diff --git a/.github/scripts/spellcheck_conf/wordlist.txt b/.github/scripts/spellcheck_conf/wordlist.txt
index 9332a4389e347b28b1aecc0bd3f092d74229561b..4e30058b4b0e46d2e5621b78031dcc3dee38a347 100644
--- a/.github/scripts/spellcheck_conf/wordlist.txt
+++ b/.github/scripts/spellcheck_conf/wordlist.txt
@@ -1351,6 +1351,11 @@ Weaviate
MediaGen
SDXL
SVD
+Agentic
+AutoGen
+DeepLearning
+Deeplearning
+Llamaindex
KV
KVs
XSUM
diff --git a/recipes/quickstart/agents/dlai/AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb b/recipes/quickstart/agents/dlai/AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bfb533e039a13002eda5bf76098a5654bc6e533c
--- /dev/null
+++ b/recipes/quickstart/agents/dlai/AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb
@@ -0,0 +1,314 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7a4b75bb-d60a-41e3-abca-1ca0f0bf1201",
+ "metadata": {},
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "51581f90-911f-46ef-82dd-f3ca9c1d4b96",
+ "metadata": {},
+ "source": [
+ "This notebook ports the DeepLearning.AI short course [AI Agentic Design Patterns with AutoGen Lesson 4 Tool Use and Conversational Chess](https://learn.deeplearning.ai/courses/ai-agentic-design-patterns-with-autogen/lesson/5/tool-use-and-conversational-chess) to using Llama 3. \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,
+ "id": "f9824ea5-3791-4638-a09d-43eb2c906d79",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "!pip install chess\n",
+ "!pip install pyautogen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a082a6dc-ceb1-4a3e-b3ae-afcb835de6da",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import chess\n",
+ "import chess.svg\n",
+ "from typing_extensions import Annotated"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fbcdd9ea-f589-463d-a306-3fb3fcde770c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "board = chess.Board()\n",
+ "\n",
+ "made_move = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d27858c-4a0b-40f6-bd58-01b19c33ab38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_legal_moves(\n",
+ " \n",
+ ") -> Annotated[str, \"A list of legal moves in UCI format\"]:\n",
+ " return \"Possible moves are: \" + \",\".join(\n",
+ " [str(move) for move in board.legal_moves]\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "67742daa-9d9a-46b3-9466-beb96d535334",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import SVG\n",
+ "\n",
+ "def make_move(\n",
+ " move: Annotated[str, \"A move in UCI format.\"]\n",
+ ") -> Annotated[str, \"Result of the move.\"]:\n",
+ " move = chess.Move.from_uci(move)\n",
+ " board.push_uci(str(move))\n",
+ " global made_move\n",
+ " made_move = True\n",
+ " \n",
+ " svg_str = chess.svg.board(\n",
+ " board,\n",
+ " arrows=[(move.from_square, move.to_square)],\n",
+ " fill={move.from_square: \"gray\"},\n",
+ " size=200\n",
+ " )\n",
+ " display(\n",
+ " SVG(data=svg_str)\n",
+ " )\n",
+ " \n",
+ " # Get the piece name.\n",
+ " piece = board.piece_at(move.to_square)\n",
+ " piece_symbol = piece.unicode_symbol()\n",
+ " piece_name = (\n",
+ " chess.piece_name(piece.piece_type).capitalize()\n",
+ " if piece_symbol.isupper()\n",
+ " else chess.piece_name(piece.piece_type)\n",
+ " )\n",
+ " return f\"Moved {piece_name} ({piece_symbol}) from \"\\\n",
+ " f\"{chess.SQUARE_NAMES[move.from_square]} to \"\\\n",
+ " f\"{chess.SQUARE_NAMES[move.to_square]}.\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e84508c0-0465-4be8-a97b-2e702265bcfb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# base url from https://console.groq.com/docs/openai\n",
+ "config_list = [\n",
+ " {\n",
+ " \"model\": \"llama3-70b-8192\",\n",
+ " \"base_url\": \"https://api.groq.com/openai/v1\",\n",
+ " 'api_key': 'your_groq_api_key', # get a free key at https://console.groq.com/keys\n",
+ " },\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86dbb782-61f0-4b61-aab5-41fd12c26f51",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from autogen import ConversableAgent\n",
+ "\n",
+ "# Player white agent\n",
+ "player_white = ConversableAgent(\n",
+ " name=\"Player White\",\n",
+ " system_message=\"You are a chess player and you play as white. \"\n",
+ " \"First call get_legal_moves(), to get a list of legal moves in UCI format. \"\n",
+ " \"Then call make_move(move) to make a move. Finally, tell the proxy what you have moved and ask the black to move\", # added \"Finally...\" to make the agents work\n",
+ " llm_config={\"config_list\": config_list,\n",
+ " \"temperature\": 0,\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1c57411c-183a-44ea-95ab-33c0e97feb74",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Player black agent\n",
+ "player_black = ConversableAgent(\n",
+ " name=\"Player Black\",\n",
+ " system_message=\"You are a chess player and you play as black. \"\n",
+ " \"First call get_legal_moves(), to get a list of legal moves in UCI format. \"\n",
+ " \"Then call make_move(move) to make a move. Finally, tell the proxy what you have moved and ask the white to move\", # added \"Finally...\" to make the agents work\n",
+ " llm_config={\"config_list\": config_list,\n",
+ " \"temperature\": 0,\n",
+ " },)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60e5cb2d-4273-45a9-af40-0ffb1ada0009",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def check_made_move(msg):\n",
+ " global made_move\n",
+ " if made_move:\n",
+ " made_move = False\n",
+ " return True\n",
+ " else:\n",
+ " return False\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "be4c7b55-9d50-4aa8-ae4b-3b959ffbb298",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "board_proxy = ConversableAgent(\n",
+ " name=\"Board Proxy\",\n",
+ " llm_config=False,\n",
+ " is_termination_msg=check_made_move,\n",
+ " default_auto_reply=\"Please make a move.\",\n",
+ " human_input_mode=\"NEVER\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e122875c-8bff-4212-8a1b-5f91d253fdd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from autogen import register_function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20edcb8c-5b7b-438e-b476-1cb16d14ef62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for caller in [player_white, player_black]:\n",
+ " register_function(\n",
+ " get_legal_moves,\n",
+ " caller=caller,\n",
+ " executor=board_proxy,\n",
+ " name=\"get_legal_moves\",\n",
+ " description=\"Call this tool to get all legal moves in UCI format.\",\n",
+ " )\n",
+ " \n",
+ " register_function(\n",
+ " make_move,\n",
+ " caller=caller,\n",
+ " executor=board_proxy,\n",
+ " name=\"make_move\",\n",
+ " description=\"Call this tool to make a move.\",\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b254ea02-0a81-4e9f-91fa-788dead9ffb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "player_black.llm_config[\"tools\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3715f56c-8ab8-4563-8f00-233beb3959b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "player_white.register_nested_chats(\n",
+ " trigger=player_black,\n",
+ " chat_queue=[\n",
+ " {\n",
+ " \"sender\": board_proxy,\n",
+ " \"recipient\": player_white,\n",
+ " \"summary_method\": \"last_msg\",\n",
+ " }\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "player_black.register_nested_chats(\n",
+ " trigger=player_white,\n",
+ " chat_queue=[\n",
+ " {\n",
+ " \"sender\": board_proxy,\n",
+ " \"recipient\": player_black,\n",
+ " \"summary_method\": \"last_msg\",\n",
+ " }\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eda4f544-ab4c-4e9e-bceb-f93ad57c4026",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "board = chess.Board()\n",
+ "\n",
+ "chat_result = player_black.initiate_chat(\n",
+ " player_white,\n",
+ " message=\"Let's play chess! Your move.\",\n",
+ " max_turns=3,\n",
+ ")"
+ ]
+ }
+ ],
+ "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": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/recipes/quickstart/agents/dlai/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb b/recipes/quickstart/agents/dlai/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6f29e4e73df12db1368886acad70a33bf1f5246a
--- /dev/null
+++ b/recipes/quickstart/agents/dlai/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb
@@ -0,0 +1,784 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "de56ee05-3b71-43c9-8cbf-6ad9b3233f38",
+ "metadata": {},
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16ba1896-6867-4c68-9951-b0aadb819782",
+ "metadata": {},
+ "source": [
+ "This notebook ports the DeepLearning.AI short course [AI Agents in LangGraph Lesson 1 Build an Agent from Scratch](https://learn.deeplearning.ai/courses/ai-agents-in-langgraph/lesson/2/build-an-agent-from-scratch) to using Llama 3, with a bonus section that ports the agent from scratch code to using LangGraph, introduced in [Lession 2 LangGraph Components](https://learn.deeplearning.ai/courses/ai-agents-in-langgraph/lesson/3/langgraph-components) of the course. \n",
+ "\n",
+ "You should take the course, especially the first two lessons, before or after going through this notebook, to have a deeper understanding."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9b168b57-6ff8-41d1-8f8f-a0c4a5ff108e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install groq"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c067d5f-c58c-47c0-8ccd-9a8710711bf7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os \n",
+ "from groq import Groq\n",
+ "\n",
+ "os.environ['GROQ_API_KEY'] = 'your_groq_api_key' # get a free key at https://console.groq.com/keys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5f7d8d95-36fb-4b14-bd28-99d305c0fded",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# a quick sanity test of calling Llama 3 70b on Groq \n",
+ "# see https://console.groq.com/docs/text-chat for more info\n",
+ "client = Groq()\n",
+ "chat_completion = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=[{\"role\": \"user\", \"content\": \"what are the words Charlotte wrote for the pig?\"}]\n",
+ ")\n",
+ "print(chat_completion.choices[0].message.content)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f758c771-5afe-4008-9d7f-92a6f526778b",
+ "metadata": {},
+ "source": [
+ "### ReAct Agent from Sractch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c00c0479-0913-4a92-8991-fe5a9a936bdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client = Groq()\n",
+ "model = \"llama3-8b-8192\" # this model works with the prompt below only for the first simpler example; you'll see how to modify the prompt to make it work for a more complicated question\n",
+ "#model = \"llama3-70b-8192\" # this model works with the prompt below for both example questions \n",
+ "\n",
+ "class Agent:\n",
+ " def __init__(self, system=\"\"):\n",
+ " self.system = system\n",
+ " self.messages = []\n",
+ " if self.system:\n",
+ " self.messages.append({\"role\": \"system\", \"content\": system})\n",
+ "\n",
+ " def __call__(self, message):\n",
+ " self.messages.append({\"role\": \"user\", \"content\": message})\n",
+ " result = self.execute()\n",
+ " self.messages.append({\"role\": \"assistant\", \"content\": result})\n",
+ " return result\n",
+ "\n",
+ " def execute(self):\n",
+ " completion = client.chat.completions.create(\n",
+ " model=model,\n",
+ " temperature=0,\n",
+ " messages=self.messages)\n",
+ " return completion.choices[0].message.content\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f766fb44-e8c2-43db-af83-8b9053a334ef",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompt = \"\"\"\n",
+ "You run in a loop of Thought, Action, PAUSE, Observation.\n",
+ "At the end of the loop you output an Answer\n",
+ "Use Thought to describe your thoughts about the question you have been asked.\n",
+ "Use Action to run one of the actions available to you - then return PAUSE.\n",
+ "Observation will be the result of running those actions.\n",
+ "\n",
+ "Your available actions are:\n",
+ "\n",
+ "calculate:\n",
+ "e.g. calculate: 4 * 7 / 3\n",
+ "Runs a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary\n",
+ "\n",
+ "average_dog_weight:\n",
+ "e.g. average_dog_weight: Collie\n",
+ "returns average weight of a dog when given the breed\n",
+ "\n",
+ "Example session:\n",
+ "\n",
+ "Question: How much does a Bulldog weigh?\n",
+ "Thought: I should look the dogs weight using average_dog_weight\n",
+ "Action: average_dog_weight: Bulldog\n",
+ "PAUSE\n",
+ "\n",
+ "You will be called again with this:\n",
+ "\n",
+ "Observation: A Bulldog weights 51 lbs\n",
+ "\n",
+ "You then output:\n",
+ "\n",
+ "Answer: A bulldog weights 51 lbs\n",
+ "\"\"\".strip()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93ab1290-625b-4b69-be4d-210fca43a513",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def calculate(what):\n",
+ " return eval(what)\n",
+ "\n",
+ "def average_dog_weight(name):\n",
+ " if name in \"Scottish Terrier\": \n",
+ " return(\"Scottish Terriers average 20 lbs\")\n",
+ " elif name in \"Border Collie\":\n",
+ " return(\"a Border Collies average weight is 37 lbs\")\n",
+ " elif name in \"Toy Poodle\":\n",
+ " return(\"a toy poodles average weight is 7 lbs\")\n",
+ " else:\n",
+ " return(\"An average dog weights 50 lbs\")\n",
+ "\n",
+ "known_actions = {\n",
+ " \"calculate\": calculate,\n",
+ " \"average_dog_weight\": average_dog_weight\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "52f900d9-15f0-4f48-9bf3-6165c70e4b42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot = Agent(prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1b612c9-2a7d-4325-b36f-182899252538",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = abot(\"How much does a toy poodle weigh?\")\n",
+ "print(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e27dda33-c76d-4a19-8aef-02ba5389e7a6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot.messages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b6e85ca1-85af-43e3-a5ea-c5faf0935361",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# manually call the exeternal func (tool) for now\n",
+ "result = average_dog_weight(\"Toy Poodle\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a9263ac7-fa81-4c95-91c8-a6c0741ab7f8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cb309710-0693-422f-a739-38ca9455e497",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "next_prompt = \"Observation: {}\".format(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd567e42-b5a9-4e4e-8807-38bb1d6c80a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot(next_prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "255bf148-bf85-40c5-b33e-d849a42c127b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot.messages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c286a6d5-b5b3-473b-bad6-aa6f1468e603",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot = Agent(prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f5e13b6e-e68e-45c2-b688-a257b531e482",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "question = \"\"\"I have 2 dogs, a border collie and a scottish terrier. \\\n",
+ "What is their combined weight\"\"\"\n",
+ "abot(question)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "049202f1-585f-42c3-8511-08eca7e5ed0e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot.messages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f086f19a-30fe-40ca-aafb-f1ce7c28982d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "next_prompt = \"Observation: {}\".format(average_dog_weight(\"Border Collie\"))\n",
+ "print(next_prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1747c78d-642d-4f57-81a0-27218eab3958",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot(next_prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "85809d8f-cd95-4e0a-acb7-9705817bea70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot.messages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e77591fa-4e04-4eb6-8a40-ca26a71765f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "next_prompt = \"Observation: {}\".format(average_dog_weight(\"Scottish Terrier\"))\n",
+ "print(next_prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1f72b638-de07-4972-bbdb-8c8602f3d143",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot(next_prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb5bf29d-22f9-4c0d-aea6-7e9c99e71835",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot.messages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a67add73-b3c3-42be-9c54-f8a6ac828869",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "next_prompt = \"Observation: {}\".format(eval(\"37 + 20\"))\n",
+ "print(next_prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "801fda04-9756-4ae4-9990-559216d38be8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot(next_prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56f7b9f4-289f-498d-8bc8-da9bb7365d52",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot.messages"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "155ee9b3-a4f9-43dd-b23e-0f268f72f198",
+ "metadata": {},
+ "source": [
+ "### Automate the ReAct action execution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b2196f8-88e6-4eb4-82b0-cf251a07e313",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "\n",
+ "# automate the action execution above to make the whole ReAct (Thought - Action- Observsation) process fully automated\n",
+ "action_re = re.compile('^Action: (\\w+): (.*)$') # python regular expression to selection action"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea5710d6-5d9a-46ff-a275-46311257d9fd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def query(question, max_turns=5):\n",
+ " i = 0\n",
+ " bot = Agent(prompt) # set system prompt\n",
+ " next_prompt = question\n",
+ " while i < max_turns:\n",
+ " i += 1\n",
+ " result = bot(next_prompt)\n",
+ " print(result)\n",
+ " actions = [\n",
+ " action_re.match(a)\n",
+ " for a in result.split('\\n')\n",
+ " if action_re.match(a)\n",
+ " ]\n",
+ " if actions:\n",
+ " # There is an action to run\n",
+ " action, action_input = actions[0].groups()\n",
+ " if action not in known_actions:\n",
+ " raise Exception(\"Unknown action: {}: {}\".format(action, action_input))\n",
+ " print(\" -- running {} {}\".format(action, action_input))\n",
+ "\n",
+ " # key to make the agent process fully automated:\n",
+ " # programtically call the external func with arguments, with the info returned by LLM\n",
+ " observation = known_actions[action](action_input) \n",
+ "\n",
+ " print(\"Observation:\", observation)\n",
+ " next_prompt = \"Observation: {}\".format(observation)\n",
+ " else:\n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "232d0818-7580-424b-9538-1e2b1c15360b",
+ "metadata": {},
+ "source": [
+ "#### Using model \"llama3-8b-8192\", the code below will cause an invalid syntax error because the Action returned is calculate: (average_dog_weight: Border Collie) + (average_dog_weight: Scottish Terrier), instead of the expected \"Action: average_dog_weight: Border Collie\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bb0095f3-b3f1-48cf-b3fb-36049b6b91f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "question = \"\"\"I have 2 dogs, a border collie and a scottish terrier. \\\n",
+ "What is their combined weight\"\"\"\n",
+ "query(question)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "952ffac8-5ec2-48f3-8049-d03c130dad0d",
+ "metadata": {},
+ "source": [
+ "#### Prompt engineering in action:\n",
+ "REPLACE \"Use Thought to describe your thoughts about the question you have been asked. Use Action to run one of the actions available to you - then return PAUSE.\" with \n",
+ "\"First, use Thought to describe your thoughts about the question you have been asked, and generate Action to run one of the actions available to you, then return PAUSE.\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ec791ad6-b39a-4f46-b149-704c23d6c506",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompt = \"\"\"\n",
+ "You run in a loop of Thought, Action, PAUSE, Observation.\n",
+ "At the end of the loop you output an Answer.\n",
+ "First, use Thought to describe your thoughts about the question you have been asked, and generate Action to run one of the actions available to you, then return PAUSE.\n",
+ "Observation will be the result of running those actions.\n",
+ "\n",
+ "Your available actions are:\n",
+ "\n",
+ "calculate:\n",
+ "e.g. calculate: 4 * 7 / 3\n",
+ "Runs a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary\n",
+ "\n",
+ "average_dog_weight:\n",
+ "e.g. average_dog_weight: Collie\n",
+ "returns average weight of a dog when given the breed\n",
+ "\n",
+ "Example session:\n",
+ "\n",
+ "Question: How much does a Bulldog weigh?\n",
+ "Thought: I should look the dogs weight using average_dog_weight\n",
+ "Action: average_dog_weight: Bulldog\n",
+ "PAUSE\n",
+ "\n",
+ "You will be called again with this:\n",
+ "\n",
+ "Observation: A Bulldog weights 51 lbs\n",
+ "\n",
+ "You then output:\n",
+ "\n",
+ "Answer: A bulldog weights 51 lbs\n",
+ "\"\"\".strip()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90bcf731-4d89-473b-98e1-53826da149f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "question = \"\"\"I have 2 dogs, a border collie and a scottish terrier. \\\n",
+ "What is their combined weight\"\"\"\n",
+ "query(question)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09d30a8f-3783-4ee5-a48e-7d89e22a508a",
+ "metadata": {},
+ "source": [
+ "### Bonus: Port the Agent Implementation to LangGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b5ed82e-2d70-45ac-b026-904da211f81a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install langchain\n",
+ "!pip install langgraph\n",
+ "!pip install langchain_openai\n",
+ "!pip install langchain_community\n",
+ "!pip install httpx\n",
+ "!pip install langchain-groq"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ed3b90-688e-4aa2-8e43-e951af29a57f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langgraph.graph import StateGraph, END\n",
+ "from typing import TypedDict, Annotated\n",
+ "import operator\n",
+ "from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, ToolMessage\n",
+ "from langchain_openai import ChatOpenAI\n",
+ "from langchain_community.tools.tavily_search import TavilySearchResults"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c555f945-7db0-4dc9-9ea5-5632bf941fe4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_groq import ChatGroq\n",
+ "\n",
+ "model = ChatGroq(temperature=0, model_name=\"llama3-8b-8192\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7755a055-fa1f-474f-b558-230cc5a67a33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_core.tools import tool\n",
+ "from langgraph.prebuilt import ToolNode\n",
+ "\n",
+ "@tool\n",
+ "def calculate(what):\n",
+ " \"\"\"Runs a calculation and returns the number.\"\"\"\n",
+ " return eval(what)\n",
+ "\n",
+ "@tool\n",
+ "def average_dog_weight(name):\n",
+ " \"\"\"Returns the average weight of a dog.\"\"\"\n",
+ " if name in \"Scottish Terrier\":\n",
+ " return(\"Scottish Terriers average 20 lbs\")\n",
+ " elif name in \"Border Collie\":\n",
+ " return(\"a Border Collies average weight is 37 lbs\")\n",
+ " elif name in \"Toy Poodle\":\n",
+ " return(\"a toy poodles average weight is 7 lbs\")\n",
+ " else:\n",
+ " return(\"An average dog weights 50 lbs\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a003862-8fd2-45b1-8fe4-78d7cd5888d9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompt = \"\"\"\n",
+ "You run in a loop of Thought, Action, Observation.\n",
+ "At the end of the loop you output an Answer\n",
+ "Use Thought to describe your thoughts about the question you have been asked.\n",
+ "Use Action to run one of the actions available to you.\n",
+ "Observation will be the result of running those actions.\n",
+ "\n",
+ "Your available actions are:\n",
+ "\n",
+ "calculate:\n",
+ "e.g. calculate: 4 * 7 / 3\n",
+ "Runs a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary\n",
+ "\n",
+ "average_dog_weight:\n",
+ "e.g. average_dog_weight: Collie\n",
+ "returns average weight of a dog when given the breed\n",
+ "\n",
+ "Example session:\n",
+ "\n",
+ "Question: How much does a Bulldog weigh?\n",
+ "Thought: I should look the dogs weight using average_dog_weight\n",
+ "Action: average_dog_weight: Bulldog\n",
+ "\n",
+ "You will be called again with this:\n",
+ "\n",
+ "Observation: A Bulldog weights 51 lbs\n",
+ "\n",
+ "You then output:\n",
+ "\n",
+ "Answer: A bulldog weights 51 lbs\n",
+ "\"\"\".strip()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "471c0aa7-547f-4d5f-9e99-73ef47101d41",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AgentState(TypedDict):\n",
+ " messages: Annotated[list[AnyMessage], operator.add]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "530e8a60-085a-4485-af03-bafc6b2c1d88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Agent:\n",
+ "\n",
+ " def __init__(self, model, tools, system=\"\"):\n",
+ " self.system = system\n",
+ " graph = StateGraph(AgentState)\n",
+ " graph.add_node(\"llm\", self.call_llm)\n",
+ " graph.add_node(\"action\", self.take_action)\n",
+ " graph.add_conditional_edges(\n",
+ " \"llm\",\n",
+ " self.exists_action,\n",
+ " {True: \"action\", False: END}\n",
+ " )\n",
+ " graph.add_edge(\"action\", \"llm\")\n",
+ " graph.set_entry_point(\"llm\")\n",
+ " self.graph = graph.compile()\n",
+ " self.tools = {t.name: t for t in tools}\n",
+ " self.model = model.bind_tools(tools)\n",
+ "\n",
+ " def exists_action(self, state: AgentState):\n",
+ " result = state['messages'][-1]\n",
+ " return len(result.tool_calls) > 0\n",
+ "\n",
+ " def call_llm(self, state: AgentState):\n",
+ " messages = state['messages']\n",
+ " if self.system:\n",
+ " messages = [SystemMessage(content=self.system)] + messages\n",
+ " message = self.model.invoke(messages)\n",
+ " return {'messages': [message]}\n",
+ "\n",
+ " def take_action(self, state: AgentState):\n",
+ " tool_calls = state['messages'][-1].tool_calls\n",
+ " results = []\n",
+ " for t in tool_calls:\n",
+ " print(f\"Calling: {t}\")\n",
+ " result = self.tools[t['name']].invoke(t['args'])\n",
+ " results.append(ToolMessage(tool_call_id=t['id'], name=t['name'], content=str(result)))\n",
+ " print(\"Back to the model!\")\n",
+ " return {'messages': results}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3db8dcea-d4eb-46df-bd90-55acd4c5520a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abot = Agent(model, [calculate, average_dog_weight], system=prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "72c62e36-9321-40d2-86d8-b3c9caf3020f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [HumanMessage(content=\"How much does a Toy Poodle weigh?\")]\n",
+ "result = abot.graph.invoke({\"messages\": messages})\n",
+ "result['messages'], result['messages'][-1].content\n",
+ "\n",
+ "# the code above will cause an error because Llama 3 8B incorrectly returns an extra \"calculate\" tool call"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56b4c622-b306-4aa3-84e6-4ccd6d6f272f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# using the Llama 3 70B will fix the error\n",
+ "model = ChatGroq(temperature=0, model_name=\"llama3-70b-8192\")\n",
+ "abot = Agent(model, [calculate, average_dog_weight], system=prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "629ca375-979a-45d7-bad8-7240ae9ad844",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Toy Poodle case sensitive here - can be fixed easily by modifying def average_dog_weight\n",
+ "messages = [HumanMessage(content=\"How much does a Toy Poodle weigh?\")]\n",
+ "result = abot.graph.invoke({\"messages\": messages})\n",
+ "result['messages'], result['messages'][-1].content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30e253ae-e742-4df8-92e6-fadfc3826003",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [HumanMessage(content=\"I have 2 dogs, a border collie and a scottish terrier. What are their average weights? Total weight?\")]\n",
+ "result = abot.graph.invoke({\"messages\": messages})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "238ec75c-4ff6-4561-bb0a-895530a61e47",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result['messages'], result['messages'][-1].content"
+ ]
+ }
+ ],
+ "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": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/recipes/quickstart/agents/dlai/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb b/recipes/quickstart/agents/dlai/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..989344445689febd8a97cd4312ad90a0afbad3aa
--- /dev/null
+++ b/recipes/quickstart/agents/dlai/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 1 Router Engine](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/2/router-query-engine) to using Llama 3. \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": "094c60c2-d782-4baf-bfc3-913f53ac1ff3"
+ },
+ "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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Wv_-jm5XGoWo",
+ "outputId": "d0dd37f4-ca33-4704-d221-6ea80ee09eb5"
+ },
+ "outputs": [],
+ "source": [
+ "import os \n",
+ "os.environ['GROQ_API_KEY'] = 'your_groq_api_key' # get a free key at https://console.groq.com/keys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aUi629qVUsG5",
+ "outputId": "015a6a1a-a9e1-4c4f-dcaf-7d341b1a0b0e"
+ },
+ "outputs": [],
+ "source": [
+ "!wget \"https://openreview.net/pdf?id=VtmBAGCN7o\" -O metagpt.pdf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "auZQalH5J7CU"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.core import SimpleDirectoryReader\n",
+ "\n",
+ "documents = SimpleDirectoryReader(input_files=[\"metagpt.pdf\"]).load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GFfUjJypJ7Eq"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.core.node_parser import SentenceSplitter\n",
+ "\n",
+ "splitter = SentenceSplitter(chunk_size=1024)\n",
+ "nodes = splitter.get_nodes_from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bguUp2D5LhST"
+ },
+ "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=\"llama3-8b-8192\") #, api_key=GROQ_API_TOKEN)\n",
+ "Settings.llm = llm\n",
+ "#llm.complete(\"Who wrote the book godfather\").text\n",
+ "\n",
+ "Settings.embed_model = HuggingFaceEmbedding(\n",
+ " model_name=\"BAAI/bge-small-en-v1.5\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "pkmnSHsMJ7Hg"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.core import SummaryIndex, VectorStoreIndex\n",
+ "\n",
+ "summary_index = SummaryIndex(nodes)\n",
+ "vector_index = VectorStoreIndex(nodes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "BnMq-qO9ezcE"
+ },
+ "outputs": [],
+ "source": [
+ "summary_query_engine = summary_index.as_query_engine(\n",
+ " response_mode=\"tree_summarize\",\n",
+ " use_async=True,\n",
+ ")\n",
+ "vector_query_engine = vector_index.as_query_engine()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vnK7YXlVLojh"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.core.tools import QueryEngineTool\n",
+ "\n",
+ "summary_tool = QueryEngineTool.from_defaults(\n",
+ " query_engine=summary_query_engine,\n",
+ " description=(\n",
+ " \"Useful for summarization questions related to MetaGPT\"\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "vector_tool = QueryEngineTool.from_defaults(\n",
+ " query_engine=vector_query_engine,\n",
+ " description=(\n",
+ " \"Useful for retrieving specific context from the MetaGPT paper.\"\n",
+ " ),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "EvLJilU4LomU"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.core.query_engine.router_query_engine import RouterQueryEngine\n",
+ "from llama_index.core.selectors import LLMSingleSelector\n",
+ "\n",
+ "query_engine = RouterQueryEngine(\n",
+ " selector=LLMSingleSelector.from_defaults(),\n",
+ " query_engine_tools=[\n",
+ " summary_tool,\n",
+ " vector_tool,\n",
+ " ],\n",
+ " verbose=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "O_HfeD6TMCJf"
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KAjQW6M1LopK",
+ "outputId": "fd85f083-599f-40b0-8fed-c3c96b09c5c9"
+ },
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\"What is the summary of the document?\")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ySNeKv3PLosR",
+ "outputId": "9568d416-9ec1-4f50-dbbf-61a208205113"
+ },
+ "outputs": [],
+ "source": [
+ "print(len(response.source_nodes))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KOP52yxpLou-",
+ "outputId": "168a6ff3-2a2a-4588-dc2f-978ae8c0bbc1"
+ },
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"How do agents share information with other agents? This is not a summarization question.\"\n",
+ ")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "luGziGzfMHbD"
+ },
+ "outputs": [],
+ "source": [
+ "def get_router_query_engine(file_path: str):\n",
+ " \"\"\"Get router query engine.\"\"\"\n",
+ "\n",
+ " documents = SimpleDirectoryReader(input_files=[file_path]).load_data()\n",
+ "\n",
+ " splitter = SentenceSplitter(chunk_size=1024)\n",
+ " nodes = splitter.get_nodes_from_documents(documents)\n",
+ "\n",
+ " summary_index = SummaryIndex(nodes)\n",
+ " vector_index = VectorStoreIndex(nodes)\n",
+ "\n",
+ " summary_query_engine = summary_index.as_query_engine(\n",
+ " response_mode=\"tree_summarize\",\n",
+ " use_async=True,\n",
+ " )\n",
+ " vector_query_engine = vector_index.as_query_engine()\n",
+ "\n",
+ " summary_tool = QueryEngineTool.from_defaults(\n",
+ " query_engine=summary_query_engine,\n",
+ " description=(\n",
+ " \"Useful for summarization questions related to MetaGPT\"\n",
+ " ),\n",
+ " )\n",
+ "\n",
+ " vector_tool = QueryEngineTool.from_defaults(\n",
+ " query_engine=vector_query_engine,\n",
+ " description=(\n",
+ " \"Useful for retrieving specific context from the MetaGPT paper.\"\n",
+ " ),\n",
+ " )\n",
+ "\n",
+ " query_engine = RouterQueryEngine(\n",
+ " selector=LLMSingleSelector.from_defaults(),\n",
+ " query_engine_tools=[\n",
+ " summary_tool,\n",
+ " vector_tool,\n",
+ " ],\n",
+ " verbose=True\n",
+ " )\n",
+ " return query_engine\n",
+ "\n",
+ "query_engine = get_router_query_engine(\"metagpt.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "SHqbTBxQNryl",
+ "outputId": "ca2f16e9-2274-42e6-fdd5-3218d8f0c3f8"
+ },
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\"Tell me about the ablation study results?\")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5r1MHbLOPT8Y"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "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": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/recipes/quickstart/agents/dlai/Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb b/recipes/quickstart/agents/dlai/Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..755b503ad3c415d36d1d0bb5e5e25773d33b2b25
--- /dev/null
+++ b/recipes/quickstart/agents/dlai/Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb
@@ -0,0 +1,581 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2ba1b4ef-3b96-4e7e-b5d0-155b839db73c",
+ "metadata": {},
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f91905c8-21ca-4d81-9614-b9c7344d08c3",
+ "metadata": {},
+ "source": [
+ "This notebook ports the DeepLearning.AI short course [Functions, Tools and Agents with LangChain Lesson 1 OpenAI Function Calling](https://learn.deeplearning.ai/courses/functions-tools-agents-langchain/lesson/2/openai-function-calling) to using Llama 3. \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,
+ "id": "31bfe801-e24d-459b-8b3f-e91a34024368",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install groq"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "88659373-0deb-45eb-8934-0b02d70bd047",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "# Example dummy function hard coded to return the same weather\n",
+ "# In production, this could be your backend API or an external API\n",
+ "def get_current_weather(location, unit=\"fahrenheit\"):\n",
+ " \"\"\"Get the current weather in a given location\"\"\"\n",
+ " weather_info = {\n",
+ " \"location\": location,\n",
+ " \"temperature\": \"72\",\n",
+ " \"unit\": unit,\n",
+ " \"forecast\": [\"sunny\", \"windy\"],\n",
+ " }\n",
+ " return json.dumps(weather_info)\n",
+ "\n",
+ "known_functions = {\n",
+ " \"get_current_weather\": get_current_weather\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "359a584a-5b26-4497-afb4-72b63027edb9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# https://console.groq.com/docs/tool-use#models\n",
+ "# Groq API endpoints support tool use for programmatic execution of specified operations through requests with explicitly defined \n",
+ "# operations. With tool use, Groq API model endpoints deliver structured JSON output that can be used to directly invoke functions.\n",
+ "\n",
+ "from groq import Groq\n",
+ "import os\n",
+ "import json\n",
+ "\n",
+ "client = Groq(api_key = 'your_groq_api_key' # get a free key at https://console.groq.com/keys')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5cc17dc9-2827-4d39-a13d-a4ed5f53c8e6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "functions = [\n",
+ " {\n",
+ " \"name\": \"get_current_weather\",\n",
+ " \"description\": \"Get the current weather in a given location\",\n",
+ " \"parameters\": {\n",
+ " \"type\": \"object\",\n",
+ " \"properties\": {\n",
+ " \"location\": {\n",
+ " \"type\": \"string\",\n",
+ " \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
+ " },\n",
+ " \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
+ " },\n",
+ " \"required\": [\"location\"],\n",
+ " },\n",
+ " }\n",
+ "]\n",
+ "\n",
+ "tools = [\n",
+ " {\n",
+ " \"type\": \"function\",\n",
+ " \"function\": {\n",
+ " \"name\": \"get_current_weather\",\n",
+ " \"description\": \"Get the current weather in a given location\",\n",
+ " \"parameters\": {\n",
+ " \"type\": \"object\",\n",
+ " \"properties\": {\n",
+ " \"location\": {\n",
+ " \"type\": \"string\",\n",
+ " \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
+ " },\n",
+ " \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
+ " },\n",
+ " \"required\": [\"location\"],\n",
+ " },\n",
+ " }\n",
+ " }\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a64d28e-b169-4855-b3c2-d6722c56394c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What's the weather like in Boston?\"\n",
+ " }\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a733c1e1-c7f2-4707-b1be-02179df0abc6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " functions=functions,\n",
+ " #tools=tools, # you can also replace functions with tools, as specified in https://console.groq.com/docs/tool-use \n",
+ " max_tokens=4096, \n",
+ " temperature=0\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9058073d-cf91-4747-9860-7e2a1d774acf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ffd4ed64-0436-499e-a7e5-4224833b72f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response_message = response.choices[0].message\n",
+ "response_message"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5458444a-a448-4c5b-b06c-47ab6cd25626",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response_message.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c669a048-1a3e-43e9-b98f-d0b6a3a0f4c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response_message.function_call"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27f3de5d-5110-486e-8b07-5086939d364d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "json.loads(response_message.function_call.arguments)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b69e6497-9e68-47d4-99ae-d45db6c1a8db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "args = json.loads(response_message.function_call.arguments)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f41a7162-9ce8-4353-827b-f6f3bb278218",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "get_current_weather(args)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bb0546f2-de55-417a-9b38-66787b673fb7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "function_call = response.choices[0].message.function_call\n",
+ "function_call"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0dd1fcf0-7105-4cad-82b5-22ce3b24fc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "function_call.name, function_call.arguments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e6d58efe-0ada-48a2-b12b-6bff948a2983",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# by defining and using known_functions, we can programatically call function\n",
+ "function_response = known_functions[function_call.name](function_call.arguments)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cee6ca19-6924-4a7b-ba7f-7b1a33344ca0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "function_response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8480be29-3326-4d95-8742-dff976a7ab2e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add the message returned by tool and query LLM again to get final answer\n",
+ "messages.append(\n",
+ "{\n",
+ " \"role\": \"function\",\n",
+ " \"name\": function_call.name,\n",
+ " \"content\": function_response,\n",
+ "}\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a42e35f-c601-4c14-8de5-bdbba01dc622",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9a2d1ee-9e41-480a-a5cc-62c273d3a179",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " temperature=0\n",
+ ")\n",
+ "\n",
+ "response.choices[0].message.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54019c56-11cf-465a-a440-296081adee93",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"hi!\",\n",
+ " }\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "922724ec-1744-4ccf-9a86-5f1823dce0e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " functions=functions,\n",
+ " function_call=\"none\", # default is auto (let LLM decide if using function call or not. can also be none, or a dict {{\"name\": \"func_name\"}\n",
+ " temperature=0\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "04c3152a-f51b-45cb-a27c-0672337520b4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "582fac7c-0de7-420c-8150-038e74be4b9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response_message = response.choices[0].message\n",
+ "response_message"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3d62357-04c9-459c-b36a-89e58444ea63",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"hi!\",\n",
+ " }\n",
+ "]\n",
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " functions=functions,\n",
+ " function_call=\"auto\", # default is auto (let LLM decide if using function call or not. can also be none, or a dict {{\"name\": \"func_name\"}\n",
+ " temperature=0\n",
+ ")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "632df69d-85bc-4e44-814c-7c1d2fe97228",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"hi!\",\n",
+ " }\n",
+ "]\n",
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " functions=functions,\n",
+ " function_call=\"none\", # default is auto (let LLM decide if using function call or not. can also be none, or a dict {{\"name\": \"func_name\"}\n",
+ " temperature=0\n",
+ ")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c773ab17-a620-44eb-877f-9e0bc23fb00b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What's the weather in Boston?\",\n",
+ " }\n",
+ "]\n",
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " functions=functions,\n",
+ " function_call=\"none\", # default is auto (let LLM decide if using function call or not. can also be none, or a dict {{\"name\": \"func_name\"}\n",
+ " temperature=0\n",
+ ")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c4a8ee80-83ae-4189-837c-54bb9c93c315",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"hi!\",\n",
+ " }\n",
+ "]\n",
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " functions=functions,\n",
+ " function_call={\"name\": \"get_current_weather\"}, # default is auto (let LLM decide if using function call or not. can also be none, or a dict {{\"name\": \"func_name\"}\n",
+ " temperature=0\n",
+ ")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "daa5801a-2e71-4630-a8cd-7e84d1214f51",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What's the weather like in Boston!\",\n",
+ " }\n",
+ "]\n",
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ " functions=functions,\n",
+ " function_call={\"name\": \"get_current_weather\"}, # default is auto (let LLM decide if using function call or not. can also be none, or a dict {{\"name\": \"func_name\"}\n",
+ " temperature=0\n",
+ ")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de5924d4-4225-48d1-a390-e44f3167d547",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "function_call = response.choices[0].message.function_call\n",
+ "function_call.name, function_call.arguments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fb9f3340-b905-47f3-a478-cf3d786faa1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "args = json.loads(response.choices[0].message.function_call.arguments)\n",
+ "observation = known_functions[function_call.name](args)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c31e9b5-99ed-46f3-8849-133c71ea87d4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "observation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b73c550e-5aa2-49de-8422-0c3e706f1df4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages.append(\n",
+ " {\n",
+ " \"role\": \"function\",\n",
+ " \"name\": function_call.name,\n",
+ " \"content\": observation,\n",
+ " }\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c60302f1-07e2-4f22-bd60-b54e1ea2e3db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a35f7f3d-4e39-4744-b5e3-2065e67eea28",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = client.chat.completions.create(\n",
+ " model=\"llama3-70b-8192\",\n",
+ " messages=messages,\n",
+ ")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d4745e1-0477-4b6b-84de-9c82e0bc2452",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response.choices[0].message.content"
+ ]
+ }
+ ],
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+ "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",
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diff --git a/recipes/quickstart/agents/dlai/README.md b/recipes/quickstart/agents/dlai/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..25a4029a1196d30d6c4309624cdced2bc8f3efd0
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+++ b/recipes/quickstart/agents/dlai/README.md
@@ -0,0 +1,11 @@
+# Quickstart Llama 3 Powered Agent Examples Ported from DeepLearning.ai Short Courses
+
+The notebooks in this folder are ported from the 4 recent agent short courses on [Deeplearning.ai](https://www.deeplearning.ai) to use Llama 3 to build agent apps from scratch or with open source frameworks (LangChain, LlamaIndex, AutoGen).
+
+1. [Functions Tools and Agents with LangChain L1 Function Calling](Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb)
+
+2. [AI Agents in LangGraph L1 Build an Agent from Scratch](AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb)
+
+3. [Building Agentic RAG with Llamaindex L1 Router Engine](Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb)
+
+4. [AI Agentic Design Patterns with AutoGen L4 Tool Use and Conversational Chess](AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb)