@

security: remove hardcoded API keys and harden secret hygiene

- Scrub hardcoded HuggingFace tokens and OpenAI keys from LLM_Intro.ipynb
(source lines rewritten to load from .env; cell outputs redacted)
- Delete scratch notebooks (Untitled.ipynb) and stale checkpoints that
also contained keys
- Harden .gitignore (.env*, data/, *.duckdb, .ipynb_checkpoints/, __pycache__)
- Add .env.example template (placeholders, no real secrets)

Note: exposed keys must still be rotated/revoked by the account owner.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
@

.env.example

@@ -0,0 +1,32 @@
+# Copy this file to `.env` and fill in your own values. NEVER commit `.env`.
+
+# ---- Secrets (API keys) ----
+HUGGINGFACEHUB_API_TOKEN=your_hf_token_here
+OPENAI_API_KEY=your_openai_key_here
+GOOGLE_API_KEY=your_google_key_here
+
+# ---- DuckDB ----
+DUCKDB_PATH=data/complaints.duckdb
+TABLE=complaints
+
+# ---- Embedding backend ----
+# hf     -> EMBED_MODEL=sentence-transformers/all-MiniLM-L6-v2  EMBED_DIM=384
+# google -> EMBED_MODEL=models/gemini-embedding-001            EMBED_DIM=768
+EMBED_PROVIDER=hf
+EMBED_MODEL=sentence-transformers/all-MiniLM-L6-v2
+EMBED_DIM=384
+
+# ---- Backfill throttling (Gemini free tier ~100 req/min) ----
+EMBED_MAX_ROWS=500
+EMBED_CHUNK=50
+EMBED_SLEEP=1.0
+
+# ---- Chat LLMs ----
+HF_MODEL=Qwen/Qwen2.5-72B-Instruct
+HF_PROVIDER=novita
+OPENAI_MODEL=gpt-4o
+GOOGLE_CHAT_MODEL=gemini-2.0-flash
+
+# ---- NHTSA data source APIs ----
+VPIC_URL=https://vpic.nhtsa.dot.gov/api/vehicles
+COMPLAINTS_URL=https://api.nhtsa.gov/complaints/complaintsByVehicle

.gitignore

@@ -0,0 +1,24 @@
+# Secrets / environment
+.env
+.env.*
+!.env.example
+
+# DuckDB data + local datasets
+data/
+*.duckdb
+*.duckdb.wal
+my_dataset/
+
+# Notebook checkpoints
+.ipynb_checkpoints/
+
+# Python
+__pycache__/
+*.py[cod]
+.venv/
+venv/
+*.egg-info/
+
+# OS / editor
+.DS_Store
+Thumbs.db

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}

LLM_Intro.ipynb

@@ -0,0 +1,659 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b947c78e",
+   "metadata": {},
+   "source": [
+    "<font color='green'>\n",
+    "Pip install is the command you use to install Python packages with the help of a tool called Pip package manager.\n",
+    "<br><br>Installing LangChain package\n",
+    "</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d4a481b-60ed-4177-93ea-a234186eb787",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Scripts\\python.exe\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "print(sys.executable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6347bffa-cd6e-4fab-b987-e91c21d353ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Installed into: d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Scripts\\python.exe\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install -q \"huggingface_hub<1.0,>=0.34.0\"\n",
+    "\n",
+    "print(f\"✅ Installed into: {sys.executable}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8236fd59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install langchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f9c241d",
+   "metadata": {},
+   "source": [
+    "## Let's use open-source LLM hosted on Hugging Face"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "78a5b3f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# huggingface_hub pinned <1.0 — 1.x drops the InferenceClient(api_key=...) arg\n",
+    "# that langchain_huggingface uses, and breaks transformers.\n",
+    "#!pip install langchain-huggingface langchain \"huggingface_hub<1.0,>=0.34.0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2152cf",
+   "metadata": {},
+   "source": [
+    "<font color='green'>Imports the Python built-in module called \"os.\"\n",
+    "This module provides a way to interact with the operating system, such as accessing environment variables, working with files and directories, executing shell commands, etc\n",
+    "<br><br>\n",
+    "The environ attribute is a dictionary-like object that contains the environment variables of the current operating system session\n",
+    "<br><br>\n",
+    "By accessing os.environ, you can retrieve and manipulate environment variables within your Python program. For example, you can retrieve the value of a specific environment variable using the syntax os.environ['VARIABLE_NAME'], where \"VARIABLE_NAME\" is the name of the environment variable you want to access.\n",
+    "</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6ea1b4d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "# HUGGINGFACEHUB_API_TOKEN is loaded from .env (see load_dotenv); never hardcode it here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dfc23e4",
+   "metadata": {},
+   "source": [
+    "<font color='green'>\n",
+    "LangChain has built a Wrapper around HuggingFace APIs, using which we can get access to all the services HuggingFace provides.\n",
+    "<br>\n",
+    "The code snippet below imports a specific class called 'HuggingFaceEndpoint'(Wrapper around HuggingFace large language models) from 'langchain_huggingface' library.\n",
+    "\n",
+    "<font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4c808209",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_huggingface import HuggingFaceEndpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8cdfdbb",
+   "metadata": {},
+   "source": [
+    "<font color='green'>Here we are instantiating a language model object called HuggingFaceEndpoint, for our natural language processing tasks.\n",
+    "<br><br>\n",
+    "The parameter repo_id is provided\n",
+    "<font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8a492cf3-15f3-4bd2-b602-29b702c6e1b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "huggingface_hub:       0.36.2\n",
+      "langchain_huggingface: 1.2.2\n",
+      "langchain:             1.3.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "from importlib.metadata import version\n",
+    "\n",
+    "print(f\"huggingface_hub:       {version('huggingface_hub')}\")\n",
+    "print(f\"langchain_huggingface: {version('langchain-huggingface')}\")\n",
+    "print(f\"langchain:             {version('langchain')}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a12912cc-10a9-4e13-8aae-be96267261c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The currency of India is the Indian Rupee (INR).\n",
+      "\n",
+      "To convert 1 Malaysian Ringgit (MYR) to Indian Rupee (INR), you can use a currency converter or check the current exchange rate from a reliable financial source, as exchange rates fluctuate frequently.\n",
+      "\n",
+      "As of the latest available data, 1 MYR is approximately 18.50 INR. However, for the most accurate and up-to-date conversion, please refer to a current financial news source or a reliable currency converter online.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace\n",
+    "\n",
+    "# Load keys from .env file\n",
+    "load_dotenv()\n",
+    "\n",
+    "# Notes on why this shape:\n",
+    "#  - task=\"conversational\": instruct/chat models on HF Inference are served\n",
+    "#    as chat-completions, NOT \"text-generation\".\n",
+    "#  - provider=\"novita\": route to a provider that actually hosts the model\n",
+    "#    and is live (featherless-ai was returning 503).\n",
+    "#  - ChatHuggingFace wraps the endpoint so it calls the chat API correctly.\n",
+    "llm = HuggingFaceEndpoint(\n",
+    "    repo_id=\"Qwen/Qwen2.5-72B-Instruct\",\n",
+    "    task=\"conversational\",\n",
+    "    provider=\"novita\",\n",
+    "    huggingfacehub_api_token=os.getenv(\"HUGGINGFACEHUB_API_TOKEN\"),\n",
+    ")\n",
+    "chat = ChatHuggingFace(llm=llm)\n",
+    "\n",
+    "completion = chat.invoke(\"What is the currency of India and convert one MYR to INR?\")\n",
+    "print(completion.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5432711b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The currency of India is the Indian Rupee (INR). As an AI language model, I don't have real-time data access, so I can't provide you with the current exchange rate between MYR and INR. However, you can easily find this information on various financial websites or mobile apps that offer currency conversion services.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "from huggingface_hub import InferenceClient\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "client = InferenceClient(\n",
+    "    api_key=os.getenv(\"HUGGINGFACEHUB_API_TOKEN\"),\n",
+    ")\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "    model=\"Qwen/Qwen2.5-1.5B-Instruct\",   # ← free, works on free tier\n",
+    "    messages=[\n",
+    "        {\"role\": \"user\", \"content\":  \"What is the currency of India and convert one MYR to INR today's rate?\"}\n",
+    "    ],\n",
+    "    max_tokens=200,\n",
+    ")\n",
+    "\n",
+    "print(response.choices[0].message.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32c225b4",
+   "metadata": {},
+   "source": [
+    "## Let's use Proprietary LLM from - OpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a0e925c",
+   "metadata": {},
+   "source": [
+    "<font color='green'>\n",
+    "Installing Openai package, which includes the classes that we can use to communicate with Openai services\n",
+    "<font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5cb5a1a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f274e9c5",
+   "metadata": {},
+   "source": [
+    "<font color='green'>\n",
+    "Imports the Python built-in module called \"os.\"\n",
+    "<br>This module provides a way to interact with the operating system, such as accessing environment variables, working with files and directories, executing shell commands, etc\n",
+    "<br><br>\n",
+    "The environ attribute is a dictionary-like object that contains the environment variables of the current operating system session\n",
+    "<br><br>\n",
+    "By accessing os.environ, you can retrieve and manipulate environment variables within your Python program. For example, you can retrieve the value of a specific environment variable using the syntax os.environ['VARIABLE_NAME'], where \"VARIABLE_NAME\" is the name of the environment variable you want to access.\n",
+    "<font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a4d278e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "# OPENAI_API_KEY is loaded from .env (see load_dotenv); never hardcode it here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aef06cb",
+   "metadata": {},
+   "source": [
+    "<font color='green'>\n",
+    "LangChain has built a Wrapper around OpenAI APIs, using which we can get access to all the services OpenAI provides.\n",
+    "<br>\n",
+    "The code snippet below imports a specific class called 'OpenAI'(Wrapper around OpenAI large language models) from 'langchain-openai' library.\n",
+    "\n",
+    "<font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "db64eaa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a9d124f",
+   "metadata": {},
+   "source": [
+    "<font color='green'>Here we are instantiating a language model object called OpenAI, for our natural language processing tasks.\n",
+    "<br><br>\n",
+    "The parameter model_name is provided with the value \"gpt-3.5-turbo-instruct\" which is a specific version or variant of a language model (examples - gpt-3.5-turbo, text-ada-001 and more).\n",
+    "<font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9a3be989",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(model=\"gpt-4o\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "396c7904",
+   "metadata": {},
+   "source": [
+    "<font color='green'>\n",
+    "Here language model is represented by the object \"llm,\" which is being utilized to generate a completion or response based on a specific query. \n",
+    "<br><br>\n",
+    "The query, stored in the \"our_query\" variable is bieng passed to the model through llm object.\n",
+    "<font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a2e267f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "our_query = \"What is the currency of India and convert one MYR to INR today's rate?\"\n",
+    "\n",
+    "completion = llm.invoke(our_query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "39f1b87e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The currency of India is the Indian Rupee (INR). Exchange rates fluctuate frequently, so to get the most accurate and up-to-date rate for converting Malaysian Ringgit (MYR) to Indian Rupees (INR), you should check a reliable financial news source, a bank, or a currency conversion website. As of my last update, I don't have access to real-time data, so it's best to look up the current exchange rate online or through a financial service.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(completion.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d0d8a94b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The currency of India is the Indian Rupee (INR). Exchange rates fluctuate frequently, so to get the most accurate and up-to-date rate for converting Malaysian Ringgit (MYR) to Indian Rupees (INR), you should check a reliable financial news source, a bank, or a currency conversion website. As of my last update, I don't have access to real-time data, so it's best to look up the current exchange rate online or through a financial service.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(completion.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65a783d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting datasets\n",
+      "  Using cached datasets-5.0.0-py3-none-any.whl.metadata (23 kB)\n",
+      "Requirement already satisfied: filelock in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (3.29.6)\n",
+      "Requirement already satisfied: numpy>=1.17 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (2.5.1)\n",
+      "Requirement already satisfied: pyarrow>=21.0.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (24.0.0)\n",
+      "Requirement already satisfied: dill<0.4.2,>=0.3.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (0.4.1)\n",
+      "Requirement already satisfied: pandas in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (2.3.3)\n",
+      "Requirement already satisfied: requests>=2.32.2 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (2.34.2)\n",
+      "Requirement already satisfied: httpx<1.0.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (0.28.1)\n",
+      "Requirement already satisfied: tqdm>=4.66.3 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (4.68.4)\n",
+      "Requirement already satisfied: xxhash in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (3.8.1)\n",
+      "Requirement already satisfied: multiprocess<0.70.20 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (0.70.19)\n",
+      "Collecting fsspec<=2026.4.0,>=2023.1.0 (from fsspec[http]<=2026.4.0,>=2023.1.0->datasets)\n",
+      "  Using cached fsspec-2026.4.0-py3-none-any.whl.metadata (10 kB)\n",
+      "Requirement already satisfied: huggingface-hub<2.0,>=0.25.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (0.36.2)\n",
+      "Requirement already satisfied: packaging in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (26.2)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from datasets) (6.0.3)\n",
+      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (3.14.1)\n",
+      "Requirement already satisfied: anyio in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from httpx<1.0.0->datasets) (4.14.1)\n",
+      "Requirement already satisfied: certifi in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from httpx<1.0.0->datasets) (2026.6.17)\n",
+      "Requirement already satisfied: httpcore==1.* in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from httpx<1.0.0->datasets) (1.0.9)\n",
+      "Requirement already satisfied: idna in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from httpx<1.0.0->datasets) (3.18)\n",
+      "Requirement already satisfied: h11>=0.16 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from httpcore==1.*->httpx<1.0.0->datasets) (0.16.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (4.16.0)\n",
+      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (2.7.1)\n",
+      "Requirement already satisfied: aiosignal>=1.4.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (1.4.0)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (26.1.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (1.8.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (6.7.1)\n",
+      "Requirement already satisfied: propcache>=0.2.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (0.5.2)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.17.0 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.4.0,>=2023.1.0->datasets) (1.24.2)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from requests>=2.32.2->datasets) (3.4.9)\n",
+      "Requirement already satisfied: urllib3<3,>=1.26 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from requests>=2.32.2->datasets) (2.7.0)\n",
+      "Requirement already satisfied: colorama in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from tqdm>=4.66.3->datasets) (0.4.6)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from pandas->datasets) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from pandas->datasets) (2026.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from pandas->datasets) (2026.2)\n",
+      "Requirement already satisfied: six>=1.5 in d:\\2026\\July\\Agentic-RAG-with-LangGraph-and-Ollama\\.venv\\Lib\\site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+      "Using cached datasets-5.0.0-py3-none-any.whl (555 kB)\n",
+      "Using cached fsspec-2026.4.0-py3-none-any.whl (203 kB)\n",
+      "Installing collected packages: fsspec, datasets\n",
+      "\n",
+      "  Attempting uninstall: fsspec\n",
+      "\n",
+      "    Found existing installation: fsspec 2026.6.0\n",
+      "\n",
+      "    Uninstalling fsspec-2026.6.0:\n",
+      "\n",
+      "      Successfully uninstalled fsspec-2026.6.0\n",
+      "\n",
+      "   ---------------------------------------- 0/2 [fsspec]\n",
+      "   ---------------------------------------- 0/2 [fsspec]\n",
+      "   ---------------------------------------- 0/2 [fsspec]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   -------------------- ------------------- 1/2 [datasets]\n",
+      "   ---------------------------------------- 2/2 [datasets]\n",
+      "\n",
+      "Successfully installed datasets-5.0.0 fsspec-2026.4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#!pip install datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97e2464f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset({\n",
+      "    features: ['text', 'label'],\n",
+      "    num_rows: 2\n",
+      "})\n",
+      "Value('string')\n",
+      "Column([1, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from datasets import Dataset\n",
+    "\n",
+    "# Define your raw structured data\n",
+    "raw_data = {\n",
+    "    \"text\": [\"I love machine learning\", \"Hugging Face makes AI easy\"],\n",
+    "    \"label\": [1, 1]\n",
+    "}\n",
+    "\n",
+    "# Convert to a Hugging Face Dataset object\n",
+    "dataset = Dataset.from_pandas(pd.DataFrame(raw_data))\n",
+    "print(dataset)\n",
+    "print(dataset[\"text\"])\n",
+    "print(dataset[\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4ea59a37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset({\n",
+      "    features: ['text', 'label'],\n",
+      "    num_rows: 10\n",
+      "})\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    \"text\": [\n",
+    "        \"I love machine learning\",\n",
+    "        \"Hugging Face makes AI easy\",\n",
+    "        \"Natural language processing is interesting\",\n",
+    "        \"Deep learning models are powerful\",\n",
+    "        \"AI is transforming industries\",\n",
+    "        \"Data science is exciting\",\n",
+    "        \"Python is widely used in AI\",\n",
+    "        \"Models require good datasets\",\n",
+    "        \"Learning AI step by step is helpful\",\n",
+    "        \"Custom datasets improve performance\"\n",
+    "    ],\n",
+    "    \"label\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "dataset = Dataset.from_pandas(df)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7c13f568",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09acd1375a0d4365a564944c8bd94a23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Saving the dataset (0/1 shards):   0%|          | 0/10 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset.save_to_disk(\"my_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "46c552ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: 200\n",
+      "URL: https://api.nhtsa.gov/complaints/complaintsByVehicle?make=Tesla&model=Model+3&modelYear=2020\n",
+      "\n",
+      "Total complaints found: 435\n",
+      "Message: Results returned successfully\n",
+      "\n",
+      "--- First Complaint ---\n",
+      "ODI Number:   11751847\n",
+      "Component:    UNKNOWN OR OTHER,FORWARD COLLISION AVOIDANCE\n",
+      "Date:         07/16/2026\n",
+      "Description:  ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "url = \"https://api.nhtsa.gov/complaints/complaintsByVehicle\"\n",
+    "params = {\"make\": \"Tesla\", \"model\": \"Model 3\", \"modelYear\": \"2020\"}\n",
+    "\n",
+    "r = requests.get(url, params=params)\n",
+    "\n",
+    "print(\"Status:\", r.status_code)\n",
+    "print(\"URL:\", r.url)\n",
+    "\n",
+    "# Parse JSON properly\n",
+    "data = r.json()\n",
+    "\n",
+    "print(f\"\\nTotal complaints found: {data['count']}\")\n",
+    "print(f\"Message: {data['message']}\")\n",
+    "\n",
+    "# Show first complaint\n",
+    "if data['results']:\n",
+    "    first = data['results'][0]\n",
+    "    print(\"\\n--- First Complaint ---\")\n",
+    "    print(f\"ODI Number:   {first.get('odiNumber')}\")\n",
+    "    print(f\"Component:    {first.get('components')}\")\n",
+    "    print(f\"Date:         {first.get('dateOfIncident')}\")\n",
+    "    print(f\"Description:  {first.get('description', '')[:200]}...\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.12.8.final.0)",
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Untitled.ipynb

@@ -1,72 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e4271b05-0ca4-4cc9-b534-e4ac8d74ef07",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "12 + 12"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9fdb7b0c-b3a9-492e-8c45-6d15fd288da9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello World\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Hello World')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa2e4e6b-9803-4733-9b2f-9b4dbd19aa73",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Agentic RAG (Python 3.12.8)",
-   "language": "python",
-   "name": "agentic-rag"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}