.pdf\",\n",
    "    \"finetune_fair_diffusion.pdf\",\n",
    "    \"knowledge_card.pdf\",\n",
    "    \"metra.pdf\",\n",
    "    \"vr_mcl.pdf\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m-RoimylMmU1",
    "outputId": "95f616e4-b479-4fe7-dba0-862fd596dad4"
   },
   "outputs": [],
   "source": [
    "for url, paper in zip(urls, papers):\n",
    "  !wget \"{url}\" -O \"{paper}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BEfcjCQbMp2i",
    "outputId": "81c48305-9569-470c-b66f-e1b615423cd2"
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "paper_to_tools_dict = {}\n",
    "for paper in papers:\n",
    "    print(f\"Getting tools for paper: {paper}\")\n",
    "    vector_tool, summary_tool = get_doc_tools(paper, Path(paper).stem)\n",
    "    paper_to_tools_dict[paper] = [vector_tool, summary_tool]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "E2wXRc15Mp5Y"
   },
   "outputs": [],
   "source": [
    "all_tools = [t for paper in papers for t in paper_to_tools_dict[paper]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PKd8_J1vMp8a"
   },
   "outputs": [],
   "source": [
    "# define an \"object\" index and retriever over these tools\n",
    "from llama_index.core import VectorStoreIndex\n",
    "from llama_index.core.objects import ObjectIndex\n",
    "\n",
    "obj_index = ObjectIndex.from_objects(\n",
    "    all_tools,\n",
    "    index_cls=VectorStoreIndex,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jys0qlogM069"
   },
   "outputs": [],
   "source": [
    "obj_retriever = obj_index.as_retriever(similarity_top_k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q6iWXlbsM3LX"
   },
   "outputs": [],
   "source": [
    "tools = obj_retriever.retrieve(\n",
    "    \"Tell me about the eval dataset used in MetaGPT and SWE-Bench\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    },
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    "outputId": "ad06205d-1996-4d6c-8a95-3c13824c75bd"
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  {
   "cell_type": "code",
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    "id": "qsD37iTpM6gb"
   },
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   "source": [
    "# The LlamaIndex's FunctionCallingAgentWorker API doesn't work correctly with Fireworks Llama, so we use ReActAgent here.\n",
    "from llama_index.core.agent import ReActAgent\n",
    "\n",
    "agent = ReActAgent.from_tools(\n",
    "    tool_retriever=obj_retriever,\n",
    "    llm=llm,\n",
    "    system_prompt=\"\"\" \\\n",
    "You are an agent designed to answer queries over a set of given papers.\n",
    "Please always use the tools provided to answer a question. Do not rely on prior knowledge.\\\n",
    "\"\"\",    \n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "M6gM7iq6M8De",
    "outputId": "392e2376-1aff-46bb-ab23-1da137fa27ed"
   },
   "outputs": [],
   "source": [
    "response = agent.query(\n",
    "    \"Tell me about the evaluation dataset used \"\n",
    "    \"in MetaGPT and compare it against SWE-Bench\"\n",
    ")\n",
    "print(str(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9SsWMO7qM-NX"
   },
   "outputs": [],
   "source": [
    "response = agent.query(\n",
    "    \"Compare and contrast the LoRA papers (LongLoRA, LoftQ). \"\n",
    "    \"Analyze the approach in each paper first. \"\n",
    ")"
   ]
  }
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