Upload agent.ipynb

unit2/langgraph/agent.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "98f5e36a-da49-4ae2-8c74-b910a2f992fc",
+   "metadata": {},
+   "source": [
+    "# Agent\n",
+    "\n",
+    "In this notebook, **we're going to build a simple agent using using LangGraph**.\n",
+    "\n",
+    "This notebook is part of the <a href=\"https://www.hf.co/learn/agents-course\">Hugging Face Agents Course</a>, a free course from beginner to expert, where you learn to build Agents.\n",
+    "\n",
+    "![Agents course share](https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/share.png)\n",
+    "\n",
+    "As seen in the Unit 1, an agent needs 3 steps as introduced in the ReAct architecture :\n",
+    "[ReAct](https://react-lm.github.io/), a general agent architecture.\n",
+    "  \n",
+    "* `act` - let the model call specific tools \n",
+    "* `observe` - pass the tool output back to the model \n",
+    "* `reason` - let the model reason about the tool output to decide what to do next (e.g., call another tool or just respond directly)\n",
+    "\n",
+    "\n",
+    "![Agent](https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/LangGraph/Agent.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "63edff5a-724b-474d-9db8-37f0ae936c76",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -q -U langchain_openai langchain_core langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "356a6482",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Please setp your own key.\n",
+    "os.environ[\"OPENAI_API_KEY\"]=\"sk-xxxxxx\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "71795ff1-d6a7-448d-8b55-88bbd1ed3dbe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "from typing import List\n",
+    "from langchain.schema import HumanMessage\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "vision_llm = ChatOpenAI(model=\"gpt-4o\")\n",
+    "\n",
+    "def extract_text(img_path: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Extract text from an image file using a multimodal model.\n",
+    "\n",
+    "    Args:\n",
+    "        img_path: A local image file path (strings).\n",
+    "\n",
+    "    Returns:\n",
+    "        A single string containing the concatenated text extracted from each image.\n",
+    "    \"\"\"\n",
+    "    all_text = \"\"\n",
+    "    try:\n",
+    "       \n",
+    "        # Read image and encode as base64\n",
+    "        with open(img_path, \"rb\") as image_file:\n",
+    "            image_bytes = image_file.read()\n",
+    "\n",
+    "        image_base64 = base64.b64encode(image_bytes).decode(\"utf-8\")\n",
+    "\n",
+    "        # Prepare the prompt including the base64 image data\n",
+    "        message = [\n",
+    "            HumanMessage(\n",
+    "                content=[\n",
+    "                    {\n",
+    "                        \"type\": \"text\",\n",
+    "                        \"text\": (\n",
+    "                            \"Extract all the text from this image. \"\n",
+    "                            \"Return only the extracted text, no explanations.\"\n",
+    "                        ),\n",
+    "                    },\n",
+    "                    {\n",
+    "                        \"type\": \"image_url\",\n",
+    "                        \"image_url\": {\n",
+    "                            \"url\": f\"data:image/png;base64,{image_base64}\"\n",
+    "                        },\n",
+    "                    },\n",
+    "                ]\n",
+    "            )\n",
+    "        ]\n",
+    "\n",
+    "        # Call the vision-capable model\n",
+    "        response = vision_llm.invoke(message)\n",
+    "\n",
+    "        # Append extracted text\n",
+    "        all_text += response.content + \"\\n\\n\"\n",
+    "\n",
+    "        return all_text.strip()\n",
+    "    except Exception as e:\n",
+    "        # You can choose whether to raise or just return an empty string / error message\n",
+    "        error_msg = f\"Error extracting text: {str(e)}\"\n",
+    "        print(error_msg)\n",
+    "        return \"\"\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4o\")\n",
+    "\n",
+    "def divide(a: int, b: int) -> float:\n",
+    "    \"\"\"Divide a and b.\"\"\"\n",
+    "    return a / b\n",
+    "\n",
+    "tools = [\n",
+    "    divide,\n",
+    "    extract_text\n",
+    "]\n",
+    "llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2cec014-3023-405c-be79-de8fc7adb346",
+   "metadata": {},
+   "source": [
+    "Let's create our LLM and prompt it with the overall desired agent behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "deb674bc-49b2-485a-b0c3-4d7b05a0bfac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import TypedDict, Annotated, List, Any, Optional\n",
+    "from langchain_core.messages import AnyMessage\n",
+    "from langgraph.graph.message import add_messages\n",
+    "class AgentState(TypedDict):\n",
+    "    # The input document\n",
+    "    input_file:  Optional[str]  # Contains file path, type (PNG)\n",
+    "    messages: Annotated[list[AnyMessage], add_messages]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d061813f-ebc0-432c-91ec-3b42b15c30b6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+    "\n",
+    "\n",
+    "# AgentState\n",
+    "def assistant(state: AgentState):\n",
+    "    # System message\n",
+    "    textual_description_of_tool=\"\"\"\n",
+    "extract_text(img_path: str) -> str:\n",
+    "    Extract text from an image file using a multimodal model.\n",
+    "\n",
+    "    Args:\n",
+    "        img_path: A local image file path (strings).\n",
+    "\n",
+    "    Returns:\n",
+    "        A single string containing the concatenated text extracted from each image.\n",
+    "divide(a: int, b: int) -> float:\n",
+    "    Divide a and b\n",
+    "\"\"\"\n",
+    "    image=state[\"input_file\"]\n",
+    "    sys_msg = SystemMessage(content=f\"You are an helpful agent that can analyse some images and run some computatio without provided tools :\\n{textual_description_of_tool} \\n You have access to some otpional images. Currently the loaded images is : {image}\")\n",
+    "\n",
+    "\n",
+    "    return {\"messages\": [llm_with_tools.invoke([sys_msg] + state[\"messages\"])],\"input_file\":state[\"input_file\"]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eb43343-9a6f-42cb-86e6-4380f928633c",
+   "metadata": {},
+   "source": [
+    "We define a `Tools` node with our list of tools.\n",
+    "\n",
+    "The `Assistant` node is just our model with bound tools.\n",
+    "\n",
+    "We create a graph with `Assistant` and `Tools` nodes.\n",
+    "\n",
+    "We add `tools_condition` edge, which routes to `End` or to `Tools` based on  whether the `Assistant` calls a tool.\n",
+    "\n",
+    "Now, we add one new step:\n",
+    "\n",
+    "We connect the `Tools` node *back* to the `Assistant`, forming a loop.\n",
+    "\n",
+    "* After the `assistant` node executes, `tools_condition` checks if the model's output is a tool call.\n",
+    "* If it is a tool call, the flow is directed to the `tools` node.\n",
+    "* The `tools` node connects back to `assistant`.\n",
+    "* This loop continues as long as the model decides to call tools.\n",
+    "* If the model response is not a tool call, the flow is directed to END, terminating the process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "aef13cd4-05a6-4084-a620-2e7b91d9a72f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from langgraph.graph import START, StateGraph\n",
+    "from langgraph.prebuilt import tools_condition\n",
+    "from langgraph.prebuilt import ToolNode\n",
+    "from IPython.display import Image, display\n",
+    "\n",
+    "# Graph\n",
+    "builder = StateGraph(AgentState)\n",
+    "\n",
+    "# Define nodes: these do the work\n",
+    "builder.add_node(\"assistant\", assistant)\n",
+    "builder.add_node(\"tools\", ToolNode(tools))\n",
+    "\n",
+    "# Define edges: these determine how the control flow moves\n",
+    "builder.add_edge(START, \"assistant\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"assistant\",\n",
+    "    # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools\n",
+    "    # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END\n",
+    "    tools_condition,\n",
+    ")\n",
+    "builder.add_edge(\"tools\", \"assistant\")\n",
+    "react_graph = builder.compile()\n",
+    "\n",
+    "# Show\n",
+    "display(Image(react_graph.get_graph(xray=True).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "75602459-d8ca-47b4-9518-3f38343ebfe4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "messages = [HumanMessage(content=\"Divide 6790 by 5\")]\n",
+    "\n",
+    "messages = react_graph.invoke({\"messages\": messages,\"input_file\":None})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "b517142d-c40c-48bf-a5b8-c8409427aa79",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Divide 6790 by 5\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  divide (call_s0G5ewtIQyHUCOv0fClsCpgh)\n",
+      " Call ID: call_s0G5ewtIQyHUCOv0fClsCpgh\n",
+      "  Args:\n",
+      "    a: 6790\n",
+      "    b: 5\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: divide\n",
+      "\n",
+      "1358.0\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The result of dividing 6790 by 5 is 1358.0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for m in messages['messages']:\n",
+    "    m.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08386393-c270-43a5-bde2-2b4075238971",
+   "metadata": {},
+   "source": [
+    "## Training program\n",
+    "MR Wayne left a note with his training program for the week. I came up with a recipe for dinner leaft in a note.\n",
+    "\n",
+    "you can find the document [HERE](https://huggingface.co/datasets/agents-course/course-images/blob/main/en/unit2/LangGraph/Batman_training_and_meals.png), so download it and upload it in the local folder.\n",
+    "\n",
+    "![Training](https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/LangGraph/Batman_training_and_meals.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "f6e97e84-3b05-4aaf-a38f-1de9b73cd37f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "messages = [HumanMessage(content=\"According the note provided by MR wayne in the provided images. What's the list of items I should buy for the dinner menu ?\")]\n",
+    "\n",
+    "messages = react_graph.invoke({\"messages\": messages,\"input_file\":\"Batman_training_and_meals.png\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "17686d52-c7ba-407b-a13f-f6c37668e5b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "According the note provided by MR wayne in the provided images. What's the list of tiems I should buy for the dinner menu ?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  extract_text (call_JalVBOR82hwRknFcplnLoTtG)\n",
+      " Call ID: call_JalVBOR82hwRknFcplnLoTtG\n",
+      "  Args:\n",
+      "    img_path: Batman_training_and_meals.png\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: extract_text\n",
+      "\n",
+      "TRAINING SCHEDULE\n",
+      "For the week of 2/20-2/26\n",
+      "\n",
+      "SUNDAY 2/20\n",
+      "MORNING\n",
+      "30 minute jog\n",
+      "30 minute meditation\n",
+      "\n",
+      "EVENING\n",
+      "clean and jerk lifts—3 reps/8 sets. 262 lbs.\n",
+      "5 sets metabolic conditioning:\n",
+      "10 mile run\n",
+      "12 kettlebell swings\n",
+      "12 pull-ups\n",
+      "30 minutes flexibility\n",
+      "30 minutes sparring\n",
+      "\n",
+      "MONDAY 2/21\n",
+      "MORNING\n",
+      "30 minute jog\n",
+      "30 minutes traditional kata (focus on Japanese forms)\n",
+      "\n",
+      "EVENING\n",
+      "5 sets 20 foot rope climb\n",
+      "30 minutes gymnastics (work on muscle ups in\n",
+      "particular)\n",
+      "high bar jumps—12 reps/8 sets\n",
+      "crunches—50 reps/5 sets\n",
+      "30 minutes heavy bag\n",
+      "30 minutes flexibility\n",
+      "20 minutes target practice\n",
+      "\n",
+      "TUESDAY 2/22\n",
+      "MORNING\n",
+      "30 minute jog\n",
+      "30 minutes yoga\n",
+      "\n",
+      "EVENING\n",
+      "off day\n",
+      "leg heavy dead lift—5 reps/7 sets. 600 lbs.\n",
+      "clean and jerk lift—3 reps/10 sets\n",
+      "30 minutes sparring\n",
+      "\n",
+      "WEDNESDAY 2/23\n",
+      "OFF DAY\n",
+      "\n",
+      "MORNING\n",
+      "20-mile run—last week’s time was 4:50 per mile.\n",
+      "Need to better that time by a half a minute.\n",
+      "\n",
+      "EVENING\n",
+      "skill training only\n",
+      "30 minutes yoga\n",
+      "30 minutes meditation\n",
+      "30 minutes body basics\n",
+      "30 minutes bow basics\n",
+      "30 minutes sword basics\n",
+      "30 minutes observational\n",
+      "exercise\n",
+      "30 minutes kata\n",
+      "30 minutes pressure points\n",
+      "30 minutes modus and pressure points\n",
+      "\n",
+      "THURSDAY 2/24\n",
+      "MORNING\n",
+      "30 minute jog\n",
+      "30 minute meditation\n",
+      "30 minutes traditional kata\n",
+      "(focus on Japanese forms)\n",
+      "\n",
+      "EVENING\n",
+      "squats—10 reps/5 sets. 525 lbs.\n",
+      "30 minutes flexibility\n",
+      "crunches—50 reps/5 sets\n",
+      "20 minutes target practice\n",
+      "30 minutes heavy bag\n",
+      "\n",
+      "FRIDAY 2/25\n",
+      "MORNING\n",
+      "30 minute jog\n",
+      "30 minute meditation\n",
+      "\n",
+      "EVENING\n",
+      "clean and jerk lifts—3 reps/8 sets. 262 lbs.\n",
+      "5 sets metabolic conditioning:\n",
+      "10 mile run\n",
+      "12 kettlebell swings\n",
+      "12 pull-ups\n",
+      "30 minutes flexibility\n",
+      "30 minutes sparring\n",
+      "\n",
+      "SATURDAY 2/26)\n",
+      "MORNING\n",
+      "30 minute jog\n",
+      "30 minutes yoga\n",
+      "\n",
+      "EVENING\n",
+      "crunches—50 reps/5 sets\n",
+      "squats—(5 reps/10 sets. 525 lbs.\n",
+      "push-ups—60 reps/sets\n",
+      "30 minutes monkey bars\n",
+      "30 minute pommel horse\n",
+      "30 minutes heavy bag\n",
+      "2 mile swim\n",
+      "\n",
+      "In an effort to inspire the all- important Dark Knight to take time out of his busy schedule and actually consume a reasonable amount of sustenance, I have taken the liberty of composing a menu for today's scheduled natal to its my hope that these elegantly prepared courses will not share the fate of their predecessors -mated cold and untouched on a computer console.\n",
+      "-A\n",
+      "\n",
+      "W A Y N E M A N O R\n",
+      "\n",
+      "Tuesday's Menu\n",
+      "\n",
+      "Breakfast\n",
+      "six poached eggs laid over artichoke bottoms with a sage pesto sauce\n",
+      "thinly sliced baked ham\n",
+      "mixed organic fresh fruit bowl\n",
+      "freshly squeezed orange juice\n",
+      "organic, grass-fed milk\n",
+      "4 grams branched-chain amino acid\n",
+      "2 grams fish oil\n",
+      "\n",
+      "Lunch\n",
+      "local salmon with a ginger glaze\n",
+      "organic asparagus with lemon garlic dusting\n",
+      "Asian yam soup with diced onions\n",
+      "2 grams fish oil\n",
+      "\n",
+      "Dinner\n",
+      "grass-fed local sirloin steak\n",
+      "bed of organic spinach and piquillo peppers\n",
+      "oven-baked golden herb potato\n",
+      "2 grams fish oil\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "For the dinner menu, you should buy the following items:\n",
+      "\n",
+      "1. Grass-fed local sirloin steak\n",
+      "2. Organic spinach\n",
+      "3. Piquillo peppers\n",
+      "4. Potatoes (for oven-baked golden herb potato)\n",
+      "5. Fish oil (2 grams)\n",
+      "\n",
+      "Ensure the steak is grass-fed and the spinach and peppers are organic for the best quality meal.\n"
+     ]
+    }
+   ],
+   "source": [
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+    "    m.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b96c8456-4093-4cd6-bc5a-f611967ab709",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
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